Starting in mid-July of 2020, despite many delays due to Covid-19, census takers began interviewing households who had not yet responded online or via the mail to the U.S. 2020 Census. The federal census, required by the United States’ Constitution, happens once every 10 years and each time, there are new variations in enumeration (counting) techniques and what statistical data to collect. There are processes around “how” to count and then also “what” to count; the data collected needs to be useful for governance and allocation yet also respectful of privacy and remain fair and impartial for the entire U.S. population. In 2019 and 2020, hundreds of thousands of temporary workers from local communities were hired to go out into the field as census takers as well as staff offices and provide supervision. This 22nd federal census count began in January 2020 with remote portions of Alaska, where the territory was still frozen and traversable. These employed citizens are just one aspect of how the census is truly a community event. Let’s dive into the history of the U.S. Census and also learn why this count is so important.

Historical population as enumerated and corrected from 1790 through 2020. North Carolina was one of the 13 original States and by the time of the 1790 census had essentially its current boundaries. The Census is mandated by the United States Constitution and was first completed for 1790. The population has been counted every ten years hence, with some limitations. In 1790 census coverage included most of the State, except for areas in the west, parts of which were not enumerated until 1840. The population for 1810 includes Walton County, enumerated as part of Georgia although actually within North Carolina. Historical populations shown here reflect the population of the respective named county and not necessarily the population of the area of the county as it was defined for a particular census. County boundaries shown in maps reflect boundaries as defined in 2020. Historic boundaries for some counties may include additional geographic areas or may be smaller than the current geographic boundaries. Notes below list the county or counties with which the population of a currently defined county were enumerated historically (Current County: Population counted in). The current 100 counties have been in place since the 1920 Census, although some modifications to the county boundaries have occurred since that time. For historical county boundaries see: Atlas of Historical County Boundaries Project (newberry.org)County Notes: Note 1: Total for 1810 includes population (1,026) of Walton County, reported as a Georgia county but later determined to be situated in western North Carolina. Total for 1890 includes 2 Indians in prison, not reported by county. Note 2: Alexander: *Iredell, Burke, Wilkes. Note 3: Avery: *Caldwell, Mitchell, Watauga. Note 4: Buncombe: *Burke, Rutherford; see also note 22. Note 5: Caldwell: *Burke, Wilkes, Yancey. Note 6: Cleveland: *Rutherford, Lincoln. Note 7: Columbus: *Bladen, Brunswick. Note 8: Dare: *Tyrrell, Currituck, Hyde. Note 9: Hoke: *Cumberland, Robeson. Note 10: Jackson: *Macon, Haywood. Note 11: Lee: *Moore, Chatham. Note 12: Lenoir: *Dobbs (Greene); Craven. Note 13: McDowell: *Burke, Rutherford. Note 14: Madison: *Buncombe, Yancey. Note 15: Mitchell: *Yancey, Watauga. Note 16: Pamlico: *Craven, Beaufort. Note 17: Polk: *Rutherford, Henderson. Note 18: Swain: *Jackson, Macon. Note 19: Transylvania: *Henderson, Jackson. Note 20: Union: *Mecklenburg, Anson. Note 21: Vance: *Granville, Warren, Franklin. Note 22: Walton: Created in 1803 as a Georgia county and reported in 1810 as part of Georgia; abolished after a review of the State boundary determined that its area was located in North Carolina. By 1820 it was part of Buncombe County. Note 23: Watauga: *Ashe, Yancey, Wilkes; Burke. Note 24: Wilson: *Edgecombe, Nash, Wayne, Johnston. Note 25: Yancey: *Burke, Buncombe. Note 26: Alleghany: *Ashe. Note 27: Haywood: *Buncombe. Note 28: Henderson: *Buncombe. Note 29: Person: Caswell. Note 30: Clay: Cherokee. Note 31: Graham: Cherokee. Note 32: Harnett: Cumberland. Note 33: Macon: Haywood.

