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| Documentation: | ACS 2011 (1-Year Estimates) |
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Publisher:
U.S. Census Bureau
Survey:
ACS 2011 (1-Year Estimates)
| Document: | ACS 2011-1yr Summary File: Technical Documentation |
| citation: | Social Explorer; U.S. Census Bureau; American Community Survey 2011 Summary File: Technical Documentation. |
Chapter Contents
Subject DefinitionsHousing VariablesPopulation Variables
001-099 WESTERN EUROPE (EXCEPT SPAIN)
100-180 EASTERN EUROPE AND FORMER SOVIET UNION
181-199 EUROPE, N.E.C.
200-299 HISPANIC CATEGORIES (INCLUDING SPAIN)
300-359 WEST INDIES (EXCEPT HISPANIC)
360-399 CENTRAL & SOUTH AMERICA (EXCEPT HISPANIC)
400-499 NORTH AFRICA AND SOUTHWEST ASIA
500-599 SUBSAHARAN AFRICA
600-699 SOUTH ASIA
700-799 OTHER ASIA
800-899 PACIFIC
900-994 NORTH AMERICA (EXCEPT HISPANIC)
995-999 RESIDUAL AND NO RESPONSE
| 1 | ALSATIAN |
| 2 | ANDORRAN |
| 3 | AUSTRIAN |
| 4 | TIROL |
| 5 | BASQUE |
| 6 | FRENCH BASQUE |
| 7 | SPANISH BASQUE |
| 8 | BELGIAN |
| 9 | FLEMISH |
| 10 | WALLOON |
| 11 | BRITISH |
| 12 | BRITISH ISLES |
| 13 | CHANNEL ISLANDER |
| 14 | GIBRALTAR |
| 15 | CORNISH |
| 16 | CORSICAN |
| 17 | CYPRIOT |
| 18 | GREEK CYPRIOTE |
| 19 | TURKISH CYPRIOTE |
| 20 | DANISH |
| 21 | DUTCH |
| 22 | ENGLISH |
| 23 | FAEROE ISLANDER |
| 24 | FINNISH |
| 25 | KARELIAN |
| 26 | FRENCH |
| 27 | LORRAINE |
| 28 | BRETON |
| 29 | FRISIAN |
| 30 | FRIULIAN |
| 31 | LADIN |
| 32 | GERMAN |
| 33 | BAVARIA |
| 34 | BERLIN |
| 35 | HAMBURG |
| 36 | HANNOVER |
| 37 | HESSIAN |
| 38 | LUBECKER |
| 39 | POMERANIAN |
| 40 | PRUSSIAN |
| 41 | SAXON |
| 42 | SUDETENLANDER |
| 43 | WESTPHALIAN |
| 44 | EAST GERMAN |
| 45 | WEST GERMAN |
| 46 | GREEK |
| 47 | CRETAN |
| 48 | CYCLADES |
| 49 | ICELANDER |
| 50 | IRISH |
| 51 | ITALIAN |
| 52 | TRIESTE |
| 53 | ABRUZZI |
| 54 | APULIAN |
| 55 | BASILICATA |
| 56 | CALABRIAN |
| 57 | AMALFIN |
| 58 | EMILIA ROMAGNA |
| 59 | ROME |
| 60 | LIGURIAN |
| 61 | LOMBARDIAN |
| 62 | MARCHE |
| 63 | MOLISE |
| 64 | NEAPOLITAN |
| 65 | PIEDMONTESE |
| 66 | PUGLIA |
| 67 | SARDINIAN |
| 68 | SICILIAN |
| 69 | TUSCANY |
| 70 | TRENTINO |
| 71 | UMBRIAN |
| 72 | VALLE DAOST |
| 73 | VENETIAN |
| 74 | SAN MARINO |
| 75 | LAPP |
| 76 | LIECHTENSTEINER |
| 77 | LUXEMBURGER |
| 78 | MALTESE |
| 79 | MANX |
| 80 | MONEGASQUE |
| 81 | NORTH IRISH |
| 82 | NORWEGIAN |
| 83 | OCCITAN |
| 84 | PORTUGUESE |
| 85 | AZORES ISLANDER |
| 86 | MADEIRA ISLANDER |
| 87 | SCOTCH IRISH |
| 88 | SCOTTISH |
| 89 | SWEDISH |
| 90 | ALAND ISLANDER |
| 91 | SWISS |
| 92 | SUISSE |
| 93 | SWITZER |
| 94 | IRISH SCOTCH |
| 95 | ROMANSCH |
| 96 | SUISSE ROMANE |
| 97 | WELSH |
| 98 | SCANDINAVIAN |
| 99 | CELTIC |
| 100 | ALBANIAN |
| 101 | AZERBAIJANI |
| 102 | BELORUSSIAN |
| 103 | BULGARIAN |
| 104 | CARPATHO RUSYN |
| 105 | CARPATHIAN |
| 106 | RUSYN |
| 107 | RUTHENIAN |
| 108 | COSSACK |
| 109 | CROATIAN |
| 110 | NOT USED |
| 111 | CZECH |
| 112 | BOHEMIAN |
| 113 | MORAVIAN |
| 114 | CZECHOSLOVAKIAN |
| 115 | ESTONIAN |
| 116 | LIVONIAN |
| 117 | FINNO UGRIAN |
| 118 | MORDOVIAN |
| 119 | VOYTAK |
| 120 | GRUZIIA |
| 121 | NOT USED |
| 122 | GERMAN FROM RUSSIA |
| 123 | VOLGA |
| 124 | ROM |
| 125 | HUNGARIAN |
| 126 | MAGYAR |
| 127 | KALMYK |
| 128 | LATVIAN |
| 129 | LITHUANIAN |
| 130 | MACEDONIAN |
| 131 | MONTENEGRIN |
| 132 | NORTH CAUCASIAN |
| 133 | NORTH CAUCASIAN TURKIC |
| 134-139 | NOT USED |
| 140 | OSSETIAN |
| 141 | NOT USED |
| 142 | POLISH |
| 143 | KASHUBIAN |
| 144 | ROMANIAN |
| 145 | BESSARABIAN |
| 146 | MOLDAVIAN |
| 147 | WALLACHIAN |
| 148 | RUSSIAN |
| 149 | NOT USED |
| 150 | MUSCOVITE |
| 151 | NOT USED |
| 152 | SERBIAN |
| 153 | SLOVAK |
| 154 | SLOVENE |
| 155 | SORBIAN/WEND |
| 156 | SOVIET TURKIC |
| 157 | BASHKIR |
| 158 | CHUVASH |
| 159 | GAGAUZ |
| 160 | MESKNETIAN |
| 161 | TUVINIAN |
| 162 | NOT USED |
| 163 | YAKUT |
| 164 | SOVIET UNION |
| 165 | TATAR |
| 166 | NOT USED |
| 167 | SOVIET CENTRAL ASIA |
| 168 | TURKESTANI |
| 169 | UZBEG |
| 170 | GEORGIA CIS |
| 171 | UKRAINIAN |
| 172 | LEMKO |
| 173 | BIOKO |
| 174 | HUSEL |
| 175 | WINDISH |
| 176 | YUGOSLAVIAN |
| 177 | HERZEGOVINIAN |
| 178 | SLAVIC |
| 179 | SLAVONIAN |
| 180 | TAJIK |
| 181 | CENTRAL EUROPEAN |
| 182 | NOT USED |
| 183 | NORTHERN EUROPEAN |
| 184 | NOT USED |
| 185 | SOUTHERN EUROPEAN |
| 186 | NOT USED |
| 187 | WESTERN EUROPEAN |
| 188-189 | NOT USED |
| 190 | EASTERN EUROPEAN |
| 191 | BUKOVINA |
| 192 | NOT USED |
| 193 | SILESIAN |
| 194 | GERMANIC |
| 195 | EUROPEAN |
| 196 | GALICIAN |
| 197-199 | NOT USED |
| 200 | SPANIARD |
| 201 | ANDALUSIAN |
| 202 | ASTURIAN |
| 203 | CASTILLIAN |
| 204 | CATALONIAN |
| 205 | BALEARIC ISLANDER |
| 206 | GALLEGO |
| 207 | VALENCIAN |
| 208 | CANARY ISLANDER |
| 209 | NOT USED |
| 210 | MEXICAN |
| 211 | MEXICAN AMERICAN |
| 212 | MEXICANO |
| 213 | CHICANO |
| 214 | LA RAZA |
| 215 | MEXICAN AMERICAN INDIAN |
| 216-217 | NOT USED |
| 218 | MEXICAN STATE |
| 219 | MEXICAN INDIAN |
| 220 | NOT USED |
| 221 | COSTA RICAN |
| 222 | GUATEMALAN |
| 223 | HONDURAN |
| 224 | NICARAGUAN |
| 225 | PANAMANIAN |
| 226 | SALVADORAN |
| 227 | CENTRAL AMERICAN |
| 228 | NOT USED |
| 229 | CANAL ZONE |
| 230 | NOT USED |
| 231 | ARGENTINEAN |
| 232 | BOLIVIAN |
| 233 | CHILEAN |
| 234 | COLOMBIAN |
| 235 | ECUADORIAN |
| 236 | PARAGUAYAN |
| 237 | PERUVIAN |
| 238 | URUGUAYAN |
| 239 | VENEZUELAN |
| 240-247 | NOT USED |
| 248 | CRIOLLO |
| 249 | SOUTH AMERICAN |
| 250 | LATIN AMERICAN |
| 251 | LATIN |
| 252 | LATINO |
| 253-260 | NOT USED |
| 261 | PUERTO RICAN |
| 262-270 | NOT USED |
| 271 | CUBAN |
| 272-274 | NOT USED |
| 275 | DOMINICAN |
| 276-289 | NOT USED |
| 290 | HISPANIC |
| 291 | SPANISH |
| 292 | CALIFORNIO |
| 293 | TEJANO |
| 294 | NUEVO MEXICANO |
| 295 | SPANISH AMERICAN |
| 296-299 | NOT USED |
| 300 | BAHAMIAN |
| 301 | BARBADIAN |
| 302 | BELIZEAN |
| 303 | BERMUDAN |
| 304 | CAYMAN ISLANDER |
| 305-307 | NOT USED |
| 308 | JAMAICAN |
| 309 | NOT USED |
| 310 | DUTCH WEST INDIES |
| 311 | ARUBA ISLANDER |
| 312 | ST MAARTEN ISLANDER |
| 313 | NOT USED |
| 314 | TRINIDADIAN TOBAGONIAN |
| 315 | TRINIDADIAN |
| 316 | TOBAGONIAN |
| 317 | U S VIRGIN ISLANDER |
| 318 | ST CROIX ISLANDER |
| 319 | ST JOHN ISLANDER |
| 320 | ST THOMAS ISLANDER |
| 321 | BRITISH VIRGIN ISLANDER |
| 322 | BRITISH WEST INDIES |
| 323 | TURKS AND CAICOS ISLANDER |
| 324 | ANGUILLA ISLANDER |
| 325 | ANTIGUA AND BARBUDA |
| 326 | MONTSERRAT ISLANDER |
| 327 | KITTS/NEVIS ISLANDER |
| 328 | DOMINICA ISLANDER |
| 329 | GRENADIAN |
| 330 | VINCENT-GRENADINE ISLANDER |
| 331 | ST LUCIA ISLANDER |
| 332 | FRENCH WEST INDIES |
| 333 | GUADELOUPE ISLANDER |
| 334 | CAYENNE |
| 335 | WEST INDIAN |
| 336 | HAITIAN |
| 337-359 | NOT USED |
| 360 | BRAZILIAN |
| 361-364 | NOT USED |
| 365 | SAN ANDRES |
| 366-369 | NOT USED |
| 370 | GUYANESE |
| 371-374 | NOT USED |
| 375 | PROVIDENCIA |
| 376-379 | NOT USED |
| 380 | SURINAM |
| 381-399 | NOT USED |
| 400 | ALGERIAN |
| 401 | NOT USED |
| 402 | EGYPTIAN |
| 403 | NOT USED |
| 404 | LIBYAN |
| 405 | NOT USED |
| 406 | MOROCCAN |
| 407 | IFNI |
| 408 | TUNISIAN |
| 409-410 | NOT USED |
| 411 | NORTH AFRICAN |
| 412 | ALHUCEMAS |
| 413 | BERBER |
| 414 | RIO DE ORO |
| 415 | BAHRAINI |
| 416 | IRANIAN |
| 417 | IRAQI |
| 418 | NOT USED |
| 419 | ISRAELI |
| 420 | NOT USED |
| 421 | JORDANIAN |
| 422 | TRANSJORDAN |
| 423 | KUWAITI |
| 424 | NOT USED |
| 425 | LEBANESE |
| 426 | NOT USED |
| 427 | SAUDI ARABIAN |
| 428 | NOT USED |
| 429 | SYRIAN |
| 430 | NOT USED |
| 431 | ARMENIAN |
| 432-433 | NOT USED |
| 434 | TURKISH |
| 435 | YEMENI |
| 436 | OMANI |
| 437 | MUSCAT |
| 438 | TRUCIAL STATES |
| 439 | QATAR |
| 440 | NOT USED |
| 441 | BEDOUIN |
| 442 | KURDISH |
| 443 | NOT USED |
| 444 | KURIA MURIA ISLANDER |
| 445-464 | NOT USED |
| 465 | PALESTINIAN |
| 466 | GAZA STRIP |
| 467 | WEST BANK |
| 468-469 | NOT USED |
| 470 | SOUTH YEMEN |
| 471 | ADEN |
| 472-479 | NOT USED |
| 480 | UNITED ARAB EMIRATES |
| 481 | NOT USED |
| 483 | ASSYRIAN |
| 484 | CHALDEAN |
| 485 | SYRIAC |
| 486-489 | NOT USED |
| 490 | MIDEAST |
| 491-494 | NOT USED |
| 495 | ARAB |
| 496 | ARABIC |
| 497-499 | NOT USED |
| 500 | ANGOLAN |
| 501 | NOT USED |
| 502 | BENIN |
| 503 | NOT USED |
| 504 | BOTSWANA |
| 505 | NOT USED |
| 506 | BURUNDIAN |
| 507 | NOT USED |
| 508 | CAMEROON |
| 509 | NOT USED |
| 510 | CAPE VERDEAN |
| 511 | NOT USED |
| 512 | CENTRAL AFRICAN REPUBLIC |
| 513 | CHADIAN |
| 514 | NOT USED |
| 515 | CONGOLESE |
| 516 | CONGO BRAZZAVILLE |
| 517-518 | NOT USED |
| 519 | DJIBOUTI |
| 520 | EQUATORIAL GUINEA |
| 521 | CORSICO ISLANDER |
| 522 | ETHIOPIAN |
| 523 | ERITREAN |
| 524 | NOT USED |
| 525 | GABONESE |
| 526 | NOT USED |
| 527 | GAMBIAN |
| 528 | NOT USED |
| 529 | GHANIAN |
| 530 | GUINEAN |
| 531 | GUINEA BISSAU |
| 532 | IVORY COAST |
| 533 | NOT USED |
| 534 | KENYAN |
| 535-537 | NOT USED |
| 538 | LESOTHO |
| 539-540 | NOT USED |
| 541 | LIBERIAN |
| 542 | NOT USED |
| 543 | MADAGASCAN |
| 544 | NOT USED |
| 545 | MALAWIAN |
| 546 | MALIAN |
| 547 | MAURITANIAN |
| 548 | NOT USED |
| 549 | MOZAMBICAN |
| 550 | NAMIBIAN |
| 551 | NIGER |
| 552 | NOT USED |
| 553 | NIGERIAN |
| 554 | FULANI |
| 555 | HAUSA |
| 556 | IBO |
| 557 | TIV |
| 558 | YORUBA |
| 559-560 | NOT USED |
| 561 | RWANDAN |
| 562-563 | NOT USED |
| 564 | SENEGALESE |
| 565 | NOT USED |
| 566 | SIERRA LEONEAN |
| 567 | NOT USED |
| 568 | SOMALIAN |
| 569 | SWAZILAND |
| 570 | SOUTH AFRICAN |
| 571 | UNION OF SOUTH AFRICA |
| 572 | AFRIKANER |
| 573 | NATALIAN |
| 574 | ZULU |
| 575 | NOT USED |
| 576 | SUDANESE |
| 577 | DINKA |
| 578 | NUER |
| 579 | FUR |
| 580 | BAGGARA |
| 581 | NOT USED |
| 582 | TANZANIAN |
| 583 | TANGANYIKAN |
| 584 | ZANZIBAR ISLANDER |
| 585 | NOT USED |
| 586 | TOGO |
| 587 | NOT USED |
| 588 | UGANDAN |
| 589 | UPPER VOLTAN |
| 590 | VOLTA |
| 591 | ZAIRIAN |
| 592 | ZAMBIAN |
| 593 | ZIMBABWEAN |
| 594 | AFRICAN ISLANDS (EXCEPT MADAGASCAR) |
| 595 | MAURITIAN |
| 596 | CENTRAL AFRICAN |
| 597 | EASTERN AFRICAN |
| 598 | WESTERN AFRICAN |
| 599 | AFRICAN |
| 600 | AFGHANISTAN |
| 601 | BALUCHISTAN |
| 602 | PATHAN |
| 603 | BANGLADESHI |
| 604-606 | NOT USED |
| 607 | BHUTANESE |
| 608 | NOT USED |
| 609 | NEPALI |
| 610-614 | NOT USED |
| 615 | ASIAN INDIAN |
| 616 | KASHMIR |
| 617 | NOT USED |
| 618 | BENGALI |
| 619 | NOT USED |
| 620 | EAST INDIAN |
| 621 | NOT USED |
| 622 | ANDAMAN ISLANDER |
| 623 | NOT USED |
| 624 | ANDHRA PRADESH |
| 625 | NOT USED |
| 626 | ASSAMESE |
| 627 | NOT USED |
| 628 | GOANESE |
| 629 | NOT USED |
| 630 | GUJARATI |
| 631 | NOT USED |
| 632 | KARNATAKAN |
| 633 | NOT USED |
| 634 | KERALAN |
| 635 | NOT USED |
| 636 | MADHYA PRADESH |
| 637 | NOT USED |
| 638 | MAHARASHTRAN |
| 639 | NOT USED |
| 640 | MADRAS |
| 641 | NOT USED |
| 642 | MYSORE |
| 643 | NOT USED |
| 644 | NAGALAND |
| 645 | NOT USED |
| 646 | ORISSA |
| 647 | NOT USED |
| 648 | PONDICHERRY |
| 649 | NOT USED |
| 650 | PUNJAB |
| 651 | NOT USED |
| 652 | RAJASTHAN |
| 653 | NOT USED |
| 654 | SIKKIM |
| 655 | NOT USED |
| 656 | TAMIL NADU |
| 657 | NOT USED |
| 658 | UTTAR PRADESH |
| 659-674 | NOT USED |
| 675 | EAST INDIES |
| 676-679 | NOT USED |
| 680 | PAKISTANI |
| 681-689 | NOT USED |
| 690 | SRI LANKAN |
| 691 | SINGHALESE |
| 692 | VEDDAH |
| 693-694 | NOT USED |
| 695 | MALDIVIAN |
| 696-699 | NOT USED |
| 700 | BURMESE |
| 701 | NOT USED |
| 702 | SHAN |
| 703 | CAMBODIAN |
| 704 | KHMER |
| 705 | NOT USED |
| 706 | CHINESE |
| 707 | CANTONESE |
| 708 | MANCHURIAN |
| 709 | MANDARIN |
| 710-711 | NOT USED |
| 712 | MONGOLIAN |
| 713 | NOT USED |
| 714 | TIBETAN |
| 715 | NOT USED |
| 716 | HONG KONG |
| 717 | NOT USED |
| 718 | MACAO |
| 719 | NOT USED |
| 720 | FILIPINO |
| 721-729 | NOT USED |
| 730 | INDONESIAN |
| 731 | NOT USED |
| 732 | BORNEO |
| 733 | NOT USED |
| 734 | JAVA |
| 735 | NOT USED |
| 736 | SUMATRA |
| 737-739 | NOT USED |
| 740 | JAPANESE |
| 741 | ISSEI |
| 742 | NISEI |
| 743 | SANSEI |
| 744 | YONSEI |
| 745 | GONSEI |
| 746 | RYUKYU ISLANDER |
| 747 | NOT USED |
| 748 | OKINAWAN |
| 749 | NOT USED |
| 750 | KOREAN |
| 751-764 | NOT USED |
| 765 | LAOTIAN |
| 766 | MEO |
| 767 | NOT USED |
| 768 | HMONG |
| 769 | NOT USED |
| 770 | MALAYSIAN |
| 771 | NORTH BORNEO |
| 772-773 | NOT USED |
| 774 | SINGAPOREAN |
| 775 | NOT USED |
| 776 | THAI |
| 777 | BLACK THAI |
| 778 | WESTERN LAO |
| 779-781 | NOT USED |
| 782 | TAIWANESE |
| 783 | FORMOSAN |
| 784 | NOT USED |
| 785 | VIETNAMESE |
| 786 | KATU |
| 787 | MA |
| 788 | MNONG |
| 789 | NOT USED |
| 790 | MONTAGNARD |
| 791 | NOT USED |
| 792 | INDO CHINESE |
| 793 | EURASIAN |
| 794 | AMERASIAN |
| 795 | ASIAN |
| 796-799 | NOT USED |
| 800 | AUSTRALIAN |
| 801 | TASMANIAN |
| 802 | AUSTRALIAN ABORIGINE |
| 803 | NEW ZEALANDER |
| 804 | TUVALUAN |
| 805 | NORFOLK ISLANDER |
| 806-807 | NOT USED |
| 808 | POLYNESIAN |
| 809 | KAPINGAMARANGAN |
| 810 | MAORI |
| 811 | HAWAIIAN |
| 812 | NOT USED |
| 813 | PART HAWAIIAN |
| 814 | SAMOAN |
| 815 | TONGAN |
| 816 | TOKELAUAN |
| 817 | COOK ISLANDER |
| 818 | TAHITIAN |
| 819 | NIUEAN |
| 820 | MICRONESIAN |
| 821 | GUAMANIAN |
| 822 | CHAMORRO ISLANDER |
| 823 | SAIPANESE |
| 824 | PALAUAN |
| 825 | MARSHALLESE |
| 826 | KOSRAEAN |
| 827 | PONAPEAN |
| 828 | TRUKESE (CHUUKESE) |
| 829 | YAPESE |
| 830 | CAROLINIAN |
| 831 | KIRIBATESE |
| 832 | NAURUAN |
| 833 | TARAWA ISLANDER |
| 834 | TINIAN ISLANDER |
| 835-839 | NOT USED |
| 840 | MELANESIAN |
| 841 | FIJIAN |
| 842 | NOT USED |
| 843 | NEW GUINEAN |
| 844 | PAPUAN |
| 845 | SOLOMON ISLANDER |
| 846 | NEW CALEDONIAN |
| 847 | VANUATUAN |
| 848-849 | NOT USED |
| 850 | PACIFIC ISLANDER |
| 851-859 | NOT USED |
| 860 | PACIFIC |
| 861 | NOT USED |
| 862 | CHAMOLINIAN |
| 863-899 | NOT USED |
| 900 | AFRO AMERICAN |
| 901 | AFRO |
| 902 | AFRICAN AMERICAN |
| 903 | BLACK |
| 904 | NEGRO |
| 905 | NONWHITE |
| 906 | COLORED |
| 907 | CREOLE |
| 908 | MULATTO |
| 909-912 | NOT USED |
| 913 | CENTRAL AMERICAN INDIAN |
| 914 | SOUTH AMERICAN INDIAN |
| 915-916 | NOT USED |
| 917 | NATIVE AMERICAN |
| 918 | INDIAN |
| 919 | CHEROKEE |
| 920 | AMERICAN INDIAN |
| 921 | ALEUT |
| 922 | ESKIMO |
| 923 | INUIT |
| 924 | WHITE |
| 925 | ANGLO |
| 926 | NOT USED |
| 927 | APPALACHIAN |
| 928 | ARYAN |
| 929 | PENNSYLVANIA GERMAN |
| 930 | GREENLANDER |
| 931 | CANADIAN |
| 932 | NOT USED |
| 933 | NEWFOUNDLAND |
| 934 | NOVA SCOTIA |
| 935 | FRENCH CANADIAN |
| 936 | ACADIAN |
| 937 | CAJUN |
| 938 | NOT USED |
| 939 | AMERICAN |
| 940 | UNITED STATES |
| 941 | ALABAMA |
| 942 | ALASKA |
| 943 | ARIZONA |
| 944 | ARKANSAS |
| 945 | CALIFORNIA |
| 946 | COLORADO |
| 947 | CONNECTICUT |
| 948 | DISTRICT OF COLUMBIA |
| 949 | DELAWARE |
| 950 | FLORIDA |
| 951 | IDAHO |
| 952 | ILLINOIS |
| 953 | INDIANA |
| 954 | IOWA |
| 955 | KANSAS |
| 956 | KENTUCKY |
| 957 | LOUISIANA |
| 958 | MAINE |
| 959 | MARYLAND |
| 960 | MASSACHUSETTS |
| 961 | MICHIGAN |
| 962 | MINNESOTA |
| 963 | MISSISSIPPI |
| 964 | MISSOURI |
| 965 | MONTANA |
| 966 | NEBRASKA |
| 967 | NEVADA |
| 968 | NEW HAMPSHIRE |
| 969 | NEW JERSEY |
| 970 | NEW MEXICO |
| 971 | NEW YORK |
| 972 | NORTH CAROLINA |
| 973 | NORTH DAKOTA |
| 974 | OHIO |
| 975 | NOT USED |
| 976 | OKLAHOMA |
| 977 | OREGON |
| 978 | PENNSYLVANIA |
| 979 | RHODE ISLAND |
| 980 | SOUTH CAROLINA |
| 981 | SOUTH DAKOTA |
| 982 | TENNESSEE |
| 983 | TEXAS |
| 984 | UTAH |
| 985 | VERMONT |
| 986 | VIRGINIA |
| 987 | WASHINGTON |
| 988 | WEST VIRGINIA |
| 989 | WISCONSIN |
| 990 | WYOMING |
| 991 | GEORGIA |
| 992 | NOT USED |
| 993 | SOUTHERNER |
| 994 | NORTH AMERICAN |
| 995 | MIXTURE |
| 996 | UNCODABLE ENTRIES |
| 997 | NOT USED |
| 998 | OTHER RESPONSES |
| 999 | NOT REPORTED |
Institutional Group Quarters:
Correctional facilities for adults
101. Federal detention centers
102. Federal prisons
103. State prisons
104. Local jails and other municipal confinement facilities
105. Correctional residential facilities
106. Military disciplinary barracks and jailsJuvenile facilities
201. Group homes for juveniles (non-correctional)
202. Residential treatment centers for juveniles (non-correctional)
203. Correctional facilities intended for juvenilesNursing facilities/skilled-nursing facilities
301. Nursing facilities/skilled-nursing facilitiesOther institutional facilities
401. Mental (psychiatric) hospitals and psychiatric units in other hospitals
402. Hospitals with patients who have no usual home elsewhere
403. In-patient hospice facilities
404. Military treatment facilities with assigned patients
405. Residential schools for people with disabilities
Noninstitutional Group Quarters:
College/university student housing
501. College/university student housing
Military Quarters
601. Military quarters
602. Military ships
Other noninstitutional facilities
701. Emergency and transitional shelters (with sleeping facilities) for people experiencing homelessness
801. Group homes intended for adults
802. Residential treatment centers for adults
901. Workers' group living quarters and Job Corps centers
902. Religious group quarters
Correctional facilities for adults
101. Federal detention centers
102. Federal prisons
103. State prisons
104. Local jails and other municipal confinement facilities
105. Correctional residential facilities
106. Military disciplinary barracks and jailsJuvenile facilities
201. Group homes for juveniles (non-correctional)
202. Residential treatment centers for juveniles (non-correctional)
203. Correctional facilities intended for juvenilesNursing facilities/skilled-nursing facilities
301. Nursing facilities/skilled-nursing facilitiesOther institutional facilities
401. Mental (psychiatric) hospitals and psychiatric units in other hospitals
402. Hospitals with patients who have no usual home elsewhere
403. In-patient hospice facilities
404. Military treatment facilities with assigned patients
405. Residential schools for people with disabilities
Noninstitutional Group Quarters:
College/university student housing
501. College/university student housing
Military Quarters
601. Military quarters
602. Military ships
Other noninstitutional facilities
701. Emergency and transitional shelters (with sleeping facilities) for people experiencing homelessness
801. Group homes intended for adults
802. Residential treatment centers for adults
901. Workers' group living quarters and Job Corps centers
902. Religious group quarters
| 001-199, 300-999 Not Spanish/Hispanic | 200-209 Spaniard | 210-220 Mexican |
| 221-230 Central American | 231-249 South American | 250-259 Latin American |
| 260-269 Puerto Rican | 270-274 Cuban | 275-279 Dominican |
| 280-299 Other Spanish/Hispanic |
| 001-099 Not used | 100 Not Hispanic/Spanish (CHECK BOX) | 101 Not Hispanic/Spanish |
| 102-109 Not Used | 110 Portuguese | 111 Azorean |
| 112 Brazilian | 113-115 Not Used | 116 Belizean |
| 117 British Honduran | 118 Haitian | 119 Dominica Island |
| 120 Basque | 121 Sephardic | 122-129 Not used |
| 130 White | 131-134 Not used | 135 Black (African American) |
| 136-144 Not used | 145 American Indian | 146 Alaska Native |
| 147-149 Not used | 150 Other Asian | 151 Asian Indian |
| 152 Chinese | 153 Filipino | 154 Japanese |
| 155 Korean | 156 Vietnamese | 157-159 Not used |
| 160 Native Hawaiian | 161-165 Not used | 166 Other Pacific Islander |
| 167 Guamanian or Chamorro | 168 Samoan | 169-199 Not used |
| 200 Spaniard | 201 Andalusian | 202 Asturian |
| 203 Castillian | 204 Catalonian | 205 Balearic Islander |
| 206 Gallego | 207 Valencian | 208 Canarian |
| 209 Spanish Basque |
| 210 Mexican (CHECK BOX) | 211 Mexican | 212 Mexican American |
| 213 Mexicano | 214 Chicano | 215 La Raza |
| 216 Mexican American Indian | 217 Not Used | 218 Mexican State |
| 219 Mexican Indian | 220 Not Used |
| 221 Costa Rican | 222 Guatemalan | 223 Honduran |
| 224 Nicaraguan | 225 Panamanian | 226 Salvadoran |
| 227 Central American | 228 Central American Indian | 229 Canal Zone |
| 230 Not Used |
| 231 Argentinean | 232 Bolivian | 233 Chilean |
| 234 Colombian | 235 Ecuadorian | 236 Paraguayan |
| 237 Peruvian | 238 Uruguayan | 239 Venezuelan |
| 240 South American Indian | 241 Criollo | 242 South American |
| 243-249 Not Used |
| 250 Latin American | 251 Latin | 252 Latino |
| 253-259 Not Used |
| 260 Puerto Rican (CHECK BOX) | 261 Puerto Rican | 262-269 Not Used |
| 270 Cuban (CHECK BOX) | 271 Cuban | 272-274 Not used |
| 275 Dominican | 276-279 Not Used |
| 280 Other Spanish/Hispanic (CHECK BOX) | 281 Hispanic | 282 Spanish |
| 283 Californio | 284 Tejano | 285 Nuevo Mexicano |
| 286 Spanish American | 287 Spanish American Indian | 288 Meso American Indian |
| 289 Mestizo | 290 Caribbean | |
| 291-298 Not Used | 299 Other Spanish/Hispanic, N.E.C. |
Industry 2007 Description |
2007 Census Code | 2007 NAICS Code |
| Agriculture, Forestry, Fishing, and Hunting, and Mining | 0170-0490 | 11-21 |
Agriculture, Forestry, Fishing, and Hunting |
0170-0290 | 11 |
Crop production |
170 | 111 |
Animal production |
180 | 112 |
Forestry except logging |
190 | 1131, 1132 |
Logging |
270 | 1133 |
Fishing, hunting and trapping |
280 | 114 |
Support activities for agriculture and forestry |
290 | 115 |
Mining, Quarrying, and Oil and Gas Extraction |
0370-0490 | 21 |
Oil and gas extraction |
370 | 211 |
Coal mining |
380 | 2121 |
Metal ore mining |
390 | 2122 |
Nonmetallic mineral mining and quarrying |
470 | 2123 |
Not specified type of mining |
480 | Part of 21 |
Support activities for mining |
490 | 213 |
| Construction | 770 | 23 |
Construction (the cleaning of buildings and dwellings is incidental during construction and immediately after construction) |
770 | 23 |
| Manufacturing | 1070-3990 | 31-33 |
Animal food, grain and oilseed milling |
1070 | 3111, 3112 |
Sugar and confectionery products |
1080 | 3113 |
Fruit and vegetable preserving and specialty food manufacturing |
1090 | 3114 |
Dairy product manufacturing |
1170 | 3115 |
Animal slaughtering and processing |
1180 | 3116 |
Retail bakeries |
1190 | 311811; 3118 exc. |
Bakeries, except retail |
1270 | 311811 |
Seafood and other miscellaneous foods, n.e.c. |
1280 | 3117, 3119 |
Not specified food industries |
1290 | Part of 311 |
Beverage manufacturing |
1370 | 3121 |
Tobacco manufacturing |
1390 | 3122 |
Fiber, yarn, and thread mills |
1470 | 3131 |
Fabric mills, except knitting mills |
1480 | 3132 exc. 31324 |
Textile and fabric finishing and fabric coating mills |
1490 | 3133 |
Carpet and rug mills |
1570 | 31411 |
Textile product mills, except carpet and rug |
1590 | 314 exc. 31411 |
Knitting fabric mills, and apparel knitting mills |
1670 | 31324, 3151 |
Cut and sew apparel manufacturing |
1680 | 3152 |
Apparel accessories and other apparel manufacturing |
1690 | 3159 |
Footwear manufacturing |
1770 | 3162 |
Leather tanning and finishing and other allied products manufacturing |
1790 | 3161, 3169 |
Pulp, paper, and paperboard mills |
1870 | 3221 |
Paperboard containers and boxes |
1880 | 32221 |
Miscellaneous paper and pulp products |
1890 | 32222,32223, 32229 |
Printing and related support activities |
1990 | 3231 |
Petroleum refining |
2070 | 32411 |
Miscellaneous petroleum and coal products |
2090 | 32412,32419 |
Resin, synthetic rubber, and fibers and filaments manufacturing |
2170 | 3252 |
Agricultural chemical manufacturing |
2180 | 3253 |
Pharmaceutical and medicine manufacturing |
2190 | 3254 |
Paint, coating, and adhesive manufacturing |
2270 | 3255 |
Soap, cleaning compound, and cosmetics manufacturing |
2280 | 3256 |
Industrial and miscellaneous chemicals |
2290 | 3251, 3259 |
Plastics product manufacturing |
2370 | 3261 |
Tire manufacturing |
2380 | 32621 |
Rubber products, except tires, manufacturing |
2390 | 32622, 32629 |
Pottery, ceramics, and plumbing fixture manufacturing |
2470 | 32711 |
Structural clay product manufacturing |
2480 | 32712 |
Glass and glass product manufacturing |
2490 | 3272 |
Cement, concrete, lime, and gypsum product manufacturing |
2570 | 3273, 3274 |
Miscellaneous nonmetallic mineral product manufacturing |
2590 | 3279 |
Iron and steel mills and steel product manufacturing |
2670 | 3311, 3312 |
Aluminum production and processing |
2680 | 3313 |
Nonferrous metal (except aluminum) production and processing |
2690 | 3314 |
Foundries |
2770 | 3315 |
Metal forgings and stampings |
2780 | 3321 |
Cutlery and hand tool manufacturing |
2790 | 3322 |
Structural metals, and boiler, tank, and shipping container manufacturing |
2870 | 3323, 3324 |
Machine shops; turned product; screw, nut, and bolt manufacturing |
2880 | 3327 |
Coating, engraving, heat treating, and allied activities |
2890 | 3328 |
Ordnance |
2970 | 332992, 332993, 332994, 332995 |
Miscellaneous fabricated metal products manufacturing |
2980 | 3325, 3326, 3329 exc. 332992, 332993, 332994, 332995 |
Not specified metal industries |
2990 | Part of 331 and 332 |
Agricultural implement manufacturing |
3070 | 33311 |
Construction, and mining and oil and gas field machinery manufacturing |
3080 | 33312, 33313 |
Commercial and service industry machinery manufacturing |
3090 | 3333 |
Metalworking machinery manufacturing |
3170 | 3335 |
Engines, turbines, and power transmission equipment manufacturing |
3180 | 3336 |
Machinery manufacturing, n.e.c. |
3190 | 3332, 3334, 3339 |
Not specified machinery manufacturing |
3290 | Part of 333 |
Computer and peripheral equipment manufacturing |
3360 | 3341 |
Communications, and audio and video equipment manufacturing |
3370 | 3342, 3343 |
Navigational, measuring, electromedical, and control instruments manufacturing |
3380 | 3345 |
Electronic component and product manufacturing, n.e.c. |
3390 | 3344, 3346 |
Household appliance manufacturing |
3470 | 3352 |
Electric lighting and electrical equipment manufacturing, and other electrical component manufacturing, n.e.c. |
3490 | 3351, 3353, 3359 |
Motor vehicles and motor vehicle equipment manufacturing |
3570 | 3361, 3362, 3363 |
Aircraft and parts manufacturing |
3580 | 336411, 336412, 336413 |
Aerospace products and parts manufacturing |
3590 | 336414, 336415, 336419 |
Railroad rolling stock manufacturing |
3670 | 3365 |
Ship and boat building |
3680 | 3366 |
Other transportation equipment manufacturing |
3690 | 3369 |
Sawmills and wood preservation |
3770 | 3211 |
Veneer, plywood, and engineered wood products |
3780 | 3212 |
Prefabricated wood buildings and mobile homes |
3790 | 321991, 321992 |
Miscellaneous wood products |
3870 | 3219 exc. 321991, 321992 |
Furniture and related product manufacturing |
3890 | 337 |
Medical equipment and supplies manufacturing |
3960 | 3391 |
Sporting and athletic goods, and doll, toy and game manufacturing |
3970 | 33992,33993 |
Miscellaneous manufacturing, n.e.c. |
3980 | 3399 exc. 33992, 33993 |
Not specified manufacturing industries |
3990 | Part of 31, 32, 33 |
| Wholesale Trade | 4070-4590 | 42 |
Motor vehicles, parts and supplies merchant wholesalers |
4070 | 4231 |
Furniture and home furnishing merchant wholesalers |
4080 | 4232 |
Lumber and other construction materials merchant wholesalers |
4090 | 4233 |
Professional and commercial equipment and supplies merchant wholesalers |
4170 | 4234 |
Metals and minerals, except petroleum, merchant wholesalers |
4180 | 4235 |
Electrical and electronic goods merchant wholesalers |
4190 | 4236 |
Hardware, plumbing and heating equipment, and supplies merchant wholesalers |
4260 | 4237 |
Machinery, equipment, and supplies merchant wholesalers |
4270 | 4238 |
Recyclable material merchant wholesalers |
4280 | 42393 |
Miscellaneous durable goods merchant wholesalers |
4290 | 4239 exc. 42393 |
Paper and paper products merchant wholesalers |
4370 | 4241 |
Drugs, sundries, and chemical and allied products merchant wholesalers |
4380 | 4242, 4246 |
Apparel, fabrics, and notions merchant wholesalers |
4390 | 4243 |
Groceries and related products merchant wholesalers |
4470 | 4244 |
Farm product raw materials merchant wholesalers |
4480 | 4245 |
Petroleum and petroleum products merchant wholesalers |
4490 | 4247 |
Alcoholic beverages merchant wholesalers |
4560 | 4248 |
Farm supplies merchant wholesalers |
4570 | 42491 |
Miscellaneous nondurable goods merchant wholesalers |
4580 | 4249 exc. 42491 |
Wholesale electronic markets and agents and brokers |
4585 | 4251 |
Not specified wholesale trade |
4590 | Part of 42 |
| Retail Trade | 4670-5790 | 44-45 |
Automobile dealers |
4670 | 4411 |
Other motor vehicle dealers |
4680 | 4412 |
Auto parts, accessories, and tire stores |
4690 | 4413 |
Furniture and home furnishings stores |
4770 | 442 |
Household appliance stores |
4780 | 443111 |
Radio, TV, and computer stores |
4790 | 443112, 44312 |
Building material and supplies dealers |
4870 | 4441 exc. 44413 |
Hardware stores |
4880 | 44413 |
Lawn and garden equipment and supplies stores |
4890 | 4442 |
Grocery stores |
4970 | 4451 |
Specialty food stores |
4980 | 4452 |
Beer, wine, and liquor stores |
4990 | 4453 |
Pharmacies and drug stores |
5070 | 44611 |
Health and personal care, except drug, stores |
5080 | 446 exc. 44611 |
Gasoline stations |
5090 | 447 |
Clothing stores |
5170 | 4481 |
Shoe stores |
5180 | 44821 |
Jewelry, luggage, and leather goods stores |
5190 | 4483 |
Sporting goods, camera, and hobby and toy stores |
5270 | 44313, 45111, 45112 |
Sewing, needlework, and piece goods stores |
5280 | 45113 |
Music stores |
5290 | 45114, 45122 |
Book stores and news dealers |
5370 | 45121 |
Department stores and discount stores |
5380 | 45211 |
Miscellaneous general merchandise stores |
5390 | 4529 |
Retail florists |
5470 | 4531 |
Office supplies and stationery stores |
5480 | 45321 |
Used merchandise stores |
5490 | 4533 |
Gift, novelty, and souvenir shops |
5570 | 45322 |
Miscellaneous retail stores |
5580 | 4539 |
Electronic shopping |
5590 | 454111 |
Electronic auctions |
5591 | 454112 |
Mail order houses |
5592 | 454113 |
Vending machine operators |
5670 | 4542 |
Fuel dealers |
5680 | 45431 |
Other direct selling establishments |
5690 | 45439 |
Not specified retail trade |
5790 | Part of 44, 45 |
| Transportation and Warehousing and Utilities | 6070-6390, 0570-0690 | 48-49, 22 |
Transportation and Warehousing |
6070-6390 | 48-49 |
Air transportation |
6070 | 481 |
Rail transportation |
6080 | 482 |
Water transportation |
6090 | 483 |
Truck transportation |
6170 | 484 |
Bus service and urban transit |
6180 | 4851, 4852, 4854, 4855, 4859 |
Taxi and limousine service |
6190 | 4853 |
Pipeline transportation |
6270 | 486 |
Scenic and sightseeing transportation |
6280 | 487 |
Services incidental to transportation |
6290 | 488 |
Postal Service |
6370 | 491 |
Couriers and messengers |
6380 | 492 |
Warehousing and storage |
6390 | 493 |
Utilities |
0570-0690 | 22 |
Electric power generation, transmission and distribution |
570 | 2211 |
Natural gas distribution |
580 | 2212 |
Electric and gas, and other combinations |
590 | Pts. 2211, 2212 |
Water, steam, air-conditioning, and irrigation systems |
670 | 22131, 22133 |
Sewage treatment facilities |
680 | 22132 |
Not specified utilities |
690 | Part of 22 |
| Information | 6470-6780 | 51 |
Newspaper publishers |
6470 | 51111 |
Periodical, book, and directory publishers |
6480 | 5111 exc. 51111 |
Software publishers |
6490 | 5112 |
Motion picture and video industries |
6570 | 5121 |
Sound recording industries |
6590 | 5122 |
Radio and television broadcasting and cable subscription programming |
6670 | 515 |
Internet publishing and broadcasting and web search portals |
6672 | 51913 |
Wired telecommunications carriers |
6680 | 5171 |
Other telecommunications services |
6690 | 517 exc. 5171 |
Data processing, hosting, and related services |
6695 | 5182 |
Libraries and archives |
6770 | 51912 |
Other information services |
6780 | 5191 exc. 51912, 51913 |
| Finance and Insurance, and Real Estate, and Rental and Leasing | 6870-7190 | 52-53 |
Finance and Insurance |
6870-6990 | 52 |
Banking and related activities |
6870 | 521, 52211,52219 |
Savings institutions, including credit unions |
6880 | 52212, 52213 |
Non-depository credit and related activities |
6890 | 5222, 5223 |
Securities, commodities, funds, trusts, and other financial investments |
6970 | 523, 525 |
Insurance carriers and related activities |
6990 | 524 |
Real Estate and Rental and Leasing |
7070-7190 | 53 |
Real estate |
7070 | 531 |
Automotive equipment rental and leasing |
7080 | 5321 |
Video tape and disk rental |
7170 | 53223 |
Other consumer goods rental |
7180 | 53221, 53222, 53229, 5323 |
Commercial, industrial, and other intangible assets rental and leasing |
7190 | 5324, 533 |
| Professional, Scientific, and Management, and Administrative, and Waste Management Services | 7270-7790 | 54-56 |
Professional, Scientific, and Technical Services |
7270-7490 | 54 |
Legal services |
7270 | 5411 |
Accounting, tax preparation, bookkeeping, and payroll services |
7280 | 5412 |
Architectural, engineering, and related services |
7290 | 5413 |
Specialized design services |
7370 | 5414 |
Computer systems design and related services |
7380 | 5415 |
Management, scientific, and technical consulting services |
7390 | 5416 |
Scientific research and development services |
7460 | 5417 |
Advertising and related services |
7470 | 5418 |
Veterinary services |
7480 | 54194 |
Other professional, scientific, and technical services |
7490 | 5419 exc. 54194 |
Management of companies and enterprises |
7570 | 55 |
Management of companies and enterprises |
7570 | 55 |
Administrative and support and waste management services |
7580-7790 | 56 |
Employment services |
7580 | 5613 |
Business support services |
7590 | 5614 |
Travel arrangements and reservation services |
7670 | 5615 |
Investigation and security services |
7680 | 5616 |
Services to buildings and dwellings (except cleaning during construction and immediately after construction) |
7690 | 5617 exc. 56173 |
Landscaping services |
7770 | 56173 |
Other administrative and other support services |
7780 | 5611, 5612, 5619 |
Waste management and remediation services |
7790 | 562 |
| Educational Services, and Health Care and Social Assistance | 7860-8470 | 61-62 |
Educational Services |
7860-7890 | 61 |
Elementary and secondary schools |
7860 | 6111 |
Colleges and universities, including junior colleges |
7870 | 6112, 6113 |
Business, technical, and trade schools and training |
7880 | 6114, 6115 |
Other schools and instruction, and educational support services |
7890 | 6116, 6117 |
Health Care and Social Assistance |
7970-8470 | 62 |
Offices of physicians |
7970 | 6211 |
Offices of dentists |
7980 | 6212 |
Offices of chiropractors |
7990 | 62131 |
Offices of optometrists |
8070 | 62132 |
Offices of other health practitioners |
8080 | 6213 exc. 62131, 62132 |
Outpatient care centers |
8090 | 6214 |
Home health care services |
8170 | 6216 |
Other health care services |
8180 | 6215, 6219 |
Hospitals |
8190 | 622 |
Nursing care facilities |
8270 | 6231 |
Residential care facilities, without nursing |
8290 | 6232, 6233, 6239 |
Individual and family services |
8370 | 6241 |
Community food and housing, and emergency services |
8380 | 6242 |
Vocational rehabilitation services |
8390 | 6243 |
Child day care services |
8470 | 6244 |
| Arts, Entertainment, and Recreation, and Accommodation and Food Services | 8560-8690 | 71-72 |
Arts, Entertainment, and Recreation |
8560-8590 | 71 |
Independent artists, performing arts, spectator sports, and related industries |
8560 | 711 |
Museums, art galleries, historical sites, and similar institutions |
8570 | 712 |
Bowling centers |
8580 | 71395 |
Other amusement, gambling, and recreation industries |
8590 | 713 exc. 71395 |
Accommodation and Food Services |
8660-8690 | 72 |
Traveler accommodation |
8660 | 7211 |
Recreational vehicle parks and camps, and rooming and boarding houses |
8670 | 7212, 7213 |
Restaurants and other food services |
8680 | 722 exc. 7224 |
Drinking places, alcoholic beverages |
8690 | 7224 |
| Other Services, Except Public Administration | 8770-9290 | 81 |
Automotive repair and maintenance |
8770 | 8111 exc. 811192 |
Car washes |
8780 | 811192 |
Electronic and precision equipment repair and maintenance |
8790 | 8112 |
Commercial and industrial machinery and equipment repair and maintenance |
8870 | 8113 |
Personal and household goods repair and maintenance |
8880 | 8114 exc. 81143 |
Footwear and leather goods repair |
8890 | 81143 |
Barber shops |
8970 | 812111 |
Beauty salons |
8980 | 812112 |
Nail salons and other personal care services |
8990 | 812113, 81219 |
Drycleaning and laundry services |
9070 | 8123 |
Funeral homes, and cemeteries and crematories |
9080 | 8122 |
Other personal services |
9090 | 8129 |
Religious organizations |
9160 | 8131 |
Civic, social, advocacy organizations, and grantmaking and giving services |
9170 | 8132, 8133, 8134 |
Labor unions |
9180 | 81393 |
Business, professional, political, and similar organizations |
9190 | 8139 exc. 81393 |
Private households |
9290 | 814 |
| Public Administration | 9370-9590 | 92 |
Executive offices and legislative bodies |
9370 | 92111, 92112, 92114, pt. 92115 |
Public finance activities |
9380 | 92113 |
Other general government and support |
9390 | 92119 |
Justice, public order, and safety activities |
9470 | 922, pt. 92115 |
Administration of human resource programs |
9480 | 923 |
Administration of environmental quality and housing programs |
9490 | 924, 925 |
Administration of economic programs and space research |
9570 | 926, 927 |
National security and international affairs |
9590 | 928 |
| Military | 9670-9870 | 928110 |
U. S. Army |
9670 | 928110 |
U. S. Air Force |
9680 | 928110 |
U. S. Navy |
9690 | 928110 |
U. S. Marines |
9770 | 928110 |
U. S. Coast Guard |
9780 | 928110 |
Armed Forces, Branch not specified |
9790 | 928110 |
Military Reserves or National Guard |
9870 | 928110 |
Unemployed and last worked 5 years ago or earlier or never worked |
9920 | none |
| 601 Jamaican Creole | 602 Krio | 603 Hawaiian Pidgin |
| 604 Pidgin | 605 Gullah | 606 Saramacca |
| 607 German | 608 Pennsylvania Dutch | 609 Yiddish |
| 610 Dutch | 611 Afrikaans | 612 Frisian |
| 613 Luxembourgian | 614 Swedish | 615 Danish |
| 616 Norwegian | 617 Icelandic | 618 Faroese |
| 619 Italian | 620 French | 621 Provencal |
| 622 Patois | 623 French Creole | 624 Cajun |
| 625 Spanish | 626 Catalonian | 627 Ladino |
| 628 Pachuco | 629 Portuguese | 630 Papia Mentae |
| 631 Romanian | 632 Rhaeto-Romanic | 633 Welsh |
| 634 Breton | 635 Irish Gaelic | 636 Scottic Gaelic |
| 637 Greek | 638 Albanian | 639 Russian |
| 640 Bielorussian | 641 Ukrainian | 642 Czech |
| 643 Kashubian | 644 Lusatian | 645 Polish |
| 646 Slovak | 647 Bulgarian | 648 Macedonian |
| 649 Serbocroatian | 650 Croatian | 651 Serbian |
| 652 Slovene | 653 Lithuanian | 654 Lettish |
| 655 Armenian | 656 Persian | 657 Pashto |
| 658 Kurdish | 659 Balochi | 660 Tadzhik |
| 661 Ossete | 662 India N.E.C. | 663 Hindi |
| 664 Bengali | 665 Panjabi | 666 Marathi |
| 667 Gujarathi | 668 Bihari | 669 Rajasthani |
| 670 Oriya | 671 Urdu | 672 Assamese |
| 673 Kashmiri | 674 Nepali | 675 Sindhi |
| 676 Pakistan N.E.C. | 677 Sinhalese | 678 Romany |
| 679 Finnish | 680 Estonian | 681 Lapp |
| 682 Hungarian | 683 Other Uralic Lang. | 684 Chuvash |
| 685 Karakalpak | 686 Kazakh | 687 Kirghiz |
| 688 Karachay | 689 Uighur | 690 Azerbaijani |
| 691 Turkish | 692 Turkmen | 693 Yakut |
| 694 Mongolian | 695 Tungus | 696 Caucasian |
| 697 Basque | 698 Dravidian | 699 Brahui |
| 700 Gondi | 701 Telugu | 702 Kannada |
| 703 Malayalam | 704 Tamil | 705 Kurukh |
| 706 Munda | 707 Burushaski | 708 Chinese |
| 709 Hakka | 710 Kan, Hsiang | 711 Cantonese |
| 712 Mandarin | 713 Fuchow | 714 Formosan |
| 715 Wu | 716 Tibetan | 717 Burmese |
| 718 Karen | 719 Kachin | 720 Thai |
| 721 Mien | 722 Hmong | 723 Japanese |
| 724 Korean | 725 Laotian | 726 Mon-Khmer, Cambodian |
| 727 Paleo-Siberian | 728 Vietnamese | 729 Muong |
| 730 Buginese | 731 Moluccan | 732 Indonesian |
| 733 Achinese | 734 Balinese | 735 Cham |
| 736 Javanese | 737 Madurese | 738 Malagasy |
| 739 Malay | 740 Minangkabau | 741 Sundanese |
| 742 Tagalog | 743 Bisayan | 744 Sebuano |
| 745 Pangasinan | 746 Ilocano | 747 Bikol |
| 748 Pampangan | 749 Gorontalo | 750 Micronesian |
| 751 Carolinian | 752 Chamorro | 753 Gilbertese |
| 754 Kusaiean | 755 Marshallese | 756 Mokilese |
| 757 Mortlockese | 758 Nauruan | 759 Palau |
| 760 Ponapean | 761 Trukese | 762 Ulithean |
| 763 Woleai-Ulithi | 764 Yapese | 765 Melanesian |
| 766 Polynesian | 767 Samoan | 768 Tongan |
| 769 Niuean | 770 Tokelauan | 771 Fijian |
| 772 Marquesan | 773 Rarotongan | 774 Maori |
| 775 Nukuoro | 776 Hawaiian | 777 Arabic |
| 778 Hebrew | 779 Syriac | 780 Amharic |
| 781 Berber | 782 Chadic | 783 Cushite |
| 784 Sudanic | 785 Nilotic | 786 Nilo-Hamitic |
| 787 Nubian | 788 Saharan | 789 Nilo-Saharan |
| 790 Khoisan | 791 Swahili | 792 Bantu |
| 793 Mande | 794 Fulani | 795 Gur |
| 796 Kru, Ibo, Yoruba | 797 Efik | 798 Mbum (And Related) |
| 799 African | 800 Unangan/Aleut | 801 Alutiiq/Sugpiaq |
| 802 Eskimo | 803 Inupiaq | 804 St Lawrence Island Yupik |
| 805 Central Alaskan Yup'ik | 806 Algonquian | 807 Arapaho |
| 808 Atsina | 809 Blackfoot | 810 Cheyenne |
| 811 Cree | 812 Delaware | 813 Fox |
| 814 Kickapoo | 815 Menomini | 816 French Cree |
| 817 Miami | 818 Micmac | 819 Ojibwa |
| 820 Ottawa | 821 Passamaquoddy | 822 Penobscot |
| 823 Abnaki | 824 Potawatomi | 825 Shawnee |
| 826 Wiyot | 827 Yurok | 828 Kutenai |
| 829 Makah | 830 Kwakiutl | 831 Nootka |
| 833 Lower Chehalis | 834 Upper Chehalis | 835 Clallam |
| 836 Coeur D'Alene | 837 Columbia | 838 Cowlitz |
| 839 Salish | 840 Nootsack | 841 Okanogan |
| 842 Puget Sound Salish | 843 Quinault | 844 Tillamook |
| 845 Twana | 846 Haida | 847 Athapascan |
| 848 Ahtena | 849 Han | 850 Ingalit |
| 851 Koyukon | 852 Kuchin | 853 Upper Kuskokwim |
| 854 Tanaina | 855 Tanana | 856 Tanacross |
| 857 Upper Tanana | 858 Tutchone | 859 Chasta Costa |
| 860 Hupa | 861 Other Athapascan-Eyak | 862 Apache |
| 863 Kiowa | 864 Navaho | 865 Eyak |
| 866 Tlingit | 867 Mountain Maidu | 868 Northwest Maidu |
| 869 Southern Maidu | 870 Coast Miwok | 871 Plains Miwok |
| 872 Sierra Miwok | 873 Nomlaki | 874 Patwin |
| 875 Wintun | 876 Foothill North Yokuts | 877 Tachi |
| 878 Santiam | 879 Siuslaw | 880 Klamath |
| 881 Nez Perce | 882 Sahaptian | 883 Upper Chinook |
| 884 Tsimshian | 885 Achumawi | 886 Atsugewi |
| 887 Karok | 888 Pomo | 889 Shastan |
| 890 Washo | 891 Up River Yuman | 892 Cocomaricopa |
| 893 Mohave | 894 Yuma | 895 Diegueno |
| 896 Delta River Yuman | 897 Upland Yuman | 898 Havasupai |
| 899 Walapai | 900 Yavapai | 901 Chumash |
| 902 Tonkawa | 903 Yuchi | 904 Crow |
| 905 Hidatsa | 906 Mandan | 907 Dakota |
| 908 Chiwere | 909 Winnebago | 910 Kansa |
| 911 Omaha | 912 Osage | 913 Ponca |
| 914 Quapaw | 915 Alabama | 916 Choctaw |
| 917 Mikasuki | 918 Hichita | 919 Koasati |
| 920 Muskogee | 921 Chetemacha | 922 Yuki |
| 923 Wappo | 924 Keres | 925 Iroquois |
| 926 Mohawk | 927 Oneida | 928 Onondaga |
| 929 Cayuga | 930 Seneca | 931 Tuscarora |
| 932 Wyandot | 933 Cherokee | 934 Arikara |
| 935 Caddo | 936 Pawnee | 937 Wichita |
| 938 Comanche | 939 Mono | 940 Paiute |
| 941 Northern Paiute | 942 Southern Paiute | 943 Chemehuevi |
| 944 Kawaiisu | 945 Ute | 946 Shoshoni |
| 947 Panamint | 948 Hopi | 949 Cahuilla |
| 950 Cupeno | 951 Luiseno | 952 Serrano |
| 953 Tubatulabal | 954 Pima | 955 Yaqui |
| 956 Aztecan | 957 Sonoran, n.e.c. | 959 Picuris |
| 960 Tiwa | 961 Sandia | 962 Tewa |
| 963 Towa | 964 Zuni | 965 Chinook Jargon |
| 966 American Indian | 967 Misumalpan | 968 Mayan Languages |
| 969 Tarascan | 970 Mapuche | 971 Oto-Manguen |
| 972 Quechua | 973 Aymara | 974 Arawakian |
| 975 Chibchan | 976 Tupi-Guarani | 977 Jicarilla |
| 978 Chiricahua | 979 San Carlos | 980 Kiowa-apache |
| 981 Kalispel | 982 Spokane | 983-997 Not Used |
| 998 Specified Not Listed | 999 Not Specified |
| Occupation 2010 Description | 2010 Census Code | 2010 SOC Code |
The 2010 census occupation classification list has 539 codes including 4 military codes. |
||
| Management, Business, Science, and Arts Occupations: | 0010-3540 | 11-0000 - 29-0000 |
Management, Business, and Financial Occupations: |
0010-0950 | 11-0000 - 13-0000 |
Management Occupations: |
0010-0430 | 11-0000 |
Chief executives |
0010 | 11-1011 |
General and operations managers |
0020 | 11-1021 |
Legislators |
0030 | 11-1031 |
Advertising and promotions managers |
0040 | 11-2011 |
Marketing and sales managers |
0050 | 11-2020 |
Public relations and fundraising managers |
0060 | 11-2031 |
Administrative services managers |
0100 | 11-3011 |
Computer and information systems managers |
0110 | 11-3021 |
Financial managers |
0120 | 11-3031 |
Compensation and benefits managers |
0135 | 11-3111 |
Human resources managers |
0136 | 11-3121 |
Training and development managers |
0137 | 11-3131 |
Industrial production managers |
0140 | 11-3051 |
Purchasing managers |
0150 | 11-3061 |
Transportation, storage, and distribution managers |
0160 | 11-3071 |
Farmers, ranchers, and other agricultural managers |
0205 | 11-9013 |
Construction managers |
0220 | 11-9021 |
Education administrators |
0230 | 11-9030 |
Architectural and engineering managers |
0300 | 11-9041 |
Food service managers |
0310 | 11-9051 |
Funeral service managers |
0325 | 11-9061 |
Gaming managers |
0330 | 11-9071 |
Lodging managers |
0340 | 11-9081 |
Medical and health services managers |
0350 | 11-9111 |
Natural sciences managers |
0360 | 11-9121 |
Postmasters and mail superintendents |
0400 | 11-9131 |
Property, real estate, and community association managers |
0410 | 11-9141 |
Social and community service managers |
0420 | 11-9151 |
Emergency management directors |
0425 | 11-9161 |
Managers, all other |
0430 | 11-9199 |
Business and Financial Operations Occupations: |
0500-0950 | 13-0000 |
Agents and business managers of artists, performers, and athletes |
0500 | 13-1011 |
Buyers and purchasing agents, farm products |
0510 | 13-1021 |
Wholesale and retail buyers, except farm products |
0520 | 13-1022 |
Purchasing agents, except wholesale, retail, and farm products |
0530 | 13-1023 |
Claims adjusters, appraisers, examiners, and investigators |
0540 | 13-1030 |
Compliance officers |
0565 | 13-1041 |
Cost estimators |
0600 | 13-1051 |
Human resources workers |
0630 | 13-1070 |
Compensation, benefits, and job analysis specialists |
0640 | 13-1141 |
Training and development specialists |
0650 | 13-1151 |
Logisticians |
0700 | 13-1081 |
Management analysts |
0710 | 13-1111 |
Meeting, convention, and event planners |
0725 | 13-1121 |
Fundraisers |
0726 | 13-1131 |
Market research analysts and marketing specialists |
0735 | 13-1161 |
Business operations specialists, all other |
0740 | 13-1199 |
Accountants and auditors |
0800 | 13-2011 |
Appraisers and assessors of real estate |
0810 | 13-2021 |
Budget analysts |
0820 | 13-2031 |
Credit analysts |
0830 | 13-2041 |
Financial analysts |
0840 | 13-2051 |
Personal financial advisors |
0850 | 13-2052 |
Insurance underwriters |
0860 | 13-2053 |
Financial examiners |
0900 | 13-2061 |
Credit counselors and loan officers |
0910 | 13-2070 |
Tax examiners and collectors, and revenue agents |
0930 | 13-2081 |
Tax preparers |
0940 | 13-2082 |
Financial specialists, all other |
0950 | 13-2099 |
Computer, Engineering, and Science Occupations: |
1000-1965 | 15-0000 - 19-0000 |
Computer and mathematical occupations: |
1000-1240 | 15-0000 |
Computer and information research scientists |
1005 | 15-1111 |
Computer systems analysts |
1006 | 15-1121 |
Information security analysts |
1007 | 15-1122 |
Computer programmers |
1010 | 15-1131 |
Software developers, applications and systems software |
1020 | 15-113X |
Web developers |
1030 | 15-1134 |
Computer support specialists |
1050 | 15-1150 |
Database administrators |
1060 | 15-1141 |
Network and computer systems administrators |
1105 | 15-1142 |
Computer network architects |
1106 | 15-1143 |
Computer occupations, all other |
1107 | 15-1199 |
Actuaries |
1200 | 15-2011 |
Mathematicians |
1210 | 15-2021 |
Operations research analysts |
1220 | 15-2031 |
Statisticians |
1230 | 15-2041 |
Miscellaneous mathematical science occupations |
1240 | 15-2090 |
Architecture and Engineering Occupations: |
1300-1560 | 17-0000 |
Architects, except naval |
1300 | 17-1010 |
Surveyors, cartographers, and photogrammetrists |
1310 | 17-1020 |
Aerospace engineers |
1320 | 17-2011 |
Agricultural engineers |
1330 | 17-2021 |
Biomedical engineers |
1340 | 17-2031 |
Chemical engineers |
1350 | 17-2041 |
Civil engineers |
1360 | 17-2051 |
Computer hardware engineers |
1400 | 17-2061 |
Electrical and electronics engineers |
1410 | 17-2070 |
Environmental engineers |
1420 | 17-2081 |
Industrial engineers, including health and safety |
1430 | 17-2110 |
Marine engineers and naval architects |
1440 | 17-2121 |
Materials engineers |
1450 | 17-2131 |
Mechanical engineers |
1460 | 17-2141 |
Mining and geological engineers, including mining safety engineers |
1500 | 17-2151 |
Nuclear engineers |
1510 | 17-2161 |
Petroleum engineers |
1520 | 17-2171 |
Engineers, all other |
1530 | 17-2199 |
Drafters |
1540 | 17-3010 |
Engineering technicians, except drafters |
1550 | 17-3020 |
Surveying and mapping technicians |
1560 | 17-3031 |
Life, Physical, and Social Science Occupations: |
1600-1965 | 19-0000 |
Agricultural and food scientists |
1600 | 19-1010 |
Biological scientists |
1610 | 19-1020 |
Conservation scientists and foresters |
1640 | 19-1030 |
Medical scientists |
1650 | 19-1040 |
Life scientists, all other |
1660 | 19-1099 |
Astronomers and physicists |
1700 | 19-2010 |
Atmospheric and space scientists |
1710 | 19-2021 |
Chemists and materials scientists |
1720 | 19-2030 |
Environmental scientists and geoscientists |
1740 | 19-2040 |
Physical scientists, all other |
1760 | 19-2099 |
Economists |
1800 | 19-3011 |
Survey researchers |
1815 | 19-3022 |
Psychologists |
1820 | 19-3030 |
Sociologists |
1830 | 19-3041 |
Urban and regional planners |
1840 | 19-3051 |
Miscellaneous social scientists and related workers |
1860 | 19-3090 |
Agricultural and food science technicians |
1900 | 19-4011 |
Biological technicians |
1910 | 19-4021 |
Chemical technicians |
1920 | 19-4031 |
Geological and petroleum technicians |
1930 | 19-4041 |
Nuclear technicians |
1940 | 19-4051 |
Social science research assistants |
1950 | 19-4061 |
Miscellaneous life, physical, and social science technicians |
1965 | 19-4090 |
Education, Legal, Community Service, Arts, and Media Occupations: |
2000-2960 | 21-0000 - 27-0000 |
Community and Social Service Occupations: |
2000-2060 | 21-0000 |
Counselors |
2000 | 21-1010 |
Social workers |
2010 | 21-1020 |
Probation officers and correctional treatment specialists |
2015 | 21-1092 |
Social and human service assistants |
2016 | 21-1093 |
Miscellaneous community and social service specialists, including health educators and community health workers |
2025 | 21-109X |
Clergy |
2040 | 21-2011 |
Directors, religious activities and education |
2050 | 21-2021 |
Religious workers, all other |
2060 | 21-2099 |
Legal Occupations: |
2100-2160 | 23-0000 |
Lawyers |
2100 | 23-1011 |
Judicial law clerks |
2105 | 23-1012 |
Judges, magistrates, and other judicial workers |
2110 | 23-1020 |
Paralegals and legal assistants |
2145 | 23-2011 |
Miscellaneous legal support workers |
2160 | 23-2090 |
Education, Training, and Library Occupations: |
2200-2550 | 25-0000 |
Postsecondary teachers |
2200 | 25-1000 |
Preschool and kindergarten teachers |
2300 | 25-2010 |
Elementary and middle school teachers |
2310 | 25-2020 |
Secondary school teachers |
2320 | 25-2030 |
Special education teachers |
2330 | 25-2050 |
Other teachers and instructors |
2340 | 25-3000 |
Archivists, curators, and museum technicians |
2400 | 25-4010 |
Librarians |
2430 | 25-4021 |
Library technicians |
2440 | 25-4031 |
Teacher assistants |
2540 | 25-9041 |
Other education, training, and library workers |
2550 | 25-90XX |
Arts, Design, Entertainment, Sports, and Media Occupations: |
2600-2960 | 27-0000 |
Artists and related workers |
2600 | 27-1010 |
Designers |
2630 | 27-1020 |
Actors |
2700 | 27-2011 |
Producers and directors |
2710 | 27-2012 |
Athletes, coaches, umpires, and related workers |
2720 | 27-2020 |
Dancers and choreographers |
2740 | 27-2030 |
Musicians, singers, and related workers |
2750 | 27-2040 |
Entertainers and performers, sports and related workers, all other |
2760 | 27-2099 |
Announcers |
2800 | 27-3010 |
News analysts, reporters and correspondents |
2810 | 27-3020 |
Public relations specialists |
2825 | 27-3031 |
Editors |
2830 | 27-3041 |
Technical writers |
2840 | 27-3042 |
Writers and authors |
2850 | 27-3043 |
Miscellaneous media and communication workers |
2860 | 27-3090 |
Broadcast and sound engineering technicians and radio operators |
2900 | 27-4010 |
Photographers |
2910 | 27-4021 |
Television, video, and motion picture camera operators and editors |
2920 | 27-4030 |
Media and communication equipment workers, all other |
2960 | 27-4099 |
Healthcare Practitioners and Technical Occupations: |
3000-3540 | 29-0000 |
Chiropractors |
3000 | 29-1011 |
Dentists |
3010 | 29-1020 |
Dietitians and nutritionists |
3030 | 29-1031 |
Optometrists |
3040 | 29-1041 |
Pharmacists |
3050 | 29-1051 |
Physicians and surgeons |
3060 | 29-1060 |
Physician assistants |
3110 | 29-1071 |
Podiatrists |
3120 | 29-1081 |
Audiologists |
3140 | 29-1181 |
Occupational therapists |
3150 | 29-1122 |
Physical therapists |
3160 | 29-1123 |
Radiation therapists |
3200 | 29-1124 |
Recreational therapists |
3210 | 29-1125 |
Respiratory therapists |
3220 | 29-1126 |
Speech-language pathologists |
3230 | 29-1127 |
Exercise physiologists |
3235 | 29-1128 |
Therapists, all other |
3245 | 29-1129 |
Veterinarians |
3250 | 29-1131 |
Registered nurses |
3255 | 29-1141 |
Nurse anesthetists |
3256 | 29-1151 |
Nurse midwives |
3257 | 29-1161 |
Nurse practitioners |
3258 | 29-1171 |
Health diagnosing and treating practitioners, all other |
3260 | 29-1199 |
Clinical laboratory technologists and technicians |
3300 | 29-2010 |
Dental hygienists |
3310 | 29-2021 |
Diagnostic related technologists and technicians |
3320 | 29-2030 |
Emergency medical technicians and paramedics |
3400 | 29-2041 |
Health practitioner support technologists and technicians |
3420 | 29-2050 |
Licensed practical and licensed vocational nurses |
3500 | 29-2061 |
Medical records and health information technicians |
3510 | 29-2071 |
Opticians, dispensing |
3520 | 29-2081 |
Miscellaneous health technologists and technicians |
3535 | 29-2090 |
Other healthcare practitioners and technical occupations |
3540 | 29-9000 |
| Service Occupations: | 3600-4650 | 31-0000 - 39-0000 |
Healthcare Support Occupations: |
3600-3655 | 31-0000 |
Nursing, psychiatric, and home health aides |
3600 | 31-1010 |
Occupational therapy assistants and aides |
3610 | 31-2010 |
Physical therapist assistants and aides |
3620 | 31-2020 |
Massage therapists |
3630 | 31-9011 |
Dental assistants |
3640 | 31-9091 |
Medical assistants |
3645 | 31-9092 |
Medical transcriptionists |
3646 | 31-9094 |
Pharmacy aides |
3647 | 31-9095 |
Veterinary assistants and laboratory animal caretakers |
3648 | 31-9096 |
Phlebotomists |
3649 | 31-9097 |
Healthcare support workers, all other, including medical equipment preparers |
3655 | 31-909X |
Protective Service Occupations: |
3700-3955 | 33-0000 |
First-line supervisors of correctional officers |
3700 | 33-1011 |
First-line supervisors of police and detectives |
3710 | 33-1012 |
First-line supervisors of fire fighting and prevention workers |
3720 | 33-1021 |
First-line supervisors of protective service workers, all other |
3730 | 33-1099 |
Firefighters |
3740 | 33-2011 |
Fire inspectors |
3750 | 33-2020 |
Bailiffs, correctional officers, and jailers |
3800 | 33-3010 |
Detectives and criminal investigators |
3820 | 33-3021 |
Fish and game wardens |
3830 | 33-3031 |
Parking enforcement workers |
3840 | 33-3041 |
Police and sheriff's patrol officers |
3850 | 33-3051 |
Transit and railroad police |
3860 | 33-3052 |
Animal control workers |
3900 | 33-9011 |
Private detectives and investigators |
3910 | 33-9021 |
Security guards and gaming surveillance officers |
3930 | 33-9030 |
Crossing guards |
3940 | 33-9091 |
Transportation security screeners |
3945 | 33-9093 |
Lifeguards and other recreational, and all other protective service workers |
3955 | 33-909X |
Food Preparation and Serving Related Occupations: |
4000-4160 | 35-0000 |
Chefs and head cooks |
4000 | 35-1011 |
First-line supervisors of food preparation and serving workers |
4010 | 35-1012 |
Cooks |
4020 | 35-2010 |
Food preparation workers |
4030 | 35-2021 |
Bartenders |
4040 | 35-3011 |
Combined food preparation and serving workers, including fast food |
4050 | 35-3021 |
Counter attendants, cafeteria, food concession, and coffee shop |
4060 | 35-3022 |
Waiters and waitresses |
4110 | 35-3031 |
Food servers, nonrestaurant |
4120 | 35-3041 |
Dining room and cafeteria attendants and bartender helpers |
4130 | 35-9011 |
Dishwashers |
4140 | 35-9021 |
Hosts and hostesses, restaurant, lounge, and coffee shop |
4150 | 35-9031 |
Food preparation and serving related workers, all other |
4160 | 35-9099 |
Building and Grounds Cleaning and Maintenance Occupations: |
4200-4250 | 37-0000 |
First-line supervisors of housekeeping and janitorial workers |
4200 | 37-1011 |
First-line supervisors of landscaping, lawn service, and groundskeeping workers |
4210 | 37-1012 |
Janitors and building cleaners |
4220 | 37-201X |
Maids and housekeeping cleaners |
4230 | 37-2012 |
Pest control workers |
4240 | 37-2021 |
Grounds maintenance workers |
4250 | 37-3010 |
Personal Care and Service Occupations: |
4300-4650 | 39-0000 |
First-line supervisors of gaming workers |
4300 | 39-1010 |
First-line supervisors of personal service workers |
4320 | 39-1021 |
Animal trainers |
4340 | 39-2011 |
Nonfarm animal caretakers |
4350 | 39-2021 |
Gaming services workers |
4400 | 39-3010 |
Motion picture projectionists |
4410 | 39-3021 |
Ushers, lobby attendants, and ticket takers |
4420 | 39-3031 |
Miscellaneous entertainment attendants and related workers |
4430 | 39-3090 |
Embalmers and funeral attendants |
4460 | 39-40XX |
Morticians, undertakers, and funeral directors |
4465 | 39-4031 |
Barbers |
4500 | 39-5011 |
Hairdressers, hairstylists, and cosmetologists |
4510 | 39-5012 |
Miscellaneous personal appearance workers |
4520 | 39-5090 |
Baggage porters, bellhops, and concierges |
4530 | 39-6010 |
Tour and travel guides |
4540 | 39-7010 |
Childcare workers |
4600 | 39-9011 |
Personal care aides |
4610 | 39-9021 |
Recreation and fitness workers |
4620 | 39-9030 |
Residential advisors |
4640 | 39-9041 |
Personal care and service workers, all other |
4650 | 39-9099 |
| Sales and Office Occupations: | 4700-5940 | 41-0000 - 43-0000 |
Sales and Related Occupations: |
4700-4965 | 41-0000 |
First-line supervisors of retail sales workers |
4700 | 41-1011 |
First-line supervisors of non-retail sales workers |
4710 | 41-1012 |
Cashiers |
4720 | 41-2010 |
Counter and rental clerks |
4740 | 41-2021 |
Parts salespersons |
4750 | 41-2022 |
Retail salespersons |
4760 | 41-2031 |
Advertising sales agents |
4800 | 41-3011 |
Insurance sales agents |
4810 | 41-3021 |
Securities, commodities, and financial services sales agents |
4820 | 41-3031 |
Travel agents |
4830 | 41-3041 |
Sales representatives, services, all other |
4840 | 41-3099 |
Sales representatives, wholesale and manufacturing |
4850 | 41-4010 |
Models, demonstrators, and product promoters |
4900 | 41-9010 |
Real estate brokers and sales agents |
4920 | 41-9020 |
Sales engineers |
4930 | 41-9031 |
Telemarketers |
4940 | 41-9041 |
Door-to-door sales workers, news and street vendors, and related workers |
4950 | 41-9091 |
Sales and related workers, all other |
4965 | 41-9099 |
Office and Administrative Support Occupations: |
5000-5940 | 43-0000 |
First-line supervisors of office and administrative support workers |
5000 | 43-1011 |
Switchboard operators, including answering service |
5010 | 43-2011 |
Telephone operators |
5020 | 43-2021 |
Communications equipment operators, all other |
5030 | 43-2099 |
Bill and account collectors |
5100 | 43-3011 |
Billing and posting clerks |
5110 | 43-3021 |
Bookkeeping, accounting, and auditing clerks |
5120 | 43-3031 |
Gaming cage workers |
5130 | 43-3041 |
Payroll and timekeeping clerks |
5140 | 43-3051 |
Procurement clerks |
5150 | 43-3061 |
Tellers |
5160 | 43-3071 |
Financial clerks, all other |
5165 | 43-3099 |
Brokerage clerks |
5200 | 43-4011 |
Correspondence clerks |
5210 | 43-4021 |
Court, municipal, and license clerks |
5220 | 43-4031 |
Credit authorizers, checkers, and clerks |
5230 | 43-4041 |
Customer service representatives |
5240 | 43-4051 |
Eligibility interviewers, government programs |
5250 | 43-4061 |
File clerks |
5260 | 43-4071 |
Hotel, motel, and resort desk clerks |
5300 | 43-4081 |
Interviewers, except eligibility and loan |
5310 | 43-4111 |
Library assistants, clerical |
5320 | 43-4121 |
Loan interviewers and clerks |
5330 | 43-4131 |
New accounts clerks |
5340 | 43-4141 |
Order clerks |
5350 | 43-4151 |
Human resources assistants, except payroll and timekeeping |
5360 | 43-4161 |
Receptionists and information clerks |
5400 | 43-4171 |
Reservation and transportation ticket agents and travel clerks |
5410 | 43-4181 |
Information and record clerks, all other |
5420 | 43-4199 |
Cargo and freight agents |
5500 | 43-5011 |
Couriers and messengers |
5510 | 43-5021 |
Dispatchers |
5520 | 43-5030 |
Meter readers, utilities |
5530 | 43-5041 |
Postal service clerks |
5540 | 43-5051 |
Postal service mail carriers |
5550 | 43-5052 |
Postal service mail sorters, processors, and processing machine operators |
5560 | 43-5053 |
Production, planning, and expediting clerks |
5600 | 43-5061 |
Shipping, receiving, and traffic clerks |
5610 | 43-5071 |
Stock clerks and order fillers |
5620 | 43-5081 |
Weighers, measurers, checkers, and samplers, recordkeeping |
5630 | 43-5111 |
Secretaries and administrative assistants |
5700 | 43-6010 |
Computer operators |
5800 | 43-9011 |
Data entry keyers |
5810 | 43-9021 |
Word processors and typists |
5820 | 43-9022 |
Desktop publishers |
5830 | 43-9031 |
Insurance claims and policy processing clerks |
5840 | 43-9041 |
Mail clerks and mail machine operators, except postal service |
5850 | 43-9051 |
Office clerks, general |
5860 | 43-9061 |
Office machine operators, except computer |
5900 | 43-9071 |
Proofreaders and copy markers |
5910 | 43-9081 |
Statistical assistants |
5920 | 43-9111 |
Office and administrative support workers, all other |
5940 | 43-9199 |
| Natural Resources, Construction, and Maintenance Occupations: | 6005-7630 | 45-0000 - 49-0000 |
Farming, Fishing, and Forestry Occupations: |
6005-6130 | 45-0000 |
First-line supervisors of farming, fishing, and forestry workers |
6005 | 45-1011 |
Agricultural inspectors |
6010 | 45-2011 |
Animal breeders |
6020 | 45-2021 |
Graders and sorters, agricultural products |
6040 | 45-2041 |
Miscellaneous agricultural workers |
6050 | 45-2090 |
Fishers and related fishing workers |
6100 | 45-3011 |
Hunters and trappers |
6110 | 45-3021 |
Forest and conservation workers |
6120 | 45-4011 |
Logging workers |
6130 | 45-4020 |
Construction and Extraction Occupations: |
6200-6940 | 47-0000 |
First-line supervisors of construction trades and extraction workers |
6200 | 47-1011 |
Boilermakers |
6210 | 47-2011 |
Brickmasons, blockmasons, and stonemasons |
6220 | 47-2020 |
Carpenters |
6230 | 47-2031 |
Carpet, floor, and tile installers and finishers |
6240 | 47-2040 |
Cement masons, concrete finishers, and terrazzo workers |
6250 | 47-2050 |
Construction laborers |
6260 | 47-2061 |
Paving, surfacing, and tamping equipment operators |
6300 | 47-2071 |
Pile-driver operators |
6310 | 47-2072 |
Operating engineers and other construction equipment operators |
6320 | 47-2073 |
Drywall installers, ceiling tile installers, and tapers |
6330 | 47-2080 |
Electricians |
6355 | 47-2111 |
Glaziers |
6360 | 47-2121 |
Insulation workers |
6400 | 47-2130 |
Painters, construction and maintenance |
6420 | 47-2141 |
Paperhangers |
6430 | 47-2142 |
Pipelayers, plumbers, pipefitters, and steamfitters |
6440 | 47-2150 |
Plasterers and stucco masons |
6460 | 47-2161 |
Reinforcing iron and rebar workers |
6500 | 47-2171 |
Roofers |
6515 | 47-2181 |
Sheet metal workers |
6520 | 47-2211 |
Structural iron and steel workers |
6530 | 47-2221 |
Solar photovoltaic installers |
6540 | 47-2231 |
Helpers, construction trades |
6600 | 47-3010 |
Construction and building inspectors |
6660 | 47-4011 |
Elevator installers and repairers |
6700 | 47-4021 |
Fence erectors |
6710 | 47-4031 |
Hazardous materials removal workers |
6720 | 47-4041 |
Highway maintenance workers |
6730 | 47-4051 |
Rail-track laying and maintenance equipment operators |
6740 | 47-4061 |
Septic tank servicers and sewer pipe cleaners |
6750 | 47-4071 |
Miscellaneous construction and related workers |
6765 | 47-4090 |
Derrick, rotary drill, and service unit operators, oil, gas, and mining |
6800 | 47-5010 |
Earth drillers, except oil and gas |
6820 | 47-5021 |
Explosives workers, ordnance handling experts, and blasters |
6830 | 47-5031 |
Mining machine operators |
6840 | 47-5040 |
Roof bolters, mining |
6910 | 47-5061 |
Roustabouts, oil and gas |
6920 | 47-5071 |
Helpers--extraction workers |
6930 | 47-5081 |
Other extraction workers |
6940 | 47-50XX |
Installation, Maintenance, and Repair Occupations: |
7000-7630 | 49-0000 |
First-line supervisors of mechanics, installers, and repairers |
7000 | 49-1011 |
Computer, automated teller, and office machine repairers |
7010 | 49-2011 |
Radio and telecommunications equipment installers and repairers |
7020 | 49-2020 |
Avionics technicians |
7030 | 49-2091 |
Electric motor, power tool, and related repairers |
7040 | 49-2092 |
Electrical and electronics installers and repairers, transportation equipment |
7050 | 49-2093 |
Electrical and electronics repairers, industrial and utility |
7100 | 49-209X |
Electronic equipment installers and repairers, motor vehicles |
7110 | 49-2096 |
Electronic home entertainment equipment installers and repairers |
7120 | 49-2097 |
Security and fire alarm systems installers |
7130 | 49-2098 |
Aircraft mechanics and service technicians |
7140 | 49-3011 |
Automotive body and related repairers |
7150 | 49-3021 |
Automotive glass installers and repairers |
7160 | 49-3022 |
Automotive service technicians and mechanics |
7200 | 49-3023 |
Bus and truck mechanics and diesel engine specialists |
7210 | 49-3031 |
Heavy vehicle and mobile equipment service technicians and mechanics |
7220 | 49-3040 |
Small engine mechanics |
7240 | 49-3050 |
Miscellaneous vehicle and mobile equipment mechanics, installers, and repairers |
7260 | 49-3090 |
Control and valve installers and repairers |
7300 | 49-9010 |
Heating, air conditioning, and refrigeration mechanics and installers |
7315 | 49-9021 |
Home appliance repairers |
7320 | 49-9031 |
Industrial and refractory machinery mechanics |
7330 | 49-904X |
Maintenance and repair workers, general |
7340 | 49-9071 |
Maintenance workers, machinery |
7350 | 49-9043 |
Millwrights |
7360 | 49-9044 |
Electrical power-line installers and repairers |
7410 | 49-9051 |
Telecommunications line installers and repairers |
7420 | 49-9052 |
Precision instrument and equipment repairers |
7430 | 49-9060 |
Wind turbine service technicians |
7440 | 49-9081 |
Coin, vending, and amusement machine servicers and repairers |
7510 | 49-9091 |
Commercial divers |
7520 | 49-9092 |
Locksmiths and safe repairers |
7540 | 49-9094 |
Manufactured building and mobile home installers |
7550 | 49-9095 |
Riggers |
7560 | 49-9096 |
Signal and track switch repairers |
7600 | 49-9097 |
Helpers--installation, maintenance, and repair workers |
7610 | 49-9098 |
Other installation, maintenance, and repair workers |
7630 | 49-909X |
| Production, Transportation, and Material Moving Occupations: | 7700-9750 | 51-0000 - 53-0000 |
Production Occupations: |
7700-8965 | 51-0000 |
First-line supervisors of production and operating workers |
7700 | 51-1011 |
Aircraft structure, surfaces, rigging, and systems assemblers |
7710 | 51-2011 |
Electrical, electronics, and electromechanical assemblers |
7720 | 51-2020 |
Engine and other machine assemblers |
7730 | 51-2031 |
Structural metal fabricators and fitters |
7740 | 51-2041 |
Miscellaneous assemblers and fabricators |
7750 | 51-2090 |
Bakers |
7800 | 51-3011 |
Butchers and other meat, poultry, and fish processing workers |
7810 | 51-3020 |
Food and tobacco roasting, baking, and drying machine operators and tenders |
7830 | 51-3091 |
Food batchmakers |
7840 | 51-3092 |
Food cooking machine operators and tenders |
7850 | 51-3093 |
Food processing workers, all other |
7855 | 51-3099 |
Computer control programmers and operators |
7900 | 51-4010 |
Extruding and drawing machine setters, operators, and tenders, metal and plastic |
7920 | 51-4021 |
Forging machine setters, operators, and tenders, metal and plastic |
7930 | 51-4022 |
Rolling machine setters, operators, and tenders, metal and plastic |
7940 | 51-4023 |
Cutting, punching, and press machine setters, operators, and tenders, metal and plastic |
7950 | 51-4031 |
Drilling and boring machine tool setters, operators, and tenders, metal and plastic |
7960 | 51-4032 |
Grinding, lapping, polishing, and buffing machine tool setters, operators, and tenders, metal and plastic |
8000 | 51-4033 |
Lathe and turning machine tool setters, operators, and tenders, metal and plastic |
8010 | 51-4034 |
Milling and planing machine setters, operators, and tenders, metal and plastic |
8020 | 51-4035 |
Machinists |
8030 | 51-4041 |
Metal furnace operators, tenders, pourers, and casters |
8040 | 51-4050 |
Model makers and patternmakers, metal and plastic |
8060 | 51-4060 |
Molders and molding machine setters, operators, and tenders, metal and plastic |
8100 | 51-4070 |
Multiple machine tool setters, operators, and tenders, metal and plastic |
8120 | 51-4081 |
Tool and die makers |
8130 | 51-4111 |
Welding, soldering, and brazing workers |
8140 | 51-4120 |
Heat treating equipment setters, operators, and tenders, metal and plastic |
8150 | 51-4191 |
Layout workers, metal and plastic |
8160 | 51-4192 |
Plating and coating machine setters, operators, and tenders, metal and plastic |
8200 | 51-4193 |
Tool grinders, filers, and sharpeners |
8210 | 51-4194 |
Metal workers and plastic workers, all other |
8220 | 51-4199 |
Prepress technicians and workers |
8250 | 51-5111 |
Printing press operators |
8255 | 51-5112 |
Print binding and finishing workers |
8256 | 51-5113 |
Laundry and dry-cleaning workers |
8300 | 51-6011 |
Pressers, textile, garment, and related materials |
8310 | 51-6021 |
Sewing machine operators |
8320 | 51-6031 |
Shoe and leather workers and repairers |
8330 | 51-6041 |
Shoe machine operators and tenders |
8340 | 51-6042 |
Tailors, dressmakers, and sewers |
8350 | 51-6050 |
Textile bleaching and dyeing machine operators and tenders |
8360 | 51-6061 |
Textile cutting machine setters, operators, and tenders |
8400 | 51-6062 |
Textile knitting and weaving machine setters, operators, and tenders |
8410 | 51-6063 |
Textile winding, twisting, and drawing out machine setters, operators, and tenders |
8420 | 51-6064 |
Extruding and forming machine setters, operators, and tenders, synthetic and glass fibers |
8430 | 51-6091 |
Fabric and apparel patternmakers |
8440 | 51-6092 |
Upholsterers |
8450 | 51-6093 |
Textile, apparel, and furnishings workers, all other |
8460 | 51-6099 |
Cabinetmakers and bench carpenters |
8500 | 51-7011 |
Furniture finishers |
8510 | 51-7021 |
Model makers and patternmakers, wood |
8520 | 51-7030 |
Sawing machine setters, operators, and tenders, wood |
8530 | 51-7041 |
Woodworking machine setters, operators, and tenders, except sawing |
8540 | 51-7042 |
Woodworkers, all other |
8550 | 51-7099 |
Power plant operators, distributors, and dispatchers |
8600 | 51-8010 |
Stationary engineers and boiler operators |
8610 | 51-8021 |
Water and wastewater treatment plant and system operators |
8620 | 51-8031 |
Miscellaneous plant and system operators |
8630 | 51-8090 |
Chemical processing machine setters, operators, and tenders |
8640 | 51-9010 |
Crushing, grinding, polishing, mixing, and blending workers |
8650 | 51-9020 |
Cutting workers |
8710 | 51-9030 |
Extruding, forming, pressing, and compacting machine setters, operators, and tenders |
8720 | 51-9041 |
Furnace, kiln, oven, drier, and kettle operators and tenders |
8730 | 51-9051 |
Inspectors, testers, sorters, samplers, and weighers |
8740 | 51-9061 |
Jewelers and precious stone and metal workers |
8750 | 51-9071 |
Medical, dental, and ophthalmic laboratory technicians |
8760 | 51-9080 |
Packaging and filling machine operators and tenders |
8800 | 51-9111 |
Painting workers |
8810 | 51-9120 |
Photographic process workers and processing machine operators |
8830 | 51-9151 |
Semiconductor processors |
8840 | 51-9141 |
Adhesive bonding machine operators and tenders |
8850 | 51-9191 |
Cleaning, washing, and metal pickling equipment operators and tenders |
8860 | 51-9192 |
Cooling and freezing equipment operators and tenders |
8900 | 51-9193 |
Etchers and engravers |
8910 | 51-9194 |
Molders, shapers, and casters, except metal and plastic |
8920 | 51-9195 |
Paper goods machine setters, operators, and tenders |
8930 | 51-9196 |
Tire builders |
8940 | 51-9197 |
Helpers--production workers |
8950 | 51-9198 |
Production workers, all other |
8965 | 51-9199 |
Transportation and Material Moving Occupations: |
9000-9750 | 53-0000 |
Transportation Occupations: |
9000-9420 | 53-1000 - 53-6000 |
Supervisors of transportation and material moving workers |
9000 | 53-1000 |
Aircraft pilots and flight engineers |
9030 | 53-2010 |
Air traffic controllers and airfield operations specialists |
9040 | 53-2020 |
Flight attendants |
9050 | 53-2031 |
Ambulance drivers and attendants, except emergency medical technicians |
9110 | 53-3011 |
Bus drivers |
9120 | 53-3020 |
Driver/sales workers and truck drivers |
9130 | 53-3030 |
Taxi drivers and chauffeurs |
9140 | 53-3041 |
Motor vehicle operators, all other |
9150 | 53-3099 |
Locomotive engineers and operators |
9200 | 53-4010 |
Railroad brake, signal, and switch operators |
9230 | 53-4021 |
Railroad conductors and yardmasters |
9240 | 53-4031 |
Subway, streetcar, and other rail transportation workers |
9260 | 53-40XX |
Sailors and marine oilers |
9300 | 53-5011 |
Ship and boat captains and operators |
9310 | 53-5020 |
Ship engineers |
9330 | 53-5031 |
Bridge and lock tenders |
9340 | 53-6011 |
Parking lot attendants |
9350 | 53-6021 |
Automotive and watercraft service attendants |
9360 | 53-6031 |
Transportation inspectors |
9410 | 53-6051 |
Transportation attendants, except flight attendants |
9415 | 53-6061 |
Other transportation workers |
9420 | 53-60XX |
Material Moving Occupations: |
9500-9750 | 53-7000 |
Conveyor operators and tenders |
9500 | 53-7011 |
Crane and tower operators |
9510 | 53-7021 |
Dredge, excavating, and loading machine operators |
9520 | 53-7030 |
Hoist and winch operators |
9560 | 53-7041 |
Industrial truck and tractor operators |
9600 | 53-7051 |
Cleaners of vehicles and equipment |
9610 | 53-7061 |
Laborers and freight, stock, and material movers, hand |
9620 | 53-7062 |
Machine feeders and offbearers |
9630 | 53-7063 |
Packers and packagers, hand |
9640 | 53-7064 |
Pumping station operators |
9650 | 53-7070 |
Refuse and recyclable material collectors |
9720 | 53-7081 |
Mine shuttle car operators |
9730 | 53-7111 |
Tank car, truck, and ship loaders |
9740 | 53-7121 |
Material moving workers, all other |
9750 | 53-7199 |
| Military Specific Occupations: | 9800-9830 | 55-0000 |
Military officer special and tactical operations leaders |
9800 | 55-1010 |
First-line enlisted military supervisors |
9810 | 55-2010 |
Military enlisted tactical operations and air/weapons specialists and crew members |
9820 | 55-3010 |
Military, rank not specified |
9830 | none |
Unemployed, with no work experience in the last 5 years or earlier or never worked |
9920 | none |
This section contains the numerical list of U.S. States, U.S. Island Areas, foreign countries and other areas. This list was used to assign a three-digit code to birthplaces and state or foreign country of previous residences and places of work reported by each person. The alphabetical list used in coding included abbreviations and alternate names, including some historical names for countries and names of States, provinces, counties, or other subdivisions of countries.