Note 34: Catawba: Lincoln. Note 35: Gaston: Lincoln. Note 36: Cabarrus: Mecklenburg.
Note 37: Stanly: Montgomery. Note 38: Pender: New Hanover. Note 39: Alamance: Orange.
Note 40: Durham: Orange, Wake. Note 41: Scotland: Richmond. Note 42: Davidson: Rowan. Note 43: Davie: Rowan.Note 44: Forsyth: Stokes. Note 45: Yadkin: Surry.
Note 46: Washington: Tyrrell.Note 47: Ashe: Wilkes. Part III. Population of Counties, Earliest Census to 1990The 1840 population of Person County, NC should be 9,790. The 1840 population of Perquimans County, NC should be 7,346.

Initially taken in 1838 to demonstrate the stability and significance of the African American community and to forestall the abrogation of African American voting rights, the Quaker and Abolitionist census of African Americans was continued in 1847 and 1856 and present an invaluable view of the mid-nineteenth century African American population of Philadelphia. Although these censuses list only household heads, providing aggregate information for other household members, and exclude the substantial number of African Americans living in white households, they provide data not found in the federal population schedules. When combined with the information on African Americans taken from the four federal censuses, they offer researchers a richly detailed view of Philadelphia's African American community spanning some forty years. The three censuses are not of equal inclusiveness or quality, however. The 1838 and 1847 enumerations cover only the "old" City of Philadelphia (river-to-river and from Vine to South Streets) and the immediate surrounding districts (Spring Garden, Northern Liberties, Southwark, Moyamensing, Kensington--1838, West Philadelphia--1847); the 1856 survey includes African Americans living throughout the newly enlarged city which, as today, conforms to the boundaries of Philadelphia County. In spite of this deficiency in areal coverage, the earlier censuses are superior historical documents. The 1838 and 1847 censuses contain data on a wide range of social and demographic variables describing the household indicating address, household size, occupation, whether members were born in Pennsylvania, status-at-birth, debts, taxes, number of children attending school, names of beneficial societies and churches (1838), property brought to Philadelphia from other states (1838), sex composition (1847), age structure (1847), literacy (1847), size of rooms and number of people per room (1847), and miscellaneous remarks (1847). While the 1856 census includes the household address and reports literacy, occupation, status-at-birth, and occasional passing remarks about individual households and their occupants, it excludes the other informational categories. Moreover, unlike the other two surveys, it lists the occupations of only higher status African Americans, excluding unskilled and semiskilled designations, and records the status-at-birth of adults only. Indeed, it even fails to provide data permitting the calculation of the size and age and sex structure of households. Variables for each household head and his household include (differ slightly by census year): name, sex, status-at-birth, occupation, wages, real and personal property, literacy, education, religion, membership in beneficial societies and temperance societies, taxes, rents, dwelling size, address, slave or free birth.

Context. This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 million cells.

Content. The data published in this repository correspond to a first release, i.e. a diachronic slice of one register every 8 to 9 years. Unfortunately, the remaining data are currently under embargo. Their publication will take place as soon as possible, and at the latest by the end of 2023. In the meantime, the data presented here correspond to a large subset of 2,844 pages, which already allows to investigate most research hypotheses.

Description. The population censuses, digitized by the Archives of the city of Lausanne, continuously cover the evolution of the population in Lausanne throughout the 19th century, starting in 1805, with only one long interruption from 1814 to 1831. Highly detailed, they are an invaluable source for studying migration, economic and social history, and traces of cultural exchanges not only with Bern, but also with France and Italy. Indeed, the system of tracing family origin, specific to Switzerland, allows to follow the migratory movements of families long before the censuses appeared. The bourgeoisie is also an essential economic tracer. In addition, censuses extensively describe the organization of the social fabric into family nuclei, around which gravitate various boarders, workers, servants or apprentices, often living in the same apartment with the family.

Production. The structure and richness of censuses have also provided an opportunity to develop automatic methods for processing structured documents. The processing of censuses includes several steps, from the identification of text segments to the restructuring of information as digital tabular data, through Handwritten Text Recognition and the automatic segmentation of the structure using neural networks. Please note that the detailed extraction methodology, as well as the complete evaluation of performance and reliability is published in:

Petitpierre R., Rappo L., Kramer M. (2023). An end-to-end pipeline for historical censuses processing. International Journal on Document Analysis and Recognition (IJDAR). doi: 10.1007/s10032-023-00428-9

Data structure. The data are structured in rows and columns, with each row corresponding to a household. Multiple entries in the same column for a single household are separated by vertical bars ⟨|⟩. The center point ⟨·⟩ indicates an empty entry. For some columns (e.g., street name, house number, owner name), an empty entry indicates that the last non-empty value should be carried over. The page number is in the last column.

Liability. The data presented here are not curated nor verified. They are the raw results of the extraction, the reliability of which was thoroughly assessed in the above-mentioned publication. We insist on the fact that for any reuse of this data for research purposes, the implementation of an appropriate methodology is necessary. This may typically include string distance heuristics, or statistical methodologies to deal with noise and uncertainty.

The data sets in this repository allow users to link people among the U.S. decennial censuses, using the "histid" identifier. The census data sets users will need are indexed by Ancestry.com and are hosted by IPUMS at https://usa.ipums.org/usa-action/samples. Users will need to download the full-count census for each year and be sure to select the "histid" variable that is available under the Person/Historical Technical drop-down menu.As of 7/12/21, links are available between the 1900-1910, 1910-1920, and 1900-1920 censuses.A detailed account of how these links are created and a description of the data and its characteristics are available in the following article:Price, J., Buckles, K., Van Leeuwen, J., & Riley, I. (2021). Combining family history and machine learning to link historical records: The Census Tree data set. Explorations in Economic History, 80, 101391.https://www.sciencedirect.com/science/article/pii/S0014498321000024

Historical population counts for municipalities in the State of Vermont (1791-2020) compiled by the Vermont Historical Society (years 1791-2010) then appended with 2020 Census counts.An attempt was made to convert counts to current town names to allow for analyses of population change of an area over time. The Historical Society notes, “For example, the census numbers from Kellyvale are counted as the town of Lowell because the name was changed in 1831. Cabot is included in Washington County records, even though it was in Caledonia County through the 1850 census.” This does create some issues where there are changes in geography such as boundary changes, annexations, and new incorporations (such as Rutland City splitting off from Rutland Town).The Historical Society collected the data from a variety of sources.The 1791-2010 data was extracted from PDF’s by VCGI Open Data Fellow Kendal Fortney in 2017.

The Census Tree is the largest-ever database of record links among the historical U.S. censuses, with over 700 million links for people living in the United States between 1850 and 1940. These links allow researchers to construct a longitudinal dataset that is highly representative of the population, and that includes women, Black Americans, and other under-represented populations at unprecedented rates. Each .csv file consists of a crosswalk between the two years indicated in the filename, using the IPUMS histids. For more information, consult the included Read Me file, and visit https://censustree.org.

This training dataset includes a total of 34,913 manually transcribed text segments. It is dedicated to the handwritten text recognition (HTR) of historical sources, typically tabular records, such as censuses. This dataset is based on a sample of 83 pages from the 19th century (1805-1898) censuses of Lausanne, Switzerland. The primary language of the documents is French, although many germanic names and toponyms are also found.

The training data are formatted and provided on the model of the Bentham dataset. The format thus simply consists in a list of jpeg images, one per text segments, and their corresponding transcription, stored in a txt file. The file naming convention is 'yyyy-ppp-n', where 'y' stands for the year of publication of the census, and 'p' for the page number.

The digitized documents are provided by the Archives of the City of Lausanne.

Please note that the annotation and extraction methodology, as well as the complete evaluation of performance, including HTR benchmark and post-correction performance is published in :

Petitpierre R., Rappo L., Kramer M. (2023). An end-to-end pipeline for historical censuses processing. International Journal on Document Analysis and Recognition (IJDAR). doi: 10.1007/s10032-023-00428-9

Tabular dataset resulting from automatic extraction are also available on Zenodo :

Petitpierre R., Rappo L., Kramer M., di Lenardo I. (2023). 1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History. Zenodo. doi: 10.5281/zenodo.7711640