The following list does not include any of these alternate names, but does include continent and area names used as defaults if a specific country was not named but a broader region or area was reported. The names of foreign countries shown on this list and in the publications reflect the most commonly used names for this country, not their official or legal names. Each entry shown on the following list has a unique code.
The U.S. States and U.S. Island Areas were assigned their Federal Information Processing Standards (FIPS) code preceded by a zero. For foreign countries codes were generally assigned by listing the countries or areas in alphabetical order within eight broad continent or regional areas: (1) Europe, (2) Asia, (3) Northern America, (4) Central America, (5) Caribbean, (6) South America, (7) Africa, and (8) Oceania.
138,139,140,141,142,143,144,145 United Kingdom: 138,141,142,143,144,145 United Kingdom, excluding England and Scotland: 127, 121, 135 Norway: 129,130,131 Portugal: 105,148,149 Czechoslovakia (includes Czech Republic and Slovakia): 147,154,167 Yugoslavia:
207,209,232,240,225 China: 207, 232, 225 China, excluding Hong Kong and Taiwan: 217, 220, 221 Korea:
The following list does not include any of these alternate names, but does include continent and area names used as defaults if a specific country was not named but a broader region or area was reported. The names of foreign countries shown on this list and in the publications reflect the most commonly used names for this country, not their official or legal names. Each entry shown on the following list has a unique code.
The U.S. States and U.S. Island Areas were assigned their Federal Information Processing Standards (FIPS) code preceded by a zero. For foreign countries codes were generally assigned by listing the countries or areas in alphabetical order within eight broad continent or regional areas: (1) Europe, (2) Asia, (3) Northern America, (4) Central America, (5) Caribbean, (6) South America, (7) Africa, and (8) Oceania.
| 001-059 United States | 060-099 U.S. Outlying Areas and Puerto Rico |
| 100-157, 160, 162-199 Europe | 158-159, 161, 200-299 Asia |
| 300-399 Americas | 400-499 Africa |
| 500-553 Oceania | 554-999 At Sea/Abroad, Not Reported |
001-059 United States
| 001 Alabama | 002 Alaska | 003 Not Used |
| 004 Arizona | 005 Arkansas | 006 California |
| 007 Not Used | 008 Colorado | 009 Connecticut |
| 010 Delaware | 011 District of Columbia | 012 Florida |
| 013 Georgia | 014 Not Used | 015 Hawaii |
| 016 Idaho | 017 Illinois | 018 Indiana |
| 019 Iowa | 020 Kansas | 021 Kentucky |
| 022 Louisiana | 023 Maine | 024 Maryland |
| 025 Massachusetts | 026 Michigan | 027 Minnesota |
| 028 Mississippi | 029 Missouri | 030 Montana |
| 031 Nebraska | 032 Nevada | 033 New Hampshire |
| 034 New Jersey | 035 New Mexico | 036 New York |
| 037 North Carolina | 038 North Dakota | 039 Ohio |
| 040 Oklahoma | 041 Oregon | 042 Pennsylvania |
| 043 Not Used | 044 Rhode Island | 045 South Carolina |
| 046 South Dakota | 047 Tennessee | 048 Texas |
| 049 Utah | 050 Vermont | 051 Virginia |
| 052 Not Used | 053 Washington | 054 West Virginia |
| 055 Wisconsin | 056 Wyoming | 057-059 Not Used |
060-099 U.S. Outlying Areas and Puerto Rico
| 060 American Samoa | 066 Guam | 067 Johnston Atoll |
| 069 Northern Marianas | 071 Midway Islands | 072 Puerto Rico |
| 076 Navassa Island | 078 U.S. Virgin Islands | 079 Wake Island |
| 081 Baker Island | 084 Howland Island | 086 Jarvis Island |
| 089 Kingman Reef | 095 Palmyra Atoll | 096 U.S. Island Area |
| 097-099 Not Used |
100-157, 160, 162-199 Europe
106-108,118,119,121,127,135,136,138-145 Northern Europe 101,102,103,109,110,122,123,125,126,137 Western Europe 115,116,120,124,129-131,133,134,146 Southern Europe 100,104,105,117,128,132,147-157,160,162,163,164,165,167, 168 Eastern Europe| 100 Albania | 101 Andorra | 102 Austria |
| 103 Belgium | 104 Bulgaria | 105 Czechoslovakia |
| 106 Denmark | 107 Faroe Islands | 108 Finland |
| 109 France | 110 Germany | 111-114 Not Used |
| 115 Gibraltar | 116 Greece | 117 Hungary |
| 118 Iceland | 119 Ireland | 120 Italy |
| 121 Jan Mayan | 122 Liechtenstein | 123 Luxembourg |
| 124 Malta | 125 Monaco | 126 Netherlands |
| 127 Norway | 128 Poland | 129 Portugal |
| 130 Azores Islands | 131 Madeira Islands | 132 Romania |
| 133 San Marino | 134 Spain | 135 Svalbard |
| 136 Sweden | 137 Switzerland | 138 United Kingdom |
| 139 England | 140 Scotland | 141 Wales |
| 142 Northern Ireland | 143 Guernsey | 144 Jersey |
| 145 Isle of Man | 146 Vatican City | 147 Yugoslavia |
| 148 Czech Republic | 149 Slovakia | 150 Bosnia and Herzegovina |
| 151 Croatia | 152 Macedonia | 153 Slovenia |
| 154 Serbia | 155 Estonia | 156 Latvia |
| 157 Lithuania | 160 Belarus | 162 Moldova |
| 163 Russia | 164 Ukraine | 165 USSR |
| 166 Europe | 167 Kosovo | 168 Montenegro |
| 169-199 Not Used |
158-159, 161, 200-299 Asia
207,209,215,217,220,221,225,228,232,240 Eastern Asia 200,202,203,210,212,218,219,227,229,231,238,241,244,246 South Central Asia 204,205,206,211,223,226,233,236,242,247,250 South Eastern Asia 158,159,161,201,208,213,214,216,222,224,230,234,235,239,243,245,248 Western Asia| 158 Armenia | 159 Azerbaijan | 161 Georgia |
| 200 Afghanistan | 201 Bahrain | 202 Bangladesh |
| 203 Bhutan | 204 Brunei | 205 Burma (Myanmar) |
| 206 Cambodia | 207 China | 208 Cyprus |
| 209 Hong Kong | 210 India | 211 Indonesia |
| 212 Iran | 213 Iraq | 214 Israel |
| 215 Japan | 216 Jordan | 217 Korea |
| 218 Kazakhstan | 219 Kyrgyzstan | 220 South Korea |
| 221 North Korea | 222 Kuwait | 223 Laos |
| 224 Lebanon | 225 Macau | 226 Malaysia |
| 227 Maldives | 228 Mongolia | 229 Nepal |
| 230 Oman | 231 Pakistan | 232 Paracel Islands |
| 233 Philippines | 234 Qatar | 235 Saudi Arabia |
| 236 Singapore | 237 Spratley Islands | 238 Sri Lanka |
| 239 Syria | 240 Taiwan | 241 Tajikistan |
| 242 Thailand | 243 Turkey | 244 Turkmenistan |
| 245 United Arab Emirates | 246 Uzbekistan | 247 Vietnam |
| 248 Yemen | 249 Asia | 250 East Timor |
| 251-299 Not Used |
300-399 Americas
300-302, 304-309 Northern America
| 300 Bermuda | 301 Canada | 302 Greenland |
| 304 St. Pierre & Miquelon | 305 North America | 306-309 Not Used |
303, 310-399 Latin America
303, 310-317 Central America
303, 310-317 Central America
| 303 Mexico | 310 Belize | 311 Costa Rica |
| 312 El Salvador | 313 Guatemala | 314 Honduras |
| 315 Nicaragua | 316 Panama | 317 Central America |
| 318-319 Not Used | 321 Antigua & | 322 Aruba |
| 320 Anguilla | Barbuda | 325 British Virgin Islands |
| 323 Bahamas | 324 Barbados | 328 Dominica |
| 326 Cayman Islands | 327 Cuba | 331 Guadeloupe |
| 329 Dominican Republic | 330 Grenada | 334 Martinique |
| 332 Haiti | 333 Jamaica | 337 St. Barthelemy |
| 335 Montserrat | 336 Netherlands Antilles | 340 St. Vincent & the Grenadines |
| 338 St. Kitts-Nevis | 339 St. Lucia | 343 West Indies |
| 341 Trinidad & Tobago | 342 Turks & Caicos Islands | 346 Saba |
| 344 Bonaire | 345 Curacao | 349-359 Not Used |
| 347 Sint Eustatius | 348 Sint Maarten |
360-399 South America
360-399 South America
| 360 Argentina | 361 Bolivia | 362 Brazil |
| 363 Chile | 364 Colombia | 365 Ecuador |
| 366 Falkland Islands | 367 French Guiana | 368 Guyana |
| 369 Paraguay | 370 Peru | 371 Suriname |
| 372 Uruguay | 373 Venezuela | 374 South America |
| 375-399 Not Used |
400-499 Africa
404,406,411,413,416-418,422,426,427,431,432,435,437,441,442,445,446,448,453,455,457,460,461 Eastern Africa 401,407,409,410,412,415,419,443,459 Middle Africa 400,414,430,436,451,456,458 Northern Africa 403,428,438,449,452 Southern Africa 402,405,408,420,421,423-425,429,433,434,439,440,444,447,450,454 Western Africa| 400 Algeria | 401 Angola | 402 Benin |
| 403 Botswana | 404 British Indian Ocean Territory | 407 Cameroon |
| 405 Burkina Faso | 406 Burundi | 410 Chad |
| 408 Cape Verde | 409 Central African Republic | 413 Djibouti |
| 411 Comoros | 412 Congo | 416 Ethiopia |
| 414 Egypt | 415 Equatorial Guinea | 419 Gabon |
| 417 Eritrea | 418 Europa Island | 422 Glorioso Islands |
| 420 Gambia | 421 Ghana | 425 Ivory Coast |
| 423 Guinea | 424 Guinea-Bissau | 428 Lesotho |
| 426 Juan De Nova Island | 427 Kenya | 431 Madagascar |
| 429 Liberia | 430 Libya | 434 Mauritania |
| 432 Malawi | 433 Mali | 437 Mozambique |
| 435 Mayotte | 436 Morocco | 440 Nigeria |
| 438 Namibia | 439 Niger | 443 Sao Tome & Principe |
| 441 Reunion | 442 Rwanda | 446 Seychelles |
| 444 Senegal | 445 Mauritius | 449 South Africa |
| 447 Sierra Leone | 448 Somalia | 452 Swaziland |
| 450 St. Helena | 451 Sudan | 455 Tromelin Island |
| 453 Tanzania | 454 Togo | 458 Western Sahara |
| 456 Tunisia | 457 Uganda | 461 Zimbabwe |
| 459 Democratic Republic of Congo (Zaire) | 460 Zambia | 462 Africa |
| 463-499 Not Used |
500-553 Oceania
501,502,506,507,515,517 Australia and New Zealand Subregion| 500 Not Used | 501 Australia | 502 Christmas Island, Indian |
| 503-504 Not Used | 505 Cook Islands | Ocean |
| 507 Heard & McDonald Islands | 508 Fiji | 506 Coral Sea Islands |
| 510 Kiribati | 511 Marshall Islands | 509 French Polynesia |
| 513 Nauru | 514 New Caledonia | 512 Micronesia |
| 516 Niue | 517 Norfolk Island | 515 New Zealand |
| 519 Papua New Guinea | 520 Pitcairn Islands | 518 Palau |
| 522 Tokelau | 523 Tonga | 521 Solomon Islands |
| 525 Vanuatu | 526 Wallis & Futuna Islands | 524 Tuvalu |
| 528 Oceania | 529-553 Not Used | 527 Samoa |
554-999 At Sea/Abroad, Not Reported
| 554 At Sea | 555 Abroad | 556-999 Not Used |
| 100-199 | WHITE |
| 100 | White (Checkbox) |
| 101 | White ethnic group, not elsewhere classified |
| 102 | Arab |
| 103 | English |
| 104 | French |
| 105 | German |
| 106 | Irish |
| 107 | Italian |
| 108 | Near Easterner |
| 109 | Polish |
| 110 | Scottish |
| 111 | Armenian |
| 112 | Assyrian |
| 113 | Egyptian |
| 114 | Iranian |
| 115 | Iraqi |
| 116 | Lebanese |
| 117 | Middle East |
| 118 | Palestinian |
| 119 | Syrian |
| 120 | Other Arab |
| 121 | Afghanistani |
| 122 | Israeli |
| 123 | Not Used |
|   |   |
| 124 | Cajun |
| 125 | Moroccan |
| 126 | North African |
| 127 | United Arab Emirates |
| 128 | Azerbaijani |
| 129 | Aryan |
| 130-139 | Not Used |
|   |   |
| 140 | Multiple WHITE responses |
| 141-149 | Not Used |
|   |   |
| 150 | White |
| 151 | Caucasian |
| 152-199 | Not Used |
|   |   |
| 200-299 | BLACK OR AFRICAN AMERICAN |
|   |   |
| 200 | Black, African Am., or Negro (Checkbox) |
| 201 | Black ethnic group, not elsewhere classified |
| 202 | African |
| 203 | African American |
| 204 | Afro-American |
|   |   |
| 205 | Nigritian |
| 206 | Negro |
| 207 | Bahamian |
| 208 | Barbadian |
| 209 | Batswana (Botswana) |
| 210-212 | Not Used |
|   |   |
| 213 | Ethiopian |
| 214 | Haitian |
| 215 | Jamaican |
| 216 | Liberian |
| 217 | Not Used |
|   |   |
| 218 | Namibian |
| 219 | Nigerian |
| 220 | Other African |
| 221-222 | Not Used |
|   |   |
| 223 | Trinidad and Tobago |
| 224 | West Indies |
| 225 | Zaire |
| 226-227 | Not Used |
|   |   |
| 228 | South African |
| 229 | Not Used |
|   |   |
| 230 | Dominica Islander |
| 231-233 | Not Used |
|   |   |
| 234 | Cayenne |
| 235-239 | Not Used |
|   |   |
| 240 | Multiple BLACK OR AFRICAN AMERICAN responses |
| 241-249 | Not Used |
|   |   |
| 250 | Black |
| 251-299 | Not Used |
|   |   |
| 300-399, A01- |   |
| Z99 | AMERICAN INDIAN AND ALASKA NATIVE |
|   |   |
| 300 | American Indian or Alaska Native (Checkbox) |
| 301-399 | Not Used |
|   |   |
| AMERICAN INDIAN TRIBES |   |
|   |   |
| Abenaki |   |
| A01 | Abenaki Nation of Missisquoi |
| A02 | Koasek (Cowasuck) Traditional Band of the Sovereign Abenaki Nation |
| A03-A04 | Not Used |
|   |   |
|   |   |
| Algonquian |   |
| A05 | Algonquian |
| A06-A08 | Not Used |
|   |   |
|   |   |
| Apache |   |
| A09 | Apache |
| A10 | Not Used |
| A11 | Fort Sill Apache (Chiricahua) |
| A12 | Jicarilla Apache Nation |
| A13 | Lipan Apache |
| A14 | Mescalero Apache Tribe of the Mescalero Reservation, New Mexico |
| A15 | Apache Tribe of Oklahoma |
| A16 | Tonto Apache Tribe of Arizona |
| A17 | San Carlos Apache Tribe of the San Carlos Reservation |
| A18 | White Mountain Apache Tribe of the Fort Apache Reservation, Arizona |
| A19-A23 | Not Used |
|   |   |
|   |   |
| Arapaho |   |
| A24 | Arapaho |
| A25 | Northern Arapaho |
| A26 | Southern Arapaho |
| A27 | Arapaho Tribe of the Wind River Reservation, Wyoming |
| A28-A33 | Not Used |
|   |   |
|   |   |
| Assiniboine |   |
| A34 | Assiniboine |
| A35-A37 | Not Used |
|   |   |
|   |   |
| Assiniboine Sioux |   |
| A38 | Assiniboine Sioux |
| A39 | Fort Peck Assiniboine and Sioux Tribes of the Fort Peck Indian Reservation |
| A40 | Fort Peck Assiniboine |
| A41 | Fort Peck Sioux |
| A42-A44 | Not Used |
|   |   |
|   |   |
| Blackfeet |   |
| A45 | Blackfeet Tribe of the Blackfeet Indian Reservation of Montana |
| A46-A50 | Not Used |
|   |   |
|   |   |
| Brotherton |   |
| A51 | Brotherton |
| A52 | Not Used |
|   |   |
|   |   |
| Burt Lake |   |
| A53 | Burt Lake Chippewa |
| A54 | Burt Lake Band of Ottawa and Chippewa Indians |
| A55 | Burt Lake Ottawa |
|   |   |
| Caddo |   |
| A56 | Caddo |
| A57 | Caddo Nation of Oklahoma |
| A58 | Caddo Adais Indians |
| A59-A60 | Not Used |
|   |   |
|   |   |
| Cahuilla |   |
| A61 | Agua Caliente Band of Cahuilla Indians |
| A62 | Augustine Band of Cahuilla Indians |
| A63 | Cabazon Band of Mission Indians |
|   |   |
| A64 | Cahuilla |
| A65 | Los Coyotes Band of Cahuilla and Cupeno Indians |
| A66 | Morongo Band of Cahuilla Mission Indians |
| A67 | Santa Rosa Band of Cahuilla Indians |
| A68 | Torres-Martinez Desert Cahuilla Indians |
| A69 | Ramona Band or Village of Cahuilla |
| A70-A74 | Not Used |
|   |   |
|   |   |
| California Tribes |   |
| A75 | Cahto Indian Tribe of the Laytonville Rancheria |
| A76 | Chimariko |
| A77-A78 | Not Used |
|   |   |
|   |   |
| A79 | Kawaiisu |
| A80 | Kern River Paiute Council |
| A81 | Mattole |
| A82 | Red Wood |
| A83 | Santa Rosa Indian Community |
| A84 | Takelma |
| A85 | Wappo |
| A86 | Yana |
| A87 | Yuki |
| A88 | Bear River Band of Rohnerville Rancheria |
| A89 | California Valley Miwok Tribe |
| A90 | Redding Rancheria, California |
| A91 | (see under Tolowa) |
| A92 | Cher-Ae Heights Indian Community of the Trinidad Rancheria |
| A93-A99 | Not Used |
| B01-B03 | Not Used |
|   |   |
|   |   |
| Catawba |   |
| B04 | Catawba Indian Nation |
| B05-B06 | Not Used |
|   |   |
|   |   |
| Cayuse |   |
| B07 | Cayuse |
| B08-B10 | Not Used |
|   |   |
|   |   |
| Chehalis |   |
| B11 | Confederated Tribes of the Chehalis Reservation, Washington |
| B12-B13 | Not Used |
|   |   |
|   |   |
| Chemakuan |   |
| B14 | Chemakuan |
| B15 | Hoh Indian Tribe of the Hoh Reservation, Washington |
| B16 | Quileute Tribe of the Quileute Reservation, Washington |
| B17-B18 | Not Used |
|   |   |
|   |   |
| Chemehuevi |   |
| B19 | Chemehuevi Indian Tribe |
| B20 | Not Used |
|   |   |
| Cherokee |   |
| B21 | Cherokee |
| B22 | Cherokee Alabama |
| B23 | Cherokee Tribe of Northeast Alabama |
| B24 | Cher-O-Creek Intratribal Indians |
| B25 | Eastern Band of Cherokees |
| B26 | Echota Cherokee Tribe of Alabama |
| B27 | Georgia Eastern Cherokee |
| B28 | Northern Cherokee Nation of Missouri and Arkansas |
| B29 | Tuscola |
| B30 | United Keetoowah Band of Cherokee |
| B31 | Cherokee Nation of Oklahoma (Western Cherokee) |
| B32 | Southeastern Cherokee Council |
| B33 | Sac River Band of the Chickamauga-Cherokee |
| B34 | White River Band of the Chickamauga-Cherokee |
| B35 | Four Winds Cherokee |
| B36 | Cherokee of Georgia |
| B37 | Piedmont American Indian Association-Lower Eastern Cherokee Nation SC (PAIA) |
| B38 | United Cherokee Ani-Yun-Wiya Nation |
| B39 | Cherokee Bear Clan of South Carolina |
|   |   |
| Cheyenne |   |
| B40 | Cheyenne |
| B41 | Northern Cheyenne Tribe of the Northern Cheyenne Reservation, Montana |
| B42 | Southern Cheyenne |
| B43-B45 | Not Used |
|   |   |
|   |   |
| Cheyenne-Arapaho |   |
| B46 | Cheyenne and Arapaho Tribes, Oklahoma |
| B47-B48 | Not Used |
|   |   |
|   |   |
| Chickahominy |   |
| B49 | Chickahominy Indian Tribe |
| B50 | Chickahominy Eastern Band |
| B51-B52 | Not Used |
|   |   |
|   |   |
| Chickasaw |   |
| B53 | Chickasaw Nation |
| B54 | Chaloklowa Chickasaw |
| B55-B56 | Not Used |
|   |   |
|   |   |
| Chinook |   |
| B57 | Chinook |
| B58 | Clatsop |
| B59 | Columbia River Chinook |
| B60 | Kathlamet |
| B61 | Upper Chinook |
| B62 | Wakiakum Chinook |
| B63 | Willapa Chinook |
| B64 | Wishram |
| B65-B66 | Not Used |
|   |   |
|   |   |
|   |   |
| Chippewa |   |
| B67 | Bad River Band of the Lake Superior Tribe |
| B68 | Bay Mills Indian Community |
| B69 | Bois Forte Band of Chippewa |
| B70 | Not Used |
|   |   |
|   |   |
| B71 | Chippewa |
| B72 | Fond du Lac |
| B73 | Grand Portage |
| B74 | Grand Traverse Band of Ottawa and Chippewa Indians |
| B75 | Keweenaw Bay Indian Community |
| B76 | Lac Court Oreilles Band of Lake Superior Chippewa |
| B77 | Lac du Flambeau |
| B78 | Lac Vieux Desert Band of Lake Superior Chippewa Indians |
| B79 | Lake Superior Chippewa |
| B80 | Leech Lake |
| B81 | Little Shell Tribe of Chippewa Indians of Montana |
| B82 | Mille Lacs |
| B83 | Minnesota Chippewa |
| B84 | Not Used |
|   |   |
|   |   |
| B85 | Red Cliff Band of Lake Superior Chippewa |
| B86 | Red Lake Band of Chippewa Indians |
| B87 | Saginaw Chippewa Indian Tribe |
| B88 | St. Croix Chippewa |
| B89 | Sault Ste. Marie Tribe of Chippewa Indians |
| B90 | Sokaogon Chippewa Community |
| B91 | Turtle Mountain Band of Chippewa Indians of North Dakota |
| B92 | White Earth |
| B93 | Swan Creek Black River Confederate Tribe |
| B94-B99 | Not Used |
|   |   |
|   |   |
| Chippewa Cree |   |
| C01 | Not Used |
|   |   |
|   |   |
| C02 | Chippewa-Cree Indians of the Rocky Boy's Reservation |
| C03-C04 | Not Used |
|   |   |
|   |   |
| Chitimacha |   |
| C05 | Chitimacha Tribe of Louisiana |
| C06 | Pointe Au-Chien Indian Tribe |
| C07 | Not Used |
|   |   |
|   |   |
| Choctaw |   |
| C08 | Choctaw |
| C09 | Clifton Choctaw |
| C10 | Jena Band of Choctaw |
| C11 | Mississippi Band of Choctaw Indians |
| C12 | MOWA Band of Choctaw Indians |
| C13 | Choctaw Nation of Oklahoma |
| C14-C16 | Not Used |
|   |   |
|   |   |
|   |   |
| Choctaw-Apache |   |
| C17 | Choctaw-Apache Community of Ebarb |
| C18-C19 | Not Used |
|   |   |
|   |   |
| Chumash |   |
| C20 | Chumash |
| C21 | Santa Ynez Band of Chumash Mission Indians |
| C22 | San Luis Rey Mission Indian |
| C23-C24 | Not Used |
|   |   |
|   |   |
| Clear Lake |   |
| C25 | Clear Lake |
| Coeur DAlene |   |
| C26 | Coeur DAlene Tribe |
| C27-C28 | Not Used |
|   |   |
|   |   |
| Coharie |   |
| C29 | Coharie Indian Tribe |
| C30-C31 | Not Used |
|   |   |
|   |   |
| Colorado River Indian |   |
| C32 | Colorado River Indian Tribes |
| C33-C34 | Not Used |
|   |   |
|   |   |
| Colville |   |
| C35 | Confederated Tribes of the Colville Reservation |
| C36-C38 | Not Used |
|   |   |
|   |   |
| Comanche |   |
| C39 | Comanche Nation, Oklahoma |
| C40-C43 | Not Used |
|   |   |
|   |   |
| Coos, Lower Umpqua, and Siuslaw |   |
| C44 | Confederated Tribes of Coos, Lower Umpqua, and Siuslaw Indians |
| C45 | Not Used |
|   |   |
|   |   |
| Coos |   |
| C46 | Coos |
| Coquille |   |
| C47 | Coquille Indian Tribe |
| C48 | Not Used |
|   |   |
|   |   |
| Costanoan |   |
| C49 | Costanoan |
| C50-C51 | Not Used |
|   |   |
|   |   |
| Coushatta |   |
| C52 | Alabama-Coushatta Tribe of Texas |
| C53 | Coushatta |
|   |   |
| C54-C55 | Not Used |
|   |   |
|   |   |
| Cowlitz |   |
| C56 | Cowlitz Indian Tribe |
| C57-C58 | Not Used |
|   |   |
|   |   |
| Cree |   |
| C59 | Cree |
| C60-C63 | Not Used |
|   |   |
|   |   |
| Creek |   |
| C64 | Alabama Creek |
| C65 | Alabama Quassarte Tribal Town |
| C66 | Muscogee (Creek) Nation |
| C67 | Eastern Creek |
| C68 | Eastern Muscogee |
| C69 | Kialegee Tribal Town |
| C70 | Lower Muscogee Creek Tama Tribal Town |
| C71 | MaChis Lower Creek Indian Tribe |
| C72 | Poarch Band of Creek Indians |
| C73 | Principal Creek Indian Nation |
| C74 | Lower Creek Muscogee Tribe East, Star Clan |
| C75 | Thlopthlocco Tribal Town |
| C76 | Tuckabachee |
| C77-C80 | Not Used |
|   |   |
|   |   |
| Croatan |   |
| C81 | Croatan |
| C82 | Not Used |
|   |   |
|   |   |
| Crow |   |
| C83 | Crow Tribe of Montana |
| C84-C86 | Not Used |
|   |   |
|   |   |
| Cumberland |   |
| C87 | Cumberland County Association for Indian People |
| C88 | Not Used |
|   |   |
|   |   |
| Cupeno |   |
| C89 | Agua Caliente |
| C90 | Cupeno |
| C91-C92 | Not Used |
|   |   |
|   |   |
| Delaware |   |
| C93 | Delaware (Lenni-Lenape) |
| C94 | Delaware Tribe of Indians, Oklahoma |
| C95 | Not Used |
|   |   |
|   |   |
| C96 | Munsee |
| C97 | Delaware Nation |
| C98 | Ramapough Lenape Nation (Ramapough Mountain) |
| C99 | New Jersey Sand Hill Band of Indians, Inc |
|   |   |
| D01 | Allegheny Lenape |
| D02-D04 | Not Used |
|   |   |
|   |   |
| Diegueno (Kumeyaay) |   |
| D05 | Barona Group of Capitan Grande Band |
| D06 | Campo Band of Diegueno Mission Indians |
| D07 | Capitan Grande Band of Diegueno Mission Indians |
| D08 | Ewiiaapaayp Band of Kumeyaay Indians |
| D09 | Diegueno (Kumeyaay) |
| D10 | La Posta Band of Diegueno Mission Indians |
| D11 | Manzanita Band of Diegueno Mission Indians |
| D12 | Mesa Grande Band of Diegueno Mission Indians |
| D13 | San Pasqual Band of Diegueno Mission Indians |
| D14 | Iipay Nation of Santa Ysabel |
| D15 | Sycuan Band of the Kumeyaay Nation |
| D16 | Viejas (Baron Long) Group of Capitan Grande Band |
| D17 | Inaja Band of Diegueno Mission Indians of the Inaja and Cosmit Reservation |
| D18 | Jamul Indian Village |
| D19 | Not Used |
|   |   |
|   |   |
| Eastern Tribes |   |
| D20 | Attacapa |
| D21 | Biloxi |
| D22 | Georgetown |
| D23 | Moor |
| D24 | Nansemond Indian Tribe |
| D25 | Natchez Indian Tribe of South Carolina (Kusso-Natchez; Edisto) |
| D26 | Nausu Waiwash |
| D27 | (see under Nipmuc) |
| D28 | Golden Hill Paugussett |
| D29 | Pocomoke Acohonock |
| D30 | Southeastern Indians |
| D31 | Susquehanock |
| D32 | Biloxi-Chitimacha-Choctaw Confederation |
| D33 | Tunica Biloxi Indian Tribe of Louisiana |
| D34 | Waccamaw Siouan Indian Tribe |
| D35 | Beaver Creek Indians |
| D36 | Wicomico |
| D37 | Meherrin Indian Tribe |
| D38 | Santee Indian Organization |
| D39 | Santee Indian Nation of South Carolina |
| D40 | Pee Dee Indian Tribe of South Carolina |
| D41 | Pee Dee Indian Nation of Upper South Carolina |
|   |   |
| Esselen |   |
| D42 | Esselen |
| D43 | Not Used |
|   |   |
|   |   |
| Fort Belknap |   |
| D44 | Fort Belknap Indian Community of the Fort Belknap Reservation |
| Three Affiliated Tribes of North Dakota |   |
| D45 | Three Affiliated Tribes of Ft. Berthold Reservation, North Dakota |
| D46 | Mandan |
| D47 | Hidatsa |
| D48 | Arikara (Sahnish) |
|   |   |
| Fort McDowell |   |
| D49 | Fort McDowell Yavapai Nation |
| D50 | Not Used |
|   |   |
|   |   |
| Fort Hall |   |
| D51 | Shoshone-Bannock Tribes of the Fort Hall Reservation |
| D52 | Lemhi-Shoshone |
| D53 | Bannock |
| D54 | Not Used |
|   |   |
|   |   |
| Gabrieleno |   |
| D55 | Gabrieleno |
| Fernandeno Tataviam Band of Mission Indians |   |
| D56 | Fernandeno Tataviam Band of Mission Indians |
|   |   |
| Grand Ronde |   |
| D57 | Confederated Tribes of the Grand Ronde Community of Oregon |
|   |   |
| Guilford |   |
| D58 | Guilford Native American Association |
| D59 | Not Used |
|   |   |
|   |   |
| Gros Ventres |   |
| D60 | Atsina |
| D61 | Gros Ventres |
| D62-D63 | Not Used |
|   |   |
|   |   |
| Haliwa-Saponi |   |
| D64 | Haliwa-Saponi Indian Tribe |
| D65-D67 | Not Used |
|   |   |
|   |   |
| Ho-Chunk Nation |   |
| D68 | Ho-Chunk Nation |
| D69 | Not Used |
|   |   |
|   |   |
| Hoopa |   |
| D70 | Hoopa Valley Tribe |
| D71 | Trinity |
| D72 | Whilkut |
|   |   |
| D73 | Not Used |
|   |   |
|   |   |
| Hopi |   |
| D74 | Hopi Tribe of Arizona |
| D75 | Arizona Tewa |
|   |   |
| Hoopa Extension |   |
| D76 | Hoopa Extension |
| D77 | Not Used |
|   |   |
|   |   |
| Houma |   |
| D78 | United Houma Nation |
| D79-D86 | Not Used |
|   |   |
|   |   |
| Iowa |   |
| D87 | Iowa |
| D88 | Iowa Tribe of Kansas and Nebraska |
| D89 | Iowa Tribe of Oklahoma |
| D90 | Not Used |
|   |   |
|   |   |
| Sappony (Indians of Person County) |   |
| D91 | Sappony |
| D92 | Not Used |
|   |   |
|   |   |
| Iroquois |   |
| D93 | Cayuga Nation |
| D94 | Iroquois |
| D95 | Mohawk |
| D96 | Oneida |
| D97 | Onondaga Nation |
| D98 | Seneca |
| D99 | Seneca Nation |
| E01 | Seneca-Cayuga Tribe of Oklahoma |
| E02 | Tonawanda Band of Seneca Indians |
| E03 | Tuscarora Nation |
| E04 | Wyandotte Nation, Oklahoma |
| E05 | Oneida Nation of New York |
| E06-E09 | Not Used |
|   |   |
|   |   |
| Juaneno (Acjachemem) |   |
| E10 | Juaneno (Acjachemem) |
| E11-E12 | Not Used |
|   |   |
|   |   |
| Kalispel |   |
| E13 | Kalispel Indian Community |
| E14-E16 | Not Used |
|   |   |
|   |   |
| Karuk |   |
| E17 | Karuk Tribe of California |
| E18-E20 | Not Used |
| Kaw |   |
| E21 | Kaw Nation |
| E22-E23 | Not Used |
|   |   |
|   |   |
| Kickapoo |   |
| E24 | Kickapoo |
| E25 | Kickapoo Tribe of Oklahoma |
| E26 | Kickapoo Traditional Tribe of Texas |
| E27 | Kickapoo Tribe of Indians in Kansas |
| E28-E29 | Not Used |
|   |   |
|   |   |
| Kiowa |   |
| E30 | Kiowa |
| E31 | Kiowa Indian Tribe of Oklahoma |
| E32-E36 | Not Used |
|   |   |
|   |   |
| SKlallam |   |
| E37 | Jamestown SKlallam Tribe of Washington |
| E38 | Klallam |
| E39 | Lower Elwha Tribal Community of the Lower Elwha Reservation, Washington |
| E40 | Port Gamble S'Klallam Tribe |
| E41-E43 | Not Used |
|   |   |
|   |   |
| Klamath |   |
| E44 | Klamath Indian Tribe of Oregon |
| E45-E47 | Not Used |
|   |   |
|   |   |
| Konkow |   |
| E48 | Konkow |
| E49 | Not Used |
|   |   |
|   |   |
| Kootenai |   |
| E50 | Kootenai |
| E51 | Kootenai Tribe of Idaho |
| E52 | Not Used |
|   |   |
|   |   |
| Lassik |   |
| E53 | Lassik |
| E54-E58 | Not Used |
|   |   |
|   |   |
| Long Island |   |
| E59 | Matinecock |
| E60 | Montauk |
| E61 | Poospatuck |
| E62 | Setauket |
| E63-E65 | Not Used |
|   |   |
|   |   |
| Luiseno |   |
| E66 | La Jolla Band of Luiseno Mission Indians |
| E67 | Luiseno |
| E68 | Pala Band of Luiseno Mission Indians |
| E69 | Pauma Band of Luiseno Mission Indians |
| E70 | Pechanga Band of Luiseno Mission Indians |
| E71 | Soboba Band of Luiseno Indians |
| E72 | Twenty-Nine Palms Band of Luiseno Mission Indians |
| E73 | Temecula |
| E74 | Rincon Band of Luiseno Mission Indians |
| E75-E77 | Not Used |
|   |   |
|   |   |
| Lumbee |   |
| E78 | Lumbee Indian Tribe |
| E79-E83 | Not Used |
|   |   |
|   |   |
| Lummi |   |
| E84 | Lummi Tribe |
| E85 | Not Used |
|   |   |
|   |   |
| Maidu |   |
| E86 | United Auburn Indian Community |
| E87 | Mooretown Rancheria of Maidu Indians |
| E88 | Maidu |
| E89 | Mountain Maidu |
| E90 | Nisenen (Nishinam) |
| E91 | Mechoopda Indian Tribe of Chico Rancheria |
| E92 | Berry Creek Rancheria of Maidu Indians |
| E93 | Enterprise Rancheria of Maidu Indians |
| E94 | Greenville Rancheria of Maidu Indians |
|   |   |
| Makah |   |
| E95 | Makah Indian Tribe |
| E96-E99 | Not Used |
|   |   |
|   |   |
| Maliseet |   |
| F01 | Maliseet |
| F02 | Houlton Band of Maliseet Indians |
| F03-F08 | Not Used |
|   |   |
|   |   |
| Mattaponi |   |
| F09 | Mattaponi Indian Tribe |
| F10 | Upper Mattaponi Tribe |
| Menominee |   |
| F11 | Menominee Indian Tribe |
| F12-F14 | Not Used |
|   |   |
|   |   |
| Metrolina |   |
| F15 | Metrolina Native American Association |
| F16 | Not Used |
|   |   |
|   |   |
| Miami |   |
| F17 | Illinois Miami |
| F18 | Indiana Miami |
| F19 | Miami |
| F20 | Miami Tribe of Oklahoma |
| F21-F23 | Not Used |
|   |   |
|   |   |
| Miccosukee |   |
| F24 | Miccosukee Tribe of Indians of Florida |
| F25-F26 | Not Used |
|   |   |
|   |   |
| Micmac |   |
| F27 | Aroostook Band of Micmac Indians |
| F28 | Micmac |
| F29-F30 | Not Used |
|   |   |
|   |   |
| Mission Indians |   |
| F31 | Mission Indians |
| F32 | Cahuilla Band of Mission Indians |
| F33 | Not Used |
|   |   |
|   |   |
| Miwok/Me-Wuk |   |
| F34 | Ione Band of Miwok Indians |
| F35 | Shingle Springs Band of Miwok Indians |
| F36 | Miwok/Me-Wuk |
| F37 | Jackson Rancheria of Me-Wuk Indians of California |
| F38 | Tuolumne Band of Me-Wuk Indians of California |
| F39 | Buena Vista Rancheria of Me-Wuk Indians of California |
| F40 | Chicken Ranch Rancheria of Me-Wuk Indians |
| F41 | Not Used |
|   |   |
|   |   |
| Modoc |   |
| F42 | Modoc |
| F43 | Modoc Tribe of Oklahoma |
| F44-F45 | Not Used |
|   |   |
|   |   |
| Mohegan |   |
| F46 | Mohegan Indian Tribe |
| F47 | Not Used |
|   |   |
|   |   |
| Monacan |   |
| F48 | Monacan Indian Nation |
| Mono |   |
| F49 | Mono |
| F50 | North Fork Rancheria of Mono Indians |
| F51 | Cold Springs Rancheria of Mono Indians |
| F52 | Big Sandy Band of Western Mono Indians |
| Nanticoke |   |
| F53 | Nanticoke |
| F54-F55 | Not Used |
|   |   |
| Nanticoke Lenni-Lenape |   |
| F56 | Nanticoke Lenni-Lenape |
|   |   |
| Narragansett |   |
| F57 | Narragansett Indian Tribe |
| F58-F61 | Not Used |
|   |   |
|   |   |
| Navajo |   |
| F62-F63 | Not Used |
|   |   |
|   |   |
| F64 | Navajo Nation |
| F65-F70 | Not Used |
|   |   |
|   |   |
| Nez Perce |   |
| F71 | Nez Perce Tribe of Idaho (Nimiipuu) |
| F72-F74 | Not Used |
|   |   |
|   |   |
| Nipmuc |   |
| F75 | Hassanamisco Band of the Nipmuc Nation |
| F76 | Chaubunagungamaug Nipmuck |
| D27 | Nipmuc |
|   |   |
| Nomlaki |   |
| F77 | Nomlaki |
| F78 | Paskenta Band of Nomlaki Indians |
| F79 | Not Used |
|   |   |
|   |   |
| Northwest Tribes |   |
| F80 | Alsea |
| F81 | Celilo |
| F82 | Columbia |
| F83 | Kalapuya |
| F84 | Molalla |
| F85 | Talakamish |
| F86 | Tenino |
| F87 | Tillamook |
| F88 | Wenatchee |
| F89-F94 | Not Used |
|   |   |
|   |   |
| Omaha |   |
| F95 | Omaha Tribe of Nebraska |
| F96-F98 | Not Used |
|   |   |
|   |   |
| Oneida Tribe |   |
| F99 | Oneida Tribe of Indians of Wisconsin |
|   |   |
| Oregon Athabascan |   |
| G01 | Oregon Athabascan |
| G02-G03 | Not Used |
|   |   |
|   |   |
|   |   |
| Osage |   |
| G04 | Osage Tribe, Oklahoma |
| G05-G09 | Not Used |
|   |   |
|   |   |
| Otoe-Missouria |   |
| G10 | Otoe-Missouria Tribe of Indians |
| G11-G13 | Not Used |
|   |   |
|   |   |
| Ottawa |   |
| G14 | Not Used |
|   |   |
|   |   |
| G15 | Little River Band of Ottawa Indians of Michigan |
| G16 | Ottawa Tribe of Oklahoma |
| G17 | Ottawa |
| G18 | Little Traverse Bay Bands of Odawa Indians |
| G19 | Grand River Band of Ottawa Indians |
| G20-G22 | Not Used |
|   |   |
|   |   |
| Paiute |   |
| G23 | Big Pine Paiute Tribe of the Owens Valley |
| G24 | Bridgeport Paiute Indian Colony |
| G25 | Burns Paiute Tribe |
| G26 | Cedarville Rancheria |
| G27 | Fort Bidwell Indian Community |
| G28 | Fort Independence Indian Community |
| G29 | Kaibab Band of Paiute Indians of the Kaibab Indian Reservation |
| G30 | Las Vegas Tribe of Paiute Indians of the Las Vegas Indian Colony |
| G31 | Not Used |
|   |   |
|   |   |
| G32 | Lovelock Paiute Tribe of the Lovelock Indian Colony, Nevada |
| G33 | Malheur Paiute |
| G34 | Moapa Band of Paiute Indians of the Moapa River Indian Reservation, Nevada |
| G35 | Northern Paiute |
| G36 | Not Used |
|   |   |
|   |   |
| G37 | Paiute |
| G38 | Pyramid Lake Paiute Tribe of the Pyramid Lake Reservation, Nevada |
| G39 | San Juan Southern Paiute Tribe of Arizona |
| G40 | Paiute Indian Tribe of Utah (Southern Paiute) |
| G41 | Summit Lake Paiute Tribe of Nevada |
| G42 | Utu Utu Gwaitu Paiute Tribe of the Benton Paiute Reservation, California |
| G43 | Walker River Paiute Tribe of the Walker River Reservation, Nevada |
| G44 | Yerington Paiute Tribe of the Yerington Colony and Campbell Ranch, Nevada |
| G45 | Yahooskin Band of Snake |
| G46 | Not Used |
|   |   |
|   |   |
| G47 | Susanville Indian Rancheria, California |
| G48 | Winnemucca Indian Colony of Nevada |
| G49 | Not Used |
|   |   |
|   |   |
| Pamunkey |   |
| G50 | Pamunkey Indian Tribe |
| G51-G52 | Not Used |
|   |   |
| Passamaquoddy |   |
| G53 | Indian Township |
| G54 | Passamaquoddy Tribe of Maine |
| G55 | Pleasant Point Passamaquoddy |
| G56-G60 | Not Used |
|   |   |
|   |   |
| Pawnee |   |
| G61 | Pawnee Nation of Oklahoma |
| G62 | Pawnee |
| G63-G67 | Not Used |
|   |   |
|   |   |
| Penobscot |   |
| G68 | Penobscot Tribe of Maine |
| G69-G71 | Not Used |
|   |   |
|   |   |
| Peoria |   |
| G72 | Peoria Tribe of Indians of Oklahoma |
| G73 | Peoria |
| G74-G76 | Not Used |
|   |   |
|   |   |
| Pequot |   |
| G77 | Mashantucket Pequot Tribe of Connecticut |
| G78 | Pequot |
| G79 | Paucatuck Eastern Pequot |
| G80 | Eastern Pequot |
| G81-G83 | Not Used |
|   |   |
|   |   |
| Pima |   |
| G84 | Gila River Indian Community of the Gila River Indian Reservation |
| G85 | Pima |
| G86 | Salt River Pima-Maricopa Indian Community |
| G87 | Peeposh |
| G88-G91 | Not Used |
|   |   |
|   |   |
| Piscataway |   |
| G92 | Piscataway |
| G93-G95 | Not Used |
|   |   |
|   |   |
| Pit River |   |
| G96 | Pit River Tribe of California |
| G97 | Alturas Indian Rancheria |
| G98 | Not Used |
|   |   |
|   |   |
| Pomo |   |
| G99 | Big Valley Band of Pomo Indians of the Big Valley Rancheria |
| H01 | Central Pomo |
| H02 | Dry Creek Rancheria of Pomo Indians |
| H03 | Eastern Pomo |
| H04 | Kashia Band of Pomo Indians of the Stewarts Point Rancheria |
| H05 | Northern Pomo |
| H06 | Pomo |
|   |   |
| H07 | Scotts Valley Band of Pomo Indians of California |
| H08 | Stonyford |
| H09 | Elem Indian Colony of the Sulphur Bank Rancheria |
| H10 | Sherwood Valley Rancheria of Pomo Indians of California |
| H11 | Guidiville Rancheria of California |
| H12 | Lytton Rancheria of California |
| H13 | Cloverdale Rancheria of Pomo Indians of California |
| H14 | Coyote Valley Band of Pomo Indians of California |
| H15-H20 | (see under Ponca) |
| H21-H33 | (see under Potawatomi) |
| H34-H37 | (see under Powhatan) |
| H38-H65 | (see under Pueblo) |
| H66 | Hopland Band of Pomo Indians |
| H67 | Manchester Band of Pomo Indians of the Manchester-Point Arena Rancheria |
| H68 | Middletown Rancheria of Pomo Indians |
| H69 | Pinoleville Pomo Nation |
| H70-H92 | (see under Puget Sound Salish) |
| H93 | Potter Valley Tribe |
| H94 | Redwood Valley Rancheria of Pomo Indians |
| H95 | Robinson Rancheria of Pomo Indians |
| H96 | Habematolel Pomo of Upper Lake (Upper Lake Band of Pomo Indians of Upper Lake Rancheria) |
| H97 | Federated Indians of Graton Rancheria |
| H98 | Lower Lake Rancheria Koi Nation |
|   |   |
| Ponca |   |
| H15 | Ponca Tribe of Nebraska |
| H16 | Ponca Tribe of Indians of Oklahoma |
| H17 | Ponca |
| H18-H20 | Not Used |
|   |   |
|   |   |
| Potawatomi |   |
| H21 | Citizen Potawatomi Nation, Oklahoma |
| H22 | Forest County Potawatomi Community, Wisconsin |
| H23 | Hannahville Potawatomi Indian Tribe, Michigan |
| H24 | Nottawaseppi Huron Band of the Potawatomi, Michigan |
| H25 | Pokagon Band of Potawatomi Indians |
| H26 | Potawatomi |
| H27 | Prairie Band of Potawatomi Nation, Kansas |
| H28 | Wisconsin Potawatomi |
| H29 | Match-e-be-nash-she-wish Band of Pottawatomi Indians |
| H30-H33 | Not Used |
|   |   |
|   |   |
| Powhatan |   |
| H34 | Powhatan |
| H35-H37 | Not Used |
|   |   |
|   |   |
| Pueblo |   |
| H38 | Pueblo of Acoma |
| H39 | Not Used |
| H40 | Pueblo of Cochiti |
| H41 | Not Used |
| H42 | Pueblo of Isleta |
| H43 | Pueblo of Jemez |
| H44 | Not Used |
| H45 | Pueblo of Laguna |
| H46 | Pueblo of Nambe |
| H47 | Pueblo of Picuris |
| H48 | Piro Manso Tiwa Tribe |
| H49 | Pueblo of Pojoaque |
| H50 | Pueblo |
| H51 | Pueblo of San Felipe |
| H52 | Pueblo of San Ildefonso |
| H53 | Ohkay Owingeh, New Mexico |
| H54 | Not Used |
| H55 | San Juan |
| H56 | Pueblo of Sandia |
| H57 | Pueblo of Santa Ana |
| H58 | Pueblo of Santa Clara |
| H59 | Pueblo of Santo Domingo |
| H60 | Pueblo of Taos |
| H61 | Pueblo of Tesuque |
| H62 | Not Used |
| H63 | Ysleta Del Sur Pueblo of Texas |
| H64 | Pueblo of Zia |
| H65 | Zuni Tribe of the Zuni Reservation |
| H66-H69 | (see under Pomo) |
|   |   |
| Puget Sound Salish |   |
| H70 | Marietta Band of Nooksack |
| H71 | Duwamish |
| H72 | Kikiallus |
| H73 | Lower Skagit |
| H74 | Muckleshoot Indian Tribe |
| H75 | Nisqually Indian Tribe |
| H76 | Nooksack Indian Tribe |
| H77 | Not Used |
| H78 | Puget Sound Salish |
| H79 | Puyallup Tribe |
| H80 | Samish Indian Tribe |
| H81 | Sauk-Suiattle Indian Tribe |
| H82 | Skokomish Indian Tribe of the Skokomish Indian Reservation, Washington |
| H83 | Skykomish |
| H84 | Snohomish |
| H85 | Snoqualmie Tribe |
| H86 | Squaxin Island Tribe of the Squaxin Island Reservation, Washington |
| H87 | Steilacoom |
| H88 | Stillaguamish |
| H89 | The Suquamish Tribe |
| H90 | Swinomish Indian Tribal Community |
| H91 | Tulalip Tribes |
| H92 | Upper Skagit Indian Tribe |
| H93-H98 | (see under Pomo) |
|   |   |
| Quapaw |   |
| H99 | Quapaw Tribe of Indians, Oklahoma |
| I01-I99 | Not Used |
|   |   |
|   |   |
| Quinault |   |
| J01 | Quinault Tribe |
| J02-J04 | Not Used |
|   |   |
|   |   |
| Rappahannock |   |
| J05 | Rappahannock Indian Tribe |
| J06 | Not Used |
|   |   |
|   |   |
| Reno-Sparks |   |
| J07 | Reno-Sparks Indian Colony, Nevada |
| J08-J13 | Not Used |
|   |   |
|   |   |
| Round Valley |   |
| J14 | Round Valley Indian Tribes |
| J15-J18 | Not Used |
|   |   |
|   |   |
| Sac and Fox |   |
| J19 | Sac and Fox Tribe of the Mississippi in Iowa |
| J20 | Sac and Fox Nation of Missouri in Kansas and Nebraska |
| J21 | Sac and Fox Nation, Oklahoma |
| J22 | Sac and Fox |
| J23-J27 | Not Used |
|   |   |
|   |   |
| Salinan |   |
| J28 | Salinan |
| J29-J30 | Not Used |
|   |   |
|   |   |
| Salish |   |
| J31 | Salish |
| J32-J34 | Not Used |
|   |   |
|   |   |
| Salish and Kootenai |   |
| J35 | Confederated Salish and Kootenai Tribes of the Flathead Nation |
| J36-J37 | Not Used |
|   |   |
|   |   |
| Saponi |   |
| J38 | Saponi |
|   |   |
| Schaghticoke |   |
| J39 | Schaghticoke |
| J40-J46 | Not Used |
|   |   |
|   |   |
| Seminole |   |
| J47 | Big Cypress Reservation |
| J48 | Brighton Reservation |
| J49 | Seminole Tribe of Florida |
| J50 | Hollywood Reservation (Dania) |
| J51 | Seminole Nation of Oklahoma |
| J52 | Seminole |
| J53 | Not Used |
| J54 | Tampa Reservation |
| J55-J57 | Not Used |
|   |   |
|   |   |
| Serrano |   |
| J58 | San Manuel Band of Serrano Mission Indians |
| J59 | Serrano |
| J60-J61 | Not Used |
|   |   |
|   |   |
| Shasta |   |
| J62 | Shasta |
| J63 | Quartz Valley Indian Reservation |
| J64-J65 | Not Used |
|   |   |
|   |   |
| Shawnee |   |
| J66 | Absentee Shawnee Tribe of Indians of Oklahoma |
| J67 | Eastern Shawnee |
| J68 | Shawnee |
| J69 | Piqua Shawnee Tribe |
| J70 | Shawnee Tribe, Oklahoma |
| J71 | Shawnee Nation United Remnant Band |
| J72 | East of the River Shawnee |
| J73 | Not Used |
|   |   |
|   |   |
| Shinnecock |   |
| J74 | Shinnecock |
| J75-J77 | Not Used |
|   |   |
|   |   |
| Shoalwater Bay |   |
| J78 | Shoalwater Bay Tribe of the Shoalwater Bay Reservation, Washington |
| J79-J80 | Not Used |
|   |   |
|   |   |
| Shoshone |   |
| J81 | Duckwater Shoshone Tribe |
| J82 | Ely Shoshone Tribe |
| J83 | Confederated Tribes of the Goshute Reservation |
| J84 | Not Used |
| J85 | Shoshone |
| J86 | Skull Valley Band of Goshute Indians of Utah |
| J87 | Not Used |
| J88 | Death Valley Timbi-Sha Shoshone |
| J89 | Northwestern Band of Shoshone Nation of Utah (Washakie) |
| J90 | Eastern Shoshone (Wind River) |
| J91 | Yomba Shoshone Tribe of the Yomba Reservation, Nevada |
| J92 | Not Used |
|   |   |
| Te-Moak Tribes of Western Shoshone Indians of Nevada |   |
| J93 | Te-Moak Tribes of Western Shoshone Indians of Nevada |
| J94 | Battle Mountain Band |
| J95 | Elko Band |
| J96 | South Fork Band |
| J97 | Wells Band |
| J98-J99 | Not Used |
|   |   |
|   |   |
| Paiute-Shoshone |   |
| K01 | Shoshone-Paiute Tribes of the Duck Valley Reservation |
| K02 | Paiute-Shoshone Tribe of the Fallon Reservation and Colony, Nevada |
| K03 | Fort McDermitt Paiute and Shoshone Tribe of Nevada and Oregon |
| K04 | Shoshone Paiute |
| K05 | Bishop Paiute Tribe |
| K06 | Lone Pine |
| K07-K09 | Not Used |
|   |   |
|   |   |
| Siletz |   |
| K10 | Confederated Tribes of Siletz Indians of Oregon |
| K11-K15 | Not Used |
|   |   |
|   |   |
| Sioux |   |
| K16 | Not Used |
| K17 | Brule Sioux |
| K18 | Cheyenne River Sioux Tribe of the Cheyenne River Reservation, South Dakota |
| K19 | Crow Creek Sioux Tribe of the Crow Creek Reservation, South Dakota |
| K20 | Dakota Sioux |
| K21 | Flandreau Santee Sioux Tribe of South Dakota |
| K22-K23 | Not Used |
| K24 | Lower Brule Sioux Tribe of the Lower Brule Reservation, South Dakota |
| K25 | Lower Sioux Indian Community in the State of Minnesota |
| K26 | Mdewakanton Sioux |
| K27 | Not Used |
| K28 | Oglala Sioux Tribe of the Pine Ridge Reservation, South Dakota |
| K29 | Not Used |
| K30 | Pipestone Sioux |
| K31 | Prairie Island Indian Community |
| K32 | Shakopee Mdewakanton Sioux Community (Prior Lake) |
| K33 | Rosebud Sioux Tribe of the Rosebud Indian Reservation, South Dakota |
| K34 | Not Used |
| K35 | Santee Sioux Nation, Nebraska |
| K36 | Sioux |
| K37 | Sisseton-Wahpeton Oyate of the Lake Traverse Reservation, South Dakota |
| K38 | Not Used |
| K39 | Spirit Lake Tribe |
| K40 | Standing Rock Sioux Tribe |
| K41 | Teton Sioux |
| K42 | Not Used |
| K43 | Upper Sioux Community |
| K44 | Wahpekute Sioux |
| K45 | Not Used |
| K46 | Wazhaza Sioux |
| K47 | Yankton Sioux Tribe of South Dakota |
| K48 | Yanktonai Sioux |
| K49-K53 | Not Used |
|   |   |
|   |   |
| Siuslaw |   |
| K54 | Siuslaw |
| K55-K58 | Not Used |
|   |   |
|   |   |
| Spokane |   |
| K59 | Spokane Tribe |
| K60-K66 | Not Used |
|   |   |
|   |   |
| Stockbridge-Munsee |   |
| K67 | Stockbridge-Munsee Community |
| K68-K76 | Not Used |
|   |   |
|   |   |
| Ak-Chin |   |
| K77 | Ak-Chin Indian Community of the Maricopa Indian Reservation |
| Tohono OOdham |   |
| K78 | Gila Bend |
| K79 | San Xavier |
| K80 | Sells |
| K81 | Tohono OOdham Nation of Arizona |
| K82-K86 | Not Used |
|   |   |
|   |   |
| Tolowa |   |
| K87 | Tolowa |
| K88 | Big Lagoon Rancheria |
| K89 | Elk Valley Rancheria |
| A91 | Smith River Rancheria |
| Tonkawa |   |
| K90 | Tonkawa Tribe of Indians of Oklahoma |
| K91-K93 | Not Used |
|   |   |
|   |   |
| Tygh |   |
| K94 | Tygh |
| K95-K96 | Not Used |
|   |   |
|   |   |
| Umatilla |   |
| K97 | Confederated Tribes of the Umatilla Indian Reservation |
| K98-K99 | Not Used |
|   |   |
|   |   |
| Umpqua |   |
| L01 | Cow Creek Band of Umpqua Indians of Oregon |
| L02 | Umpqua |
| L03-L05 | Not Used |
|   |   |
|   |   |
| Ute |   |
| L06 | Not Used |
| L07 | Ute Indian Tribe of the Uintah and Ouray Reservation, Utah |
| L08 | Ute Mountain Ute Tribe |
| L09 | Ute |
| L10 | Southern Ute Indian Tribe of the Southern Ute Reservation |
| L11-L14 | Not Used |
|   |   |
|   |   |
| Wailaki |   |
| L15 | Wailaki |
| L16-L18 | Not Used |
|   |   |
|   |   |
| Walla Walla |   |
| L19 | Walla Walla |
| L20-L21 | Not Used |
|   |   |
|   |   |
| Wampanoag |   |
| L22 | Wampanoag Tribe of Gay Head (Aquinnah) |
| L23 | Mashpee Wampanoag Tribe |
| L24 | Wampanoag |
| L25 | Seaconeke Wampanoag |
| L26 | Pocasset Wampanoag |
| L27 | Herring Pond Wampanoag Tribe |
| L28 | Pokanoket (Royal House of Pokanoket) |
| L29 | Ponkapoag |
| L30 | Chappaquiddick Tribe of the Wampanoag Indian Nation |
| L31 | Assonet Band of the Wampanoag Nation |
| L32 | Not Used |
|   |   |
|   |   |
| Warm Springs |   |
| L33 | Confederated Tribes of Warm Springs |
| Wascopum |   |
| L34 | Wascopum |
| L35-L37 | Not Used |
|   |   |
|   |   |
| Washoe |   |
| L38 | Alpine |
| L39-L40 | Not Used |
|   |   |
|   |   |
| L41 | Washoe Tribe of Nevada and California |
| L42-L46 | Not Used |
|   |   |
|   |   |
| Wichita and Affiliated Tribes, Oklahoma |   |
| L47 | Wichita |
| L48 | Keechi |
| L49 | Waco |
| L50 | Tawakonie |
| L51 | Not Used |
|   |   |
|   |   |
| Wind River |   |
| L52 | Wind River |
| L53-L54 | Not Used |
|   |   |
| Winnebago |   |
| L55 | Not Used |
|   |   |
|   |   |
| L56 | Winnebago Tribe of Nebraska |
| L57 | Winnebago |
| L58-L65 | Not Used |
|   |   |
|   |   |
| Wintun |   |
| L66 | Wintun |
| L67 | Cachil Dehe Band of Wintun Indians of the Colusa Rancheria |
| L68 | Cortina Indian Rancheria of Wintun Indians |
| L69 | Rumsey Indian Rancheria of Wintun Indians |
| L70 | Not Used |
|   |   |
|   |   |
| Wintun-Wailaki |   |
| L71 | Grindstone Indian Rancheria of Wintun-Wailaki Indians |
| Wiyot |   |
| L72 | Wiyot Tribe, California |
| L73 | Not Used |
|   |   |
|   |   |
| L74 | Blue Lake Rancheria |
| L75-L78 | Not Used |
|   |   |
|   |   |
| Yakama |   |
| L79 | Confederated Tribes and Bands of the Yakama Nation |
| L80-L84 | Not Used |
|   |   |
|   |   |
| Yakama Cowlitz |   |
| L85 | Yakama Cowlitz |
| L86-L90 | Not Used |
|   |   |
|   |   |
| Yaqui |   |
| L91 | Not Used |
|   |   |
|   |   |
| L92 | Pascua Yaqui Tribe of Arizona |
| L93 | Yaqui |
| L94-L99 | Not Used |
|   |   |
|   |   |
| Yavapai Apache |   |
| M01 | Yavapai Apache Nation of the Camp Verde Indian Reservation |
| M02-M06 | Not Used |
|   |   |
|   |   |
| Yokuts |   |
| M07 | Picayune Rancheria of Chukchansi Indians |
| M08 | Tachi |
| M09 | Tule River Indian Tribe |
| M10 | Yokuts |
| M11 | Table Mountain Rancheria |
| M12-M15 | Not Used |
|   |   |
|   |   |
| Yuchi |   |
| M16 | Yuchi |
| M17 | Tla |
|   |   |
| M18 | Tla Wilano |
| M19 | Ani-stohini/Unami |
| M20-M21 | Not Used |
|   |   |
|   |   |
| Yuman |   |
| M22 | Cocopah Tribe of Arizona |
| M23 | Havasupai Tribe of the Havasupai Reservation |
| M24 | Hualapai Indian Tribe of the Hualapai Indian Reservation |
| M25 | Maricopa |
| M26 | Fort Mojave Indian Tribe of Arizona, California, and Nevada |
| M27 | Quechan Tribe of the Fort Yuma Indian Reservation |
| M28 | Yavapai-Prescott Tribe of the Yavapai Reservation |
| M29-M33 | Not Used |
|   |   |
|   |   |
| Yurok |   |
| M34 | Resighini Rancheria |
| M35 | Yurok Tribe |
| M36-M38 | Not Used |
| M39 | Multiple AMERICAN INDIAN and ALASKA NATIVE responses |
| M40 | Multiple AMERICAN INDIAN responses |
| Tribe Not Specified |   |
| M41 | American Indian |
| M42 | Tribal responses, not elsewhere classified |
| M43 | Not Used |
|   |   |
|   |   |
| ALASKA NATIVE |   |
| Alaska Native Not Specified |   |
| M44 | Alaska Indian |
| M45-M46 | Not Used |
| M47 | Alaska Native |
| M48-M51 | Not Used |
|   |   |
|   |   |
| Alaskan Athabascan |   |
| M52 | Ahtna, Inc. Corporation |
| M53 | Alaskan Athabascan |
| M54 | Alatna Village |
| M55 | Alexander |
| M56 | Allakaket Village |
| M57 | Alanvik |
| M58 | Anvik Village |
| M59 | Arctic Village |
| M60 | Beaver Village |
| M61 | Birch Creek Tribe |
| M62 | Native Village of Cantwell |
| M63 | Chalkyitsik Village |
| M64 | Chickaloon Native Village |
| M65 | Cheesh-Na Tribe (Chistochina) |
| M66 | Native Village of Chitina |
| M67 | Circle Native Community |
| M68 | Cook Inlet |
| M69 | Not Used |
| M70 | Copper River |
| M71 | Village of Dot Lake |
| M72 | Doyon |
| M73 | Native Village of Eagle |
| M74 | Eklutna Native Village |
| M75 | Evansville Village (Bettles Field) |
| M76 | Native Village of Fort Yukon |
| M77 | Native Village of Gakona |
| M78 | Galena Village (Louden Village) |
| M79 | Organized Village of Grayling (Holikachuk) |
| M80 | Gulkana Village |
| M81 | Healy Lake Village |
| M82 | Holy Cross Village |
| M83 | Hughes Village |
| M84 | Huslia Village |
| M85 | Village of Iliamna |
| M86 | Village of Kaltag |
| M87 | Native Village of Kluti Kaah (Copper Center) |
| M88 | Knik Tribe |
| M89 | Koyukuk Native Village |
| M90 | Lake Minchumina |
| M91 | Lime Village |
| M92 | McGrath Native Village |
| M93 | Manley Village Council (Manley Hot Springs) |
| M94 | Mentasta Traditional Council |
| M95 | Native Village of Minto |
| M96 | Nenana Native Association |
| M97 | Nikolai Village |
| M98 | Ninilchik Village Traditional Council |
| M99 | Nondalton Village |
| N01 | Northway Village |
| N02 | Nulato Village |
| N03 | Pedro Bay Village |
| N04 | Rampart Village |
| N05 | Native Village of Ruby |
| N06 | Village of Salamatoff |
| N07 | Seldovia Village Tribe |
| N08 | Slana |
| N09 | Shageluk Native Village |
| N10 | Native Village of Stevens |
| N11 | Village of Stony River |
| N12 | Takotna Village |
| N13 | Native Village of Tanacross |
| N14 | Not Used |
| N15 | Native Village of Tanana |
| N16 | Tanana Chiefs |
| N17 | Native Village of Tazlina |
| N18 | Telida Village |
| N19 | Native Village of Tetlin |
| N20 | Tok |
| N21 | Native Village of Tyonek |
| N22 | Village of Venetie |
| N23 | Wiseman |
| N24 | Kenaitze Indian Tribe |
| N25-N27 | Not Used |
|   |   |
|   |   |
| Tlingit-Haida |   |
| N28 | Angoon Community Association |
| N29 | Central Council of the Tlingit and Haida Indian Tribes |
| N30 | Chilkat Indian Village (Klukwan) |
| N31 | Chilkoot Indian Association (Haines) |
| N32 | Craig Community Association |
| N33 | Douglas Indian Association |
| N34 | Haida |
| N35 | Hoonah Indian Association |
| N36 | Hydaburg Cooperative Association |
| N37 | Organized Village of Kake |
| N38 | Organized Village of Kasaan |
| N39 | Not Used |
| N40 | Ketchikan Indian Corporation |
| N41 | Klawock Cooperative Association |
| N42 | Not Used |
| N43 | Pelican |
| N44 | Petersburg Indian Association |
| N45 | Organized Village of Saxman |
| N46 | Sitka Tribe of Alaska |
| N47 | Tenakee Springs |
| N48 | Tlingit |
| N49 | Wrangell Cooperative Association |
| N50 | Yakutat Tlingit Tribe |
| N51-N55 | Not Used |
| N56-N58 | (see under Tsimshian) |
| N59 | Not Used |
| N60 | Sealaska Corporation (Southeast Alaska) |
| N61-N64 | Not Used |
| N65 | Skagway Village |
| N66 | Not Used |
|   |   |
|   |   |
| Tsimshian |   |
| N56 | Metlakatla Indian Community, Annette Island Reserve |
| N57 | Tsimshian |
| N58 | Not Used |
|   |   |
|   |   |
| Inupiat |   |
| N67 | American Eskimo |
| N68 | Eskimo |
| N69 | Greenland Eskimo |
| N70-N74 | Not Used |
| N75 | Inuit |
| N76-N78 | Not Used |
| N79 | Native Village of Ambler |
| N80 | Not Used |
| N81 | Village of Anaktuvuk Pass |
| N82 | Inupiat Community of the Arctic Slope |
| N83 | Arctic Slope Corporation |
| N84 | Atqasuk Village (Atkasook) |
| N85 | Native Village of Barrow Inupiat Traditional Government |
| N86 | Bering Straits Inupiat |
| N87 | Native Village of Brevig Mission |
| N88 | Native Village of Buckland |
| N89 | Chinik Eskimo Community (Golovin) |
| N90 | Native Village of Council |
| N91 | Native Village of Deering |
| N92 | Native Village of Elim |
| N93 | Not Used |
| N94 | Native Village of Diomede (Inalik) |
| N95 | Not Used |
| N96 | Inupiat (Inupiaq) |
| N97 | Kaktovik Village (Barter Island) |
| N98 | Kawerak |
| N99 | Native Village of Kiana |
| O01-O99 | Not Used |
| P01 | Native Village of Kivalina |
| P02 | Native Village of Kobuk |
| P03 | Native Village of Kotzebue |
| P04 | Native Village of Koyuk |
| P05-P06 | Not Used |
| P07 | Nana Inupiat |
| P08 | Native Village of Noatak |
| P09 | Nome Eskimo Community |
| P10 | Noorvik Native Community |
| P11 | Native Village of Nuiqsut (Nooiksut) |
| P12 | Native Village of Point Hope |
| P13 | Native Village of Point Lay |
| P14 | Native Village of Selawik |
| P15 | Native Village of Shaktoolik |
| P16 | Native Village of Shishmaref |
| P17 | Native Village of Shungnak |
| P18 | Village of Solomon |
| P19 | Native Village of Teller |
| P20 | Native Village of Unalakleet |
| P21 | Village of Wainwright |
| P22 | Native Village of Wales |
| P23 | Native Village of White Mountain |
| P24 | Not Used |
| P25 | Native Village of Marys Igloo |
| P26 | King Island Native Community |
| P27-P29 | Not Used |
| P30-P32 | (see under Yupik) |
| P33-P35 | Not Used |
| P36 | Chevak Native Village |
| P37 | Native Village of Mekoryuk |
|   |   |
| Yupik |   |
| P30 | Native Village of Gambell |
| P31 | Native Village of Savoonga |
| P32 | Siberian Yupik |
| P33-P37 | (see under Inupiat) |
| P38 | Akiachak Native Community |
| P39 | Akiak Native Community |
| P40 | Village of Alakanuk |
| P41 | Native Village of Aleknagik |
| P42 | Yupiit of Andreafski |
| P43 | Village of Aniak |
| P44 | Village of Atmautluak |
| P45 | Orutsararmiut Native Village (Bethel) |
| P46 | Village of Bill Moores Slough |
| P47 | Bristol Bay |
| P48 | Calista |
| P49 | Village of Chefornak |
| P50 | Native Village of Hamilton |
| P51 | Native Village of Chuathbaluk |
| P52 | Village of Clarks Point |
| P53 | Village of Crooked Creek |
| P54 | Curyung Tribal Council (Native Village of Dillingham) |
| P55 | Native Village of Eek |
| P56 | Native Village of Ekuk |
| P57 | Ekwok Village |
| P58 | Emmonak Village |
| P59 | Native Village of Goodnews Bay |
| P60 | Native Village of Hooper Bay (Naparagamiut) |
| P61 | Iqurmuit Traditional Council |
| P62 | Village of Kalskag |
| P63 | Native Village of Kasigluk |
| P64 | Native Village of Kipnuk |
| P65 | New Koliganek Village Council |
| P66 | Native Village of Kongiganak |
| P67 | Village of Kotlik |
| P68 | Organized Village of Kwethluk |
| P69 | Native Village of Kwigillingok |
| P70 | Levelock Village |
| P71 | Village of Lower Kalskag |
| P72 | Manokotak Village |
| P73 | Native Village of Marshall (Fortuna Ledge) |
| P74 | Village of Ohogamiut |
| P75 | Asacarsarmiut Tribe |
| P76 | Naknek Native Village |
| P77 | Native Village of Napaimute |
| P78 | Native Village of Napakiak |
| P79 | Native Village of Napaskiak |
| P80 | Newhalen Village |
| P81 | New Stuyahok Village |
| P82 | Newtok Village |
| P83 | Native Village of Nightmute |
| P84 | Native Village of Nunapitchuk |
| P85 | Oscarville Traditional Village |
| P86 | Pilot Station Traditional Village |
| P87 | Native Village of Pitka's Point |
| P88 | Platinum Traditional Village |
| P89 | Portage Creek Village (Ohgsenakale) |
| P90 | Native Village of Kwinhagak |
| P91 | Village of Red Devil |
| P92 | Native Village of Saint Michael |
| P93 | Native Village of Scammon Bay |
| P94 | Native Village of Nunam Iqua (Sheldons Point) |
| P95 | Village of Sleetmute |
| P96 | Stebbins Community Association |
| P97 | Traditional Village of Togiak |
| P98 | Nunakauyarmiut Tribe (Toksook Bay) |
| P99 | Tuluksak Native Community |
| Q01-Q99 | Not Used |
| R01 | Native Village of Tuntutuliak |
| R02 | Native Village of Tununak |
| R03 | Twin Hills Village |
| R04 | Yupik (Yupik Eskimo) |
| R05 | Not Used |
| R06 | Native Village of Georgetown |
| R07 | Algaaciq Native Village (St. Marys) |
| R08 | Umkumiute Native Village |
| R09 | Chuloonawick Native Village |
| R10 | Not Used |
|   |   |
|   |   |
| Aleut |   |
| R11 | Aleut |
| R12-R15 | Not Used |
| R16 | Alutiiq |
| R17 | Native Village of Afognak |
| R18-R22 | Not Used |
| R23 | Native Village of Tatitlek |
| R24 | Ugashik Village |
| R25-R27 | Not Used |
| R28 | Bristol Bay Aleut |
| R29 | Chignik Bay Tribal Council (Native Village of Chignik) |
| R30 | Chignik Lake Village |
| R31 | Egegik Village |
| R32 | Igiugig Village |
| R33 | Ivanoff Bay Village |
| R34 | King Salmon Tribe |
| R35 | Kokhanok Village |
| R36 | Native Village of Perryville |
| R37 | Native Village of Pilot Point |
| R38 | Native Village of Port Heiden |
| R39-R42 | Not Used |
| R43 | Native Village of Chanega (Chenega) |
| R44 | Chugach Aleut |
| R45 | Chugach Corporation |
| R46 | Native Village of Nanwalek (English Bay) |
| R47 | Native Village of Port Graham |
| R48-R50 | Not Used |
| R51 | Native Village of Eyak (Cordova) |
| R52-R54 | Not Used |
| R55 | Native Village of Akhiok |
| R56 | Agdaagux Tribe of King Cove |
| R57 | Native Village of Karluk |
| R58 | Native Village of Kanatak |
| R59 | Kodiak |
| R60 | Koniag Aleut |
| R61 | Native Village of Larsen Bay |
| R62 | Village of Old Harbor |
| R63 | Native Village of Ouzinkie |
| R64 | Native Village of Port Lions |
| R65 | Lesnoi Village (Woody Island) |
| R66 | Sunaq Tribe of Kodiak |
| R67 | Sugpiaq |
| R68-R74 | Not Used |
| R75 | Native Village of Akutan |
| R76 | Aleut Corporation |
| R77-R78 | Not Used |
| R79 | Native Village of Atka |
| R80 | Native Village of Belkofski |
| R81 | Native Village of Chignik Lagoon |
| R82 | King Cove |
| R83 | Native Village of False Pass |
| R84 | Native Village of Nelson Lagoon |
| R85 | Native Village of Nikolski |
| R86 | Pauloff Harbor Village |
| R87 | Qagan Tayagungin Tribe of Sand Point Village |
| R88 | Qawalangin Tribe of Unalaska |
| R89 | Saint George Island |
| R90 | Saint Paul Island |
| R91 | Not Used |
| R92 | South Naknek Village |
| R93 | Unangan (Unalaska) |
| R94 | Not Used |
| R95 | Native Village of Unga |
| R96 | Kaguyak Village |
| R97-R98 | Not Used |
| R99 | Multiple ALASKA NATIVE responses |
| S01-S99 | Not Used |
|   |   |
|   |   |
|   |   |
| CANADIAN AND LATIN AMERICAN INDIAN |   |
|   |   |
| Canadian and French American Indian |   |
|   |   |
| T01 | Canadian Indian |
| T02 | French Canadian/French American Indian |
| T03 | Abenaki Canadian |
| T04 | Acadia Band |
| T05 | Ache Dene Koe |
| T06 | Ahousaht |
| T07 | Alderville First Nation |
| T08 | Alexandria Band |
| T09 | Algonquins of Barriere Lake |
| T10 | Batchewana First Nation |
| T11 | Beardys and Okemasis Band |
| T12 | Beausoleil |
| T13 | Beecher Bay |
| T14 | Beothuk |
| T15 | Bella Coola (Nuxalk Nation) |
| T16 | Big Cove |
| T17 | Big Grassy |
| T18 | Bigstone Cree Nation |
| T19 | Bonaparte Band |
| T20 | Boston Bar First Nation |
| T21 | Bridge River |
| T22 | Brokenhead Ojibway Nation |
| T23 | Buffalo Point Band |
| T24 | Caldwell |
| T25 | Campbell River Band |
| T26 | Cape Mudge Band |
| T27 | Carcross/Tagish First Nation |
| T28 | Caribou |
| T29 | Carrier Nation |
| T30 | Carry the Kettle Band |
| T31 | Cheam Band |
| T32 | Chemainus First Nation |
| T33 | Chilcotin Nation |
| T34 | Chippewa/Ojibwe Canadian |
| T35 | Chippewa of Sarnia |
| T36 | Chippewa of the Thames |
| T37 | Clayoquot |
| T38 | Cold Lake First Nations |
| T39 | Coldwater Band |
| T40 | Comox Band |
| T41 | Coquitlam Band |
| T42 | Cote First Nation |
| T43 | Couchiching First Nation |
| T44 | Cowessess Band |
| T45 | Cowichan |
| T46 | Cree Canadian |
| T47 | Cross Lake First Nation |
| T48 | Curve Lake Band |
| T49 | Dene Canadian |
| T50 | Dene Band Nwt (Nw Terr.) |
| T51 | Ditidaht Band |
| T52 | Dogrib |
| T53 | Eagle Lake Band |
| T54 | Eastern Cree |
| T55 | Ebb and Flow Band |
| T56 | English River First Nation |
| T57 | Eskasoni |
| T58 | Esquimalt |
| T59 | Fisher River |
| T60 | Five Nations |
| T61 | Fort Alexander Band |
| T62 | Garden River Nation |
| T63 | Gibson Band |
| T64 | Gitksan |
| T65 | Gitlakdamix Band |
| T66 | Grassy Narrows First Nation |
| T67 | Gull Bay Band |
| T68 | Gwichya Gwich'in |
| T69 | Heiltsuk Band |
| T70 | Hesquiaht Band |
| T71 | Hiawatha First Nation |
| T72 | Hope Band (Chawathill Nation) |
| T73 | Huron |
| T74 | Huron of Lorretteville |
| T75 | Innu (Montagnais) |
| T76 | Interior Salish |
| T77 | James Bay Cree |
| T78 | James Smith Cree Nation |
| T79 | Kahkewistahaw First Nation |
| T80 | Kamloops Band |
| T81 | Kanaka Bar |
| T82 | Kanesatake Band |
| T83 | Kaska Dena |
| T84 | Keeseekoose Band |
| T85 | Kincolith Band |
| T86 | Kingsclear Band |
| T87 | Kitamaat |
| T88 | Kitigan Zibi Anishinabeg |
| T89 | Klahoose First Nation |
| T90 | Kwakiutl |
| T91 | Kyuquot Band |
| T92 | Lakahahmen Band |
| T93 | Lake Manitoba Band |
| T94 | Lake St. Martin Band |
| T95 | Lennox Island Band |
| T96 | Liard River First Nation |
| T97 | Lillooet |
| T98 | Little Shuswap Band |
| T99 | Long Plain First Nation |
| U01 | Lower Nicola Indian Band |
| U02 | Malahat First Nation |
| U03 | Matachewan Band |
| U04 | Mcleod Lake |
| U05 | Metis |
| U06 | Millbrook First Nation |
| U07 | Mississaugas of the Credit |
| U08 | Mohawk Bay of Quinte |
| U09 | Mohawk Canadian |
| U10 | Mohawk Kahnawake |
| U11 | Mohican Canadian |
| U12 | Musqueam Band |
| U13 | Namgis First Nation (Nimpkish) |
| U14 | Nanaimo (Snuneymuxw) |
| U15 | Nanoose First Nation |
| U16 | Naskapi |
| U17 | Nation Huronne Wendat |
| U18 | Nipissing First Nation |
| U19 | North Thompson Band (Simpcw First Nation) |
| U20 | NQuatqua (Anderson Lake) |
| U21 | Nuu-chah-nulth (Nootka) |
| U22 | Odanak |
| U23 | Ohiaht Band |
| U24 | Oneida Nation of the Thames |
| U25 | Opaskwayak Cree Nation |
| U26 | Osoyoos Band |
| U27 | Pacheedaht First Nation |
| U28 | Pauquachin |
| U29 | Peepeekisis |
To obtain additional information on these and other American Community Survey (ACS) subjects, see the list of Census 2000/2010 Contacts on the Internet at
http://www.census.gov/contacts/www/c-census2000.html.
http://www.census.gov/contacts/www/c-census2000.html.
These definitions apply to the data collected in both the United States and Puerto Rico. The text specifically notes any differences. References about comparability to the previous ACS years refer only to the ACS in the United States. Beginning in 2006, the population in group quarters is included in the data tabulations.
For additional information about the data in previous decennial censuses, see http://www.census.gov/prod/cen2000/doc/sf4.pdf, Appendix B and subject definitions for American Community Survey years prior to 2005.
The weighting methodology in the 2006 ACS was modified in order to ensure consistent estimates of occupied housing units, households, and householders. For more information on the 2006 weighting methodology changes, see "User Notes" on the ACS website (http://www.census.gov/acs/www/data_documentation/documentation_main/). There were no significant changes to the 2007 or 2008 weighting methodology. Beginning in 2009, the weighting methodology has changed to include the use of controls for total population for incorporated places and minor civil divisions.
For subject definitions from previous years, visit http://www.census.gov/acs/www/.
For subject definitions from previous years, visit http://www.census.gov/acs/www/.
Living quarters are classified as either housing units or group quarters. Living quarters are usually found in structures intended for residential use, but also may be found in structures intended for nonresidential use as well as in places such as tents, vans, and emergency and transitional shelters.
A housing unit may be a house, an apartment, a mobile home, a group of rooms or a single room that is occupied (or, if vacant, intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other individuals in the building and which have direct access from outside the building or through a common hall. For vacant units, the criteria of separateness and direct access are applied to the intended occupants whenever possible. If that information cannot be obtained, the criteria are applied to the previous occupants.
Both occupied and vacant housing units are included in the housing unit inventory. Boats, recreational vehicles (RVs), vans, tents, railroad cars, and the like are included only if they are occupied as someone's current place of residence. Vacant mobile homes are included provided they are intended for occupancy on the site where they stand. Vacant mobile homes on dealers' sales lots, at the factory, or in storage yards are excluded from the housing inventory. Also excluded from the housing inventory are quarters being used entirely for nonresidential purposes, such as a store or an office, or quarters used for the storage of business supplies or inventory, machinery, or agricultural products.
Both occupied and vacant housing units are included in the housing unit inventory. Boats, recreational vehicles (RVs), vans, tents, railroad cars, and the like are included only if they are occupied as someone's current place of residence. Vacant mobile homes are included provided they are intended for occupancy on the site where they stand. Vacant mobile homes on dealers' sales lots, at the factory, or in storage yards are excluded from the housing inventory. Also excluded from the housing inventory are quarters being used entirely for nonresidential purposes, such as a store or an office, or quarters used for the storage of business supplies or inventory, machinery, or agricultural products.
A housing unit is classified as occupied if it is the current place of residence of the person or group of people living in it at the time of interview, or if the occupants are only temporarily absent from the residence for two months or less, that is, away on vacation or a business trip. If all the people staying in the unit at the time of the interview are staying there for two months or less, the unit is considered to be temporarily occupied and classified as "vacant." The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living quarters. The living quarters occupied by staff personnel within any group quarters are separate housing units if they satisfy the housing unit criteria of separateness and direct access; otherwise, they are considered group quarters.
Occupied rooms or suites of rooms in hotels, motels, and similar places are classified as housing units only when occupied by permanent residents, that is, people who consider the hotel as their current place of residence or have no current place of residence elsewhere. If any of the occupants in rooming or boarding houses, congregate housing, or continuing care facilities live separately from others in the building and have direct access, their quarters are classified as separate housing units.
Occupied rooms or suites of rooms in hotels, motels, and similar places are classified as housing units only when occupied by permanent residents, that is, people who consider the hotel as their current place of residence or have no current place of residence elsewhere. If any of the occupants in rooming or boarding houses, congregate housing, or continuing care facilities live separately from others in the building and have direct access, their quarters are classified as separate housing units.
A housing unit is vacant if no one is living in it at the time of interview. Units occupied at the time of interview entirely by persons who are staying two months or less and who have a more permanent residence elsewhere are considered to be temporarily occupied, and are classified as "vacant."
New units not yet occupied are classified as vacant housing units if construction has reached a point where all exterior windows and doors are installed and final usable floors are in place. Vacant units are excluded from the housing inventory if they are open to the elements, that is, the roof, walls, windows, and/or doors no longer protect the interior from the elements. Also, excluded are vacant units with a sign that they are condemned or they are to be demolished.
New units not yet occupied are classified as vacant housing units if construction has reached a point where all exterior windows and doors are installed and final usable floors are in place. Vacant units are excluded from the housing inventory if they are open to the elements, that is, the roof, walls, windows, and/or doors no longer protect the interior from the elements. Also, excluded are vacant units with a sign that they are condemned or they are to be demolished.
Group Quarters (GQs) are places where people live or stay, in a group living arrangement that is owned or managed by an entity or organization providing housing and/or services for the residents. These services may include custodial or medical care, as well as other types of assistance, and residency is commonly restricted to those receiving these services. People living in GQs usually are not related to each other. GQs include such places as college residence halls, residential treatment centers, skilled nursing facilities, group homes, military barracks, correctional facilities, workers' dormitories, and facilities for people experiencing homelessness. GQs are defined according to the housing and/or services provided to residents, and are identified by census GQ type codes.
In January 2006, the American Community Survey (ACS) was expanded to include the population living in GQ facilities. The ACS GQ sample encompasses 12 independent samples; like the housing unit (HU) sample, a new GQ sample is introduced each month. The GQ data collection lasts only 6 weeks and does not include a formal nonresponse follow- up operation. The GQ data collection operation is conducted in two phases. First, U.S. Census Bureau Field Representatives (FRs) conduct interviews with the GQ facility contact person or administrator of the selected GQ (GQ level), and second, the FR conducts interviews with a sample of individuals from the facility (person level).
The GQ-level data collection instrument is an automated Group Quarters Facility Questionnaire (GQFQ). Information collected by the FR using the GQFQ during the GQ- level interview is used to determine or verify the type of facility, population size, and the sample of individuals to be interviewed. FRs conduct GQ-level data collection at approximately 20,000 individual GQ facilities each year.
A list of the GQ facilities (and their respective type codes) that is in scope for the 2011 ACS can be found in the 2011 Code List.
In January 2006, the American Community Survey (ACS) was expanded to include the population living in GQ facilities. The ACS GQ sample encompasses 12 independent samples; like the housing unit (HU) sample, a new GQ sample is introduced each month. The GQ data collection lasts only 6 weeks and does not include a formal nonresponse follow- up operation. The GQ data collection operation is conducted in two phases. First, U.S. Census Bureau Field Representatives (FRs) conduct interviews with the GQ facility contact person or administrator of the selected GQ (GQ level), and second, the FR conducts interviews with a sample of individuals from the facility (person level).
The GQ-level data collection instrument is an automated Group Quarters Facility Questionnaire (GQFQ). Information collected by the FR using the GQFQ during the GQ- level interview is used to determine or verify the type of facility, population size, and the sample of individuals to be interviewed. FRs conduct GQ-level data collection at approximately 20,000 individual GQ facilities each year.
A list of the GQ facilities (and their respective type codes) that is in scope for the 2011 ACS can be found in the 2011 Code List.
Though the American Community Survey (ACS) was expanded to include the population living in GQ facilities in 2006 the ACS began field testing early. The pretest in 2001 prevented the ACS from going into 2006 without determining whether or not the new processes, type codes, and procedures would produce the desired outcome for the ACS GQ data collection operation.
In 2001, the ACS GQ operational staff and other ACS staff implemented a number of changes in the GQ operation, the greatest of which was developing an automated Group Quarters Facility Questionnaire (GQFQ). The staff developed the GQFQ based on the decennial Other Living Quarters (OLQ) questionnaire used in the 2004 Census test. However, in order to make that questionnaire script fit with the ACS operation, the developers made some modifications, such as dropping the listing component, and adding the ability to capture multiple GQ types within the special place or GQ sampled.
Along with the introduction of an automated GQFQ, the ACS made the decision to use the revised GQ definitions planned for Census 2010, even though the definitions of GQ types were still evolving. The pretest used a draft version of the GQ definitions that existed at the end of November 2004. Since these definitions will continue to evolve over the next several years, the ACS needed a GQFQ that could easily adopt future revisions to the definitions. Thus, the developers designed a flexible GQFQ. It was through this flexibility that group quarter types have been able to be added or dropped (e.g. YMCA/YWCA and hostels).
In 2001, the ACS GQ operational staff and other ACS staff implemented a number of changes in the GQ operation, the greatest of which was developing an automated Group Quarters Facility Questionnaire (GQFQ). The staff developed the GQFQ based on the decennial Other Living Quarters (OLQ) questionnaire used in the 2004 Census test. However, in order to make that questionnaire script fit with the ACS operation, the developers made some modifications, such as dropping the listing component, and adding the ability to capture multiple GQ types within the special place or GQ sampled.
Along with the introduction of an automated GQFQ, the ACS made the decision to use the revised GQ definitions planned for Census 2010, even though the definitions of GQ types were still evolving. The pretest used a draft version of the GQ definitions that existed at the end of November 2004. Since these definitions will continue to evolve over the next several years, the ACS needed a GQFQ that could easily adopt future revisions to the definitions. Thus, the developers designed a flexible GQFQ. It was through this flexibility that group quarter types have been able to be added or dropped (e.g. YMCA/YWCA and hostels).
The total group quarters population in the ACS may not be comparable with Census 2000 because there are some Census 2000 GQ types that were out of scope in the ACS such as domestic violence shelters, soup kitchens, regularly scheduled mobile food vans, targeted non-sheltered outdoor locations, crews of maritime vessels and living quarters for victims of natural disasters. Also, there are some Census 2000 GQ type categories that are no longer valid (residential care facility providing "Protective Oversight," hospitals/wards for the chronically ill and hospitals/wards for drug/alcohol abuse). The exclusion of these GQ types from the ACS may result in a small bias in some ACS estimates to the extent that the excluded population is different from the included population. Furthermore, only a sample of GQ facilities throughout the United States and Puerto Rico are selected for the ACS. ACS controls the GQ sample at the state level only. Therefore, for lower levels of geography, particularly when there are relatively few GQs in a geographic area, the ACS estimate of the GQ population may vary from the estimate from Census 2000.
When comparing the 2011 ACS data with 2008 ACS data the data should be compared with caution at the National and State level. It should not be compared below the State level because the weighting for the group quarters (GQ) population is not controlled below the state level. Because of this users may observe greater fluctuations in year-to-year ACS estimates of the GQ population at sub-state levels than at state levels. The causes of these fluctuations typically are the result of either GQs that have closed or where the current population of the GQ is significantly different than the expected population as reflected on the sampling frame. Substantial changes in the ACS GQ estimates can impact ACS estimates of total population characteristics for areas where either the GQ population is a substantial proportion of the total population or where the GQ population may have very different characteristics than the total population as a whole. Users can assess the impact that year-to- year changes in sub-state GQ total population estimates have on the changes in total ACS population estimates by accessing Table B26001 on American Fact Finder. Users should also use their local knowledge to help determine whether the year-to-year change in the ACS estimate represents a real change in the GQ population or may be the result of the lack of adequate population controls for sub-state areas.
When comparing ACS GQ data across the years that group quarters data have been collected, it must be noted that beginning in 2008 military transient quarters, YMCA / YWCA and hostels were no longer in scope. These data were collected in 2006 and 2007.
A complete list and definitions of the GQs that have been included in the ACS can be found in the 2011 Code List.
When comparing the 2011 ACS data with 2008 ACS data the data should be compared with caution at the National and State level. It should not be compared below the State level because the weighting for the group quarters (GQ) population is not controlled below the state level. Because of this users may observe greater fluctuations in year-to-year ACS estimates of the GQ population at sub-state levels than at state levels. The causes of these fluctuations typically are the result of either GQs that have closed or where the current population of the GQ is significantly different than the expected population as reflected on the sampling frame. Substantial changes in the ACS GQ estimates can impact ACS estimates of total population characteristics for areas where either the GQ population is a substantial proportion of the total population or where the GQ population may have very different characteristics than the total population as a whole. Users can assess the impact that year-to- year changes in sub-state GQ total population estimates have on the changes in total ACS population estimates by accessing Table B26001 on American Fact Finder. Users should also use their local knowledge to help determine whether the year-to-year change in the ACS estimate represents a real change in the GQ population or may be the result of the lack of adequate population controls for sub-state areas.
When comparing ACS GQ data across the years that group quarters data have been collected, it must be noted that beginning in 2008 military transient quarters, YMCA / YWCA and hostels were no longer in scope. These data were collected in 2006 and 2007.
A complete list and definitions of the GQs that have been included in the ACS can be found in the 2011 Code List.
The data on acreage were obtained from Housing Question 4 in the 2011 American Community Survey. This question was asked at occupied and vacant one-family houses and mobile homes. The data for vacant units were obtained by asking a neighbor, real estate agent, building manager, or anyone else who had knowledge of the vacant unit in question.
This question determines a range of acres (cuerdas) on which the house or mobile home is located. A major purpose for this question, in conjunction with Housing Question 5 on agricultural sales, is to identify farm units. In previous American Community Surveys and in the 2000 Census, this question was used to determine single-family homes on 10 or more acres (cuerdas). The land may consist of more than one tract or plot. These tracts or plots are usually adjoining; however, they may be separated by a road, creek, another piece of land, etc.
In the American Community Surveys prior to 2004 and in Census 2000, acreage was one of the variables used to determine specified owner- and renter-occupied housing units.
This question determines a range of acres (cuerdas) on which the house or mobile home is located. A major purpose for this question, in conjunction with Housing Question 5 on agricultural sales, is to identify farm units. In previous American Community Surveys and in the 2000 Census, this question was used to determine single-family homes on 10 or more acres (cuerdas). The land may consist of more than one tract or plot. These tracts or plots are usually adjoining; however, they may be separated by a road, creek, another piece of land, etc.
In the American Community Surveys prior to 2004 and in Census 2000, acreage was one of the variables used to determine specified owner- and renter-occupied housing units.
The 1996-1998 question asked, "Is this house or mobile home on less than 1 acre, 1 to less than 10 acres, or 10 or more acres." Since 1999, the question wording was changed to ask, "How many acres is this house or mobile home on?" and the second response category was modified to "1 to 9.9 acres."
Data on the sales of agricultural crops were obtained from Housing Question 5 in the 2011 American Community Survey. The question was asked at occupied one-family houses and mobile homes located on lots of 1 or more acres. Data for this question exclude units on lots of less than 1 acre, units located in structures containing two or more units, and all vacant units. This question refers to the total amount (before taxes and expenses) received in the 12 months prior to the interview, from the sale of crops, vegetables, fruits, nuts, livestock and livestock products, and nursery and forest products, produced on "this property." Respondents new to a unit were to estimate total agricultural sales from the 12 months prior to the interview even if some portion of the sales had been made by previous occupants of the unit.
This question is used mainly to classify housing units as farm or nonfarm residences, not to provide detailed information on the sale of agricultural products. Detailed information on the sale of agricultural products is provided by the Census of Agriculture, which is conducted by the U.S. Department of Agriculture/National Agricultural Statistics Service (see
http://www.agcensus.usda.gov/).
This question is used mainly to classify housing units as farm or nonfarm residences, not to provide detailed information on the sale of agricultural products. Detailed information on the sale of agricultural products is provided by the Census of Agriculture, which is conducted by the U.S. Department of Agriculture/National Agricultural Statistics Service (see
http://www.agcensus.usda.gov/).
On the 1996-1998 American Community Survey questionnaires, there were just two response categories to indicate whether or not the amount of sales was over $1,000. Since 1999, the question has included a series of response categories for the amount of the agricultural sales.
The data on bedrooms were obtained from Housing Question 7b in the 2011 American Community Survey. The question was asked at both occupied and vacant housing units. The number of bedrooms is the count of rooms designed to be used as bedrooms, that is, the number of rooms that would be listed as bedrooms if the house, apartment, or mobile home were on the market for sale or for rent. Included are all rooms intended to be used as bedrooms even if they currently are being used for some other purpose. A housing unit consisting of only one room is classified, by definition, as having no bedroom.
Bedrooms provide the basis for estimating the amount of living and sleeping spaces within a housing unit. These data allow officials to evaluate the adequacy of the housing stock to shelter the population, and to determine any housing deficiencies in neighborhoods. The data also allow officials to track the changing physical characteristics of the housing inventory over time.
Bedrooms provide the basis for estimating the amount of living and sleeping spaces within a housing unit. These data allow officials to evaluate the adequacy of the housing stock to shelter the population, and to determine any housing deficiencies in neighborhoods. The data also allow officials to track the changing physical characteristics of the housing inventory over time.
The 1996-1998 American Community Survey question provided a response category for "None" and space for the respondent to enter a number of bedrooms. From 1999-2007, the question provided pre-coded response categories from "No bedroom" to "5 or more bedrooms." Starting in 2008, the question became the second part of a two-part question that linked the number of "rooms" and number of "bedrooms" questions together. In addition, the wording of the question was changed to ask, "How many of these rooms are bedrooms?" Additional changes introduced in 2008 included removing the pre- coded response categories and adding a write-in box for the respondent to enter the number of bedrooms, providing the rule to use for defining a "bedroom" as an instruction, and providing an additional instruction addressing efficiency and studio apartments - "If this is an efficiency/studio apartment, print '0'."
The Census Bureau tested the changes introduced to the 2008 version of the bedrooms question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Rooms and Bedrooms" on the ACS website (www.census.gov/acs).
Caution should be used when comparing American Community Survey data on bedrooms from the years 2008 and after with both pre-2008 ACS and Census 2000 data. Changes made to the bedrooms question between the 2007 and 2008 ACS involving the wording as well as the response option resulted in an inconsistency in the ACS data. This inconsistency in the data was most noticeable as an increase in "No bedroom" responses and as a decrease in "1 bedroom" to "3 bedrooms" responses.
The data for business on property were obtained from Housing Question 6 in the 2011 American Community Survey. The question was asked at occupied and vacant one-family houses and mobile homes. A business must be easily recognizable from the outside. It usually will have a separate outside entrance and have the appearance of a business, such as a grocery store, restaurant, or barbershop. It may be either attached to the house or mobile home or be located elsewhere on the property. Those housing units in which a room is used for business or professional purposes and have no recognizable alterations to the outside are not considered to have a business. Medical offices are considered businesses for tabulation purposes.
In American Community Surveys prior to 2004 and in Census 2000, business on property was one of the variables used to determine specified owner- and renter-occupied housing units.
Business on property provides information on whether certain housing units should be excluded from statistics on rent, value, and shelter costs. The data provide a means to allow comparisons to be made to earlier census data by identifying information for comparable select groups of housing units without a business or medical office on the property.
In American Community Surveys prior to 2004 and in Census 2000, business on property was one of the variables used to determine specified owner- and renter-occupied housing units.
Business on property provides information on whether certain housing units should be excluded from statistics on rent, value, and shelter costs. The data provide a means to allow comparisons to be made to earlier census data by identifying information for comparable select groups of housing units without a business or medical office on the property.
Since 1999, the 1996-1998 ACS questions were changed to add parentheses to the question wording: "Is there a business (such as a store or barber shop) or a medical office on this property?"
The data on condominium housing units were obtained from Housing Question 13 in the 2011 American Community Survey. The question was asked at both occupied and vacant housing units.
Condominium is a type of ownership that enables a person to own an apartment or house in a development of similarly owned units and to hold a common or joint ownership in some or all of the common areas and facilities such as land, roof, hallways, entrances, elevators, swimming pool, etc. Condominiums may be single-family houses as well as units in apartment buildings. A unit does not need to be occupied by the owner to be counted as a condominium.
A condominium fee normally is charged monthly to the owners of the individual condominium units by the condominium owners' association to cover operating, maintenance, administrative, and improvement costs of the common property (grounds, halls, lobby, parking areas, laundry rooms, swimming pool, etc.). The costs for utilities and/or fuels may be included in the condominium fee if the units do not have separate meters.
Data on condominium fees may include real estate taxes and/or insurance payments for the common property, but do not include real estate taxes or fire, hazard, and flood insurance reported in Housing Questions 17 and 18 (in the 2011 American Community Survey) for the individual unit.
Amounts reported were the regular monthly payment, even if paid by someone outside the household or remain unpaid. Costs were estimated as closely as possible when exact costs were not known.
The data from this question were added to payments for mortgages (both first, second, home equity loans, and other junior mortgages); real estate taxes; fire hazard, and flood insurance payments; and utilities and fuels to derive "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income" for condominium owners. These data provide information on the cost of home ownership and offer an excellent measure of housing affordability and excessive shelter costs.
By listing the condominium status and fee separately on the questionnaire, the data also serve to improving the accuracy of estimating monthly housing costs for mortgaged owners.
Data on condominium fees may include real estate taxes and/or insurance payments for the common property, but do not include real estate taxes or fire, hazard, and flood insurance reported in Housing Questions 17 and 18 (in the 2011 American Community Survey) for the individual unit.
Amounts reported were the regular monthly payment, even if paid by someone outside the household or remain unpaid. Costs were estimated as closely as possible when exact costs were not known.
The data from this question were added to payments for mortgages (both first, second, home equity loans, and other junior mortgages); real estate taxes; fire hazard, and flood insurance payments; and utilities and fuels to derive "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income" for condominium owners. These data provide information on the cost of home ownership and offer an excellent measure of housing affordability and excessive shelter costs.
By listing the condominium status and fee separately on the questionnaire, the data also serve to improving the accuracy of estimating monthly housing costs for mortgaged owners.
Since 1996, the American Community Survey included the question on condominium status with one that asked for condominium fees. The words "or mobile home," and an instruction for renters to enter the amount of the condominium fee only if the fee was in addition to rent, were added starting in 1999.
The data on contract rent (also referred to as "rent asked" for vacant units) were obtained from Housing Question 15a in the 2011 American Community Survey. The question was asked at occupied housing units that were for rent, vacant housing units that were for rent, and vacant units rented but not occupied at the time of interview.
Housing units that are renter occupied without payment of rent are shown separately as "No rent paid." The unit may be owned by friends or relatives who live elsewhere and who allow occupancy without charge. Rent-free houses or apartments may be provided to compensate caretakers, ministers, tenant farmers, sharecroppers, or others.
Contract rent is the monthly rent agreed to or contracted for, regardless of any furnishings, utilities, fees, meals, or services that may be included. For vacant units, it is the monthly rent asked for the rental unit at the time of interview.
If the contract rent includes rent for a business unit or for living quarters occupied by another household, only that part of the rent estimated to be for the respondent's unit was included. Excluded was any rent paid for additional units or for business premises.
If a renter pays rent to the owner of a condominium or cooperative, and the condominium fee or cooperative carrying charge also is paid by the renter to the owner, the condominium fee or carrying charge was included as rent.
If a renter receives payments from lodgers or roomers who are listed as members of the household, the rent without deduction for any payments received from the lodgers or roomers, was to be reported. The respondent was to report the rent agreed to or contracted for even if paid by someone else such as friends or relatives living elsewhere, a church or welfare agency, or the government through subsidies or vouchers.
Contract rent provides information on the monthly housing cost expenses for renters. When the data is used in conjunction with utility costs and income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels, and to provide assistance to agencies in determining policies on fair rent.
Housing units that are renter occupied without payment of rent are shown separately as "No rent paid." The unit may be owned by friends or relatives who live elsewhere and who allow occupancy without charge. Rent-free houses or apartments may be provided to compensate caretakers, ministers, tenant farmers, sharecroppers, or others.
Contract rent is the monthly rent agreed to or contracted for, regardless of any furnishings, utilities, fees, meals, or services that may be included. For vacant units, it is the monthly rent asked for the rental unit at the time of interview.
If the contract rent includes rent for a business unit or for living quarters occupied by another household, only that part of the rent estimated to be for the respondent's unit was included. Excluded was any rent paid for additional units or for business premises.
If a renter pays rent to the owner of a condominium or cooperative, and the condominium fee or cooperative carrying charge also is paid by the renter to the owner, the condominium fee or carrying charge was included as rent.
If a renter receives payments from lodgers or roomers who are listed as members of the household, the rent without deduction for any payments received from the lodgers or roomers, was to be reported. The respondent was to report the rent agreed to or contracted for even if paid by someone else such as friends or relatives living elsewhere, a church or welfare agency, or the government through subsidies or vouchers.
Contract rent provides information on the monthly housing cost expenses for renters. When the data is used in conjunction with utility costs and income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels, and to provide assistance to agencies in determining policies on fair rent.
The median divides the rent distribution into two equal parts: one-half of the cases falling below the median contract rent and one-half above the median. Quartiles divide the rent distribution into four equal parts. Median and quartile contract rent are computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Appendix A.") In computing median and quartile contract rent, units reported as "No rent paid" are excluded. Median and quartile rent calculations are rounded to the nearest whole dollar. Upper and lower quartiles can be used to note large rent differences among various geographic areas. (For more information on medians and quartiles, see "Derived Measures.")
Aggregate contract rent is calculated by adding all of the contract rents for occupied housing units in an area. Aggregate contract rent is subject to rounding, which means that all cells in a matrix are rounded to the nearest hundred dollars. (For more information, see "Aggregate" under "Derived Measures.")
Aggregate rent asked is calculated by adding all of the rents for vacant-for-rent housing units in an area. Aggregate rent asked is subject to rounding, which means that all cells in a matrix are rounded to the nearest hundred dollars. (For more information, see "Aggregate" under "Derived Measures.")
Since 1996, the American Community Survey questionnaires provided a space for the respondent to enter a dollar amount. The words "or mobile home" were added to the question starting in 1999 to be more inclusive of the structure type. Since 2004, contract rent has been shown for all renter-occupied housing units. In previous years (1996-2003), it was shown only for specified renter-occupied housing units.
Data on contract rent in the American Community Survey should not be compared to Census 2000 contract rent data. For Census 2000, tables were not released for total renter-occupied units. The universe in Census 2000 was "specified renter-occupied housing units" whereas the universe in the ACS is "renter occupied housing units," thus comparisons cannot be made between these two data sets.
The data on Food Stamp benefits were obtained from Housing Question 12 in the 2011 American Community Survey. The Food Stamp Act of 1977 defines this federally-funded program as one intended to "permit low-income households to obtain a more nutritious diet" (from Title XIII of Public Law 95-113, The Food Stamp Act of 1977, declaration of policy). Food purchasing power is increased by providing eligible households with coupons or cards that can be used to purchase food. The Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA) administers the Food Stamp Program through state and local welfare offices. The Food Stamp Program is the major national income support program to which all low-income and low-resource households, regardless of household characteristics, are eligible.
On October 1, 2008, the Federal Food Stamp program was renamed SNAP (Supplemental Nutrition Assistance Program).
Respondents were asked if one or more of the current members received food stamps or a food stamp benefit card during the past 12 months. Respondents were also asked to include benefits from the Supplemental Nutrition Assistance Program (SNAP) in order to incorporate the program name change.
On October 1, 2008, the Federal Food Stamp program was renamed SNAP (Supplemental Nutrition Assistance Program).
Respondents were asked if one or more of the current members received food stamps or a food stamp benefit card during the past 12 months. Respondents were also asked to include benefits from the Supplemental Nutrition Assistance Program (SNAP) in order to incorporate the program name change.
The 1996-1998 American Community Survey asked for a 12- month amount for the value of the food stamps following the Yes response category. For the 1999-2002 ACS, the words "Food Stamps" were capitalized in the question following the Yes response category, and the instruction "Past 12 months' value - Dollars" was added. Since 2003, the words "received during the past 12 months" were added to the question following the Yes response category. Beginning in 2008, the value of food stamps received was no longer collected; the wording of the question was changed from "At anytime during the past 12 months" to "In the past 12 months," and the term '"food stamp benefit card' was added.
Adding the text "food stamps benefit card" to the question text and removing the dollar amount portion of the question resulted in a statistically significant increase in the recipiency rate for food stamps because of a decrease in item nonresponse rate.
Adding the text "food stamps benefit card" to the question text and removing the dollar amount portion of the question resulted in a statistically significant increase in the recipiency rate for food stamps because of a decrease in item nonresponse rate.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Many types of GQ populations have food stamp distributions that are very different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the food stamp distribution. This is particularly true for areas with a substantial GQ population.
The Census Bureau tested the changes introduced to the 2008 version of the Food Stamp benefits question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Food Stamps" on the ACS website (http://www.census.gov/acs/www/).
The Census Bureau tested the changes introduced to the 2008 version of the Food Stamp benefits question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Food Stamps" on the ACS website (http://www.census.gov/acs/www/).
The data on gross rent were obtained from answers to Housing Questions 11a-d and 15a in the 2011 American Community Survey. Gross rent is the contract rent plus the estimated average monthly cost of utilities (electricity, gas, and water and sewer) and fuels (oil, coal, kerosene, wood, etc.) if these are paid by the renter (or paid for the renter by someone else). Gross rent is intended to eliminate differentials that result from varying practices with respect to the inclusion of utilities and fuels as part of the rental payment. The estimated costs of water and sewer, and fuels are reported on a 12-month basis but are converted to monthly figures for the tabulations. Renter units occupied without payment of rent are shown separately as "No rent paid" in the tabulations.
Gross rent provides information on the monthly housing cost expenses for renters. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels, and to provide assistance to agencies in determining policies on fair rent.
Gross rent provides information on the monthly housing cost expenses for renters. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels, and to provide assistance to agencies in determining policies on fair rent.
To inflate gross rent amounts from previous years, the dollar values are inflated to the latest year's dollar values by multiplying by a factor equal to the average annual Consumer Price Index (CPI-U-RS) factor for the current year, divided by the average annual CPI-U-RS factor for the earlier/earliest year.
Median gross rent divides the gross rent distribution into two equal parts: one-half of the cases falling below the median gross rent and one-half above the median. Median gross rent is computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Appendix A") Median gross rent is rounded to the nearest whole dollar. (For more information on medians, see "Derived Measures.")
Aggregate gross rent is calculated by adding together all the gross rents for all specified housing units in an area. Aggregate gross rent is rounded to the nearest hundred dollars. (For more information, see "Aggregate" under "Derived Measures.")
Since 1996, the American Community Survey questions have remained the same.
Data on gross rent in the American Community Survey should not be compared to Census 2000 gross rent data. For Census 2000, tables were not released for total renter-occupied units. The universe in Census 2000 was "specified renter-occupied housing units" whereas the universe in the ACS is "renter occupied housing units," thus comparisons cannot be made between these two data sets.
Gross rent as a percentage of household income is a computed ratio of monthly gross rent to monthly household income (total household income divided by 12). The ratio is computed separately for each unit and is rounded to the nearest tenth. Units for which no rent is paid and units occupied by households that reported no income or a net loss comprise the category, "Not computed."
Gross rent as a percentage of household income provides information on the monthly housing cost expenses for renters. The information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels, and to provide assistance to agencies in determining policies on fair rent.
Gross rent as a percentage of household income provides information on the monthly housing cost expenses for renters. The information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels, and to provide assistance to agencies in determining policies on fair rent.
This measure divides the gross rent as a percentage of household income distribution into two equal parts: one-half of the cases falling below the median gross rent as a percentage of household income and one-half above the median. Median gross rent as a percentage of household income is computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Appendix A.") Median gross rent as a percentage of household income is rounded to the nearest tenth. (For more information on medians, see "Derived Measures.")
Data on gross rent as a percentage of household income in the American Community Survey should not be compared to Census 2000 gross rent as a percentage of household income data. For Census 2000, tables were not released for total renter-occupied units. The universe in Census 2000 was "specified renter-occupied housing units" whereas the universe in the ACS is "renter occupied housing units," thus comparisons cannot be made between these two data sets.
The data on house heating fuel were obtained from Housing Question 10 in the 2011 American Community Survey. The question was asked at occupied housing units. The data show the type of fuel used most to heat the house, apartment, or mobile home.
House heating fuel provides information on energy supply and consumption. These data are used by planners to identify the types of fuel used in certain areas and the consequences this usage may have on the area. The data also serve to aid in forecasting the need for future energy needs and power facilities such as generating plants, long distance pipelines for oil or natural gas, and long distance transmission lines for electricity.
House heating fuel is categorized on the ACS questionnaire as follows:
House heating fuel provides information on energy supply and consumption. These data are used by planners to identify the types of fuel used in certain areas and the consequences this usage may have on the area. The data also serve to aid in forecasting the need for future energy needs and power facilities such as generating plants, long distance pipelines for oil or natural gas, and long distance transmission lines for electricity.
House heating fuel is categorized on the ACS questionnaire as follows:
This category includes gas piped through underground pipes from a central system to serve the neighborhood.
This category includes liquid propane gas stored in bottles or tanks that are refilled or exchanged when empty.
This category includes electricity that is generally supplied by means of above or underground electric power lines.
This category includes fuel oil, kerosene, gasoline, alcohol, and other combustible liquids.
This category includes purchased wood, wood cut by household members on their property or elsewhere, driftwood, sawmill or construction scraps, or the like.
This category includes heat provided by sunlight that is collected, stored, and actively distributed to most of the rooms.
This category includes units that do not use any fuel or that do not have heating equipment.
Since 1996, the American Community Survey questions have remained the same.
This question is based on the count of people in occupied housing units. All people occupying the housing unit are counted, including the householder, occupants related to the householder, and lodgers, roomers, boarders, and so forth.
A measure obtained by dividing the number of people living in occupied housing units by the total number of occupied housing units. This measure is rounded to the nearest hundredth.
A measure obtained by dividing the number of people living in owner-occupied housing units by the total number of owner- occupied housing units. This measure is rounded to the nearest hundredth.
A measure obtained by dividing the number of people living in renter-occupied housing units by the total number of renter- occupied housing units. This measure is rounded to the nearest hundredth.
The data on fire, hazard, and flood insurance were obtained from Housing Question 18 in the 2011 American Community Survey. The question was asked of owner-occupied units. The statistics for this question refer to the annual premium for fire, hazard, and flood insurance on the property (land and buildings), that is, policies that protect the property and its contents against loss due to damage by fire, lightning, winds, hail, flood, explosion, and so on.
Liability policies are included only if they are paid with the fire, hazard, and flood insurance premiums and the amounts for fire, hazard, and flood cannot be separated. Premiums are reported even if they have not been paid or are paid by someone outside the household. When premiums are paid on other than a yearly basis, the premiums are converted to a yearly basis.
The payment for fire, hazard, and flood insurance is added to payments for real estate taxes, utilities, fuels, and mortgages (both first, second, home equity loans, and other junior mortgages) to derive "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income." These data provide information on the cost of home ownership and offer an excellent measure of housing affordability and excessive shelter costs.
A separate question (19d in the 2011 American Community Survey) determines whether insurance premiums are included in the mortgage payment to the lender(s). This makes it possible to avoid counting these premiums twice in the computations.
Liability policies are included only if they are paid with the fire, hazard, and flood insurance premiums and the amounts for fire, hazard, and flood cannot be separated. Premiums are reported even if they have not been paid or are paid by someone outside the household. When premiums are paid on other than a yearly basis, the premiums are converted to a yearly basis.
The payment for fire, hazard, and flood insurance is added to payments for real estate taxes, utilities, fuels, and mortgages (both first, second, home equity loans, and other junior mortgages) to derive "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income." These data provide information on the cost of home ownership and offer an excellent measure of housing affordability and excessive shelter costs.
A separate question (19d in the 2011 American Community Survey) determines whether insurance premiums are included in the mortgage payment to the lender(s). This makes it possible to avoid counting these premiums twice in the computations.
Median fire, hazard, and flood insurance divides the fire, hazard, and flood insurance distribution into two equal parts: one-half of the cases falling below the median fire, hazard, and flood insurance and one-half above the median. Median fire, hazard, and flood insurance is computed on the basis of a standard distribution (see the "Standard Distributions" section under "Appendix A.") Median fire, hazard, and flood insurance is rounded to the nearest whole dollar. (For more information on medians, see "Derived Measures.")
Data on kitchen facilities were obtained from Housing Question 8d-f in the 2011 American Community Survey. The question was asked at both occupied and vacant housing units. A unit has complete kitchen facilities when it has all three of the following facilities: (d) a sink with a faucet, (e) a stove or range, and (f) a refrigerator. All kitchen facilities must be located in the house, apartment, or mobile home, but they need not be in the same room. A housing unit having only a microwave or portable heating equipment such as a hot plate or camping stove should not be considered as having complete kitchen facilities. An icebox is not considered to be a refrigerator.
Kitchen facilities provide an indication of living standards and assess the quality of household facilities within the housing inventory. These data provide assistance in determining areas that are eligible for programs and funding, such as Meals on Wheels. The data also serve to aid in the development of policies based on fair market rent, and to identify areas in need of rehabilitation loans or grants.
Kitchen facilities provide an indication of living standards and assess the quality of household facilities within the housing inventory. These data provide assistance in determining areas that are eligible for programs and funding, such as Meals on Wheels. The data also serve to aid in the development of policies based on fair market rent, and to identify areas in need of rehabilitation loans or grants.
The 1996-1998 American Community Survey questions asked whether the house or apartment had complete kitchen facilities, requiring that the three facilities all be in the same unit. In 1999, "mobile home" was added to the question, along with the capitalization of the word "COMPLETE" for emphasis. Starting in 2008, the structure of the question changed and combined kitchen facilities with plumbing facilities and telephone service availability into one question to ask, "Does this house, apartment, or
mobile home have-" and provided the respondent with a "Yes" or "No" checkbox for each component needed for complete facilities. Also in 2008, the component "sink with piped water" was changed to "sink with a faucet."
mobile home have-" and provided the respondent with a "Yes" or "No" checkbox for each component needed for complete facilities. Also in 2008, the component "sink with piped water" was changed to "sink with a faucet."
Caution should be used when comparing American Community Survey data on kitchen facilities from the years 2008 and after with both pre-2008 ACS and Census 2000 data. Changes made to the kitchen facilities question between the 2007 and 2008 ACS involving the wording as well as the response option resulted in an inconsistency in the ACS data. This inconsistency in the data was most noticeable as an increase in housing units "lacking complete kitchen facilities."
The data on meals included in the rent were obtained from Housing Question 15b in the 2011 American Community Survey. The question was asked of occupied housing units that were rented and vacant housing units that were for rent at the time of enumeration. These data only include rental units which meals are included in the rent, or if occupants contract for either their meals or a meal plan in order to live in the unit. Renters in continuing care or life facilities are included in this category if their contracts cover meal services.
The meals included in rent allows for a measurement on the amount of congregate housing within the housing inventory. Congregate housing is considered to be housing units where the rent includes meals and other services.
The meals included in rent allows for a measurement on the amount of congregate housing within the housing inventory. Congregate housing is considered to be housing units where the rent includes meals and other services.
Since 1996, the American Community Survey questions have been the same. Starting in 2004, meals included in rent is shown for all renter-occupied housing units. In previous years (1996-2003), it was shown only for specified renter- occupied housing units.
The data on mobile home costs were obtained from Housing Question 21 in the 2011 American Community Survey. The question was asked at owner-occupied mobile homes.
These data include the total yearly costs for personal property taxes, land or site rent, registration fees, and license fees on all owner-occupied mobile homes. The instructions are to exclude real estate taxes already reported in Question 17 in the 2011 American Community Survey.
Costs are estimated as closely as possible when exact costs are not known. Amounts are the total for an entire 12-month billing period, even if they are paid by someone outside the household or remain unpaid.
The data from this question are added to payments for mortgages; real estate taxes; fire, hazard, and flood insurance payments; utilities; and fuels to derive "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income" for mobile home owners. These data provide information on the cost of home ownership and offer an excellent measure of housing affordability and excessive shelter costs.
These data include the total yearly costs for personal property taxes, land or site rent, registration fees, and license fees on all owner-occupied mobile homes. The instructions are to exclude real estate taxes already reported in Question 17 in the 2011 American Community Survey.
Costs are estimated as closely as possible when exact costs are not known. Amounts are the total for an entire 12-month billing period, even if they are paid by someone outside the household or remain unpaid.
The data from this question are added to payments for mortgages; real estate taxes; fire, hazard, and flood insurance payments; utilities; and fuels to derive "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income" for mobile home owners. These data provide information on the cost of home ownership and offer an excellent measure of housing affordability and excessive shelter costs.
The 1996-1998 American Community Survey questions were the same. Starting in 1999, the question had a lead-in question on whether the respondent had an installment loan or a contract on the mobile home. The question then asked for total costs including any installment loan.
The data for monthly housing costs are developed from a distribution of "Selected Monthly Owner Costs" for owner-occupied units and "Gross Rent" for renter-occupied units. The owner-occupied categories are further separated into those with a mortgage and those without a mortgage. See the sections on "Selected Monthly Owner Costs" and "Gross Rent" for more details on what characteristics are included in each measure and how these data are comparable to previous ACS and Census 2000 data.
Monthly housing costs provide information on the cost of monthly housing expenses for owners and renters. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels.
Monthly housing costs provide information on the cost of monthly housing expenses for owners and renters. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels.
This measure divides the monthly housing costs distribution into two equal parts: one-half of the cases falling below the median monthly housing costs and one-half above the median. Medians are shown separately for units "with a mortgage" and for units "not mortgaged." Median monthly housing costs are computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Appendix A.") Median monthly housing costs are rounded to the nearest whole dollar.
The data for monthly housing costs as a percentage of household income are developed from a distribution of "Selected Monthly Owner Costs as a Percentage of Household Income" for owner-occupied and "Gross Rent as a Percentage of Household Income" for renter-occupied units. The owner-occupied categories are further separated into those with a mortgage and those without a mortgage. See sections on "Selected Monthly Owner Costs as a Percentage of Household Income" and "Gross Rent as a Percentage of Household Income" for more details on what characteristics are included in each measure and how these data are comparable to previous ACS and Census 2000 data.
Monthly housing costs as a percentage of household income provide information on the cost of monthly housing expenses for owners and renters. The information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels.
Monthly housing costs as a percentage of household income provide information on the cost of monthly housing expenses for owners and renters. The information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels.
The data on mortgage payment were obtained from Housing Question 19b in the 2011 American Community Survey. The question was asked at owner-occupied units that have a mortgage, deed of trust, or similar debt; or contract to purchase. The question provides the regular monthly amount required to be paid to the lender for the first mortgage (deed of trust, contract to purchase, or similar debt) on the property. Amounts are included even if the payments are delinquent or paid by someone else. The amounts reported are included in the computation of "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income" for units with a mortgage.
The amounts reported include everything paid to the lender including principal and interest payments, real estate taxes, fire, hazard, and flood insurance payments, and mortgage insurance premiums. Separate questions determine whether real estate taxes and fire, hazard, and flood insurance payments are included in the mortgage payment to the lender. This makes it possible to avoid counting these components twice in the computation of "Selected Monthly Owner Costs."
Mortgage payment provides information on the monthly housing cost expenses for owners with a mortgage. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs aimed to meet the needs of people at different economic levels.
The amounts reported include everything paid to the lender including principal and interest payments, real estate taxes, fire, hazard, and flood insurance payments, and mortgage insurance premiums. Separate questions determine whether real estate taxes and fire, hazard, and flood insurance payments are included in the mortgage payment to the lender. This makes it possible to avoid counting these components twice in the computation of "Selected Monthly Owner Costs."
Mortgage payment provides information on the monthly housing cost expenses for owners with a mortgage. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs aimed to meet the needs of people at different economic levels.
Data on mortgage payment in the American Community Survey can be compared to previous ACS and Census 2000 mortgage payment data. For Census 2000, tables for both total owner-occupied housing units and specified owner-occupied housing units were released, thus comparisons can be made only when comparing the same universes between the two data sets.
The data on mortgage status were obtained from Housing Questions 19a and 20a in the 2011 American Community Survey. The questions were asked at owner-occupied units. "Mortgage" refers to all forms of debt where the property is pledged as security for repayment of the debt, including deeds of trust; trust deeds; contracts to purchase; land contracts; junior mortgages; and home equity loans.
A mortgage is considered a first mortgage if it has prior claim over any other mortgage or if it is the only mortgage on the property. All other mortgages (second, third, etc.) are considered junior mortgages. A home equity loan is generally a junior mortgage. If no first mortgage is reported, but a junior mortgage or home equity loan is reported, then the loan is considered a first mortgage.
In most data products, the tabulations for "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income" usually are shown separately for units "with a mortgage" and for units "not mortgaged." The category "not mortgaged" is comprised of housing units owned free and clear of debt.
Mortgage status provides information on the cost of home ownership. When the data is used in conjunction with mortgage payment data, the information determines shelter costs for living quarters. These data can be use in the development of housing programs aimed to meet the needs of people at different economic levels. The data also serve to evaluate the magnitude of and to plan facilities for condominiums, which are becoming an important source of supply of new housing in many areas.
A mortgage is considered a first mortgage if it has prior claim over any other mortgage or if it is the only mortgage on the property. All other mortgages (second, third, etc.) are considered junior mortgages. A home equity loan is generally a junior mortgage. If no first mortgage is reported, but a junior mortgage or home equity loan is reported, then the loan is considered a first mortgage.
In most data products, the tabulations for "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income" usually are shown separately for units "with a mortgage" and for units "not mortgaged." The category "not mortgaged" is comprised of housing units owned free and clear of debt.
Mortgage status provides information on the cost of home ownership. When the data is used in conjunction with mortgage payment data, the information determines shelter costs for living quarters. These data can be use in the development of housing programs aimed to meet the needs of people at different economic levels. The data also serve to evaluate the magnitude of and to plan facilities for condominiums, which are becoming an important source of supply of new housing in many areas.
Data on mortgage status in the American Community Survey can be compared to previous ACS and Census 2000 mortgage status data. For Census 2000, tables for both total owner-occupied housing units and specified owner-occupied housing units were released, thus comparisons can be made only when comparing the same universes between the two data sets.
Occupants per room is obtained by dividing the number of people in each occupied housing unit by the number of rooms in the unit. The figures show the number of occupied housing units having the specified ratio of people per room. Although the Census Bureau has no official definition of crowded units, many users consider units with more than one occupant per room to be crowded. Occupants per room is rounded to the nearest hundredth.
This data is the basis for estimating the amount of living and sleeping spaces within a housing unit. These data allow officials to plan and allocate funding for additional housing to relieve crowded housing conditions. The data also serve to aid in planning for future services and infrastructure, such as home energy assistance programs and the development of waste treatment facilities.
This data is the basis for estimating the amount of living and sleeping spaces within a housing unit. These data allow officials to plan and allocate funding for additional housing to relieve crowded housing conditions. The data also serve to aid in planning for future services and infrastructure, such as home energy assistance programs and the development of waste treatment facilities.
Caution should be used when comparing American Community Survey data on occupants per room from the years 2008 and after with both pre-2008 ACS and Census 2000 data. Changes made to the rooms question between the 2007 and 2008 ACS involving the wording as well as the response option resulted in an inconsistency in the ACS data. This inconsistency in the data was most noticeable as an increase in "1 room" responses and as a decrease in "2 rooms" to "6 rooms" responses.
The data on plumbing facilities were obtained from Housing Question 8 a, b, and c in the 2011 American Community Survey. The question was asked at both occupied and vacant housing units. Complete plumbing facilities include: (a) hot and cold running water, (b) a flush toilet, and (c) a bathtub or shower. All three facilities must be located inside the house, apartment, or mobile home, but not necessarily in the same room. Housing units are classified as lacking complete plumbing facilities when any of the three facilities is not present.
Plumbing facilities provide an indication of living standards and assess the quality of household facilities within the housing inventory. These data provide assistance in the assessment of water resources and to serve as an aid to identify possible areas of ground water contamination. The data also are used to forecast the need for additional water and sewage facilities, aid in the development of policies based on fair market rent, and to identify areas in need of rehabilitation loans or grants.
Plumbing facilities provide an indication of living standards and assess the quality of household facilities within the housing inventory. These data provide assistance in the assessment of water resources and to serve as an aid to identify possible areas of ground water contamination. The data also are used to forecast the need for additional water and sewage facilities, aid in the development of policies based on fair market rent, and to identify areas in need of rehabilitation loans or grants.
The 1996-2007 American Community Survey questions were stand-alone questions that asked the respondent to answer either "Yes, has all three facilities" or "No" to the question of whether the housing unit had complete plumbing facilities, requiring that the facilities all be in the same unit. Starting in 2008, the structure of the question changed and combined plumbing facilities with kitchen facilities and telephone service availability into one question to ask, "Does this house, apartment, or mobile home have -" and provided the respondent with a "Yes" or "No" checkbox for each component needed for complete facilities. An additional change introduced in 2008 included changing the description of the component "hot and cold piped water" to "hot and cold running water."
Caution should be used when comparing American Community Survey data on plumbing facilities from the years 2008 and after with both pre-2008 ACS and Census 2000 data. Changes made to the plumbing facilities question between 2007 and 2008 ACS involving the wording as well as the response option resulted in an inconsistency in the ACS data. This inconsistency in the data was most noticeable as an increase in housing units "lacking complete plumbing facilities."
Data tables for Puerto Rico were not shown. Research indicated that the questions on plumbing facilities that were introduced in 2008 in the stateside American Community Survey and the Puerto Rico Community Survey may not have been appropriate for Puerto Rico.
Data tables for Puerto Rico were not shown. Research indicated that the questions on plumbing facilities that were introduced in 2008 in the stateside American Community Survey and the Puerto Rico Community Survey may not have been appropriate for Puerto Rico.
The data shown for population in occupied units is the total population minus any people living in group quarters. All people occupying the housing unit are counted, including the householder, occupants related to the householder, and lodgers, roomers, boarders, and so forth.
Population in occupied housing units provides information on the population within the housing inventory. The data allow the identification of population patterns within areas to assist in developing housing programs. These data also serve to aid officials in tracking the changing population characteristics of the housing inventory over time.
Population in occupied housing units provides information on the population within the housing inventory. The data allow the identification of population patterns within areas to assist in developing housing programs. These data also serve to aid officials in tracking the changing population characteristics of the housing inventory over time.
The data on poverty status of households were derived from answers to the income questions. Since poverty is defined at the family level and not the household level, the poverty status of the household is determined by the poverty status of the householder. Households are classified as poor when the total income of the householder's family is below the appropriate poverty threshold. (For nonfamily householders, their own income is compared with the appropriate threshold.) The income of people living in the household who are unrelated to the householder is not considered when determining the poverty status of a household, nor does their presence affect the family size in determining the appropriate threshold. The poverty thresholds vary depending on three criteria: size of family, number of related children, and, for 1- and 2-person families, age of householder. See the table "The 2011 Poverty Factors" in Appendix A. (For more information, see "Poverty Status" and "Income" under "Population Variables.")
The data on real estate taxes were obtained from Housing Question 17 in the 2011 American Community Survey. The question was asked at owner-occupied units. The statistics from this question refer to the total amount of all real estate taxes on the entire property (land and buildings) payable to all taxing jurisdictions, including special assessments, school taxes, county taxes, and so forth.
Real estate taxes include state, local, and all other real estate taxes even if delinquent, unpaid, or paid by someone who is not a member of the household. However, taxes due from prior years are not included. If taxes are paid on other than a yearly basis, the payments are converted to a yearly basis.
The payment for real estate taxes is added to payments for fire, hazard, and flood insurance; utilities and fuels; and mortgages (both first and second mortgages, home equity loans, and other junior mortgages) to derive "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income." These data provide information on the cost of home ownership and offer an excellent measure of housing affordability and excessive shelter costs.
A separate question (Question 19c in the 2008 American Community Survey) determines whether real estate taxes are included in the mortgage payment to the lender(s). This makes it possible to avoid counting taxes twice in the computations.
Real estate taxes include state, local, and all other real estate taxes even if delinquent, unpaid, or paid by someone who is not a member of the household. However, taxes due from prior years are not included. If taxes are paid on other than a yearly basis, the payments are converted to a yearly basis.
The payment for real estate taxes is added to payments for fire, hazard, and flood insurance; utilities and fuels; and mortgages (both first and second mortgages, home equity loans, and other junior mortgages) to derive "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income." These data provide information on the cost of home ownership and offer an excellent measure of housing affordability and excessive shelter costs.
A separate question (Question 19c in the 2008 American Community Survey) determines whether real estate taxes are included in the mortgage payment to the lender(s). This makes it possible to avoid counting taxes twice in the computations.
Data on real estate taxes in the American Community Survey should not be compared to Census 2000 real estate taxes data. The universe in Census 2000 was "specified owner-occupied housing units" whereas the universe in the ACS is "owner occupied housing units," thus comparisons cannot be made between these two data sets.
The data on rooms were obtained from Housing Question 7a in the 2011 American Community Survey. The question was asked at both occupied and vacant housing units. The statistics on rooms are in terms of the number of housing units with a specified number of rooms. The intent of this question is to count the number of whole rooms used for living purposes.
For each unit, rooms include living rooms, dining rooms, kitchens, bedrooms, finished recreation rooms, enclosed porches suitable for year-round use, and lodger's rooms. Excluded are strip or pullman kitchens, bathrooms, open porches, balconies, halls or foyers, half-rooms, utility rooms, unfinished attics or basements, or other unfinished space used for storage. A partially divided room is a separate room only if there is a partition from floor to ceiling, but not if the partition consists solely of shelves or cabinets.
Rooms provide the basis for estimating the amount of living and sleeping spaces within a housing unit. These data allow officials to plan and allocate funding for additional housing to relieve crowded housing conditions. The data also serve to aid in planning for future services and infrastructure, such as home energy assistance programs and the development of waste treatment facilities.
For each unit, rooms include living rooms, dining rooms, kitchens, bedrooms, finished recreation rooms, enclosed porches suitable for year-round use, and lodger's rooms. Excluded are strip or pullman kitchens, bathrooms, open porches, balconies, halls or foyers, half-rooms, utility rooms, unfinished attics or basements, or other unfinished space used for storage. A partially divided room is a separate room only if there is a partition from floor to ceiling, but not if the partition consists solely of shelves or cabinets.
Rooms provide the basis for estimating the amount of living and sleeping spaces within a housing unit. These data allow officials to plan and allocate funding for additional housing to relieve crowded housing conditions. The data also serve to aid in planning for future services and infrastructure, such as home energy assistance programs and the development of waste treatment facilities.
This measure divides the room distribution into two equal parts: one-half of the cases falling below the median number of rooms and one-half above the median. In computing median rooms, the whole number is used as the midpoint of the interval; thus, the category "3 rooms" is treated as an interval ranging from 2.5 to 3.5 rooms. Median rooms is rounded to the nearest tenth. (For more information on medians, see the discussion under "Derived Measures.")
Aggregate rooms is calculated by adding all of the rooms for housing units in an area. (For more information on aggregates, see "Derived Measures.")
The 1996-1998 American Community Survey question provided a space for a write-in entry on the number of rooms. From 1999-2007 the question provided response categories from "1 room" to "9 or more rooms." Starting in 2008, the response categories were removed and a write-in box was added for the respondent to enter the number of rooms. Additional changes introduced in 2008 included adding the word "separate" to the question stem, adding an instruction that defines a "room," adding an inclusion instruction to include bedrooms and kitchens in the count of rooms, and changing the current exclusion instruction by removing the word "half-room" and adding the phrase "unfinished basements."
The Census Bureau tested the changes introduced to the 2008 version of the rooms question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Rooms and Bedrooms" on the ACS website (http://www.census.gov/acs/www/).
Caution should be used when comparing American Community Survey data on rooms from the years 2008 and after with both pre-2008 ACS and Census 2000 data. Changes made to the rooms question between the 2007 and 2008 ACS involving the wording as well as the response option resulted in an inconsistency in the ACS data. This inconsistency in the data was most noticeable as an increase in "1 room" response and as a decrease in "2 rooms" to "6 rooms" responses.
The data on second mortgages or home equity loan payments were obtained from Housing Questions 20a and 20b in the 2011 American Community Survey. The questions were asked at owner-occupied units. Question 20a asks whether a second mortgage or a home equity loan exists on the property. Question 20b provides the regular monthly amount required to be paid to the lender on all second and junior mortgages and home equity loans. Amounts are included even if the payments are delinquent or paid by someone else. The amounts reported are included in the computation of "Selected Monthly Owner Costs" and "Selected Monthly Owner Costs as a Percentage of Household Income" for units with a mortgage.
All mortgages other than first mortgages (for example, second, third, etc.) are classified as "junior" mortgages. A second mortgage is a junior mortgage that gives the lender a claim against the property that is second to the claim of the holder of the first mortgage. Any other junior mortgage(s) would be subordinate to the second mortgage. A home equity loan is a line of credit available to the borrower that is secured by real estate. It may be placed on a property that already has a first or second mortgage, or it may be placed on a property that is owned free and clear.
If the respondents answered that no first mortgage existed, but a second mortgage or a home equity loan did, a computer edit assigned the unit a first mortgage and made the first mortgage monthly payment the amount reported in the second mortgage. The second mortgage/home equity loan data were then made "No" in Question 20a and blank in Question 20b.
Second mortgage or home equity loan data provide information on the monthly housing cost expenses for owners. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs aimed to meet the needs of people at different economic levels.
By listing the second mortgage or home equity loan question separately on the questionnaire from other housing cost questions, the data also serve to improving the accuracy of estimating monthly housing costs for mortgaged owners.
All mortgages other than first mortgages (for example, second, third, etc.) are classified as "junior" mortgages. A second mortgage is a junior mortgage that gives the lender a claim against the property that is second to the claim of the holder of the first mortgage. Any other junior mortgage(s) would be subordinate to the second mortgage. A home equity loan is a line of credit available to the borrower that is secured by real estate. It may be placed on a property that already has a first or second mortgage, or it may be placed on a property that is owned free and clear.
If the respondents answered that no first mortgage existed, but a second mortgage or a home equity loan did, a computer edit assigned the unit a first mortgage and made the first mortgage monthly payment the amount reported in the second mortgage. The second mortgage/home equity loan data were then made "No" in Question 20a and blank in Question 20b.
Second mortgage or home equity loan data provide information on the monthly housing cost expenses for owners. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs aimed to meet the needs of people at different economic levels.
By listing the second mortgage or home equity loan question separately on the questionnaire from other housing cost questions, the data also serve to improving the accuracy of estimating monthly housing costs for mortgaged owners.
Data on second mortgages or home equity loans in the American Community Survey can be compared to previous ACS and Census 2000 second mortgages or home equity loans data.
The variable "Selected Conditions" is defined for owner- and renter-occupied housing units as having at least one of the following conditions: 1) lacking complete plumbing facilities, 2) lacking complete kitchen facilities, 3) with 1.01 or more occupants per room, 4) selected monthly owner costs as a percentage of household income greater than 30 percent, and 5) gross rent as a percentage of household income greater than 30 percent.
Selected conditions provide information in assessing the quality of the housing inventory and its occupants. The data is used to easily identify those homes in which the quality of living and housing can be considered substandard.
Selected conditions provide information in assessing the quality of the housing inventory and its occupants. The data is used to easily identify those homes in which the quality of living and housing can be considered substandard.
The data on selected monthly owner costs were obtained from Housing Questions 11 and Questions 17 through 21 in the 2011 American Community Survey. The data were obtained for owner-occupied units. Selected monthly owner costs are the sum of payments for mortgages, deeds of trust, contracts to purchase, or similar debts on the property (including payments for the first mortgage, second mortgages, home equity loans, and other junior mortgages); real estate taxes; fire, hazard, and flood insurance on the property; utilities (electricity, gas, and water and sewer); and fuels (oil, coal, kerosene, wood, etc.). It also includes, where appropriate, the monthly condominium fee for condominiums (Question 13) and mobile home costs (Question 21) (installment loan payments, personal property taxes, site rent, registration fees, and license fees). Selected monthly owner costs were tabulated for all owner-occupied units, and usually are shown separately for units "with a mortgage" and for units "not mortgaged."
Selected monthly owner costs provide information on the monthly housing cost expenses for owners. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels.
Selected monthly owner costs provide information on the monthly housing cost expenses for owners. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels.
To inflate selected monthly owner costs from previous years, the dollar values are inflated to the latest year's dollar values by multiplying by a factor equal to the average annual Consumer Price Index (CPI-U- RS) factor for the current year, divided by the average annual CPI-U-RS factor for the earlier/earliest year.
This measure divides the selected monthly owner costs distribution into two equal parts: one-half of the cases falling below the median selected monthly owner costs and one-half above the median. Medians are shown separately for units "with a mortgage" and for units "not mortgaged." Median selected monthly owner costs are computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Appendix A.") Median selected monthly owner costs are rounded to the nearest whole dollar.
Since 1996, the American Community Survey questions have been the same. The American Community Survey collected the monthly cost of electricity and gas, and the 12-month cost of water and sewer. Since 2004, selected monthly owner costs has been shown for all owner-occupied housing units. In previous years (1996-2003), the question was shown only for specified owner-occupied housing units.
Caution should be used when comparing selected monthly owner costs data between the American Community Survey and Census 2000. For Census 2000, tables for both total owner-occupied housing units and specified owner-occupied housing units were released, thus comparisons can be made only when comparing the same universes between the two data sets. Additionally, for Census 2000 tables with full distributions were released for total owner-occupied housing units but medians were not shown.
The information on selected monthly owner costs as a percentage of household income is the computed ratio of selected monthly owner costs to monthly household income. The ratio was computed separately for each unit and rounded to the nearest whole percentage. The data are tabulated only for owner-occupied units.
Separate distributions are often shown for units "with a mortgage" and for units "not mortgaged." Units occupied by households reporting no income or a net loss are included in the "not computed" category. (For more information, see the discussion under "Selected Monthly Owner Costs.")
Selected monthly owner costs as a percentage of household income provide information on the monthly housing cost expenses for owners. The information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels.
Separate distributions are often shown for units "with a mortgage" and for units "not mortgaged." Units occupied by households reporting no income or a net loss are included in the "not computed" category. (For more information, see the discussion under "Selected Monthly Owner Costs.")
Selected monthly owner costs as a percentage of household income provide information on the monthly housing cost expenses for owners. The information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels.
This measure divides the selected monthly owner costs as a percentage of household income distribution into two equal parts: one-half of the cases falling below the median selected monthly owner costs as a percentage of household income and one-half above the median. Median selected monthly owner costs as a percentage of household income is computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Appendix A.") Median selected monthly owner costs as a percentage of household income is rounded to the nearest tenth. (For more information on medians, see "Derived Measures.")
Caution should be used when comparing selected monthly owner costs as a percentage of household income data between the American Community Survey and Census 2000. For Census 2000, tables for both total owner-occupied housing units and specified owner-occupied housing units were released, thus comparisons can be made only when comparing the same universes between the two data sets. Additionally, for Census 2000 tables with full distributions were released for total owner-occupied housing units but medians were not shown.
Specified owner-occupied units include only 1-family houses on less than 10 acres (cuerdas) without a business or medical office on the property. The data for "specified units" exclude mobile homes, houses with a business or medical office, houses on 10 or more acres (cuerdas), and housing units in multiunit buildings.
Specified owner-occupied unit information is used to maintain a comparable universe between the American Community Survey and earlier census data. Financial housing characteristics in earlier census data were based on a specified owner-occupied unit, however the ACS does not provide information solely for this universe. Therefore, the characteristics for a specified owner-occupied unit are maintained within the PUMS file to ensure comparisons can be made between the two data sets.
Specified owner-occupied unit information is used to maintain a comparable universe between the American Community Survey and earlier census data. Financial housing characteristics in earlier census data were based on a specified owner-occupied unit, however the ACS does not provide information solely for this universe. Therefore, the characteristics for a specified owner-occupied unit are maintained within the PUMS file to ensure comparisons can be made between the two data sets.
Prior to 1990, much of the owner-occupied housing inventory was comprised of single-family homes, either detached or attached. Therefore, earlier census data provided financial housing characteristics for the specified owner-occupied unit universe. However, the housing market began to change during the 1990's as an increasing number of units in multiunit structures were constructed and sold as condominiums, as well as the increase of mobile homes as an option for lower-income owners to purchase a home. As a result of these changes, the ACS abandoned the concept of the specified owner- occupied universe to ensure housing data was provided for all owner-occupied units.
The ACS only publishes financial housing characteristics for all units. The ACS PUMS file will provide the individual characteristics of a specified owner-occupied unit to allow comparisons to be made between the ACS and earlier census data. Census 2000 data provide financial housing characteristics for both all owner-occupied units and the more restricted universe of specified owner-occupied units.
Specified renter-occupied units are renter-occupied units that exclude 1-family houses on 10 or more acres (cuerdas).
Specified renter-occupied unit information is used to maintain a comparable universe between the American Community Survey and earlier census data. Financial housing characteristics in earlier census data were based on a specified renter-occupied unit, however the ACS does not provide information solely for this universe. Therefore, the characteristics for a specified renter-occupied unit are maintained within the PUMS file to ensure comparisons can be made between the two data sets.
Specified renter-occupied unit information is used to maintain a comparable universe between the American Community Survey and earlier census data. Financial housing characteristics in earlier census data were based on a specified renter-occupied unit, however the ACS does not provide information solely for this universe. Therefore, the characteristics for a specified renter-occupied unit are maintained within the PUMS file to ensure comparisons can be made between the two data sets.
The ACS only publishes financial housing characteristics for total renter- occupied units, whereas for Census 2000 tables were only released for specified renter- occupied units. Therefore, comparisons between these two data sets cannot be made, unless the characteristics of a specified renter-occupied are used to construct the same universe within the ACS PUMS file.
The data on telephones were obtained from Housing Question 8g in the 2011 American Community Survey. The question was asked at occupied housing units.
The question asked whether telephone service was available in the house, apartment, or mobile home. A telephone must be in working order and service available in the house, apartment, or mobile home that allows the respondent to both make and receive calls. Households whose service has been discontinued for nonpayment or other reasons are not counted as having telephone service available.
The availability of telephone service provides information on the isolation of households. These data help assess the level of communication access amongst elderly and low-income households. The data also serve to aid in the development of emergency telephone, medical, or crime prevention services.
The question asked whether telephone service was available in the house, apartment, or mobile home. A telephone must be in working order and service available in the house, apartment, or mobile home that allows the respondent to both make and receive calls. Households whose service has been discontinued for nonpayment or other reasons are not counted as having telephone service available.
The availability of telephone service provides information on the isolation of households. These data help assess the level of communication access amongst elderly and low-income households. The data also serve to aid in the development of emergency telephone, medical, or crime prevention services.
For the 1996-1998 American Community Survey, the question asked whether there was a telephone in the house or apartment. A telephone must be inside the house or apartment for the unit to be classified as having a telephone and units where the respondent used a telephone located inside the building but not in the respondent's living quarters were classified as having no telephone. In 1999, the words "or mobile home" were added question to be more inclusive of the structure type. In 2004, instructions that accompanied the ACS mail questionnaire advised respondents that if the household members used cell phones to answer that the house, apartment, or mobile home had telephone service. Starting in 2008, the structure of the question changed and combined telephone service availability with plumbing facilities and kitchen facilities into one question to ask, "Does this house, apartment, or mobile home have -" and provided the respondent with a "Yes" or "No" checkbox for each component needed for complete facilities. In 2008 the instruction "Include cell phones'' was added.
The Census Bureau tested the changes introduced to the 2008 version of the telephone service available question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Facilities" on the ACS website (http://www.census.gov/acs).
Caution should be used when comparing American Community Survey data on telephone service availability from the years 2008 and after with both pre-2008 ACS and Census 2000 data. Changes made to the telephone service availability question between the 2007 and 2008 ACS involving the structure of the question as well as the introduction of an instruction to include cell phones resulted in an inconsistency in the ACS data. This inconsistency in the data was most noticeable as an increase in the number of respondents answering "yes" to the question.
The data for tenure were obtained from Housing Question 14 in the 2011 American Community Survey. The question was asked at occupied housing units. Occupied housing units are classified as either owner occupied or renter occupied.
Tenure provides a measurement of home ownership, which has served as an indicator of the nation's economy for decades. These data are used to aid in the distribution of funds for programs such as those involving mortgage insurance, rental housing, and national defense housing. Data on tenure allows planners to evaluate the overall viability of housing markets and to assess the stability of neighborhoods. The data also serve in understanding the characteristics of owner occupied and renter occupied units to aid builders, mortgage lenders, planning officials, government agencies, etc., in the planning of housing programs and services.
Tenure provides a measurement of home ownership, which has served as an indicator of the nation's economy for decades. These data are used to aid in the distribution of funds for programs such as those involving mortgage insurance, rental housing, and national defense housing. Data on tenure allows planners to evaluate the overall viability of housing markets and to assess the stability of neighborhoods. The data also serve in understanding the characteristics of owner occupied and renter occupied units to aid builders, mortgage lenders, planning officials, government agencies, etc., in the planning of housing programs and services.
A housing unit is owner occupied if the owner or co-owner lives in the unit even if it is mortgaged or not fully paid for. The owner or co-owner must live in the unit and usually is Person 1 on the questionnaire. The unit is "Owned by you or someone in this household with a mortgage or loan" if it is being purchased with a mortgage or some other debt arrangement such as a deed of trust, trust deed, contract to purchase, land contract, or purchase agreement. The unit also is considered owned with a mortgage if it is built on leased land and there is a mortgage on the unit. Mobile homes occupied by owners with installment loan balances also are included in this category.
A housing unit is "Owned by you or someone in this household free and clear (without a mortgage or loan)" if there is no mortgage or other similar debt on the house, apartment, or mobile home including units built on leased land if the unit is owned outright without a mortgage.
A housing unit is "Owned by you or someone in this household free and clear (without a mortgage or loan)" if there is no mortgage or other similar debt on the house, apartment, or mobile home including units built on leased land if the unit is owned outright without a mortgage.
All occupied housing units which are not owner occupied, whether they are rented or occupied without payment of rent, are classified as renter occupied. "No rent paid" units are separately identified in the rent tabulations. Such units are generally provided free by friends or relatives or in exchange for services such as resident manager, caretaker, minister, or tenant farmer. Housing units on military bases also are classified in the "No rent paid" category. "Rented" includes units in continuing care, sometimes called life care arrangements. These arrangements usually involve a contract between one or more individuals and a health services provider guaranteeing the individual shelter, usually a house or apartment, and services, such as meals or transportation to shopping or recreation. (For more information, see "Meals Included in Rent.")
From 1996-2007 the American Community Survey questions were the same. Starting in 2008, the instruction "Mark (X) ONE box." was added following the question, and the instruction "Include home equity loans." was added following the response category "Owned by you or someone in this household with a mortgage or loan?" Additional changes introduced in 2008 included revising the wording of two of the response categories from "Rented for cash rent?" to "Rented?" and "Occupied without payment of cash rent?" to "Occupied without payment of rent?"
The data on units in structure (also referred to as "type of structure") were obtained from Housing Question 1 in the 2011 American Community Survey. The question was asked at occupied and vacant housing units. A structure is a separate building that either has open spaces on all sides or is separated from other structures by dividing walls that extend from ground to roof. In determining the number of units in a structure, all housing units, both occupied and vacant, are counted. Stores and office space are excluded. The data are presented for the number of housing units in structures of specified type and size, not for the number of residential buildings.
The units in structure provides information on the housing inventory by subdividing the inventory into one-family homes, apartments, and mobile homes. When the data is used in conjunction with tenure, year structure built, and income, units in structure serves as the basic identifier of housing used in many federal programs. The data also serve to aid in the planning of roads, hospitals, utility lines, schools, playgrounds, shopping centers, emergency preparedness plans, and energy consumption and supplies.
The units in structure provides information on the housing inventory by subdividing the inventory into one-family homes, apartments, and mobile homes. When the data is used in conjunction with tenure, year structure built, and income, units in structure serves as the basic identifier of housing used in many federal programs. The data also serve to aid in the planning of roads, hospitals, utility lines, schools, playgrounds, shopping centers, emergency preparedness plans, and energy consumption and supplies.
Both occupied and vacant mobile homes to which no permanent rooms have been added are counted in this category. Mobile homes used only for business purposes or for extra sleeping space and mobile homes for sale on a dealer's lot, at the factory, or in storage are not counted in the housing inventory.
This is a 1-unit structure detached from any other house, that is, with open space on all four sides. Such structures are considered detached even if they have an adjoining shed or garage. A one-family house that contains a business is considered detached as long as the building has open space on all four sides. Mobile homes to which one or more permanent rooms have been added or built also are included.
This is a 1-unit structure that has one or more walls extending from ground to roof separating it from adjoining structures. In row houses (sometimes called townhouses), double houses, or houses attached to nonresidential structures, each house is a separate, attached structure if the dividing or common wall goes from ground to roof.
These are units in structures containing 2 or more housing units, further categorized as units in structures with 2, 3 or 4, 5 to 9, 10 to 19, 20 to 49, and 50 or more apartments.
This category is for any living quarters occupied as a housing unit that does not fit the previous categories. Examples that fit this category are houseboats, railroad cars, campers, and vans. Recreational vehicles, boats, vans, tents, railroad cars, and the like are included only if they are occupied as someone's current place of residence.
The 1996-1998 American Community Survey question provided the response category, "a mobile home or trailer." Starting in 1999, the ACS response category dropped "or trailer" to read as "a mobile home."
The data on utility costs were obtained from Housing Questions 11a through 11d in the 2011 American Community Survey. The questions were asked of occupied housing units. The questions about electricity and gas asked for the monthly costs, and the questions about water/sewer and other fuels (oil, coal, wood, kerosene, etc.) asked for the yearly costs.
Costs are recorded if paid by or billed to occupants, a welfare agency, relatives, or friends. Costs that are paid by landlords, included in the rent payment, or included in condominium or cooperative fees are excluded.
The cost of utilities provides information on the cost of either home ownership or renting. When the data is used as part of monthly housing costs and in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels, and to provide assistance in forecasting future utility services and energy supplies.
Costs are recorded if paid by or billed to occupants, a welfare agency, relatives, or friends. Costs that are paid by landlords, included in the rent payment, or included in condominium or cooperative fees are excluded.
The cost of utilities provides information on the cost of either home ownership or renting. When the data is used as part of monthly housing costs and in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels, and to provide assistance in forecasting future utility services and energy supplies.
The American Community Survey questions ask for monthly costs for electricity and gas, and yearly costs for water/sewer and other fuels. Since 1999, the words "or mobile home" were added to each question, and Question 11b, which asked "Last month, what was the cost of gas for this house, apartment, or mobile home?" had an additional response category, "included in electricity payment entered above."
Research has shown that respondents tended to overstate their expenses for electricity and gas when compared to utility company records. There is some evidence that this overstatement is reduced when yearly costs are asked rather than monthly costs. Caution should be exercised in using these data for direct analysis because costs are not reported for certain kinds of units such as renter-occupied units with all utilities included in the rent and owner-occupied condominium units with utilities included in the condominium fee.
The data on vacancy status were obtained only for a sample of cases in the computer-assisted personal interview (known as "CAPI") follow-up by field representatives. Data on vacancy status were obtained at the time of the personal visit. Vacancy status and other characteristics of vacant units were determined by field representatives obtaining information from landlords, owners, neighbors, rental agents, and others.
Vacancy status has long been used as a basic indicator of the housing market and provides information on the stability and quality of housing for certain areas. The data is used to assess the demand for housing, to identify housing turnover within areas, and to better understand the population within the housing market over time. These data also serve to aid in the development of housing programs to meet the needs of persons at different economic levels.
Vacant units are subdivided according to their housing market classification as follows:
Vacancy status has long been used as a basic indicator of the housing market and provides information on the stability and quality of housing for certain areas. The data is used to assess the demand for housing, to identify housing turnover within areas, and to better understand the population within the housing market over time. These data also serve to aid in the development of housing programs to meet the needs of persons at different economic levels.
Vacant units are subdivided according to their housing market classification as follows:
These are vacant units offered "for rent," and vacant units offered either "for rent" or "for sale."
These are vacant units rented but not yet occupied, including units where money has been paid or agreed upon, but the renter has not yet moved in.
These are vacant units being offered "for sale only," including units in cooperatives and condominium projects if the individual units are offered "for sale only." If units are offered either "for rent" or "for sale" they are included in the "for rent" classification.
These are vacant units sold but not yet occupied, including units that have been sold recently, but the new owner has not yet moved in.
These are vacant units used or intended for use only in certain seasons or for weekends or other occasional use throughout the year. Seasonal units include those used for summer or winter sports or recreation, such as beach cottages and hunting cabins. Seasonal units also may include quarters for such workers as herders and loggers. Interval ownership units, sometimes called shared-ownership or timesharing condominiums, also are included here.
These include vacant units intended for occupancy by migratory workers employed in farm work during the crop season. (Work in a cannery, a freezer plant, or a food-processing plant is not farm work.)
If a vacant unit does not fall into any of the categories specified above, it is classified as "Other vacant." For example, this category includes units held for occupancy by a caretaker or janitor, and units held for personal reasons of the owner.
The homeowner vacancy rate is the proportion of the homeowner inventory that is vacant "for sale." It is computed by dividing the number of vacant units "for sale only" by the sum of the owner-occupied units, vacant units that are "for sale only," and vacant units that have been sold but not yet occupied, and then multiplying by 100. This measure is rounded to the nearest tenth.
The rental vacancy rate is the proportion of the rental inventory that is vacant "for rent." It is computed by dividing the number of vacant units "for rent" by the sum of the renter-occupied units, vacant units that are "for rent," and vacant units that have been rented but not yet occupied, and then multiplying by 100. This measure is rounded to the nearest tenth.
The proportion of the housing inventory that is vacant- for-sale only and vacant-for-rent. It is computed by dividing the sum of vacant-for-sale only housing units and vacant-for-rent housing units, by the sum of occupied units, vacant-for-sale only housing units, vacant-sold-not occupied housing units, vacant-for-rent housing units, and vacant-rented-not-occupied housing units, and then multiplying by 100. This measure is rounded to the nearest tenth.
The 1996-2004 American Community Survey and Census 2000 used a single vacancy status category for units that were either "Rented or sold, not occupied." Since the 2005 ACS, there have been two separate categories, "Rented, not occupied" and "Sold, not occupied." This change created consistency among the ACS, the Housing Vacancy Survey, and the proposed 2010 Census vacancy status response options. The revised categories were incorporated in the calculations of the rental vacancy rate and the homeowner vacancy rate.
A housing unit occupied at the time of interview entirely by people who will be there for 2 months or less.
In CATI and CAPI interviews, the data for current residence elsewhere were obtained after creating the roster of people staying at the sample unit and after asking the current residence questions. Temporarily occupied units are sample units occupied at the time of interview entirely by people who will be there for 2 months or less. At sample units where all the people are staying less than 2 months, the respondent is asked a subset of the questions from the housing section, including the question on vacancy status.
The current residence concept is unique to the American Community Survey. By using the current residence to decide for whom to collect survey information, the ACS can provide a more accurate description of an area's social and economic characteristics. Most surveys, as well as the decennial census, use the concept of usual residence. Usual residence is defined as the place where a person lives and sleeps most of the time. The census defines everyone as having only one usual residence.
In CATI and CAPI interviews, the data for current residence elsewhere were obtained after creating the roster of people staying at the sample unit and after asking the current residence questions. Temporarily occupied units are sample units occupied at the time of interview entirely by people who will be there for 2 months or less. At sample units where all the people are staying less than 2 months, the respondent is asked a subset of the questions from the housing section, including the question on vacancy status.
The current residence concept is unique to the American Community Survey. By using the current residence to decide for whom to collect survey information, the ACS can provide a more accurate description of an area's social and economic characteristics. Most surveys, as well as the decennial census, use the concept of usual residence. Usual residence is defined as the place where a person lives and sleeps most of the time. The census defines everyone as having only one usual residence.
The data on value (also referred to as "price asked" for vacant units) were obtained from Housing Question 16 in the 2011 American Community Survey. The question was asked at housing units that were owned, being bought, vacant for sale, or sold not occupied at the time of the survey. Value is the respondent's estimate of how much the property (house and lot, mobile home and lot, or condominium unit) would sell for if it were for sale. If the house or mobile home was owned or being bought, but the land on which it sits was not, the respondent was asked to estimate the combined value of the house or mobile home and the land. For vacant units, value was the price asked for the property. Value was tabulated separately for all owner-occupied and vacant-for-sale housing units, as well as owner- occupied and vacant-for-sale mobile homes.
The value of a home provides information on neighborhood quality, housing affordability, and wealth. These data provide socioeconomic information not captured by household income and comparative information on the state of local housing markets. The data also serve to aid in the development of housing programs designed to meet the housing needs of persons at different economic levels.
The value of a home provides information on neighborhood quality, housing affordability, and wealth. These data provide socioeconomic information not captured by household income and comparative information on the state of local housing markets. The data also serve to aid in the development of housing programs designed to meet the housing needs of persons at different economic levels.
Since value collected before 2008 is the only dollar amount captured on the questionnaire in specified intervals, the category boundaries for previous years are not adjusted for inflation. In the comparison profiles, however, the median value is adjusted for inflation by multiplying a factor equal to the average annual CPI-U-RS factor for the current year, divided by the average annual CPI-U-RS factor for the earlier/earliest year.
The median divides the value distribution into two equal parts: one-half of the cases falling below the median value of the property (house and lot, mobile home and lot, or condominium unit) and one-half above the median. Quartiles divide the value distribution into four equal parts. Median and quartile value are computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Appendix A.") Median and quartile value calculations are rounded to the nearest hundred dollars. Upper and lower quartiles can be used to note large value differences among various geographic areas. (For more information on medians and quartiles, see "Derived Measures.")
Aggregate value is calculated by adding all of the value estimates for owner occupied housing units in an area. Aggregate value is rounded to the nearest hundred dollars. (For more information on aggregates, see "Derived Measures.")
The 1996-1998 American Community Survey question provided a space for the respondent to enter a dollar amount. From 1999-2007 the question provided 19 pre-coded response categories from "Less than $10,000" to "$250,000 or more - Specify.'" Starting in 2004, value was shown for all owner-occupied housing units, unlike from1996-2003 in which value was shown only for specified owner-occupied housing units. Changes introduced in 2008 were removing the pre-coded response categories and adding a write-in box for the respondent to enter the property value amount in dollars, and revising the wording of the question to ask, "About how much do you think this house and lot, apartment, or mobile home (and lot, if owned) would sell for if it were for sale?"
The Census Bureau tested the changes introduced to the 2008 version of the value question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Property Value" on the ACS website http://www.census.gov/acs.
Caution should be used when comparing American Community Survey data on value from the years 2008 and after with pre-2008 ACS data. Changes made to the value question between the 2007 and 2008 ACS involving the response option may have resulted in an inconsistency in the value distribution for some areas. In 2007 and previous years, the ACS value question included categorical response options with a write-in for values over $250,000. Beginning in 2008, the response option became solely a write-in.
Caution should also be used when comparing value data from the ACS produced in 2008 or later with Census 2000 value data. The 2008 or later ACS provides solely a write-in response option while Census 2000 collected data in categories. Additionally, Census 2000 tables on value were released for both total owner-occupied housing units and specified owner-occupied housing units, thus comparisons can be made only when comparing the same universes between the two data sets.
Caution should also be used when comparing value data from the ACS produced in 2008 or later with Census 2000 value data. The 2008 or later ACS provides solely a write-in response option while Census 2000 collected data in categories. Additionally, Census 2000 tables on value were released for both total owner-occupied housing units and specified owner-occupied housing units, thus comparisons can be made only when comparing the same universes between the two data sets.
The data on vehicles available were obtained from Housing Question 9 in the 2011 American Community Survey. The question was asked at occupied housing units. These data show the number of passenger cars, vans, and pickup or panel trucks of one-ton capacity or less kept at home and available for the use of household members. Vehicles rented or leased for one month or more, company vehicles, and police and government vehicles are included if kept at home and used for non-business purposes. Dismantled or immobile vehicles are excluded. Vehicles kept at home but used only for business purposes also are excluded.
The availability of vehicles provides information for numerous transportation programs. When the data is used in conjunction with place-of-work and journey-to-work data, the information can provide insight into vehicle travel and aid in forecasting future travel and its effect on transportation systems. The data also serve to aid in the development of emergency and evacuation planning, special transportation services, and forecasting future energy consumption and needs.
The availability of vehicles provides information for numerous transportation programs. When the data is used in conjunction with place-of-work and journey-to-work data, the information can provide insight into vehicle travel and aid in forecasting future travel and its effect on transportation systems. The data also serve to aid in the development of emergency and evacuation planning, special transportation services, and forecasting future energy consumption and needs.
The 1996-1998 American Community Survey question provided a space for the respondent to enter the number of vehicles. Since 1999, the American Community Survey question provided seven pre-coded response categories ranging from "None" to "6 or more."
The data on year householder moved into unit were obtained from answers to Housing Question 3 in the 2011 American Community Survey, which was asked at occupied housing units. These data refer to the year of the latest move by the householder. If the householder moved back into a housing unit he or she previously occupied, the year of the latest move was reported. If the householder moved from one apartment to another within the same building, the year the householder moved into the present apartment was reported. The intent is to establish the year the present occupancy by the householder began. The year that the householder moved in is not necessarily the same year other members of the household moved in, although in the great majority of cases an entire household moves at the same time.
The year the householder moved into the unit provides information on the specific period of time when mobility occurs, especially for recent movers. These data help to measure neighborhood stability and to identify transient communities. The data also is used to assess the amount of displacement caused by floods and other natural disasters, and as an aid to evaluate the changes in service requirements.
The year the householder moved into the unit provides information on the specific period of time when mobility occurs, especially for recent movers. These data help to measure neighborhood stability and to identify transient communities. The data also is used to assess the amount of displacement caused by floods and other natural disasters, and as an aid to evaluate the changes in service requirements.
Median year householder moved into unit divides the distribution into two equal parts: one-half of the cases falling below the median year householder moved into unit and one-half above the median. Median year householder moved into unit is computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Appendix A.") Median year householder moved into unit is rounded to the nearest calendar year. (For more information on medians, see "Derived Measures.")
Since 1996, the question provided two write-in spaces for the respondent to enter month and year the householder (person 1) moved into the house, apartment, or mobile home.
The data on year structure built were obtained from Housing Question 2 in the 2011 American Community Survey. The question was asked at both occupied and vacant housing units. Year structure built refers to when the building was first constructed, not when it was remodeled, added to, or converted. Housing units under construction are included as vacant housing if they meet the housing unit definition, that is, all exterior windows, doors, and final usable floors are in place. For mobile homes, houseboats, RVs, etc., the manufacturer's model year was assumed to be the year built. The data relate to the number of units built during the specified periods that were still in existence at the time of interview.
The year the structure was built provides information on the age of housing units. These data help identify new housing construction and measures the disappearance of old housing from the inventory, when used in combination with data from previous years. The data also serve to aid in the development of formulas to determine substandard housing and provide assistance in forecasting future services, such as energy consumption and fire protection.
The year the structure was built provides information on the age of housing units. These data help identify new housing construction and measures the disappearance of old housing from the inventory, when used in combination with data from previous years. The data also serve to aid in the development of formulas to determine substandard housing and provide assistance in forecasting future services, such as energy consumption and fire protection.
Median year structure built divides the distribution into two equal parts: one-half of the cases falling below the median year structure built and one-half above the median. Median year structure built is computed on the basis of a standard distribution (See the "Standard Distributions" section under "Appendix A.") The median is rounded to the nearest calendar year. Median age of housing can be obtained by subtracting median year structure built from survey year. For example, if the median year structure built is 1969, the median age of housing in that area is 40 years (2011 minus 1970). (For more information on medians, see "Derived Measures.")
The 1996-1998 American Community Survey question provided a write-in space for the respondent to enter a year the structure was built. From 1999-2007 the question provided 9 pre-coded response categories, which showed ranges of years, and from 2003-2007 the response categories were updated to provide detail for recently built structures. Starting in 2008, the response category "2000 or later" and the instruction "Specify yeaf with a write-in box replaced the two categories "2000 to 2004" and "2005 or later."
Data on year structure built are more susceptible to errors of response and non-reporting than data for many other questions because respondents must rely on their memory or on estimates by people who have lived in the neighborhood a long time.
Respondents who reported speaking a language other than English were asked to indicate their English-speaking ability based on one of the following categories: "Very well," "Well," "Not well," or "Not at all." Those who answered "Well," "Not well," or "Not at all" are sometimes referred as "Less than 'very well.'" Respondents were not instructed on how to interpret the response categories in this question.
This variable identifies households that may need English language assistance. This arises when no one 14 and over meets either of two conditions (1) they speak English at home or (2) even though they speak another language, they also report that they speak English "very well."
After data are collected for each person in the household, this variable checks if all people 14 and over speak a language other than English. If so, the variable checks the English-speaking ability responses to see if all people 14 and over speak English "Less than 'very well.'" If all household members 14 and over speak a language other than English and speak English "Less than 'very well,'" the household is considered part of this group that may be in need of English language assistance. All members of a household were identified in this group, including members under 14 years old who may have spoken only English.
Government agencies use information on language spoken at home for their programs that serve the needs of the foreign-born and specifically those who have difficulty with English. Under the Voting Rights Act, language is needed to meet statutory requirements for making voting materials available in minority languages. The Census Bureau is directed, using data about language spoken at home and the ability to speak English, to identify minority groups that speak a language other than English and to assess their English-speaking ability. The U.S. Department of Education uses these data to prepare a report to Congress on the social and economic status of children served by different local school districts.
Government agencies use information on language spoken at home for their programs that serve the needs of the foreign-born and specifically those who have difficulty with English. Under the Voting Rights Act, language is needed to meet statutory requirements for making voting materials available in minority languages. The Census Bureau is directed, using data about language spoken at home and the ability to speak English, to identify minority groups that speak a language other than English and to assess their English-speaking ability. The U.S. Department of Education uses these data to prepare a report to Congress on the social and economic status of children served by different local school districts. State and local agencies concerned with aging develop health care and other services tailored to the language and cultural diversity of the elderly under the Older Americans Act.
After data are collected for each person in the household, this variable checks if all people 14 and over speak a language other than English. If so, the variable checks the English-speaking ability responses to see if all people 14 and over speak English "Less than 'very well.'" If all household members 14 and over speak a language other than English and speak English "Less than 'very well,'" the household is considered part of this group that may be in need of English language assistance. All members of a household were identified in this group, including members under 14 years old who may have spoken only English.
Government agencies use information on language spoken at home for their programs that serve the needs of the foreign-born and specifically those who have difficulty with English. Under the Voting Rights Act, language is needed to meet statutory requirements for making voting materials available in minority languages. The Census Bureau is directed, using data about language spoken at home and the ability to speak English, to identify minority groups that speak a language other than English and to assess their English-speaking ability. The U.S. Department of Education uses these data to prepare a report to Congress on the social and economic status of children served by different local school districts.