The 1940 Census Public Use Microdata Sample Project was assembled through a collaborative effort between the United States Bureau of the Census and the Center for Demography and Ecology at the University of Wisconsin. The collection contains a stratified 1-percent sample of households, with separate records for each household, for each "sample line" respondent, and for each person in the household. These records were encoded from microfilm copies of original handwritten enumeration schedules from the 1940 Census of Population. Geographic identification of the location of the sampled households includes Census regions and divisions, states (except Alaska and Hawaii), standard metropolitan areas (SMAs), and state economic areas (SEAs). Accompanying the data collection is a codebook that includes an abstract, descriptions of sample design, processing procedures and file structure, a data dictionary (record layout), category code lists, and a glossary. Also included is a procedural history of the 1940 Census. Each of the 20 subsamples contains three record types: household, sample line, and person. Household variables describe the location and condition of the household. The sample line records contain variables describing demographic characteristics such as nativity, marital status, number of children, veteran status, wage deductions for Social Security, and occupation. Person records also contain variables describing demographic characteristics including nativity, marital status, family membership, education, employment status, income, and occupation. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08236.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This component of the Philadelphia Social History Project examines the demographic composition of city grid squares using census data from years 1850, 1860, 1870, and 1880. The collection consists of two types of data files: (1) grid tallies, and (2) grid dictionaries. The grid tally files consist of counts of individuals living in PSHP grid squares, with totals broken down by race/ethnicity, sex, and age. The grid dictionary files link lines in the census manuscripts to PSHP grid squares, allowing users to follow the movements of census-takers as they moved house-to-house on foot, adding individuals to the printed census manuscript forms. The "grid" network consists of a set of vertical and horizontal lines drawn at fixed intervals across a city map, forming the foundation for the spatial organization of the data. The grid dictionary files show when census-takers crossed from one grid square to another; each row in the grid dictionary describes a set of rows that are in a specific grid square by listing the starting page/line and the ending page/line.

This data collection contains detailed county and state-level ecological and descriptive data for the United States for the years 1790 to 2002. Parts 1-43 are an update to HISTORICAL, DEMOGRAPHIC, ECONOMIC, AND SOCIAL DATA: THE UNITED STATES, 1790-1970 (ICPSR 0003). Parts 1-41 contain data from the 1790-1970 censuses. They include extensive information about the social and political character of the United States, including a breakdown of population by state, race, nationality, number of families, size of the family, births, deaths, marriages, occupation, religion, and general economic condition. Parts 42 and 43 contain data from the 1840 and 1870 Censuses of Manufacturing, respectively. These files include information about the number of persons employed in various industries and the quantities of different types of manufactured products. Parts 44-50 provide county-level data from the United States Census of Agriculture for 1840 to 1900. They also include the state and national totals for the variables. The files provide data about the number, types, and prices of various agricultural products. Parts 51-57 contain data on religious bodies and church membership for 1906, 1916, 1926, 1936, and 1952, respectively. Parts 58-69 consist of data from the CITY DATA BOOKS for 1944, 1948, 1952, 1956, 1962, 1967, 1972, 1977, 1983, 1988, 1994, and 2000, respectively. These files contain information about population, climate, housing units, hotels, birth and death rates, school enrollment and education expenditures, employment in various industries, and city government finances. Parts 70-81 consist of data from the COUNTY DATA BOOKS for 1947, 1949, 1952, 1956, 1962, 1967, 1972, 1977, 1983, 1988, 1994, and 2000, respectively. These files include information about population, employment, housing, agriculture, manufacturing, retail, services, trade, banking, Social Security, local governments, school enrollment, hospitals, crime, and income. Parts 82-84 contain data from USA COUNTIES 1998. Due to the large number of variables from this source, the data were divided into into three separate data files. Data include information on population, vital statistics, school enrollment, educational attainment, Social Security, labor force, personal income, poverty, housing, trade, farms, ancestry, commercial banks, and transfer payments. Parts 85-106 provide data from the United States Census of Agriculture for 1910 to 2002. They provide data about the amount, types, and prices of various agricultural products. Also, these datasets contain extensive information on the amount, expenses, sales, values, and production of farms and machinery. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR02896.v3. We highly recommend using the ICPSR version, as they made this dataset available in multiple data formats and updated the data through 2002.