Government agencies use information on language spoken at home for their programs that serve the needs of the foreign-born and specifically those who have difficulty with English. Under the Voting Rights Act, language is needed to meet statutory requirements for making voting materials available in minority languages. The Census Bureau is directed, using data about language spoken at home and the ability to speak English, to identify minority groups that speak a language other than English and to assess their English-speaking ability. The U.S. Department of Education uses these data to prepare a report to Congress on the social and economic status of children served by different local school districts. State and local agencies concerned with aging develop health care and other services tailored to the language and cultural diversity of the elderly under the Older Americans Act.
The English Language Ability question has been the same since the beginning of ACS. "Households in which no one 14 and over speaks English only or speaks a language other than English and speaks English 'very well'" has been calculated the same way in all years of ACS data collection, but has sometimes been termed "Linguistic Isolation."
Ideally, the data on ability to speak English represented a person's perception of their own English-speaking ability. However, because one household member usually completes American Community Survey questionnaires, the responses may have represented the perception of another household member.
The data on age were derived from answers to Question 4. The age classification is based on the age of the person in complete years at the time of interview. Both age and date of birth are used in combination to calculate the most accurate age at the time of the interview. Respondents are asked to give an age in whole, completed years as of interview date as well as the month, day and year of birth. People are not to round an age up if the person is close to having a birthday and to estimate an age if the exact age is not known. An additional instruction on babies also asks respondents to print "0" for babies less than one year old. Inconsistently reported and missing values are assigned or imputed based on the values of other variables for that person, from other people in the household, or from people in other households ("hot deck" imputation).
Age is asked for all persons in a household or group quarters. On the mailout/mailback paper questionnaire for households, both age and date of birth are asked for persons listed as person numbers 1-5 on the form. Only age (in years) is initially asked for persons listed as 612 on the mailout/mailback paper questionnaire. If a respondent indicates that there are more than 5 people living in the household, then the household is eligible for Failed Edit Follow-up (FEFU). During FEFU operations, telephone center staffers call respondents to obtain missing data. This includes asking date of birth for any person in the household missing date of birth information. In Computer Assisted Telephone Interviews (CATI) and Computer Assisted Personal Interview (CAPI) instruments both age and date of birth is asked for all persons. In 2006, the ACS began collecting data in group quarters (GQs). This included asking both age and date of birth for persons living in a group quarters. For additional data collection methodology, please see www.census.gov/acs.
Data on age are used to determine the applicability of other questions for a particular individual and to classify other characteristics in tabulations. Age data are needed to interpret most social and economic characteristics used to plan and analyze programs and policies. Age is central for any number of federal programs that target funds or services to children, working-age adults, women of childbearing age, or the older population. The U.S. Department of Education uses census age data in its formula for allotment to states. The U.S. Department of Veterans Affairs uses age to develop its mandated state projections on the need for hospitals, nursing homes, cemeteries, domiciliary services, and other benefits for veterans. For more information on the use of age data in Federal programs, please see www.census.gov/acs.
Age is asked for all persons in a household or group quarters. On the mailout/mailback paper questionnaire for households, both age and date of birth are asked for persons listed as person numbers 1-5 on the form. Only age (in years) is initially asked for persons listed as 612 on the mailout/mailback paper questionnaire. If a respondent indicates that there are more than 5 people living in the household, then the household is eligible for Failed Edit Follow-up (FEFU). During FEFU operations, telephone center staffers call respondents to obtain missing data. This includes asking date of birth for any person in the household missing date of birth information. In Computer Assisted Telephone Interviews (CATI) and Computer Assisted Personal Interview (CAPI) instruments both age and date of birth is asked for all persons. In 2006, the ACS began collecting data in group quarters (GQs). This included asking both age and date of birth for persons living in a group quarters. For additional data collection methodology, please see www.census.gov/acs.
Data on age are used to determine the applicability of other questions for a particular individual and to classify other characteristics in tabulations. Age data are needed to interpret most social and economic characteristics used to plan and analyze programs and policies. Age is central for any number of federal programs that target funds or services to children, working-age adults, women of childbearing age, or the older population. The U.S. Department of Education uses census age data in its formula for allotment to states. The U.S. Department of Veterans Affairs uses age to develop its mandated state projections on the need for hospitals, nursing homes, cemeteries, domiciliary services, and other benefits for veterans. For more information on the use of age data in Federal programs, please see www.census.gov/acs.
The median age is the age that divides the population into two equal-size groups. Half of the population is older than the median age and half is younger. Median age is based on a standard distribution of the population by single years of age and is shown to the nearest tenth of a year. (See the sections on "Standard Distributions" and "Medians" under "Derived Measures.")
The age dependency ratio is derived by dividing the combined under 18 years and 65 years and over populations by the 18-to-64 population and multiplying by 100.
The old-age dependency ratio is derived by dividing the population 65 years and over by the 18-to-64 population and multiplying by 100.
The child dependency ratio is derived by dividing the population under 18 years by the 18-to-64 population, and multiplying by 100.
The 1996-2002 American Community Survey question asked for month, day, and year of birth before age. Since 2003, the American Community Survey question asked for age, followed by month, day, and year of birth. In 2008, an additional instruction was provided with the age and date of birth question on the American Community Survey questionnaire to report babies as age 0 when the child was less than 1 year old. The addition of this instruction occurred after 2005 National Census Test results indicated increased accuracy of age reporting for babies less than one year old.
Beginning in 2006, the population living in group quarters (GQ) was included in the American Community Survey population universe. Some types of group quarters have populations with age distributions that are very different from that of the household population. The inclusion of the GQ population could therefore have a noticeable impact on the age distribution for a given geographic area. This is particularly true for areas with a substantial GQ population. For example, in areas with large colleges and universities, the percent of individuals 18-24 would increase due to the inclusion of GQs in the American Community Survey universe.
Caution should be taken when comparing population in age groups across time. The entire population continually ages into older age groups over time, and babies fill in the youngest age group. Therefore, the population of a certain age is made up of a completely different group of people in one time period than in another (e.g. one age group in 2000 versus same age group in 2011). Since populations occasionally experience booms/increases and busts/decreases in births, deaths, or migration (for example, the postwar Baby Boom from 1946-1964), one should not necessarily expect that the population in an age group in one year should be similar in size or proportion to the population in the same age group in a different period in time. For example, Baby Boomers were age 36 to 54 in Census 2000 while they were age 46 to 64 in the 2011 ACS. The age structure and distribution would therefore shift in those age groups to reflect the change in people occupying those age- specific groups over time.
Data users should also be aware of methodology differences that may exist between different data sources if they are comparing American Community Survey age data to data sources, such as Population Estimates or Decennial Census data. For example, the American Community Survey data are that of a respondent-based survey and subject to various quality measures, such as sampling and nonsampling error, response rates and item allocation error. This differs in design and methodology from other data sources, such as Population Estimates, which is not a survey and involves computational methodology to derive intercensal estimates of the population. While ACS estimates are controlled to Population Estimates for age at the nation, state and county levels of geography as part of the ACS weighting procedure, variation may exist in the age structure of a population at lower levels of geography when comparing different time periods or comparing across time due to the absence of controls below the county geography level. For more information on American Community Survey data accuracy and weighting procedures, please see www.census.gov/acs.
It should also be noted that although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns, and estimates of housing units for states and counties.
Data users should also be aware of methodology differences that may exist between different data sources if they are comparing American Community Survey age data to data sources, such as Population Estimates or Decennial Census data. For example, the American Community Survey data are that of a respondent-based survey and subject to various quality measures, such as sampling and nonsampling error, response rates and item allocation error. This differs in design and methodology from other data sources, such as Population Estimates, which is not a survey and involves computational methodology to derive intercensal estimates of the population. While ACS estimates are controlled to Population Estimates for age at the nation, state and county levels of geography as part of the ACS weighting procedure, variation may exist in the age structure of a population at lower levels of geography when comparing different time periods or comparing across time due to the absence of controls below the county geography level. For more information on American Community Survey data accuracy and weighting procedures, please see www.census.gov/acs.
It should also be noted that although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns, and estimates of housing units for states and counties.
Ancestry refers to a person's ethnic origin, heritage, descent, or "roots," which may reflect their place of birth or that of previous generations of their family. Some ethnic identities, such as "Egyptian" or "Polish" can be traced to geographic areas outside the United States, while other ethnicities such as "Pennsylvania German" or "Cajun" evolved in the United States.
The intent of the ancestry question was not to measure the degree of attachment the respondent had to a particular ethnicity, but simply to establish that the respondent had a connection to and self-identified with a particular ethnic group. For example, a response of "Irish" might reflect total involvement in an Irish community or only a memory of ancestors several generations removed from the individual.
The data on ancestry were derived from answers to Question 13. The question was based on self-identification; the data on ancestry represent self-classification by people according to the ancestry group(s) with which they most closely identify.
The Census Bureau coded the responses into a numeric representation of over 1,000 categories. To do so, responses initially were processed through an automated coding system; then, those that were not automatically assigned a code were coded by individuals trained in coding ancestry responses. The code list reflects the results of the Census Bureau's own research and consultations with many ethnic experts. Many decisions were made to determine the classification of responses. These decisions affected the grouping of the tabulated data. For example, the "Indonesian" category includes the responses of "Indonesian," "Celebesian," "Moluccan," and a number of other responses.
The ancestry question allowed respondents to report one or more ancestry groups. Generally, only the first two responses reported were coded. If a response was in terms of a dual ancestry, for example, "Irish English," the person was assigned two codes, in this case one for Irish and another for English. However, in certain cases, multiple responses such as "French Canadian," "Scotch-Irish," "Greek Cypriot," and "Black Dutch" were assigned a single code reflecting their status as unique groups. If a person reported one of these unique groups in addition to another group, for example, "Scotch-Irish English," resulting in three terms, that person received one code for the unique group (Scotch-Irish) and another one for the remaining group (English). If a person reported "English Irish French," only English and Irish were coded. If there were more than two ancestries listed and one of the ancestries was a part of another, such as "German Bavarian Hawaiian," the responses were coded using the more detailed groups (Bavarian and Hawaiian).
The Census Bureau accepted "American" as a unique ethnicity if it was given alone or with one other ancestry. There were some groups such as "American Indian," "Mexican American," and "African American" that were coded and identified separately.
The ancestry question is asked for every person in the American Community Survey, regardless of age, place of birth, Hispanic origin, or race.
Ancestry identifies the ethnic origins of the population, and Federal agencies regard this information as essential for fulfilling many important needs. Ancestry is required to enforce provisions under the Civil Rights Act, which prohibits discrimination based upon race, sex, religion, and national origin. More generally, these data are needed to measure the social and economic characteristics of ethnic groups and to tailor services to accommodate cultural differences. The Department of Labor draws samples for surveys that provide employment statistics and other related information for ethnic groups using ancestry.
The ACS data on ancestry are released annually on the Census Bureau's internet site. The Detailed Tables (B04001-B04007) contain estimates of over 100 different ancestry groups for the nation, states, and many other geographic areas, while the Special Population Profiles contain characteristics of different ancestry groups.
In all tabulations, when respondents provided an unclassifiable ethnic identity (for example, "multi-national," "adopted," or "I have no idea"), the answer was included in "Unclassified or not reported."
The tabulations on ancestry show two types of data- one where estimates represent the number of people, and the other where estimates represent the number of responses. If you want to know how many people reported an ancestry, use the estimates based on people. If you want to know how many reports there were of a certain ancestry, use the estimates based on reports. The difference between the two types of data presentations represents the fact that people can provide more than one ancestry, therefore can be counted twice in the same ancestry category. Examples are provided below.
The following are the types of estimates shown:
The intent of the ancestry question was not to measure the degree of attachment the respondent had to a particular ethnicity, but simply to establish that the respondent had a connection to and self-identified with a particular ethnic group. For example, a response of "Irish" might reflect total involvement in an Irish community or only a memory of ancestors several generations removed from the individual.
The data on ancestry were derived from answers to Question 13. The question was based on self-identification; the data on ancestry represent self-classification by people according to the ancestry group(s) with which they most closely identify.
The Census Bureau coded the responses into a numeric representation of over 1,000 categories. To do so, responses initially were processed through an automated coding system; then, those that were not automatically assigned a code were coded by individuals trained in coding ancestry responses. The code list reflects the results of the Census Bureau's own research and consultations with many ethnic experts. Many decisions were made to determine the classification of responses. These decisions affected the grouping of the tabulated data. For example, the "Indonesian" category includes the responses of "Indonesian," "Celebesian," "Moluccan," and a number of other responses.
The ancestry question allowed respondents to report one or more ancestry groups. Generally, only the first two responses reported were coded. If a response was in terms of a dual ancestry, for example, "Irish English," the person was assigned two codes, in this case one for Irish and another for English. However, in certain cases, multiple responses such as "French Canadian," "Scotch-Irish," "Greek Cypriot," and "Black Dutch" were assigned a single code reflecting their status as unique groups. If a person reported one of these unique groups in addition to another group, for example, "Scotch-Irish English," resulting in three terms, that person received one code for the unique group (Scotch-Irish) and another one for the remaining group (English). If a person reported "English Irish French," only English and Irish were coded. If there were more than two ancestries listed and one of the ancestries was a part of another, such as "German Bavarian Hawaiian," the responses were coded using the more detailed groups (Bavarian and Hawaiian).
The Census Bureau accepted "American" as a unique ethnicity if it was given alone or with one other ancestry. There were some groups such as "American Indian," "Mexican American," and "African American" that were coded and identified separately.
The ancestry question is asked for every person in the American Community Survey, regardless of age, place of birth, Hispanic origin, or race.
Ancestry identifies the ethnic origins of the population, and Federal agencies regard this information as essential for fulfilling many important needs. Ancestry is required to enforce provisions under the Civil Rights Act, which prohibits discrimination based upon race, sex, religion, and national origin. More generally, these data are needed to measure the social and economic characteristics of ethnic groups and to tailor services to accommodate cultural differences. The Department of Labor draws samples for surveys that provide employment statistics and other related information for ethnic groups using ancestry.
The ACS data on ancestry are released annually on the Census Bureau's internet site. The Detailed Tables (B04001-B04007) contain estimates of over 100 different ancestry groups for the nation, states, and many other geographic areas, while the Special Population Profiles contain characteristics of different ancestry groups.
In all tabulations, when respondents provided an unclassifiable ethnic identity (for example, "multi-national," "adopted," or "I have no idea"), the answer was included in "Unclassified or not reported."
The tabulations on ancestry show two types of data- one where estimates represent the number of people, and the other where estimates represent the number of responses. If you want to know how many people reported an ancestry, use the estimates based on people. If you want to know how many reports there were of a certain ancestry, use the estimates based on reports. The difference between the two types of data presentations represents the fact that people can provide more than one ancestry, therefore can be counted twice in the same ancestry category. Examples are provided below.
The following are the types of estimates shown:
Includes all people who reported only one ethnic group such as "German." Also included in this category are people with only a multiple- term response such as "Scotch-Irish" who are assigned a single code because they represent one distinct group. For example, in this type of table, the count for German would be interpreted as "The number of people who reported that German was their only ancestry."
Includes all people who reported more than one group, such as "German" and "Irish" and were assigned two ancestry codes. The German line on this table would be interpreted as "The number of people who responded that German was part of their multiple ancestry."
Includes all people who reported each ancestry, regardless of whether it was their first or second ancestry, or part of a single or multiple response. This estimate is the sum of the two estimates above (for Single and Multiple ancestry). People can be listed twice in this table. For example, if someone reports their ancestry as "German and Danish", they will be listed once in German and once in Danish, and therefore the sum of the rows would not equal the total population. Interpret the German line of this table as "The total number of people who reported they had German ancestry."
Includes the first response of all people who reported at least one codeable entry. For example, in this type of table, the count for German would include all those who reported only German and those who reported German first and then some other group. The German line of this table could be interpreted as "The number of times German was listed as the first, or only, ancestry."
Includes the second response of all people who reported a multiple ancestry. Thus, the count for German in this category includes all people who reported German as the second response, regardless of the first response provided. The German line in this table is interpreted as "The number of times German was listed as a second ancestry."
Includes the total number of ancestries reported and coded. If a person reported a multiple ancestry such as "German Danish," that response was counted twice in the tabulations--once in the German category and again in the Danish category. Also, if a person reported two different types of German ancestry, such as "Bavarian Hamburger", they would be counted twice in the German category on this type of table. Thus, each line of this table represents the number of reports for that ancestry type, not the number of people (although sometimes that number is the same). Likewise, the sum of the estimates in each of the rows in this type of presentation is not the total population but the total of all responses. The German line in this table is interpreted as "The number of times a German ancestry was reported."
The question on ancestry has been asked on the American Community Survey since 1996. The question wording has never changed, although placement of the question changed slightly. Also, the examples listed below the write-in lines changed in 1999, but have remained the same since then.
The question on ancestry was first asked in the 1980 Census. It replaced the question on parental place of birth, in order to include ancestral heritage for people whose families have been in the U.S. for more than two generations. The question was also asked in the 1990 and 2000 censuses.
From 1996 to 1999, the ACS editing system used answers to the race and place of birth questions to clarify ancestry responses of "Indian," where possible. In 2000 and subsequent years, the editing was expanded to aid interpretation of two-word ancestries, such as "Black Irish."
The question on ancestry was first asked in the 1980 Census. It replaced the question on parental place of birth, in order to include ancestral heritage for people whose families have been in the U.S. for more than two generations. The question was also asked in the 1990 and 2000 censuses.
From 1996 to 1999, the ACS editing system used answers to the race and place of birth questions to clarify ancestry responses of "Indian," where possible. In 2000 and subsequent years, the editing was expanded to aid interpretation of two-word ancestries, such as "Black Irish."
Although some experts consider religious affiliation a component of ethnic identity, the ancestry question was not designed to collect any information concerning religion. The Census Bureau is prohibited from collecting information on religion. Thus, if a religion was given as an answer to the ancestry question, it was coded as an "Other" response.
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. Some types of GQ populations may have ancestry distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the ancestry distribution. This is particularly true for areas with a substantial GQ population.
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. Some types of GQ populations may have ancestry distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the ancestry distribution. This is particularly true for areas with a substantial GQ population.
The data are comparable to Census 2000, as long as some caution is used. Response rates to the ancestry question are generally higher for ACS than for Census, and data are never generated for missing ancestry responses, therefore some ancestry groups are reported more heavily in ACS than in Census 2000.
In 2010, there were two major changes to the coding rules. If up to two ancestries were listed, both were coded, even if one was the specific of the other or if one was American. Also, race groups and Hispanic groups were coded with the same priority as non-race and non-Hispanic groups. For example, "Haitian Black French" would previously have been coded Haitian and French, but now would be coded Haitian and Black.
See the 2011 Code List for Ancestry Code List.
In 2010, there were two major changes to the coding rules. If up to two ancestries were listed, both were coded, even if one was the specific of the other or if one was American. Also, race groups and Hispanic groups were coded with the same priority as non-race and non-Hispanic groups. For example, "Haitian Black French" would previously have been coded Haitian and French, but now would be coded Haitian and Black.
See the 2011 Code List for Ancestry Code List.
For the 1996-1998 American Community Survey, the data on fertility (also referred to as "children ever born") were derived from answers to Question 17, which was asked of all women 15 years old and over regardless of marital status. Stillbirths, stepchildren, and adopted children were excluded from the number of children ever born. Ever-married women were instructed to include all children born to them before and during their most recent marriage, children no longer living, and children living away from home, as well as children who were still living in the home. Never-married women were instructed to include all children born to them. The question on children ever born was asked to measure lifetime fertility experience of women up to the survey date.
Data were most frequently presented in terms of the aggregate number of children ever born to women in the specified category and in terms of the rate per 1,000 women.
Beginning in 1999, American Community Survey data on fertility were derived from questions that asked if the person had given birth in the past 12 months. See the section on "Fertility" for more information.
Data were most frequently presented in terms of the aggregate number of children ever born to women in the specified category and in terms of the rate per 1,000 women.
Beginning in 1999, American Community Survey data on fertility were derived from questions that asked if the person had given birth in the past 12 months. See the section on "Fertility" for more information.
The 1996-1998 American Community Survey used a write-in space for the number and a response category for "None." No question addressed "children ever born" after 1998.
The data available for 1996-1998 are only available for a limited number of geographies.
The data on citizenship status were derived from answers to Question 8. This question was asked about Persons 1 through 5 in the ACS.
Respondents were asked to select one of five categories: (1) born in the United States, (2) born in Puerto Rico, Guam, the U.S. Virgin Islands, or Northern Marianas, (3) born abroad of U.S. citizen parent or parents, (4) U.S. citizen by naturalization, or (5) not a U.S citizen. Respondents indicating they are a U.S. citizen by naturalization are also asked to print their year of naturalization. People born in American Samoa, although not explicitly listed, are included in the second response category.
For the Puerto Rico Community Survey, respondents were asked to select one of five categories: (1) born in Puerto Rico, (2) born in a U.S. state, District of Columbia, Guam, the U.S. Virgin Islands, or Northern Marianas, (3) born abroad of U.S. citizen parent or parents, (4) U.S. citizen by naturalization, or (5) not a U.S. citizen. Respondents indicating they are a U.S. citizen by naturalization are also asked to print their year of naturalization. People born in American Samoa, although not explicitly listed, are included in the second response category.
When no information on citizenship status was reported for a person, information for other household members, if available, was used to assign a citizenship status to the respondent. All cases of nonresponse that were not assigned a citizenship status based on information from other household members were allocated the citizenship status of another person with similar characteristics who provided complete information. In cases of conflicting responses, place of birth information is used to edit citizenship status. For example, if a respondent states he or she was born in Puerto Rico but was not a U.S. citizen, the edits use the response to the place of birth question to change the respondent's status to "U.S. citizen at birth."
Respondents were asked to select one of five categories: (1) born in the United States, (2) born in Puerto Rico, Guam, the U.S. Virgin Islands, or Northern Marianas, (3) born abroad of U.S. citizen parent or parents, (4) U.S. citizen by naturalization, or (5) not a U.S citizen. Respondents indicating they are a U.S. citizen by naturalization are also asked to print their year of naturalization. People born in American Samoa, although not explicitly listed, are included in the second response category.
For the Puerto Rico Community Survey, respondents were asked to select one of five categories: (1) born in Puerto Rico, (2) born in a U.S. state, District of Columbia, Guam, the U.S. Virgin Islands, or Northern Marianas, (3) born abroad of U.S. citizen parent or parents, (4) U.S. citizen by naturalization, or (5) not a U.S. citizen. Respondents indicating they are a U.S. citizen by naturalization are also asked to print their year of naturalization. People born in American Samoa, although not explicitly listed, are included in the second response category.
When no information on citizenship status was reported for a person, information for other household members, if available, was used to assign a citizenship status to the respondent. All cases of nonresponse that were not assigned a citizenship status based on information from other household members were allocated the citizenship status of another person with similar characteristics who provided complete information. In cases of conflicting responses, place of birth information is used to edit citizenship status. For example, if a respondent states he or she was born in Puerto Rico but was not a U.S. citizen, the edits use the response to the place of birth question to change the respondent's status to "U.S. citizen at birth."
Respondents who indicated that they were born in the United States, Puerto Rico, a U.S. Island Area (such as Guam), or abroad of American (U.S. citizen) parent or parents are considered U.S. citizens at birth. Foreign-born people who indicated that they were U.S. citizens through naturalization also are considered U.S. citizens.
Respondents who indicated that they were not U.S. citizens at the time of the survey.
The native population includes anyone who was a U.S. citizen or a U.S. national at birth. This includes respondents who indicated they were born in the United States, Puerto Rico, a U.S. Island Area (such as Guam), or abroad of American (U.S. citizen) parent or parents.
The foreign-born population includes anyone who was not a U.S. citizen or a U.S. national at birth. This includes respondents who indicated they were a U.S. citizen by naturalization or not a U.S. citizen.
The American Community Survey questionnaires do not ask about immigration status. The population surveyed includes all people who indicated that the United States was their usual place of residence on the survey date. The foreign-born population includes naturalized U.S. citizens, lawful permanent residents (i.e. immigrants), temporary migrants (e.g., foreign students), humanitarian migrants (e.g., refugees), and unauthorized migrants (i.e. people illegally present in the United States).
The responses to this question are used to determine the U.S. citizen and non-U.S. citizen populations as well as to determine the native and foreign-born populations.
The American Community Survey questionnaires do not ask about immigration status. The population surveyed includes all people who indicated that the United States was their usual place of residence on the survey date. The foreign-born population includes naturalized U.S. citizens, lawful permanent residents (i.e. immigrants), temporary migrants (e.g., foreign students), humanitarian migrants (e.g., refugees), and unauthorized migrants (i.e. people illegally present in the United States).
The responses to this question are used to determine the U.S. citizen and non-U.S. citizen populations as well as to determine the native and foreign-born populations.
In the 1996-1998 American Community Survey, the third response category was "Yes, born abroad of American parent(s)." However, since 1999 in the American Community Survey and since the 2005 Puerto Rico Community Survey, the response category was "Yes, born abroad of American parent or parents." In 2008, respondents who indicated that they were a U.S. citizen by naturalization were also asked to print their year of naturalization. Also in 2008, modifications in wording were made to both the third response category (changed from "Yes, born abroad of American parent or parents" to "Yes, born abroad of U.S. citizen parent or parents") and the fifth response category (changed from "No, not a citizen of the United States" to "No, not a U.S. citizen").
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have citizenship status distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the citizenship status distribution. This is particularly true for areas with substantial GQ populations.
Class of worker categorizes people according to the type of ownership of the employing organization. Class of worker data were derived from answers to question 41. Question 41 provides respondents with 8 class of worker categories from which they are to select one. These categories are:
The class of worker categories are defined as follows:
- An employee of a private, for-profit company or business, or of an individual, for wages, salary, or commissions.
- An employee of a private, not-for-profit, tax-exempt, or charitable organization.
- A local government employee (city, county, etc.).
- A state government employee.
- A Federal government employee.
- Self-employed in own not incorporated business, professional practice, or farm.
- Self-employed in own incorporated business, professional practice, or farm.
- Working without pay in a family business or farm.
The class of worker categories are defined as follows:
Includes people who worked for wages, salary, commission, tips, pay-in-kind, or piece rates for a private, for-profit employer or a private not-for-profit, tax-exempt or charitable organization. Self-employed people whose business was incorporated are included with private wage and salary workers because they are paid employees of their own companies.
ACS tabulations present data separately for these subcategories: "Employee of private company workers," "Private not-for-profit wage and salary workers," and "Self-employed in own incorporated business workers."
ACS tabulations present data separately for these subcategories: "Employee of private company workers," "Private not-for-profit wage and salary workers," and "Self-employed in own incorporated business workers."
Includes people who were employees of any local, state, or Federal governmental unit, regardless of the activity of the particular agency. For ACS tabulations, the data are presented separately for the three levels of government.
Employees of Indian tribal governments, foreign governments, the United Nations, or other formal international organizations controlled by governments were classified as "Federal government workers."
The government categories include all government workers, though government workers may work in different industries. For example, people who work in a public elementary school or city owned bus line are coded as local government class of workers.
Employees of Indian tribal governments, foreign governments, the United Nations, or other formal international organizations controlled by governments were classified as "Federal government workers."
The government categories include all government workers, though government workers may work in different industries. For example, people who work in a public elementary school or city owned bus line are coded as local government class of workers.
Includes people who worked for profit or fees in their own unincorporated business, profession, or trade, or who operated a farm.
Includes people who worked without pay in a business or on a farm operated by a relative.
A computer edit and allocation process excludes all responses that should not be included in the universe and evaluates the consistency of the remaining responses. Class of worker responses are checked for consistency with the industry and occupation data provided for that respondent. Occasionally respondents do not report a response for class of worker, industry, or occupation. Certain types of incomplete entries are corrected using the Alphabetical Index of Industries and Occupations. If one or more of the three codes (occupation, industry, or class of worker) is blank after the edit, a code is assigned from a donor respondent who is a "similar" person based on questions such as age, sex, educational attainment, income, employment status, and weeks worked. If all of the labor force and income data are blank, all of these economic questions are assigned from a "similar" person who had provided all the necessary data.
These data are used to formulate policy and programs for employment and career development and training. Companies use these data to decide where to locate new plants, stores, or offices.
These data are used to formulate policy and programs for employment and career development and training. Companies use these data to decide where to locate new plants, stores, or offices.
Class of worker data have been collected during decennial censuses since 1910. Starting with the 2010 Census, class of worker data will no longer be collected during the decennial census. Long form data collection has transitioned to the American Community Survey. The American Community Survey began collecting data on class of worker in 1996. The questions on class of worker were designed to be consistent with the 1990 Census questions on class of worker. The 1996-1998 ACS class of worker question had an additional response category for "Active duty U.S. Armed Forces member." People who marked this category were tabulated as Federal government workers. A check box was added to the employer name questionnaire item in 1999. This check box is to be marked by anyone "now on active duty in the Armed Forces..." This information is used by the industry and occupation coders to assist in assigning proper industry codes for active duty military.
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. Some types of GQ populations have class of worker distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the class of worker distribution in some geographic areas with a substantial GQ population.
Data on occupation, industry, and class of worker are collected for the respondent's current primary job or the most recent job for those who are not employed but have worked in the last 5 years. Other labor force questions, such as questions on earnings or work hours, may have different reference periods and may not limit the response to the primary job. Although the prevalence of multiple jobs is low, data on some labor force items may not exactly correspond to the reported occupation, industry, or class of worker of a respondent.
Data on occupation, industry, and class of worker are collected for the respondent's current primary job or the most recent job for those who are not employed but have worked in the last 5 years. Other labor force questions, such as questions on earnings or work hours, may have different reference periods and may not limit the response to the primary job. Although the prevalence of multiple jobs is low, data on some labor force items may not exactly correspond to the reported occupation, industry, or class of worker of a respondent.
Class of worker categories have remained consistent since the implementation of the American Community Survey in 1996. The 1996-1998 ACS class of worker question had an additional response category for "Active duty U.S. Armed Forces member" in order to assist industry and occupation coders in assigning proper industry codes for active duty military. People who selected this category were tabulated as Federal government workers. Active duty U.S. Armed Forces have been coded as Federal government workers from 1996 to 2011.
See also, Industry and Occupation.
See also, Industry and Occupation.
Under the conceptual framework of disability described by the Institute of Medicine (IOM) and the International Classification of Functioning, Disability, and Health (ICF), disability is defined as the product of interactions among individuals' bodies; their physical, emotional, and mental health; and the physical and social environment in which they live, work, or play. Disability exists where this interaction results in limitations of activities and restrictions to full participation at school, at work, at home, or in the community. For example, disability may exist where a person is limited in their ability to work due to job discrimination against persons with specific health conditions; or, disability may exist where a child has difficulty learning because the school cannot accommodate the child's deafness.
Furthermore, disability is a dynamic concept that changes over time as one's health improves or declines, as technology advances, and as social structures adapt. As such, disability is a continuum in which the degree of difficulty may also increase or decrease. Because disability exists along a continuum, various cut-offs are used to allow for a simpler understanding of the concept, the most common of which is the dichotomous "With a disability"/"no disability" differential.
Measuring this complex concept of disability with a short set of six questions is difficult. Because of the multitude of possible functional limitations that may present as disabilities, and in the absence of information on external factors that influence disability, surveys like the ACS are limited to capturing difficulty with only selected activities. As such, people identified by the ACS as having a disability are, in fact, those who exhibit difficulty with specific functions and may, in the absence of accommodation, have a disability. While this definition is different from the one described by the IOM and ICF conceptual frameworks, it relates to the programmatic definitions used in most Federal and state legislation.
In an attempt to capture a variety of characteristics that encompass the definition of disability, the ACS identifies serious difficulty with four basic areas of functioning - hearing, vision, cognition, and ambulation. These functional limitations are supplemented by questions about difficulties with selected activities from the Katz Activities of Daily Living (ADL) and Lawton Instrumental Activities of Daily Living (IADL) scales, namely difficulty bathing and dressing, and difficulty performing errands such as shopping. Overall, the ACS attempts to capture six aspects of disability, which can be used together to create an overall disability measure, or independently to identify populations with specific disability types.
Information on disability is used by a number of federal agencies to distribute funds and develop programs for people with disabilities. For example, data about the size, distribution, and needs of the disabled population are essential for developing disability employment policy. For the Americans with Disabilities Act, data about functional limitations are important to ensure that comparable public transportation services are available for all segments of the population. Federal grants are awarded, under the Older Americans Act, based on the number of elderly people with physical and mental disabilities.
Furthermore, disability is a dynamic concept that changes over time as one's health improves or declines, as technology advances, and as social structures adapt. As such, disability is a continuum in which the degree of difficulty may also increase or decrease. Because disability exists along a continuum, various cut-offs are used to allow for a simpler understanding of the concept, the most common of which is the dichotomous "With a disability"/"no disability" differential.
Measuring this complex concept of disability with a short set of six questions is difficult. Because of the multitude of possible functional limitations that may present as disabilities, and in the absence of information on external factors that influence disability, surveys like the ACS are limited to capturing difficulty with only selected activities. As such, people identified by the ACS as having a disability are, in fact, those who exhibit difficulty with specific functions and may, in the absence of accommodation, have a disability. While this definition is different from the one described by the IOM and ICF conceptual frameworks, it relates to the programmatic definitions used in most Federal and state legislation.
In an attempt to capture a variety of characteristics that encompass the definition of disability, the ACS identifies serious difficulty with four basic areas of functioning - hearing, vision, cognition, and ambulation. These functional limitations are supplemented by questions about difficulties with selected activities from the Katz Activities of Daily Living (ADL) and Lawton Instrumental Activities of Daily Living (IADL) scales, namely difficulty bathing and dressing, and difficulty performing errands such as shopping. Overall, the ACS attempts to capture six aspects of disability, which can be used together to create an overall disability measure, or independently to identify populations with specific disability types.
Information on disability is used by a number of federal agencies to distribute funds and develop programs for people with disabilities. For example, data about the size, distribution, and needs of the disabled population are essential for developing disability employment policy. For the Americans with Disabilities Act, data about functional limitations are important to ensure that comparable public transportation services are available for all segments of the population. Federal grants are awarded, under the Older Americans Act, based on the number of elderly people with physical and mental disabilities.
In the 2011 American Community Survey, disability concepts were asked in questions 17 through 19. Question 17 had two subparts and was asked of all persons regardless of age. Question 18 had three subparts and was asked of people age 5 years and older. Question 19 was asked of people age 15 years and older.
Hearing difficulty was derived from question 17a, which asked respondents if they were "deaf or ... [had] serious difficulty hearing."
Vision difficulty was derived from question 17b, which asked respondents if they were "blind or ... [had] serious difficulty seeing even when wearing glasses." Prior to the 2008 ACS, hearing and vision difficulty were asked in a single question under the label "Sensory disability."
Cognitive difficulty was derived from question 18a, which asked respondents if due to physical, mental, or emotional condition, they had "serious difficulty concentrating, remembering, or making decisions." Prior to the 2008 ACS, the question on cognitive functioning asked about difficulty "learning, remembering, or concentrating" under the label "Mental disability."
Ambulatory difficulty was derived from question 18b, which asked respondents if they had "serious difficulty walking or climbing stairs." Prior to 2008, the ACS asked if respondents had "a condition that substantially limits one or more basic physical activities such as walking, climbing stairs, reaching, lifting, or carrying." This measure was labeled "Physical difficulty" in ACS data products.
Self-care difficulty was derived from question 18c, which asked respondents if they had "difficulty dressing or bathing." Difficulty with these activities are two of six specific Activities of Daily Living (ADLs) often used by health care providers to assess patients' self- care needs. Prior to the 2008 ACS, the question on self-care limitations asked about difficulty "dressing, bathing, or getting around inside the home," under the label "Self-care disability."
Independent living difficulty was derived from question 19, which asked respondents if due to a physical, mental, or emotional condition, they had difficulty "doing errands alone such as visiting a doctor's office or shopping." Difficulty with this activity is one of several Instrumental Activities of Daily Living (IADL) used by health care providers in making care decisions. Prior to the 2008 ACS, a similar measure on difficulty "going outside the home alone to shop or visit a doctor's office" was asked under the label "Go-outside-home disability."
Disability status is determined from the answers from these six types of difficulty. For children under 5 years old, hearing and vision difficulty are used to determine disability status. For children between the ages of 5 and 14, disability status is determined from hearing, vision, cognitive, ambulatory, and self-care difficulties. For people aged 15 years and older, they are considered to have a disability if they have difficulty with any one of the six difficulty types.
The universe for most disability data tabulations is the civilian noninstitutionalized population. Some types of GQ populations have disability distributions that are different from the household population. The inclusion of the noninstitutionalized GQ population could therefore have a noticeable impact on the disability distribution. This is particularly true for areas with a substantial noninstitutionalized GQ population. For a discussion of the effect of group quarters data has on estimates of disability status, see "Disability Status and the Characteristics of People in Group Quarters: A Brief Analysis of Disability Prevalence among the Civilian Noninstitutionalized and Total Populations in the American Community Survey" (http://www.census.gov/hhes/www/disability/GQdisability.pdf).
Beginning in 2008, questions on disability represent a conceptual and empirical break from earlier years of the ACS. Hence, the Census Bureau does not recommend any comparisons of 2011 disability data to 2007 and earlier ACS disability data.
Research suggests that combining the new separate measures of hearing and vision difficulty to generate a sensory difficulty measure does not create a comparable estimate to the old Sensory disability estimates in prior ACS products. Likewise, the cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty measures are based on different sets of activities and different question wordings from similar measures in ACS questionnaires prior to 2008 and thus should not be compared. Because the overall measure of disability status beginning in 2008 is based on different measures of difficulty, these estimates should also not be compared to prior years. For additional information on the differences between the ACS disability questions beginning in 2008 and prior ACS disability questions, see "Review of Changes to the Measurement of Disability in the 2008 American Community Survey" (http://www.census.gov/hhes/www/disability/2008ACS_disability.pdf).
The 2011 disability estimates should also not be compared with disability estimates from Census 2000 for reasons similar to the ones made above. ACS disability estimates should also not be compared with more detailed measures of disability from sources such as the National Health Interview Survey and the Survey of Income and Program Participation.
The 2011 ACS disability estimates are comparable with the ACS disability estimates from 2008,2009, and 2010.
Research suggests that combining the new separate measures of hearing and vision difficulty to generate a sensory difficulty measure does not create a comparable estimate to the old Sensory disability estimates in prior ACS products. Likewise, the cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty measures are based on different sets of activities and different question wordings from similar measures in ACS questionnaires prior to 2008 and thus should not be compared. Because the overall measure of disability status beginning in 2008 is based on different measures of difficulty, these estimates should also not be compared to prior years. For additional information on the differences between the ACS disability questions beginning in 2008 and prior ACS disability questions, see "Review of Changes to the Measurement of Disability in the 2008 American Community Survey" (http://www.census.gov/hhes/www/disability/2008ACS_disability.pdf).
The 2011 disability estimates should also not be compared with disability estimates from Census 2000 for reasons similar to the ones made above. ACS disability estimates should also not be compared with more detailed measures of disability from sources such as the National Health Interview Survey and the Survey of Income and Program Participation.
The 2011 ACS disability estimates are comparable with the ACS disability estimates from 2008,2009, and 2010.
Educational attainment data are needed for use in assessing the socioeconomic condition of the U.S. population. Government agencies also require these data for funding allocations and program planning and implementation. These data are needed to determine the extent of illiteracy rates of citizens in language minorities in order to meet statutory requirements under the Voting Rights Act. Based on data about educational attainment, school districts are allocated funds to provide classes in basic skills to adults who have not completed high school.
Data on educational attainment were derived from answers to Question 11, which was asked of all respondents. Educational attainment data are tabulated for people 18 years old and over. Respondents are classified according to the highest degree or the highest level of school completed. The question included instructions for persons currently enrolled in school to report the level of the previous grade attended or the highest degree received.
The educational attainment question included a response category that allowed people to report completing the 12th grade without receiving a high school diploma. Respondents who received a regular high school diploma and did not attend college were instructed to report "Regular high school diploma." Respondents who received the equivalent of a high school diploma (for example, passed the test of General Educational Development (G.E.D.)), and did not attend college, were instructed to report "GED or alternative credential." "Some college" is in two categories: "Some college credit, but less than 1 year of college credit" and "1 or more years of college credit, no degree." The category "Associate's degree" included people whose highest degree is an associate's degree, which generally requires 2 years of college level work and is either in an occupational program that prepares them for a specific occupation, or an academic program primarily in the arts and sciences. The course work may or may not be transferable to a bachelor's degree. Master's degrees include the traditional MA and MS degrees and field-specific degrees, such as MSW, MEd, MBA, MLS, and MEng. Instructions included in the respondent instruction guide for mailout/mailback respondents only provided the following examples of professional school degrees: Medicine, dentistry, chiropractic, optometry, osteopathic medicine, pharmacy, podiatry, veterinary medicine, law, and theology. The order in which degrees were listed suggested that doctorate degrees were "higher" than professional school degrees, which were "higher" than master's degrees. If more than one box was filled, the response was edited to the highest level or degree reported.
The instructions further specified that schooling completed in foreign or ungraded school systems should be reported as the equivalent level of schooling in the regular American system. The instructions specified that certificates or diplomas for training in specific trades or from vocational, technical or business schools were not to be reported. Honorary degrees awarded for a respondent's accomplishments were not to be reported.
Data on educational attainment were derived from answers to Question 11, which was asked of all respondents. Educational attainment data are tabulated for people 18 years old and over. Respondents are classified according to the highest degree or the highest level of school completed. The question included instructions for persons currently enrolled in school to report the level of the previous grade attended or the highest degree received.
The educational attainment question included a response category that allowed people to report completing the 12th grade without receiving a high school diploma. Respondents who received a regular high school diploma and did not attend college were instructed to report "Regular high school diploma." Respondents who received the equivalent of a high school diploma (for example, passed the test of General Educational Development (G.E.D.)), and did not attend college, were instructed to report "GED or alternative credential." "Some college" is in two categories: "Some college credit, but less than 1 year of college credit" and "1 or more years of college credit, no degree." The category "Associate's degree" included people whose highest degree is an associate's degree, which generally requires 2 years of college level work and is either in an occupational program that prepares them for a specific occupation, or an academic program primarily in the arts and sciences. The course work may or may not be transferable to a bachelor's degree. Master's degrees include the traditional MA and MS degrees and field-specific degrees, such as MSW, MEd, MBA, MLS, and MEng. Instructions included in the respondent instruction guide for mailout/mailback respondents only provided the following examples of professional school degrees: Medicine, dentistry, chiropractic, optometry, osteopathic medicine, pharmacy, podiatry, veterinary medicine, law, and theology. The order in which degrees were listed suggested that doctorate degrees were "higher" than professional school degrees, which were "higher" than master's degrees. If more than one box was filled, the response was edited to the highest level or degree reported.
The instructions further specified that schooling completed in foreign or ungraded school systems should be reported as the equivalent level of schooling in the regular American system. The instructions specified that certificates or diplomas for training in specific trades or from vocational, technical or business schools were not to be reported. Honorary degrees awarded for a respondent's accomplishments were not to be reported.
This category includes people whose highest degree was a high school diploma or its equivalent, people who attended college but did not receive a degree, and people who received an associate's, bachelor's, master's, or professional or doctorate degree. People who reported completing the 12th grade but not receiving a diploma are not included.
This category includes people of compulsory school attendance age or above who were not enrolled in school and were not high school graduates. These people may be referred to as "high school dropouts." There is no restriction on when they "dropped out" of school; therefore, they may have dropped out before high school and never attended high school.
Since 1999, the American Community Survey question does not have the response category for "Vocational, technical, or business school degree" that the 1996-1998 American Community Surveys question had. Starting in 1999, the American Community Survey question had two categories for some college: "Some college credit, but less than 1 year" and "1 or more years of college, no degree." The 1996-1998 American Community Survey question had one category: "Some college but no degree."
In the 1996-1998 American Community Survey, the educational attainment question was used to estimate level of enrollment. Since 1999, a question regarding grade of enrollment was included.
The 1999-2007 American Community Survey attainment question grouped grade categories below high school into the following three categories: "Nursery school to 4th grade," "5th grade or 6th grade," and "7th grade or 8 grade." The 1996-1998 American Community Survey question allowed a write-in for highest grade completed for grades 1-11 in addition to "Nursery or preschool" and "Kindergarten."
Beginning in 2008, the American Community Survey attainment question was changed to the following categories for levels up to "Grade 12, no diploma": "Nursery school," "Kindergarten," "Grade 1 through grade 11," and "12th grade, no diploma." The survey question allowed a write-in for the highest grade completed for grades 1-11. In addition, the category that was previously "High school graduate (including GED)" was broken into two categories: "Regular high school diploma" and "GED or alternative credential." The term "credit" for the two some college categories was emphasized. The phrase "beyond a bachelor's degree" was added to the professional degree category.
In the 1996-1998 American Community Survey, the educational attainment question was used to estimate level of enrollment. Since 1999, a question regarding grade of enrollment was included.
The 1999-2007 American Community Survey attainment question grouped grade categories below high school into the following three categories: "Nursery school to 4th grade," "5th grade or 6th grade," and "7th grade or 8 grade." The 1996-1998 American Community Survey question allowed a write-in for highest grade completed for grades 1-11 in addition to "Nursery or preschool" and "Kindergarten."
Beginning in 2008, the American Community Survey attainment question was changed to the following categories for levels up to "Grade 12, no diploma": "Nursery school," "Kindergarten," "Grade 1 through grade 11," and "12th grade, no diploma." The survey question allowed a write-in for the highest grade completed for grades 1-11. In addition, the category that was previously "High school graduate (including GED)" was broken into two categories: "Regular high school diploma" and "GED or alternative credential." The term "credit" for the two some college categories was emphasized. The phrase "beyond a bachelor's degree" was added to the professional degree category.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have educational attainment distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the educational attainment distribution. This is particularly true for areas with a substantial GQ population.
The Census Bureau tested the changes introduced to the 2008 version of the educational attainment question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Educational Attainment" on the ACS website (www.census.gov/acs).
The Census Bureau tested the changes introduced to the 2008 version of the educational attainment question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Educational Attainment" on the ACS website (www.census.gov/acs).
New questions were added to the 2008 ACS CATI/CAPI instrument. Respondents who received a high school diploma, GED or equivalent were also asked if they had completed any college credit. Therefore, data users may notice a decrease in the number of high school graduates relative to previous years because those people are now being captured in the "Some college credit, but less than 1 year of college credit" or "1 or more years of college credit, no degree" categories. For more information, see the report titled Report P.2.b: "Evaluation Report Covering Educational Attainment" on the ACS website (www.census.gov/acs).
Data about educational attainment are also collected from the decennial Census and from the Current Population Survey (CPS). ACS data is generally comparable to data from the Census. For more information about the comparability of ACS and CPS data, please see the link for the Fact Sheet and the Comparison Report from the CPS Educational Attainment page.
Data about educational attainment are also collected from the decennial Census and from the Current Population Survey (CPS). ACS data is generally comparable to data from the Census. For more information about the comparability of ACS and CPS data, please see the link for the Fact Sheet and the Comparison Report from the CPS Educational Attainment page.
The data on employment status were derived from Questions 29 and 35 to 37 in the 2011 American Community Survey. (In the 1999-2002 American Community Survey, data were derived from Questions 22 and 28 to 30; in the 1996-1998 American Community Survey, data were derived from Questions 21 and 28 to 30.) The questions were asked of all people 15 years old and over. The series of questions on employment status was designed to identify, in this sequence: (1) people who worked at any time during the reference week; (2) people on temporary layoff who were available for work; (3) people who did not work during the reference week but who had jobs or businesses from which they were temporarily absent (excluding layoff); (4) people who did not work during the reference week, but who were looking for work during the last four weeks and were available for work during the reference week; and (5) people not in the labor force. (For more information, see the discussion under "Reference Week.")
The employment status data shown in American Community Survey tabulations relate to people 16 years old and over.
Employment status is key to understanding work and unemployment patterns and the availability of workers. Based on labor market areas and unemployment levels, the U.S. Department of Labor identifies service delivery areas and determines amounts to be allocated to each for job training. The impact of immigration on the economy and job markets is determined partially by labor force data, and this information is included in required reports to Congress. The Office of Management and Budget, under the Paperwork Reduction Act, uses data about employed workers as part of the criteria for defining metropolitan areas. The Bureau of Economic Analysis uses this information, in conjunction with other data, to develop its state per capita income estimates used in the allocation formulas and eligibility criteria for many federal programs such as Medicaid.
The employment status data shown in American Community Survey tabulations relate to people 16 years old and over.
Employment status is key to understanding work and unemployment patterns and the availability of workers. Based on labor market areas and unemployment levels, the U.S. Department of Labor identifies service delivery areas and determines amounts to be allocated to each for job training. The impact of immigration on the economy and job markets is determined partially by labor force data, and this information is included in required reports to Congress. The Office of Management and Budget, under the Paperwork Reduction Act, uses data about employed workers as part of the criteria for defining metropolitan areas. The Bureau of Economic Analysis uses this information, in conjunction with other data, to develop its state per capita income estimates used in the allocation formulas and eligibility criteria for many federal programs such as Medicaid.
This category includes all civilians 16 years old and over who either (1) were "at work," that is, those who did any work at all during the reference week as paid employees, worked in their own business or profession, worked on their own farm, or worked 15 hours or more as unpaid workers on a family farm or in a family business; or (2) were "with a job but not at work," that is, those who did not work during the reference week but had jobs or businesses from which they were temporarily absent due to illness, bad weather, industrial dispute, vacation, or other personal reasons. Excluded from the employed are people whose only activity consisted of work around the house or unpaid volunteer work for religious, charitable, and similar organizations; also excluded are all institutionalized people and people on active duty in the United States Armed Forces.
All civilians 16 years old and over are classified as unemployed if they (1) were neither "at work" nor "with a job but not at work" during the reference week, and (2) were actively looking for work during the last 4 weeks, and (3) were available to start a job. Also included as unemployed are civilians who did not work at all during the reference week, were waiting to be called back to a job from which they had been laid off, and were available for work except for temporary illness. Examples of job seeking activities are:
- Registering at a public or private employment office
- Meeting with prospective employers
- Investigating possibilities for starting a professional practice or opening a business
- Placing or answering advertisements
- Writing letters of application
- Being on a union or professional register
Consists of people classified as employed or unemployed in accordance with the criteria described above.
The unemployment rate represents the number of unemployed people as a percentage of the civilian labor force. For example, if the civilian labor force equals 100 people and 7 people are unemployed, then the unemployment rate would be 7 percent.
All people classified in the civilian labor force plus members of the U.S. Armed Forces (people on active duty with the United States Army, Air Force, Navy, Marine Corps, or Coast Guard).
The labor force participation rate represents the proportion of the population that is in the labor force. For example, if there are 100 people in the population 16 years and over, and 64 of them are in the labor force, then the labor force participation rate for the population 16 years and over would be 64 percent.
All people 16 years old and over who are not classified as members of the labor force. This category consists mainly of students, homemakers, retired workers, seasonal workers interviewed in an off season who were not looking for work, institutionalized people, and people doing only incidental unpaid family work (less than 15 hours during the reference week).
This term appears in connection with several subjects: employment status, journey-to-work questions, class of worker, weeks worked in the past 12 months, and number of workers in family in the past 12 months. The meaning varies and, therefore, should be determined in each case by referring to the definition of the subject in which it appears. When used in the concepts "workers in family" and "full-time, year-round workers," the term "worker" relates to the meaning of work defined for the "work experience" subject.
Worked Last Week (Question 29): From 1999-2007, an italicized instruction was added to the question to help respondents determine what to count as work. Starting in 2008, the instruction was removed and the question was separated into two parts in an effort to give respondents - particularly people with irregular kinds of work arrangements - two opportunities to grasp and respond to the correct intent of the question.
On Layoff (Question 35a): Starting in 1999, the "Yes, on temporary layoff from most recent job" and "Yes, permanently laid off from most recent job" response categories were condensed into a single "Yes" category. An additional question (Q35b) was added to determine the temporary/permanent layoff distinction.
Temporarily Absent (Question 35b): Starting in 2008, the temporarily absent question included a revised list of examples of work absences.
Recalled to Work (Question 35c): This question was added in the 1999 American Community Survey to determine if a respondent who reported being on layoff from a job had been informed that he or she would be recalled to work within 6 months or been given a date to return to work.
Looking for Work (Question 36): Starting in 2008, the actively looking for work question was modified to emphasize 'active' job-searching activities.
Available to Work (Question 37): Starting in 1999, the "Yes, if a job had been offered" and "Yes, if recalled from layoff' response categories were condensed into one category, "Yes, could have gone to work." Starting in 2008, the actively looking for work question was modified to emphasize 'active' job-searching activities.
On Layoff (Question 35a): Starting in 1999, the "Yes, on temporary layoff from most recent job" and "Yes, permanently laid off from most recent job" response categories were condensed into a single "Yes" category. An additional question (Q35b) was added to determine the temporary/permanent layoff distinction.
Temporarily Absent (Question 35b): Starting in 2008, the temporarily absent question included a revised list of examples of work absences.
Recalled to Work (Question 35c): This question was added in the 1999 American Community Survey to determine if a respondent who reported being on layoff from a job had been informed that he or she would be recalled to work within 6 months or been given a date to return to work.
Looking for Work (Question 36): Starting in 2008, the actively looking for work question was modified to emphasize 'active' job-searching activities.
Available to Work (Question 37): Starting in 1999, the "Yes, if a job had been offered" and "Yes, if recalled from layoff' response categories were condensed into one category, "Yes, could have gone to work." Starting in 2008, the actively looking for work question was modified to emphasize 'active' job-searching activities.
The data may understate the number of employed people because people who have irregular, casual, or unstructured jobs sometimes report themselves as not working. The number of employed people "at work" is probably overstated in the data (and conversely, the number of employed "with a job, but not at work" is understated) since some people on vacation or sick leave erroneously reported themselves as working. This problem has no effect on the total number of employed people. The reference week for the employment data is not the same for all people. Since people can change their employment status from one week to another, the lack of a uniform reference week may mean that the employment data do not reflect the reality of the employment situation of any given week. (For more information, see the discussion under "Reference Week.")
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations have employment status distributions that are different from the household population. All institutionalized people are placed in the "not in labor force category." The inclusion of the GQ population could therefore have a noticeable impact on the employment status distribution. This is particularly true for areas with a substantial GQ population. For example, in areas having a large state prison population, the employment rate would be expected to decrease because the base of the percentage, which now includes the population in correctional institutions, is larger.
The Census Bureau tested the changes introduced to the 2008 version of the employment status questions in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for these questions as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Employment Status" on the ACS website (www.census.gov/acs).
Along with the 2008 ACS release, the Census Bureau produced a research note comparing 2007 and 2008 ACS employment estimates to 2007 and 2008 Current Population Survey (CPS)/Local Area Unemployment Statistics (LAUS) estimates. The research note shows that the changes to the employment status series of questions in the 2008 ACS will make ACS labor force data more consistent with benchmark data from the CPS and LAUS program. For more information, see "Changes to the American Community Survey between 2007 and 2008 and the Effects on the Estimates of Employment and Unemployment" (http://www.census.gov/hhes/www/laborfor/researchnote092209.html).
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations have employment status distributions that are different from the household population. All institutionalized people are placed in the "not in labor force category." The inclusion of the GQ population could therefore have a noticeable impact on the employment status distribution. This is particularly true for areas with a substantial GQ population. For example, in areas having a large state prison population, the employment rate would be expected to decrease because the base of the percentage, which now includes the population in correctional institutions, is larger.
The Census Bureau tested the changes introduced to the 2008 version of the employment status questions in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for these questions as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Employment Status" on the ACS website (www.census.gov/acs).
Along with the 2008 ACS release, the Census Bureau produced a research note comparing 2007 and 2008 ACS employment estimates to 2007 and 2008 Current Population Survey (CPS)/Local Area Unemployment Statistics (LAUS) estimates. The research note shows that the changes to the employment status series of questions in the 2008 ACS will make ACS labor force data more consistent with benchmark data from the CPS and LAUS program. For more information, see "Changes to the American Community Survey between 2007 and 2008 and the Effects on the Estimates of Employment and Unemployment" (http://www.census.gov/hhes/www/laborfor/researchnote092209.html).
Since employment data from the American Community Survey are obtained from respondents in households, they differ from statistics based on reports from individual business establishments, farm enterprises, and certain government programs. People employed at more than one job are counted only once in the American Community Survey and are classified according to the job at which they worked the greatest number of hours during the reference week. In statistics based on reports from business and farm establishments, people who work for more than one establishment may be counted more than once. Moreover, some tabulations may exclude private household workers, unpaid family workers, and self-employed people, but may include workers less than 16 years of age.
An additional difference in the data arises from the fact that people who had a job but were not at work are included with the employed in the American Community Survey statistics, whereas many of these people are likely to be excluded from employment figures based on establishment payroll reports. Furthermore, the employment status data in tabulations include people on the basis of place of residence regardless of where they work, whereas establishment data report people at their place of work regardless of where they live. This latter consideration is particularly significant when comparing data for workers who commute between areas.
For several reasons, the unemployment figures of the Census Bureau are not comparable with published figures on unemployment compensation claims. For example, figures on unemployment compensation claims exclude people who have exhausted their benefit rights, new workers who have not earned rights to unemployment insurance, and people losing jobs not covered by unemployment insurance systems (including some workers in agriculture, domestic services, and religious organizations, and self-employed and unpaid family workers). In addition, the qualifications for drawing unemployment compensation differ from the definition of unemployment used by the Census Bureau. People working only a few hours during the week and people with a job but not at work are sometimes eligible for unemployment compensation but are classified as "Employed" in the American Community Survey. Differences in the geographical distribution of unemployment data arise because the place where claims are filed may not necessarily be the same as the place of residence of the unemployed worker.
For guidance on differences in employment and unemployment estimates from different sources, go to http://www.census.gov/hhes/www/laborfor/laborguidance082504.html.
An additional difference in the data arises from the fact that people who had a job but were not at work are included with the employed in the American Community Survey statistics, whereas many of these people are likely to be excluded from employment figures based on establishment payroll reports. Furthermore, the employment status data in tabulations include people on the basis of place of residence regardless of where they work, whereas establishment data report people at their place of work regardless of where they live. This latter consideration is particularly significant when comparing data for workers who commute between areas.
For several reasons, the unemployment figures of the Census Bureau are not comparable with published figures on unemployment compensation claims. For example, figures on unemployment compensation claims exclude people who have exhausted their benefit rights, new workers who have not earned rights to unemployment insurance, and people losing jobs not covered by unemployment insurance systems (including some workers in agriculture, domestic services, and religious organizations, and self-employed and unpaid family workers). In addition, the qualifications for drawing unemployment compensation differ from the definition of unemployment used by the Census Bureau. People working only a few hours during the week and people with a job but not at work are sometimes eligible for unemployment compensation but are classified as "Employed" in the American Community Survey. Differences in the geographical distribution of unemployment data arise because the place where claims are filed may not necessarily be the same as the place of residence of the unemployed worker.
For guidance on differences in employment and unemployment estimates from different sources, go to http://www.census.gov/hhes/www/laborfor/laborguidance082504.html.
The data on fertility were derived from Question 17 in 1999-2002, Question 18 in 2003-2007, question 23 in 2008, and question 24 since 2009. The question asked if the person had given birth in the past 12 months, and was asked of all women 15 to 50 years old regardless of marital status. From this question, we are able to determine geographies with high numbers of women with births and the characteristics of these women, such as age and marital status. When fertility was not reported, it was imputed according to the woman's age and marital status and the possibility there was an infant in the household.
Data are most frequently presented in terms of the aggregate number of women who had a birth in the past 12 months in the specified category, and in terms of the rate per 1,000 women.
Data are most frequently presented in terms of the aggregate number of women who had a birth in the past 12 months in the specified category, and in terms of the rate per 1,000 women.
This measure estimates the number of children a group of 1,000 women would have by the end of their childbearing years if they all experienced the same-age specific birth rates between ages 15-50 in a given year. This rate is used for comparisons among different population groups--for example, women in different geographical areas--as the rate accounts for differences in the age distribution in those areas. It is calculated by summing the age-specific birth rates for women in 5-year age groups between ages 15-19 and 40-44 and ages 45-50 and multiplying these rates by 5--or by 6 for the final age group- representing the number of years in each age group. The sum of these individual rates is then multiplied by 1,000 to represent the numbers of births per 1,000 women.
The 1996-1998 American Community Survey collected data on "children ever born." (See the section on "Children Ever Born" for more information.) In 1999, the American Community Survey began collecting data on children born in the last 12 months.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have fertility distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the fertility distribution. This is particularly true for areas with a substantial GQ population.
The data on fertility can be compared to previous ACS years and to similar data collected in the Current Population Survey (CPS) and Survey of Income and Program Participation (SIPP), and from the National Center for Health Statistics. All of these surveys have slightly different ways of determining the reference period but generally show births occurring over a period of 12 months.
Field of degree data are used by the National Science Foundation to study the characteristics of the population with science and engineering degrees and occupations.
Data on field of bachelor's degree were derived from answers to Question 12. This question was asked only to person with a bachelor's degree or higher. Eligible respondents were asked to list the specific major(s) of any bachelor's degree received. This question does not ask for the field of any other type of degree earned (such as master's or doctorate).
An automated computer system coded write-in responses to Question 12 into 192 areas. Clerical coding categorized any write-in responses that could not be autocoded by the computer. Respondents listing multiple fields were assigned a code for each field, with a maximum of 10 fields per respondent.
The majors were further classified into a category scheme detailed in Appendix A.
Data on field of bachelor's degree were derived from answers to Question 12. This question was asked only to person with a bachelor's degree or higher. Eligible respondents were asked to list the specific major(s) of any bachelor's degree received. This question does not ask for the field of any other type of degree earned (such as master's or doctorate).
An automated computer system coded write-in responses to Question 12 into 192 areas. Clerical coding categorized any write-in responses that could not be autocoded by the computer. Respondents listing multiple fields were assigned a code for each field, with a maximum of 10 fields per respondent.
The majors were further classified into a category scheme detailed in Appendix A.
The field of degree question first appeared in the 2009 ACS. The inclusion of a field of degree question on the ACS was proposed to provide field of degree data annually for small levels of geography and to assist in building a sampling frame for the National Science Foundation's (NSF) National Survey of College Graduates (NSCG).
Because of its introduction in 2009, the 201 field of degree data can only be compared to the 2009 and 2010 ACS survey. This data may be roughly comparable to the National Survey of College Graduates and the National Survey of Recent College Graduates, although the sampling frame and survey instruments differ between the surveys. Field of degree data was also collected in the Survey of Income and Program Participation (SIPP) from1984 to 2004. However, these data would not be comparable to ACS due to differences in data collection period, methodology and collection methods. For example, the SIPP only collects data for respondents who are 15 years and older and does not include group quarters.
The foreign-born population includes anyone who was not a U.S. citizen or a U.S. national at birth. This includes respondents who indicated they were a U.S. citizen by naturalization or not a U.S. citizen. See Citizenship Status.
Data on grandparents as caregivers were derived from Questions 25a through 25c. Data were collected on whether a grandchild lives with a grandparent in the household, whether the grandparent has responsibility for the basic needs of the grandchild, and the duration of that responsibility.
This was determined by a "Yes" answer to the question, "Does this person have any of his/her own grandchildren under the age of 18 living in this house or apartment?" This question was asked of people 15 years of age and over. Because of the low numbers of persons under 30 years old living with their grandchildren, data were only tabulated for people 30 and over.
This question determines if the grandparent is financially responsible for food, shelter, clothing, day care, etc., for any or all grandchildren living in the household. In selected tabulations, grandparent responsibility is further classified by presence of parent (of the grandchild).
The answer refers to the grandchild for whom the grandparent has been responsible for the longest period of time. Duration categories ranged from less than 6 months to 5 or more years.
This set of questions was added to the American Community Survey in 1999 to comply with legislation passed in the 104th Congress requiring that the decennial census program obtain information about grandparents who have primary responsibility for the care of their grandchildren.
The response categories for length of time caring for grandchildren were modified slightly between the 1999 and 2000 American Community Survey questionnaires to match the 2000 decennial census questionnaire. The question has remained unchanged since then.
The response categories for length of time caring for grandchildren were modified slightly between the 1999 and 2000 American Community Survey questionnaires to match the 2000 decennial census questionnaire. The question has remained unchanged since then.
Before 2006, ACS grandparents data had a universe of people in households (which was the same as that in Census 2000 and the CPS). Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have grandparents as caregivers distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the grandparents as caregivers distribution. This is particularly true for areas with a substantial GQ population.
In 2011, data on health insurance coverage were derived from answers to Question 16, which was asked of all respondents. Respondents were instructed to report their current coverage and to mark "yes" or "no" for each of the eight types listed (labeled as parts 16a to 16h).
Health insurance coverage in the ACS and other Census Bureau surveys define coverage to include plans and programs that provide comprehensive health coverage. Plans that provide insurance for specific conditions or situations such as cancer and long-term care policies are not considered coverage. Likewise, other types of insurance like dental, vision, life, and disability insurance are not considered health insurance coverage.
In defining types of coverage, write-in responses were reclassified into one of the first seven types of coverage or determined not to be a coverage type. Write-in responses that referenced the coverage of a family member were edited to assign coverage based on responses from other family members. As a result, only the first seven types of health coverage are included in the microdata file.
An eligibility edit was applied to give Medicaid, Medicare, and TRICARE coverage to individuals based on program eligibility rules. TRICARE or other military health care was given to active-duty military personnel and their spouses and children. Medicaid or other means-tested public coverage was given to foster children, certain individuals receiving Supplementary Security Income or Public Assistance, and the spouses and children of certain Medicaid beneficiaries. Medicare coverage was given to people 65 and older who received Social Security or Medicaid benefits.
People were considered insured if they reported at least one "yes" to Questions 16a to 16f. People who had no reported health coverage, or those whose only health coverage was Indian Health Service, were considered uninsured. For reporting purposes, the Census Bureau broadly classifies health insurance coverage as private health insurance or public coverage. Private health insurance is a plan provided through an employer or union, a plan purchased by an individual from a private company, or TRICARE or other military health care. Respondents reporting a "yes" to the types listed in parts a, b, or e were considered to have private health insurance. Public health coverage includes the federal programs Medicare, Medicaid, and VA Health Care (provided through the Department of Veterans Affairs); the Children's Health Insurance Program (CHIP); and individual state health plans. Respondents reporting a "yes" to the types listed in c, d, or f were considered to have public coverage. The types of health insurance are not mutually exclusive; people may be covered by more than one at the same time.
The U.S. Department of Health and Human Services, as well as other federal agencies, use data on health insurance coverage to more accurately distribute resources and better understand state and local health insurance needs.
- Insurance through a current or former employer or union (of this person or another family member)
- Insurance purchased directly from an insurance company (by this person or another family member)
- Medicare, for people 65 and older, or people with certain disabilities
- Medicaid, Medical Assistance, or any kind of government-assistance plan for those with low incomes or a disability
- TRICARE or other military health care
- VA (including those who have ever used or enrolled for VA health care)
- Indian Health Service
- Any other type of health insurance or health coverage plan
Health insurance coverage in the ACS and other Census Bureau surveys define coverage to include plans and programs that provide comprehensive health coverage. Plans that provide insurance for specific conditions or situations such as cancer and long-term care policies are not considered coverage. Likewise, other types of insurance like dental, vision, life, and disability insurance are not considered health insurance coverage.
In defining types of coverage, write-in responses were reclassified into one of the first seven types of coverage or determined not to be a coverage type. Write-in responses that referenced the coverage of a family member were edited to assign coverage based on responses from other family members. As a result, only the first seven types of health coverage are included in the microdata file.
An eligibility edit was applied to give Medicaid, Medicare, and TRICARE coverage to individuals based on program eligibility rules. TRICARE or other military health care was given to active-duty military personnel and their spouses and children. Medicaid or other means-tested public coverage was given to foster children, certain individuals receiving Supplementary Security Income or Public Assistance, and the spouses and children of certain Medicaid beneficiaries. Medicare coverage was given to people 65 and older who received Social Security or Medicaid benefits.
People were considered insured if they reported at least one "yes" to Questions 16a to 16f. People who had no reported health coverage, or those whose only health coverage was Indian Health Service, were considered uninsured. For reporting purposes, the Census Bureau broadly classifies health insurance coverage as private health insurance or public coverage. Private health insurance is a plan provided through an employer or union, a plan purchased by an individual from a private company, or TRICARE or other military health care. Respondents reporting a "yes" to the types listed in parts a, b, or e were considered to have private health insurance. Public health coverage includes the federal programs Medicare, Medicaid, and VA Health Care (provided through the Department of Veterans Affairs); the Children's Health Insurance Program (CHIP); and individual state health plans. Respondents reporting a "yes" to the types listed in c, d, or f were considered to have public coverage. The types of health insurance are not mutually exclusive; people may be covered by more than one at the same time.
The U.S. Department of Health and Human Services, as well as other federal agencies, use data on health insurance coverage to more accurately distribute resources and better understand state and local health insurance needs.
The ACS began asking questions about health insurance coverage in 2008. Because 2008 was the first year of collection, the Census Bureau limited the number and type of data products to simple age breakdowns of overall, private, and public coverage status. The evaluation of the 2008 data suggested that the data were of good quality, so the Census Bureau expanded the data products to include estimates of the specific types of coverage along with estimates about social, economic, and demographic details for people with and without health insurance.
For the 2008 data released September 2009, there was no eligibility edit applied. The eligibility edit that was developed for the 2009 was applied to the 2008 data during spring 2010. New estimates of health insurance coverage with this data are available at http://www.census.gov/hhes/www/hlthins/hlthins.html.
For the 2008 data released September 2009, there was no eligibility edit applied. The eligibility edit that was developed for the 2009 was applied to the 2008 data during spring 2010. New estimates of health insurance coverage with this data are available at http://www.census.gov/hhes/www/hlthins/hlthins.html.
The universe for most health insurance coverage estimates is the civilian noninstitutionalized population, which excludes active-duty military personnel and the population living in correctional facilities and nursing homes. Some noninstitutionalized GQ populations have health insurance coverage distributions that are different from the household population (e.g., the prevalence of private health insurance among residents of college dormitories is higher than the household population). The proportion of the universe that is in the noninstitutionalized GQ populations could therefore have a noticeable impact on estimates of the health insurance coverage. Institutionalized GQ populations may also have health insurance coverage distributions that are different from the civilian noninstitutionalized population, the distributions in the published tables may differ slightly from how they would look if the total population were represented.
Health insurance coverage was added to the 2008 ACS and so no equivalent measure is available from previous ACS surveys or Census 2000. Because of the addition of the eligibility edit to 2009 ACS health insurance, data users should be careful as to which 2008 ACS estimates they use to make comparisons. National, state, county and place-level 2008 1-year data incorporating the eligibility edit are available at http://www.census.gov/hhes/www/hlthins/data/acs/2008/re-run.html; they are comparable to the 2009 estimates in American Fact Finder. For more information on the logical coverage (eligibility) edits, please see http://www.census.gov/hhes/www/hlthins/publications/coverage_edits_final .pdf.
Because coverage in the ACS references an individual's current status, caution should be taken when making comparisons to other surveys which may define coverage as "at any time in the last year" or "throughout the past year." A discussion of how the ACS health insurance estimates relate to other survey health insurance estimates can be found in A Preliminary Evaluation of Health Insurance Coverage in the 2008 American Community Survey (http://www.census.gov/hhes/www/hlthins/data/acs/2008/2008ACS_healthins.pdf).
Because coverage in the ACS references an individual's current status, caution should be taken when making comparisons to other surveys which may define coverage as "at any time in the last year" or "throughout the past year." A discussion of how the ACS health insurance estimates relate to other survey health insurance estimates can be found in A Preliminary Evaluation of Health Insurance Coverage in the 2008 American Community Survey (http://www.census.gov/hhes/www/hlthins/data/acs/2008/2008ACS_healthins.pdf).
The data on the Hispanic or Latino population were derived from answers to a question that was asked of all people. The terms "Hispanic," "Latino," and "Spanish" are used interchangeably. Some respondents identify with all three terms while others may identify with only one of these three specific terms. Hispanics or Latinos who identify with the terms "Hispanic," "Latino," or "Spanish" are those who classify themselves in one of the specific Hispanic, Latino, or Spanish categories listed on the questionnaire ("Mexican," "Puerto Rican," or "Cuban") as well as those who indicate that they are "another Hispanic, Latino, or Spanish origin." People who do not identify with one of the specific origins listed on the questionnaire but indicate that they are "another Hispanic, Latino, or Spanish origin" are those whose origins are from Spain, the Spanish-speaking countries of Central or South America, or the Dominican Republic. Up to two write-in responses to the "another Hispanic, Latino, or Spanish origin" category are coded.
Origin can be viewed as the heritage, nationality group, lineage, or country of birth of the person or the person's parents or ancestors before their arrival in the United States. People who identify their origin as Hispanic, Latino, or Spanish may be of any race.
Hispanic origin is used in numerous programs and is vital in making policy decisions. These data are needed to determine compliance with provisions of antidiscrimination in employment and minority recruitment legislation. Under the Voting Rights Act, data about Hispanic origin are essential to ensure enforcement of bilingual election rules. Hispanic origin classifications used by the Census Bureau and other federal agencies meet the requirements of standards issued by the Office of Management and Budget in 1997 (Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity). These standards set forth guidance for statistical collection and reporting on race and ethnicity used by all federal agencies.
Some tabulations are shown by the origin of the householder. In all cases where the origin of households, families, or occupied housing units is classified as Hispanic, Latino, or Spanish, the origin of the householder is used. (For more information, see the discussion of householder under "Household Type and Relationship.")
Origin can be viewed as the heritage, nationality group, lineage, or country of birth of the person or the person's parents or ancestors before their arrival in the United States. People who identify their origin as Hispanic, Latino, or Spanish may be of any race.
Hispanic origin is used in numerous programs and is vital in making policy decisions. These data are needed to determine compliance with provisions of antidiscrimination in employment and minority recruitment legislation. Under the Voting Rights Act, data about Hispanic origin are essential to ensure enforcement of bilingual election rules. Hispanic origin classifications used by the Census Bureau and other federal agencies meet the requirements of standards issued by the Office of Management and Budget in 1997 (Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity). These standards set forth guidance for statistical collection and reporting on race and ethnicity used by all federal agencies.
Some tabulations are shown by the origin of the householder. In all cases where the origin of households, families, or occupied housing units is classified as Hispanic, Latino, or Spanish, the origin of the householder is used. (For more information, see the discussion of householder under "Household Type and Relationship.")
There were two types of coding operations: (1) automated coding where a write-in response was automatically coded if it matched a write-in response already contained in a database known as the "master file," and (2) expert coding, which took place when a write-in response did not match an entry already on the master file, and was sent to expert coders familiar with the subject matter. During the coding process, subject-matter specialists reviewed and coded written entries from the "Yes, another Hispanic, Latino or Spanish origin" write-in response category on the Hispanic origin question.
If an individual did not provide a Hispanic origin response, their origin was allocated using specific rules of precedence of household relationship. For example, if origin was missing for a natural-born child in the household, then either the origin of the householder, another natural-born child, or spouse of the householder was allocated. If Hispanic origin was not reported for anyone in the household and origin could not be obtained from a response to the race question, then the Hispanic origin of a householder in a previously processed household with the same race was allocated. Surnames (Spanish and Non-Spanish) were used to assist in allocating an origin or race.
Beginning in 1996, the American Community Survey question was worded "Is this person Spanish/Hispanic/Latino?" In 2008, the question wording changed to "Is this person of Hispanic, Latino, or Spanish origin?" From 1999 to 2007, the Hispanic origin question provided an instruction, "Mark (X) the "No" box if not Spanish/Hispanic/Latino." The 2008 question, as well as the 1996 to 1998 questions, did not have this instruction. In addition, in 2008, the "Yes, another Hispanic, Latino, or Spanish" category provided examples of six Hispanic origin groups (Argentinean, Colombian, Dominican, Nicaraguan, Salvadoran, Spaniard, and so on).
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have Hispanic or Latino origin distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the Hispanic or Latino origin distribution. This is particularly true for areas with a substantial GQ population.
The ACS question on Hispanic origin was revised in 2008 to make it consistent with the Census 2010 Hispanic origin question. The reporting of specific Hispanic groups (e.g., Colombian, Dominican, Spaniard, etc.) increased at the national level. The change in estimates for 2011 may be due to demographic changes, as well as factors including questionnaire changes, differences in ACS population controls, and methodological differences in the population estimates. Caution should be used when comparing 2011 estimates to estimates from previous years. The 2011 Hispanic origin question is different from the Census 2000 question on Hispanic origin, therefore comparisons should be made with caution. More information about the changes in the estimates is available at http://www.census.gov/population/hispanic/files/acs08researchnote.pdf.
See the 2011 Code List for Hispanic Origin Code List.
See the 2011 Code List for Hispanic Origin Code List.
The data on relationship to householder were derived from answers to Question 2, relationship to the householder, which was asked of all people in housing units. The question on relationship is essential for classifying the population info families and other groups. Information about changes in the composition of the American family, from the number of people living alone to the number of children living with only one parent, is essential for planning and carrying out a number of federal programs, such as families in poverty.
The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multigenerational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status.
The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multigenerational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status.
A household includes all the people who occupy a housing unit. (People not living in households are classified as living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and which have direct access from the outside of the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living arrangements.
A measure obtained by dividing the number of people in households by the number of households. In cases where people in households are cross- classified by race or Hispanic origin, people in the household are classified by the race or Hispanic origin of the householder rather than the race or Hispanic origin of each individual. Average household size is rounded to the nearest hundredth.
One person in each household is designated as the householder. In most cases, this is the person, or one of the people, in whose name the home is owned, being bought, or rented and who is listed on line one of the survey questionnaire. If there is no such person in the household, any adult household member 15 years old and over could be designated as the householder.
Households are classified by type according to the sex of the householder and the presence of relatives. Two types of householders are distinguished: a family householder and a non- family householder. A family householder is a householder living with one or more individuals related to him or her by birth, marriage, or adoption. The householder and all people in the household related to him or her are family members. A nonfamily householder is a householder living alone or with non-relatives only.
Households are classified by type according to the sex of the householder and the presence of relatives. Two types of householders are distinguished: a family householder and a non- family householder. A family householder is a householder living with one or more individuals related to him or her by birth, marriage, or adoption. The householder and all people in the household related to him or her are family members. A nonfamily householder is a householder living alone or with non-relatives only.
Includes a person married to and living with a householder who is of the opposite sex of the householder. The category "husband or wife" includes people in formal marriages, as well as people in common-law marriages. In tabulations, unless otherwise specified, "Spouse" does not include same-sex married couples even if the marriage was performed in a state issuing marriage certificates for same-sex couples.
Includes a son or daughter by birth, a stepchild, or adopted child of the householder, regardless of the child's age or marital status. The category excludes sons-in-law, daughters- in-law, and foster children.
- Biological son or daughter - The son or daughter of the householder by birth.
- Adopted son or daughter - The son or daughter of the householder by legal adoption. If a stepson or stepdaughter has been legally adopted by the householder, the child is then classified as an adopted child.
- Stepson or stepdaughter - The son or daughter of the householder through marriage but not by birth, excluding sons-in-law and daughters-in-law. If a stepson or stepdaughter of the householder has been legally adopted by the householder, the child is then classified as an adopted child.
A never-married child under 18 years who is a son or daughter by birth, a stepchild, or an adopted child of the householder. In certain tabulations, own children are further classified as living with two parents or with one parent only. Own children of the householder living with two parents are by definition found only in married-couple families. (Note: When used in "EMPLOYMENT STATUS" tabulations, own child refers to a never married child under the age of 18 in a family or a subfamily who is a son or daughter, by birth, marriage, or adoption, of a member of the householder's family, but not necessarily of the householder.)
Any child under 18 years old who is related to the householder by birth, marriage, or adoption. Related children of the householder include ever-married as well as never-married children. Children, by definition, exclude persons under 18 years who maintain households or are spouses or unmarried partners of householders.
In tabulations, the category "other relatives" includes any household member related to the householder by birth, marriage, or adoption, but not included specifically in another relationship category. In certain detailed tabulations, the following categories may be shown:
- Grandchild - The grandson or granddaughter of the householder.
- Brother/Sister - The brother or sister of the householder, including stepbrothers, stepsisters, and brothers and sisters by adoption. Brothers-in-law and sisters-in-law are included in the "Other Relative" category on the questionnaire.
- Parent - The father or mother of the householder, including a stepparent or adoptive parent. Fathers-in-law and mothers-in-law are included in the "Parent-in-law" category on the questionnaire.
- Parent-in-law - The mother-in-law or father-in-law of the householder.
- Son-in-law or daughter-in-law - The spouse of the child of the householder.
- Other Relatives - Anyone not listed in a reported category above who is related to the householder by birth, marriage, or adoption (brother-in-law, grandparent, nephew, aunt, cousin, and so forth).
This category includes any household member, including foster children, not related to the householder by birth, marriage, or adoption. The following categories may be presented in more detailed tabulations:
- Roomer or Boarder - A roomer or boarder is a person who lives in a room in the household of the householder. Some sort of cash or noncash payment (e.g., chores) is usually made for their living accommodations.
- Housemate or Roommate - A housemate or roommate is a person age 15 years and over, who is not related to the householder, and who shares living quarters primarily in order to share expenses.
- Unmarried Partner - An unmarried partner is a person age 15 years and over, who is not related to the householder, who shares living quarters, and who has a close personal relationship with the householder. Same-sex spouses are included in this category for tabulation purposes and for public use data files.
- Foster Child - A foster child is a person who is under 21 years old placed by the local government in a household to receive parental care. Foster children may be living in the household for just a brief period or for several years. Foster children are nonrelatives of the householder. If the foster child is also related to the householder, the child is classified as that specific relative.
- Other Nonrelatives - Anyone who is not related by birth, marriage, or adoption to the householder and who is not described by the categories given above.
n unrelated individual is: (1) a householder living alone or with nonrelatives only, (2) a household member who is not related to the householder, or (3) a person living in group quarters who is not an inmate of an institution.
A family consists of a householder and one or more other people living in the same household who are related to the householder by birth, marriage, or adoption. All people in a household who are related to the householder are regarded as members of his or her family. A family household may contain people not related to the householder, but those people are not included as part of the householder's family in tabulations. Thus, the number of family households is equal to the number of families, but family households may include more members than do families. A household can contain only one family for purposes of tabulations. Not all households contain families since a household may be comprised of a group of unrelated people or of one person living alone - these are called nonfamily households. Families are classified by type as either a "married- couple family" or "other family" according to the sex of the householder and the presence of relatives. The data on family type are based on answers to questions on sex and relationship that were asked of all people.
- Female Householder, No Husband Present - A family with a female householder and no spouse of householder present.
Family households and married-couple families do not include same-sex married couples even if the marriage was performed in a state issuing marriage certificates for same-sex couples. Same sex couple households are included in the family households category if there is at least one additional person related to the householder by birth or adoption.
- Married-Couple Family - A family in which the householder and his or her spouse are listed as members of the same household.
- Other Family:
- Female Householder, No Husband Present - A family with a female householder and no spouse of householder present.
Family households and married-couple families do not include same-sex married couples even if the marriage was performed in a state issuing marriage certificates for same-sex couples. Same sex couple households are included in the family households category if there is at least one additional person related to the householder by birth or adoption.
A measure obtained by dividing the number of people in families by the total number of families (or family householders). In cases where the measures, "people in family" or "people per family" are cross-tabulated by race or Hispanic origin, the race or Hispanic origin refers to the householder rather than the race or Hispanic origin of each individual. Average family size is rounded to the nearest hundredth.
A subfamily is a married couple (husband and wife interviewed as members of the same household) with or without never-married children under 18 years old, or one parent with one or more never-married children under 18 years old. A subfamily does not maintain its own household, but lives in a household where the householder or householder's spouse is a relative. The number of subfamilies is not included in the count of families, since subfamily members are counted as part of the householder's family. Subfamilies are defined during processing of data.
In selected tabulations, subfamilies are further classified by type: married-couple subfamilies, with or without own children; mother-child subfamilies; and father-child subfamilies.
In some labor force tabulations, children in both one-parent families and one-parent subfamilies are included in the total number of children living with one parent, while children in both married-couple families and married-couple subfamilies are included in the total number of children living with two parents.
In selected tabulations, subfamilies are further classified by type: married-couple subfamilies, with or without own children; mother-child subfamilies; and father-child subfamilies.
In some labor force tabulations, children in both one-parent families and one-parent subfamilies are included in the total number of children living with one parent, while children in both married-couple families and married-couple subfamilies are included in the total number of children living with two parents.
A householder living alone or with nonrelatives only. Same-sex couple households with no relatives of the householder present are tabulated in nonfamily households.
An unmarried-partner household is a household other than a "married-couple household" that includes a householder and an "unmarried partner." An "unmarried partner" can be of the same sex or of the opposite sex as the householder. An "unmarried partner" in an "unmarried-partner household" is an adult who is unrelated to the householder, but shares living quarters and has a close personal relationship with the householder. An unmarried-partner household also may be a family household or a nonfamily household, depending on the presence or absence of another person in the household who is related to the householder. There may be only one unmarried partner per household, and an unmarried partner may not be included in a married-couple household, as the householder cannot have both a spouse and an unmarried partner. Same-sex married couples are included in the count of unmarried-partner households for tabulations purposes and for public use data files.
Between 1996 and 2007, the question response categories remained the same. In 2008, the "Son or daughter" category was expanded to "Biological son or daughter," "Adopted son or daughter," and "Stepson or stepdaughter." Also "In-law" was expanded to "Parent-in-law" and "Son-in-law or daughter-in-law."
Unlike the Current Population Survey (CPS) and the Survey of Income and Program Participation (SIPP), the ACS relationship question does not have a parent pointer to identify whether both parents are present. For example, if a child lives with unmarried parents, we only know the relationship of the child to the householder, not to the other parent. So a count of children living with two biological parents is not precise.
The relationship categories for the most part can be compared to previous ACS years and to similar data collected in the decennial census, CPS, and SIPP. With the change in 2008 from "In-law" to the 2 categories of "Parent-in-law" and "Son- in-law or daughter-in-law", caution should be exercised when comparing data on in-laws from previous years. "In-law" encompassed any type of in-law such as sister-in-law. Combining "Parent-in-law" and "son-in-law or daughter-in-law" does not represent all "in-laws" in 2008. The same can be said of comparing the 3 categories of "biological" "step", and "adopted" child in 2008 to "Child" in previous years. Before 2008, respondents may have considered anyone under 18 as "child" and chosen that category. The ACS includes "foster child" as a category. However, the 2010 census did not contain this category, and "foster children" were included in the "Other nonrelative" category. Therefore, comparison of "foster child" cannot be made to the 2010 census.
The data on income were derived from answers to Questions 47 and 48, which were asked of the population 15 years old and over. "Total income" is the sum of the amounts reported separately for wage or salary income; net self-employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income.
Receipts from the following sources are not included as income: capital gains, money received from the sale of property (unless the recipient was engaged in the business of selling such property); the value of income "in kind" from food stamps, public housing subsidies, medical care, employer contributions for individuals, etc.; withdrawal of bank deposits; money borrowed; tax refunds; exchange of money between relatives living in the same household; gifts and lump-sum inheritances, insurance payments, and other types of lump- sum receipts.
Income is a vital measure of general economic circumstances. Income data are used to determine poverty status, to measure economic well-being, and to assess the need for assistance. These data are included in federal allocation formulas for many government programs. For instance:
Receipts from the following sources are not included as income: capital gains, money received from the sale of property (unless the recipient was engaged in the business of selling such property); the value of income "in kind" from food stamps, public housing subsidies, medical care, employer contributions for individuals, etc.; withdrawal of bank deposits; money borrowed; tax refunds; exchange of money between relatives living in the same household; gifts and lump-sum inheritances, insurance payments, and other types of lump- sum receipts.
Income is a vital measure of general economic circumstances. Income data are used to determine poverty status, to measure economic well-being, and to assess the need for assistance. These data are included in federal allocation formulas for many government programs. For instance:
Under the Older Americans Act, funds for food, health care, and legal services are distributed to local agencies based on data about elderly people with low incomes. Data about income at the state and county levels are used to allocate funds for food, health care, and classes in meal planning to low-income women with children.
Income data are used to identify local areas eligible for grants to stimulate economic recovery, run job-training programs, and define areas such as empowerment or enterprise zones.
Under the Low-Income Home Energy Assistance Program, income data are used to allocate funds to areas for home energy aid. Under the Community Development Block Grant Program, funding for housing assistance and other community development is based on income and other census data.
Data about poor children are used to allocate funds to counties and school districts. These funds provide resources and services to improve the education of economically disadvantaged children.
In household surveys, respondents tend to underreport income. Asking the list of specific sources of income helps respondents remember all income amounts that have been received, and asking total income increases the overall response rate and thus, the accuracy of the answers to the income questions. The eight specific sources of income also provide needed detail about items such as earnings, retirement income, and public assistance.
In household surveys, respondents tend to underreport income. Asking the list of specific sources of income helps respondents remember all income amounts that have been received, and asking total income increases the overall response rate and thus, the accuracy of the answers to the income questions. The eight specific sources of income also provide needed detail about items such as earnings, retirement income, and public assistance.
The eight types of income reported in the American Community Survey are defined as follows:
Wage or salary income includes total money earnings received for work performed as an employee during the past 12 months. It includes wages, salary, Armed Forces pay, commissions, tips, piece-rate payments, and cash bonuses earned before deductions were made for taxes, bonds, pensions, union dues, etc.
Self-employment income includes both farm and non-farm self-employment income.
Farm self-employment income includes net money income (gross receipts minus operating expenses) from the operation of a farm by a person on his or her own account, as an owner, renter, or sharecropper. Gross receipts include the value of all products sold, government farm programs, money received from the rental of farm equipment to others, and incidental receipts from the sale of wood, sand, gravel, etc. Operating expenses include cost of feed, fertilizer, seed, and other farming supplies, cash wages paid to farmhands, depreciation charges, rent, interest on farm mortgages, farm building repairs, farm taxes (not state and federal personal income taxes), etc. The value of fuel, food, or other farm products used for family living is not included as part of net income.
Non-farm self-employment income includes net money income (gross receipts minus expenses) from one's own business, professional enterprise, or partnership. Gross receipts include the value of all goods sold and services rendered. Expenses include costs of goods purchased, rent, heat, light, power, depreciation charges, wages and salaries paid, business taxes (not personal income taxes), etc.
Interest, dividends, or net rental income includes interest on savings or bonds, dividends from stockholdings or membership in associations, net income from rental of property to others and receipts from boarders or lodgers, net royalties, and periodic payments from an estate or trust fund.
Social Security income includes Social Security pensions and survivor benefits, permanent disability insurance payments made by the Social Security Administration prior to deductions for medical insurance, and railroad retirement insurance checks from the U.S. government. Medicare reimbursements are not included.
Supplemental Security Income (SSI) is a nationwide U.S. assistance program administered by the Social Security Administration that guarantees a minimum level of income for needy aged, blind, or disabled individuals. The Puerto Rico Community Survey questionnaire asks about the receipt of SSI; however, SSI is not a federally-administered program in Puerto Rico. Therefore, it is probably not being interpreted by most respondents in the same manner as SSI in the United States. The only way a resident of Puerto Rico could have appropriately reported SSI would have been if they lived in the United States at any time during the past 12-month reference period and received SSI.
Public assistance income includes general assistance and Temporary Assistance to Needy Families (TANF). Separate payments received for hospital or other medical care (vendor payments) are excluded. This does not include Supplemental Security Income (SSI) or noncash benefits such as Food Stamps. The terms "public assistance income" and "cash public assistance" are used interchangeably in the 2011 ACS data products.
Retirement income includes: (1) retirement pensions and survivor benefits from a former employer; labor union; or federal, state, or local government; and the U.S. military; (2) disability income from companies or unions; federal, state, or local government; and the U.S. military; (3) periodic receipts from annuities and insurance; and (4) regular income from IRA and Keogh plans. This does not include Social Security income.
All other income includes unemployment compensation, worker's compensation, Department of Veterans Affairs (VA) payments, alimony and child support, contributions received periodically from people not living in the household, military family allotments, and other kinds of periodic income other than earnings.
This includes the income of the householder and all other individuals 15 years old and over in the household, whether they are related to the householder or not. Because many households consist of only one person, average household income is usually less than average family income. Although the household income statistics cover the past 12 months, the characteristics of individuals and the composition of households refer to the time of interview. Thus, the income of the household does not include amounts received by individuals who were members of the household during all or part of the past 12 months if these individuals no longer resided in the household at the time of interview. Similarly, income amounts reported by individuals who did not reside in the household during the past 12 months but who were members of the household at the time of interview are included. However, the composition of most households was the same during the past 12 months as at the time of interview.
In compiling statistics on family income, the incomes of all members 15 years old and over related to the householder are summed and treated as a single amount. Although the family income statistics cover the past 12 months, the characteristics of individuals and the composition of families refer to the time of interview. Thus, the income of the family does not include amounts received by individuals who were members of the family during all or part of the past 12 months if these individuals no longer resided with the family at the time of interview. Similarly, income amounts reported by individuals who did not reside with the family during the past 12 months but who were members of the family at the time of interview are included. However, the composition of most families was the same during the past 12 months as at the time of interview.
Income for individuals is obtained by summing the eight types of income for each person 15 years old and over. The characteristics of individuals are based on the time of interview even though the amounts are for the past 12 months.
The median divides the income distribution into two equal parts: one-half of the cases falling below the median income and one-half above the median. For households and families, the median income is based on the distribution of the total number of households and families including those with no income. The median income for individuals is based on individuals 15 years old and over with income. Median income for households, families, and individuals is computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Derived Measures.") Median income is rounded to the nearest whole dollar. Median income figures are calculated using linear interpolation. (For more information on medians and interpolation, see "Derived Measures.")
Aggregate income is the sum of all incomes for a particular universe. Aggregate income is subject to rounding, which means that all cells in a matrix are rounded to the nearest hundred dollars. (For more information, see "Aggregate" under "Derived Measures.")
Mean income is the amount obtained by dividing the aggregate income of a particular statistical universe by the number of units in that universe. For example, mean household income is obtained by dividing total household income by the total number of households. (The aggregate used to calculate mean income is rounded. For more information, see "Aggregate income.")
For the various types of income, the means are based on households having those types of income. For household income and family income, the mean is based on the distribution of the total number of households and families including those with no income. The mean income for individuals is based on individuals 15 years old and over with income. Mean income is rounded to the nearest whole dollar.
Care should be exercised in using and interpreting mean income values for small subgroups of the population. Because the mean is influenced strongly by extreme values in the distribution, it is especially susceptible to the effects of sampling variability, misreporting, and processing errors. The median, which is not affected by extreme values, is, therefore, a better measure than the mean when the population base is small. The mean, nevertheless, is shown in some data products for most small subgroups because, when weighted according to the number of cases, the means can be computed for areas and groups other than those shown in Census Bureau tabulations. (For more information on means, see "Derived Measures.")
For the various types of income, the means are based on households having those types of income. For household income and family income, the mean is based on the distribution of the total number of households and families including those with no income. The mean income for individuals is based on individuals 15 years old and over with income. Mean income is rounded to the nearest whole dollar.
Care should be exercised in using and interpreting mean income values for small subgroups of the population. Because the mean is influenced strongly by extreme values in the distribution, it is especially susceptible to the effects of sampling variability, misreporting, and processing errors. The median, which is not affected by extreme values, is, therefore, a better measure than the mean when the population base is small. The mean, nevertheless, is shown in some data products for most small subgroups because, when weighted according to the number of cases, the means can be computed for areas and groups other than those shown in Census Bureau tabulations. (For more information on means, see "Derived Measures.")
Negative incomes are converted to zero for these measures. These measures are the quintile cutoffs, along with the 95th percentile of the distribution. (For more information on quintiles, see "Derived Measures.")
Means of household income by quintiles are calculated by dividing aggregate household income in each quintile by the number of households in each quintile (one-fifth of the total number of households). (For more information on aggregates, see "Aggregate Income." For more information on quintiles, see "Derived Measures.")
Negative incomes are converted to zero for these measures. These measures are the aggregate household income in each quintile as a percentage of the total aggregate household income. (For more information on aggregates, see "Aggregate income." For more information on quintiles, see "Derived Measures.")
Negative incomes are converted to zero. The Gini index of income inequality measures the dispersion of the household income distribution. (For more information on the Gini index, see "Derived Measures.")
Earnings are defined as the sum of wage or salary income and net income from self-employment. "Earnings" represent the amount of income received regularly for people 16 years old and over before deductions for personal income taxes, Social Security, bond purchases, union dues, Medicare deductions, etc. An individual with earnings is one who has either wage/salary income or self-employment income, or both. Respondents who "break even" in self-employment income and therefore have zero self-employment earnings also are considered "individuals with earnings."
The median divides the earnings distribution into two equal parts: one- half of the cases falling below the median and one-half above the median. Median earnings is restricted to individuals 16 years old and over with earnings and is computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Derived Measures.") Median earnings figures are calculated using linear interpolation. (For more information on medians and interpolation, see "Derived Measures.")
Aggregate earnings are the sum of wage/salary and net self- employment income for a particular universe of people 16 years old and over. Aggregate earnings are rounded to the nearest hundred dollars. (For more information, see "Aggregate" under "Derived Measures.")
Mean earnings is calculated by dividing aggregate earnings by the population 16 years old and over with earnings. (The aggregate used to calculate mean earnings is rounded. For more information, see ''Aggregate earnings.'') Mean earnings is rounded to the nearest whole dollar. (For more information on means, see "Derived Measures.")
Women's earnings as a percentage of men's earnings is defined as median earnings for females who worked fulltime, year-round divided by median earnings for males who worked full-time, year-round, multiplied by 100. (For more information see "full-time, year-round workers" under "Usual hours worked per weeks worked in the past 12 months" and "Median earnings.")
Per capita income is the mean income computed for every man, woman, and child in a particular group including those living in group quarters. It is derived by dividing the aggregate income of a particular group by the total population in that group. (The aggregate used to calculate per capita income is rounded. For more information, see "Aggregate" under "Derived Measures.") Per capita income is rounded to the nearest whole dollar. (For more information on means, see "Derived Measures.")
Income components were reported for the 12 months preceding the interview month. Monthly Consumer Price Indices (CPI) factors were used to inflation-adjust these components to a reference calendar year (January through December). For example, a household interviewed in March 2011 reports their income for March 2010 through February 2011. Their income is adjusted to the 2011 reference calendar year by multiplying their reported income by 2011 average annual CPI (January-December 2011) and then dividing by the average CPI for March 2010-February2011.
In order to inflate income amounts from previous years, the dollar values on individual records are inflated to the latest year's dollar values by multiplying by a factor equal to the average annual CPI-U-RS factor for the current year, divided by the average annual CPI-U- RS factor for the earlier/earliest year.
In order to inflate income amounts from previous years, the dollar values on individual records are inflated to the latest year's dollar values by multiplying by a factor equal to the average annual CPI-U-RS factor for the current year, divided by the average annual CPI-U- RS factor for the earlier/earliest year.
The 1998 ACS questionnaire deleted references to Aid to Families with Dependent Children (AFDC) because of welfare law reforms.
In 1999, the ACS questions were changed to be consistent with the questions for the Census 2000. The instructions are slightly different to reflect differences in the reference periods. The ACS asks about the past 12 months, and the questions for the decennial census ask about the previous calendar year.
In 1999, the ACS questions were changed to be consistent with the questions for the Census 2000. The instructions are slightly different to reflect differences in the reference periods. The ACS asks about the past 12 months, and the questions for the decennial census ask about the previous calendar year.
Since answers to income questions are frequently based on memory and not on records, many people tend to forget minor or sporadic sources of income and, therefore, underreport their income. Underreporting tends to be more pronounced for income sources that are not derived from earnings, such as public assistance, interest, dividends, and net rental income.
Extensive computer editing procedures were instituted in the data processing operation to reduce some of these reporting errors and to improve the accuracy of the income data. These procedures corrected various reporting deficiencies and improved the consistency of reported income questions associated with work experience and information on occupation and class of worker. For example, if people reported they were self employed on their own farm, not incorporated, but had reported only wage and salary earnings, the latter amount was shifted to self-employment income. Also, if any respondent reported total income only, the amount was generally assigned to one of the types of income questions according to responses to the work experience and class-of-worker questions. Another type of problem involved non- reporting of income data. Where income information was not reported, procedures were devised to impute appropriate values with either no income or positive or negative dollar amounts for the missing entries. (For more information on imputation, see "Accuracy of the Data" on the ACS website www.census.gov/acs.)
In income tabulations for households and families, the lowest income group (for example, less than $10,000) includes units that were classified as having no income in the past 12 months. Many of these were living on income "in kind," savings, or gifts, were newly created families, or were families in which the sole breadwinner had recently died or left the household. However, many of the households and families who reported no income probably had some money income that was not reported in the American Community Survey.
Users should exercise caution when comparing income and earnings estimates for individuals since the 2006 ACS to earlier years because of the introduction of group quarters. Household and family income estimates are not affected by the inclusion of group quarters.
Users should exercise caution when comparing medians from the 2011 ACS to earlier years. There was a change between 2008 and 2009 1-Year and 3-Year Data Products in Income and Earnings median calculations. Medians above $75,000 were most likely to be affected.
Extensive computer editing procedures were instituted in the data processing operation to reduce some of these reporting errors and to improve the accuracy of the income data. These procedures corrected various reporting deficiencies and improved the consistency of reported income questions associated with work experience and information on occupation and class of worker. For example, if people reported they were self employed on their own farm, not incorporated, but had reported only wage and salary earnings, the latter amount was shifted to self-employment income. Also, if any respondent reported total income only, the amount was generally assigned to one of the types of income questions according to responses to the work experience and class-of-worker questions. Another type of problem involved non- reporting of income data. Where income information was not reported, procedures were devised to impute appropriate values with either no income or positive or negative dollar amounts for the missing entries. (For more information on imputation, see "Accuracy of the Data" on the ACS website www.census.gov/acs.)
In income tabulations for households and families, the lowest income group (for example, less than $10,000) includes units that were classified as having no income in the past 12 months. Many of these were living on income "in kind," savings, or gifts, were newly created families, or were families in which the sole breadwinner had recently died or left the household. However, many of the households and families who reported no income probably had some money income that was not reported in the American Community Survey.
Users should exercise caution when comparing income and earnings estimates for individuals since the 2006 ACS to earlier years because of the introduction of group quarters. Household and family income estimates are not affected by the inclusion of group quarters.
Users should exercise caution when comparing medians from the 2011 ACS to earlier years. There was a change between 2008 and 2009 1-Year and 3-Year Data Products in Income and Earnings median calculations. Medians above $75,000 were most likely to be affected.
The income data shown in ACS tabulations are not directly comparable with those that may be obtained from statistical summaries of income tax returns. Income, as defined for federal tax purposes, differs somewhat from the Census Bureau concept. Moreover, the coverage of income tax statistics is different because of the exemptions for people having small amounts of income and the inclusion of net capital gains in tax returns. Furthermore, members of some families file separate returns and others file joint returns; consequently, the tax reporting unit is not consistent with the census household, family, or person units.
The earnings data shown in ACS tabulations are not directly comparable with earnings records of the Social Security Administration (SSA). The earnings record data for SSA excludes the earnings of some civilian government employees, some employees of nonprofit organizations, workers covered by the Railroad Retirement Act, and people not covered by the program because of insufficient earnings. Because ACS data are obtained from household questionnaires, they may differ from SSA earnings record data, which are based upon employers' reports and the federal income tax returns of self-employed people.
The Commerce Department's Bureau of Economic Analysis (BEA) publishes annual data on aggregate and per-capita personal income received by the population for states, metropolitan areas, and selected counties. Aggregate income estimates based on the income statistics shown in ACS products usually would be less than those shown in the BEA income series for several reasons. The ACS data are obtained from a household survey, whereas the BEA income series is estimated largely on the basis of data from administrative records of business and governmental sources. Moreover, the definitions of income are different. The BEA income series includes some questions not included in the income data shown in ACS publications, such as income "in kind," income received by nonprofit institutions, the value of services of banks and other financial intermediaries rendered to people without the assessment of specific charges, and Medicare payments. On the other hand, the ACS income data include contributions for support received from people not residing in the same household if the income is received on a regular basis.
In comparing income for the most recent year with income from earlier years, users should note that an increase or decrease in money income does not necessarily represent a comparable change in real income, unless adjusted for inflation.
The earnings data shown in ACS tabulations are not directly comparable with earnings records of the Social Security Administration (SSA). The earnings record data for SSA excludes the earnings of some civilian government employees, some employees of nonprofit organizations, workers covered by the Railroad Retirement Act, and people not covered by the program because of insufficient earnings. Because ACS data are obtained from household questionnaires, they may differ from SSA earnings record data, which are based upon employers' reports and the federal income tax returns of self-employed people.
The Commerce Department's Bureau of Economic Analysis (BEA) publishes annual data on aggregate and per-capita personal income received by the population for states, metropolitan areas, and selected counties. Aggregate income estimates based on the income statistics shown in ACS products usually would be less than those shown in the BEA income series for several reasons. The ACS data are obtained from a household survey, whereas the BEA income series is estimated largely on the basis of data from administrative records of business and governmental sources. Moreover, the definitions of income are different. The BEA income series includes some questions not included in the income data shown in ACS publications, such as income "in kind," income received by nonprofit institutions, the value of services of banks and other financial intermediaries rendered to people without the assessment of specific charges, and Medicare payments. On the other hand, the ACS income data include contributions for support received from people not residing in the same household if the income is received on a regular basis.
In comparing income for the most recent year with income from earlier years, users should note that an increase or decrease in money income does not necessarily represent a comparable change in real income, unless adjusted for inflation.
Industry data describe the kind of business conducted by a person's employing organization. Industry data were derived from answers to questions 42 through 44. Question 42 asks: "For whom did this person work?" Question 43 asks: "What kind of business or industry was this?" Question 44 provides 4 check boxes from which respondents are to select one to indicate whether the business was primarily manufacturing, wholesale trade, retail trade, or other (agriculture, construction, service, government, etc.).
These questions were asked of all people 15 years old and over who had worked in the past 5 years. For employed people, the data refer to the person's job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people and people who are not currently employed but report having a job within the last five years, the data refer to their last job.
These questions were asked of all people 15 years old and over who had worked in the past 5 years. For employed people, the data refer to the person's job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people and people who are not currently employed but report having a job within the last five years, the data refer to their last job.
Written responses to the industry questions are coded using the industry classification system developed for Census 2000 and modified in 2002 and again in 2007. This system consists of 269 categories for employed people, including military, classified into 20 sectors. The modified 2007 census industry classification was developed from the 2007 North American Industry Classification System (NAICS) published by the Executive Office of the President, Office of Management and Budget. The NAICS was developed to increase comparability in industry definitions between the United States, Mexico, and Canada. It provides industry classifications that group establishments into industries based on the activities in which they are primarily engaged. The NAICS was created for establishment designations and provides detail about the smallest operating establishment, while the American Community Survey data are collected from households and differ in detail and nature from those obtained from establishment surveys. Because of potential disclosure issues, the census industry classification system, while defined in NAICS terms, cannot reflect the full detail for all categories that the NAICS provides.
Respondents provided the data for the tabulations by writing on the questionnaires descriptions of their kind of business or industry. Clerical staff in the National Processing Center in Jeffersonville, Indiana converted the written questionnaire descriptions to codes by comparing these descriptions to entries in the Alphabetical Index of Industries and Occupations.
The industry category, "Public administration," is limited to regular government functions such as legislative, judicial, administrative, and regulatory activities. Other government organizations such as public schools, public hospitals, and bus lines are classified by industry according to the activity in which they are engaged.
Some occupation groups are related closely to certain industries. Operators of transportation equipment, farm operators and workers, and healthcare providers account for major portions of their respective industries of transportation, agriculture, and health care. However, the industry categories include people in other occupations. For example, people employed in agriculture include truck drivers and bookkeepers; people employed in the transportation industry include mechanics, freight handlers, and payroll clerks; and people employed in the health care industry include janitors, security guards, and secretaries.
Respondents provided the data for the tabulations by writing on the questionnaires descriptions of their kind of business or industry. Clerical staff in the National Processing Center in Jeffersonville, Indiana converted the written questionnaire descriptions to codes by comparing these descriptions to entries in the Alphabetical Index of Industries and Occupations.
The industry category, "Public administration," is limited to regular government functions such as legislative, judicial, administrative, and regulatory activities. Other government organizations such as public schools, public hospitals, and bus lines are classified by industry according to the activity in which they are engaged.
Some occupation groups are related closely to certain industries. Operators of transportation equipment, farm operators and workers, and healthcare providers account for major portions of their respective industries of transportation, agriculture, and health care. However, the industry categories include people in other occupations. For example, people employed in agriculture include truck drivers and bookkeepers; people employed in the transportation industry include mechanics, freight handlers, and payroll clerks; and people employed in the health care industry include janitors, security guards, and secretaries.
Following the coding operation, a computer edit and allocation process excludes all responses that should not be included in the universe, and evaluates the consistency of the remaining responses. The codes for industry are checked for consistency with the occupation and class of worker data provided for that respondent. Occasionally respondents supply industry descriptions that are not sufficiently specific for precise classification, or they do not report on these questions at all. Certain types of incomplete entries are corrected using the Alphabetical Index of Industries and Occupations. If one or more of the three codes (industry, occupation, or class of worker) is blank after the edit, a code is assigned from a donor respondent who is a "similar" person based on questions such as age, sex, educational attainment, income, employment status, and weeks worked. If all of the labor force and income data are blank, all of these economic questions are assigned from a "similar" person who had provided all the necessary data.
These questions describe the industrial composition of the American labor force. Data are used to formulate policy and programs for employment, career development and training, and to measure compliance with antidiscrimination policies. Companies use these data to decide where to locate new plants, stores, or offices.
These questions describe the industrial composition of the American labor force. Data are used to formulate policy and programs for employment, career development and training, and to measure compliance with antidiscrimination policies. Companies use these data to decide where to locate new plants, stores, or offices.
Industry data have been collected during decennial censuses intermittently since 1820 and on a continuous basis since 1910. Starting with the 2010 Census, industry data will no longer be collected during the decennial census. Long form data collection has transitioned to the American Community Survey. The American Community Survey began collecting data on industry in 1996. The questions on industry were designed to be consistent with the 1990 Census questions on industry. In the 1990 Census and starting with the 1999 ACS, a check box was added to the employer name questionnaire item that was to be marked by anyone "now on active duty in the Armed Forces..." This information is used by the industry and occupation coders to assist in assigning proper industry codes for active duty military. Prior to 1999, the 1996-1998 ACS class of worker question had an additional response category for "Active duty U.S. Armed Forces member." Other than this exception, American Community Survey questions on industry have remained consistent between 1996 and 2011.
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. Some types of GQ populations have industry distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the industry distribution in some geographic areas with a substantial GQ population.
Data on occupation, industry, and class of worker are collected for the respondent's current primary job or the most recent job for those who are not employed but have worked in the last 5 years. Other labor force questions, such as questions on earnings or work hours, may have different reference periods and may not limit the response to the primary job. Although the prevalence of multiple jobs is low, data on some labor force items may not exactly correspond to the reported occupation, industry, or class of worker of a respondent.
Data on occupation, industry, and class of worker are collected for the respondent's current primary job or the most recent job for those who are not employed but have worked in the last 5 years. Other labor force questions, such as questions on earnings or work hours, may have different reference periods and may not limit the response to the primary job. Although the prevalence of multiple jobs is low, data on some labor force items may not exactly correspond to the reported occupation, industry, or class of worker of a respondent.
Comparability of industry data was affected by a number of factors, primarily the system used to classify the questionnaire responses. Changes in the industry classification system limit comparability of the data from one year to another. These changes are needed to recognize the "birth" of new industries, the "death" of others, the growth and decline in existing industries, and the desire of analysts and other users for more detail in the presentation of the data. Probably the greatest cause of noncomparability is the movement of a segment from one category to another. Changes in the nature of jobs, respondent terminology, and refinement of category composition made these movements necessary.
ACS data from 1996 to 1999 used the same industry classification systems used for the 1990 census; therefore, the data are comparable. Since 1990, the industry classification has had major revisions to reflect the shift from the Standard Industrial Classification (SIC) to the North American Industry Classification System (NAICS). These changes were reflected in the Census 2000 industry codes. The 2000-2002 ACS data used the same industry and occupation classification systems used for the 2000 census, therefore, the data are comparable. In 2002, NAICS underwent another change and the industry codes were changed accordingly. Because of the possibility of new industries being added to the list of codes, the Census Bureau needed to have more flexibility in adding codes. Consequently, in 2002, industry census codes were expanded from three-digit codes to four-digit codes. The changes to these code classifications mean that the ACS data from 2003-2011 are not completely comparable to the data from earlier surveys. In 2007, NAICS was updated again. This resulted in a minor change in the industry data that will cause it to not be completely comparable to previous years. The changes were concentrated in the Information Sector where one census code was added (6672) and two were deleted (6675, 6692). For more information on industry comparability across classification systems, please see technical paper #65: The Relationship Between the 1990 Census and Census 2000 Industry and Occupation Classification Systems.
See the 2011 Code List for Industry Code List.
See also, Occupation and Class of Worker.
ACS data from 1996 to 1999 used the same industry classification systems used for the 1990 census; therefore, the data are comparable. Since 1990, the industry classification has had major revisions to reflect the shift from the Standard Industrial Classification (SIC) to the North American Industry Classification System (NAICS). These changes were reflected in the Census 2000 industry codes. The 2000-2002 ACS data used the same industry and occupation classification systems used for the 2000 census, therefore, the data are comparable. In 2002, NAICS underwent another change and the industry codes were changed accordingly. Because of the possibility of new industries being added to the list of codes, the Census Bureau needed to have more flexibility in adding codes. Consequently, in 2002, industry census codes were expanded from three-digit codes to four-digit codes. The changes to these code classifications mean that the ACS data from 2003-2011 are not completely comparable to the data from earlier surveys. In 2007, NAICS was updated again. This resulted in a minor change in the industry data that will cause it to not be completely comparable to previous years. The changes were concentrated in the Information Sector where one census code was added (6672) and two were deleted (6675, 6692). For more information on industry comparability across classification systems, please see technical paper #65: The Relationship Between the 1990 Census and Census 2000 Industry and Occupation Classification Systems.
See the 2011 Code List for Industry Code List.
See also, Occupation and Class of Worker.
The data on place of work were derived from answers to Question 30, which was asked of people who indicated in Question 29 that they worked at some time during the reference week. (See "Reference Week.")
Data were tabulated for workers 16 years old and over, that is, members of the Armed Forces and civilians who were at work during the reference week. Data on place of work refer to the geographic location at which workers carried out their occupational activities during the reference week. In the American Community Survey, the exact address (number and street name) of the place of work was asked, as well as the place (city, town, or post office); whether the place of work was inside or outside the limits of that city or town; and the county, state or foreign country, and ZIP Code. In the Puerto Rico Community Survey, the question asked for the exact address, including the development or condominium name, as well as the place; whether or not the place of work was inside or outside the limits of that city or town; the municipio or U.S. county. Respondents also were asked to "enter Puerto Rico or name of U.S. state or foreign country" and the ZIP Code. If the respondent's employer operated in more than one location, the exact address of the location or branch where he or she worked was requested. When the number and street name were unknown, a description of the location, such as the building name or nearest street or intersection, was to be entered. People who worked at more than one location during the reference week were asked to report the location at which they worked the greatest number of hours. People who regularly worked in several locations each day during the reference week were requested to give the address at which they began work each day. For cases in which daily work did not begin at a central place each day, the respondent was asked to provide as much information as possible to describe the area in which he or she worked most during the reference week.
Place-of-work data may show a few workers who made unlikely daily work trips (e.g., workers who lived in New York and worked in California). This result is attributable to people who worked during the reference week at a location that was different from their usual place of work, such as people away from home on business.
In areas where the workplace address was geographically coded to the block level, people were tabulated as working inside or outside a specific place based on the location of that address regardless of the response to Question 30c concerning city/town limits. In areas where it was impossible to code the workplace address to the block level, or the coding system was unable to match the employer name and street address responses, people were tabulated as working inside or outside a specific place based on the combination of state, county, ZIP Code, place name, and city limits indicator. The city limits indicator was used only in coding decisions when there were multiple geographic codes to select from, after matching on the state, county, place, and ZIP Code responses. The accuracy of place-of- work data for census designated places (CDPs) may be affected by the extent to which their census names were familiar to respondents, and by coding problems caused by similarities between the CDP name and the names of other geographic jurisdictions in the same vicinity.
Place-of-work data are given for selected minor civil divisions (MCDs), (generally cities, towns, and townships) in the 12 strong MCD states (Connecticut, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Wisconsin), based on the responses to the place of work question. Many towns and townships are regarded locally as equivalent to a place, and therefore, were reported as the place of work. When a respondent reported a locality or incorporated place that formed a part of a township or town, the coding and tabulating procedure was designed to include the response in the total for the township or town.
Data were tabulated for workers 16 years old and over, that is, members of the Armed Forces and civilians who were at work during the reference week. Data on place of work refer to the geographic location at which workers carried out their occupational activities during the reference week. In the American Community Survey, the exact address (number and street name) of the place of work was asked, as well as the place (city, town, or post office); whether the place of work was inside or outside the limits of that city or town; and the county, state or foreign country, and ZIP Code. In the Puerto Rico Community Survey, the question asked for the exact address, including the development or condominium name, as well as the place; whether or not the place of work was inside or outside the limits of that city or town; the municipio or U.S. county. Respondents also were asked to "enter Puerto Rico or name of U.S. state or foreign country" and the ZIP Code. If the respondent's employer operated in more than one location, the exact address of the location or branch where he or she worked was requested. When the number and street name were unknown, a description of the location, such as the building name or nearest street or intersection, was to be entered. People who worked at more than one location during the reference week were asked to report the location at which they worked the greatest number of hours. People who regularly worked in several locations each day during the reference week were requested to give the address at which they began work each day. For cases in which daily work did not begin at a central place each day, the respondent was asked to provide as much information as possible to describe the area in which he or she worked most during the reference week.
Place-of-work data may show a few workers who made unlikely daily work trips (e.g., workers who lived in New York and worked in California). This result is attributable to people who worked during the reference week at a location that was different from their usual place of work, such as people away from home on business.
In areas where the workplace address was geographically coded to the block level, people were tabulated as working inside or outside a specific place based on the location of that address regardless of the response to Question 30c concerning city/town limits. In areas where it was impossible to code the workplace address to the block level, or the coding system was unable to match the employer name and street address responses, people were tabulated as working inside or outside a specific place based on the combination of state, county, ZIP Code, place name, and city limits indicator. The city limits indicator was used only in coding decisions when there were multiple geographic codes to select from, after matching on the state, county, place, and ZIP Code responses. The accuracy of place-of- work data for census designated places (CDPs) may be affected by the extent to which their census names were familiar to respondents, and by coding problems caused by similarities between the CDP name and the names of other geographic jurisdictions in the same vicinity.
Place-of-work data are given for selected minor civil divisions (MCDs), (generally cities, towns, and townships) in the 12 strong MCD states (Connecticut, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Wisconsin), based on the responses to the place of work question. Many towns and townships are regarded locally as equivalent to a place, and therefore, were reported as the place of work. When a respondent reported a locality or incorporated place that formed a part of a township or town, the coding and tabulating procedure was designed to include the response in the total for the township or town.
The characteristics of workers may be shown using either residence-based or workplace-based geography. If you are interested in the number and characteristics of workers living in a specific area, you should use the standard (residence- based) journey-to-work tables. If you are interested in the number and characteristics of workers who work in a specific area, you should use the workplace-based journey-to-work tables. Because place-of-work information for workers cannot always be specified below the place level, the workplace-based tables are presented only for selected geographic areas.
The data on means of transportation to work were derived from answers to Question 31, which was asked of people who indicated in Question 29 that they worked at some time during the reference week. (See "Reference Week.") Means of transportation to work refers to the principal mode of travel or type of conveyance that the worker usually used to get from home to work during the reference week.
People who used different means of transportation on different days of the week were asked to specify the one they used most often, that is, the greatest number of days. People who used more than one means of transportation to get to work each day were asked to report the one used for the longest distance during the work trip. The category, "Car, truck, or van," includes workers using a car (including company cars but excluding taxicabs), a truck of one- ton capacity or less, or a van. The category, "Public transportation," includes workers who used a bus or trolley bus, streetcar or trolley car, subway or elevated, railroad, or ferryboat, even if each mode is not shown separately in the tabulation. "Carro publico" is included in the public transportation category in Puerto Rico. The category, "Other means," includes workers who used a mode of travel that is not identified separately within the data distribution. The category, "Other means," may vary from table to table, depending on the amount of detail shown in a particular distribution.
The means of transportation data for some areas may show workers using modes of public transportation that are not available in those areas (for example, subway or elevated riders in a metropolitan area where there is no subway or elevated service). This result is largely due to people who worked during the reference week at a location that was different from their usual place of work (such as people away from home on business in an area where subway service was available), and people who used more than one means of transportation each day but whose principal means was unavailable where they lived (for example, residents of nonmetropolitan areas who drove to the fringe of a metropolitan area, and took the commuter railroad most of the distance to work).
People who used different means of transportation on different days of the week were asked to specify the one they used most often, that is, the greatest number of days. People who used more than one means of transportation to get to work each day were asked to report the one used for the longest distance during the work trip. The category, "Car, truck, or van," includes workers using a car (including company cars but excluding taxicabs), a truck of one- ton capacity or less, or a van. The category, "Public transportation," includes workers who used a bus or trolley bus, streetcar or trolley car, subway or elevated, railroad, or ferryboat, even if each mode is not shown separately in the tabulation. "Carro publico" is included in the public transportation category in Puerto Rico. The category, "Other means," includes workers who used a mode of travel that is not identified separately within the data distribution. The category, "Other means," may vary from table to table, depending on the amount of detail shown in a particular distribution.
The means of transportation data for some areas may show workers using modes of public transportation that are not available in those areas (for example, subway or elevated riders in a metropolitan area where there is no subway or elevated service). This result is largely due to people who worked during the reference week at a location that was different from their usual place of work (such as people away from home on business in an area where subway service was available), and people who used more than one means of transportation each day but whose principal means was unavailable where they lived (for example, residents of nonmetropolitan areas who drove to the fringe of a metropolitan area, and took the commuter railroad most of the distance to work).
The data on private vehicle occupancy were derived from answers to Question 32. This question was asked of people who indicated in Question 29 that they worked at some time during the reference week and who reported in Question 31 that their means of transportation to work was "Car, truck, or van." Data were tabulated for workers 16 years old and over, that is, members of the Armed Forces and civilians who were at work during the reference week. (See "Reference Week.")
Private vehicle occupancy refers to the number of people who usually rode to work in the vehicle during the reference week. The category, "Drove alone," includes people who usually drove alone to work as well as people who were driven to work by someone who then drove back home or to a non-work destination. The category, "Carpooled," includes workers who reported that two or more people usually rode to work in the vehicle during the reference week.
Private vehicle occupancy refers to the number of people who usually rode to work in the vehicle during the reference week. The category, "Drove alone," includes people who usually drove alone to work as well as people who were driven to work by someone who then drove back home or to a non-work destination. The category, "Carpooled," includes workers who reported that two or more people usually rode to work in the vehicle during the reference week.
Workers per car, truck, or van is a ratio obtained by dividing the aggregate number of workers who reported using a car, truck, or van to get to work by the number of such vehicles that they used. Workers per car, truck, or van is rounded to the nearest hundredth. This measure also may be known as "Workers per private vehicle."
Theaggregate number of vehicles used in commuting is derived by counting each person who drove alone as occupying one vehicle, each person who reported being in a two-person carpool as occupying one-half of a vehicle, each person who reported being in a three-person carpool as occupying one-third of a vehicle, and so on, then summing all the vehicles. This aggregate is used in the calculation for "workers per car, truck, or van."
The data on time leaving home to go to work were derived from answers to Question 33. This question was asked of people who indicated in Question 29 that they worked at some time during the reference week, and who reported in Question 31 that they worked outside their home. The departure time refers to the time of day that the respondent usually left home to go to work during the reference week. (See "Reference Week.")
The data on travel time to work were derived from answers to Question 34. This question was asked of people who indicated in Question 29 that they worked at some time during the reference week, and who reported in Question 31 that they worked outside their home. Travel time to work refers to the total number of minutes that it usually took the worker to get from home to work during the reference week. The elapsed time includes time spent waiting for public transportation, picking up passengers in carpools, and time spent in other activities related to getting to work. (See "Reference Week.")
Aggregate travel time to work is calculated by adding all of the travel times (in minutes) for workers who did not work at home. Aggregate travel times of workers having specific characteristics also are computed. The aggregate travel time is subject to rounding, which means that all cells in a matrix are rounded to the nearest 5 minutes. (For more information, see "Aggregate" under "Derived Measures.")
Mean travel time to work (in minutes) is the average travel time that workers usually took to get from home to work (one way) during the reference week. This measure is obtained by dividing the total number of minutes taken to get from home to work (the aggregate travel time) by the number of workers 16 years old and over who did not work at home. The travel time includes time spent waiting for public transportation, picking up passengers and carpools, and time spent in other activities related to getting to work. Mean travel times of workers having specific characteristics also are computed. For example, the mean travel time of workers traveling 45 or more minutes to work is computed by dividing the aggregate travel time of workers whose travel times were 45 or more minutes by the number of workers whose travel times were 45 or more minutes. The aggregate travel time to work used to calculate mean travel time to work is rounded. (For more information, see "Aggregate Travel Time to Work (in Minutes).") Mean travel time is rounded to the nearest tenth of a minute. (For more information on means, see "Derived Measures.")
The data on time arriving at work from home were derived from answers to Question 33 (Time Leaving Home to Go to Work) and from answers to Question 34 (Travel Time to Work). These questions were asked of people who indicated in Question 29 that they worked at some time during the reference week, and who reported in Question 31 that they worked outside their home. The arrival time is calculated by adding the travel time to work to the reported time leaving home to go to work. These data are presented with other characteristics of workers at their workplace. (See "Time Leaving Home to Go to Work" and "Travel Time to Work.")
The responses to the place of work and journey to work questions provide basic knowledge about commuting patterns and the characteristics of commuter travel. The commuting data are essential for planning highway improvement and developing public transportation services, as well as for designing programs to ease traffic problems during peak periods, conserve energy, reduce pollution, and estimate and project the demand for alternative-fueled vehicles. These data are required to develop standards for reducing work-related vehicle trips and increasing passenger occupancy during peak period of travel. The Bureau of Economic Analysis (BEA) plans to use county-level data in computing gross commuting flows to develop place-of-residence earning estimates from place-of-work estimates by industry. In addition, BEA also plans to use these data for state personal income estimates for determining federal fund allocations.
The responses to the place of work and journey to work questions provide basic knowledge about commuting patterns and the characteristics of commuter travel. The commuting data are essential for planning highway improvement and developing public transportation services, as well as for designing programs to ease traffic problems during peak periods, conserve energy, reduce pollution, and estimate and project the demand for alternative-fueled vehicles. These data are required to develop standards for reducing work-related vehicle trips and increasing passenger occupancy during peak period of travel. The Bureau of Economic Analysis (BEA) plans to use county-level data in computing gross commuting flows to develop place-of-residence earning estimates from place-of-work estimates by industry. In addition, BEA also plans to use these data for state personal income estimates for determining federal fund allocations.
Starting in 1999, the American Community Survey questions differ from the 1996-1998 questions in that the labels on the write-in spaces and format of the skip instructions were modified to provide clarifications.
Beginning in 2004, the category, "Public transportation" for means of transportation was tabulated to exclude workers who used taxicab as their means of transportation.
The 2004 American Community Survey marked the first time that workplace-based tables were released as a part of a standard census data product.
Beginning in 2004, the category, "Public transportation" for means of transportation was tabulated to exclude workers who used taxicab as their means of transportation.
The 2004 American Community Survey marked the first time that workplace-based tables were released as a part of a standard census data product.
Beginning in 2006, the group quarters (GQ) population is included in the ACS. Some types of GQ populations have place of work distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the place of work distribution. This is particularly true for areas with a substantial GQ population.
The data on place of work is related to a reference week, that is, the calendar week preceding the date on which the respondents completed their questionnaires or were interviewed. This week is not the same for all respondents because data were collected over a 12-month period. The lack of a uniform reference week means that the place-of-work data reported in the survey will not exactly match the distribution of workplace locations observed or measured during an actual workweek.
The place-of-work data are estimates of people 16 years and over who were both employed and at work during the reference week (including people in the Armed Forces). People who did not work during the reference week but had jobs or businesses from which they were temporarily absent due to illness, bad weather, industrial dispute, vacation, or other personal reasons are not included in the place-of-work data. Therefore, the data on place of work understate the total number of jobs or total employment in a geographic area during the reference week. It also should be noted that people who had irregular, casual, or unstructured jobs during the reference week might have erroneously reported themselves as not working.
The address where the individual worked most often during the reference week was recorded on the questionnaire. If a worker held two jobs, only data about the primary job (the job where one worked the greatest number of hours during the preceding week) was requested. People who regularly worked in several locations during the reference week were requested to give the address at which they began work each day. For cases in which daily work was not begun at a central place each day, the respondent was asked to provide as much information as possible to describe the area in which he or she worked most during the reference week.
The data on place of work is related to a reference week, that is, the calendar week preceding the date on which the respondents completed their questionnaires or were interviewed. This week is not the same for all respondents because data were collected over a 12-month period. The lack of a uniform reference week means that the place-of-work data reported in the survey will not exactly match the distribution of workplace locations observed or measured during an actual workweek.
The place-of-work data are estimates of people 16 years and over who were both employed and at work during the reference week (including people in the Armed Forces). People who did not work during the reference week but had jobs or businesses from which they were temporarily absent due to illness, bad weather, industrial dispute, vacation, or other personal reasons are not included in the place-of-work data. Therefore, the data on place of work understate the total number of jobs or total employment in a geographic area during the reference week. It also should be noted that people who had irregular, casual, or unstructured jobs during the reference week might have erroneously reported themselves as not working.
The address where the individual worked most often during the reference week was recorded on the questionnaire. If a worker held two jobs, only data about the primary job (the job where one worked the greatest number of hours during the preceding week) was requested. People who regularly worked in several locations during the reference week were requested to give the address at which they began work each day. For cases in which daily work was not begun at a central place each day, the respondent was asked to provide as much information as possible to describe the area in which he or she worked most during the reference week.
This data source is comparable to the decennial censuses for all journey to work variables. Since both the American Community Survey and the decennial censuses are related to a "reference week" that has some variability, the data do not reflect any single week. Since the American Community Survey data are collected over 12 months, the reference week in American Community Survey has a greater range of variation. (See "Reference Week.")
For more detailed information regarding the difference of place of work and journey to work in the ACS and Census 2000, see Estimates about Journey to Work from the 2005 ACS, C2SS, and Census 2000 on the ACS website (www.census.gov/acs).
See the 2011 Code List for Place of Work Code List.
For more detailed information regarding the difference of place of work and journey to work in the ACS and Census 2000, see Estimates about Journey to Work from the 2005 ACS, C2SS, and Census 2000 on the ACS website (www.census.gov/acs).
See the 2011 Code List for Place of Work Code List.
Data on language spoken at home were derived from answers to questions 14a and 14b. These questions were asked only of persons 5 years of age and older. Instructions mailed with the American Community Survey questionnaire instructed respondents to mark "Yes" on Question 14a if they sometimes or always spoke a language other than English at home, and "No" if a language was spoken only at school - or if speaking was limited to a few expressions or slang. For Question 14b, respondents printed the name of the non-English language they spoke at home. If the person spoke more than one non-English language, they reported the language spoken most often. If the language spoken most frequently could not be determined, the respondent reported the language learned first.
Questions 14a and 14b referred to languages spoken at home in an effort to measure the current use of languages other than English. This category excluded respondents who spoke a language other than English exclusively outside of the home.
An automated computer system coded write-in responses to Question 14b into more than 380 detailed language categories. This automated procedure compared write-in responses with a master computer code list - which contained approximately 55,000 previously coded language names and variants - and then assigned a detailed language category to each write- in response. The computerized matching assured that identical alphabetic entries received the same code. Clerical coding categorized any write-in responses that did not match the computer dictionary. When multiple languages other than English were specified, only the first was coded.
The write-in responses represented the names people used for languages they spoke. They may not have matched the names or categories used by professional linguists. The categories used were sometimes geographic and sometimes linguistic. The table in Appendix A provides an illustration of the content of the classification schemes used to present language data.
Questions 14a and 14b referred to languages spoken at home in an effort to measure the current use of languages other than English. This category excluded respondents who spoke a language other than English exclusively outside of the home.
An automated computer system coded write-in responses to Question 14b into more than 380 detailed language categories. This automated procedure compared write-in responses with a master computer code list - which contained approximately 55,000 previously coded language names and variants - and then assigned a detailed language category to each write- in response. The computerized matching assured that identical alphabetic entries received the same code. Clerical coding categorized any write-in responses that did not match the computer dictionary. When multiple languages other than English were specified, only the first was coded.
The write-in responses represented the names people used for languages they spoke. They may not have matched the names or categories used by professional linguists. The categories used were sometimes geographic and sometimes linguistic. The table in Appendix A provides an illustration of the content of the classification schemes used to present language data.
In households where one or more people spoke a language other than English, the household language assigned to all household members was the non- English language spoken by the first person with a non-English language. This assignment scheme ranked household members in the following order: householder, spouse, parent, sibling, child, grandchild, other relative, stepchild, unmarried partner, housemate or roommate, and other nonrelatives. Therefore, a person who spoke only English may have had a non-English household language assigned during tabulations by household language Government agencies use information on language spoken at home for their programs that serve the needs of the foreign-born and specifically those who have difficulty with English. Under the Voting Rights Act, language is needed to meet statutory requirements for making voting materials available in minority languages. The Census Bureau is directed, using data about language spoken at home and the ability to speak English, to identify minority groups that speak a language other than English and to assess their English-speaking ability. The U.S. Department of Education uses these data to prepare a report to Congress on the social and economic status of children served by different local school districts.
Government agencies use information on language spoken at home for their programs that serve the needs of the foreign-born and specifically those who have difficulty with English. Under the Voting Rights Act, language is needed to meet statutory requirements for making voting materials available in minority languages. The Census Bureau is directed, using data about language spoken at home and the ability to speak English, to identify minority groups that speak a language other than English and to assess their English-speaking ability. The U.S. Department of Education uses these data to prepare a report to Congress on the social and economic status of children served by different local school districts. State and local agencies concerned with aging develop health care and other services tailored to the language and cultural diversity of the elderly under the Older Americans Act.
Government agencies use information on language spoken at home for their programs that serve the needs of the foreign-born and specifically those who have difficulty with English. Under the Voting Rights Act, language is needed to meet statutory requirements for making voting materials available in minority languages. The Census Bureau is directed, using data about language spoken at home and the ability to speak English, to identify minority groups that speak a language other than English and to assess their English-speaking ability. The U.S. Department of Education uses these data to prepare a report to Congress on the social and economic status of children served by different local school districts. State and local agencies concerned with aging develop health care and other services tailored to the language and cultural diversity of the elderly under the Older Americans Act.
The Language Spoken Questions have changed only once since ACS began. Examples of languages were listed immediately followed the question "What is this language?" in the 1996-1998 questionnaire. Starting in 1999, the list of languages was moved to below the write-in box.
The language question is about current use of a non-English language, not about ability to speak another language or the use of such a language in the past. People who speak a language other than English outside of the home are not reported as speaking a language other than English. Similarly, people whose mother tongue is a non- English language but who do not currently use the language at home do not report the language. Some people who speak a language other than English at home may have first learned that language in school. These people are expected to indicate speaking English "Very well."
The data on marital status and marital history were derived from answers to Questions 20 through 23. The marital status question is asked to determine the status of the person at the time of interview. Many government programs need accurate information on marital status, such as the number of married women in the labor force, elderly widowed individuals, or young single people who may establish homes of their own. The marital history data enables multiple agencies to more accurately measure the effects of federal and state policies and programs that focus on the well-being of families. Marital history data can provide estimates of marriage and divorce rates and duration, as well as flows into and out of marriage. This information is critical for more refined analyses of eligibility for program services and benefits, and of changes resulting from federal policies and programs.
Before 2008, the marital status question was asked of all people. Beginning in 2008, the question on marital status was asked only for people 15 years old and over. People 15 and over were asked whether they were "now married," "widowed," "divorced," "separated," or "never married." People in common-law marriages were allowed to report the marital status they considered the most appropriate. When marital status was not reported, it was imputed according to the person's relationship to the householder, sex, and age.
Differences in the number of married males and females occur because there is no step in the weighting process to equalize the weighted estimates of husbands and wives.
Before 2008, the marital status question was asked of all people. Beginning in 2008, the question on marital status was asked only for people 15 years old and over. People 15 and over were asked whether they were "now married," "widowed," "divorced," "separated," or "never married." People in common-law marriages were allowed to report the marital status they considered the most appropriate. When marital status was not reported, it was imputed according to the person's relationship to the householder, sex, and age.
Differences in the number of married males and females occur because there is no step in the weighting process to equalize the weighted estimates of husbands and wives.
Includes all people who have never been married, including people whose only marriage(s) was annulled.
Includes people ever married at the time of interview (including those now married, separated, widowed, or divorced).
Includes people whose current marriage has not ended through widowhood, divorce, or separation (regardless of previous marital history). The category may also include couples who live together or people in common-law marriages if they consider this category the most appropriate. In certain tabulations, currently married people are further classified as "spouse present" or "spouse absent." In tabulations, unless otherwise specified, "now married" does not include same-sex married people even if the marriage was performed in a state issuing marriage certificates for same-sex couples.
Includes people legally separated or otherwise absent from their spouse because of marital discord. Those without a final divorce decree are classified as "separated." This category also includes people who have been deserted or who have parted because they no longer want to live together but who have not obtained a divorce.
Includes people who are legally divorced and who have not remarried. Those without a final divorce decree are classified as "separated."
In selected tabulations, data for married and separated people are reorganized and combined with information on the presence of the spouse in the same household.
In selected tabulations, data for married and separated people are reorganized and combined with information on the presence of the spouse in the same household.
All people whose current marriage has not ended by widowhood or divorce. This category includes people defined above as "separated."
- Spouse Absent, Other - Married people whose wife or husband was not reported as a member of the same household, excluding separated. Included is any person whose spouse was employed and living away from home or in an institution or serving away from home in the Armed Forces.
Differences between the number of married males and the number of married females occur because: some husbands and wives have their usual residence in different areas; and husbands and wives do not have the same weights. By definition, the numbers would be the same.
- Spouse Present - Married people whose wife or husband was reported as a member of the same household, including those whose spouses may have been temporarily absent for such reasons as travel or hospitalization.
- Spouse Absent - Married people whose wife or husband was not reported as a member of the same household or people reporting they were married and living in a group quarters facility.
- Spouse Absent, Other - Married people whose wife or husband was not reported as a member of the same household, excluding separated. Included is any person whose spouse was employed and living away from home or in an institution or serving away from home in the Armed Forces.
Differences between the number of married males and the number of married females occur because: some husbands and wives have their usual residence in different areas; and husbands and wives do not have the same weights. By definition, the numbers would be the same.
The median age at first marriage is calculated indirectly by estimating the proportion of young people who will marry during their lifetime, calculating one-half of this proportion, and determining the age (at the time of the survey) of people at this half-way mark by osculatory interpolation. It does not represent the actual median age of the population who married during the calendar year. It is shown to the nearest tenth of a year. Henry S. Shryock and Jacob S. Siegel outline the osculatory procedure in Methods and Materials of Demography, First Edition (May 1973), Volume 1, pages 291-296.
Beginning in 2008, people 15 years and over who were ever married (married, widowed, separated, or divorced) were asked if they had been married, widowed, or divorced in the past 12 months. They were asked how many times (once, two times, three or more times) they have been married, and the year of their last marriage.
The word "current" was dropped from the 1996-1998 question. Since 1999, the question states, "What is this person's marital status?" The American Community Survey began providing the median age at first marriage with the 2004 data. Data on marital history were first collected in 2008 at the request of the Department of Health and Human Services to provide more detailed annual information on the marital status of the population. Before 2008, the marital status question was asked of all people and only tabulated for those 15 and over. In 2008, marital status was moved from the basic demographic section, at the beginning of the ACS questionnaire, to the detailed person section - a part of the questionnaire where questions were asked of only people 15 and over. The marital history questions follow the marital status question on the questionnaire.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations have marital status distributions that are very different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the marital status distribution. This is particularly true for areas with a substantial GQ population.
The data on marital status can be compared to previous ACS years and to similar data collected on CPS and SIPP. Marital status is no longer asked on the Decennial Census. The marital history data, and particularly marriage and divorce rates derived from the questions asking if the person got married or divorced in the past 12 months is comparable to vital statistics collected by the National Center for Health Statistics (NCHS).
The native population includes anyone who was a U.S. citizen or a U.S. national at birth. This includes respondents who indicated they were born in the United States, Puerto Rico, a U.S. Island Area (such as Guam), or abroad of American (U.S. citizen) parent or parents. See Citizenship Status.
Nativity of parent indicates the nativity (native or foreign born) of the parent(s) of children living in a family or subfamily with one or more parents present in the household. It applies to "own children," that is, never married children under 18 years of age living with one or more of their parents. (See also "Own Child.") The nativity of the child's parent(s) is determined by the citizenship status of the parent(s). A person is considered native if he/she is a native United States citizen at birth, and foreign born if he/she is not a United States citizen at birth. (See also "Place of Birth.")
Nativity of parent does not provide information about children over the age of 18 who may live in the same household as their parents, or children of any age who live apart from their parents.
Occupation describes the kind of work a person does on the job. Occupation data were derived from answers to questions 45 and 46. Question 45 asks: "What kind of work was this person doing?" Question 46 asks: "What were this person's most important activities or duties?"
These questions were asked of all people 15 years old and over who had worked in the past 5 years. For employed people, the data refer to the person's job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people and people who are not currently employed but report having a job within the last five years, the data refer to their last job.
These questions describe the work activity and occupational experience of the American labor force. Data are used to formulate policy and programs for employment, career development and training; to provide information on the occupational skills of the labor force in a given area to analyze career trends; and to measure compliance with antidiscrimination policies. Companies use these data to decide where to locate new plants, stores, or offices.
These questions were asked of all people 15 years old and over who had worked in the past 5 years. For employed people, the data refer to the person's job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people and people who are not currently employed but report having a job within the last five years, the data refer to their last job.
These questions describe the work activity and occupational experience of the American labor force. Data are used to formulate policy and programs for employment, career development and training; to provide information on the occupational skills of the labor force in a given area to analyze career trends; and to measure compliance with antidiscrimination policies. Companies use these data to decide where to locate new plants, stores, or offices.
Occupation statistics are compiled from data that are coded based on the Standard Occupational Classification (SOC) Manual: 2011, published by the Executive Office of the President, Office of Management and Budget. Census occupation codes, based on the 2011 SOC, provide 539 specific occupational categories, for employed people, including military, arranged into 23 major occupational groups.
Respondents provided the data for the tabulations by writing on the questionnaires descriptions of the kind of work and activities they are doing. Clerical staff in the National Processing Center in Jeffersonville, Indiana converted the written questionnaire descriptions to codes by comparing these descriptions to entries in the Alphabetical Index of Industries and Occupations.
Some occupation groups are related closely to certain industries. Operators of transportation equipment, farm operators and workers, and healthcare providers account for major portions of their respective industries of transportation, agriculture, and health care. However, the industry categories include people in other occupations. For example, people employed in agriculture include truck drivers and bookkeepers; people employed in the transportation industry include mechanics, freight handlers, and payroll clerks; and people employed in the health care industry include janitors, security guards, and secretaries.
Respondents provided the data for the tabulations by writing on the questionnaires descriptions of the kind of work and activities they are doing. Clerical staff in the National Processing Center in Jeffersonville, Indiana converted the written questionnaire descriptions to codes by comparing these descriptions to entries in the Alphabetical Index of Industries and Occupations.
Some occupation groups are related closely to certain industries. Operators of transportation equipment, farm operators and workers, and healthcare providers account for major portions of their respective industries of transportation, agriculture, and health care. However, the industry categories include people in other occupations. For example, people employed in agriculture include truck drivers and bookkeepers; people employed in the transportation industry include mechanics, freight handlers, and payroll clerks; and people employed in the health care industry include janitors, security guards, and secretaries.
Following the coding operation, a computer edit and allocation process excludes all responses that should not be included in the universe, and evaluates the consistency of the remaining responses. The codes for occupation are checked for consistency with the industry and class of worker data provided for that respondent. Occasionally respondents supply occupation descriptions that are not sufficiently specific for precise classification, or they do not report on these questions at all. Certain types of incomplete entries are corrected using the Alphabetical Index of Industries and Occupations. If one or more of the three codes (occupation, industry, or class of worker) is blank after the edit, a code is assigned from a donor respondent who is a "similar" person based on questions such as age, sex, educational attainment, income, employment status, and weeks worked. If all of the labor force and income data are blank, all of these economic questions are assigned from a "similar" person who had provided all the necessary data.
Occupation data have been collected during decennial censuses since 1850. Starting with the 2010 Census, occupation data will no longer be collected during the decennial census. Long form data collection has transitioned to the American Community Survey. The American Community Survey began collecting data on occupation in 1996. The questions on occupation were designed to be consistent with the 1990 Census questions on occupation. American Community Survey questions on occupation have remained consistent between 1996 and 2011.
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. Some types of GQ populations have occupational distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the occupational distribution in some geographic areas with a substantial GQ population.
Data on occupation, industry, and class of worker are collected for the respondent's current primary job or the most recent job for those who are not employed but have worked in the last 5 years. Other labor force questions, such as questions on earnings or work hours, may have different reference periods and may not limit the response to the primary job. Although the prevalence of multiple jobs is low, data on some labor force items may not exactly correspond to the reported occupation, industry, or class of worker of a respondent.
Data on occupation, industry, and class of worker are collected for the respondent's current primary job or the most recent job for those who are not employed but have worked in the last 5 years. Other labor force questions, such as questions on earnings or work hours, may have different reference periods and may not limit the response to the primary job. Although the prevalence of multiple jobs is low, data on some labor force items may not exactly correspond to the reported occupation, industry, or class of worker of a respondent.
Comparability of occupation data was affected by a number of factors, primarily the system used to classify the questionnaire responses. Changes in the occupational classification system limit comparability of the data from one year to another. These changes are needed to recognize the "birth" of new occupations, the "death" of others, the growth and decline in existing occupations, and the desire of analysts and other users for more detail in the presentation of the data. Probably the greatest cause of noncomparability is the movement of a segment from one category to another. Changes in the nature of jobs, respondent terminology, and refinement of category composition made these movements necessary.
ACS data from 1996 to 1999 used the same occupation classification systems used for the 1990 census; therefore, the data are comparable. Since 1990, the occupation classification has been revised to reflect changes within the Standard Occupational Classification (SOC). The SOC was updated in 2000 and these changes were reflected in the Census 2000 occupation codes. The 2000-2002 ACS data used the same occupation classification systems used for Census 2000, therefore, the data are comparable. Because of the possibility of new occupations being added to the list of codes, the Census Bureau needed to have more flexibility in adding codes. Consequently, in 2002, census occupation codes were expanded from three-digit codes to four-digit codes. For occupation, this entailed adding a "0" to the end of each occupation code. The SOC was revised once more in 2011. Based on the 2011 SOC changes, Census codes were revised resulting in a net gain of 30 Census occupation codes (from 509 occupations to 539 occupations). Most of these changes were concentrated in information technology, healthcare, printing, and human resources occupations. For more information on occupational comparability across classification systems, please see technical paper #65: The Relationship Between the 1990 Census and Census 2000 Industry and Occupation Classification Systems. For information on the 2011 SOC and Census codes, please see the summary of 2011 changes and the Census 2002 to 2011 occupation crosswalk.
See the 2011 Code List for Occupation Code List.
See also, Industry and Class of Worker.
ACS data from 1996 to 1999 used the same occupation classification systems used for the 1990 census; therefore, the data are comparable. Since 1990, the occupation classification has been revised to reflect changes within the Standard Occupational Classification (SOC). The SOC was updated in 2000 and these changes were reflected in the Census 2000 occupation codes. The 2000-2002 ACS data used the same occupation classification systems used for Census 2000, therefore, the data are comparable. Because of the possibility of new occupations being added to the list of codes, the Census Bureau needed to have more flexibility in adding codes. Consequently, in 2002, census occupation codes were expanded from three-digit codes to four-digit codes. For occupation, this entailed adding a "0" to the end of each occupation code. The SOC was revised once more in 2011. Based on the 2011 SOC changes, Census codes were revised resulting in a net gain of 30 Census occupation codes (from 509 occupations to 539 occupations). Most of these changes were concentrated in information technology, healthcare, printing, and human resources occupations. For more information on occupational comparability across classification systems, please see technical paper #65: The Relationship Between the 1990 Census and Census 2000 Industry and Occupation Classification Systems. For information on the 2011 SOC and Census codes, please see the summary of 2011 changes and the Census 2002 to 2011 occupation crosswalk.
See the 2011 Code List for Occupation Code List.
See also, Industry and Class of Worker.
The data on place of birth were derived from answers to Question 7. Respondents were asked to select one of two categories: (1) in the United States, or (2) outside the United States. In the American Community Survey, respondents selecting category (1) were then asked to report the name of the state while respondents selecting category (2) were then asked to report the name of the foreign country, or Puerto Rico, Guam, etc. In the Puerto Rico Community Survey, respondents selecting category (1) were also asked to report the name of the state, while respondents selecting category (2) were then asked to print Puerto Rico or the name of the foreign country, or U.S. Virgin Islands, Guam, etc. People not reporting a place of birth were assigned the state or country of birth of another family member, or were allocated the response of another individual with similar characteristics. People born outside the United States were asked to report their place of birth according to current international boundaries. Since numerous changes in boundaries of foreign countries have occurred in the last century, some people may have reported their place of birth in terms of boundaries that existed at the time of their birth or emigration, or in accordance with their own national preference.
The place of birth questions along with the citizenship status question provide essential data for setting and evaluating immigration policies and laws. Knowing the characteristics of immigrants helps legislators and others understand how different immigrant groups are assimilated. Federal agencies require these data to develop programs for refugees and other foreign-born individuals. Vital information on lifetime migration among states also comes from the place of birth question.
The place of birth questions along with the citizenship status question provide essential data for setting and evaluating immigration policies and laws. Knowing the characteristics of immigrants helps legislators and others understand how different immigrant groups are assimilated. Federal agencies require these data to develop programs for refugees and other foreign-born individuals. Vital information on lifetime migration among states also comes from the place of birth question.
Information on place of birth and citizenship status was used to classify the population into two major categories: native and foreign born.
The native population includes anyone who was a U.S. citizen at birth. The native population includes those born in the United States, Puerto Rico, American Samoa, Guam, the Northern Marianas, or the U.S. Virgin Islands, as well as those born abroad of at least one U.S. citizen parent. The native population is divided into the following groups: people born in the state in which they resided at the time of the survey; people born in a different state, by region; people born in Puerto Rico or one of the U.S. Island Areas; and people born abroad with at least one U.S. citizen parent. (See also "Citizenship Status.")
The foreign-born population includes anyone who was not a U.S. citizen at birth. This includes respondents who indicated they were a U.S. citizen by naturalization or not a U.S. citizen. (See also "Citizenship Status.")
The foreign-born population is shown by selected area, country, or region of birth. The places of birth shown in data products were chosen based on the number of respondents who reported that area or country of birth.
The foreign-born population is shown by selected area, country, or region of birth. The places of birth shown in data products were chosen based on the number of respondents who reported that area or country of birth.
The 1996-1998 American Community Survey question asked respondents to write in the U.S. state, territory, commonwealth or foreign country where this person was born. Beginning in 1999, the question asked "Where was this person born?" and provided two check-boxes, each with a write-in space.
Beginning in 2006, the group quarters (GQ) population is included in the ACS. Some types of GQ populations may have place of birth distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the place of birth distribution. This is particularly true for areas with a substantial GQ population.
Poverty statistics in ACS products adhere to the standards specified by the Office of Management and Budget in Statistical Policy Directive 14. The Census Bureau uses a set of dollar value thresholds that vary by family size and composition to determine who is in poverty. Further, poverty thresholds for people living alone or with nonrelatives (unrelated individuals) vary by age (under 65 years or 65 years and older). The poverty thresholds for two-person families also vary by the age of the householder. If a family's total income is less than the dollar value of the appropriate threshold, then that family and every individual in it are considered to be in poverty. Similarly, if an unrelated individual's total income is less than the appropriate threshold, then that individual is considered to be in poverty.
In determining the poverty status of families and unrelated individuals, the Census Bureau uses thresholds (income cutoffs) arranged in a two-dimensional matrix. The matrix consists of family size (from one person to nine or more people) cross-classified by presence and number of family members under 18 years old (from no children present to eight or more children present). Unrelated individuals and two-person families are further differentiated by age of reference person (RP) (under 65 years old and 65 years old and over).
To determine a person's poverty status, one compares the person's total family income in the last 12 months with the poverty threshold appropriate for that person's family size and composition (see example below). If the total income of that person's family is less than the threshold appropriate for that family, then the person is considered "below the poverty level," together with every member of his or her family. If a person is not living with anyone related by birth, marriage, or adoption, then the person's own income is compared with his or her poverty threshold. The total number of people below the poverty level is the sum of people in families and the number of unrelated individuals with incomes in the last 12 months below the poverty threshold.
Since ACS is a continuous survey, people respond throughout the year. Because the income questions specify a period covering the last 12 months, the appropriate poverty thresholds are determined by multiplying the base-year poverty thresholds (1982) by the average of the monthly inflation factors for the 12 months preceding the data collection. See the table in Appendix A titled "Poverty Thresholds in 1982, by Size of Family and Number of RelatedChildren Under 18 Years (Dollars)," for appropriate base thresholds. See the table "The 2011 Poverty Factors" in Appendix A for the appropriate adjustment based on interview month.
For example, consider a family of three with one child under 18 years of age, interviewed in July 2011 and reporting a total family income of $14,000 for the last 12 months (July 2010 to June 2011). The base year (1982) threshold for such a family is $7,765, while the average of the 12 inflation factors is 2.24574 Multiplying $7,765 by 2.24574 determines the appropriate poverty threshold for this family type, which is $17,438 Comparing the family's income of $14,000 with the poverty threshold shows that the family and all people in the family are considered to have been in poverty. The only difference for determining poverty status for unrelated individuals is that the person's individual total income is compared with the threshold rather than the family's income.
To determine a person's poverty status, one compares the person's total family income in the last 12 months with the poverty threshold appropriate for that person's family size and composition (see example below). If the total income of that person's family is less than the threshold appropriate for that family, then the person is considered "below the poverty level," together with every member of his or her family. If a person is not living with anyone related by birth, marriage, or adoption, then the person's own income is compared with his or her poverty threshold. The total number of people below the poverty level is the sum of people in families and the number of unrelated individuals with incomes in the last 12 months below the poverty threshold.
Since ACS is a continuous survey, people respond throughout the year. Because the income questions specify a period covering the last 12 months, the appropriate poverty thresholds are determined by multiplying the base-year poverty thresholds (1982) by the average of the monthly inflation factors for the 12 months preceding the data collection. See the table in Appendix A titled "Poverty Thresholds in 1982, by Size of Family and Number of RelatedChildren Under 18 Years (Dollars)," for appropriate base thresholds. See the table "The 2011 Poverty Factors" in Appendix A for the appropriate adjustment based on interview month.
For example, consider a family of three with one child under 18 years of age, interviewed in July 2011 and reporting a total family income of $14,000 for the last 12 months (July 2010 to June 2011). The base year (1982) threshold for such a family is $7,765, while the average of the 12 inflation factors is 2.24574 Multiplying $7,765 by 2.24574 determines the appropriate poverty threshold for this family type, which is $17,438 Comparing the family's income of $14,000 with the poverty threshold shows that the family and all people in the family are considered to have been in poverty. The only difference for determining poverty status for unrelated individuals is that the person's individual total income is compared with the threshold rather than the family's income.
Poverty status was determined for all people except institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years old. These groups were excluded from the numerator and denominator when calculating poverty rates.
Specified poverty levels are adjusted thresholds that are obtained by multiplying the official thresholds by specific factor. Using the threshold cited from the previous example (a family of three with one related child under 18 years responding in July 2011), the dollar value at 125 percent of the poverty threshold was $ 21,798 ($ 17,438x 1.25).
Income deficit represents the difference between the total income in the last 12 months of families and unrelated individuals below the poverty level and their respective poverty thresholds. In computing the income deficit, families reporting a net income loss are assigned zero dollars and for such cases the deficit is equal to the poverty threshold.
This measure provides an estimate of the amount, which would be required to raise the incomes of all poor families and unrelated individuals to their respective poverty thresholds. The income deficit is thus a measure of the degree of the impoverishment of a family or unrelated individual. However, please use caution when comparing the average deficits of families with different characteristics. Apparent differences in average income deficits may, to some extent, be a function of differences in family size.
This measure provides an estimate of the amount, which would be required to raise the incomes of all poor families and unrelated individuals to their respective poverty thresholds. The income deficit is thus a measure of the degree of the impoverishment of a family or unrelated individual. However, please use caution when comparing the average deficits of families with different characteristics. Apparent differences in average income deficits may, to some extent, be a function of differences in family size.
Aggregate income deficit refers only to those families or unrelated individuals who are classified as below the poverty level. It is defined as the group (e.g., type of family) sum total of differences between the appropriate threshold and total family income or total personal income. Aggregate income deficit is subject to rounding, which means that all cells in a matrix are rounded to the nearest hundred dollars. (For more information, see "Aggregate" under "Derived Measures.")
Mean income deficit represents the amount obtained by dividing the aggregate income deficit for a group below the poverty level by the number of families (or unrelated individuals) in that group. (The aggregate used to calculate mean income deficit is rounded. For more information, see "Aggregate Income Deficit.") As mentioned above, please use caution when comparing mean income deficits of families with different characteristics, as apparent differences may, to some extent, be a function of differences in family size. Mean income deficit is rounded to the nearest whole dollar. (For more information on means, see "Derived Measures.")
Since poverty is defined at the family level and not the household level, the poverty status of the household is determined by the poverty status of the householder. Households are classified as poor when the total income of the householder's family in the last 12 months is below the appropriate poverty threshold. (For nonfamily householders, their own income is compared with the appropriate threshold.) The income of people living in the household who are unrelated to the householder is not considered when determining the poverty status of a household, nor does their presence affect the family size in determining the appropriate threshold. The poverty thresholds vary depending upon three criteria: size of family, number of children, and, for one- and two- person families, age of the householder.
Derivation of the Current Poverty Measure - When the original poverty definition was developed in 1964 by the Social Security Administration (SSA), it focused on family food consumption. The U.S. Department of Agriculture (USDA) used its data about the nutritional needs of children and adults to construct food plans for families. Within each food plan, dollar amounts varied according to the total number of people in the family and the family's composition, that is, the number of children within each family. The cheapest of these plans, the Economy Food Plan, was designed to address the dietary needs of families on an austere budget.
Since the USDA's 1955 Food Consumption Survey showed that families of three or more people across all income levels spent roughly one-third of their income on food, the SSA multiplied the cost of the Economy Food Plan by three to obtain dollar figures for total family income. These dollar figures, with some adjustments, later became the official poverty thresholds. Since the Economy Food Plan budgets varied by family size and composition, so too did the poverty thresholds. For two-person families, the thresholds were adjusted by slightly higher factors because those households had higher fixed costs. Thresholds for unrelated individuals were calculated as a fixed proportion of the corresponding thresholds for two-person families.
The poverty thresholds are revised annually to allow for changes in the cost of living as reflected in the Consumer Price Index for All Urban Consumers (CPI-U). The poverty thresholds are the same for all parts of the country; they are not adjusted for regional, state, or local variations in the cost of living.
Since the USDA's 1955 Food Consumption Survey showed that families of three or more people across all income levels spent roughly one-third of their income on food, the SSA multiplied the cost of the Economy Food Plan by three to obtain dollar figures for total family income. These dollar figures, with some adjustments, later became the official poverty thresholds. Since the Economy Food Plan budgets varied by family size and composition, so too did the poverty thresholds. For two-person families, the thresholds were adjusted by slightly higher factors because those households had higher fixed costs. Thresholds for unrelated individuals were calculated as a fixed proportion of the corresponding thresholds for two-person families.
The poverty thresholds are revised annually to allow for changes in the cost of living as reflected in the Consumer Price Index for All Urban Consumers (CPI-U). The poverty thresholds are the same for all parts of the country; they are not adjusted for regional, state, or local variations in the cost of living.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. The part of the group quarters population in the poverty universe (for example, people living in group homes or those living in agriculture workers' dormitories) is many times more likely to be in poverty than people living in households. Direct comparisons of the data would likely result in erroneous conclusions about changes in the poverty status of all people in the poverty universe.
Because of differences in survey methodology (questionnaire design, method of data collection, sample size, etc.), the poverty rate estimates obtained from American Community Survey data may differ from those reported in the Current Population Survey, Annual Social and Economic Supplement, and those reported in Census 2000. Please refer to
(http://www.census.gov/hhes/www/poverty/about/datasources/description.html) for more details.
(http://www.census.gov/hhes/www/poverty/about/datasources/description.html) for more details.
The data on race were derived from answers to the question on race that was asked of all people. The U.S. Census Bureau collects race data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self- identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. In addition, it is recognized that the categories of the race item include racial and national origin or sociocultural groups. People may choose to report more than one race to indicate their racial mixture, such as "American Indian"and "White." People who identify their origin as Hispanic, Latino, or Spanish may be of any race.
The racial classifications used by the Census Bureau adhere to the October 30, 1997, Federal Register notice entitled, "Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity" issued by OMB. These standards govern the categories used to collect and present federal data on race and ethnicity. OMB requires five minimum categories (White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander) for race. The race categories are described below with a sixth category, "Some Other Race," added with OMB approval. In addition to the five race groups, OMB also states that respondents should be offered the option of selecting one or more races.
If an individual did not provide a race response, the race or races of the householder or other household members were imputed using specific rules of precedence of household relationship. For example, if race was missing for a natural-born child in the household, then either the race or races of the householder, another natural-born child, or spouse of the householder were imputed.
If race was not reported for anyone in the household, then the race or races of a householder in a previously processed household were imputed.
Definitions from OMB guide the Census Bureau in classifying written responses to the race question:
The racial classifications used by the Census Bureau adhere to the October 30, 1997, Federal Register notice entitled, "Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity" issued by OMB. These standards govern the categories used to collect and present federal data on race and ethnicity. OMB requires five minimum categories (White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander) for race. The race categories are described below with a sixth category, "Some Other Race," added with OMB approval. In addition to the five race groups, OMB also states that respondents should be offered the option of selecting one or more races.
If an individual did not provide a race response, the race or races of the householder or other household members were imputed using specific rules of precedence of household relationship. For example, if race was missing for a natural-born child in the household, then either the race or races of the householder, another natural-born child, or spouse of the householder were imputed.
If race was not reported for anyone in the household, then the race or races of a householder in a previously processed household were imputed.
Definitions from OMB guide the Census Bureau in classifying written responses to the race question:
A person having origins in any of the original peoples of Europe, the Middle East, or North Africa. It includes people who indicate their race as "White" or report entries such as Irish, German, Italian, Lebanese, Arab, Moroccan, or Caucasian.
A person having origins in any of the Black racial groups of Africa. It includes people who indicate their race as "Black, African Am., or Negro" or report entries such as African American, Kenyan, Nigerian, or Haitian.
A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment. This category includes people who indicate their race as "American Indian or Alaska Native" or report entries such as Navajo, Blackfeet, Inupiat, Yup'ik, or Central American Indian groups, or South American Indian groups.
Respondents who identified themselves as "American Indian or Alaska Native" were asked to report their enrolled or principal tribe. Therefore, tribal data in tabulations reflect the written entries reported on the questionnaires. Some of the entries (for example, Metlakatla Indian Community and Umatilla) represent reservations or a confederation of tribes on a reservation. The information on tribe is based on self-identification and, therefore, does not reflect any designation of federally or state-recognized tribe. The information for the 2011 ACS Detail Race tables were derived from the American Indian and Alaska Native Tribal Classification List for the 2010 Census, which was updated through 2009 based on the annual Federal Register notice entitled "Indian Entities Recognized and Eligible to Receive Services From the United States Bureau of Indian Affairs," Department of the Interior, Bureau of Indian Affairs, issued by OMB, and through consultation with American Indian and Alaska Native communities and leaders.
The American Indian categories shown in the 2011 ACS Detailed Race tables represent tribal groupings, which refer to the combining of individual American Indian tribes, such as Fort Sill Apache, Mescalero Apache, and San Carlos Apache, into the general Apache tribal grouping.
The Alaska Native categories shown in the 2011 ACS Detailed Race tables represent tribal groupings, which refer to the combining of individual Alaska Native tribes, such as King Salmon Tribe, Native Village of Kanatak, and Sun'aq Tribe of Kodiak, into the general Aleut tribal grouping.
Respondents who identified themselves as "American Indian or Alaska Native" were asked to report their enrolled or principal tribe. Therefore, tribal data in tabulations reflect the written entries reported on the questionnaires. Some of the entries (for example, Metlakatla Indian Community and Umatilla) represent reservations or a confederation of tribes on a reservation. The information on tribe is based on self-identification and, therefore, does not reflect any designation of federally or state-recognized tribe. The information for the 2011 ACS Detail Race tables were derived from the American Indian and Alaska Native Tribal Classification List for the 2010 Census, which was updated through 2009 based on the annual Federal Register notice entitled "Indian Entities Recognized and Eligible to Receive Services From the United States Bureau of Indian Affairs," Department of the Interior, Bureau of Indian Affairs, issued by OMB, and through consultation with American Indian and Alaska Native communities and leaders.
The American Indian categories shown in the 2011 ACS Detailed Race tables represent tribal groupings, which refer to the combining of individual American Indian tribes, such as Fort Sill Apache, Mescalero Apache, and San Carlos Apache, into the general Apache tribal grouping.
The Alaska Native categories shown in the 2011 ACS Detailed Race tables represent tribal groupings, which refer to the combining of individual Alaska Native tribes, such as King Salmon Tribe, Native Village of Kanatak, and Sun'aq Tribe of Kodiak, into the general Aleut tribal grouping.
Includes respondents who provide a response of another American Indian tribe not shown separately, such as Abenaki, Catawba, Eastern Tribes, Kickapoo, Mattaponi, Quapaw, Shawnee, or Yuchi.
Includes people who provide a generic term such as "American Indian" or tribal groupings not elsewhere classified.
Includes people who provide a generic term such as "Alaska Indian" or "Alaska Native" or tribal groupings not elsewhere classified.
Includes respondents who checked the American Indian or Alaska Native response category on the ACS questionnaire and did not write in a specific group or wrote in a generic term such as "American Indian or Alaska Native."
A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam. It includes people who indicate their race as "Asian Indian," "Chinese," "Filipino," "Korean," "Japanese," "Vietnamese," and "Other Asian" or provide other detailed Asian responses.
Includes respondents who indicate their race as "Asian Indian" or report entries such as India or East Indian.
Includes respondents who indicate their race as "Chinese" or report entries such as China or Chinese American.
Includes respondents who indicate their race as "Filipino" or report entries such as Philippines or Filipino American.
Includes respondents who report entries such as Hmong or Mong. Indonesian. Includes respondents who report entries such as Indonesian or Indonesia.
Includes respondents who indicate their race as "Japanese" or report entries such as Japan or Japanese American.
Includes respondents who indicate their race as "Korean" or report entries such as Korea or Korean American.
Includes respondents who indicate their race as "Vietnamese" or report entries such as Vietnam or Vietnamese American.
Includes respondents who provide a response of another Asian group not shown separately, such as Iwo Jiman, Maldivian, or Singaporean.
Includes respondents who checked the "Other Asian" response category on the ACS questionnaire and did not write in a specific group or wrote in a generic term such as "Asian," or "Asiatic."
A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands. It includes people who indicate their race as "Native Hawaiian," "Guamanian or Chamorro," "Samoan," and "Other Pacific Islander" or provide other detailed Pacific Islander responses.
Includes respondents who indicate their race as "Native Hawaiian" or report entries such as Part Hawaiian or Hawaiian.
Includes respondents who indicate their race as "Samoan" or report entries such as American Samoan or Western Samoan.
Includes respondents who provide a response of another Polynesian group, such as Tahitian, Tokelauan, or wrote in a generic term such as "Polynesian."
Includes respondents who indicate their race as "Guamanian or Chamorro" or report entries such as Chamorro or Guam.
Includes respondents who provide a response of another Micronesian group, such as Carolinian, Chuukese, I-Kiribati, Kosraean, Mariana Islander, Palauan, Pohnpeian, Saipanese, Yapese, or wrote in a generic term such as "Micronesian."
Includes respondents who provide a response of another Melanesian group, such as Papua New Guinean, Ni-Vanuatu (New Hebrides Islander), Solomon Islander, or wrote in a generic term such as "Melanesian."
Includes respondents who checked the Other Pacific Islander response category on the ACS questionnaire and did not write in a specific group or wrote in a generic term such as "Pacific Islander."
Includes all other responses not included in the "White," "Black or African American," "American Indian or Alaska Native," "Asian," and "Native Hawaiian or Other Pacific Islander" race categories described above. Respondents reporting entries such as multiracial, mixed, interracial, or a Hispanic, Latino, or Spanish group (for example, Mexican, Puerto Rican, Cuban, or Spanish) in response to the race question are included in this category.
People may chose to provide two or more races either by checking two or more race response check boxes, by providing multiple responses, or by some combination of check boxes and other responses. The race response categories shown on the questionnaire are collapsed into the five minimum race groups identified by OMB, and the Census Bureau's "Some Other Race" category. For data product purposes, "Two or More Races" refers to combinations of two or more of the following race categories
- White
- Black or African American
- American Indian or Alaska Native
- Asian
- Native Hawaiian or Other Pacific Islander
- Some Other Race
Given the many possible ways of displaying data on race, data products will provide varying levels of detail. There are several concepts used to display and tabulate race information for the six major race categories (White; Black or African American; American Indian or Alaska Native; Asian; Native Hawaiian or Other Pacific Islander; and Some Other Race) and the various details within these groups.
The concept "race alone" includes people who reported a single entry (i.e., Korean) and no other race, as well as people who reported two or more entries within the same major race group (i.e., Asian). For example, respondents who reported Korean and Vietnamese are part of the larger "Asian alone" race group.
The concept "race alone or in combination" includes people who reported a single race alone (i.e., Asian) and people who reported that race in combination with one or more of the other major race groups (i.e., White, Black or African American, American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, and Some Other Race). The "race alone or in combination" concept, therefore, represents the maximum number of people who reported as that race group, either alone, or in combination with another race(s). The sum of the six individual race "alone or in combination" categories may add to more than the total population because people who reported more than one race were tallied in each race category.
The concept "race alone or in any combination" applies only to detailed race iteration groups, such as American Indian and Alaska Native tribes, detailed Asian groups, and detailed Pacific Islander groups. For example, Korean alone or in any combination includes people who reported a single response (i.e., Korean), people who reported Korean and another Asian group (i.e., Korean and Vietnamese), and people who reported Korean in combination with one or more other non-Asian race groups (i.e., White, Black or African American, American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, or Some Other Race).
The concept "race alone" includes people who reported a single entry (i.e., Korean) and no other race, as well as people who reported two or more entries within the same major race group (i.e., Asian). For example, respondents who reported Korean and Vietnamese are part of the larger "Asian alone" race group.
The concept "race alone or in combination" includes people who reported a single race alone (i.e., Asian) and people who reported that race in combination with one or more of the other major race groups (i.e., White, Black or African American, American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, and Some Other Race). The "race alone or in combination" concept, therefore, represents the maximum number of people who reported as that race group, either alone, or in combination with another race(s). The sum of the six individual race "alone or in combination" categories may add to more than the total population because people who reported more than one race were tallied in each race category.
The concept "race alone or in any combination" applies only to detailed race iteration groups, such as American Indian and Alaska Native tribes, detailed Asian groups, and detailed Pacific Islander groups. For example, Korean alone or in any combination includes people who reported a single response (i.e., Korean), people who reported Korean and another Asian group (i.e., Korean and Vietnamese), and people who reported Korean in combination with one or more other non-Asian race groups (i.e., White, Black or African American, American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, or Some Other Race).
The 2011 ACS included an automated review, computer edit, and coding operation on a 100 percent basis for the write-in responses to the race question, similar to that used in the 2010 Census. There were two types of coding operations: (1) automated coding where a write-in response was automatically coded if it matched a write-in response already contained in a database known as the "master file" and (2) expert coding, which took place when a write-in response did not match an entry already on the master file and was sent to expert coders familiar with the subject matter. During the coding process, subject-matter specialists reviewed and coded written entries from the response areas on the race question: American Indian or Alaska Native, Other Asian, Other Pacific Islander, and Some Other Race. Up to 30 text characters were collected from each race write-in area, and up to two responses were coded and tabulated from each separate race write-in area.
- The sequence of the questions on race and Hispanic origin was switched. In the 1996-1998 ACS, the question on race immediately followed the question on Hispanic origin. This approach differed from the 1990 census, where the question on race preceded the question on Hispanic origin with two intervening questions.
- The 1990 census category, "Black or Negro" was changed to "Black, African Am."
- The 1990 census category, "Other race," was renamed "Some other race." A separate "Multiracial" category was added. The instruction to "print the race(s) or group below" pertained to both the "Some other race" and "Multiracial" categories.
- The "Indian (Amer.)," "Other Asian/Pacific Islander," "Some other race," and "Multiracial" response categories all shared a single write-in area.
- The response category "Black, African Am." was changed to "Black, African Am., or Negro" to correspond with the Census 2000 response category.
- The separate 1990 census and 1996-1998 ACS response categories "Indian (Amer.)," "Eskimo," and "Aleut," were combined into one response category, "American Indian or Alaska Native." Respondents were asked to "print name of enrolled or principal tribe" on a separate write-in line to correspond with the Census 2000 response category.
- The 1990 Asian or Pacific Islander category was separated into two categories, "Asian" and "Native Hawaiian or Other Pacific Islander." Also, the six detailed Asian groups were alphabetized; and the three detailed Pacific Islander groups were alphabetized after the Native Hawaiian response category.
- The response category "Hawaiian" was changed to "Native Hawaiian." The response category "Guamanian" was changed to "Guamanian or Chamorro." The response category "Other Asian/Pacific Islander" was split into two separate response categories, "Other Asian," and "Other Pacific Islander." These changes correspond to those in the Census 2000 response categories.
- The separate "multiracial" response category was dropped. Rather, respondents were instructed to "Mark [x] one or more races to indicate what this person considers himself/herself to be." Respondents were allowed to select more than one category for race in Census 2000.
- In the American Community Survey, the "Other Asian," "Other Pacific Islander," and "Some other race" response categories shared the same write-in area. On the Census 2000 questionnaire, only the "Other Asian" and "Other Pacific Islander" response categories shared the same write-in area, and the "Some other race" category had a separate write-in area.
- The response category "Black, African Am., or Negro" was changed to "Black or African American."
- Separate questions on race and Hispanic origin were included on the questionnaire. These questions were identical to the questions used in the United States.
- The wording of the race question was changed to read, "What is Person 1's race? Mark (X) one or more boxes" and the reference to what this person considers him/herself to be was deleted.
- The response category "Black or African American" was changed to "Black, African Am., or Negro."
- Examples were added to the "Other Asian" response categories (Hmong, Laotian, Thai, Pakistani, Cambodian, and so on) and the "Other Pacific Islander" response categories (Fijian, Tongan, and so on).
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have race distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the race distribution. This is particularly true for areas with a substantial GQ population.
The data on race in the American Community Survey are not directly comparable across all years. Ongoing research conducted following the 1990 census affected the ACS question on race since its inception in 1996. Also, the October 1997 revised standards for federal data on race and ethnicity issued by the OMB led to changes in the question on race for Census 2000. Consequently, in order to achieve consistency, other census-administered surveys such as the ACS were modified to reflect changes required by OMB.
See the 2011 Code List for Race Code List.
See the 2011 Code List for Race Code List.
The data on employment status and journey to work relate to the reference week, that is, the calendar week preceding the date on which the respondents completed their questionnaires or were interviewed. This week is not the same for all respondents since the interviewing was conducted over a 12-month period. The occurrence of holidays during the relative reference week could affect the data on actual hours worked during the reference week, but probably had no effect on overall measurement of employment status.
The data on residence 1 year ago were derived from answers to Question 15, which were asked of the population 1 year and older. For the American Community Survey, people who had moved from another residence in the United States or Puerto Rico 1 year earlier were asked to report the exact address (number and street name); the name of the city, town, or post office; the name of the U.S. county or municipio in Puerto Rico; state or Puerto Rico; and the ZIP Code where they lived 1 year ago. People living outside the United States and Puerto Rico were asked to report the name of the foreign country or U.S. Island Area where they were living 1 year ago.
For the Puerto Rico Community Survey, people who moved from another residence in Puerto Rico or the United States 1 year ago were asked to report the exact address, including the development or condominium name; the name of the city, town, or post office; the municipio in Puerto Rico (county equivalent) or county in the U.S.; and the ZIP Code where they lived. People living outside Puerto Rico and the United States were asked to report the name of the foreign country or U.S. Island Area where they were living 1 year ago.