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin; for the United States, States, Counties; and for Puerto Rico and its Municipios: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

BackgroundAddressing contemporary anti-Asian racism and its impacts on health requires understanding its historical roots, including discriminatory restrictions on immigration, citizenship, and land ownership. Archival secondary data such as historical census records provide opportunities to quantitatively analyze structural dynamics that affect the health of Asian immigrants and Asian Americans. Census data overcome weaknesses of other data sources, such as small sample size and aggregation of Asian subgroups. This article explores the strengths and limitations of early twentieth-century census data for understanding Asian Americans and structural racism.MethodsWe used California census data from three decennial census spanning 1920–1940 to compare two criteria for identifying Asian Americans: census racial categories and Asian surname lists (Chinese, Indian, Japanese, Korean, and Filipino) that have been validated in contemporary population data. This paper examines the sensitivity and specificity of surname classification compared to census-designated “color or race” at the population level.ResultsSurname criteria were found to be highly specific, with each of the five surname lists having a specificity of over 99% for all three census years. The Chinese surname list had the highest sensitivity (ranging from 0.60–0.67 across census years), followed by the Indian (0.54–0.61) and Japanese (0.51–0.62) surname lists. Sensitivity was much lower for Korean (0.40–0.45) and Filipino (0.10–0.21) surnames. With the exception of Indian surnames, the sensitivity values of surname criteria were lower for the 1920–1940 census data than those reported for the 1990 census. The extent of the difference in sensitivity and trends across census years vary by subgroup.DiscussionSurname criteria may have lower sensitivity in detecting Asian subgroups in historical data as opposed to contemporary data as enumeration procedures for Asians have changed across time. We examine how the conflation of race, ethnicity, and nationality in the census could contribute to low sensitivity of surname classification compared to census-designated “color or race.” These results can guide decisions when operationalizing race in the context of specific research questions, thus promoting historical quantitative study of Asian American experiences. Furthermore, these results stress the need to situate measures of race and racism in their specific historical context.

An appreciation of historical landuse and its effects is crucial when interpreting the structure, composition, and spatial characteristics of modern forests. The Harvard Forest has compiled many different historical data sources in an ongoing effort to understand how anthropogenic disturbances have shaped our modern landscapes. Estimates of town land use and land cover were gathered from a variety of sources, including tax valuations (1801-1860) and state agricultural census records (1865-1905). Data prior to 1801 rarely cover the entire state and are excluded from these datasets. Data on forest structure are available for several time periods, including 1885 and 1895 (Agricultural Censuses) and 1916-1920s (State Forester’s reports).

This dataset, generated on 2020-09-29, contains all IPv4 addresses who ever responded to our ISI-west (w) censuses, starting from census it11w (internet_address_survey_it11w-20060307), e.g. it11w, it12w, ... Each IP address is given a history of replies (as a bit-string with 's representing ICMP_ECHO_REPLY received from this IP, and 's corresponding to no-replies. We also include numerical scores that are intended to reflect the likelihood that the given IP address will repond in the future. The scores are based on census data from it76w to it91w.

Alcobendas City Council. Official population data of Alcobendas by census sections since 2014. The figures refer to 1 January of each year.

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators.

Latest edition information

For the second edition (October 2022), the data and documentation have been revised.

(1) This hierarchical file contains 202,112 records. There are approximately 157 variables and two record types: family and person. Family records contain approximately 58 variables, and person records contain approximately 99 variables. (2) Each family and person record contains a weight, which must be used in any analysis. (3) This data file was obtained from the Data Program and Library Service (DPLS), University of Wisconsin. Some data management operations intended to store the data more efficiently were performed by DPLS. That organization also revised the original Census Bureau documentation. (4) The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site. This data collection supplies standard monthly labor force data as well as supplemental data on work experience, income, and migration. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Information on demographic characteristics, such as age, sex, race, educational attainment, marital status, veteran status, household relationship, and Hispanic origin, is available for each person in the household enumerated. Persons in the civilian noninstitutional population of the United States living in households and members of the armed forces living in civilian housing units in 1969. Datasets: DS1: Current Population Survey: Annual Demographic File, 1969 A national probability sample was used in selecting housing units.