Residence 1 year ago is used in conjunction with location of current residence to determine the extent of residential mobility of the population and the resulting redistribution of the population across the various states, metropolitan areas, and regions of the country.
When no information on previous residence was reported for a person, information for other family members, if available, was used to assign a location of residence 1 year ago. All cases of nonresponse or incomplete response that were not assigned a previous residence based on information from other family members were allocated the previous residence of another person with similar characteristics who provided complete information.
The tabulation category, "Same house," includes all people 1 year and over who did not move during the 1 year as well as those who had moved and returned to their residence 1 year ago. The category, "Different house in the United States" includes people who lived in the United States 1 year ago but in a different house or apartment from the one they occupied at the time of interview. These movers are then further subdivided according to the type of move.
In most tabulations, movers within the U.S. are divided into three groups according to their previous residence: "Different house, same county," "Different county, same state," and "Different state." The last group may be further subdivided into region of residence 1 year ago. An additional category, "Abroad," includes those whose previous residence was in a foreign country, Puerto Rico, American Samoa, Guam, the Northern Marianas, or the U.S. Virgin Islands, including members of the Armed Forces and their dependents. Some tabulations show movers who were residing in Puerto Rico or one of the U.S. Island Areas 1 year ago separately from those residing in foreign countries.
In most tabulations, movers within Puerto Rico are divided into two groups according to their residence 1 year ago: "Same municipio," and "Different municipio." Other tabulations show movers within or between metropolitan areas similar to the stateside tabulations.
For the Puerto Rico Community Survey, people who moved from another residence in Puerto Rico or the United States 1 year ago were asked to report the exact address, including the development or condominium name; the name of the city, town, or post office; the municipio in Puerto Rico (county equivalent) or county in the U.S.; and the ZIP Code where they lived. People living outside Puerto Rico and the United States were asked to report the name of the foreign country or U.S. Island Area where they were living 1 year ago.
Residence 1 year ago is used in conjunction with location of current residence to determine the extent of residential mobility of the population and the resulting redistribution of the population across the various states, metropolitan areas, and regions of the country.
When no information on previous residence was reported for a person, information for other family members, if available, was used to assign a location of residence 1 year ago. All cases of nonresponse or incomplete response that were not assigned a previous residence based on information from other family members were allocated the previous residence of another person with similar characteristics who provided complete information.
The tabulation category, "Same house," includes all people 1 year and over who did not move during the 1 year as well as those who had moved and returned to their residence 1 year ago. The category, "Different house in the United States" includes people who lived in the United States 1 year ago but in a different house or apartment from the one they occupied at the time of interview. These movers are then further subdivided according to the type of move.
In most tabulations, movers within the U.S. are divided into three groups according to their previous residence: "Different house, same county," "Different county, same state," and "Different state." The last group may be further subdivided into region of residence 1 year ago. An additional category, "Abroad," includes those whose previous residence was in a foreign country, Puerto Rico, American Samoa, Guam, the Northern Marianas, or the U.S. Virgin Islands, including members of the Armed Forces and their dependents. Some tabulations show movers who were residing in Puerto Rico or one of the U.S. Island Areas 1 year ago separately from those residing in foreign countries.
In most tabulations, movers within Puerto Rico are divided into two groups according to their residence 1 year ago: "Same municipio," and "Different municipio." Other tabulations show movers within or between metropolitan areas similar to the stateside tabulations.
The characteristics of movers may be shown using either current residence-based or previous residence-based geography. If you are interested in the number and characteristics of movers living in a specific area, you should use the standard (residence-based) tables. If you are interested in the number and characteristics of movers who previous residence was in a specific area, you should use the residence-1-year-ago-based tables. Because residence-1-year-ago information for movers cannot always be specified below the place level, the previous residence-based tables are presented only for selected geographic areas.
Residence 1 year ago is used to assess the residential stability and the effects of migration in both urban and rural areas. This item provides information on the mobility of our population. Knowing the number and characteristics of movers is essential for federal programs dealing with employment, housing, education, and the elderly. The U.S. Department of Veterans Affairs develops its mandated projection of the need for hospitals and other veteran benefits for each state with migration data about veterans. The Census Bureau develops state age and sex estimates and small-area population projections based on data about residence 1 year ago.
Residence 1 year ago is used to assess the residential stability and the effects of migration in both urban and rural areas. This item provides information on the mobility of our population. Knowing the number and characteristics of movers is essential for federal programs dealing with employment, housing, education, and the elderly. The U.S. Department of Veterans Affairs develops its mandated projection of the need for hospitals and other veteran benefits for each state with migration data about veterans. The Census Bureau develops state age and sex estimates and small-area population projections based on data about residence 1 year ago.
The 1996-1998 questions asked about residence 5 years ago. Beginning in 1999, the time period was changed to that of 1 year ago, which reflects the ongoing data collection on the American Community Survey, and allows for annual estimates of migration. Beginning in 1999, a separate write-in line and a skip instruction were added for a foreign country response. This write-in line was moved to one of the answer categories for the residence one year ago question. The migration parts (city, county, and state response areas) were also reordered. Beginning in 2003, the numerical order was changed so that part c of this question would not be displayed in a separate column of the questionnaire. Beginning with 2008, a write-in space for street address was included and the questions were reworded on both the ACS and the PRCS so that the geographic specificity is maintained for movers within and between the U.S. and Puerto Rico. Municipio of previous residence in Puerto Rico is available for people living in the United States as a result of this change. For more information, see the report titled Report P.3: Evaluation Report Covering Residence 1 Year Ago (Migration).
Beginning in 2006, the group quarters (GQ) population is included in the ACS. Some types of GQ populations have residence one year ago (migration) distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the residence one year ago (migration) distribution. This is particularly true for areas with a substantial GQ population.
School enrollment data are used to assess the socioeconomic condition of school-age children. Government agencies also require these data for funding allocations and program planning and implementation.
Data on school enrollment and grade or level attending were derived from answers to Question 10. People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to "include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree". Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.
School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in "vocational, technical, or business school" such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers.
Data on school enrollment and grade or level attending were derived from answers to Question 10. People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to "include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree". Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.
School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in "vocational, technical, or business school" such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers.
Includes people who attended school in the reference period and indicated they were enrolled by marking one of the questionnaire categories for "public school, public college," or "private school, private college, home school." The instruction guide defines a public school as "any school or college controlled and supported primarily by a local, county, state, or federal government." Private schools are defined as schools supported and controlled primarily by religious organizations or other private groups. Home schools are defined as "parental-guided education outside of public or private school for grades 1-12." Respondents who marked both the "public" and "private" boxes are edited to the first entry, "public."
From 1999-2007, in the American Community Survey, people reported to be enrolled in "public school, public college" or "private school, private college" were classified by grade or level according to responses to Question 10b, "What grade or level was this person attending?" Seven levels were identified: "nursery school, preschool;" "kindergarten;" elementary "grade 1 to grade 4" or "grade 5 to grade 8;" high school "grade 9 to grade 12;" "college undergraduate years (freshman to senior);" and "graduate or professional school (for example: medical, dental, or law school)."
In 2008, the school enrollment questions had several changes. "Home school" was explicitly included in the "private school, private college" category. For question 10b the categories changed to the following "Nursery school, preschool," "Kindergarten," "Grade 1 through grade 12," "College undergraduate years (freshman to senior)," "Graduate or professional school beyond a bachelor's degree (for example: MA or PhD program, or medical or law school)." The survey question allowed a write-in for the grades enrolled from 1-12.
In 2008, the school enrollment questions had several changes. "Home school" was explicitly included in the "private school, private college" category. For question 10b the categories changed to the following "Nursery school, preschool," "Kindergarten," "Grade 1 through grade 12," "College undergraduate years (freshman to senior)," "Graduate or professional school beyond a bachelor's degree (for example: MA or PhD program, or medical or law school)." The survey question allowed a write-in for the grades enrolled from 1-12.
Since 1999, the American Community Survey enrollment status question (Question 10a) refers to "regular school or college," while the 1996-1998 American Community Survey did not restrict reporting to "regular" school, and contained an additional category for the "vocational, technical or business school."
The 1996-1998 American Community Survey used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 American Community Survey questionnaire.
The 1996-1998 American Community Survey used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 American Community Survey questionnaire.
Beginning in 2006, the population universe in the American Community Survey includes people living in group quarters. Data users may see slight differences in levels of school enrollment in any given geographic area due to the inclusion of this population. The extent of this difference, if any, depends on the type of group quarters present and whether the group quarters population makes up a large proportion of the total population. For example, in areas that are home to several colleges and universities, the percent of individuals 18 to 24 who were enrolled in college or graduate school would increase, as people living in college dormitories are now included in the universe.
Data about level of enrollment are also collected from the decennial Census and from the Current Population Survey (CPS). ACS data is generally comparable to data from the Census. Although it should be noted that the ACS reference period was 3 months preceding the date of interview, while the Census 2000 reference period was any time since February 1, 2000. For more information about the comparability of ACS and CPS data, please see the link for the Fact Sheet from the CPS School Enrollment page.
Data on school enrollment also are collected and published by other federal, state, and local government agencies. Because these data are obtained from administrative records of school systems and institutions of higher learning, they are only roughly comparable to data from population censuses and surveys. Differences in definitions and concepts, subject matter covered, time references, and data collection methods contribute to the differences in estimates. At the local level, the difference between the location of the institution and the residence of the student may affect the comparability of census and administrative data because census data are collected from and based on a respondent's residence. Differences between the boundaries of school districts and census geographic units also may affect these comparisons.
Data on school enrollment also are collected and published by other federal, state, and local government agencies. Because these data are obtained from administrative records of school systems and institutions of higher learning, they are only roughly comparable to data from population censuses and surveys. Differences in definitions and concepts, subject matter covered, time references, and data collection methods contribute to the differences in estimates. At the local level, the difference between the location of the institution and the residence of the student may affect the comparability of census and administrative data because census data are collected from and based on a respondent's residence. Differences between the boundaries of school districts and census geographic units also may affect these comparisons.
The data on sex were derived from answers to Question 3. Individuals were asked to mark either "male" or "female" to indicate their biological sex. For most cases in which sex was invalid, the appropriate entry was determined from other information provided for that person, such as the person's given (i.e., first) name and household relationship. Otherwise, sex was allocated from a hot deck.
Sex is asked for all persons in a household or group quarters. On the mailout/mailback paper questionnaire for households, sex is asked for all persons listed on the form. This form accommodates asking sex for up to 12 people listed as living or residing in the household for at least 2 months. If a respondent indicates that more people are listed as part of the total persons living in the household than the form can accommodate, or if any person included on the form is missing sex, then the household is eligible for Failed Edit Follow-up (FEFU). During FEFU operations, telephone center staffers call respondents to obtain missing data. This includes asking sex for any person in the household missing sex information. In Computer Assisted Telephone Interviews (CATI) and Computer Assisted Personal Interview (CAPI) instruments sex is asked for all persons. In 2006, the ACS began collecting data in group quarters (GQs). This included asking sex for persons living in a group quarters. For additional data collection methodology, please see www.census.gov/acs.
Data on sex are used to determine the applicability of other questions for a particular individual and to classify other characteristics in tabulations. The sex data collected on the forms are aggregated and provide the number of males and females in the population. These data are needed to interpret most social and economic characteristics used to plan and analyze programs and policies. Data about sex are critical because so many federal programs must differentiate between males and females. The U.S. Departments of Education and Health and Human Services are required by statute to use these data to fund, implement, and evaluate various social and welfare programs, such as the Special Supplemental Food Program for Women, Infants, and Children (WIC) or the Low-Income Home Energy Assistance Program (LIHEAP). Laws to promote equal employment opportunity for women also require census data on sex. The U.S. Department of Veterans Affairs must use census data to develop its state projections of veterans' facilities and benefits. For more information on the use of sex data in Federal programs, please see www.census.gov/acs.
Sex is asked for all persons in a household or group quarters. On the mailout/mailback paper questionnaire for households, sex is asked for all persons listed on the form. This form accommodates asking sex for up to 12 people listed as living or residing in the household for at least 2 months. If a respondent indicates that more people are listed as part of the total persons living in the household than the form can accommodate, or if any person included on the form is missing sex, then the household is eligible for Failed Edit Follow-up (FEFU). During FEFU operations, telephone center staffers call respondents to obtain missing data. This includes asking sex for any person in the household missing sex information. In Computer Assisted Telephone Interviews (CATI) and Computer Assisted Personal Interview (CAPI) instruments sex is asked for all persons. In 2006, the ACS began collecting data in group quarters (GQs). This included asking sex for persons living in a group quarters. For additional data collection methodology, please see www.census.gov/acs.
Data on sex are used to determine the applicability of other questions for a particular individual and to classify other characteristics in tabulations. The sex data collected on the forms are aggregated and provide the number of males and females in the population. These data are needed to interpret most social and economic characteristics used to plan and analyze programs and policies. Data about sex are critical because so many federal programs must differentiate between males and females. The U.S. Departments of Education and Health and Human Services are required by statute to use these data to fund, implement, and evaluate various social and welfare programs, such as the Special Supplemental Food Program for Women, Infants, and Children (WIC) or the Low-Income Home Energy Assistance Program (LIHEAP). Laws to promote equal employment opportunity for women also require census data on sex. The U.S. Department of Veterans Affairs must use census data to develop its state projections of veterans' facilities and benefits. For more information on the use of sex data in Federal programs, please see www.census.gov/acs.
The sex ratio represents the balance between the male and female populations. Ratios above 100 indicate a larger male population, and ratios below 100 indicate a larger female population. This measure is derived by dividing the total number of males by the total number of females and then multiplying by 100. It is rounded to the nearest tenth.
Sex has been asked of all persons living in a household since the 1996 ACS Test phase. When group quarters were included in the survey universe in 2006, sex was asked of all person in group quarters as well.
Beginning in 2008, the layout of the sex question response categories was changed to a horizontal side-by-side layout from a vertically stacked layout on the mail paper ACS questionnaire
Beginning in 2008, the layout of the sex question response categories was changed to a horizontal side-by-side layout from a vertically stacked layout on the mail paper ACS questionnaire
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. Some types of GQ populations have sex distributions that are very different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the sex distribution. This is particularly true for a given geographic area. This is particularly true for areas with a substantial GQ population.
The Census Bureau tested the changes introduced to the 2008 version of the sex question in the 2007 ACS Grid-Sequential Test (www.census.gov/acs). The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008.
The Census Bureau tested the changes introduced to the 2008 version of the sex question in the 2007 ACS Grid-Sequential Test (www.census.gov/acs). The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008.
Sex is generally comparable across different data sources and data years.
However, data users should still be aware of methodological differences that may exist between different data sources if they are comparing American Community Survey sex data to other data sources, such as Population Estimates or Decennial Census data. For example, the American Community Survey data are that of a respondent-based survey and subject to various quality measures, such as sampling and nonsampling error, response rates and item allocation. This differs in design and methodology from other data sources, such as Population Estimates, which is not a survey and involves computational methodology to derive intercensal estimates of the population. While ACS estimates are controlled to Population Estimates for sex at the nation, state and county levels of geography as part of the ACS weighting procedure, variation may exist in the sex structure of a population at lower levels of geography when comparing different time periods or comparing across time due to the absence of controls below the county geography level. For more information on American Community Survey data accuracy and weighting procedures, please see www.census.gov/acs.
It should also be noted that although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties .
However, data users should still be aware of methodological differences that may exist between different data sources if they are comparing American Community Survey sex data to other data sources, such as Population Estimates or Decennial Census data. For example, the American Community Survey data are that of a respondent-based survey and subject to various quality measures, such as sampling and nonsampling error, response rates and item allocation. This differs in design and methodology from other data sources, such as Population Estimates, which is not a survey and involves computational methodology to derive intercensal estimates of the population. While ACS estimates are controlled to Population Estimates for sex at the nation, state and county levels of geography as part of the ACS weighting procedure, variation may exist in the sex structure of a population at lower levels of geography when comparing different time periods or comparing across time due to the absence of controls below the county geography level. For more information on American Community Survey data accuracy and weighting procedures, please see www.census.gov/acs.
It should also be noted that although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties .
Data on veteran status and period of military service were derived from answers to Questions 26 and 27.
Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty, not counting the 4-6 months for initial training or yearly summer camps. All other civilians are classified as nonveterans.
While it is possible for 17 year olds to be veterans of the Armed Forces, ACS data products are restricted to the population 18 years and older.
Answers to this question provide specific information about veterans. Veteran status is used to identify people with active duty military service and service in the military Reserves and the National Guard. ACS data define civilian veteran as a person 18 years old and over who served (even for a short time), but is not now serving on acting duty in the U.S. Army, Navy, Air Force, Marine Corps or Coast Guard, or who served as a Merchant Marine seaman during World War II. Individuals who have training for Reserves or National Guard but no active duty service are not considered veterans in the ACS. These data are used primarily by the Department of Veterans Affairs to measure the needs of veterans.
Other uses include:
While it is possible for 17 year olds to be veterans of the Armed Forces, ACS data products are restricted to the population 18 years and older.
Answers to this question provide specific information about veterans. Veteran status is used to identify people with active duty military service and service in the military Reserves and the National Guard. ACS data define civilian veteran as a person 18 years old and over who served (even for a short time), but is not now serving on acting duty in the U.S. Army, Navy, Air Force, Marine Corps or Coast Guard, or who served as a Merchant Marine seaman during World War II. Individuals who have training for Reserves or National Guard but no active duty service are not considered veterans in the ACS. These data are used primarily by the Department of Veterans Affairs to measure the needs of veterans.
Other uses include:
- Used at state and county levels to plan programs for medical and nursing home care for veterans.
- Used by the Department of Veterans Affairs (VA) to plan the locations and sizes of veterans' cemeteries.
- Used by local agencies, under the Older Americans Act, to develop health care and other services for elderly veterans.
For the 1999-2002 American Community Survey, the question was changed to match the Census 2000 item. The response categories were modified by expanding the "No active duty service" answer category to distinguish persons whose only military service was for training in the Reserves or National Guard, from persons with no military experience whatsoever.
Beginning in 2003, the "Yes, on active duty in the past, but not now" category was split into two categories. Veterans are now asked whether or not their service ended in the last 12 months.
Beginning in 2003, the "Yes, on active duty in the past, but not now" category was split into two categories. Veterans are now asked whether or not their service ended in the last 12 months.
There may be a tendency for the following kinds of persons to report erroneously that they served on active duty in the Armed Forces: (a) persons who served in the National Guard or Military Reserves but were never called to duty; (b) civilian employees or volunteers for the USO, Red Cross, or the Department of Defense (or its predecessors, the Department of War and the Department of the Navy); and (c) employees of the Merchant Marine or Public Health Service.
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. Some types of GQ populations may have period of military service and veteran status distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the period of service and veteran status distributions. This is particularly true for areas with a substantial GQ population.
Beginning in 2006, the population in group quarters (GQ) was included in the ACS. Some types of GQ populations may have period of military service and veteran status distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the period of service and veteran status distributions. This is particularly true for areas with a substantial GQ population.
The ACS has two separate questions for veteran status and period of military service, whereas in Census 2000, it was a two-part question. The wording for the veteran status question remains the same, however, the response categories have changed over time (see the section "Question/Concept History").
The Group Quarters (GQ) population was included in the 2006 ACS and not included in prior years of ACS data, thus comparisons should be made only if the geographic area of interest does not include a substantial GQ population.
For comparisons to the Current Population Survey (CPS), please see "Comparison of ACS and ASEC Data on Veteran Status and Period of Military Service: 2007."
The Group Quarters (GQ) population was included in the 2006 ACS and not included in prior years of ACS data, thus comparisons should be made only if the geographic area of interest does not include a substantial GQ population.
For comparisons to the Current Population Survey (CPS), please see "Comparison of ACS and ASEC Data on Veteran Status and Period of Military Service: 2007."
People who indicate that they had ever served on active duty in the past or were currently on active duty are asked to indicate the period or periods in which they served. Currently, there are 11 periods of service on the ACS questionnaire. Respondents are instructed to mark a box for each period in which they served, even if just for part of the period. The periods were determined by the Department of Veterans Affairs and generally alternate between peacetime and wartime, with a few exceptions. The responses to this question are edited for consistency and reasonableness. The edit eliminates inconsistencies between reported period(s) of service and age of the person; it also removes reported combinations of periods containing unreasonable gaps (for example, it will not accept a response that indicated the person had served in World War II and in the Vietnam era, but not in the Korean conflict).
Period of military service distinguishes veterans who served during wartime periods from those whose only service was during peacetime. Questions about period of military service provide necessary information to estimate the number of veterans who are eligible to receive specific benefits.
Period of military service distinguishes veterans who served during wartime periods from those whose only service was during peacetime. Questions about period of military service provide necessary information to estimate the number of veterans who are eligible to receive specific benefits.
In 1999, the response categories were modified by closing the "August 1990 or later (including Persian Gulf War)" period at March 1995, and adding the "April 1995" or later category.
For the 2001-2002 American Community Survey question, the response category was changed from "Korean conflict" to "Korean War."
Beginning in 2003, the response categories for the question were modified in several ways. The first category "April 1995 or later" was changed to "September 2001 or later" to reflect the era that began after the events of September 11, 2001; the second category "August 1990 to March 1995" was then expanded to "August 1990 to August 2001 (including Persian Gulf War)." The category "February 1955 to July 1964" was split into two categories: "March 1961 to July 1964" and "February 1955 to February 1961." To match the revised dates for war-time periods of the Department of Veterans Affairs, the dates for the "World War II" category were changed from "September 1940 to July 1947" to "December 1941 to December 1946," and the dates for the "Korean War" were changed from "June 1950 to January 1955" to "July 1950 to January 1955." To increase specificity, the "Some other time" category was split into two categories: "January 1947 to June 1950" and "November 1941 or earlier."
For the 2001-2002 American Community Survey question, the response category was changed from "Korean conflict" to "Korean War."
Beginning in 2003, the response categories for the question were modified in several ways. The first category "April 1995 or later" was changed to "September 2001 or later" to reflect the era that began after the events of September 11, 2001; the second category "August 1990 to March 1995" was then expanded to "August 1990 to August 2001 (including Persian Gulf War)." The category "February 1955 to July 1964" was split into two categories: "March 1961 to July 1964" and "February 1955 to February 1961." To match the revised dates for war-time periods of the Department of Veterans Affairs, the dates for the "World War II" category were changed from "September 1940 to July 1947" to "December 1941 to December 1946," and the dates for the "Korean War" were changed from "June 1950 to January 1955" to "July 1950 to January 1955." To increase specificity, the "Some other time" category was split into two categories: "January 1947 to June 1950" and "November 1941 or earlier."
There may be a tendency for people to mark the most recent period in which they served or the period in which they began their service, but not all periods in which they served.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have period of military service and veteran status distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the period of service and veteran status distributions. This is particularly true for areas with a substantial GQ population.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have period of military service and veteran status distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the period of service and veteran status distributions. This is particularly true for areas with a substantial GQ population.
Since Census 2000, the period of military service categories on the ACS questionnaire were updated to: 1) include the most recent period "September 2001 or later;" 2) list all "peace time" periods without showing a date-breakup in the list; and 3) update the Korean War and World War II dates to match the official dates as listed in US Code, Title 38. While the response categories differ slightly from those in Census 2000, data from the two questions can still be compared to one another.
Due to an editing error, veteran's period of service (VPS) prior to 2007 was being incorrectly assigned for some individuals. The majority of the errors misclassified some people who reported only serving during the Vietnam Era as having served in the category "Gulf War and Vietnam Era." The remainder of the errors misclassified some people who reported only serving between the Vietnam Era and Gulf War as having served in the category "Gulf War."
The Group Quarters (GQ) population was included in the 2006 ACS and not included in prior years of ACS data, thus comparisons should be made only if the geographic area of interest does not include a substantial GQ population.
For comparisons to the Current Population Survey (CPS), please see "Comparison of ACS and ASEC Data on Veteran Status and Period of Military Service: 2007."
Due to an editing error, veteran's period of service (VPS) prior to 2007 was being incorrectly assigned for some individuals. The majority of the errors misclassified some people who reported only serving during the Vietnam Era as having served in the category "Gulf War and Vietnam Era." The remainder of the errors misclassified some people who reported only serving between the Vietnam Era and Gulf War as having served in the category "Gulf War."
The Group Quarters (GQ) population was included in the 2006 ACS and not included in prior years of ACS data, thus comparisons should be made only if the geographic area of interest does not include a substantial GQ population.
For comparisons to the Current Population Survey (CPS), please see "Comparison of ACS and ASEC Data on Veteran Status and Period of Military Service: 2007."
Data on service-connected disability- rating status and service-connected disability ratings were derived from answers to Questions 28a and 28b.
People who indicated they had served on active duty in the U.S. Armed Forces, military Reserves, or National Guard, or trained with the Reserves or National Guard or were now on active duty were asked to indicate whether or not they had a Department of Veterans Affairs (VA) service-connected disability rating. These disabilities are evaluated according to the VA Schedule for Rating Disabilities in Title 38, U.S. Code of Federal Regulations, Part 4.
"Service-connected" means the disability was a result of disease or injury incurred or aggravated during active military service.
The Department of Veterans Affairs (VA) uses a priority system to allocate health care services among veterans enrolled in its programs. Data on service-connected disability status and ratings are used by the Department of Veterans Affairs to measure the demand for VA health care services in local market areas across the country as well as to classify veterans into priority groups for VA health care enrollment.
"Service-connected" means the disability was a result of disease or injury incurred or aggravated during active military service.
The Department of Veterans Affairs (VA) uses a priority system to allocate health care services among veterans enrolled in its programs. Data on service-connected disability status and ratings are used by the Department of Veterans Affairs to measure the demand for VA health care services in local market areas across the country as well as to classify veterans into priority groups for VA health care enrollment.
This question was added to the American Community Survey in 2008. For more information, see "Evaluation Report Covering Service-Connected Disability."
There may be a tendency for people to erroneously report having a 0 percent rating when they have no service-connected disability rating at all.
This question is asked of people who reported having a VA service-connected disability rating. These ratings are graduated according to degrees of disability on a scale from 0 to 100 percent, in increments of 10 percent. The ratings determine the amount of compensation payments made to the veterans. A zero-rating, which is different than having no rating at all, means a disability exists but it is not so disabling that it entitles the veteran to compensation payments.
The Department of Veterans Affairs (VA) uses a priority system to allocate health care services among veterans enrolled in its programs. Data on service-connected disability status and ratings are used by the Department of Veterans Affairs to measure the demand for VA health care services in local market areas across the country as well as to classify veterans into priority groups for VA health care enrollment.
The Department of Veterans Affairs (VA) uses a priority system to allocate health care services among veterans enrolled in its programs. Data on service-connected disability status and ratings are used by the Department of Veterans Affairs to measure the demand for VA health care services in local market areas across the country as well as to classify veterans into priority groups for VA health care enrollment.
This question was added to the American Community Survey in 2008. For more information, see "Evaluation Report Covering Service-Connected Disability."
There may be a tendency for people to erroneously report having a 0 percent rating when they have no service-connected disability rating at all.
The data on work experience were derived from answers to Questions 38, 39, and 40. This term relates to work status in the past 12 months, weeks worked in the past 12 months, and usual hours worked per week worked in the past 12 months.
To comply with provisions of the Civil Rights Act, the U.S. Department of Justice uses these data to determine the availability of individuals for work. Government agencies, in considering the programmatic and policy aspects of providing federal assistance to areas, have emphasized the requirements for reliable data to determine the employment resources available. Data about the number of weeks and hours worked last year are essential because these data allow the characterization of workers by full-time/part-time and full-year/part-year status. Data about working last year are also necessary for collecting accurate income data by defining the universe of persons who should have earnings as part of their total income.
To comply with provisions of the Civil Rights Act, the U.S. Department of Justice uses these data to determine the availability of individuals for work. Government agencies, in considering the programmatic and policy aspects of providing federal assistance to areas, have emphasized the requirements for reliable data to determine the employment resources available. Data about the number of weeks and hours worked last year are essential because these data allow the characterization of workers by full-time/part-time and full-year/part-year status. Data about working last year are also necessary for collecting accurate income data by defining the universe of persons who should have earnings as part of their total income.
The data on work status in the past 12 months were derived from answers to Question 38. People 16 years old and over who worked 1 or more weeks according to the criteria described below are classified as "Worked in the past 12 months." All other people 16 years old and over are classified as "Did not work in the past 12 months."
The data on weeks worked in the past 12 months were derived from responses to Question 39, which was asked of people 16 years old and over who indicated that they worked during the past 12 months.
The data pertain to the number of weeks during the past 12 months in which a person did any work for pay or profit (including paid vacation and paid sick leave) or worked without pay on a family farm or in a family business. Weeks of active service in the Armed Forces are also included.
The data pertain to the number of weeks during the past 12 months in which a person did any work for pay or profit (including paid vacation and paid sick leave) or worked without pay on a family farm or in a family business. Weeks of active service in the Armed Forces are also included.
The data on usual hours worked per week worked in the past 12 months were derived from answers to Question 40. This question was asked of people 16 years old and over who indicated that they worked during the past 12 months.
The data pertain to the number of hours a person usually worked during the weeks worked in the past 12 months. The respondent was to report the number of hours worked per week in the majority of the weeks he or she worked in the past 12 months. If the hours worked per week varied considerably during the past 12 months, the respondent was to report an approximate average of the hours worked per week.
People 16 years old and over who reported that they usually worked 35 or more hours each week during the weeks they worked are classified as "Usually worked full time;" people who reported that they usually worked 1 to 34 hours are classified as "Usually worked part time."
The data pertain to the number of hours a person usually worked during the weeks worked in the past 12 months. The respondent was to report the number of hours worked per week in the majority of the weeks he or she worked in the past 12 months. If the hours worked per week varied considerably during the past 12 months, the respondent was to report an approximate average of the hours worked per week.
People 16 years old and over who reported that they usually worked 35 or more hours each week during the weeks they worked are classified as "Usually worked full time;" people who reported that they usually worked 1 to 34 hours are classified as "Usually worked part time."
Aggregate usual hours worked is the sum of the values for usual hours worked each week of all the people in a particular universe. (For more information, see "Aggregate" under "Derived Measures.")
Mean usual hours worked is the number obtained by dividing the aggregate number of hours worked each week of a particular universe by the number of people in that universe. For example, mean usual hours worked for workers 16 to 64 years old is obtained by dividing the aggregate usual hours worked each week for workers 16 to 64 years old by the total number of workers 16 to 64 years old. Mean usual hours worked values are rounded to the nearest one-tenth of an hour. (For more information, see "Mean" under "Derived Measures.")
All people 16 years old and over who usually worked 35 hours or more per week for 50 to 52 weeks in the past 12 months.
The term "worker" as used for these data is defined based on the criteria for work status in the past 12 months.
Beginning in 2008, the weeks worked question was separated into 2 parts: part (a) asked whether the respondent worked 50 or more weeks in the past 12 months and part (b) asked respondents who answered 'no' to part (a) how many weeks they worked, even for a few hours.
It is probable that the number of people who worked in the past 12 months and the number of weeks worked are understated since there is some tendency for respondents to forget intermittent or short periods of employment or to exclude weeks worked without pay. There may also be a tendency for people not to include weeks of paid vacation among their weeks worked; one result may be that the American Community Survey figures understate the number of people who worked "50 to 52 weeks."
The American Community Survey data refer to the 12 months preceding the date of interview. Since not all people in the American Community Survey were interviewed at the same time, the reference period for the American Community Survey data is neither fixed nor uniform.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have work experience distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the work experience distribution. This is particularly true for areas with a substantial GQ population.
The Census Bureau tested the changes introduced to the 2008 version of the weeks worked question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Weeks Worked" (www.census.gov/acs).
The American Community Survey data refer to the 12 months preceding the date of interview. Since not all people in the American Community Survey were interviewed at the same time, the reference period for the American Community Survey data is neither fixed nor uniform.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have work experience distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the work experience distribution. This is particularly true for areas with a substantial GQ population.
The Census Bureau tested the changes introduced to the 2008 version of the weeks worked question in the 2006 ACS Content Test. The results of this testing show that the changes may introduce an inconsistency in the data produced for this question as observed from the years 2007 to 2008, see "2006 ACS Content Test Evaluation Report Covering Weeks Worked" (www.census.gov/acs).
The data on year of entry were derived from answers to Question 9. This question was asked about Persons 1 through 5 in the ACS, and was restricted to those persons who on Question 8 answered that they were in citizenship categories (2) born in Puerto Rico, Guam, the U.S. Virgin Islands, or Northern Marianas, (3) born abroad of U.S. citizen parent or parents, (4) U.S. citizen by naturalization, or (5) not a U.S citizen.
All respondents born outside the United States were asked for the year in which they came to live in the United States. This includes people born in Puerto Rico and U.S. Island Areas; people born abroad of an U.S. citizen parent or parents; and the foreign born. (See "Citizenship Status.") For the Puerto Rico Community Survey, respondents were asked for the year in which they came to live in Puerto Rico.
The responses to this question indicate when persons born outside of the U.S. came to live in the United States.
All respondents born outside the United States were asked for the year in which they came to live in the United States. This includes people born in Puerto Rico and U.S. Island Areas; people born abroad of an U.S. citizen parent or parents; and the foreign born. (See "Citizenship Status.") For the Puerto Rico Community Survey, respondents were asked for the year in which they came to live in Puerto Rico.
The responses to this question indicate when persons born outside of the U.S. came to live in the United States.
Since 1996, the year of entry questions for the American Community Survey and for the Puerto Rico Survey were identical. An instruction was added beginning in 1999 to "Print numbers in boxes."
Respondents were directed to indicate the year they entered the U.S. "to live." (or "to live" in Puerto Rico, for the Puerto Community Survey). For respondents who entered the U.S. (or entered Puerto Rico for the Puerto Rico Community Survey) multiple times, the interviewers were instructed to request the most recent year of entry. For respondents who entered multiple times and did not ask the interviewer for clarification or who mailed back the questionnaire without being interviewed in person, it is unclear which year of entry was provided (i.e. first or most recent).
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have year of entry distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the year of entry distribution. This is particularly true for areas with substantial GQ populations.
Beginning in 2006, the population in group quarters (GQ) is included in the ACS. Some types of GQ populations may have year of entry distributions that are different from the household population. The inclusion of the GQ population could therefore have a noticeable impact on the year of entry distribution. This is particularly true for areas with substantial GQ populations.
Year of entry was comparable across ACS years. A note of caution when comparing ACS and Census 2000 year of entry data: Census 2000 represents data collected as of April 1, 2000 and thus the "2000" year of entry category accounts only for the first quarter (Jan-Mar) in 2000. In comparison, the ACS represents data collected throughout the entire year and thus the "2000" year of entry category accounts for the entire year of 2000.
Census data products include various derived measures, such as medians, means, and percentages, as well as certain rates and ratios. Most derived measures that round to less than 0.1 are shown as zero.
The Gini is a measure of how much a distribution varies from a proportionate distribution. A purely proportionate distribution would have every value in the distribution being equal (that is 20% of the values would equal 20% of the aggregate total of all the values). This is also known as "perfect equality" - all households have an equal share of income. An example of a distribution that deviates the most from perfect equality would be have every value except one equal to zero, and one value that would be equal to the nonzero aggregate total for all the values. This is also known as "perfect inequality" - one household has all income.
The Gini ranges from zero (perfect equality) to one (perfect inequality), and it is calculated by measuring the difference between a diagonal line (the purely proportionate distribution) and the distribution of actual values (a Lorenz curve). This measure is presented for household income.
The Gini ranges from zero (perfect equality) to one (perfect inequality), and it is calculated by measuring the difference between a diagonal line (the purely proportionate distribution) and the distribution of actual values (a Lorenz curve). This measure is presented for household income.
Interpolation is frequently used to calculate medians or quartiles and to approximate standard errors from tables based on interval data. Different kinds of interpolation may be used to estimate the value of a function between two known values, depending on the form of the distribution. The most common distributional assumption is that the data are linear, resulting in linear interpolation. However, this assumption may not be valid for income data, particularly when the data are based on wide intervals. For these cases, a Pareto distribution is assumed and the median is estimated by interpolating between the logarithms of the upper and lower income limits of the median category. The Census Bureau estimates median income using the Pareto distribution within intervals when the intervals are wider than $2,500.
This measure represents an arithmetic average of a set of values. It is derived by dividing the sum (or aggregate) of a group of numerical questions by the total number of questions in that group. For example, mean household earnings is obtained by dividing the aggregate of all earnings reported by individuals with earnings living in households by the total number of households with earnings. (Additional information on means and aggregates is included in the separate explanations of many population and housing variables.)
An aggregate is the sum of the values for each of the elements in the universe. For example, aggregate household income is the sum of the incomes of all households in a given geographic area. Means are derived by dividing the aggregate by the appropriate universe. When an aggregate used as a numerator is rounded in the detailed (base) tables, the rounded value is used for the calculation of the mean.
To protect the confidentiality of responses, the aggregates shown in matrices for the list of subjects below are rounded. This means that the aggregates for these subjects, except for travel time to work, are rounded to the nearest hundred dollars. Unless special rounding rules apply (see below); $150 rounds up to $200; $149 rounds down to $100. Note that each cell in a matrix is rounded individually. This means that an aggregate value shown for the United States may not necessarily be the sum total of the aggregate values in the matrices for the states. This also means that the cells in the aggregate matrices may not add to the total and/or subtotal lines.
- If the dollar value is between -$100 and +$100, then the dollar value is rounded to $0.
- If the dollar value is less than -$100, then the dollar value is rounded to the nearest -$100.
Contract Rent, Rent Asked
Earnings in the Past 12 Months (Households)
Earnings in the Past 12 Months (Individuals)
Gross Rent*
Income Deficit in the Past 12 Months (Families)
Income Deficit in the Past 12 Months Per Family Member
Income Deficit in the Past 12 Months Per Unrelated Individual
Income in the Past 12 Months (Household/Family/Nonfamily Household)
Income in the Past 12 Months (Individuals)
Mobile Home Costs
Real Estate Taxes (Per $1,000 Value)
Rent Asked
Selected Monthly Owner Costs* by Mortgage Status
Total Mortgage Payment Travel Time to Work**
Type of Income in the Past 12 Months (Households) Value, Price Asked
[*Note: Gross Rent and Selected Monthly Owner Costs include other aggregates that also are subject to rounding. For example, Gross Rent includes aggregates of payments for "contract rent" and the "costs of utilities and fuels." Selected Monthly Owner Costs includes aggregates of payments for "mortgages, deeds of trust, contracts to purchase, or similar debts on the property (including payments for the first mortgage, second mortgage, home equity loans, and other junior mortgages); real estate taxes; fire, hazard, and flood insurance on the property, and the costs of utilities and fuels."]
[**Note: Aggregate Travel Time to Work is zero if the aggregate is zero, is rounded to 4 minutes if the aggregate is 1 to 7 minutes, and is rounded to the nearest multiple of 5 minutes for all other values (if the aggregate is not already evenly divisible by 5).]
This measure represents the middle value (if n is odd) or the average of the two middle values (if n is even) in an ordered list of n data values. The median divides the total frequency distribution into two equal parts: one-half of the cases falling below the median and one-half above the median. Each median is calculated using a standard distribution (see below). (For more information, see "Interpolation.")
For data products displayed in American FactFinder, medians that fall in the upper-most category of an open-ended distribution will be shown with a plus symbol (+) appended (e.g., "$2,000+" for contract rent), and medians that fall in the lowest category of an open-ended distribution will be shown with a minus symbol (-) appended (e.g., "$100- for contract rent"). For other data products and data files that are downloaded by users (i.e., FTP files), plus and minus signs will not be appended. Contract Rent, for example will be shown as $2001 if the median falls in the upper-most category ($2,000 or more) and $99 if the median falls in the lowest category (Less than $100). (The "Standard Distributions" section in Appendix A shows the open-ended intervals for medians.)
For data products displayed in American FactFinder, medians that fall in the upper-most category of an open-ended distribution will be shown with a plus symbol (+) appended (e.g., "$2,000+" for contract rent), and medians that fall in the lowest category of an open-ended distribution will be shown with a minus symbol (-) appended (e.g., "$100- for contract rent"). For other data products and data files that are downloaded by users (i.e., FTP files), plus and minus signs will not be appended. Contract Rent, for example will be shown as $2001 if the median falls in the upper-most category ($2,000 or more) and $99 if the median falls in the lowest category (Less than $100). (The "Standard Distributions" section in Appendix A shows the open-ended intervals for medians.)
This measure is calculated by taking the number of questions in a group possessing a characteristic of interest and dividing by the total number of questions in that group, and then multiplying by 100.
This measure divides a distribution into four equal parts. The first quartile (or lower quartile) is the value that defines the upper limit of the lowest one-quarter of the cases. The second quartile is the median. The third quartile (or upper quartile) is defined as the upper limit of the lowest three quarters of cases in the distribution. Quartiles are presented for certain financial characteristics such as housing value and contract rent. The distribution used to compute quartiles is the same as that used to compute medians for that variable.
This measure divides a distribution into five equal parts. The first quintile (or lowest quintile) is the value that defines the upper limit of the lowest one-fifth of the cases. The second quintile is the 40th percentile. The third quintile is the 60th percentile. The fourth quintile is defined as the upper limit of the lowest four fifths of cases in the distribution, or the 80th percentile. Quintiles are presented for household incomes.
This is a measure of occurrences in a given period of time divided by the possible number of occurrences during that period. For example, the homeowner vacancy rate is calculated by dividing the number of vacant units "for sale only" by the sum of owner-occupied units and vacant units that are "for sale only," and then multiplying by 100. Rates are sometimes presented as percentages.
Measures describing the quality of the ACS sample and the data collected by the ACS - including sample sizes, coverage rates, and response rates - are available for 2000 through 2006 on the ACS web page, at www.census.gov/acs. The quality measures illustrate the steps the Census Bureau takes to ensure that ACS survey data are accurate and reliable.
Beginning in 2007, the quality measures are available through American FactFinder in the B98* series of Detailed Tables.
Beginning in 2007, the quality measures are available through American FactFinder in the B98* series of Detailed Tables.
The number of addresses in each state and for the nation that were selected for the ACS sample for a particular year. Each year's sample is systematically divided into 12 monthly samples for ACS interviewing. This initial number includes addresses later determined to be commercial or nonexistent, as well as housing units that are not interviewed due to subsampling for personal visit follow-up, refusals or other reasons.
The final number of interviews across all three modes of data collection for the ACS in a given year. This number includes occupied and vacant housing units that were interviewed by mail, telephone, or personal visit methods between January 1 - December 31. It excludes addresses determined to be nonexistent or commercial, and addresses not selected in the subsample for personal visit follow-up, and addresses that are not interviewed due to refusals or other reasons.
The number of people living in GQs that could be contacted for ACS interviewing in a given year for a given geographic area. Each year's sample is systematically divided into 12 monthly samples for ACS interviewing. This initial number includes people thought to be in group quarters that were later determined to be out of scope or nonexistent, as well as people not interviewed due to the group quarter refusing entry, the person refusing to respond, or other reasons.
There are two kinds of coverage error: under-coverage and over-coverage. Under-coverage exists when housing units or people do not have a chance of being selected in the sample. Over-coverage exists when housing units or people have more than one chance of selection in the sample, or are included in the sample when they should not have been. If the characteristics of under-covered or over-covered housing units or individuals differ from those that are selected, the ACS may not provide an accurate picture of the population.
The coverage rates measure coverage error in the ACS. The coverage rate is the ratio of the ACS population or housing estimate of an area or group to the independent estimate for that area or group, times 100.
Coverage rates for the total resident population are calculated by sex at the national, state, and Puerto Rico levels, and at the national level only for total Hispanics, and non-Hispanics crossed by the five major race categories: White, Black, American Indian and Alaska Native, Asian, and Native Hawaiian and Other Pacific Islander. The total resident population includes persons in both housing units and group quarters. In addition, a coverage rate that includes only the group quarters population is calculated at the national level. Coverage rates for housing units are calculated at the national and state level, except for Puerto Rico because independent housing unit estimates are not available. These rates are weighted to reflect the probability of selection into the sample, the subsampling for personal visit follow-up, and non-response adjustment.
The coverage rates measure coverage error in the ACS. The coverage rate is the ratio of the ACS population or housing estimate of an area or group to the independent estimate for that area or group, times 100.
Coverage rates for the total resident population are calculated by sex at the national, state, and Puerto Rico levels, and at the national level only for total Hispanics, and non-Hispanics crossed by the five major race categories: White, Black, American Indian and Alaska Native, Asian, and Native Hawaiian and Other Pacific Islander. The total resident population includes persons in both housing units and group quarters. In addition, a coverage rate that includes only the group quarters population is calculated at the national level. Coverage rates for housing units are calculated at the national and state level, except for Puerto Rico because independent housing unit estimates are not available. These rates are weighted to reflect the probability of selection into the sample, the subsampling for personal visit follow-up, and non-response adjustment.
The survey response rate is the ratio of the estimate of units interviewed after data collection is complete to the estimate of all units that should have been interviewed. Separate rates are calculated for housing unit response and GQ person response. For housing units, this means all interviews after mail, telephone and personal visit follow-up. For GQ persons, this means all interviews after the personal visit. Interviews include complete and partial interviews with enough information to be processed.
All final noninterviews have been grouped into one of the following Reasons for Noninterviews:
All final noninterviews have been grouped into one of the following Reasons for Noninterviews:
Even though the ACS is a mandatory survey and households whose addresses are selected and GQ persons who are selected for the survey are required to answer the survey questions, a few are reluctant to cooperate and refuse to participate.
If the interviewer cannot find the sample address after using all possible sources, they consider it "unable to locate". For GQ persons, the individual could not be located.
Interviewers assign this code if they could not find anyone at the housing unit during the entire month's interview period. There is no equivalent rate for GQ persons.
The interviewers confirm that all household members or the GQ person are away during the entire month's interview period on vacation, a business trip, or caring for sick relatives.
The interviewer could not conduct an interview because of language barriers, was not able to get an interpreter who could translate, and the supervisor or regional office could not help complete this case.
To be considered an interviewed unit in ACS, a household or GQ person's response needs to have a minimum amount of data. Occupied housing units and GQ persons not meeting this minimum are treated as noninterviews in the estimation process. Responses for vacant housing units are not subject to a minimum data requirement
Unique situations when the reason for noninterview does not fit into one of the classifications described above. Possible reasons include "death in the family", "household quarantined", or "roads impassable".
Some group quarters refuse to allow the ACS to interview any of their residents, citing legal or other reasons.
Missing data for a particular question or item is called item nonresponse. It occurs when a respondent fails to provide an answer to a required item. The ACS also considers invalid answers as item nonresponse. The Census Bureau uses imputation methods that either use rules to determine acceptable answers or use answers from similar housing units or people who provided the item information. One type of imputation, allocation, involves using statistical procedures, such as within-household or nearest neighbor matrices populated by donors, to impute for missing values.
This rate is calculated by adding together the weighted number of allocated items across a set of person characteristics, and dividing by the total weighted number of responses across the same set of characteristics.
This rate is calculated by adding together the weighted number of allocated items across a set of household and housing unit characteristics, and dividing by the total weighted number of responses across the same set of characteristics.
These rates give an overall picture of the rate of item nonresponse for a geographic area.
Four Main Group Classifications and Thirty-Nine Subgroup Classifications of Languages Spoken at Home with Illustrative Examples
Poverty Thresholds in 1982, by Size of Family and Number of Related Children Under
18 Years Old (Dollars)
Two or More Races (57 Possible Specified Combinations)
1. White; Black or African American
2. White; American Indian and Alaska Native
3. White; Asian
4. White; Native Hawaiian and Other Pacific Islander
5. White; Some other race
6. Black or African American; American Indian and Alaska Native
7. Black or African American; Asian
8. Black or African American; Native Hawaiian and Other Pacific Islander
9. Black or African American; Some other race
10. American Indian and Alaska Native; Asian
11. American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
12. American Indian and Alaska Native; Some other race
13. Asian; Native Hawaiian and Other Pacific Islander
14. Asian; Some other race
15. Native Hawaiian and Other Pacific Islander; Some other race
16. White; Black or African American; American Indian and Alaska Native
17. White; Black or African American; Asian
18. White; Black or African American; Native Hawaiian and Other Pacific Islander
19. White; Black or African American; Some other race
20. White; American Indian and Alaska Native; Asian
21. White; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
22. White; American Indian and Alaska Native; Some other race
23. White; Asian; Native Hawaiian and Other Pacific Islander
24. White; Asian; Some other race
25. White; Native Hawaiian and Other Pacific Islander; Some other race
26. Black or African American; American Indian and Alaska Native; Asian
27. Black or African American; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
28. Black or African American; American Indian and Alaska Native; Some other race
29. Black or African American; Asian; Native Hawaiian and Other Pacific Islander
30. Black or African American; Asian; Some other race
31. Black or African American; Native Hawaiian and Other Pacific Islander; Some other race
32. American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander
33. American Indian and Alaska Native; Asian; Some other race
34. American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander; Some other race
35. Asian; Native Hawaiian and Other Pacific Islander; Some other race
36. White; Black or African American; American Indian and Alaska Native; Asian
37. White; Black or African American; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
38. White; Black or African American; American Indian and Alaska Native; Some other race
39. White; Black or African American; Asian; Native Hawaiian and Other Pacific Islander
40. White; Black or African American; Asian; Some other race
41. White; Black or African American; Native Hawaiian and Other Pacific Islander; Some other race
42. White; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander
43. White; American Indian and Alaska Native; Asian; Some other race
44. White; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander; Some other race
45. White; Asian; Native Hawaiian and Other Pacific Islander; Some other race
46. Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander
47. Black or African American; American Indian and Alaska Native; Asian; Some other race
48. Black or African American; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander; Some other race
49. Black or African American; Asian; Native Hawaiian and Other Pacific Islander; Some other race
50. American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander; Some other race
51. White; Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander
52. White; Black or African American; American Indian and Alaska Native; Asian; Some other race
53. White; Black or African American; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander; Some other race
54. White; Black or African American; Asian; Native Hawaiian and Other Pacific Islander; Some other race
55. White; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander; Some other race
56. Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander; Some other race
57. White; Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander; Some other race
In order to provide consistency in the values within and among data products, standard distributions from which medians and quartiles are calculated are used for the American Community Survey.
Standard Distribution for Median Age:
[116 data cells]
Under 1 year
1 year
2 years
3 years
4 years
5 years
.
.
.
112 years
113 years
114 years
115 years and over
Standard Distribution for Median Age at First Marriage:
[9 cells]
5 to 9 years
10 to 14 years
15 to 19 years
20 to 24 years
25 to 29 years
30 to 34 years
35 to 39 years
40 to 44 years
45 to 49 years
Standard Distribution for Median Agricultural Crop Sales:
[5 data cells]
Less than $1,000
$1,000 to $2,499
$2,500 to $4,999
$5,000 to $9,999
$10,000 or more
Standard Distribution for Median Bedrooms:
[9 data cells]
No bedroom
1 bedroom
2 bedrooms
3 bedrooms
4 bedrooms
5 bedrooms
6 bedrooms
7 bedrooms
8 or more bedrooms
Standard Distribution for Median Condominium Fees:
[15 data cells]
Less than $50
$50 to $99
$100 to $199
$200 to $299
$300 to $399
$400 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,749
$1,750 or more
Standard Distribution for Median Contract Rent/Quartile Contract Rent/Rent Asked/Gross Rent:
[23 data cells]
Less than $100
$100 to $149
$150 to $199
$200 to $249
$250 to $299
$300 to $349
$350 to $399
$400 to $449
$450 to $499
$500 to $549
$550 to $599
$600 to $649
$650 to $699
$700 to $749
$750 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,999
$2,000 to $2,499
$2,500 to $2,999
$3,000 or more
Standard Distribution for Duration of Current Marriage:
[101 data cells]
Under 1 year
1 year
2 years
3 years
4 years
5 years
.
.
.
97 years
98 years
99 years
100 years and over
Standard Distribution for Median Earnings and Median Income (Individuals):
[101 data cells]
Less than $2,500
$2,500 to $4,999
$5,000 to $7,499
$7,500 to $9,999
$10,000 to $12,499
$12,500 to $14,999
$15,000 to $17,499
$17,500 to $19,999
$20,000 to $22,499
$22,500 to $24,999
$25,000 to $27,499
$27,500 to $29,999
$30,000 to $32,499
$32,500 to $34,999
$35,000 to $37,499
$37,500 to $39,999
$40,000 to $42,499
$42,500 to $44,999
$45,000 to $47,499
$47,500 to $49,999
$50,000 to $52,499
$52,500 to $54,999
$55,000 to $57,499
$57,500 to $59,999
$60,000 to $62,499
$62,500 to $64,999
$65,000 to $67,499
$67,500 to $69,999
$70,000 to $72,499
$72,500 to $74,999
$75,000 to $77,499
$77,500 to $79,999
$80,000 to $82,499
$82,500 to $84,999
$85,000 to $87,499
$87,500 to $89,999
$90,000 to $92,499
$92,500 to $94,999
$95,000 to $97,499
$97,500 to $99,999
$100,000 to $102,499
$102,500 to $104,999
$105,000 to $107,499
$107,500 to $109,999
$110,000 to $112,499
$112,500 to $114,999
$115,000 to $117,499
$117,500 to $119,999
$120,000 to $122,499
$122,500 to $124,999
$125,000 to $127,499
$127,500 to $129,999
$130,000 to $132,499
$132,500 to $134,999
$135,000 to $137,499
$137,500 to $139,999
$140,000 to $142,499
$142,500 to $144,999
$145,000 to $147,499
$147,500 to $149,999
$150,000 to $152,499
$152,500 to $154,999
$155,000 to $157,499
$157,500 to $159,999
$160,000 to $162,499
$162,500 to $164,999
$165,000 to $167,499
$167,500 to $169,999
$170,000 to $172,499
$172,500 to $174,999
$175,000 to $177,499
$177,500 to $179,999
$180,000 to $182,499
$182,500 to $184,999
$185,000 to $187,499
$187,500 to $189,999
$190,000 to $192,499
$192,500 to $194,999
$195,000 to $197,499
$197,500 to $199,999
$200,000 to $202,499
$202,500 to $204,999
$205,000 to $207,499
$207,500 to $209,999
$210,000 to $212,499
$212,500 to $214,999
$215,000 to $217,499
$217,500 to $219,999
$220,000 to $222,499
$222,500 to $224,999
$225,000 to $227,499
$227,500 to $229,999
$230,000 to $232,499
$232,500 to $234,999
$235,000 to $237,499
$237,500 to $239,999
$240,000 to $242,499
$242,500 to $244,999
$245,000 to $247,499
$247,500 to $249,999
$250,000 or more
Standard Distribution for Median Fire, Hazard, and Flood Insurance:
[19 data cells]
$0
$1 to $49
$50 to $99
$100 to $149
$150 to $199
$200 to $249
$250 to $299
$300 to $349
$350 to $399
$400 to $449
$450 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,499
$1,500 to $1,999
$2,000 or more
Standard Distribution for Median Gross Rent as a Percentage of Household Income:
[13 data cells]
Less than 10.0 percent
10.0 to 14.9 percent
15.0 to 19.9 percent
20.0 to 24.9 percent
25.0 to 29.9 percent
30.0 to 34.9 percent
35.0 to 39.9 percent
40.0 to 49.9 percent
to 59.9 percent
60.0 to 69.9 percent
70.0 to 79.9 percent
80.0 to 89.9 percent
90.0 percent or more
Standard Distribution for Median Income in the Past 12 Months (Household/Family/Nonfamily Household):
[101 data cells]
Less than $2,500
$2,500 to $4,999
$5,000 to $7,499
$7,500 to $9,999
$10,000 to $12,499
$12,500 to $14,999
$15,000 to $17,499
$17,500 to $19,999
$20,000 to $22,499
$22,500 to $24,999
$25,000 to $27,499
$27,500 to $29,999
$30,000 to $32,499
$32,500 to $34,999
$35,000 to $37,499
$37,500 to $39,999
$40,000 to $42,499
$42,500 to $44,999
$45,000 to $47,499
$47,500 to $49,999
$50,000 to $52,499
$52,500 to $54,999
$55,000 to $57,499
$57,500 to $59,999
$60,000 to $62,499
$62,500 to $64,999
$65,000 to $67,499
$67,500 to $69,999
$70,000 to $72,499
$72,500 to $74,999
$75,000 to $77,499
$77,500 to $79,999
$80,000 to $82,499
$82,500 to $84,999
$85,000 to $87,499
$87,500 to $89,999
$90,000 to $92,499
$92,500 to $94,999
$95,000 to $97,499
$97,500 to $99,999
$100,000 to $102,499
$102,500 to $104,999
$105,000 to $107,499
$107,500 to $109,999
$110,000 to $112,499
$112,500 to $114,999
$115,000 to $117,499
$117,500 to $119,999
$120,000 to $122,499
$122,500 to $124,999
$125,000 to $127,499
$127,500 to $129,999
$130,000 to $132,499
$132,500 to $134,999
$135,000 to $137,499
$137,500 to $139,999
$140,000 to $142,499
$142,500 to $144,999
$145,000 to $147,499
$147,500 to $149,999
$150,000 to $152,499
$152,500 to $154,999
$155,000 to $157,499
$157,500 to $159,999
$160,000 to $162,499
$162,500 to $164,999
$165,000 to $167,499
$167,500 to $169,999
$170,000 to $172,499
$172,500 to $174,999
$175,000 to $177,499
$177,500 to $179,999
$180,000 to $182,499
$182,500 to $184,999
$185,000 to $187,499
$187,500 to $189,999
$190,000 to $192,499
$192,500 to $194,999
$195,000 to $197,499
$197,500 to $199,999
$200,000 to $202,499
$202,500 to $204,999
$205,000 to $207,499
$207,500 to $209,999
$210,000 to $212,499
$212,500 to $214,999
$215,000 to $217,499
$217,500 to $219,999
$220,000 to $222,499
$222,500 to $224,999
$225,000 to $227,499
$227,500 to $229,999
$230,000 to $232,499
$232,500 to $234,999
$235,000 to $237,499
$237,500 to $239,999
$240,000 to $242,499
$242,500 to $244,999
$245,000 to $247,499
$247,500 to $249,999
$250,000 or more
Standard Distribution for Median Monthly Housing Costs:
[30 cells]
Less than $100
$100 to $149
$150 to $199
$200 to $249
$250 to $299
$300 to $349
$350 to $399
$400 to $449
$450 to $499
$500 to $549
$550 to $599
$600 to $649
$650 to $699
$700 to $749
$750 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,749
$1,750 to $1,999
$2,000 to $2,499
$2,500 to $2,999
$3,000 to $3,499
$3,500 to $3,999
$4,000 to $4,499
$4,500 to $4,999
$5,000 to $5,499
$5,500 to $5,999
$6,000 or more
Standard Distribution for Median Real Estate Taxes Paid:
[14 data cells]
Less than $200
$200 to $299
$300 to $399
$400 to $599
$600 to $799
$800 to $999
$1,000 to $1,499
$1,500 to $1,999
$2,000 to $2,999
$3,000 to $3,999
$4,000 to $4,999
$5,000 to $7,499
$7,500 to $9,999
$10,000 or more
Standard Distribution for Median Rooms:
[14 data cells]
1 room
2 rooms
3 rooms
4 rooms
5 rooms
6 rooms
7 rooms
8 rooms
9 rooms
10 rooms
11 rooms
12 rooms
13 rooms
14 or more rooms
Standard Distribution for Median Selected Monthly Owner Costs/Median Selected Monthly Owner Costs by Mortgage Status (With a Mortgage):
[23 data cells]
Less than $100
$100 to $199
$200 to $299
$300 to $399
$400 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,749
$1,750 to $1,999
$2,000 to $2,499
$2,500 to $2,999
$3,000 to $3,499
$3,500 to $3,999
$4,000 to $4,499
$4,500 to $4,999
$5,000 to $5,499
$5,500 to $5,999
$6,000 or more
Standard Distribution for Median Selected Monthly Owner Costs by Mortgage Status (Without a Mortgage):
[17 data cells]
Less than $100
$100 to $149
$150 to $199
$200 to $249
$250 to $299
$300 to $349
$350 to $399
$400 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,999
$2,000 or more
Standard Distribution for Median Selected Monthly Owner Costs as a Percentage of Household Income by Mortgage Status:
[13 data cells]
Less than 10.0 percent
10.0 to 14.9 percent
15.0 to 19.9 percent
20.0 to 24.9 percent
25.0 to 29.9 percent
30.0 to 34.9 percent
35.0 to 39.9 percent
40.0 to 49.9 percent
50.0 to 59.9 percent
60.0 to 69.9 percent
70.0 to 79.9 percent
80.0 to 89.9 percent
90.0 percent or more
Standard Distribution for Median Total Mortgage Payment:
[21 data cells]
Less than $100
$100 to $199
$200 to $299
$300 to $399
$400 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,749
$1,750 to $1,999
$2,000 to $2,499
$2,500 to $2,999
$3,000 to $3,499
$3,500 to $3,999
$4,000 to $4,499
$4,500 to $4,999
$5,000 or more
Standard Distribution for Median Usual Hours Worked Per Week Worked in the Past 12 Months:
[9 data cells]
Usually worked 50 to 99 hours per week
Usually worked 45 to 49 hours per week
Usually worked 41 to 44 hours per week
Usually worked 40 hours per week
Usually worked 35 to 39 hours per week
Usually worked 30 to 34 hours per week
Usually worked 25 to 29 hours per week
Usually worked 15 to 24 hours per week
Usually worked 1 to 14 hours per week
Standard Distribution for Median Value/Quartile Value/Price Asked:
[24 data cells]
Less than $10,000
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $24,999
$25,000 to $29,999
$30,000 to $34,999
$35,000 to $39,999
$40,000 to $49,999
$50,000 to $59,999
$60,000 to $69,999
$70,000 to $79,999
$80,000 to $89,999
$90,000 to $99,999
$100,000 to $124,999
$125,000 to $149,999
$150,000 to $174,999
$175,000 to $199,999
$200,000 to $249,999
$250,000 to $299,999
$300,000 to $399,999
$400,000 to $499,999
$500,000 to $749,999
$750,000 to $999,999
$1,000,000 or more
Standard Distribution for Median Vehicles Available:
[6 data cells]
No vehicle available
1 vehicle available
2 vehicles available
3 vehicles available
4 vehicles available
5 or more vehicles available
Standard Distribution for Median Year Householder Moved Into Unit:
[16 data cells]
Moved in 2011
Moved in 2010
Moved in 2009
Moved in 2008
Moved in 2007
Moved in 2006
Moved in 2005
Moved in 2004
Moved in 2003
Moved in 2002
Moved in 2001
Moved in 2000
Moved in 1990 to 1999
Moved in 1980 to 1989
Moved in 1970 to 1979
Moved in 1969 or earlier
Standard Distribution for Median Year Structure Built:
[19 data cells]
Built in 2011
Built in 2010
Built in 2009
Built in 2008
Built in 2007
Built in 2006
Built in 2005
Built in 2004
Built in 2003
Built in 2002
Built in 2001
Built in 2000
Built 1990 to 1999
Built 1980 to 1989
Built 1970 to 1979
Built 1960 to 1969
Built 1950 to 1959
Built 1940 to 1949
Built 1939 or earlier
These rates give an overall picture of the rate of item nonresponse for a geographic area.
| Five-Group Classification | Fifteen-Group Classification | Examples |
|---|---|---|
| Science and Engineering | Computers, Mathematics and Statistics | Computer Science, Mathematics, General Statistics | Biological, Agricultural, and Environmental Sciences | Cellular and Molecular Biology, Soil Sciences, Natural Resource Management | Physical and Related Sciences | Physics, Organic chemistry, Astronomy | Psychology | Psychology, Counseling, Child psychology | Social Sciences | Criminology, Sociology, Political Science | Engineering | Chemical Engineering, Thermal engineering, Electrical engineering | Multidisciplinary Studies | Nutritional science, Cognitive science, Behavioral science |
| Science and Engineering Related | Science and Engineering Related | Pre-Med, Physical therapy, Mechanical engineering technology |
| Business | Business | Business administration, Accounting, Human resources development |
| Education | Education | Early childhood education, Higher education administration, Special education |
| Arts, Humanities, and Other | Literature and Languages | English, Foreign language and literature, Spanish | Liberal Arts and History | Philosophy, Theology, American history | Visual and Performing Arts | Interior design, Dance, Voice | Communications | Mass communications, Journalism, Public relations | Other | Public Administration, Pre-law, Kinesiology |
Four Main Group Classifications and Thirty-Nine Subgroup Classifications of Languages Spoken at Home with Illustrative Examples
| Four Main Group Classifications | Thirty-Nine Subgroup Classifications |
| Spanish | Spanish or Spanish Creole Examples: Ladino, Pachuco |
| Other Indo-European languages | French Examples: Cajun, Patois | French Creole Examples: Haitian Creole | Italian | Portuguese or Portuguese Creole Examples: Papia Mentae | German Example: Luxembourgian | Yiddish | Other West Germanic languages Examples: Dutch, Pennsylvania Dutch, Afrikaans | Scandinavian languages Examples: Danish, Norwegian, Swedish | Greek | Russian | Polish | Serbo-Croatian Examples: Bosnian, Serbian | Other Slavic languages | Examples: Czech, Slovak, Ukrainian | Armenian | Persian | Gujarati | Hindi | Urdu | Other Indic languages Examples: Bengali, Marathi, Punjabi, Romany | Other Indo-European language Examples: Albanian, Gaelic, Lithuanian, Romanian |
| Asian and Pacific Island languages | Chinese Examples: Cantonese, Formosan, Mandarin | Japanese | Korean | Mon-Khmer, Cambodian | Hmong | Thai | Laotian | Vietnamese | Other Asian languages Examples: Dravidian languages (Malayalam, Telugu, Tamil), Turkish | Tagalog | Other Pacific Island languages Examples: Chamorro, Hawaiian, Ilocano, Indonesian, Samoan |
| All other languages | Navajo | Other Native North American languages Examples: Apache, Cherokee, Dakota, Pima, Yupik | Hungarian | Arabic | Hebrew | African languages Examples: Amharic, Ibo, Yoruba, Bantu, Swahili, Somali | Other and unspecified languages Examples: Syriac, Finnish, Other languages of the Americas, not reported |
Poverty Factors and Thresholds
The 2011 Poverty Factors:| Interview Month | Poverty Factors |
|---|---|
| January | 2.22296 |
| February | 2.22775 |
| March | 2.23167 |
| April | 2.23592 |
| May | 2.24004 |
| June | 2.24377 |
| July | 2.24574 |
| August | 2.24803 |
| September | 2.25017 |
| October | 2.25231 |
| November | 2.25449 |
| December | 2.25663 |
Poverty Thresholds in 1982, by Size of Family and Number of Related Children Under
18 Years Old (Dollars)
Size of family unit |
Related children under 18 years |
||||||||
One person (unrelated individual) |
None |
One |
Two |
Three |
Four |
Five |
Six |
Seven |
Eight ormore |
| Under 65 years | |||||||||
| 65 years and over | 5,019 | ||||||||
| Two persons | 4,626 | ||||||||
| Householder under 65 years | 6,459 | 6,649 | |||||||
| Householder 65 years and over | 5,831 | 6,624 | |||||||
| Three persons | 7,546 | 7,765 | 7,772 | ||||||
| Four persons | 9,950 | 10,112 | 9,783 | 9,817 | |||||
| Five persons | 11,999 | 12,173 | 11,801 | 11,512 | 11,336 | ||||
| Six persons | 13,801 | 13,855 | 13,570 | 13,296 | 12,890 | 12,649 | |||
| Seven persons | 15,879 | 15,979 | 15,637 | 15,399 | 14,955 | 14,437 | 13,869 | ||
| Eight persons or more | 17,760 | 17,917 | 17,594 | 17,312 | 16,911 | 16,403 | 15,872 | 15,738 | |
| Nine persons or more | 21,364 | 21,468 | 21,183 | 20,943 | 20,549 | 20,008 | 19,517 | 19,397 | 18,649 |
Race Combinations
Two or More Races (57 Possible Specified Combinations)
1. White; Black or African American
2. White; American Indian and Alaska Native
3. White; Asian
4. White; Native Hawaiian and Other Pacific Islander
5. White; Some other race
6. Black or African American; American Indian and Alaska Native
7. Black or African American; Asian
8. Black or African American; Native Hawaiian and Other Pacific Islander
9. Black or African American; Some other race
10. American Indian and Alaska Native; Asian
11. American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
12. American Indian and Alaska Native; Some other race
13. Asian; Native Hawaiian and Other Pacific Islander
14. Asian; Some other race
15. Native Hawaiian and Other Pacific Islander; Some other race
16. White; Black or African American; American Indian and Alaska Native
17. White; Black or African American; Asian
18. White; Black or African American; Native Hawaiian and Other Pacific Islander
19. White; Black or African American; Some other race
20. White; American Indian and Alaska Native; Asian
21. White; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
22. White; American Indian and Alaska Native; Some other race
23. White; Asian; Native Hawaiian and Other Pacific Islander
24. White; Asian; Some other race
25. White; Native Hawaiian and Other Pacific Islander; Some other race
26. Black or African American; American Indian and Alaska Native; Asian
27. Black or African American; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
28. Black or African American; American Indian and Alaska Native; Some other race
29. Black or African American; Asian; Native Hawaiian and Other Pacific Islander
30. Black or African American; Asian; Some other race
31. Black or African American; Native Hawaiian and Other Pacific Islander; Some other race
32. American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander
33. American Indian and Alaska Native; Asian; Some other race
34. American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander; Some other race
35. Asian; Native Hawaiian and Other Pacific Islander; Some other race
36. White; Black or African American; American Indian and Alaska Native; Asian
37. White; Black or African American; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
38. White; Black or African American; American Indian and Alaska Native; Some other race
39. White; Black or African American; Asian; Native Hawaiian and Other Pacific Islander
40. White; Black or African American; Asian; Some other race
41. White; Black or African American; Native Hawaiian and Other Pacific Islander; Some other race
42. White; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander
43. White; American Indian and Alaska Native; Asian; Some other race
44. White; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander; Some other race
45. White; Asian; Native Hawaiian and Other Pacific Islander; Some other race
46. Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander
47. Black or African American; American Indian and Alaska Native; Asian; Some other race
48. Black or African American; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander; Some other race
49. Black or African American; Asian; Native Hawaiian and Other Pacific Islander; Some other race
50. American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander; Some other race
51. White; Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander
52. White; Black or African American; American Indian and Alaska Native; Asian; Some other race
53. White; Black or African American; American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander; Some other race
54. White; Black or African American; Asian; Native Hawaiian and Other Pacific Islander; Some other race
55. White; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander; Some other race
56. Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander; Some other race
57. White; Black or African American; American Indian and Alaska Native; Asian; Native Hawaiian and Other Pacific Islander; Some other race
Median Standard Distributions
In order to provide consistency in the values within and among data products, standard distributions from which medians and quartiles are calculated are used for the American Community Survey.
Standard Distribution for Median Age:
[116 data cells]
Under 1 year
1 year
2 years
3 years
4 years
5 years
.
.
.
112 years
113 years
114 years
115 years and over
Standard Distribution for Median Age at First Marriage:
[9 cells]
5 to 9 years
10 to 14 years
15 to 19 years
20 to 24 years
25 to 29 years
30 to 34 years
35 to 39 years
40 to 44 years
45 to 49 years
Standard Distribution for Median Agricultural Crop Sales:
[5 data cells]
Less than $1,000
$1,000 to $2,499
$2,500 to $4,999
$5,000 to $9,999
$10,000 or more
Standard Distribution for Median Bedrooms:
[9 data cells]
No bedroom
1 bedroom
2 bedrooms
3 bedrooms
4 bedrooms
5 bedrooms
6 bedrooms
7 bedrooms
8 or more bedrooms
Standard Distribution for Median Condominium Fees:
[15 data cells]
Less than $50
$50 to $99
$100 to $199
$200 to $299
$300 to $399
$400 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,749
$1,750 or more
Standard Distribution for Median Contract Rent/Quartile Contract Rent/Rent Asked/Gross Rent:
[23 data cells]
Less than $100
$100 to $149
$150 to $199
$200 to $249
$250 to $299
$300 to $349
$350 to $399
$400 to $449
$450 to $499
$500 to $549
$550 to $599
$600 to $649
$650 to $699
$700 to $749
$750 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,999
$2,000 to $2,499
$2,500 to $2,999
$3,000 or more
Standard Distribution for Duration of Current Marriage:
[101 data cells]
Under 1 year
1 year
2 years
3 years
4 years
5 years
.
.
.
97 years
98 years
99 years
100 years and over
Standard Distribution for Median Earnings and Median Income (Individuals):
[101 data cells]
Less than $2,500
$2,500 to $4,999
$5,000 to $7,499
$7,500 to $9,999
$10,000 to $12,499
$12,500 to $14,999
$15,000 to $17,499
$17,500 to $19,999
$20,000 to $22,499
$22,500 to $24,999
$25,000 to $27,499
$27,500 to $29,999
$30,000 to $32,499
$32,500 to $34,999
$35,000 to $37,499
$37,500 to $39,999
$40,000 to $42,499
$42,500 to $44,999
$45,000 to $47,499
$47,500 to $49,999
$50,000 to $52,499
$52,500 to $54,999
$55,000 to $57,499
$57,500 to $59,999
$60,000 to $62,499
$62,500 to $64,999
$65,000 to $67,499
$67,500 to $69,999
$70,000 to $72,499
$72,500 to $74,999
$75,000 to $77,499
$77,500 to $79,999
$80,000 to $82,499
$82,500 to $84,999
$85,000 to $87,499
$87,500 to $89,999
$90,000 to $92,499
$92,500 to $94,999
$95,000 to $97,499
$97,500 to $99,999
$100,000 to $102,499
$102,500 to $104,999
$105,000 to $107,499
$107,500 to $109,999
$110,000 to $112,499
$112,500 to $114,999
$115,000 to $117,499
$117,500 to $119,999
$120,000 to $122,499
$122,500 to $124,999
$125,000 to $127,499
$127,500 to $129,999
$130,000 to $132,499
$132,500 to $134,999
$135,000 to $137,499
$137,500 to $139,999
$140,000 to $142,499
$142,500 to $144,999
$145,000 to $147,499
$147,500 to $149,999
$150,000 to $152,499
$152,500 to $154,999
$155,000 to $157,499
$157,500 to $159,999
$160,000 to $162,499
$162,500 to $164,999
$165,000 to $167,499
$167,500 to $169,999
$170,000 to $172,499
$172,500 to $174,999
$175,000 to $177,499
$177,500 to $179,999
$180,000 to $182,499
$182,500 to $184,999
$185,000 to $187,499
$187,500 to $189,999
$190,000 to $192,499
$192,500 to $194,999
$195,000 to $197,499
$197,500 to $199,999
$200,000 to $202,499
$202,500 to $204,999
$205,000 to $207,499
$207,500 to $209,999
$210,000 to $212,499
$212,500 to $214,999
$215,000 to $217,499
$217,500 to $219,999
$220,000 to $222,499
$222,500 to $224,999
$225,000 to $227,499
$227,500 to $229,999
$230,000 to $232,499
$232,500 to $234,999
$235,000 to $237,499
$237,500 to $239,999
$240,000 to $242,499
$242,500 to $244,999
$245,000 to $247,499
$247,500 to $249,999
$250,000 or more
Standard Distribution for Median Fire, Hazard, and Flood Insurance:
[19 data cells]
$0
$1 to $49
$50 to $99
$100 to $149
$150 to $199
$200 to $249
$250 to $299
$300 to $349
$350 to $399
$400 to $449
$450 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,499
$1,500 to $1,999
$2,000 or more
Standard Distribution for Median Gross Rent as a Percentage of Household Income:
[13 data cells]
Less than 10.0 percent
10.0 to 14.9 percent
15.0 to 19.9 percent
20.0 to 24.9 percent
25.0 to 29.9 percent
30.0 to 34.9 percent
35.0 to 39.9 percent
40.0 to 49.9 percent
to 59.9 percent
60.0 to 69.9 percent
70.0 to 79.9 percent
80.0 to 89.9 percent
90.0 percent or more
Standard Distribution for Median Income in the Past 12 Months (Household/Family/Nonfamily Household):
[101 data cells]
Less than $2,500
$2,500 to $4,999
$5,000 to $7,499
$7,500 to $9,999
$10,000 to $12,499
$12,500 to $14,999
$15,000 to $17,499
$17,500 to $19,999
$20,000 to $22,499
$22,500 to $24,999
$25,000 to $27,499
$27,500 to $29,999
$30,000 to $32,499
$32,500 to $34,999
$35,000 to $37,499
$37,500 to $39,999
$40,000 to $42,499
$42,500 to $44,999
$45,000 to $47,499
$47,500 to $49,999
$50,000 to $52,499
$52,500 to $54,999
$55,000 to $57,499
$57,500 to $59,999
$60,000 to $62,499
$62,500 to $64,999
$65,000 to $67,499
$67,500 to $69,999
$70,000 to $72,499
$72,500 to $74,999
$75,000 to $77,499
$77,500 to $79,999
$80,000 to $82,499
$82,500 to $84,999
$85,000 to $87,499
$87,500 to $89,999
$90,000 to $92,499
$92,500 to $94,999
$95,000 to $97,499
$97,500 to $99,999
$100,000 to $102,499
$102,500 to $104,999
$105,000 to $107,499
$107,500 to $109,999
$110,000 to $112,499
$112,500 to $114,999
$115,000 to $117,499
$117,500 to $119,999
$120,000 to $122,499
$122,500 to $124,999
$125,000 to $127,499
$127,500 to $129,999
$130,000 to $132,499
$132,500 to $134,999
$135,000 to $137,499
$137,500 to $139,999
$140,000 to $142,499
$142,500 to $144,999
$145,000 to $147,499
$147,500 to $149,999
$150,000 to $152,499
$152,500 to $154,999
$155,000 to $157,499
$157,500 to $159,999
$160,000 to $162,499
$162,500 to $164,999
$165,000 to $167,499
$167,500 to $169,999
$170,000 to $172,499
$172,500 to $174,999
$175,000 to $177,499
$177,500 to $179,999
$180,000 to $182,499
$182,500 to $184,999
$185,000 to $187,499
$187,500 to $189,999
$190,000 to $192,499
$192,500 to $194,999
$195,000 to $197,499
$197,500 to $199,999
$200,000 to $202,499
$202,500 to $204,999
$205,000 to $207,499
$207,500 to $209,999
$210,000 to $212,499
$212,500 to $214,999
$215,000 to $217,499
$217,500 to $219,999
$220,000 to $222,499
$222,500 to $224,999
$225,000 to $227,499
$227,500 to $229,999
$230,000 to $232,499
$232,500 to $234,999
$235,000 to $237,499
$237,500 to $239,999
$240,000 to $242,499
$242,500 to $244,999
$245,000 to $247,499
$247,500 to $249,999
$250,000 or more
Standard Distribution for Median Monthly Housing Costs:
[30 cells]
Less than $100
$100 to $149
$150 to $199
$200 to $249
$250 to $299
$300 to $349
$350 to $399
$400 to $449
$450 to $499
$500 to $549
$550 to $599
$600 to $649
$650 to $699
$700 to $749
$750 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,749
$1,750 to $1,999
$2,000 to $2,499
$2,500 to $2,999
$3,000 to $3,499
$3,500 to $3,999
$4,000 to $4,499
$4,500 to $4,999
$5,000 to $5,499
$5,500 to $5,999
$6,000 or more
Standard Distribution for Median Real Estate Taxes Paid:
[14 data cells]
Less than $200
$200 to $299
$300 to $399
$400 to $599
$600 to $799
$800 to $999
$1,000 to $1,499
$1,500 to $1,999
$2,000 to $2,999
$3,000 to $3,999
$4,000 to $4,999
$5,000 to $7,499
$7,500 to $9,999
$10,000 or more
Standard Distribution for Median Rooms:
[14 data cells]
1 room
2 rooms
3 rooms
4 rooms
5 rooms
6 rooms
7 rooms
8 rooms
9 rooms
10 rooms
11 rooms
12 rooms
13 rooms
14 or more rooms
Standard Distribution for Median Selected Monthly Owner Costs/Median Selected Monthly Owner Costs by Mortgage Status (With a Mortgage):
[23 data cells]
Less than $100
$100 to $199
$200 to $299
$300 to $399
$400 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,749
$1,750 to $1,999
$2,000 to $2,499
$2,500 to $2,999
$3,000 to $3,499
$3,500 to $3,999
$4,000 to $4,499
$4,500 to $4,999
$5,000 to $5,499
$5,500 to $5,999
$6,000 or more
Standard Distribution for Median Selected Monthly Owner Costs by Mortgage Status (Without a Mortgage):
[17 data cells]
Less than $100
$100 to $149
$150 to $199
$200 to $249
$250 to $299
$300 to $349
$350 to $399
$400 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,999
$2,000 or more
Standard Distribution for Median Selected Monthly Owner Costs as a Percentage of Household Income by Mortgage Status:
[13 data cells]
Less than 10.0 percent
10.0 to 14.9 percent
15.0 to 19.9 percent
20.0 to 24.9 percent
25.0 to 29.9 percent
30.0 to 34.9 percent
35.0 to 39.9 percent
40.0 to 49.9 percent
50.0 to 59.9 percent
60.0 to 69.9 percent
70.0 to 79.9 percent
80.0 to 89.9 percent
90.0 percent or more
Standard Distribution for Median Total Mortgage Payment:
[21 data cells]
Less than $100
$100 to $199
$200 to $299
$300 to $399
$400 to $499
$500 to $599
$600 to $699
$700 to $799
$800 to $899
$900 to $999
$1,000 to $1,249
$1,250 to $1,499
$1,500 to $1,749
$1,750 to $1,999
$2,000 to $2,499
$2,500 to $2,999
$3,000 to $3,499
$3,500 to $3,999
$4,000 to $4,499
$4,500 to $4,999
$5,000 or more
Standard Distribution for Median Usual Hours Worked Per Week Worked in the Past 12 Months:
[9 data cells]
Usually worked 50 to 99 hours per week
Usually worked 45 to 49 hours per week
Usually worked 41 to 44 hours per week
Usually worked 40 hours per week
Usually worked 35 to 39 hours per week
Usually worked 30 to 34 hours per week
Usually worked 25 to 29 hours per week
Usually worked 15 to 24 hours per week
Usually worked 1 to 14 hours per week
Standard Distribution for Median Value/Quartile Value/Price Asked:
[24 data cells]
Less than $10,000
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $24,999
$25,000 to $29,999
$30,000 to $34,999
$35,000 to $39,999
$40,000 to $49,999
$50,000 to $59,999
$60,000 to $69,999
$70,000 to $79,999
$80,000 to $89,999
$90,000 to $99,999
$100,000 to $124,999
$125,000 to $149,999
$150,000 to $174,999
$175,000 to $199,999
$200,000 to $249,999
$250,000 to $299,999
$300,000 to $399,999
$400,000 to $499,999
$500,000 to $749,999
$750,000 to $999,999
$1,000,000 or more
Standard Distribution for Median Vehicles Available:
[6 data cells]
No vehicle available
1 vehicle available
2 vehicles available
3 vehicles available
4 vehicles available
5 or more vehicles available
Standard Distribution for Median Year Householder Moved Into Unit:
[16 data cells]
Moved in 2011
Moved in 2010
Moved in 2009
Moved in 2008
Moved in 2007
Moved in 2006
Moved in 2005
Moved in 2004
Moved in 2003
Moved in 2002
Moved in 2001
Moved in 2000
Moved in 1990 to 1999
Moved in 1980 to 1989
Moved in 1970 to 1979
Moved in 1969 or earlier
Standard Distribution for Median Year Structure Built:
[19 data cells]
Built in 2011
Built in 2010
Built in 2009
Built in 2008
Built in 2007
Built in 2006
Built in 2005
Built in 2004
Built in 2003
Built in 2002
Built in 2001
Built in 2000
Built 1990 to 1999
Built 1980 to 1989
Built 1970 to 1979
Built 1960 to 1969
Built 1950 to 1959
Built 1940 to 1949
Built 1939 or earlier
A group quarters is a place where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents. This isnot a typical household-type living arrangement. These services may include custodial or medical care as well as other types of assistance, and residency is commonly restricted to those receivingthese services. People living in group quarters are usually not related to each other. Group quarters include such places as college residence halls, residential treatment centers, skilled nursingfacilities, group homes, military barracks, correctional facilities, and workers dormitories.
These are community-based facilities operated for correctional purposes. The facility residents may be allowed extensive contact with the community, such as for employment or attending school, but are obligated to occupy the premises at night. Examples are halfway houses, restitution centers, and prerelease, work release, and study centers.
Stand alone, generally multi-level, federally operated correctional facilities that provide Ashort-term@confinement or custody of adults pending adjudication or sentencing. These facilities may holdpretrial detainees, holdovers, sentenced offenders, and Immigration and Customs Enforcement(ICE) inmates, formerly called Immigration and Naturalization Service (INS) inmates. Thesefacilities include: Metropolitan Correctional Centers (MCCs), Metropolitan Detention Centers(MDCs), Federal Detention Centers (FDCs), Bureau of Indian Affairs Detention Centers, ICEService Processing Centers, and ICE contract detention facilities.
Adult correctional facilities where people convicted of crimes serve their sentences. Common names include: prison, penitentiary, correctional institution, federal or state correctional facility, and conservation camp. The prisons are classified by two types of control: (1) "federal" (operated by or for the Bureau of Prisons of the Department of Justice) and (2) "state." Residents who are forensic patients or criminally insane are classified on the basis of where they resided at the time of interview. Patients in hospitals (units, wings, or floors) operated by or for federal or state correctional authorities are interviewed in the prison population. Other forensic patients will be interviewed in psychiatric hospital units and floors for long-term non-acute patients. This category may include privately operated correctional facilities.
Correctional facilities operated by or for counties, cities, and American Indian and Alaska Native tribal governments. These facilities hold adults detained pending adjudication and/or people committed after adjudication. This category also includes work farms and camps used to hold people awaiting trial or serving time on relatively short sentences. Residents who are forensic patients or criminally insane are classified on the basis of where they resided at the time of interview. Patients in hospitals (units, wings, or floors) operated by or for local correctional authorities are counted in the jail population. Other forensic patients will be interviewed in psychiatric hospital units and floors for long-term non-acute patients. This category may include privately operated correctional facilities.
Includes specialized facilities that provide strict confinement for its residents and detain juveniles awaiting adjudication, commitment or placement, and/or those being held for diagnosis or classification. Also included are correctional facilities where residents are permitted contact with the community, for purposes such as attending school or holding a job. Examples are residential training schools and farms, reception and diagnostic centers, group homes operated by or for correctional authorities, detention centers, and boot camps for juvenile delinquents.
Includes community-based group living arrangements for youth in residential settings that are able to accommodate three or more clients of a service provider. The group home provides room and board and services, including behavioral, psychological, or social programs. Generally, clients are not related to the care giver or to each other. Examples are maternity homes for unwed mothers, orphanages, and homes for abused and neglected children in need of services. Group homes for juveniles do not include residential treatment centers for juveniles or group homes operated by or for correctional authorities.
Includes facilities that primarily serve youth that provide services on-site in a highly structured livein environment for the treatment of drug/alcohol abuse, mentalillness, and emotional/behavioral disorders. These facilities are staffed 24-hours a day. The focus of a residential treatment center is on the treatment program.Residential treatment centers for juveniles do not include facilities operated by or for correctional authorities.
Includes facilities licensed to provide medical care with seven day, twenty-four hour coverage for people requiring long-term non-acute care. People in these facilities require nursing care, regardless of age. Either of these types of facilities may be referred to as nursing homes.
Includes hospitals if they have any patients who have no exit or disposition plan, or who are known as "boarder patients" or "boarder babies." All hospitals are eligible for inclusion in this category except psychiatric hospitals, units, wings or floors operated by federal, state or local correctional authorities. Patients in hospitals operated by these correctional authorities will be interviewed in the prison or jail population. Psychiatric units and hospice units in hospitals are also excluded. Only patients with no usual home elsewhere are interviewed in this category.
Includes in-patient hospice facilities (both free-standing and units in hospitals) that provide palliative, comfort, and supportive care for the terminally ill patient and their families. All patients in these GQs are included in the ACS GQ sample.
Includes psychiatric hospitals, units and floors for long-term non-acute care patients. The primary function of the hospital, unit, or floor is to provide diagnostic and treatment services for long-term non-acute patients who have psychiatric-related illness.
These facilities include military hospitals and medical centers with active duty patients assigned to the facility. Only these patients are interviewed in this category.
Includes residence halls and dormitories, which house college and university students in a group living arrangement. These facilities are owned, leased, or managed either by a college, university, or seminary, or by a private entity or organization. Fraternity and sorority housing recognized by the college or university are included as college student housing. Students attending the U.S. Naval Academy, the U.S. Military Academy (West Point), the U.S. Coast Guard Academy, and the U.S. Air Force Academy are interviewed in military group quarters.
These facilities include military personnel living in barracks (including open barrack transient quarters) and dormitories and military ships. Patients assigned to Military Treatment Facilities and people being held in military disciplinary barracks and jails are not interviewed in this category.Patients in Military Treatment Facilities with no usual home elsewhere are not interviewed in this category.
Facilities where people experiencing homelessness stay overnight. These include:1) shelters that operate on a first-come, first-serve basis where people must leave in the morning and have no guaranteed bed for the next night;2) shelters where people know that they have a bed for a specified period of time (even if they leave the building every day); and3) shelters that provide temporary shelter during extremely cold weather (such as churches). This category does not include shelters that operate only in the event of a natural disaster.Examples are emergency and transitional shelters; missions; hotels and motels used to shelter people experiencing homelessness; shelters for children who are runaways, neglected or experiencing homelessness; and similar places known to have people experiencing homelessness.
Group homes are community-based group living arrangements in residential settings that are able to accommodate three or more clients of a service provider. The group home provides room and board and services, including behavioral, psychological, or social programs. Generally, clients are not related to the care giver or to each other. Group homes do not include residential treatment centers or facilities operated by or for correctional authorities.
Residential facilities that provide treatment on-site in a highly structured live-in environment for thetreatment of drug/alcohol abuse, mental illness, and emotional/behavioral disorders. They arestaffed 24-hours a day. The focus of a residential treatment center is on the treatment program.Residential treatment centers do not include facilities operated by or for correctional authorities.
These are living quarters owned or operated by religious organizations that are intended to house theirmembers in a group living situation. This category includes such places as convents, monasteries, andabbeys.
Living quarters for students living or staying in seminaries are classified as college student housing notreligious group quarters.
Living quarters for students living or staying in seminaries are classified as college student housing notreligious group quarters.
Includes facilities such as dormitories, bunkhouses, and similar types of group living arrangements for agricultural and non-agricultural workers. This category also includes facilities that provide a full-time, year-round residential program offering a vocational training and employment program that helps young people 16-to-24 years old learn a trade, earn a high school diploma or GED and get help finding a job.
Examples are group living quarters at migratory farm worker camps, construction worker's camps, Job Corps centers, and vocational training facilities, and energy enclaves in Alaska.
Examples are group living quarters at migratory farm worker camps, construction worker's camps, Job Corps centers, and vocational training facilities, and energy enclaves in Alaska.
This document provides some basic instructions for obtaining the ACS standard errors needed to do statistical tests, as well as performing the statistical testing.
Once standard errors have been obtained, doing the statistical test to determine significance is not difficult. The determination of statistical significance takes into account the difference between the two estimates as well as the standard errors of both estimates. For two estimates, A and B, with standard errors SE(A) and SE(B), let
If Z < -1.645 or Z > 1.645, then the difference between A and B is significant at the 90 percent confidence level. Otherwise, the difference is not significant. This means that there is less than a 10 percent chance that the difference between these two estimates would be as large or larger by random chance alone.
This is the method used in determining statistical significance for the ACS Comparison Profiles published on AFF. Note that the users determination of statistical significance may not match the results in the Comparison Profile for the same pair of estimates, because the significance tests for Comparison Profiles are made using unrounded standard errors. Standard errors obtained from the rounded margins of error or confidence bounds are unlikely to match the unrounded standard error, and so statistical tests may differ.
Users may choose to apply a confidence level different from 90 percent to their tests of statistical significance. For example, if Z < -1.96 or Z > 1.96, then the difference between A and B is significant at the 95 percent confidence level.
This method can be used for any types of estimates: counts, percentages, proportions, means, medians, etc. It can be used for comparing across years, or across surveys. If one of the estimates is a fixed value or comes from a source without sampling error (such as a count from the 2010 Census), use zero for the standard error for that estimate in the above equation for Z.
NOTE: Making comparisons between ACS single-year and multiyear estimates is very difficult, and is not advised. In addition, using the rule of thumb of overlapping confidence intervals does not constitute a valid significance test and users are discouraged from using that method.
Once standard errors have been obtained, doing the statistical test to determine significance is not difficult. The determination of statistical significance takes into account the difference between the two estimates as well as the standard errors of both estimates. For two estimates, A and B, with standard errors SE(A) and SE(B), let
If Z < -1.645 or Z > 1.645, then the difference between A and B is significant at the 90 percent confidence level. Otherwise, the difference is not significant. This means that there is less than a 10 percent chance that the difference between these two estimates would be as large or larger by random chance alone.
This is the method used in determining statistical significance for the ACS Comparison Profiles published on AFF. Note that the users determination of statistical significance may not match the results in the Comparison Profile for the same pair of estimates, because the significance tests for Comparison Profiles are made using unrounded standard errors. Standard errors obtained from the rounded margins of error or confidence bounds are unlikely to match the unrounded standard error, and so statistical tests may differ.
Users may choose to apply a confidence level different from 90 percent to their tests of statistical significance. For example, if Z < -1.96 or Z > 1.96, then the difference between A and B is significant at the 95 percent confidence level.
This method can be used for any types of estimates: counts, percentages, proportions, means, medians, etc. It can be used for comparing across years, or across surveys. If one of the estimates is a fixed value or comes from a source without sampling error (such as a count from the 2010 Census), use zero for the standard error for that estimate in the above equation for Z.
NOTE: Making comparisons between ACS single-year and multiyear estimates is very difficult, and is not advised. In addition, using the rule of thumb of overlapping confidence intervals does not constitute a valid significance test and users are discouraged from using that method.
The data contained in these data products are based on the American Community Survey (ACS) sample interviewed from January 1, 2011 through December 31, 2011. The ACS sample is selected from all counties and county-equivalents in the United States. In 2006, the ACS began collection of data from sampled persons in group quarters (GQs) - for example, military barracks, college dormitories, nursing homes, and correctional facilities. Persons in group quarters are included with persons in housing units (HUs) in all 2011 ACS estimates that are based on the total population. All ACS population estimates from years prior to 2006 include only persons in housing units. The ACS, like any other statistical activity, is subject to error. The purpose of this documentation is to provide data users with a basic understanding of the ACS sample design, estimation methodology, and accuracy of the ACS data. The ACS is sponsored by the U.S. Census Bureau, and is part of the 2010 Decennial Census Program.
Additional information on the design and methodology of the ACS, including data collection and processing, can be found at: http://www.census.gov/acs/www/methodology/methodology main/.
The 2011 Accuracy of the Data from the Puerto Rico Community Survey can be found at http://www.census.gov/acs/www/Downloads/data_documentation/Accuracy/PRCS_Accuracy_of_Data_2011.pdf.
Additional information on the design and methodology of the ACS, including data collection and processing, can be found at: http://www.census.gov/acs/www/methodology/methodology main/.
The 2011 Accuracy of the Data from the Puerto Rico Community Survey can be found at http://www.census.gov/acs/www/Downloads/data_documentation/Accuracy/PRCS_Accuracy_of_Data_2011.pdf.
The ACS employs three modes of data collection:
Month 1: Addresses in sample that are determined to be mailable are sent a questionnaire via
the U.S. Postal Service.
Month 2: All mail non-responding addresses with an available phone number are sent to CATI.
Month 3: A sample of mail non-responses without a phone number, CATI non-responses, and unmailable addresses are selected and sent to CAPI.
Note that mail responses are accepted during all three months of data collection.
All Remote Alaska addresses that are in sample are assigned to one of two data collection periods, January-April, or September-December and are all sent to the CAPI mode of data collection.1 Data for these addresses are collected using CAPI only and up to four months are given to complete the interviews in Remote Alaska for each data collection period.
- Mailout/Mailback
- Computer Assisted Telephone Interview (CATI)
- Computer Assisted Personal Interview (CAPI)
Month 1: Addresses in sample that are determined to be mailable are sent a questionnaire via
the U.S. Postal Service.
Month 2: All mail non-responding addresses with an available phone number are sent to CATI.
Month 3: A sample of mail non-responses without a phone number, CATI non-responses, and unmailable addresses are selected and sent to CAPI.
Note that mail responses are accepted during all three months of data collection.
All Remote Alaska addresses that are in sample are assigned to one of two data collection periods, January-April, or September-December and are all sent to the CAPI mode of data collection.1 Data for these addresses are collected using CAPI only and up to four months are given to complete the interviews in Remote Alaska for each data collection period.
Group Quarters data collection spans six weeks, except in Remote Alaska and for Federal prisons, where the data collection time period is four months. As is done for HUs, Group Quarters in Remote Alaska are assigned to one of two data collection periods, January-April, or September-December and up to four months is allowed to complete the interviews. Similarly, all Federal prisons are assigned to September with a four month data collection window.
Field representatives have several options available to them for data collection. These include completing the questionnaire while speaking to the resident in person or over the telephone, conducting a personal interview with a proxy, such as a relative or guardian, or leaving paper questionnaires for residents to complete for themselves and then pick them up later. This last option is used for data collection in Federal prisons.
Field representatives have several options available to them for data collection. These include completing the questionnaire while speaking to the resident in person or over the telephone, conducting a personal interview with a proxy, such as a relative or guardian, or leaving paper questionnaires for residents to complete for themselves and then pick them up later. This last option is used for data collection in Federal prisons.
The universe for the ACS consists of all valid, residential housing unit addresses in all county and county equivalents in the 50 states, including the District of Columbia. The Master Address File (MAF) is a database maintained by the Census Bureau containing a listing of residential and commercial addresses in the U.S. and Puerto Rico. The MAF is updated twice each year with the Delivery Sequence Files (DSF) provided by the U.S. Postal Service. The DSF covers only the U.S. These files identify mail drop points and provide the best available source of changes and updates to the housing unit inventory. The MAF is also updated with the results from various Census Bureau field operations, including the ACS.
The universe of group quarters (GQs) valid for ACS for 2011 was significantly different than the ACS 2010 GQ universe. Results from nationwide field operations such as address canvassing and group quarters validation conducted for the 2010 Census were available in 2011 for the first time for ACS use. Results from these sources were combined with the 2010 Census universe of GQs to create the final 2011 ACS sampling frame.
As in previous years, due to operational difficulties associated with data collection, the ACS excluded certain types of GQs from the sampling universe and data collection operations. The weighting and estimation accounts for this segment of the population included in the population controls. The following GQ types were removed since they are from the 2011 GQ universe:
As in previous years, due to operational difficulties associated with data collection, the ACS excluded certain types of GQs from the sampling universe and data collection operations. The weighting and estimation accounts for this segment of the population included in the population controls. The following GQ types were removed since they are from the 2011 GQ universe:
- Soup kitchens
- Domestic violence shelters
- Regularly scheduled mobile food vans
- Targeted non-sheltered outdoor locations
- Maritime/merchant vessels
- Living quarters for victims of natural disasters
The ACS employs a two-phase, two-stage sample design. The ACS first-phase sample consists of two separate samples, Main and Supplemental, each chosen at different points in time.
Together, these constitute the first-phase sample. Both the Main and the Supplemental samples are chosen in two stages referred to as first- and second-stage sampling. Subsequent to second- stage sampling, sample addresses are randomly assigned to one of the twelve months of the sample year. The second-phase of sampling occurs when the CAPI sample is selected (see Section 2 below).
The Main sample is selected during the summer preceding the sample year. Approximately 99 percent of the sample is selected at this time. Each address in sample is randomly assigned to one of the 12 months of the sample year. Supplemental sampling occurs in January/February of the sample year and accounts for approximately 1 percent of the overall first-phase sample. The Supplemental sample is allocated to the last eight months of the sample year. A sub-sample of non-responding addresses and of any addresses deemed unmailable is selected for the CAPI data collection mode.
Several steps used to select the first-phase sample are common to both Main and Supplemental sampling. The descriptions of the steps included in the first-phase sample selection below indicate which are common to both and which are unique to either Main or Supplemental sampling.
1. First-phase Sample Selection
- Counties
- Places
- School Districts (elementary, secondary, and unified)
- American Indian Areas
- Tribal Subdivisions
- Alaska Native Village Statistical Areas
- Hawaiian Homelands
- Minor Civil Divisions - in Connecticut, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Wisconsin (these are the states where MCDs are active, functioning governmental units)
- Census Designated Places - in Hawaii only
The measure of size for all areas except American Indian Areas, Tribal Subdivisions, and Alaska Native Village Statistical Areas is an estimate of the number of occupied HUs in the area. This is calculated by multiplying the number of ACS addresses by an estimated occupancy rate at the block level. A measure of size for each Census Tract is also calculated in the same manner.
For American Indian, Tribal Subdivisions, and Alaska Native Village Statistical Areas, the measure of size is the estimated number of occupied HUs multiplied by the proportion of people reporting American Indian or Alaska Native (alone or in combination) in the 2010 Census.
Each block is then assigned the smallest measure of size from the set of all entities of which it is a part. The 2011 second-stage sampling strata and the overall first-phase sampling rates are shown in Table 1 below. There are two sets of rates shown in the table, since the sample increase from 2.9 to 3.54 million occurred midway through the 2011 sample year. The 3.54 million target sampling rates are the rates that would have been used if the entire 2011 sample was at the 3.54 million level. The sample rates represent the actual percent in sample that was delivered for the 2011 sample year.
2. Second-phase Sample Selection - Subsampling the Unmailable and Non-Responding Addresses
Most addresses determined to be unmailable are subsampled for the CAPI phase of data collection at a rate of 2-in-3. Unmailable addresses, which include Remote Alaska addresses, do not go to the CATI phase of data collection. Subsequent to CATI, all addresses for which no response has been obtained prior to CAPI are subsampled based on the expected rate of completed interviews at the tract level using the rates shown in Table 2.
Table 1. First-phase Sampling Rate Categories for the United States
Footnotes:
1MOS = Measure of size of the smallest governmental entity.
2TRACTMOS = Census Tract measure of size.
3H.R. = areas where predicted levels of completed mail and CATI interviews are > 60%.
Table 2. Second-Phase (CAPI) Subsampling Rates for the United States
Footnotes:
1 The full CAPI follow-up procedure for these two categories is new to the ACS sample design.
Together, these constitute the first-phase sample. Both the Main and the Supplemental samples are chosen in two stages referred to as first- and second-stage sampling. Subsequent to second- stage sampling, sample addresses are randomly assigned to one of the twelve months of the sample year. The second-phase of sampling occurs when the CAPI sample is selected (see Section 2 below).
The Main sample is selected during the summer preceding the sample year. Approximately 99 percent of the sample is selected at this time. Each address in sample is randomly assigned to one of the 12 months of the sample year. Supplemental sampling occurs in January/February of the sample year and accounts for approximately 1 percent of the overall first-phase sample. The Supplemental sample is allocated to the last eight months of the sample year. A sub-sample of non-responding addresses and of any addresses deemed unmailable is selected for the CAPI data collection mode.
Several steps used to select the first-phase sample are common to both Main and Supplemental sampling. The descriptions of the steps included in the first-phase sample selection below indicate which are common to both and which are unique to either Main or Supplemental sampling.
1. First-phase Sample Selection
- First-stage sampling (performed during both Main and Supplemental sampling) - First stage sampling defines the universe for the second stage of sampling through two steps. First, all addresses that were in a first-phase sample within the past four years are excluded from eligibility. This ensures that no address is in sample more than once in any five-year period. The second step is to select a 20 percent systematic sample of "new" units, i.e. those units that have never appeared on a previous MAF extract. Each new address is systematically assigned to either the current year or to one of four back- samples. This procedure maintains five equal partitions (samples) of the universe.
- Assignment of blocks to a second-stage sampling stratum (performed during Main sampling only) - Second-stage sampling uses 16 sampling strata in the U.S . The stratum level rates used in second-stage sampling account for the first-stage selection probabilities. These rates are applied at a block level to addresses in the U.S. by calculating a measure of size for each of the following geographic entities:
- Counties
- Places
- School Districts (elementary, secondary, and unified)
- American Indian Areas
- Tribal Subdivisions
- Alaska Native Village Statistical Areas
- Hawaiian Homelands
- Minor Civil Divisions - in Connecticut, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Wisconsin (these are the states where MCDs are active, functioning governmental units)
- Census Designated Places - in Hawaii only
The measure of size for all areas except American Indian Areas, Tribal Subdivisions, and Alaska Native Village Statistical Areas is an estimate of the number of occupied HUs in the area. This is calculated by multiplying the number of ACS addresses by an estimated occupancy rate at the block level. A measure of size for each Census Tract is also calculated in the same manner.
For American Indian, Tribal Subdivisions, and Alaska Native Village Statistical Areas, the measure of size is the estimated number of occupied HUs multiplied by the proportion of people reporting American Indian or Alaska Native (alone or in combination) in the 2010 Census.
Each block is then assigned the smallest measure of size from the set of all entities of which it is a part. The 2011 second-stage sampling strata and the overall first-phase sampling rates are shown in Table 1 below. There are two sets of rates shown in the table, since the sample increase from 2.9 to 3.54 million occurred midway through the 2011 sample year. The 3.54 million target sampling rates are the rates that would have been used if the entire 2011 sample was at the 3.54 million level. The sample rates represent the actual percent in sample that was delivered for the 2011 sample year.
- Calculation of the second-stage sampling rates (performed during Main sampling only) - The overall first-phase sampling rates given in Table 1 are calculated using the distribution of ACS valid addresses by second-stage sampling stratum in such a way as to yield an overall target sample size for the year of 3,540,000 in the U.S. These rates also account for expected growth of the HU inventory between Main and Supplemental of roughly 1 percent. The first-phase rates are adjusted for the first-stage sample to yield the second-stage selection probabilities1.
- Second-stage sample selection (performed in Main and Supplemental) - After each block is assigned to a second-stage sampling stratum, a systematic sample of addresses is selected from the second-stage universe (first-stage sample) within each county and county equivalent.
- Sample Month Assignment (performed in Main and Supplemental) - After the second stage of sampling, all sample addresses are randomly assigned to a sample month. Addresses selected during Main sampling are allocated to each of the 12 months. Addresses selected during Supplemental sampling are assigned to the months of May- December.
2. Second-phase Sample Selection - Subsampling the Unmailable and Non-Responding Addresses
Most addresses determined to be unmailable are subsampled for the CAPI phase of data collection at a rate of 2-in-3. Unmailable addresses, which include Remote Alaska addresses, do not go to the CATI phase of data collection. Subsequent to CATI, all addresses for which no response has been obtained prior to CAPI are subsampled based on the expected rate of completed interviews at the tract level using the rates shown in Table 2.
Table 1. First-phase Sampling Rate Categories for the United States
| Second Stage Sampling Stratum | Type of Area | Target Sampling Rate (3.54 Million) | 2011 Percent In Sample |
| 1 | 0 < MOS1< 200 | 15.00% | 14.81% |
| 2 | 200 < MOS < 400 | 10.00% | 9.94% |
| 3 | 400 < MOS < 800 | 7.00% | 6.97% |
| 4 | 800 < MOS < 1,200 | 4.93% | 4.44% |
| 5 | 0 < TRACTMOS2 < 400 | 6.16% | 5.55% |
| 6 | 0 < TRACTMOS < 400 H.R.3 | 5.67% | 5.00% |
| 7 | 400 < TRACTMOS < 1,000 | 4.93% | 4.46% |
| 8 | 400 < TRACTMOS < 1,000 H.R. | 4.53% | 4.10% |
| 9 | 1,000 < TRACTMOS < 2,000 | 2.99% | 2.71% |
| 10 | 1,000 < TRACTMOS < 2,000 H.R. | 2.75% | 2.49% |
| 11 | 2,000 < TRACTMOS < 4,000 | 1.76% | 1.61% |
| 12 | 2,000 < TRACTMOS < 4,000 H.R. | 1.62% | 1.47% |
| 13 | 4,000 < TRACTMOS < 6,000 | 1.06% | 0.96% |
| 14 | 4,000 < TRACTMOS < 6,000 H.R. | 0.97% | 0.89% |
| 15 | 6,000 < TRACTMOS | 0.62% | 0.57% |
| 16 | 6,000 < TRACTMOS H.R. | 0.57% | 0.53% |
Footnotes:
1MOS = Measure of size of the smallest governmental entity.
2TRACTMOS = Census Tract measure of size.
3H.R. = areas where predicted levels of completed mail and CATI interviews are > 60%.
Table 2. Second-Phase (CAPI) Subsampling Rates for the United States
| Address and Tract Characteristics | CAPI Subsampling Rate |
| United States | |
| Addresses in Remote Alaska1 | Take all (100%) |
| Addresses in Hawaiian Homelands, Alaska Native Village Statistical areas and a subset of American Indian areas1 | Take all (100%) |
| Unmailable addresses that are not in the previous two categories | 66.70% |
| Mailable addresses in tracts with predicted levels of completed mail and CATI interviews prior to CAPI subsampling between 0% and less than 36% | 50.00% |
| Mailable addresses in tracts with predicted levels of completed mail and CATI interviews prior to CAPI subsampling greater than 35% and less than 51% | 40.00% |
| Mailable addresses in other tracts | 33.30% |
Footnotes:
1 The full CAPI follow-up procedure for these two categories is new to the ACS sample design.
The 2011 GQ sampling frame is divided into two strata: one for small GQs (having 15 or fewer people according to 2010 Census or updated information), and one for large GQs (having more than 15 people according to 2010 Census or updated information). GQs in the first stratum are sampled using the same procedure, while GQs in the large stratum are sampled using a different method.
1. First-phase Sample Selection for Small GQ Stratum
2. Sample Selection for the Large GQ Stratum
Unlike housing unit address sampling and the small GQ sample selection, the large GQ sampling procedure has no first-stage in which sampling units are randomly assigned to one of five years. All large GQs are eligible for sampling each year. The large GQ samples are selected using a two-phase design.
All GQs in this stratum are eligible for sampling every year, regardless of their sample status in previous years. For large GQs, hits can be selected multiple times in the sample year. For most GQ types, the hits are randomly assigned throughout the year. Some GQs may have multiple hits with the same sample date if more than 12 hits are selected from the GQ. In these cases, the person sample within that month is unduplicated. The following table summarizes the 2011 state target sampling rates for the U.S.
Table 3. 2011 State Targeted Sampling Rates for the U.S.
3. Sample Month Assignment
In order to assign each hit to a panel month, all of the GQ samples from a state are combined and sorted by small/large stratum and second-phase order of selection. Consecutive samples are assigned to the twelve panel months in a predetermined order, starting with a randomly determined month, except for Federal prisons and remote Alaska. Remote Alaska GQs are assigned to January and September based on where the GQ is located. Correctional facilities have their sample clustered. All Federal prisons hits are assigned to the September panel. In non-Federal correctional facilities, all hits for a given GQ are assigned to the same panel month. However, unlike Federal prisons, the hits in state and local correctional facilities are assigned to randomly selected panels spread throughout the year.
4. Second Phase Sample: Selection of Persons in Small and Large GQs
Small GQs in the second phase sampling are "take all," i.e., every person in the selected GQ is eligible to receive a questionnaire. If the actual number of persons in the GQ exceeds 15, a field subsampling operation is performed to reduce the total number of sample persons interviewed at the GQ to 10. If the actual number of people is 15 or less, all people in the GQ will receive the questionnaire.
For each hit in the large GQs, the automated instrument uses the population count at the time of the visit and selects a subsample of 10 people from the roster. The people in this subsample receive the questionnaire.
1. First-phase Sample Selection for Small GQ Stratum
- First-stage sampling - Small GQs are only eligible to be selected for the ACS once every five years. To accomplish this, the first stage sampling procedure systematically assigned all small GQs to one of five partitions of the universe. Each partition was assigned to a particular year (2011-2015) and the one assigned to 2011 became the first stage sample. In future years, each new GQ will be systematically assigned to one of the five samples. These samples are rotated over five year periods and become the universe for selecting the second stage sample.
- Second-stage sampling - During the second stage, GQs are selected from the first stage sample in a systematic sample of 1-in-x where x is dependent upon the state's target sampling rate. Since the first stage sample is one fifth of the universe, x can be calculated as x = (1 /5) x (1 / rate) where rate is the state's target sampling rate. For example, suppose a state had a target sampling rate of 2.5%. The systematic sample would then be 1-in-8 since (1 / 5) x (1 / 0.025) = 8. Regardless of their actual size, all GQs in the small stratum have the same probability of selection.
2. Sample Selection for the Large GQ Stratum
Unlike housing unit address sampling and the small GQ sample selection, the large GQ sampling procedure has no first-stage in which sampling units are randomly assigned to one of five years. All large GQs are eligible for sampling each year. The large GQ samples are selected using a two-phase design.
- First-phase Sampling - In the large GQ stratum, GQ hits are selected using a systematic PPS (probability proportional to size) sample, with a target sampling rate that varies according to state. A hit refers to a grouping of 10 expected interviews. GQs are selected with probability proportional to its most current count of persons or capacity. For stratification, and for sampling the large GQs, a GQ measure of size (GQMOS) is computed, where GQMOS is the expected population of the GQ divided by 10. This reflects that the GQ data is collected in groups of 10 GQ persons. People are selected in hits of 10 in a systematic sample of 1-in-x where x = 1 / rate (one divided by the state's target sampling rate). For example, suppose a state had a target sampling rate of 2.5%. The hits would then be selected in a systematic sample of 1-in-40, since 1 / 0.025 = 40.
All GQs in this stratum are eligible for sampling every year, regardless of their sample status in previous years. For large GQs, hits can be selected multiple times in the sample year. For most GQ types, the hits are randomly assigned throughout the year. Some GQs may have multiple hits with the same sample date if more than 12 hits are selected from the GQ. In these cases, the person sample within that month is unduplicated. The following table summarizes the 2011 state target sampling rates for the U.S.
Table 3. 2011 State Targeted Sampling Rates for the U.S.
| Alabama | 2.04% | Louisiana | 2.21% | North Dakota | 4.13% |
| Alaska | 3.84% | Maine | 2.80% | Ohio | 2.22% |
| California, Illinois | 2.15% | Maryland | 2.12% | Oklahoma | 2.09% |
| Connecticut | 2.03% | Michigan | 2.53% | Oregon, West Virginia | 2.18% |
| Delaware | 4.18% | Minnesota | 2.24% | Puerto Rico | 2.50% |
| District of Columbia | 2.39% | Missouri, Washington | 2.01% | Rhode Island | 2.37% |
| Hawaii, New Jersey | 2.44% | Montana | 3.41% | South Carolina | 2.02% |
| Idaho | 2.75% | Nebraska, Pennsylvania | 2.34% | South Dakota | 2.89% |
| Indiana | 2.08% | Nevada, Utah | 2.25% | Tennessee | 2.13% |
| Iowa | 2.23% | New Hampshire | 2.56% | Vermont | 3.85% |
| Kansas, Mississippi | 2.16% | New Mexico | 2.36% | Wisconsin | 2.20% |
| Kentucky | 2.19% | North Carolina | 2.07% | Wyoming | 6.53% |
| All Other States | 2.00% |
3. Sample Month Assignment
In order to assign each hit to a panel month, all of the GQ samples from a state are combined and sorted by small/large stratum and second-phase order of selection. Consecutive samples are assigned to the twelve panel months in a predetermined order, starting with a randomly determined month, except for Federal prisons and remote Alaska. Remote Alaska GQs are assigned to January and September based on where the GQ is located. Correctional facilities have their sample clustered. All Federal prisons hits are assigned to the September panel. In non-Federal correctional facilities, all hits for a given GQ are assigned to the same panel month. However, unlike Federal prisons, the hits in state and local correctional facilities are assigned to randomly selected panels spread throughout the year.
4. Second Phase Sample: Selection of Persons in Small and Large GQs
Small GQs in the second phase sampling are "take all," i.e., every person in the selected GQ is eligible to receive a questionnaire. If the actual number of persons in the GQ exceeds 15, a field subsampling operation is performed to reduce the total number of sample persons interviewed at the GQ to 10. If the actual number of people is 15 or less, all people in the GQ will receive the questionnaire.
For each hit in the large GQs, the automated instrument uses the population count at the time of the visit and selects a subsample of 10 people from the roster. The people in this subsample receive the questionnaire.
The estimates that appear in this product are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given tabulation area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the tabulation area. Each sample person or housing unit record is assigned exactly one weight to be used to produce estimates of all characteristics. For example, if the weight given to a sample person or housing unit has a value 40, all characteristics of that person or housing unit are tabulated with the weight of 40.
The weighting is conducted in two main operations: a group quarters person weighting operation which assigns weights to persons in group quarters, and a household person weighting operation which assigns weights both to housing units and to persons within housing units. The group quarters person weighting is conducted first and the household person weighting second. The household person weighting is dependent on the group quarters person weighting because estimates for total population which include both group quarters and household population are controlled to the Census Bureau's official 2011 total resident population estimates.
The weighting is conducted in two main operations: a group quarters person weighting operation which assigns weights to persons in group quarters, and a household person weighting operation which assigns weights both to housing units and to persons within housing units. The group quarters person weighting is conducted first and the household person weighting second. The household person weighting is dependent on the group quarters person weighting because estimates for total population which include both group quarters and household population are controlled to the Census Bureau's official 2011 total resident population estimates.
The group quarters (GQ) person weighting for the ACS 2011 1-year estimates has changed in important ways from that of the ACS 2010 1-year estimates. For the first time, the GQ population sample has been supplemented by a large-scale whole person imputation into not-in-sample GQ facilities. For the 2011 ACS GQ data, roughly as many GQ persons were imputed as sampled. The goal of the imputation methodology was two-fold.
1.The primary objective was to establish representation of county by major GQ type group in the tabulations for each combination that exists on the ACS GQ sample frame. The seven major GQ type groups are defined by the Population Estimates Program and are given in Table 4.
2.A secondary objective was to establish representation of tract by major GQ type group for each combination that exists on the ACS GQ sample frame.
Table 4: Population Estimates Program Major GQ Type Groups
For all not-in-sample GQ facilities with an expected population of 16 or more persons (large facilities), we imputed a number of GQ persons equal to 2.5% of the expected population. For those GQ facilities with an expected population of fewer than 16 persons (small facilities), we selected a random sample of GQ facilities as needed to accomplish the two objectives given above. For those selected small GQ facilities, we imputed a number of GQ persons equal to 20% of the facility's expected population.
Interviewed GQ person records were then sampled at random to be imputed into the selected not-in-sample GQ facilities. An expanding search algorithm searched for donors within the same specific type of GQ facility and the same county. If that failed, the search included all GQ facilities of the same major GQ type group. If that still failed, the search expanded to a specific type within a larger geography, then a major GQ type group within that geography, and so on until suitable donors were found.
The weighting procedure made no distinction between sampled and imputed GQ person records. The initial weights of person records in the large GQ facilities equaled the observed or expected population of the GQ facility divided by the number of person records. The initial weights of person records in small GQ facilities equaled the observed or expected population of the GQ facility divided by the number of records, multiplied by the inverse of the fraction represented on the frame of the small GQ facilities of that tract by major GQ type group combination. As was done in previous years' weighting, we controlled the final weights to an independent set of GQ population estimates produced by the Population Estimates Program for each state by each of the seven major GQ type groups.
Lastly, the final GQ person weight was rounded to an integer. Rounding was performed so that the sum of the rounded weights were within one person of the sum of the unrounded weights for any of the groups listed below:
Major GQ Type Group Major GQ Type Group x County
Housing Unit and Household Person Weighting
The housing unit and household person weighting uses two types of geographic areas for adjustments: weighting areas and subcounty areas. Weighting areas are county-based and have been used since the first year of the ACS. Subcounty areas are based on incorporated place and minor civil divisions (MCD). Their use was introduced into the ACS in 2010.
Weighting areas are built from collections of whole counties. Census 2000 data are used to group counties of similar demographic and social characteristics. The characteristics considered in the formation include:
Subcounty areas are built from incorporated places and MCDs, with MCDs only being used in the 20 states where MCDs serve as functioning governmental units. Each subcounty area formed has a total population of at least 24,000, as determined by the July 1, 2011 Population Estimates data, which are based on the 2010 Census estimates of the population on April 1, 2010, extrapolated forward. The subcounty areas can be incorporated places, MCDs, place/MCD intersections (in counties where places and MCDs are not coexistent), 'balance of MCD,' and 'balance of county.' The latter two types group together unincorporated areas and places/MCDs that do not meet the population threshold. If two or more subcounty areas cannot be formed within a county, then the entire county is treated as a single area. Thus all counties whose total population is less than 48,000 will be treated as a single area since it is not possible to form two areas that satisfy the minimum size threshold.
The estimation procedure used to assign the weights is then performed independently within each of the ACS weighting areas.
1. Initial Housing Unit Weighting Factors - This process produced the following factors:
Selected in CAPI subsampling: SSF = 2.0, 2.5, or 3.0 according to Table 2 Not selected in CAPI subsampling: SSF = 0.0 Not a CAPI case: SSF = 1.0
Some sample addresses are unmailable. A two-thirds sample of these is sent directly to CAPI and for these cases SSF = 1.5.
Weighting Area x Month
Weighting Area x Building Type x Tract
A second factor, NIF2, is a ratio adjustment that is computed and assigned to occupied housing units based on the following groups:
Weighting Area x Building Type x MonthNIF is then computed by applying NIF1 and NIF2 for each occupied housing unit. Vacant housing units are assigned a value of NIF = 1.0. Nonresponding housing units are assigned a weight of 0.0.
NIFM is computed and assigned to occupied CAPI housing units based on the following groups:
Weighting Area x Building Type (single or multi unit) x Month
Vacant housing units or non-CAPI (mail and CATI) housing units receive a value of NIFM = 1.0.
Weighting Area x Tenure (owner or renter) x Month x Marital Status of the Householder (married/widowed or single)
Vacant housing units receive a value of MBF = 1.0. MBF is applied to the weights computed through NIF.
2. Person Weighting Factors-Initially the person weight of each person in an occupied housing unit is the product of the weighting factors of their associated housing unit (BW x ... x HPF). At this point everyone in the household has the same weight. The person weighting is done in a series of three steps which are repeated until a stopping criterion is met. These three steps form a raking ratio or raking process. These person weights are individually adjusted for each person as described below.
The three steps are as follows:
Spouse Equalization/Householder Equalization Raking Factor (SPHHEQRF)-This factor is applied to individuals based on the combination of their status of being in a married-couple or unmarried-partner household and whether they are the householder. All persons are assigned to one of four groups:
The weights of persons in the first two groups are adjusted so that their sums are each equal to the total estimate of married-couple or unmarried-partner households using the housing unit weight (BW x ... x HPF). At the same time the weights of persons in the first and third groups are adjusted so that their sum is equal to the total estimate of occupied housing units using the housing unit weight (BW x ... x HPF). The goal of this step is to produce more consistent estimates of spouses or unmarried partners and married-couple and unmarried-partner households while simultaneously producing more consistent estimates of householders, occupied housing units, and households.
Demographic Raking Factor (DEMORF)-This factor is applied to individuals based on their age, race, sex and Hispanic origin. It adjusts the person weights so that the weighted sample counts equal independent population estimates by age, race, sex, and Hispanic origin at the weighting area. Because of collapsing of groups in applying this factor, only total population is assured of agreeing with the official 2011 population estimates at the weighting area level.
This uses the following groups (note that there are 13 Age groupings):
Weighting Area x Race / Ethnicity (non-Hispanic White, non-Hispanic Black, non- Hispanic American Indian or Alaskan Native, non-Hispanic Asian, non-Hispanic Native Hawaiian or Pacific Islander, and Hispanic (any race)) x Sex x Age Groups.
These three steps are repeated several times until the estimates at the national level achieve their optimal consistency with regard to the spouse and householder equalization. The effect Person Post-Stratification Factor (PPSF) is then equal to the product (SUBEQRF x SPHHEQRF x DEMORF) from all of iterations of these three adjustments. The unrounded person weight is then the equal to the product of PPSF times the housing unit weight (BW x ... x HPF x PPSF).
3.Rounding-The final product of all person weights (BW x ... x HPF x PPSF) is rounded to an integer. Rounding is performed so that the sum of the rounded weights is within one person of the sum of the unrounded weights for any of the groups listed below:
County
County x Race
County x Race x Hispanic Origin
County x Race x Hispanic Origin x Sex
County x Race x Hispanic Origin x Sex x Age
County x Race x Hispanic Origin x Sex x Age x Tract
County x Race x Hispanic Origin x Sex x Age x Tract x Block
For example, the number of White, Hispanic, Males, Age 30 estimated for a county using the rounded weights is within one of the number produced using the unrounded weights.
4.Final Housing Unit Weighting Factors-This process produces the following factors:
County
County x Tract
County x Tract x Block
1.The primary objective was to establish representation of county by major GQ type group in the tabulations for each combination that exists on the ACS GQ sample frame. The seven major GQ type groups are defined by the Population Estimates Program and are given in Table 4.
2.A secondary objective was to establish representation of tract by major GQ type group for each combination that exists on the ACS GQ sample frame.
Table 4: Population Estimates Program Major GQ Type Groups
| Major GQ Type Group | Definition | Institutional/Non-Institutional |
| 1 | Correctional Institutions | Institutional |
| 2 | Juvenile Detention Facilities | Institutional |
| 3 | Nursing Homes | Institutional |
| 4 | Other Long-Term Care Facilities | Institutional |
| 5 | College Dormitories | Non-Institutional |
| 6 | Military Facilities | Non-Institutional |
| 7 | Other Non-Institutional Facilities | Non-Institutional |
For all not-in-sample GQ facilities with an expected population of 16 or more persons (large facilities), we imputed a number of GQ persons equal to 2.5% of the expected population. For those GQ facilities with an expected population of fewer than 16 persons (small facilities), we selected a random sample of GQ facilities as needed to accomplish the two objectives given above. For those selected small GQ facilities, we imputed a number of GQ persons equal to 20% of the facility's expected population.
Interviewed GQ person records were then sampled at random to be imputed into the selected not-in-sample GQ facilities. An expanding search algorithm searched for donors within the same specific type of GQ facility and the same county. If that failed, the search included all GQ facilities of the same major GQ type group. If that still failed, the search expanded to a specific type within a larger geography, then a major GQ type group within that geography, and so on until suitable donors were found.
The weighting procedure made no distinction between sampled and imputed GQ person records. The initial weights of person records in the large GQ facilities equaled the observed or expected population of the GQ facility divided by the number of person records. The initial weights of person records in small GQ facilities equaled the observed or expected population of the GQ facility divided by the number of records, multiplied by the inverse of the fraction represented on the frame of the small GQ facilities of that tract by major GQ type group combination. As was done in previous years' weighting, we controlled the final weights to an independent set of GQ population estimates produced by the Population Estimates Program for each state by each of the seven major GQ type groups.
Lastly, the final GQ person weight was rounded to an integer. Rounding was performed so that the sum of the rounded weights were within one person of the sum of the unrounded weights for any of the groups listed below:
Major GQ Type Group Major GQ Type Group x County
Housing Unit and Household Person Weighting
The housing unit and household person weighting uses two types of geographic areas for adjustments: weighting areas and subcounty areas. Weighting areas are county-based and have been used since the first year of the ACS. Subcounty areas are based on incorporated place and minor civil divisions (MCD). Their use was introduced into the ACS in 2010.
Weighting areas are built from collections of whole counties. Census 2000 data are used to group counties of similar demographic and social characteristics. The characteristics considered in the formation include:
- Percent in poverty
- Percent renting
- Percent in rural areas
- Race, ethnicity, age, and sex distribution
- Distance between the centroids of the counties
- Core-based Statistical Area status
Subcounty areas are built from incorporated places and MCDs, with MCDs only being used in the 20 states where MCDs serve as functioning governmental units. Each subcounty area formed has a total population of at least 24,000, as determined by the July 1, 2011 Population Estimates data, which are based on the 2010 Census estimates of the population on April 1, 2010, extrapolated forward. The subcounty areas can be incorporated places, MCDs, place/MCD intersections (in counties where places and MCDs are not coexistent), 'balance of MCD,' and 'balance of county.' The latter two types group together unincorporated areas and places/MCDs that do not meet the population threshold. If two or more subcounty areas cannot be formed within a county, then the entire county is treated as a single area. Thus all counties whose total population is less than 48,000 will be treated as a single area since it is not possible to form two areas that satisfy the minimum size threshold.
The estimation procedure used to assign the weights is then performed independently within each of the ACS weighting areas.
1. Initial Housing Unit Weighting Factors - This process produced the following factors:
- Base Weight (BW) - This initial weight is assigned to every housing unit as the inverse of its block's sampling rate.
- CAPI Subsampling Factor (SSF) - The weights of the CAPI cases are adjusted to reflect the results of CAPI subsampling. This factor is assigned to each record as follows:
Selected in CAPI subsampling: SSF = 2.0, 2.5, or 3.0 according to Table 2 Not selected in CAPI subsampling: SSF = 0.0 Not a CAPI case: SSF = 1.0
Some sample addresses are unmailable. A two-thirds sample of these is sent directly to CAPI and for these cases SSF = 1.5.
- Variation in Monthly Response by Mode (VMS)-This factor makes the total weight of the Mail, CATI, and CAPI records to be tabulated in a month equal to the total base weight of all cases originally mailed for that month. For all cases, VMS is computed and assigned based on the following groups:
Weighting Area x Month
- Noninterview Factor (NIF)-This factor adjusts the weight of all responding occupied housing units to account for nonresponding housing units. The factor is computed in two stages. The first factor, NIF1, is a ratio adjustment that is computed and assigned to occupied housings units based on the following groups:
Weighting Area x Building Type x Tract
A second factor, NIF2, is a ratio adjustment that is computed and assigned to occupied housing units based on the following groups:
Weighting Area x Building Type x MonthNIF is then computed by applying NIF1 and NIF2 for each occupied housing unit. Vacant housing units are assigned a value of NIF = 1.0. Nonresponding housing units are assigned a weight of 0.0.
- Noninterview Factor - Mode (NIFM) - This factor adjusts the weight of the responding CAPI occupied housing units to account for CAPI nonrespondents. It is computed as if NIF had not already been assigned to every occupied housing unit record. This factor is not used directly but rather as part of computing the next factor, the Mode Bias Factor.
NIFM is computed and assigned to occupied CAPI housing units based on the following groups:
Weighting Area x Building Type (single or multi unit) x Month
Vacant housing units or non-CAPI (mail and CATI) housing units receive a value of NIFM = 1.0.
- Mode Bias Factor (MBF)-This factor makes the total weight of the housing units in the groups below the same as if NIFM had been used instead of NIF. MBF is computed and assigned to occupied housing units based on the following groups:
Weighting Area x Tenure (owner or renter) x Month x Marital Status of the Householder (married/widowed or single)
Vacant housing units receive a value of MBF = 1.0. MBF is applied to the weights computed through NIF.
- Housing unit Post-stratification Factor (HPF)-This factor makes the total weight of all housing units agree with the 2011 independent housing unit estimates at the subcounty level.
2. Person Weighting Factors-Initially the person weight of each person in an occupied housing unit is the product of the weighting factors of their associated housing unit (BW x ... x HPF). At this point everyone in the household has the same weight. The person weighting is done in a series of three steps which are repeated until a stopping criterion is met. These three steps form a raking ratio or raking process. These person weights are individually adjusted for each person as described below.
The three steps are as follows:
- Subcounty Area Controls Raking Factor (SUBEQRF) - This factor is applied to individuals based on their geography. It adjusts the person weights so that the weighted sample counts equal independent population estimates of total population for the subcounty area. Because of later adjustment to the person weights, total population is not assured of agreeing exactly with the official 2011 population estimates at the subcounty level.
Spouse Equalization/Householder Equalization Raking Factor (SPHHEQRF)-This factor is applied to individuals based on the combination of their status of being in a married-couple or unmarried-partner household and whether they are the householder. All persons are assigned to one of four groups:
- Householder in a married-couple or unmarried-partner household
- Spouse or unmarried partner in a married-couple or unmarried-partner household (non-householder)
- Other householder
- Other non-householder
The weights of persons in the first two groups are adjusted so that their sums are each equal to the total estimate of married-couple or unmarried-partner households using the housing unit weight (BW x ... x HPF). At the same time the weights of persons in the first and third groups are adjusted so that their sum is equal to the total estimate of occupied housing units using the housing unit weight (BW x ... x HPF). The goal of this step is to produce more consistent estimates of spouses or unmarried partners and married-couple and unmarried-partner households while simultaneously producing more consistent estimates of householders, occupied housing units, and households.
Demographic Raking Factor (DEMORF)-This factor is applied to individuals based on their age, race, sex and Hispanic origin. It adjusts the person weights so that the weighted sample counts equal independent population estimates by age, race, sex, and Hispanic origin at the weighting area. Because of collapsing of groups in applying this factor, only total population is assured of agreeing with the official 2011 population estimates at the weighting area level.
This uses the following groups (note that there are 13 Age groupings):
Weighting Area x Race / Ethnicity (non-Hispanic White, non-Hispanic Black, non- Hispanic American Indian or Alaskan Native, non-Hispanic Asian, non-Hispanic Native Hawaiian or Pacific Islander, and Hispanic (any race)) x Sex x Age Groups.
These three steps are repeated several times until the estimates at the national level achieve their optimal consistency with regard to the spouse and householder equalization. The effect Person Post-Stratification Factor (PPSF) is then equal to the product (SUBEQRF x SPHHEQRF x DEMORF) from all of iterations of these three adjustments. The unrounded person weight is then the equal to the product of PPSF times the housing unit weight (BW x ... x HPF x PPSF).
3.Rounding-The final product of all person weights (BW x ... x HPF x PPSF) is rounded to an integer. Rounding is performed so that the sum of the rounded weights is within one person of the sum of the unrounded weights for any of the groups listed below:
County
County x Race
County x Race x Hispanic Origin
County x Race x Hispanic Origin x Sex
County x Race x Hispanic Origin x Sex x Age
County x Race x Hispanic Origin x Sex x Age x Tract
County x Race x Hispanic Origin x Sex x Age x Tract x Block
For example, the number of White, Hispanic, Males, Age 30 estimated for a county using the rounded weights is within one of the number produced using the unrounded weights.
4.Final Housing Unit Weighting Factors-This process produces the following factors:
- Householder Factor (HHF)-This factor adjusts for differential response depending on the race, Hispanic origin, sex, and age of the householder. The value of HHF for an occupied housing unit is the PPSF of the householder. Since there is no householder for vacant units, the value of HHF = 1.0 for all vacant units.
- Rounding-The final product of all housing unit weights (BW x ... x HHF) is rounded to an integer. For occupied units, the rounded housing unit weight is the same as the rounded person weight of the householder. This ensures that both the rounded and unrounded householder weights are equal to the occupied housing unit weight. The rounding for vacant housing units is then performed so that total rounded weight is within one housing unit of the total unrounded weight for any of the groups listed below:
County
County x Tract
County x Tract x Block
The Census Bureau has modified or suppressed some data on this site to protect confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified.
The Census Bureau's internal Disclosure Review Board sets the confidentiality rules for all data releases. A checklist approach is used to ensure that all potential risks to the confidentiality of the data are considered and addressed.
The Census Bureau's internal Disclosure Review Board sets the confidentiality rules for all data releases. A checklist approach is used to ensure that all potential risks to the confidentiality of the data are considered and addressed.
- Title 13, United States Code: Title 13 of the United States Code authorizes the Census Bureau to conduct censuses and surveys. Section 9 of the same Title requires that any information collected from the public under the authority of Title 13 be maintained as confidential. Section 214 of Title 13 and Sections 3559 and 3571 of Title 18 of the United States Code provide for the imposition of penalties of up to five years in prison and up to $250,000 in fines for wrongful disclosure of confidential census information.
- Disclosure Avoidance: Disclosure avoidance is the process for protecting the confidentiality of data. A disclosure of data occurs when someone can use published statistical information to identify an individual that has provided information under a pledge of confidentiality. For data tabulations, the Census Bureau uses disclosure avoidance procedures to modify or remove the characteristics that put confidential information at risk for disclosure. Although it may appear that a table shows information about a specific individual, the Census Bureau has taken steps to disguise or suppress the original data while making sure the results are still useful. The techniques used by the Census Bureau to protect confidentiality in tabulations vary, depending on the type of data. All disclosure avoidance procedures are done prior to the whole person imputation into not-in-sample GQ facilities.
- Data Swapping: Data swapping is a method of disclosure avoidance designed to protect confidentiality in tables of frequency data (the number or percent of the population with certain characteristics). Data swapping is done by editing the source data or exchanging records for a sample of cases when creating a table. A sample of households is selected and matched on a set of selected key variables with households in neighboring geographic areas that have similar characteristics (such as the same number of adults and same number of children). Because the swap often occurs within a neighboring area, there is no effect on the marginal totals for the area or for totals that include data from multiple areas. Because of data swapping, users should not assume that tables with cells having a value of one or two reveal information about specific individuals. Data swapping procedures were first used in the 1990 Census, and were used again in Census 2000 and the 2010 Census.
- Synthetic Data: The goals of using synthetic data are the same as the goals of data swapping, namely to protect the confidentiality in tables of frequency data. Persons are identified as being at risk for disclosure based on certain characteristics. The synthetic data technique then models the values for another collection of characteristics to protect the confidentiality of that individual.
- Sampling Error - The data in the ACS products are estimates of the actual figures that would have been obtained by interviewing the entire population using the same methodology. The estimates from the chosen sample also differ from other samples of housing units and persons within those housing units. Sampling error in data arises due to the use of probability sampling, which is necessary to ensure the integrity and representativeness of sample survey results. The implementation of statistical sampling procedures provides the basis for the statistical analysis of sample data. Measures used to estimate the sampling error are provided in the next section.
- Nonsampling Error - In addition to sampling error, data users should realize that other types of errors may be introduced during any of the various complex operations used to collect and process survey data. For example, operations such as data entry from questionnaires and editing may introduce error into the estimates. Another source is through the use of controls in the weighting. The controls are designed to mitigate the effects of systematic undercoverage of certain groups who are difficult to enumerate as well as to reduce the variance. The controls are based on the population estimates extrapolated from the previous census. Errors can be brought into the data if the extrapolation methods do not properly reflect the population. However, the potential risk from using the controls in the weighting process is offset by far greater benefits to the ACS estimates. These benefits include reducing the effects of a larger coverage problem found in most surveys, including the ACS, and the reduction of standard errors of ACS estimates. These and other sources of error contribute to the nonsampling error component of the total error of survey estimates. Nonsampling errors may affect the data in two ways. Errors that are introduced randomly increase the variability of the data. Systematic errors which are consistent in one direction introduce bias into the results of a sample survey. The Census Bureau protects against the effect of systematic errors on survey estimates by conducting extensive research and evaluation programs on sampling techniques, questionnaire design, and data collection and processing procedures. In addition, an important goal of the ACS is to minimize the amount of nonsampling error introduced through nonresponse for sample housing units. One way of accomplishing this is by following up on mail nonrespondents during the CATI and CAPI phases. For more information, please see the section entitled "Control of Nonsampling Error".
Sampling error is the difference between an estimate based on a sample and the corresponding value that would be obtained if the estimate were based on the entire population (as from a census). Note that sample-based estimates will vary depending on the particular sample selected from the population. Measures of the magnitude of sampling error reflect the variation in the estimates over all possible samples that could have been selected from the population using the same sampling methodology.
Estimates of the magnitude of sampling errors - in the form of margins of error - are provided with all published ACS data. The Census Bureau recommends that data users incorporate this information into their analyses, as sampling error in survey estimates could impact the conclusions drawn from the results.
Confidence Intervals and Margins of Error
Confidence Intervals - A sample estimate and its estimated standard error may be used to construct confidence intervals about the estimate. These intervals are ranges that will contain the average value of the estimated characteristic that results over all possible samples, with a known probability.
For example, if all possible samples that could result under the ACS sample design were independently selected and surveyed under the same conditions, and if the estimate and its estimated standard error were calculated for each of these samples, then:
The intervals are referred to as 68 percent, 90 percent, and 95 percent confidence intervals, respectively.
Margin of Error - Instead of providing the upper and lower confidence bounds in published ACS tables, the margin of error is provided instead. The margin of error is the difference between an estimate and its upper or lower confidence bound. Both the confidence bounds and the standard error can easily be computed from the margin of error. All ACS published margins of error are based on a 90 percent confidence level.
Standard Error = Margin of Error / 1.645
Lower Confidence Bound = Estimate - Margin of Error
Upper Confidence Bound = Estimate + Margin of Error
Note that for 2005 and earlier estimates, ACS margins of error and confidence bounds were calculated using a 90 percent confidence level multiplier of 1.65. Beginning with the 2006 data release, we are now employing a more accurate multiplier of 1.645. Margins of error and confidence bounds from previously published products will not be updated with the new multiplier. When calculating standard errors from margins of error or confidence bounds using published data for 2005 and earlier, use the 1.65 multiplier.
When constructing confidence bounds from the margin of error, the user should be aware of any "natural" limits on the bounds. For example, if a characteristic estimate for the population is near zero, the calculated value of the lower confidence bound may be negative. However, a negative number of people does not make sense, so the lower confidence bound should be reported as zero instead. However, for other estimates such as income, negative values do make sense. The context and meaning of the estimate must be kept in mind when creating these bounds. Another of these natural limits would be 100 percent for the upper bound of a percent estimate.
If the margin of error is displayed as '' (five asterisks), the estimate has been controlled to be equal to a fixed value and so it has no sampling error. When using any of the formulas in the following section, use a standard error of zero for these controlled estimates.
Limitations -The user should be careful when computing and interpreting confidence intervals.
Estimates of the magnitude of sampling errors - in the form of margins of error - are provided with all published ACS data. The Census Bureau recommends that data users incorporate this information into their analyses, as sampling error in survey estimates could impact the conclusions drawn from the results.
Confidence Intervals and Margins of Error
Confidence Intervals - A sample estimate and its estimated standard error may be used to construct confidence intervals about the estimate. These intervals are ranges that will contain the average value of the estimated characteristic that results over all possible samples, with a known probability.
For example, if all possible samples that could result under the ACS sample design were independently selected and surveyed under the same conditions, and if the estimate and its estimated standard error were calculated for each of these samples, then:
- Approximately 68 percent of the intervals from one estimated standard error below the estimate to one estimated standard error above the estimate would contain the average result from all possible samples;
- Approximately 90 percent of the intervals from 1.645 times the estimated standard error below the estimate to 1.645 times the estimated standard error above the estimate would contain the average result from all possible samples.
- Approximately 95 percent of the intervals from two estimated standard errors below the estimate to two estimated standard errors above the estimate would contain the average result from all possible samples.
The intervals are referred to as 68 percent, 90 percent, and 95 percent confidence intervals, respectively.
Margin of Error - Instead of providing the upper and lower confidence bounds in published ACS tables, the margin of error is provided instead. The margin of error is the difference between an estimate and its upper or lower confidence bound. Both the confidence bounds and the standard error can easily be computed from the margin of error. All ACS published margins of error are based on a 90 percent confidence level.
Standard Error = Margin of Error / 1.645
Lower Confidence Bound = Estimate - Margin of Error
Upper Confidence Bound = Estimate + Margin of Error
Note that for 2005 and earlier estimates, ACS margins of error and confidence bounds were calculated using a 90 percent confidence level multiplier of 1.65. Beginning with the 2006 data release, we are now employing a more accurate multiplier of 1.645. Margins of error and confidence bounds from previously published products will not be updated with the new multiplier. When calculating standard errors from margins of error or confidence bounds using published data for 2005 and earlier, use the 1.65 multiplier.
When constructing confidence bounds from the margin of error, the user should be aware of any "natural" limits on the bounds. For example, if a characteristic estimate for the population is near zero, the calculated value of the lower confidence bound may be negative. However, a negative number of people does not make sense, so the lower confidence bound should be reported as zero instead. However, for other estimates such as income, negative values do make sense. The context and meaning of the estimate must be kept in mind when creating these bounds. Another of these natural limits would be 100 percent for the upper bound of a percent estimate.
If the margin of error is displayed as '' (five asterisks), the estimate has been controlled to be equal to a fixed value and so it has no sampling error. When using any of the formulas in the following section, use a standard error of zero for these controlled estimates.
Limitations -The user should be careful when computing and interpreting confidence intervals.
- The estimated standard errors (and thus margins of error) included in these data products do not include portions of the variability due to nonsampling error that may be present in the data. In particular, the standard errors do not reflect the effect of correlated errors introduced by interviewers, coders, or other field or processing personnel. Nor do they reflect the error from imputed values due to missing responses. Thus, the standard errors calculated represent a lower bound of the total error. As a result, confidence intervals formed using these estimated standard errors may not meet the stated levels of confidence (i.e., 68, 90, or 95 percent). Thus, some care must be exercised in the interpretation of the data in this data product based on the estimated standard errors.
- Zero or small estimates; very large estimates - The value of almost all ACS characteristics is greater than or equal to zero by definition. For zero or small estimates, use of the method given previously for calculating confidence intervals relies on large sample theory, and may result in negative values which for most characteristics are not admissible. In this case the lower limit of the confidence interval is set to zero by default. A similar caution holds for estimates of totals close to a control total or estimated proportion near one, where the upper limit of the confidence interval is set to its largest admissible value. In these situations the level of confidence of the adjusted range of values is less than the prescribed confidence level.
Direct estimates of the margin of error were calculated for all estimates reported in this product. The margin of error is calculated from the variance. The variance, in most cases, is calculated using a replicate-based methodology known as successive difference replication that takes into account the sample design and estimation procedures.
The formula provided below calculates the variance using the ACS estimate (X0) and the 80 replicate estimates (Xr).
X0 is the estimate calculated using the production weight and Xr is the estimate calculated using the rth replicate weight. The standard error is the square root of the variance. The 90th percent margin of error is 1.645 times the standard error.
For more information on the formation of the replicate weights, see chapter 12 of the Design and Methodology documentation at http://www.census.gov/acs/www/Downloads/survey_methodology/Chapter_12_RevisedDec2010.pdf.
Beginning with the ACS 2011 1-year estimates, a new imputation-based methodology was incorporated into processing (see the description in the Group Quarters Person Weighting Section). An adjustment was made to the production replicate weight variance methodology to account for the non-negligible amount of additional variation being introduced by the new technique.5
Footnote: 5For more information regarding this issue, see Asiala, M. and Castro, E. 2012. Developing Replicate Weight-Based Methods to Account for Imputation Variance in a Mass Imputation Application. In JSM proceedings, Section on Survey Research Methods, Alexandria, VA: American Statistical Association.
Excluding the base weights, replicate weights were allowed to be negative in order to avoid underestimating the standard error.
Exceptions include:
Sums and Differences of Direct Standard Errors
The standard errors estimated from these tables are for individual estimates. Additional calculations are required to estimate the standard errors for sums of or the differences between two or more sample estimates.
The standard error of the sum of two sample estimates is the square root of the sum of the two individual standard errors squared plus a covariance term. That is, for standard errors
and
of estimates
and 
The covariance measures the interactions between two estimates. Currently the covariance terms are not available. Data users should use the approximation:
However, this method will underestimate or overestimate the standard error if the two estimates interact in either a positive or negative way.
The approximation formula (2) can be expanded to more than two estimates by adding in the individual standard errors squared inside the radical. As the number of estimates involved in the sum or difference increases, the results of formula (2) become increasingly different from the standard error derived directly from the ACS microdata. Care should be taken to work with the fewest number of estimates as possible. If there are estimates involved in the sum that are controlled in the weighting then the approximate standard error can be increasingly different. Several examples are provided starting on page 32 to demonstrate issues associated with approximating the standard errors when summing large numbers of estimates together.
The formula provided below calculates the variance using the ACS estimate (X0) and the 80 replicate estimates (Xr).
X0 is the estimate calculated using the production weight and Xr is the estimate calculated using the rth replicate weight. The standard error is the square root of the variance. The 90th percent margin of error is 1.645 times the standard error.
For more information on the formation of the replicate weights, see chapter 12 of the Design and Methodology documentation at http://www.census.gov/acs/www/Downloads/survey_methodology/Chapter_12_RevisedDec2010.pdf.
Beginning with the ACS 2011 1-year estimates, a new imputation-based methodology was incorporated into processing (see the description in the Group Quarters Person Weighting Section). An adjustment was made to the production replicate weight variance methodology to account for the non-negligible amount of additional variation being introduced by the new technique.5
Footnote: 5For more information regarding this issue, see Asiala, M. and Castro, E. 2012. Developing Replicate Weight-Based Methods to Account for Imputation Variance in a Mass Imputation Application. In JSM proceedings, Section on Survey Research Methods, Alexandria, VA: American Statistical Association.
Excluding the base weights, replicate weights were allowed to be negative in order to avoid underestimating the standard error.
Exceptions include:
- The estimate of the number or proportion of people, households, families, or housing units in a geographic area with a specific characteristic is zero. A special procedure is used to estimate the standard error.
-
There are either no sample observations available to compute an estimate or standard error of a median, an aggregate, a proportion, or some other ratio, or there are too few sample observations to compute a stable estimate of the standard error. The estimate is represented in the tables by "-" and the margin of error by "**" (two asterisks). - The estimate of a median falls in the lower open-ended interval or upper open-ended interval of a distribution. If the median occurs in the lowest interval, then a "-" follows the estimate, and if the median occurs in the upper interval, then a "+" follows the estimate. In both cases the margin of error is represented in the tables by "***" (three asterisks).
Sums and Differences of Direct Standard Errors
The standard errors estimated from these tables are for individual estimates. Additional calculations are required to estimate the standard errors for sums of or the differences between two or more sample estimates.
The standard error of the sum of two sample estimates is the square root of the sum of the two individual standard errors squared plus a covariance term. That is, for standard errors
and of estimates and The covariance measures the interactions between two estimates. Currently the covariance terms are not available. Data users should use the approximation:
However, this method will underestimate or overestimate the standard error if the two estimates interact in either a positive or negative way.
The approximation formula (2) can be expanded to more than two estimates by adding in the individual standard errors squared inside the radical. As the number of estimates involved in the sum or difference increases, the results of formula (2) become increasingly different from the standard error derived directly from the ACS microdata. Care should be taken to work with the fewest number of estimates as possible. If there are estimates involved in the sum that are controlled in the weighting then the approximate standard error can be increasingly different. Several examples are provided starting on page 32 to demonstrate issues associated with approximating the standard errors when summing large numbers of estimates together.
The statistic of interest may be the ratio of two estimates. First is the case where the numerator is not a subset of the denominator. The standard error of this ratio between two sample estimates is approximated as:
For a proportion (or percent), a ratio where the numerator is a subset of the denominator, a slightly different estimator is used. If
,then the standard error of this proportion is approximated as:
If
(P is the proportion and Q is its corresponding percent), then
.Note the difference between the formulas to approximate the standard error for proportions (4) and ratios (3) - the plus sign in the previous formula has been replaced with a minus sign. If the value under the radical is negative, use the ratio standard error formula above, instead.
,then the standard error of this proportion is approximated as:If
(P is the proportion and Q is its corresponding percent), then.Note the difference between the formulas to approximate the standard error for proportions (4) and ratios (3) - the plus sign in the previous formula has been replaced with a minus sign. If the value under the radical is negative, use the ratio standard error formula above, instead.This calculates the percent change from one time period to another, for example, computing the percent change of a 2011 estimate to a 2010 estimate. Normally, the current estimate is compared to the older estimate.
Let the current estimate
and the earlier estimate 
then the formula for percent change is:
This reduces to a ratio. The ratio formula above may be used to calculate the standard error. As a caveat, this formula does not take into account the correlation when calculating overlapping time periods.
Let the current estimate
and the earlier estimate then the formula for percent change is: This reduces to a ratio. The ratio formula above may be used to calculate the standard error. As a caveat, this formula does not take into account the correlation when calculating overlapping time periods.
Significant differences - Users may conduct a statistical test to see if the difference between an ACS estimate and any other chosen estimates is statistically significant at a given confidence level. "Statistically significant" means that the difference is not likely due to random chance alone. With the two estimates (Est1 and Est212
If Z > 1.645 or Z < -1.645, then the difference can be said to be statistically significant at the 90 percent confidence level. 6
Users are also cautioned to not rely on looking at whether confidence intervals for two estimates overlap or not to determine statistical significance, because there are circumstances where that method will not give the correct test result. If two confidence intervals do not overlap, then the estimates will be significantly different (i.e. the significance test will always agree). However, if two confidence intervals do overlap, then the estimates may or may not be significantly different. The Z calculation above is recommended in all cases.
Here is a simple example of why it is not recommended to use the overlapping confidence bounds rule of thumb as a substitute for a statistical test.
Let: X1 = 6.0 with SE1 = 0.5 and X2 = 5.0 with SE2 = 0.2.
The Lower Bound for X12 = 5.0 + 0.2 x 1.645 = 5.3. The confidence bounds overlap, so, the rule of thumb would indicate that the estimates are not significantly different at the 90% level.
However, if we apply the statistical significance test we obtain:
Z = 1.857 > 1.645 which means that the difference is significant (at the 90% level).
All statistical testing in ACS data products is based on the 90 percent confidence level. Users should understand that all testing was done using unrounded estimates and standard errors, and it may not be possible to replicate test results using the rounded estimates and margins of error as published.
Footnote: 6The ACS Accuracy of the Data document in 2005 used a Z statistic of +/-1.65. Data users should use +/-1.65 for estimates published in 2005 or earlier.
If Z > 1.645 or Z < -1.645, then the difference can be said to be statistically significant at the 90 percent confidence level. 6
Users are also cautioned to not rely on looking at whether confidence intervals for two estimates overlap or not to determine statistical significance, because there are circumstances where that method will not give the correct test result. If two confidence intervals do not overlap, then the estimates will be significantly different (i.e. the significance test will always agree). However, if two confidence intervals do overlap, then the estimates may or may not be significantly different. The Z calculation above is recommended in all cases.
Here is a simple example of why it is not recommended to use the overlapping confidence bounds rule of thumb as a substitute for a statistical test.
Let: X1 = 6.0 with SE1 = 0.5 and X2 = 5.0 with SE2 = 0.2.
The Lower Bound for X12 = 5.0 + 0.2 x 1.645 = 5.3. The confidence bounds overlap, so, the rule of thumb would indicate that the estimates are not significantly different at the 90% level.
However, if we apply the statistical significance test we obtain:
Z = 1.857 > 1.645 which means that the difference is significant (at the 90% level).
All statistical testing in ACS data products is based on the 90 percent confidence level. Users should understand that all testing was done using unrounded estimates and standard errors, and it may not be possible to replicate test results using the rounded estimates and margins of error as published.
Footnote: 6The ACS Accuracy of the Data document in 2005 used a Z statistic of +/-1.65. Data users should use +/-1.65 for estimates published in 2005 or earlier.
We will present some examples based on the real data to demonstrate the use of the formulas.
Example 1 - Calculating the Standard Error from the Margin of Error
The estimated number of males, never married is 43,674,898 from summary table B12001 for the United States for 2011. The margin of error is 96,892.
Standard Error = Margin of Error / 1.645
Calculating the standard error using the margin of error, we have:
Example 2 - Calculating the Standard Error of a Sum or a Difference
We are interested in the number of people who have never been married. From Example 1, we know the number of males, never married is 43,674,898. From summary table B12001 we have the number of females, never married is 37,708,516 with a margin of error of 81,869. So, the estimated number of people who have never been married is 43,674,898 + 37,708,516 = 81,383,414. To calculate the approximate standard error of this sum, we need the standard errors of the two estimates in the sum. We have the standard error for the number of males never married from example 1 as 58,901. The standard error for the number of females never married is calculated using the margin of error:
SE(37,708,516 ) = 81,869/ 1.645 = 49,768.
So using formula (2) for the approximate standard error of a sum or difference we have:
Caution: This method will underestimate or overestimate the standard error if the two estimates interact in either a positive or negative way.
To calculate the lower and upper bounds of the 90 percent confidence interval around 81,383,414 using the standard error, simply multiply 77,111 by 1.645, then add and subtract the product from 81,383,414. Thus the 90 percent confidence interval for this estimate is [81,383,414 - 1.645(77,111)] to [81,383,414 + 1.645(77,111)] or 81,256,566 to 81,510,262.
Example 3 - Calculating the Standard Error of a Proportion/Percent
We are interested in the percentage of females who have never been married to the number of people who have never been married. The number of females, never married is 37,708,516 and the number of people who have never been married is 81,383,414. To calculate the approximate standard error of this percent, we need the standard errors of the two estimates in the percent. We have the approximate standard error for the number of females never married from example 2 as 49,768 and the approximate standard error for the number of people never married calculated from example 2 as 77,111.
The estimate is (37,708,516 / 81,383,414) x 100% = 46.33%
So, using formula (4) for the approximate standard error of a proportion or percent, we have:
To calculate the lower and upper bounds of the 90 percent confidence interval around 46.33 using the standard error, simply multiply 0.04 by 1.645, then add and subtract the product from 46.33. Thus the 90 percent confidence interval for this estimate is [46.33 - 1.645(0.04)] to [46.33 + 1.645(0.04)], or 46.26% to 46.40%.
Example 4 - Calculating the Standard Error of a Ratio
Now, let us calculate the estimate of the ratio of the number of unmarried males to the number of unmarried females and its approximate standard error. From the above examples, the estimate for the number of unmarried men is 43,674,898 with a standard error of 58,901, and the estimate for the number of unmarried women is 37,708,516 with a standard error of 49,768.
The estimate of the ratio is 43,674,898 / 37,708,516 = 1.158.
Using formula (3) for the approximate standard error of this ratio we have:
The 90 percent margin of error for this estimate would be 0.00219 multiplied by 1.645, or about 0.004. The 90 percent lower and upper 90 percent confidence bounds would then be [1.158 - 1.645(0.00219)] to [1.158 + 1.645(0.00219)], or 1.154 and 1.162.
Example 5 - Calculating the Standard Error of a Product
We are interested in the number of 1-unit detached owner-occupied housing units. The number of owner-occupied housing units is 74,264,435 with a margin of error of 230,440 from subject table S2504 for 2011, and the percent of 1-unit detached owner-occupied housing units is 82.2% (0.822) with a margin of error of 0.1 (0.001). So the number of 1- unit detached owner-occupied housing units is 74,264,435 x 0.822 = 61,045,366. Calculating the standard error for the estimates using the margin of error we have:
SE(74,264,435) = 230,440/1.645 = 140,085
and
SE(0.822) = 0.001/1.645 = 0.0006079
The approximate standard error for number of 1-unit detached owner-occupied housing units is calculated using formula (5) for products as:
To calculate the lower and upper bounds of the 90 percent confidence interval around 61,045,366 using the standard error, simply multiply 123,683 by 1.645, then add and subtract the product from 61,045,366. Thus the 90 percent confidence interval for this estimate is [61,045,366 - 1.645(123,683)] to [61,045,366 + 1.645(123,683)] or 60,841,907 to 61,248,825.
Example 1 - Calculating the Standard Error from the Margin of Error
The estimated number of males, never married is 43,674,898 from summary table B12001 for the United States for 2011. The margin of error is 96,892.
Standard Error = Margin of Error / 1.645
Calculating the standard error using the margin of error, we have:
Example 2 - Calculating the Standard Error of a Sum or a Difference
We are interested in the number of people who have never been married. From Example 1, we know the number of males, never married is 43,674,898. From summary table B12001 we have the number of females, never married is 37,708,516 with a margin of error of 81,869. So, the estimated number of people who have never been married is 43,674,898 + 37,708,516 = 81,383,414. To calculate the approximate standard error of this sum, we need the standard errors of the two estimates in the sum. We have the standard error for the number of males never married from example 1 as 58,901. The standard error for the number of females never married is calculated using the margin of error:
SE(37,708,516 ) = 81,869/ 1.645 = 49,768.
So using formula (2) for the approximate standard error of a sum or difference we have:
Caution: This method will underestimate or overestimate the standard error if the two estimates interact in either a positive or negative way.
To calculate the lower and upper bounds of the 90 percent confidence interval around 81,383,414 using the standard error, simply multiply 77,111 by 1.645, then add and subtract the product from 81,383,414. Thus the 90 percent confidence interval for this estimate is [81,383,414 - 1.645(77,111)] to [81,383,414 + 1.645(77,111)] or 81,256,566 to 81,510,262.
Example 3 - Calculating the Standard Error of a Proportion/Percent
We are interested in the percentage of females who have never been married to the number of people who have never been married. The number of females, never married is 37,708,516 and the number of people who have never been married is 81,383,414. To calculate the approximate standard error of this percent, we need the standard errors of the two estimates in the percent. We have the approximate standard error for the number of females never married from example 2 as 49,768 and the approximate standard error for the number of people never married calculated from example 2 as 77,111.
The estimate is (37,708,516 / 81,383,414) x 100% = 46.33%
So, using formula (4) for the approximate standard error of a proportion or percent, we have:
To calculate the lower and upper bounds of the 90 percent confidence interval around 46.33 using the standard error, simply multiply 0.04 by 1.645, then add and subtract the product from 46.33. Thus the 90 percent confidence interval for this estimate is [46.33 - 1.645(0.04)] to [46.33 + 1.645(0.04)], or 46.26% to 46.40%.
Example 4 - Calculating the Standard Error of a Ratio
Now, let us calculate the estimate of the ratio of the number of unmarried males to the number of unmarried females and its approximate standard error. From the above examples, the estimate for the number of unmarried men is 43,674,898 with a standard error of 58,901, and the estimate for the number of unmarried women is 37,708,516 with a standard error of 49,768.
The estimate of the ratio is 43,674,898 / 37,708,516 = 1.158.
Using formula (3) for the approximate standard error of this ratio we have:
The 90 percent margin of error for this estimate would be 0.00219 multiplied by 1.645, or about 0.004. The 90 percent lower and upper 90 percent confidence bounds would then be [1.158 - 1.645(0.00219)] to [1.158 + 1.645(0.00219)], or 1.154 and 1.162.
Example 5 - Calculating the Standard Error of a Product
We are interested in the number of 1-unit detached owner-occupied housing units. The number of owner-occupied housing units is 74,264,435 with a margin of error of 230,440 from subject table S2504 for 2011, and the percent of 1-unit detached owner-occupied housing units is 82.2% (0.822) with a margin of error of 0.1 (0.001). So the number of 1- unit detached owner-occupied housing units is 74,264,435 x 0.822 = 61,045,366. Calculating the standard error for the estimates using the margin of error we have:
SE(74,264,435) = 230,440/1.645 = 140,085
and
SE(0.822) = 0.001/1.645 = 0.0006079
The approximate standard error for number of 1-unit detached owner-occupied housing units is calculated using formula (5) for products as:
To calculate the lower and upper bounds of the 90 percent confidence interval around 61,045,366 using the standard error, simply multiply 123,683 by 1.645, then add and subtract the product from 61,045,366. Thus the 90 percent confidence interval for this estimate is [61,045,366 - 1.645(123,683)] to [61,045,366 + 1.645(123,683)] or 60,841,907 to 61,248,825.
As mentioned earlier, sample data are subject to nonsampling error. This component of error could introduce serious bias into the data, and the total error could increase dramatically over that which would result purely from sampling. While it is impossible to completely eliminate nonsampling error from a survey operation, the Census Bureau attempts to control the sources of such error during the collection and processing operations. Described below are the primary sources of nonsampling error and the programs instituted for control of this error. The success of these programs, however, is contingent upon how well the instructions were carried out during the survey.
A major way to avoid coverage error in a survey is to ensure that its sampling frame, for ACS an address list in each state, is as complete and accurate as possible. The source of addresses for the ACS is the MAF. An attempt is made to assign all appropriate geographic codes to each MAF address via an automated procedure using the Census Bureau TIGER (Topologically Integrated Geographic Encoding and Referencing) files. A manual coding operation based in the appropriate regional offices is attempted for addresses which could not be automatically coded. The MAF was used as the source of addresses for selecting sample housing units and mailing questionnaires. TIGER produced the location maps for CAPI assignments. Sometimes the MAF has an address that is the duplicate of another address already on the MAF. This could occur when there is a slight difference in the address such as 123 Main Street versus 123 Maine Street.
In the CATI and CAPI nonresponse follow-up phases, efforts were made to minimize the chances that housing units that were not part of the sample were interviewed in place of units in sample by mistake. If a CATI interviewer called a mail nonresponse case and was not able to reach the exact address, no interview was conducted and the case was eligible for CAPI. During CAPI follow-up, the interviewer had to locate the exact address for each sample housing unit. If the interviewer could not locate the exact sample unit in a multi-unit structure, or found a different number of units than expected, the interviewers were instructed to list the units in the building and follow a specific procedure to select a replacement sample unit. Person overcoverage can occur when an individual is included as a member of a housing unit but does not meet ACS residency rules.
Coverage rates give a measure of undercoverage or overcoverage of persons or housing units in a given geographic area. Rates below 100 percent indicate undercoverage, while rates above 100 percent indicate overcoverage. Coverage rates are released concurrent with the release of estimates on American FactFinder in the B98 series of detailed tables. Further information about ACS coverage rates may be found at http://www.census.gov/acs/www/methodology/methodology_main/.
- Unit Nonresponse - Unit nonresponse is the failure to obtain data from housing units in the sample. Unit nonresponse may occur because households are unwilling or unable to participate, or because an interviewer is unable to make contact with a housing unit. Unit nonresponse is problematic when there are systematic or variable differences between interviewed and noninterviewed housing units on the characteristics measured by the survey. Nonresponse bias is introduced into an estimate when differences are systematic, while nonresponse error for an estimate evolves from variable differences between interviewed and noninterviewed households.
The ACS makes every effort to minimize unit nonresponse, and thus, the potential for nonresponse error. First, the ACS used a combination of mail, CATI, and CAPI data collection modes to maximize response. The mail phase included a series of three to four mailings to encourage housing units to return the questionnaire. Subsequently, mail nonrespondents (for which phone numbers are available) were contacted by CATI for an interview. Finally, a subsample of the mail and telephone nonrespondents was contacted by personal visit to attempt an interview. Combined, these three efforts resulted in a very high overall response rate for the ACS.
ACS response rates measure the percent of units with a completed interview. The higher the response rate, and consequently the lower the nonresponse rate, the less chance estimates may be affected by nonresponse bias. Response and nonresponse rates, as well as rates for specific types of nonresponse, are released concurrent with the release of estimates on American FactFinder in the B98 series of detailed tables. Further information about response and nonresponse rates may be found at http://www.census.gov/acs/www/methodology/methodology_main/.
- Item Nonresponse - Nonresponse to particular questions on the survey questionnaire and instrument allows for the introduction of error or bias into the data, since the characteristics of the nonrespondents have not been observed and may differ from those reported by respondents. As a result, any imputation procedure using respondent data may not completely reflect this difference either at the elemental level (individual person or housing unit) or on average.
Some protection against the introduction of large errors or biases is afforded by minimizing nonresponse. In the ACS, item nonresponse for the CATI and CAPI operations was minimized by the requirement that the automated instrument receive a response to each question before the next one could be asked. Questionnaires returned by mail were edited for completeness and acceptability. They were reviewed by computer for content omissions and population coverage. If necessary, a telephone follow-up was made to obtain missing information. Potential coverage errors were included in this follow-up.
Allocation tables provide the weighted estimate of persons or housing units for which a value was imputed, as well as the total estimate of persons or housing units that were eligible to answer the question. The smaller the number of imputed responses, the lower the chance that the item nonresponse is contributing a bias to the estimates. Allocation tables are released concurrent with the release of estimates on American Factfinder in the B99 series of detailed tables with the overall allocation rates across all person and housing unit characteristics in the B98 series of detailed tables. Additional information on item nonresponse and allocations can be found at http://www.census.gov/acs/www/methodology/methodology_main/.
- Interviewer monitoring - The interviewer may misinterpret or otherwise incorrectly enter information given by a respondent; may fail to collect some of the information for a person or household; or may collect data for households that were not designated as part of the sample. To control these problems, the work of interviewers was monitored carefully. Field staff were prepared for their tasks by using specially developed training packages that included hands-on experience in using survey materials. A sample of the households interviewed by CAPI interviewers was reinterviewed to control for the possibility that interviewers may have fabricated data.
- Processing Error - The many phases involved in processing the survey data represent potential sources for the introduction of nonsampling error. The processing of the survey questionnaires includes the keying of data from completed questionnaires, automated clerical review, follow-up by telephone, manual coding of write-in responses, and automated data processing. The various field, coding and computer operations undergo a number of quality control checks to insure their accurate application.
- Content Editing - After data collection was completed, any remaining incomplete or inconsistent information was imputed during the final content edit of the collected data. Imputations, or computer assignments of acceptable codes in place of unacceptable entries or blanks, were needed most often when an entry for a given item was missing or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in other surveys and previous censuses, the general procedure for changing unacceptable entries was to allocate an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. Imputing acceptable values in place of blanks or unacceptable entries enhances the usefulness of the data.
- Coverage Error
A major way to avoid coverage error in a survey is to ensure that its sampling frame, for ACS an address list in each state, is as complete and accurate as possible. The source of addresses for the ACS is the MAF. An attempt is made to assign all appropriate geographic codes to each MAF address via an automated procedure using the Census Bureau TIGER (Topologically Integrated Geographic Encoding and Referencing) files. A manual coding operation based in the appropriate regional offices is attempted for addresses which could not be automatically coded. The MAF was used as the source of addresses for selecting sample housing units and mailing questionnaires. TIGER produced the location maps for CAPI assignments. Sometimes the MAF has an address that is the duplicate of another address already on the MAF. This could occur when there is a slight difference in the address such as 123 Main Street versus 123 Maine Street.
In the CATI and CAPI nonresponse follow-up phases, efforts were made to minimize the chances that housing units that were not part of the sample were interviewed in place of units in sample by mistake. If a CATI interviewer called a mail nonresponse case and was not able to reach the exact address, no interview was conducted and the case was eligible for CAPI. During CAPI follow-up, the interviewer had to locate the exact address for each sample housing unit. If the interviewer could not locate the exact sample unit in a multi-unit structure, or found a different number of units than expected, the interviewers were instructed to list the units in the building and follow a specific procedure to select a replacement sample unit. Person overcoverage can occur when an individual is included as a member of a housing unit but does not meet ACS residency rules.
Coverage rates give a measure of undercoverage or overcoverage of persons or housing units in a given geographic area. Rates below 100 percent indicate undercoverage, while rates above 100 percent indicate overcoverage. Coverage rates are released concurrent with the release of estimates on American FactFinder in the B98 series of detailed tables. Further information about ACS coverage rates may be found at http://www.census.gov/acs/www/methodology/methodology_main/.
- Nonresponse Error
- Unit Nonresponse - Unit nonresponse is the failure to obtain data from housing units in the sample. Unit nonresponse may occur because households are unwilling or unable to participate, or because an interviewer is unable to make contact with a housing unit. Unit nonresponse is problematic when there are systematic or variable differences between interviewed and noninterviewed housing units on the characteristics measured by the survey. Nonresponse bias is introduced into an estimate when differences are systematic, while nonresponse error for an estimate evolves from variable differences between interviewed and noninterviewed households.
The ACS makes every effort to minimize unit nonresponse, and thus, the potential for nonresponse error. First, the ACS used a combination of mail, CATI, and CAPI data collection modes to maximize response. The mail phase included a series of three to four mailings to encourage housing units to return the questionnaire. Subsequently, mail nonrespondents (for which phone numbers are available) were contacted by CATI for an interview. Finally, a subsample of the mail and telephone nonrespondents was contacted by personal visit to attempt an interview. Combined, these three efforts resulted in a very high overall response rate for the ACS.
ACS response rates measure the percent of units with a completed interview. The higher the response rate, and consequently the lower the nonresponse rate, the less chance estimates may be affected by nonresponse bias. Response and nonresponse rates, as well as rates for specific types of nonresponse, are released concurrent with the release of estimates on American FactFinder in the B98 series of detailed tables. Further information about response and nonresponse rates may be found at http://www.census.gov/acs/www/methodology/methodology_main/.
- Item Nonresponse - Nonresponse to particular questions on the survey questionnaire and instrument allows for the introduction of error or bias into the data, since the characteristics of the nonrespondents have not been observed and may differ from those reported by respondents. As a result, any imputation procedure using respondent data may not completely reflect this difference either at the elemental level (individual person or housing unit) or on average.
Some protection against the introduction of large errors or biases is afforded by minimizing nonresponse. In the ACS, item nonresponse for the CATI and CAPI operations was minimized by the requirement that the automated instrument receive a response to each question before the next one could be asked. Questionnaires returned by mail were edited for completeness and acceptability. They were reviewed by computer for content omissions and population coverage. If necessary, a telephone follow-up was made to obtain missing information. Potential coverage errors were included in this follow-up.
Allocation tables provide the weighted estimate of persons or housing units for which a value was imputed, as well as the total estimate of persons or housing units that were eligible to answer the question. The smaller the number of imputed responses, the lower the chance that the item nonresponse is contributing a bias to the estimates. Allocation tables are released concurrent with the release of estimates on American Factfinder in the B99 series of detailed tables with the overall allocation rates across all person and housing unit characteristics in the B98 series of detailed tables. Additional information on item nonresponse and allocations can be found at http://www.census.gov/acs/www/methodology/methodology_main/.
- Measurement and Processing Error
- Interviewer monitoring - The interviewer may misinterpret or otherwise incorrectly enter information given by a respondent; may fail to collect some of the information for a person or household; or may collect data for households that were not designated as part of the sample. To control these problems, the work of interviewers was monitored carefully. Field staff were prepared for their tasks by using specially developed training packages that included hands-on experience in using survey materials. A sample of the households interviewed by CAPI interviewers was reinterviewed to control for the possibility that interviewers may have fabricated data.
- Processing Error - The many phases involved in processing the survey data represent potential sources for the introduction of nonsampling error. The processing of the survey questionnaires includes the keying of data from completed questionnaires, automated clerical review, follow-up by telephone, manual coding of write-in responses, and automated data processing. The various field, coding and computer operations undergo a number of quality control checks to insure their accurate application.
- Content Editing - After data collection was completed, any remaining incomplete or inconsistent information was imputed during the final content edit of the collected data. Imputations, or computer assignments of acceptable codes in place of unacceptable entries or blanks, were needed most often when an entry for a given item was missing or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in other surveys and previous censuses, the general procedure for changing unacceptable entries was to allocate an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. Imputing acceptable values in place of blanks or unacceptable entries enhances the usefulness of the data.
Several examples are provided here to demonstrate how different the approximated standard errors of sums can be compared to those derived and published with ACS microdata.
A. Suppose we wish to estimate the total number of males with income below the poverty level in the past 12 months using both state and PUMA level estimates for the state of Wyoming. Part of the collapsed table C17001 is displayed below with estimates and their margins of error in parentheses.
Table A: 2009 Estimates of Males with Income Below Poverty from table C17001: Poverty Status in the Past 12 Months by Sex by Age
2009 American FactFinder
The first way is to sum the three age groups for Wyoming:
Estimate(Male) = 8,479 + 12,976 + 1,546 = 23,001.
The first approximation for the standard error in this case gives us:
A second way is to sum the four PUMA estimates for Male to obtain:
Estimate(Male) = 5,264 + 6,508 + 4,364 + 6,865 = 23,001 as before.
The second approximation for the standard error yields:
Finally, we can sum up all three age groups for all four PUMAs to obtain an estimate based on a total of twelve estimates:
Estimate (Male) =2,041 + 2,222 + ... + 451 = 23,001
And the third approximated standard error is
However, we do know that the standard error using the published MOE is 3,309 /1.645 = 2,011.6. In this instance, all of the approximations under-estimate the published standard error and should be used with caution.
B. Suppose we wish to estimate the total number of males at the national level using age and citizenship status. The relevant data from table B05003 is displayed in table B below.
Table B: 2009 Estimates of males from B05003: Sex by Age by Citizenship Status
2009 American FactFinder
The estimate and its MOE are actually published. However, if they were not available in the tables, one way of obtaining them would be to add together the number of males under 18 and over 18 to get:
Estimate (Male) =38,146,514 + 113,228,807 = 151,375,321
And the first approximated standard error is
Another way would be to add up the estimates for the three subcategories (Native, and the two subcategories for Foreign Born: Naturalized U.S. Citizen, and Not a U.S. Citizen), for males under and over 18 years of age. From these six estimates we obtain:
Estimate(Male) = 36,747,407 + 268,445 + 1,130,662 + 95,384,433 + 7,507,308 + 10,337,066 = 151,375,321.
With a second approximated standard error of:
We do know that the standard error using the published margin of error is 27,279 / 1.645 = 16,583.0. With a quick glance, we can see that the ratio of the standard error of the first method to the published-based standard error yields 1.24; an over-estimate of roughly 24%, whereas the second method yields a ratio of 4.07 or an over-estimate of 307%. This is an example of what could happen to the approximate SE when the sum involves a controlled estimate. In this case, it is sex by age.
C. Suppose we are interested in the total number of people aged 65 or older and its standard error. Table C shows some of the estimates for the national level from table B01001 (the estimates in gray were derived for the purpose of this example only).
Table C: Some Estimates from AFF Table B01001: Sex by Age for 2009
2009 American FactFinder
To begin we find the total number of people aged 65 and over by simply adding the totals for males and females to get 16,781,175 + 22,725,542 = 39,506,717. One way we could use is summing males and female for each age category and then using their MOEs to approximate the standard error for the total number of people over 65.
Now, we calculate for the number of people aged 65 or older to be 39,506,648 using the six derived estimates and approximate the standard error:
For this example the estimate and its MOE are published in table B09017. The total number of people aged 65 or older is 39,506,648 with a margin of error of 20,689. Therefore the published- based standard error is:
SE(39,506,648) = 20,689/1.645 = 12,577.
The approximated standard error, using six derived age group estimates, yields an approximated standard error roughly 3.6 times larger than the published-based standard error.
As a note, there are two additional ways to approximate the standard error of people aged 65 and over in addition to the way used above. The first is to find the published MOEs for the males age 65 and older and of females aged 65 and older separately and then combine to find the approximate standard error for the total. The second is to use all twelve of the published estimates together, that is, all estimates from the male age categories and female age categories, to create the SE for people aged 65 and older. However, in this particular example, the results from all three ways are the same. So no matter which way you use, you will obtain the same approximation for the SE. This is different from the results seen in example A.
D. For an alternative to approximating the standard error for people 65 years and older seen in part C, we could find the estimate and its SE by summing all of the estimate for the ages less than 65 years old and subtracting them from the estimate for the total population. Due to the large number of estimates, Table D does not show all of the age groups. In addition, the estimates in part of the table shaded gray were derived for the purposes of this example only and cannot be found in base table B01001.
Table D: Some Estimates from AFF Table B01001: Sex by Age for 2009:
2009 American FactFinder
An estimate for the number of people age 65 and older is equal to the total population minus the population between the ages of zero and 64 years old:
Number of people aged 65 and older: 307,006,556 - 267,499,908 = 39,506,648.
The way to approximate the SE is the same as in part C. First we will sum male and female estimates across each age category and then approximate the MOEs. We will use that information to approximate the standard error for our estimate of interest:
...etc. ...
Again, as in Example C, the estimate and its MOE are we published in B09017. The total number of people aged 65 or older is 39,506,648 with a margin of error of 20,689. Therefore the standard error is:
SE(39,506,648) = 20,689 / 1.645 = 12,577.
The approximated standard error using the thirteen derived age group estimates yields a standard error roughly 8.2 times larger than the actual SE.
Data users can mitigate the problems shown in examples A through D to some extent by utilizing a collapsed version of a detailed table (if it is available) which will reduce the number of estimates used in the approximation. These issues may also be avoided by creating estimates and SEs using the Public Use Microdata Sample (PUMS) or by requesting a custom tabulation, a fee- based service offered under certain conditions by the Census Bureau. More information regarding custom tabulations may be found at http://www.census.gov/acs/www/data_documentation/custom tabulations/.
A. Suppose we wish to estimate the total number of males with income below the poverty level in the past 12 months using both state and PUMA level estimates for the state of Wyoming. Part of the collapsed table C17001 is displayed below with estimates and their margins of error in parentheses.
Table A: 2009 Estimates of Males with Income Below Poverty from table C17001: Poverty Status in the Past 12 Months by Sex by Age
| Characteristic | Wyoming | PUMA 00100 | PUMA 00200 | PUMA 00300 | PUMA 00400 |
| Male | 23,001 | 5,264 | 6,508 | 4,364 | 6,865 |
| -3,309 | -1,624 | -1,395 | -1,026 | -1,909 | |
| Under 18 | 8,479 | 2,041 | 2,222 | 1,999 | 2,217 |
| Years Old | -1,874 | -920 | -778 | -750 | -1,192 |
| 18 to 64 | 12,976 | 3,004 | 3,725 | 2,050 | 4,197 |
| Years Old | -2,076 | -1,049 | -935 | -635 | -1,134 |
| 65 Years and | 1,546 | 219 | 561 | 315 | 451 |
| Older | -500 | -237 | -286 | -173 | -302 |
2009 American FactFinder
The first way is to sum the three age groups for Wyoming:
Estimate(Male) = 8,479 + 12,976 + 1,546 = 23,001.
The first approximation for the standard error in this case gives us:
A second way is to sum the four PUMA estimates for Male to obtain:
Estimate(Male) = 5,264 + 6,508 + 4,364 + 6,865 = 23,001 as before.
The second approximation for the standard error yields:
Finally, we can sum up all three age groups for all four PUMAs to obtain an estimate based on a total of twelve estimates:
Estimate (Male) =2,041 + 2,222 + ... + 451 = 23,001
And the third approximated standard error is
However, we do know that the standard error using the published MOE is 3,309 /1.645 = 2,011.6. In this instance, all of the approximations under-estimate the published standard error and should be used with caution.
B. Suppose we wish to estimate the total number of males at the national level using age and citizenship status. The relevant data from table B05003 is displayed in table B below.
Table B: 2009 Estimates of males from B05003: Sex by Age by Citizenship Status
| Characteristic | Estimate | MOE |
| Male | 151,375,321 | 27,279 |
| Under 18 Years | 38,146,514 | 24,365 |
| Native | 36,747,407 | 31,397 |
| Foreign Born | 1,399,107 | 20,177 |
| Naturalized U.S. Citizen | 268,445 | 10,289 |
| Not a U.S. Citizen | 1,130,662 | 20,228 |
| 18 Years and Older | 113,228,807 | 23,525 |
| Native | 95,384,433 | 70,210 |
| Foreign Born | 17,844,374 | 59,750 |
| Naturalized U.S. Citizen | 7,507,308 | 39,658 |
| Not a U.S. Citizen | 10,337,066 | 65,533 |
2009 American FactFinder
The estimate and its MOE are actually published. However, if they were not available in the tables, one way of obtaining them would be to add together the number of males under 18 and over 18 to get:
Estimate (Male) =38,146,514 + 113,228,807 = 151,375,321
And the first approximated standard error is
Another way would be to add up the estimates for the three subcategories (Native, and the two subcategories for Foreign Born: Naturalized U.S. Citizen, and Not a U.S. Citizen), for males under and over 18 years of age. From these six estimates we obtain:
Estimate(Male) = 36,747,407 + 268,445 + 1,130,662 + 95,384,433 + 7,507,308 + 10,337,066 = 151,375,321.
With a second approximated standard error of:
We do know that the standard error using the published margin of error is 27,279 / 1.645 = 16,583.0. With a quick glance, we can see that the ratio of the standard error of the first method to the published-based standard error yields 1.24; an over-estimate of roughly 24%, whereas the second method yields a ratio of 4.07 or an over-estimate of 307%. This is an example of what could happen to the approximate SE when the sum involves a controlled estimate. In this case, it is sex by age.
C. Suppose we are interested in the total number of people aged 65 or older and its standard error. Table C shows some of the estimates for the national level from table B01001 (the estimates in gray were derived for the purpose of this example only).
Table C: Some Estimates from AFF Table B01001: Sex by Age for 2009
| Age Category | Estimate, Male | MOE, Male | Estimate, Female | MOE, Female | Total | Estimated MOE, Total |
| 65 and 66 years old | 2,492,871 | 20,194 | 2,803,516 | 23,327 | 5,296,387 | 30,854 |
| 67 to 69 years old | 3,029,709 | 18,280 | 3,483,447 | 24,287 | 6,513,225 | 30,398 |
| 70 to 74 years old | 4,088,428 | 21,588 | 4,927,666 | 26,867 | 9,016,094 | 34,466 |
| 75 to 79 years old | 3,168,175 | 19,097 | 4,204,401 | 23,024 | 7,372,576 | 29,913 |
| 80 to 84 years old | 2,258,021 | 17,716 | 3,538,869 | 25,423 | 5,796,890 | 30,987 |
| 85 years and older | 1,743,971 | 17,991 | 3,767,574 | 19,294 | 5,511,545 | 26,381 |
| Total | 16,781,175 | NA | 22,725,473 | NA | 39,506,648 | 74,932 |
2009 American FactFinder
To begin we find the total number of people aged 65 and over by simply adding the totals for males and females to get 16,781,175 + 22,725,542 = 39,506,717. One way we could use is summing males and female for each age category and then using their MOEs to approximate the standard error for the total number of people over 65.
Now, we calculate for the number of people aged 65 or older to be 39,506,648 using the six derived estimates and approximate the standard error:
For this example the estimate and its MOE are published in table B09017. The total number of people aged 65 or older is 39,506,648 with a margin of error of 20,689. Therefore the published- based standard error is:
SE(39,506,648) = 20,689/1.645 = 12,577.
The approximated standard error, using six derived age group estimates, yields an approximated standard error roughly 3.6 times larger than the published-based standard error.
As a note, there are two additional ways to approximate the standard error of people aged 65 and over in addition to the way used above. The first is to find the published MOEs for the males age 65 and older and of females aged 65 and older separately and then combine to find the approximate standard error for the total. The second is to use all twelve of the published estimates together, that is, all estimates from the male age categories and female age categories, to create the SE for people aged 65 and older. However, in this particular example, the results from all three ways are the same. So no matter which way you use, you will obtain the same approximation for the SE. This is different from the results seen in example A.
D. For an alternative to approximating the standard error for people 65 years and older seen in part C, we could find the estimate and its SE by summing all of the estimate for the ages less than 65 years old and subtracting them from the estimate for the total population. Due to the large number of estimates, Table D does not show all of the age groups. In addition, the estimates in part of the table shaded gray were derived for the purposes of this example only and cannot be found in base table B01001.
Table D: Some Estimates from AFF Table B01001: Sex by Age for 2009:
| Age Category | Estimate, Male | MOE, Male | Estimate, Female | MOE, Female | Total | Estimated MOE, Total |
| Total Population | 151,375,321 | 27,279 | 155,631,235 | 27,280 | 307,006,556 | 38,579 |
| Under 5 years | 10,853,263 | 15,661 | 10,355,944 | 14,707 | 21,209,207 | 21,484 |
| 5 to 9 years old | 10,273,948 | 43,555 | 9,850,065 | 42,194 | 20,124,013 | 60,641 |
| 10 to 14 years old | 10,532,166 | 40,051 | 9,985,327 | 39,921 | 20,517,493 | 56,549 |
| 62 to 64 years old | 4,282,178 | 25,636 | 4,669,376 | 28,769 | 8,951,554 | 38,534 |
| Total for Age 0 to 64 years old | 134,594,146 | 117,166 | 132,905,762 | 117,637 | 267,499,908 | 166,031 |
| Total for Age 65 years and older | 16,781,175 | |||||
| 120,300 | 22,725,473 | 120,758 | 39,506,648 | 170,454 |
2009 American FactFinder
An estimate for the number of people age 65 and older is equal to the total population minus the population between the ages of zero and 64 years old:
Number of people aged 65 and older: 307,006,556 - 267,499,908 = 39,506,648.
The way to approximate the SE is the same as in part C. First we will sum male and female estimates across each age category and then approximate the MOEs. We will use that information to approximate the standard error for our estimate of interest:
...etc. ...
Again, as in Example C, the estimate and its MOE are we published in B09017. The total number of people aged 65 or older is 39,506,648 with a margin of error of 20,689. Therefore the standard error is:
SE(39,506,648) = 20,689 / 1.645 = 12,577.
The approximated standard error using the thirteen derived age group estimates yields a standard error roughly 8.2 times larger than the actual SE.
Data users can mitigate the problems shown in examples A through D to some extent by utilizing a collapsed version of a detailed table (if it is available) which will reduce the number of estimates used in the approximation. These issues may also be avoided by creating estimates and SEs using the Public Use Microdata Sample (PUMS) or by requesting a custom tabulation, a fee- based service offered under certain conditions by the Census Bureau. More information regarding custom tabulations may be found at http://www.census.gov/acs/www/data_documentation/custom tabulations/.
The PRCS employs three modes of data collection:
Month 1: Addresses in sample that are determined to be mailable are sent a questionnaire via the U.S. Postal Service.
Month 2: All mail non-responding addresses with an available phone number are sent to CATI.
Month 3: A sample of mail non-responses without a phone number, CATI non-responses, and unmailable addresses are selected and sent to CAPI.
Note that mail responses are accepted during all three months of data collection.
- Mailout/Mailback
- Computer Assisted Telephone Interview (CATI)
- Computer Assisted Personal Interview (CAPI)
Month 1: Addresses in sample that are determined to be mailable are sent a questionnaire via the U.S. Postal Service.
Month 2: All mail non-responding addresses with an available phone number are sent to CATI.
Month 3: A sample of mail non-responses without a phone number, CATI non-responses, and unmailable addresses are selected and sent to CAPI.
Note that mail responses are accepted during all three months of data collection.
Field representatives have several options available to them for data collection. These include completing the questionnaire while speaking to the resident in person or over the telephone, conducting a personal interview with a proxy, such as a relative or guardian, or leaving paper questionnaires for residents to complete for themselves and then pick them up later. This last option is used for data collection in Federal prisons.
Group Quarters data collection spans six weeks, except for Federal prisons, where the data collection time period is four months. All Federal prisons are assigned to September with a four month data collection window.
Group Quarters data collection spans six weeks, except for Federal prisons, where the data collection time period is four months. All Federal prisons are assigned to September with a four month data collection window.
The universe for the PRCS consists of all valid, residential housing unit addresses in all municipios in Puerto Rico. The Master Address File (MAF) is a database maintained by the Census Bureau containing a listing of residential and commercial addresses in the U.S. and Puerto Rico. The MAF is updated twice each year with the Delivery Sequence Files provided by the U.S. Postal Service, however this update covers only the U.S. The DSF does not provide changes and updates to the MAF for Puerto Rico. The MAF is also updated with the results from various Census Bureau field operations, including the PRCS.
The universe of group quarters (GQs) valid for PRCS for 2011 was drastically different than the 2010 PRCS GQ universe. Results from nationwide field operations such as address canvassing and group quarters validation were available for the first time for PRCS use. Results from these sources were combined with the 2010 Census universe of GQs to create the final 2011 PRCS sampling frame. In addition, as a result of operational difficulties associated with data collection, the PRCS excluded certain types of GQs from the sampling universe and data collection operations. The weighting and estimation account for this segment of the population as the population in these types of GQs is included in the population controls. The following GQ types were removed from the 2011 GQ universe:
- Soup kitchens
- Domestic violence shelters
- Regularly scheduled mobile food vans
- Targeted non-sheltered outdoor locations
- Maritime/merchant vessels
- Living quarters for victims of natural disasters
The PRCS employs a two-phase, two-stage sample design. The PRCS first-phase sample consists of two separate samples, Main and Supplemental, each chosen at different points in time. Together, these constitute the first-phase sample. Both the Main and the Supplemental samples are chosen in two stages referred to as first- and second-stage sampling. Subsequent to second-stage sampling, sample addresses are randomly assigned to one of the twelve months of the sample year. The second-phase of sampling occurs when the CAPI sample is selected (see Section 2 below).
The Main sample is selected during the summer preceding the sample year. Approximately 99 percent of the sample is selected at this time. Each address in sample is randomly assigned to one of the 12 months of the sample year. Supplemental sampling occurs in January/February of the sample year and accounts for approximately 1 percent of the overall first-phase sample. The Supplemental sample is allocated to the last eight months of the sample year. A sub-sample of non-responding addresses and of any addresses deemed unmailable is selected for the CAPI data collection mode.
Several of the steps used to select the first-phase sample are common to both Main and Supplemental sampling. The descriptions of the steps included in the first-phase sample selection below indicate which are common to both and which are unique to either Main or Supplemental sampling.
1. First-phase Sample Selection
Each block is then assigned the smallest measure of size from the set of all entities of which it is a part. The second-stage sampling strata and the overall first-phase sampling rates are shown in Table 1 below.
2. Second-phase Sample Selection - Subsampling the Unmailable and Non-Responding Addresses
All addresses determined to be unmailable are subsampled for the CAPI phase of data collection at a rate of 2-in-3. Unmailable addresses do not go to the CATI phase of data collection. Subsequent to CATI, all addresses for which no response has been obtained prior to CAPI are subsampled based on the expected rate of completed interviews at the tract level using the rates shown in Table 2.
Table 2. Second-Phase (CAPI) Subsampling Rates for Puerto Rico
Footnotes:
1 For sample year 2011, the total annual Puerto Rico sample size was roughly 36,600. An additional 600 sample cases were added as a result of larger than expected growth in the number of valid addresses in Puerto Rico between main and supplemental sampling.
The Main sample is selected during the summer preceding the sample year. Approximately 99 percent of the sample is selected at this time. Each address in sample is randomly assigned to one of the 12 months of the sample year. Supplemental sampling occurs in January/February of the sample year and accounts for approximately 1 percent of the overall first-phase sample. The Supplemental sample is allocated to the last eight months of the sample year. A sub-sample of non-responding addresses and of any addresses deemed unmailable is selected for the CAPI data collection mode.
Several of the steps used to select the first-phase sample are common to both Main and Supplemental sampling. The descriptions of the steps included in the first-phase sample selection below indicate which are common to both and which are unique to either Main or Supplemental sampling.
1. First-phase Sample Selection
- First-stage sampling (performed during both Main and Supplemental sampling) - First stage sampling defines the universe for the second stage of sampling through two steps. First, all addresses that were in a first-phase sample within the past four years are excluded from eligibility. This ensures that no address is in sample more than once in any five-year period. The second step is to select a 20 percent systematic sample of new units, i.e. those units that have never appeared on a previous MAF extract. Each new address is systematically assigned to either the current year or to one of four back-samples. This procedure maintains five equal partitions of the universe.
- Assignment of blocks to a second-stage sampling stratum (performed during Main sampling only) - Second-stage sampling uses five sampling strata in PR. The stratum level rates used in second-stage sampling account for the first-stage selection probabilities. These rates are applied at a block level to addresses in PR by calculating a measure of size for Municipios.
Each block is then assigned the smallest measure of size from the set of all entities of which it is a part. The second-stage sampling strata and the overall first-phase sampling rates are shown in Table 1 below.
- Calculation of the second-stage sampling rates (performed during Main sampling only) - The overall first-phase sampling rates given in Table 1 are calculated using the distribution of PRCS valid addresses by second-stage sampling stratum in such a way as to yield an overall target sample size for the year of approximately 36,0001. These rates also account for expected growth of the HU inventory between Main and Supplementa of roughly 1 percent. The first-phase rates are adjusted for the first-stage sample to yield the second-stage selection probabilities.
- Second-stage sample selection (performed in Main and Supplemental) - After each block is assigned to a second-stage sampling stratum, a systematic sample of addresses is selected from the second-stage universe (first-stage sample) within each municipio.
- Sample Month Assignment (performed in Main and Supplemental) - After the second stage of sampling, all sample addresses are randomly assigned to a sample month. Addresses selected during Main sampling are allocated to each of the 12 months. Addresses selected during Supplemental sampling are assigned to the months of May-December.
| Type of Area | Second Stage Sampling Stratum | Target Sampling Rate (36,000) | 2011 In Sample |
| 0 < MOS1 = 200 | 1 | 15.00% | NA |
| 200 < MOS = 400 | 2 | 10.00% | NA |
| 400 < MOS = 800 | 3 | 7.00% | NA |
| 800 < MOS = 1,200 | 4 | 4.34% | 4.26% |
| 0 < TRACTMOS2 = 400 | 5 | 5.43% | 5.38% |
| 400 < TRACTMOS = 1,000 | 7 | 4.34% | 4.29% |
| 1,000 < TRACTMOS = 2,000 | 9 | 2.64% | 2.59% |
| 2,000 < TRACTMOS = 4,000 | 11 | 1.55% | 1.53% |
| 4,000 < TRACTMOS = 6,000 | 13 | 0.93% | 0.92% |
| 6,000 < TRACTMOS | 15 | 0.54% | NA |
2. Second-phase Sample Selection - Subsampling the Unmailable and Non-Responding Addresses
All addresses determined to be unmailable are subsampled for the CAPI phase of data collection at a rate of 2-in-3. Unmailable addresses do not go to the CATI phase of data collection. Subsequent to CATI, all addresses for which no response has been obtained prior to CAPI are subsampled based on the expected rate of completed interviews at the tract level using the rates shown in Table 2.
Table 2. Second-Phase (CAPI) Subsampling Rates for Puerto Rico
| Address Characteristics | CAPI Subsampling Rate |
| Unmailable addresses | 66.70% |
| Mailable addresses | 50.00% |
1 For sample year 2011, the total annual Puerto Rico sample size was roughly 36,600. An additional 600 sample cases were added as a result of larger than expected growth in the number of valid addresses in Puerto Rico between main and supplemental sampling.
The GQ sampling frame is divided into three strata: one for small GQs (having 15 or fewer people according to 2010 Census or updated information), one for GQs that were closed on 2010 Census Day, and one for large GQs (having more than 15 people according to 2010 Census or updated information). GQs in the first two strata are sampled using the same procedure, and GQs in the large stratum are sampled using different a method. The small GQ stratum and the stratum for GQs closed on Census Day are combined into one sampling stratum and sorted geographically2.
1. First-phase Sample Selection for Small GQ Stratum
Unlike housing unit address sampling and the small GQ sample selection, the large GQ sampling procedure has no first-stage in which sampling units are randomly assigned to one of five years. All large GQs are eligible for sampling each year. The large GQ samples are selected using a two-phase design.
In order to assign a panel month to each hit, all of the GQ samples from Puerto Rico are combined and sorted by small/large stratum and second-phase order of selection. Consecutive samples are assigned to the twelve panel months in a predetermined order, starting with a randomly determined month, except for Federal prisons. Correctional facilities have their sample clustered. All Federal prisons hits are assigned to the September panel. In non-Federal correctional facilities, all hits for a given GQ are assigned to the same panel month. However, unlike Federal prisons, the hits in state and local correctional facilities are assigned to randomly selected panels spread throughout the year.
4. Second Phase Sample: Selection of Persons in Small and Large GQs
Small GQs in the second phase sampling are take all, i.e., every person in the selected GQ is eligible to receive a questionnaire. If the actual number of persons in the GQ exceeds 15, a field subsampling operation is performed to reduce the total number of sample persons interviewed at the GQ to 10. If the actual number of persons in the GQ is 15 or less, all people in the GQ will receive the questionnaire.
For each hit in the large GQs, the automated instrument uses the population count at the time of the visit and selects a subsample of 10 people from the roster. The people in this subsample receive the questionnaire.
Footnotes:
2 Note that all references to the small GQ stratum include both small GQs and GQs closed on Census day.
1. First-phase Sample Selection for Small GQ Stratum
- First-stage sampling - Small GQs are only eligible to be selected for the PRCS once every five years. To accomplish this, the first stage sampling procedure systematically assigned all small GQs to one of five partitions of the universe. Each partition was assigned to a particular year (2011-2015) and the one assigned to 2011 became the first stage sample. In future years, each new GQ will be systematically assigned to one of the five samples. These samples are rotated over five year periods and become the universe for selecting the second stage sample.
- Second-stage sampling - A simple 1-in-8 systematic sample of the GQs in the first stage sample is selected. Regardless of their actual size, all GQs in the small stratum have the same probability of selection. Since the first stage sample is 20% of the universe, this yields the targeted sampling rate of 2.5%.
Unlike housing unit address sampling and the small GQ sample selection, the large GQ sampling procedure has no first-stage in which sampling units are randomly assigned to one of five years. All large GQs are eligible for sampling each year. The large GQ samples are selected using a two-phase design.
- First-phase Sampling - In the large GQ stratum, GQ hits are selected using a systematic PPS (probability proportional to size) sample, with a target sampling rate of 2.5%. A hit refers to a grouping of 10 expected interviews. GQs are selected with probability proportional to its most current count of persons or capacity. For stratification, and for sampling the large GQs, a GQ measure of size (GQMOS) is computed, where GQMOS is the expected population of the GQ divided by 10. This reflects that the GQ data is collected in groups of 10 GQ persons. People are selected in hits of 10 in a systematic sample of 1-in-40 hits. All GQs in this stratum are eligible for sampling every year, regardless of their sample status in previous years. For large GQs, hits can be selected multiple times in the sample year. For most GQ types, the hits are randomly assigned throughout the year. Some GQs may have multiple hits with the same sample date if more than 12 hits are selected from the GQ. In these cases, the person sample within that month is unduplicated.
In order to assign a panel month to each hit, all of the GQ samples from Puerto Rico are combined and sorted by small/large stratum and second-phase order of selection. Consecutive samples are assigned to the twelve panel months in a predetermined order, starting with a randomly determined month, except for Federal prisons. Correctional facilities have their sample clustered. All Federal prisons hits are assigned to the September panel. In non-Federal correctional facilities, all hits for a given GQ are assigned to the same panel month. However, unlike Federal prisons, the hits in state and local correctional facilities are assigned to randomly selected panels spread throughout the year.
4. Second Phase Sample: Selection of Persons in Small and Large GQs
Small GQs in the second phase sampling are take all, i.e., every person in the selected GQ is eligible to receive a questionnaire. If the actual number of persons in the GQ exceeds 15, a field subsampling operation is performed to reduce the total number of sample persons interviewed at the GQ to 10. If the actual number of persons in the GQ is 15 or less, all people in the GQ will receive the questionnaire.
For each hit in the large GQs, the automated instrument uses the population count at the time of the visit and selects a subsample of 10 people from the roster. The people in this subsample receive the questionnaire.
Footnotes:
2 Note that all references to the small GQ stratum include both small GQs and GQs closed on Census day.
The estimates that appear in this product are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given tabulation area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the tabulation area. Each sample person or housing unit record is assigned exactly one weight to be used to produce estimates of all characteristics. For example, if the weight given to a sample person or housing unit has a value 40, all characteristics of that person or housing unit are tabulated with the weight of 40.
The weighting is conducted in two main operations: a group quarters person weighting operation which assigns weights to persons in group quarters, and a household person weighting operation which assigns weights both to housing units and to persons within housing units. The group quarters person weighting is conducted first and the household person weighting second. The household person weighting is dependent on the group quarters person weighting because estimates for total population, which include both group quarters and household population, are controlled to the Census Bureaus official 2011 total resident population estimates.
The weighting is conducted in two main operations: a group quarters person weighting operation which assigns weights to persons in group quarters, and a household person weighting operation which assigns weights both to housing units and to persons within housing units. The group quarters person weighting is conducted first and the household person weighting second. The household person weighting is dependent on the group quarters person weighting because estimates for total population, which include both group quarters and household population, are controlled to the Census Bureaus official 2011 total resident population estimates.
The group quarters (GQ) person weighting for the PRCS 2011 1-year estimates has changed in important ways from that of the PRCS 2010 1-year estimates. For the first time, the GQ population sample has been supplemented by a large-scale whole person imputation into not-in-sample GQ facilities. For the 2011 PRCS GQ data, roughly as many GQ persons were imputed as sampled. The goal of the imputation methodology was two-fold.
1. The primary objective was to establish representation of municipio by major GQ type group in the tabulations for each combination that exists on the PRCS GQ sample frame. The seven major GQ type groups are defined by the Population Estimates Program and are given in Table 4.
2. A secondary objective was to establish representation of tract by major GQ type group for each combination that exists on the PRCS GQ sample frame.
Table 3: Population Estimates Program Major GQ Type Groups
For all not-in-sample GQ facilities with an expected population of 16 or more persons (large facilities), we imputed a number of GQ persons equal to 2.5% of the expected population. For those GQ facilities with an expected population of fewer than 16 persons (small facilities), we selected a random sample of GQ facilities as needed to accomplish the two objectives given above. For those selected small GQ facilities, we imputed a number of GQ persons equal to 20% of the facilitys expected population.
Interviewed GQ person records were then sampled at random to be imputed into the selected not-in-sample GQ facilities. An expanding search algorithm searched for donors within the same specific type of GQ facility and the same municipio. If that failed, the search included all GQ facilities of the same major GQ type group. If that still failed, the search expanded to a specific type within a larger geography, then a major GQ type group within that geography, and so on until suitable donors were found.
The weighting procedure made no distinction between sampled and imputed GQ person records. The initial weights of person records in the large GQ facilities equaled the observed or expected population of the GQ facility divided by the number of person records. The initial weights of person records in small GQ facilities equaled the observed or expected population of the GQ facility divided by the number of records, multiplied by the inverse of the fraction represented on the frame of the small GQ facilities of that tract by major GQ type group combination. As was done in previous years weighting, we controlled the final weights to an independent set of GQ population estimates produced by the Population Estimates Program for each state by each of the seven major GQ type groups.
Lastly, the final GQ person weight was rounded to an integer. Rounding was performed so that the sum of the rounded weights were within one person of the sum of the unrounded weights for any of the groups listed below:
Major GQ Type Group
Major GQ Type Group x Municipio
Housing Unit and Household Person Weighting
The housing unit and household person weighting use weighting areas built from collections of whole municipios. The 2011 Census data are used to group municipios of similar demographic and social characteristics. The characteristics considered in the formation include:
The estimation procedure used to assign the weights is then performed independently within each of the PRCS weighting areas.
1. Initial Housing Unit Weighting Factors-This process produces the following factors:
Not selected in CAPI subsampling: SSF = 0.0
Not a CAPI case: SSF = 1.0
Some sample addresses are unmailable. A two-thirds sample of these is sent directly to CAPI and for these cases SSF = 1.5.
A second factor, NIF2, is a ratio adjustment that is computed and assigned to occupied housing units based on the following groups:
Weighting Area x Building Type x Month
NIF is then computed by applying NIF1 and NIF2 for each occupied housing unit. Vacant housing units are assigned a value of NIF = 1.0. Nonresponding housing units are now assigned a weight of 0.0.
Weighting Area x Building Type (single or multi unit) x Month
Vacant housing units or non-CAPI (mail and CATI) housing units receive a value of NIFM = 1.0.
Vacant housing units receive a value of MBF = 1.0. MBF is applied to the weights computed through NIF.
2. Person Weighting Factors-Initially the person weight of each person in an occupied housing unit is the product of the weighting factors of their associated housing unit (BW x ... x MBF). At this point everyone in the household has the same weight. The person weighting is done in a series of three steps which are repeated until a stopping criterion is met. These three steps form a raking ratio or raking process. These person weights are individually adjusted for each person as described below.
The three steps are as follows:
2. Spouse or unmarried partner in a married-couple or unmarried-partner household (non-householder)
3. Other householder
4. Other non-householder
The weights of persons in the first two groups are adjusted so that their sums are each equal to the total estimate of married-couple or unmarried-partner households using the housing unit weight (BW x ... x HPF). At the same time the weights of persons in the first and third groups are adjusted so that their sum is equal to the total estimate of occupied housing units using the housing unit weight (BW x ... x HPF). The goal of this step is to produce more consistent estimates of spouses or unmarried partners and married-couple and unmarried-partner households while simultaneously producing more consistent estimates of householders, occupied housing units, and households.
3. Rounding-The final product of all person weights (BW x ... x MBF x PPSF) is rounded to an integer. Rounding is performed so that the sum of the rounded weights is within one person of the sum of the unrounded weights for any of the groups listed below:
Municipio
Municipio x Sex
Municipio x Sex x Age
Municipio x Sex x Age x Tract
Municipio x Sex x Age x Tract x Block
For example, the number of Males, Age 30 estimated for a municipio using the rounded weights is within one of the number produced using the unrounded weights.
4. Final Housing Unit Weighting Factors-This process produces the following factors:
Municipio x Tract
Municipio x Tract x Block
1. The primary objective was to establish representation of municipio by major GQ type group in the tabulations for each combination that exists on the PRCS GQ sample frame. The seven major GQ type groups are defined by the Population Estimates Program and are given in Table 4.
2. A secondary objective was to establish representation of tract by major GQ type group for each combination that exists on the PRCS GQ sample frame.
Table 3: Population Estimates Program Major GQ Type Groups
| Major GQ Type Group | Definition | Institutional / Non-Institutional |
| 1 | Correctional Institutions | Institutional |
| 2 | Juvenile Detention Facilities | Institutional |
| 3 | Nursing Homes | Institutional |
| 4 | Other Long-Term Care Facilities | Institutional |
| 5 | College Dormitories | Non-Institutional |
| 6 | Military Facilities | Non-Institutional |
| 7 | Other Non-Institutional Facilities | Non-Institutional |
For all not-in-sample GQ facilities with an expected population of 16 or more persons (large facilities), we imputed a number of GQ persons equal to 2.5% of the expected population. For those GQ facilities with an expected population of fewer than 16 persons (small facilities), we selected a random sample of GQ facilities as needed to accomplish the two objectives given above. For those selected small GQ facilities, we imputed a number of GQ persons equal to 20% of the facilitys expected population.
Interviewed GQ person records were then sampled at random to be imputed into the selected not-in-sample GQ facilities. An expanding search algorithm searched for donors within the same specific type of GQ facility and the same municipio. If that failed, the search included all GQ facilities of the same major GQ type group. If that still failed, the search expanded to a specific type within a larger geography, then a major GQ type group within that geography, and so on until suitable donors were found.
The weighting procedure made no distinction between sampled and imputed GQ person records. The initial weights of person records in the large GQ facilities equaled the observed or expected population of the GQ facility divided by the number of person records. The initial weights of person records in small GQ facilities equaled the observed or expected population of the GQ facility divided by the number of records, multiplied by the inverse of the fraction represented on the frame of the small GQ facilities of that tract by major GQ type group combination. As was done in previous years weighting, we controlled the final weights to an independent set of GQ population estimates produced by the Population Estimates Program for each state by each of the seven major GQ type groups.
Lastly, the final GQ person weight was rounded to an integer. Rounding was performed so that the sum of the rounded weights were within one person of the sum of the unrounded weights for any of the groups listed below:
Major GQ Type Group
Major GQ Type Group x Municipio
Housing Unit and Household Person Weighting
The housing unit and household person weighting use weighting areas built from collections of whole municipios. The 2011 Census data are used to group municipios of similar demographic and social characteristics. The characteristics considered in the formation include:
- Percent in poverty
- Percent renting
- Percent in rural areas
- Race/ethnicity, age, and sex distribution
- Distance between the centroids of the municipios
- Core-based Statistical Area status
The estimation procedure used to assign the weights is then performed independently within each of the PRCS weighting areas.
1. Initial Housing Unit Weighting Factors-This process produces the following factors:
- Base Weight (BW)-This initial weight is assigned to every housing unit as the inverse of its blocks sampling rate.
- CAPI Subsampling Factor (SSF)-The weights of the CAPI cases are adjusted to reflect the results of CAPI subsampling. This factor is assigned to each record as follows:
Not selected in CAPI subsampling: SSF = 0.0
Not a CAPI case: SSF = 1.0
Some sample addresses are unmailable. A two-thirds sample of these is sent directly to CAPI and for these cases SSF = 1.5.
- Variation in Monthly Response by Mode (VMS)-This factor makes the total weight of the Mail, CATI, and CAPI records to be tabulated in a month equal to the total base weight of all cases originally mailed for that month. For all cases, VMS is computed and assigned based on the following groups:
- Noninterview Factor (NIF)-This factor adjusts the weight of all responding occupied housing units to account for nonresponding housing units. The factor is computed in two stages. The first factor, NIF1, is a ratio adjustment that is computed and assigned to occupied housings units based on the following groups:
A second factor, NIF2, is a ratio adjustment that is computed and assigned to occupied housing units based on the following groups:
Weighting Area x Building Type x Month
NIF is then computed by applying NIF1 and NIF2 for each occupied housing unit. Vacant housing units are assigned a value of NIF = 1.0. Nonresponding housing units are now assigned a weight of 0.0.
- Noninterview Factor-Mode (NIFM)-This factor adjusts the weight of the responding CAPI occupied housing units to account for CAPI nonrespondents. It is computed as if NIF had not already been assigned to every occupied housing unit record. This factor is not used directly but rather as part of computing the next factor, the Mode Bias Factor.
Weighting Area x Building Type (single or multi unit) x Month
Vacant housing units or non-CAPI (mail and CATI) housing units receive a value of NIFM = 1.0.
- Mode Bias Factor (MBF)-This factor makes the total weight of the housing units in the groups below the same as if NIFM had been used instead of NIF. MBF is computed and assigned to occupied housing units based on the following groups:
Vacant housing units receive a value of MBF = 1.0. MBF is applied to the weights computed through NIF.
2. Person Weighting Factors-Initially the person weight of each person in an occupied housing unit is the product of the weighting factors of their associated housing unit (BW x ... x MBF). At this point everyone in the household has the same weight. The person weighting is done in a series of three steps which are repeated until a stopping criterion is met. These three steps form a raking ratio or raking process. These person weights are individually adjusted for each person as described below.
The three steps are as follows:
- Municipio Controls Raking Factor (SUBEQRF) - This factor is applied to individuals based on their geography. It adjusts the person weights so that the weighted sample counts equal independent population estimates of total population for the municipio. For those municipios which are their own weighting area, this adjustment factor will be 1.0. Because of later adjustments to the person weights, total population is not assured of agreeing exactly with the official 2011 population estimates for municipios which are not their own weighting area.
- Spouse Equalization/Householder Equalization Raking Factor (SPHHEQRF)-This factor is applied to individuals based on the combination of their status of being in a married-couple or unmarried-partner household and whether they are the householder. All persons are assigned to one of four groups:
2. Spouse or unmarried partner in a married-couple or unmarried-partner household (non-householder)
3. Other householder
4. Other non-householder
The weights of persons in the first two groups are adjusted so that their sums are each equal to the total estimate of married-couple or unmarried-partner households using the housing unit weight (BW x ... x HPF). At the same time the weights of persons in the first and third groups are adjusted so that their sum is equal to the total estimate of occupied housing units using the housing unit weight (BW x ... x HPF). The goal of this step is to produce more consistent estimates of spouses or unmarried partners and married-couple and unmarried-partner households while simultaneously producing more consistent estimates of householders, occupied housing units, and households.
- Demographic Raking Factor (DEMORF)-This factor is applied to individuals based on their age and sex in Puerto Rico (note that there are 13 Age groupings). It adjusts the person weights so that the weighted sample counts equal the independent population estimates by age and sex at the weighting area level. Because of collapsing of groups in applying this factor, only the total population is assured of agreeing with the official 2011 population estimates at the weighting area level.
3. Rounding-The final product of all person weights (BW x ... x MBF x PPSF) is rounded to an integer. Rounding is performed so that the sum of the rounded weights is within one person of the sum of the unrounded weights for any of the groups listed below:
Municipio
Municipio x Sex
Municipio x Sex x Age
Municipio x Sex x Age x Tract
Municipio x Sex x Age x Tract x Block
For example, the number of Males, Age 30 estimated for a municipio using the rounded weights is within one of the number produced using the unrounded weights.
4. Final Housing Unit Weighting Factors-This process produces the following factors:
- Householder Factor (HHF)-This factor adjusts for differential response depending on the sex and age of the householder. The value of HHF for an occupied housing unit is the PPSF of the householder. Since there is no householder for vacant units, the value of HHF = 1.0 for all vacant units.
- Rounding-The final product of all housing unit weights (BW x ... x HHF) is rounded to an integer. For occupied units, the rounded housing unit weight is the same as the rounded person weight of the householder. This ensures that both the rounded and unrounded householder weights are equal to the occupied housing unit weight. The rounding for vacant housing units is then performed so that total rounded weight is within one housing unit of the total unrounded weight for any of the groups listed below:
Municipio x Tract
Municipio x Tract x Block
The Census Bureau has modified or suppressed some data on this site to protect confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified.
The Census Bureaus internal Disclosure Review Board sets the confidentiality rules for all data releases. A checklist approach is used to ensure that all potential risks to the confidentiality of the data are considered and addressed.
The Census Bureaus internal Disclosure Review Board sets the confidentiality rules for all data releases. A checklist approach is used to ensure that all potential risks to the confidentiality of the data are considered and addressed.
- Title 13, United States Code: Title 13 of the United States Code authorizes the Census Bureau to conduct censuses and surveys. Section 9 of the same Title requires that any information collected from the public under the authority of Title 13 be maintained as confidential. Section 214 of Title 13 and Sections 3559 and 3571 of Title 18 of the United States Code provide for the imposition of penalties of up to five years in prison and up to $250,000 in fines for wrongful disclosure of confidential census information.
- Disclosure Avoidance: Disclosure avoidance is the process for protecting the confidentiality of data. A disclosure of data occurs when someone can use published statistical information to identify an individual that has provided information under a pledge of confidentiality. For data tabulations, the Census Bureau uses disclosure avoidance procedures to modify or remove the characteristics that put confidential information at risk for disclosure. Although it may appear that a table shows information about a specific individual, the Census Bureau has taken steps to disguise or suppress the original data while making sure the results are still useful. The techniques used by the Census Bureau to protect confidentiality in tabulations vary, depending on the type of data. All disclosure avoidance procedures are done prior to the whole person imputation into not-in-sample GQ facilities.
- Data Swapping: Data swapping is a method of disclosure avoidance designed to protect confidentiality in tables of frequency data (the number or percent of the population with certain characteristics). Data swapping is done by editing the source data or exchanging records for a sample of cases when creating a table. A sample of households is selected and matched on a set of selected key variables with households in neighboring geographic areas that have similar characteristics (such as the same number of adults and same number of children). Because the swap often occurs within a neighboring area, there is no effect on the marginal totals for the area or for totals that include data from multiple areas. Because of data swapping, users should not assume that tables with cells having a value of one or two reveal information about specific individuals. Data swapping procedures were first used in the 1990 Census, and were used again in Census 2000 and the 2010 Census.
- Synthetic Data: The goals of using synthetic data are the same as the goals of data swapping, namely to protect the confidentiality in tables of frequency data. Persons are identified as being at risk for disclosure based on certain characteristics. The synthetic data technique then models the values for another collection of characteristics to protect the confidentiality of that individual.
- Sampling Error - The data in the PRCS products are estimates of the actual figures that would have been obtained by interviewing the entire population using the same methodology. The estimates from the chosen sample also differ from other samples of housing units and persons within those housing units. Sampling error in data arises due to the use of probability sampling, which is necessary to ensure the integrity and representativeness of sample survey results. The implementation of statistical sampling procedures provides the basis for the statistical analysis of sample data. Measures used to estimate the sampling error are provided in the next section.
- Nonsampling Error - In addition to sampling error, data users should realize that other types of errors may be introduced during any of the various complex operations used to collect and process survey data. For example, operations such as data entry from questionnaires and editing may introduce error into the estimates. Another source is through the use of controls in the weighting. The controls are designed to mitigate the effects of systematic undercoverage of certain groups who are difficult to enumerate and to reduce the variance. The controls are based on the population estimates extrapolated from the previous census. Errors can be brought into the data if the extrapolation methods do not properly reflect the population. However, the potential risk from using the controls in the weighting process is offset by far greater benefits to the PRCS estimates. These benefits include reducing the effects of a larger coverage problem found in most surveys, including the PRCS, and the reduction of standard errors of PRCS estimates. These and other sources of error contribute to the nonsampling error component of the total error of survey estimates. Nonsampling errors may affect the data in two ways. Errors that are introduced randomly increase the variability of the data. Systematic errors which are consistent in one direction introduce bias into the results of a sample survey. The Census Bureau protects against the effect of systematic errors on survey estimates by conducting extensive research and evaluation programs on sampling techniques, questionnaire design, and data collection and processing procedures. In addition, an important goal of the PRCS is to minimize the amount of nonsampling error introduced through nonresponse for sample housing units. One way of accomplishing this is by following up on mail nonrespondents during the CATI and CAPI phases. For more information, see the section entitled Control of Nonsampling Error.
Sampling error is the difference between an estimate based on a sample and the corresponding value that would be obtained if the estimate were based on the entire population (as from a census). Note that sample-based estimates will vary depending on the particular sample selected from the population. Measures of the magnitude of sampling error reflect the variation in the estimates over all possible samples that could have been selected from the population using the same sampling methodology.
Estimates of the magnitude of sampling errors - in the form of margins of error - are provided with all published PRCS data. The Census Bureau recommends that data users incorporate this information into their analyses, as sampling error in survey estimates could impact the conclusions drawn from the results.
Confidence Intervals and Margins of Error
Confidence Intervals - A sample estimate and its estimated standard error may be used to construct confidence intervals about the estimate. These intervals are ranges that will contain the average value of the estimated characteristic that results over all possible samples, with a known probability.
For example, if all possible samples that could result under the PRCS sample design were independently selected and surveyed under the same conditions, and if the estimate and its estimated standard error were calculated for each of these samples, then:
1. Approximately 68 percent of the intervals from one estimated standard error below the estimate to one estimated standard error above the estimate would contain the average result from all possible samples;
2. Approximately 90 percent of the intervals from 1.645 times the estimated standard error below the estimate to 1.645 times the estimated standard error above the estimate would contain the average result from all possible samples.
3. Approximately 95 percent of the intervals from two estimated standard errors below the estimate to two estimated standard errors above the estimate would contain the average result from all possible samples.
The intervals are referred to as 68 percent, 90 percent, and 95 percent confidence intervals, respectively.
Margin of Error - Instead of providing the upper and lower confidence bounds in published PRCS tables, the margin of error is provided instead. The margin of error is the difference between an estimate and its upper or lower confidence bound. Both the confidence bounds and the standard error can easily be computed from the margin of error. All PRCS published margins of error are based on a 90 percent confidence level.
Standard Error = Margin of Error / 1.645
Lower Confidence Bound = Estimate - Margin of Error
Upper Confidence Bound = Estimate + Margin of Error
Note that for 2005, PRCS margins of error and confidence bounds were calculated using a 90 percent confidence level multiplier of 1.65. Beginning with the 2006 data release, we are now employing a more accurate multiplier of 1.645. Margins of error and confidence bounds from previously published products will not be updated with the new multiplier. When calculating standard errors from margins of error or confidence bounds using published data for 2005, use the 1.65 multiplier.
When constructing confidence bounds from the margin of error, the user should be aware of any natural limits on the bounds. For example, if a characteristic estimate for the population is near zero, the calculated value of the lower confidence bound may be negative. However, a negative number of people does not make sense, so the lower confidence bound should be reported as zero instead. However, for other estimates such as income, negative values do make sense. The context and meaning of the estimate must be kept in mind when creating these bounds. Another of these natural limits would be 100 percent for the upper bound of a percent estimate.
If the margin of error is displayed as ' ***** (five asterisks), the estimate has been controlled to be equal to a fixed value and so it has no sampling error. When using any of the formulas in the following section, use a standard error of zero for these controlled estimates.
Limitations -The user should be careful when computing and interpreting confidence intervals.
Estimates of the magnitude of sampling errors - in the form of margins of error - are provided with all published PRCS data. The Census Bureau recommends that data users incorporate this information into their analyses, as sampling error in survey estimates could impact the conclusions drawn from the results.
Confidence Intervals and Margins of Error
Confidence Intervals - A sample estimate and its estimated standard error may be used to construct confidence intervals about the estimate. These intervals are ranges that will contain the average value of the estimated characteristic that results over all possible samples, with a known probability.
For example, if all possible samples that could result under the PRCS sample design were independently selected and surveyed under the same conditions, and if the estimate and its estimated standard error were calculated for each of these samples, then:
1. Approximately 68 percent of the intervals from one estimated standard error below the estimate to one estimated standard error above the estimate would contain the average result from all possible samples;
2. Approximately 90 percent of the intervals from 1.645 times the estimated standard error below the estimate to 1.645 times the estimated standard error above the estimate would contain the average result from all possible samples.
3. Approximately 95 percent of the intervals from two estimated standard errors below the estimate to two estimated standard errors above the estimate would contain the average result from all possible samples.
The intervals are referred to as 68 percent, 90 percent, and 95 percent confidence intervals, respectively.
Margin of Error - Instead of providing the upper and lower confidence bounds in published PRCS tables, the margin of error is provided instead. The margin of error is the difference between an estimate and its upper or lower confidence bound. Both the confidence bounds and the standard error can easily be computed from the margin of error. All PRCS published margins of error are based on a 90 percent confidence level.
Standard Error = Margin of Error / 1.645
Lower Confidence Bound = Estimate - Margin of Error
Upper Confidence Bound = Estimate + Margin of Error
Note that for 2005, PRCS margins of error and confidence bounds were calculated using a 90 percent confidence level multiplier of 1.65. Beginning with the 2006 data release, we are now employing a more accurate multiplier of 1.645. Margins of error and confidence bounds from previously published products will not be updated with the new multiplier. When calculating standard errors from margins of error or confidence bounds using published data for 2005, use the 1.65 multiplier.
When constructing confidence bounds from the margin of error, the user should be aware of any natural limits on the bounds. For example, if a characteristic estimate for the population is near zero, the calculated value of the lower confidence bound may be negative. However, a negative number of people does not make sense, so the lower confidence bound should be reported as zero instead. However, for other estimates such as income, negative values do make sense. The context and meaning of the estimate must be kept in mind when creating these bounds. Another of these natural limits would be 100 percent for the upper bound of a percent estimate.
If the margin of error is displayed as ' ***** (five asterisks), the estimate has been controlled to be equal to a fixed value and so it has no sampling error. When using any of the formulas in the following section, use a standard error of zero for these controlled estimates.
Limitations -The user should be careful when computing and interpreting confidence intervals.
- The estimated standard errors (and thus margins of error) included in these data products do not include portions of the variability due to nonsampling error that may be present in the data. In particular, the standard errors do not reflect the effect of correlated errors introduced by interviewers, coders, or other field or processing personnel. Nor do they reflect the error from imputed values due to missing responses. Thus, the standard errors calculated represent a lower bound of the total error. As a result, confidence intervals formed using these estimated standard errors may not meet the stated levels of confidence (i.e., 68, 90, or 95 percent). Thus, some care must be exercised in the interpretation of the data in this data product based on the estimated standard errors.
- Zero or small estimates; very large estimates - The value of almost all PRCS characteristics is greater than or equal to zero by definition. For zero or small estimates, use of the method given previously for calculating confidence intervals relies on large sample theory, and may result in negative values which for most characteristics are not admissible. In this case the lower limit of the confidence interval is set to zero by default. A similar caution holds for estimates of totals close to a control total or estimated proportion near one, where the upper limit of the confidence interval is set to its largest admissible value. In these situations the level of confidence of the adjusted range of values is less than the prescribed confidence level.
Direct estimates of the margin of error were calculated for all estimates reported in this product. The standard errors, in most cases, are calculated using a replicate-based methodology known as successive difference replication that takes into account the sample design and estimation procedures.
The formula provided below calculates the variance using the PRCS estimate (X0) and the 80 replicate estimates (Xr).
X0 is the estimate calculated using the production weight and Xr is the estimate calculated using the rth replicate weight. The standard error is the square root of the variance. The 90th percent margin of error is 1.645 times the standard error.
For more information on the formation of the replicate weights, see chapter 12 of the Design and Methodology documentation at http://www.census.gov/acs/www/Downloads/survey_methodology/Chapter_12_RevisedDec2010.pdf.
Beginning with the PRCS 2011 1-year estimates, a new imputation-based methodology was incorporated into processing (see the description in the Group Quarters Person Weighting Section). An adjustment was made to the production replicate weight variance methodology to account for the non-negligible amount of additional variation being introduced by the new technique3.
Excluding the base weights, replicate weights were allowed to be negative in order to avoid underestimating the standard error. Exceptions include:
1. The estimate of the number or proportion of people, households, families, or housing units in a geographic area with a specific characteristic is zero. A special procedure is used to estimate the standard error.
2. There are either no sample observations available to compute an estimate or standard error of a median, an aggregate, a proportion, or some other ratio, or there are too few sample observations to compute a stable estimate of the standard error. The estimate is represented in the tables by - and the margin of error by ** (two asterisks).
3. The estimate of a median falls in the lower open-ended interval or upper open-ended interval of a distribution. If the median occurs in the lowest interval, then a - follows the estimate, and if the median occurs in the upper interval, then a + follows the estimate. In both cases the margin of error is represented in the tables by *** (three asterisks).
Footnotes:
3 For more information regarding this issue, see Asiala, M. and Castro, E. 2012. Developing Replicate Weight-Based Methods to Account for Imputation Variance in a Mass Imputation Application. In JSM proceedings, Section on Survey Research Methods, Alexandria, VA: American Statistical Association.
The formula provided below calculates the variance using the PRCS estimate (X0) and the 80 replicate estimates (Xr).
X0 is the estimate calculated using the production weight and Xr is the estimate calculated using the rth replicate weight. The standard error is the square root of the variance. The 90th percent margin of error is 1.645 times the standard error.
For more information on the formation of the replicate weights, see chapter 12 of the Design and Methodology documentation at http://www.census.gov/acs/www/Downloads/survey_methodology/Chapter_12_RevisedDec2010.pdf.
Beginning with the PRCS 2011 1-year estimates, a new imputation-based methodology was incorporated into processing (see the description in the Group Quarters Person Weighting Section). An adjustment was made to the production replicate weight variance methodology to account for the non-negligible amount of additional variation being introduced by the new technique3.
Excluding the base weights, replicate weights were allowed to be negative in order to avoid underestimating the standard error. Exceptions include:
1. The estimate of the number or proportion of people, households, families, or housing units in a geographic area with a specific characteristic is zero. A special procedure is used to estimate the standard error.
2. There are either no sample observations available to compute an estimate or standard error of a median, an aggregate, a proportion, or some other ratio, or there are too few sample observations to compute a stable estimate of the standard error. The estimate is represented in the tables by - and the margin of error by ** (two asterisks).
3. The estimate of a median falls in the lower open-ended interval or upper open-ended interval of a distribution. If the median occurs in the lowest interval, then a - follows the estimate, and if the median occurs in the upper interval, then a + follows the estimate. In both cases the margin of error is represented in the tables by *** (three asterisks).
Footnotes:
3 For more information regarding this issue, see Asiala, M. and Castro, E. 2012. Developing Replicate Weight-Based Methods to Account for Imputation Variance in a Mass Imputation Application. In JSM proceedings, Section on Survey Research Methods, Alexandria, VA: American Statistical Association.
The standard errors estimated from these tables are for individual estimates. Additional calculations are required to estimate the standard errors for sums of or the differences between two or more sample estimates.
The standard error of the sum of two sample estimates is the square root of the sum of the two individual standard errors squared plus a covariance term. That is, for standard errors and
and of estimates X1 and X2:
The covariance measures the interaction between two estimates. Currently the covariance terms are not available. Data users should use the approximation:
This method, however, will underestimate or overestimate the standard error if the two estimates interact in either a positive or negative way.
The approximation formula (2) can be expanded to more than two estimates by adding in the individual standard errors squared inside the radical. As the number of estimates involved in the sum or difference increases, the results of formula (2) become increasingly different from the standard error derived directly from the PRCS microdata. Care should be taken to work with the fewest number of estimates as possible. If there are estimates involved in the sum that are controlled in the weighting then the approximate standard error can be increasingly different. Several examples are provided starting on page 29 to demonstrate how issues associated with approximating the standard errors when summing large numbers of estimates together.
The standard error of the sum of two sample estimates is the square root of the sum of the two individual standard errors squared plus a covariance term. That is, for standard errors and
and of estimates X1 and X2:The covariance measures the interaction between two estimates. Currently the covariance terms are not available. Data users should use the approximation:
This method, however, will underestimate or overestimate the standard error if the two estimates interact in either a positive or negative way.
The approximation formula (2) can be expanded to more than two estimates by adding in the individual standard errors squared inside the radical. As the number of estimates involved in the sum or difference increases, the results of formula (2) become increasingly different from the standard error derived directly from the PRCS microdata. Care should be taken to work with the fewest number of estimates as possible. If there are estimates involved in the sum that are controlled in the weighting then the approximate standard error can be increasingly different. Several examples are provided starting on page 29 to demonstrate how issues associated with approximating the standard errors when summing large numbers of estimates together.
The statistic of interest may be the ratio of two estimates. First is the case where the numerator is not a subset of the denominator. The standard error of this ratio between two sample estimates is approximated as:
For a proportion (or percent), a ratio where the numerator is a subset of the denominator, a slightly different estimator is used. If , then the standard error of this proportion is approximated as:
If (where P is the proportion and Q is its corresponding percent), then
(where P is the proportion and Q is its corresponding percent), then . Note the difference between the formulas to approximate the standard error for proportions and ratios - the plus sign in the ratio formula has been replaced with a minus sign in proportions formula. If the value under the square root sign is negative, use the ratio standard error formula instead.
If (where P is the proportion and Q is its corresponding percent), then
(where P is the proportion and Q is its corresponding percent), then . Note the difference between the formulas to approximate the standard error for proportions and ratios - the plus sign in the ratio formula has been replaced with a minus sign in proportions formula. If the value under the square root sign is negative, use the ratio standard error formula instead.This calculates the percent change from one time period to another, for example, computing the percent change of a 2011 estimate to a 2010 estimate. Normally, the current estimate is compared to the older estimate.
Let the current estimate = and the earlier estimate = , then the formula for percent change is:
This reduces to a ratio. The ratio formula above may be used to calculate the standard error. As a caveat, this formula does not take into account the correlation when calculating overlapping time periods.
Let the current estimate = and the earlier estimate = , then the formula for percent change is:
This reduces to a ratio. The ratio formula above may be used to calculate the standard error. As a caveat, this formula does not take into account the correlation when calculating overlapping time periods.
Significant differences - Users may conduct a statistical test to see if the difference between a PRCS estimate and any other chosen estimates is statistically significant at a given confidence level. Statistically significant means that the difference is not likely due to random chance alone. With the two estimates (Est1 and Est2) and their respective standard errors (SE1 and SE2), calculate
If Z > 1.645 or Z < -1.645, then the difference can be said to be statistically significant at the 90 percent confidence level.4 Any estimate can be compared to a PRCS estimate using this method, including other PRCS estimates from the current year, the PRCS estimate for the same characteristic and geographic area but from a previous year, ACS estimates, 2010 Census counts, estimates from other Census Bureau surveys, and estimates from other sources. Not all estimates have sampling error - 2010 Census counts do not - but they should be used if they exist to give the most accurate result of the test.
Users are also cautioned to not rely on looking at whether confidence intervals for two estimates overlap or not to determine statistical significance, because there are circumstances where that method will not give the correct test result. If two confidence intervals do not overlap, then the estimates will be significantly different (i.e. the significance test will always agree). However, if two confidence intervals do overlap, then the estimates may or may not be significantly different. The Z calculation above is recommended in all cases.
Here is a simple example of why it is not recommended to use the overlapping confidence bounds rule of thumb as a substitute for a statistical test.
Let: X1 = 6.0 with SE1 = 0.5 and X2 = 5.0 with SE2 = 0.2.
The Lower Bound for X1 = 6.0 - 0.5 * 1.645 = 5.2 while the Upper Bound for X2 = 5.0 + 0.2 * 1.645 = 5.3. The confidence bounds overlap, so, the rule of thumb would indicate that the estimates are not significantly different at the 90% level.
However, if we apply the statistical significance test we obtain:
Z = 1.857 > 1.645 which means that the difference is significant (at the 90% level).
All statistical testing in PRCS data products is based on the 90 percent confidence level. Users should understand that all testing was done using unrounded estimates and standard errors, and it may not be possible to replicate test results using the rounded estimates and margins of error as published.
Footnotes:
4 The PRCS Accuracy of the Data was not separate from the ACS Accuracy of the Data in 2005, which used a Z statistic of +/-1.65. Data users should use +/-1.65 for estimates published in 2005 or earlier.
If Z > 1.645 or Z < -1.645, then the difference can be said to be statistically significant at the 90 percent confidence level.4 Any estimate can be compared to a PRCS estimate using this method, including other PRCS estimates from the current year, the PRCS estimate for the same characteristic and geographic area but from a previous year, ACS estimates, 2010 Census counts, estimates from other Census Bureau surveys, and estimates from other sources. Not all estimates have sampling error - 2010 Census counts do not - but they should be used if they exist to give the most accurate result of the test.
Users are also cautioned to not rely on looking at whether confidence intervals for two estimates overlap or not to determine statistical significance, because there are circumstances where that method will not give the correct test result. If two confidence intervals do not overlap, then the estimates will be significantly different (i.e. the significance test will always agree). However, if two confidence intervals do overlap, then the estimates may or may not be significantly different. The Z calculation above is recommended in all cases.
Here is a simple example of why it is not recommended to use the overlapping confidence bounds rule of thumb as a substitute for a statistical test.
Let: X1 = 6.0 with SE1 = 0.5 and X2 = 5.0 with SE2 = 0.2.
The Lower Bound for X1 = 6.0 - 0.5 * 1.645 = 5.2 while the Upper Bound for X2 = 5.0 + 0.2 * 1.645 = 5.3. The confidence bounds overlap, so, the rule of thumb would indicate that the estimates are not significantly different at the 90% level.
However, if we apply the statistical significance test we obtain:
Z = 1.857 > 1.645 which means that the difference is significant (at the 90% level).
All statistical testing in PRCS data products is based on the 90 percent confidence level. Users should understand that all testing was done using unrounded estimates and standard errors, and it may not be possible to replicate test results using the rounded estimates and margins of error as published.
Footnotes:
4 The PRCS Accuracy of the Data was not separate from the ACS Accuracy of the Data in 2005, which used a Z statistic of +/-1.65. Data users should use +/-1.65 for estimates published in 2005 or earlier.
Example 1 - Calculating the Standard Error from the Margin of Error
The estimated number of males, never married is 583,195 from summary table B12001 for Puerto Rico for 2011. The margin of error is 8,832.
Standard Error = Margin of Error / 1.645
Calculating the standard error using the margin of error, we have:
SE(583,195) = 8,832/ 1.645 = 5,369.
Example 2 - Calculating the Standard Error of a Sum or Difference
We are interested in the number of people who have never been married. From Example 1, we know the number of males, never married is 583,195. From summary table B12001 we have the number of females, never married is 547,586 with a margin of error of 9,011. So, the estimated number of people who have never been married is 583,195 + 547,586 = 1,130,781. To calculate the approximate standard error of this sum, we need the standard errors of the two estimates in the sum. We have the standard error for the number of males never married from Example 1 as 5,369. The standard error for the number of females never married is calculated using the margin of error:
SE(547,586) = 9,011 / 1.645 = 5,478.
So using formula (2) for the approximate standard error of a sum or difference we have:
Caution: This method will underestimate or overestimate the standard error if the two estimates interact in either a positive or negative way.
To calculate the lower and upper bounds of the 90 percent confidence interval around 1,130,781 using the standard error, simply multiply 7,670 by 1.645, then add and subtract the product from 1,130,781. Thus the 90 percent confidence interval for this estimate is [1,130,781 - 1.645(7,670)] to [1,130,781 + 1.645(7,670)] or 1,118,164 to 1,143,398.
Example 3 - Calculating the Standard Error of a Proportion/Percent
We are interested in the percentage of females who have never been married to the number of people who have never been married. The number of females, never married is 547,586 and the number of people who have never been married is 1,130,781. To calculate the approximate standard error of this percent, we need the standard errors of the two estimates in the percent. We have the approximate standard error for the number of females never married from Example 2 as 5,478 and the approximate standard error for the number of people never married calculated from example 2 as 7,670.
The estimate is (547,586 / 1,130,781) * 100% = 48.43%
So, using formula (4) for the approximate standard error of a proportion or percent, we have:
To calculate the lower and upper bounds of the 90 percent confidence interval around 48.43 using the standard error, simply multiply 0.36 by 1.645, then add and subtract the product from 48.43. Thus the 90 percent confidence interval for this estimate is [48.43 - 1.645(0.36)] to [48.43 + 1.645(0.36)], or 47.84% to 49.02%.
Example 4 - Calculating the Standard Error of a Ratio
Now, let us calculate the estimate of the ratio of the number of unmarried males to the number of unmarried females and its standard error. From the above examples, the estimate for the number of unmarried men is 583,195 with a standard error of 5,369, and the estimate for the number of unmarried women is 547,586 with a standard error of 5,478.
The estimate of the ratio is 583,195 / 547,586 = 1.065.
Using formula (3) for the approximate standard error we have:
The 90 percent margin of error for this estimate would be 0.014 multiplied by 1.645, or about 0.023. The 90 percent lower and upper 90 percent confidence bounds would then be [1.065- 1.645(0.014)] to [1.065 + 1.645(0.014)], or 1.042 and 1.088.
Example 5 - Calculating the Standard Error of a Product
We are interested in the number of 1-unit detached owner-occupied housing units. The number of owner-occupied housing units is 876,741 with a margin of error of 9,559 from subject table S2504 for 2011, and the percent of 1-unit detached owner-occupied housing units is 81.1% (0.811) with a margin of error of 0.6 (0.006). So the number of 1-unit detached owner-occupied housing units is 876,741 * 0.811 = 711,037. Calculating the standard error for the estimates using the margin of error we have:
and
The approximate standard error for number of 1-unit detached owner-occupied housing units is calculated using formula (5) for products as:
To calculate the lower and upper bounds of the 90 percent confidence interval around 711,037 using the standard error, simply multiply 5,695 by 1.645, then add and subtract the product from 711,037. Thus the 90 percent confidence interval for this estimate is [711,037 - 1.645(5,695)] to [711,037 + 1.645(5,695)] or 701,669 to 720,405.
The estimated number of males, never married is 583,195 from summary table B12001 for Puerto Rico for 2011. The margin of error is 8,832.
Standard Error = Margin of Error / 1.645
Calculating the standard error using the margin of error, we have:
SE(583,195) = 8,832/ 1.645 = 5,369.
Example 2 - Calculating the Standard Error of a Sum or Difference
We are interested in the number of people who have never been married. From Example 1, we know the number of males, never married is 583,195. From summary table B12001 we have the number of females, never married is 547,586 with a margin of error of 9,011. So, the estimated number of people who have never been married is 583,195 + 547,586 = 1,130,781. To calculate the approximate standard error of this sum, we need the standard errors of the two estimates in the sum. We have the standard error for the number of males never married from Example 1 as 5,369. The standard error for the number of females never married is calculated using the margin of error:
SE(547,586) = 9,011 / 1.645 = 5,478.
So using formula (2) for the approximate standard error of a sum or difference we have:
Caution: This method will underestimate or overestimate the standard error if the two estimates interact in either a positive or negative way.
To calculate the lower and upper bounds of the 90 percent confidence interval around 1,130,781 using the standard error, simply multiply 7,670 by 1.645, then add and subtract the product from 1,130,781. Thus the 90 percent confidence interval for this estimate is [1,130,781 - 1.645(7,670)] to [1,130,781 + 1.645(7,670)] or 1,118,164 to 1,143,398.
Example 3 - Calculating the Standard Error of a Proportion/Percent
We are interested in the percentage of females who have never been married to the number of people who have never been married. The number of females, never married is 547,586 and the number of people who have never been married is 1,130,781. To calculate the approximate standard error of this percent, we need the standard errors of the two estimates in the percent. We have the approximate standard error for the number of females never married from Example 2 as 5,478 and the approximate standard error for the number of people never married calculated from example 2 as 7,670.
The estimate is (547,586 / 1,130,781) * 100% = 48.43%
So, using formula (4) for the approximate standard error of a proportion or percent, we have:
To calculate the lower and upper bounds of the 90 percent confidence interval around 48.43 using the standard error, simply multiply 0.36 by 1.645, then add and subtract the product from 48.43. Thus the 90 percent confidence interval for this estimate is [48.43 - 1.645(0.36)] to [48.43 + 1.645(0.36)], or 47.84% to 49.02%.
Example 4 - Calculating the Standard Error of a Ratio
Now, let us calculate the estimate of the ratio of the number of unmarried males to the number of unmarried females and its standard error. From the above examples, the estimate for the number of unmarried men is 583,195 with a standard error of 5,369, and the estimate for the number of unmarried women is 547,586 with a standard error of 5,478.
The estimate of the ratio is 583,195 / 547,586 = 1.065.
Using formula (3) for the approximate standard error we have:
The 90 percent margin of error for this estimate would be 0.014 multiplied by 1.645, or about 0.023. The 90 percent lower and upper 90 percent confidence bounds would then be [1.065- 1.645(0.014)] to [1.065 + 1.645(0.014)], or 1.042 and 1.088.
Example 5 - Calculating the Standard Error of a Product
We are interested in the number of 1-unit detached owner-occupied housing units. The number of owner-occupied housing units is 876,741 with a margin of error of 9,559 from subject table S2504 for 2011, and the percent of 1-unit detached owner-occupied housing units is 81.1% (0.811) with a margin of error of 0.6 (0.006). So the number of 1-unit detached owner-occupied housing units is 876,741 * 0.811 = 711,037. Calculating the standard error for the estimates using the margin of error we have:
and
The approximate standard error for number of 1-unit detached owner-occupied housing units is calculated using formula (5) for products as:
To calculate the lower and upper bounds of the 90 percent confidence interval around 711,037 using the standard error, simply multiply 5,695 by 1.645, then add and subtract the product from 711,037. Thus the 90 percent confidence interval for this estimate is [711,037 - 1.645(5,695)] to [711,037 + 1.645(5,695)] or 701,669 to 720,405.
As mentioned earlier, sample data are subject to nonsampling error. This component of error could introduce serious bias into the data, and the total error could increase dramatically over that which would result purely from sampling. While it is impossible to completely eliminate nonsampling error from a survey operation, the Census Bureau attempts to control the sources of such error during the collection and processing operations. Described below are the primary sources of nonsampling error and the programs instituted for control of this error. The success of these programs, however, is contingent upon how well the instructions were carried out during the survey.
In the CATI and CAPI nonresponse follow-up phases, efforts were made to minimize the chances that housing units that were not part of the sample were interviewed in place of units in sample by mistake. If a CATI interviewer called a mail nonresponse case and was not able to reach the exact address, no interview was conducted and the case was eligible for CAPI. During CAPI follow-up, the interviewer had to locate the exact address for each sample housing unit. If the interviewer could not locate the exact sample unit in a multi-unit structure, or found a different number of units than expected, the interviewers were instructed to list the units in the building and follow a specific procedure to select a replacement sample unit. Person overcoverage can occur when an individual is included as a member of a housing unit but does not meet PRCS residency rules.
Coverage rates give a measure of undercoverage or overcoverage of persons or housing units in a given geographic area. Rates below 100 percent indicate undercoverage, while rates above 100 percent indicate overcoverage. Coverage rates are released concurrent with the release of estimates on American FactFinder in the B98 series of detailed tables. Further information about PRCS coverage rates may be found at http://www.census.gov/acs/www/methodology/sample_size_and_data_quality/.
The PRCS made every effort to minimize unit nonresponse, and thus, the potential for nonresponse error. First, the PRCS used a combination of mail, CATI, and CAPI data collection modes to maximize response. The mail phase included a series of three to four mailings to encourage housing units to return the questionnaire. Subsequently, mail nonrespondents (for which phone numbers are available) were contacted by CATI for an interview. Finally, a subsample of the mail and telephone nonrespondents was contacted for by personal visit to attempt an interview
PRCS response rates measure the percent of units with a completed interview. The higher the response rate, and consequently the lower the nonresponse rate, the less chance estimates may be affected by nonresponse bias. Response and nonresponse rates, as well as rates for specific types of nonresponse, are released concurrent with the release of estimates on American FactFinder in the B98 series of detailed tables. Further information about response and nonresponse rates may be found at http://www.census.gov/acs/www/methodology/sample_size_and_data_quality/.
o Item Nonresponse - Nonresponse to particular questions on the survey questionnaire and instrument allows for the introduction of error or bias into the data, since the characteristics of the nonrespondents have not been observed and may differ from those reported by respondents. As a result, any imputation procedure using respondent data may not completely reflect this difference either at the elemental level (individual person or housing unit) or on average.
Some protection against the introduction of large errors or biases is afforded by minimizing nonresponse. In the PRCS, item nonresponse for the CATI and CAPI operations was minimized by the requirement that the automated instrument receive a response to each question before the next one could be asked. Questionnaires returned by mail were edited for completeness and acceptability. They were reviewed by computer for content omissions and population coverage. If necessary, a telephone follow-up was made to obtain missing information. Potential coverage errors were included in this follow-up.
Allocation tables provide the weighted estimate of persons or housing units for which a value was imputed, as well as the total estimate of persons or housing units that were eligible to answer the question. The smaller the number of imputed responses, the lower the chance that the item nonresponse is contributing a bias to the estimates. Allocation tables are released concurrent with the release of estimates on American Factfinder in the B99 series of detailed tables with the overall allocation rates across all person and housing unit characteristics in the B98 series of detailed tables. Additional information on item nonresponse and allocations can be found at http://www.census.gov/acs/www/methodology/sample_size_and_data_quality/.
o Processing Error - The many phases involved in processing the survey data represent potential sources for the introduction of nonsampling error. The processing of the survey questionnaires includes the keying of data from completed questionnaires, automated clerical review, follow-up by telephone, manual coding of write-in responses, and automated data processing. The various field, coding and computer operations undergo a number of quality control checks to insure their accurate application.
o Content Editing - After data collection was completed, any remaining incomplete or inconsistent information was imputed during the final content edit of the collected data. Imputations, or computer assignments of acceptable codes in place of unacceptable entries or blanks, were needed most often when an entry for a given item was missing or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in other surveys and previous censuses, the general procedure for changing unacceptable entries was to allocate an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. Imputing acceptable values in place of blanks or unacceptable entries enhances the usefulness of the data.
- Coverage Error - It is possible for some sample housing units or persons to be missed entirely by the survey (undercoverage), but it is also possible for some sample housing units and persons to be counted more than once (overcoverage). Both the undercoverage and overcoverage of persons and housing units can introduce biases into the data, increase respondent burden and survey costs.
In the CATI and CAPI nonresponse follow-up phases, efforts were made to minimize the chances that housing units that were not part of the sample were interviewed in place of units in sample by mistake. If a CATI interviewer called a mail nonresponse case and was not able to reach the exact address, no interview was conducted and the case was eligible for CAPI. During CAPI follow-up, the interviewer had to locate the exact address for each sample housing unit. If the interviewer could not locate the exact sample unit in a multi-unit structure, or found a different number of units than expected, the interviewers were instructed to list the units in the building and follow a specific procedure to select a replacement sample unit. Person overcoverage can occur when an individual is included as a member of a housing unit but does not meet PRCS residency rules.
Coverage rates give a measure of undercoverage or overcoverage of persons or housing units in a given geographic area. Rates below 100 percent indicate undercoverage, while rates above 100 percent indicate overcoverage. Coverage rates are released concurrent with the release of estimates on American FactFinder in the B98 series of detailed tables. Further information about PRCS coverage rates may be found at http://www.census.gov/acs/www/methodology/sample_size_and_data_quality/.
- Nonresponse Error - Survey nonresponse is a well-known source of nonsampling error. There are two types of nonresponse error - unit nonresponse and item nonresponse. Nonresponse errors affect survey estimates to varying levels depending on amount of nonresponse and the extent to which nonrespondents differ from respondents on the characteristics measured by the survey. The exact amount of nonresponse error or bias on an estimate is almost never known. Therefore, survey researchers generally rely on proxy measures, such as the nonresponse rate, to indicate the potential for nonresponse error.
The PRCS made every effort to minimize unit nonresponse, and thus, the potential for nonresponse error. First, the PRCS used a combination of mail, CATI, and CAPI data collection modes to maximize response. The mail phase included a series of three to four mailings to encourage housing units to return the questionnaire. Subsequently, mail nonrespondents (for which phone numbers are available) were contacted by CATI for an interview. Finally, a subsample of the mail and telephone nonrespondents was contacted for by personal visit to attempt an interview
PRCS response rates measure the percent of units with a completed interview. The higher the response rate, and consequently the lower the nonresponse rate, the less chance estimates may be affected by nonresponse bias. Response and nonresponse rates, as well as rates for specific types of nonresponse, are released concurrent with the release of estimates on American FactFinder in the B98 series of detailed tables. Further information about response and nonresponse rates may be found at http://www.census.gov/acs/www/methodology/sample_size_and_data_quality/.
o Item Nonresponse - Nonresponse to particular questions on the survey questionnaire and instrument allows for the introduction of error or bias into the data, since the characteristics of the nonrespondents have not been observed and may differ from those reported by respondents. As a result, any imputation procedure using respondent data may not completely reflect this difference either at the elemental level (individual person or housing unit) or on average.
Some protection against the introduction of large errors or biases is afforded by minimizing nonresponse. In the PRCS, item nonresponse for the CATI and CAPI operations was minimized by the requirement that the automated instrument receive a response to each question before the next one could be asked. Questionnaires returned by mail were edited for completeness and acceptability. They were reviewed by computer for content omissions and population coverage. If necessary, a telephone follow-up was made to obtain missing information. Potential coverage errors were included in this follow-up.
Allocation tables provide the weighted estimate of persons or housing units for which a value was imputed, as well as the total estimate of persons or housing units that were eligible to answer the question. The smaller the number of imputed responses, the lower the chance that the item nonresponse is contributing a bias to the estimates. Allocation tables are released concurrent with the release of estimates on American Factfinder in the B99 series of detailed tables with the overall allocation rates across all person and housing unit characteristics in the B98 series of detailed tables. Additional information on item nonresponse and allocations can be found at http://www.census.gov/acs/www/methodology/sample_size_and_data_quality/.
- Measurement and Processing Error - The person completing the questionnaire or responding to the questions posed by an interviewer could serve as a source of error, although the questions were cognitively tested for phrasing, and detailed instructions for completing the questionnaire were provided to each household.
o Processing Error - The many phases involved in processing the survey data represent potential sources for the introduction of nonsampling error. The processing of the survey questionnaires includes the keying of data from completed questionnaires, automated clerical review, follow-up by telephone, manual coding of write-in responses, and automated data processing. The various field, coding and computer operations undergo a number of quality control checks to insure their accurate application.
o Content Editing - After data collection was completed, any remaining incomplete or inconsistent information was imputed during the final content edit of the collected data. Imputations, or computer assignments of acceptable codes in place of unacceptable entries or blanks, were needed most often when an entry for a given item was missing or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in other surveys and previous censuses, the general procedure for changing unacceptable entries was to allocate an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. Imputing acceptable values in place of blanks or unacceptable entries enhances the usefulness of the data.
Several examples are provided here to demonstrate how different the approximated standard errors of sums can be compared to those derived and published with PRCS microdata. ACS data is used in the examples. However they are applicable to PRCS data as well.
A. Suppose we wish to estimate the total number of males using age and citizenship status. The relevant data from table B05003 is displayed in table A below.
Table A: 2009 Estimates of males from B05003: Sex by Age by Citizenship Status
The estimate and its MOE are actually published. However, if they were not available in the tables, one way of obtaining them would be to add together the number of males under 18 and over 18 to get:
And the first approximated
Another way would be to add up the estimates for the three subcategories (Native, Foreign Born: Naturalized U.S. Citizen, and Foreign Born: Not a U.S. Citizen), for males under and over 18 years of age. From these eight estimates we obtain:
With a second approximation to the SE of:
We do know that the standard error using the published MOE is 2,996 / 1.645 = 1,821.3. With a quick glance, we can see that the ratio of the standard error of the first method to the published-based standard error yields 0.93; an under-estimate of roughly 7%, whereas the second method yields a ratio of 2.08 or an over-estimate of 108%. This is an example of what could happen to the approximate SE when the sum involves a controlled estimate. In this case, it is sex by age.
B. Suppose we are interested in the total number of people aged 65 or older and its standard error. Table B shows some of the estimates for the national level from table B01001 (the estimates in gray were derived for the purpose of this example only).
Table B: Some Estimates from AFF Table B01001: Sex by Age for 2009
To begin we find the total number of people aged 65 and over by simply adding the totals for males and females to get 240,934 + 318,468 = 559,402. One way we could approximate the standard error of this estimate is to sum the males and females for each age category and then use their MOEs to obtain the approximate standard error for the total number of people over 65.
... etc. ...
Now, we calculate the number of people aged 65 or older, 559,402, using the six derived estimates and approximate its standard error:
For this example the estimate and MOE are published in table B09017. The total number of people aged 65 or older is 559,402 with a margin of error of 2,595. Therefore the published-based standard error is:
SE(559,402) = 2,595/1,645 = 1,578
The approximated standard error, using the six derived age group estimates, yields an approximated standard error roughly 3.3 times larger than the published-based standard error.
There are two additional ways to approximate the standard error of people aged 65 and over in addition to the way used above. The first is to find the MOEs for the males age 65 and older, and females aged 65 and older separately and then combine them to find the approximated standard error.
For males it is:
And for females:
And the total is then:
The second is to use all twelve of the published estimates together, that is, all estimates from the male age categories and female age categories, to create the SE for people aged 65 and older.
And the standard error is: .
In this particular situation, the results from all three ways are the same. So no matter which way you use, you will obtain the same approximation for the standard error. This is not always the case. The corresponding section in the ACS Accuracy of the Data document gives an example where the three methods yield different answers. It is located at http://www.census.gov/acs/www/Downloads/data_documentation/Accuracy/ACS_Accuracy_of_Data_2011.pdf.
C. For an alternative to approximating the standard error for people 65 years and older seen in part B, we could find the estimate and its SE by summing all of the estimate for the ages less than 65 years old and subtracting them from the estimate for the total population. Due to the large number of estimates, the Table C does not show all of the age groups. In addition, the estimates in the part of the table shaded gray were derived for the purposes of this example only and cannot be found in base table B01001.
Table C: Some Estimates from AFF Table B01001: Sex by Age for 2009:
An estimate for the number of people age 65 and older is equal to the total population minus the population between the ages of zero and 64 years old:
Number of people aged 65 and older: 3,967,288 - 3,407,886 = 559,402.
The way to approximate the SE is the same as in part B. First we will sum males and females estimates across each age category and then approximate the MOEs. We will use that information to approximate the standard error for our estimate of interest:
... etc. ...
And the SE for the total number of people aged 65 and older is:
Again, as in Example B, the estimate and its MOE are published in table B09017. The total number of people aged 65 or older is 559,402 with a margin of error of 2,595. Therefore the standard error is:
SE(559,402) = 2,595 / 1.645 = 1,578.
The approximated standard error using the thirteen derived age group estimates yields a standard error roughly 11.5 times larger than the actual SE.
Data users can mitigate the problems shown in examples A through C to some extent by using a collapsed version of a detailed table if available which will reduce the number of estimates and SEs. These issues may also be avoided by creating estimates and SEs using the Public Use Microdata Sample (PUMS) or by requesting a custom tabulation, a fee-based service offered under certain conditions by the Census Bureau. More information regarding custom tabulations may be found at http://www.census.gov/acs/www/data_documentation/custom_tabulations/.
A. Suppose we wish to estimate the total number of males using age and citizenship status. The relevant data from table B05003 is displayed in table A below.
Table A: 2009 Estimates of males from B05003: Sex by Age by Citizenship Status
| Characteristic | Estimate | MOE |
| Male | 1905314 | 2,996 |
| Under 18 Years | 493406 | 2,300 |
| Native | 491306 | 2,312 |
| Foreign Born | 2100 | 697 |
| Naturalized U.S. Citizen | 94 | 163 |
| Not a U.S. Citizen | 2006 | 687 |
| 18 Years and Older | 1411908 | 1,572 |
| Native | 1363983 | 4,197 |
| Foreign Born | 47925 | 4,108 |
| Naturalized U.S. Citizen | 19549 | 2,150 |
| Not a U.S. Citizen | 28376 | 3,290 |
The estimate and its MOE are actually published. However, if they were not available in the tables, one way of obtaining them would be to add together the number of males under 18 and over 18 to get:
And the first approximated
Another way would be to add up the estimates for the three subcategories (Native, Foreign Born: Naturalized U.S. Citizen, and Foreign Born: Not a U.S. Citizen), for males under and over 18 years of age. From these eight estimates we obtain:
With a second approximation to the SE of:
We do know that the standard error using the published MOE is 2,996 / 1.645 = 1,821.3. With a quick glance, we can see that the ratio of the standard error of the first method to the published-based standard error yields 0.93; an under-estimate of roughly 7%, whereas the second method yields a ratio of 2.08 or an over-estimate of 108%. This is an example of what could happen to the approximate SE when the sum involves a controlled estimate. In this case, it is sex by age.
B. Suppose we are interested in the total number of people aged 65 or older and its standard error. Table B shows some of the estimates for the national level from table B01001 (the estimates in gray were derived for the purpose of this example only).
Table B: Some Estimates from AFF Table B01001: Sex by Age for 2009
| Age Category | Estimate, Male | MOE, Male | Estimate, Female | MOE, Female | Total | Estimated MOE, Total |
| 65 and 66 years old | 34,387 | 2,210 | 44,262 | 2,719 | 78,649 | 3,504 |
| 67 to 69 years old | 47,639 | 2,418 | 51,547 | 2,511 | 99,186 | 3,486 |
| 70 to 74 years old | 58,924 | 2,576 | 77,508 | 3,335 | 136,432 | 4,214 |
| 75 to 79 years old | 42,091 | 1,912 | 58,788 | 2,639 | 100,879 | 3,259 |
| 80 to 84 years old | 31,494 | 2,237 | 41,300 | 2,418 | 72,794 | 3,294 |
| 85 years and older | 26,399 | 1,790 | 45,063 | 2,595 | 71,462 | 3,152 |
| Total | 240,934 | NA | 318,468 | NA | 559,402 | 8,579 |
To begin we find the total number of people aged 65 and over by simply adding the totals for males and females to get 240,934 + 318,468 = 559,402. One way we could approximate the standard error of this estimate is to sum the males and females for each age category and then use their MOEs to obtain the approximate standard error for the total number of people over 65.
... etc. ...
Now, we calculate the number of people aged 65 or older, 559,402, using the six derived estimates and approximate its standard error:
For this example the estimate and MOE are published in table B09017. The total number of people aged 65 or older is 559,402 with a margin of error of 2,595. Therefore the published-based standard error is:
SE(559,402) = 2,595/1,645 = 1,578
The approximated standard error, using the six derived age group estimates, yields an approximated standard error roughly 3.3 times larger than the published-based standard error.
There are two additional ways to approximate the standard error of people aged 65 and over in addition to the way used above. The first is to find the MOEs for the males age 65 and older, and females aged 65 and older separately and then combine them to find the approximated standard error.
For males it is:
And for females:
And the total is then:
The second is to use all twelve of the published estimates together, that is, all estimates from the male age categories and female age categories, to create the SE for people aged 65 and older.
And the standard error is: .
In this particular situation, the results from all three ways are the same. So no matter which way you use, you will obtain the same approximation for the standard error. This is not always the case. The corresponding section in the ACS Accuracy of the Data document gives an example where the three methods yield different answers. It is located at http://www.census.gov/acs/www/Downloads/data_documentation/Accuracy/ACS_Accuracy_of_Data_2011.pdf.
C. For an alternative to approximating the standard error for people 65 years and older seen in part B, we could find the estimate and its SE by summing all of the estimate for the ages less than 65 years old and subtracting them from the estimate for the total population. Due to the large number of estimates, the Table C does not show all of the age groups. In addition, the estimates in the part of the table shaded gray were derived for the purposes of this example only and cannot be found in base table B01001.
Table C: Some Estimates from AFF Table B01001: Sex by Age for 2009:
| Age Category | Estimate, Male | MOE, Male | Estimate, Female | MOE, Female | Total | Estimated MOE, Total |
| Total Population | 1,905,314 | 2,996 | 2,061,974 | 2,996 | 3,967,288 | 4,237 |
| Under 5 years | 119,417 | 1,668 | 114,240 | 1,654 | 233,657 | 2,349 |
| 5 to 9 years old | 132,539 | 4,060 | 118,361 | 3,987 | 250,900 | 5,690 |
| 10 to 14 years old | 150,027 | 4,079 | 150,448 | 4,085 | 300,475 | 5,773 |
| ... | ... | ... | ... | ... | ... | ... |
| 62 to 64 years old | 61,327 | 3,232 | 73,135 | 3,368 | 134,462 | 4,668 |
| Total for Age 0 to 64 years old | 1,664,380 | 11,945 | 1,743,506 | 12,948 | 3,407,886 | 17,617 |
An estimate for the number of people age 65 and older is equal to the total population minus the population between the ages of zero and 64 years old:
Number of people aged 65 and older: 3,967,288 - 3,407,886 = 559,402.
The way to approximate the SE is the same as in part B. First we will sum males and females estimates across each age category and then approximate the MOEs. We will use that information to approximate the standard error for our estimate of interest:
... etc. ...
And the SE for the total number of people aged 65 and older is:
Again, as in Example B, the estimate and its MOE are published in table B09017. The total number of people aged 65 or older is 559,402 with a margin of error of 2,595. Therefore the standard error is:
SE(559,402) = 2,595 / 1.645 = 1,578.
The approximated standard error using the thirteen derived age group estimates yields a standard error roughly 11.5 times larger than the actual SE.
Data users can mitigate the problems shown in examples A through C to some extent by using a collapsed version of a detailed table if available which will reduce the number of estimates and SEs. These issues may also be avoided by creating estimates and SEs using the Public Use Microdata Sample (PUMS) or by requesting a custom tabulation, a fee-based service offered under certain conditions by the Census Bureau. More information regarding custom tabulations may be found at http://www.census.gov/acs/www/data_documentation/custom_tabulations/.