Website alows the public full access to the 1940 Census images, census maps and descriptions.

The 1940 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1940, although some persons were missed. The 1940 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 2, 2012. The 1940 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tracts, and related boundaries and numbers used for each census. The coverage is nation wide and includes territorial areas. The 1940 Census enumeration district descriptions contain written descriptions of census districts, subdivisions, and enumeration districts.

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The IPUMS microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

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Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1940 census data was collected in April 1940. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.
• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT40, reconstructed using the variable SERIAL40, and the original count is found in the variable NUMPREC40.
• Some variables are missing from this data set for specific enumeration districts. The enumeration districts with missing data can be identified using the variable EDMISS. These variables will be added in a future release.
• Coded variables derived from string variables are still in progress. These variables include: occupation, industry and migration status.
• Missing observations have been allocated and some inconsistencies have been edited for the following variables: Missing observations have been allocated and some inconsistencies have been edited for the following variables: SURSIM, SEX, SCHOOL, RELATE, RACE, OCC1950, MTONGUE, MBPL, FBPL, BPL, MARST, EMPSTAT, CITIZEN, OWNERSHP. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.
• Most inconsistent information was not edited for this release, thus there are observations outside of the universe for many variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next r

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.

This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1940 datasets.

1940 Population Census Data for Baltimore, Maryland. Refer to the 1940 codebook (codebook_1940.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

This dataset includes all households from the 1940 US census.

This dataset includes all individuals from the 1940 US census.

1940 Ancestry Census Data for Baltimore, Maryland. Refer to the 1940 codebook (codebook_1940.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

The CenSoc WWII Army Enlistment Dataset is a cleaned and harmonized version of the National Archives and Records Administration’s Electronic Army Serial Number Merged File, ca. 1938 - 1946 (2002). It contains enlistment records for over 9 million men and women who served in the United States Army, including the Army Air Corps, Women's Army Auxiliary Corps, and Enlisted Reserve Corps. We publish links between men in the CenSoc WWII Army Enlistment Dataset, Social Security Administration mortality data, and the 1940 Census. The CenSoc Enlistment-Census-1940 file links these enlistment records to the complete 1940 Census, and may be merged with IPUMS-USA census data using the HISTID identifier variable. The CenSoc Enlistment-Numident file links enlistment records to the Berkley Unified Numident Mortality Database (BUNMD), and the CenSoc Enlistment-DMF file links enlistment records to the Social Security Death Master File. For enlistment records in the Enlistment-Numident and Enlistment-DMF datasets that have been independently and additionally linked to the 1940 Census, we include the HISTID identifier variable that can be used to merge the data with IPUMS census data.

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted. These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities. The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package. The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or location because they do not fit well into the regional framework. Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values. The RMarkdown document SASAPWebsiteGraphicsCensus.Rmd is used to generate a variety of figures using these data, including the additional file Chignik_population.png. An additional set of 25 figures showing regional trends in population and income metrics are also included.

This crosswalk consists of individuals matched between the 1900 and 1940 complete-count US Censuses. Within the crosswalk, users have the option to select the linking method with which these matches were created. This version of the crosswalk contains links made by the ABE-exact (conservative and standard) method, the ABE-NYSIIS (conservative and standard) method and the ABE-NYSIIS (conservative and standard) method where race is used as a matching variable. For any chosen method, users can merge into this crosswalk a wide set of individual- and household-level variables provided publicly by IPUMS, thereby creating a historical longitudinal dataset for analysis.

1940 Employment Census Data for Baltimore, Maryland. Refer to the 1940 codebook (codebook_1940.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

1940 Education Census Data for Baltimore, Maryland. Refer to the 1940 codebook (codebook_1940.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

This study matches Canadian and US manufacturing industries at the 2-digit SIC code level for census years 1900 to 1940. Canadian figures start at 1870. Only general figures were recorded, such as number of employees, number of establishments, salary and wages, gross production, cost of input materials, gross value added. The project does have some drawbacks, such as the lack of US figures gross production, cost of materials, and lack of figures for the iron and steel industry. But for an aggregate comparison of the two countries, the numbers can be considered reliable.

1940 Dwellings Census Data for Baltimore, Maryland. Refer to the 1940 codebook (codebook_1940.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Graph and download economic data for Total Households (TTLHH) from 1940 to 2024 about household survey, households, and USA.

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

In 1844, Romania had a population of just 3.6 million people. During the early entries in this data, Romania's borders were very different and much smaller than today, and control of this area often switched hands between the Austrian, Ottoman and Russian empires. The populations during this time are based on estimates made for incomplete census data, and they show that the population grows from 3.6 million in 1844, doubling to 7.2 million in 1912, part of this growth is due to a high natural birth rate during this period, but also partly due to the changing of Romania's borders and annexation of new lands. During this time Romania gained its independence from the Ottoman Empire as a result of the Russo-Turkish War in 1878, and experienced a period of increased stability and progress.

Between 1912 and 1930 the population of Romania grew by over 10 million people. The main reason for this is the huge territories gained by Romania in the aftermath of the First World War. During the war Romania remained neutral for the first two years, after which it joined the allies; however, it was very quickly defeated and overrun by the Central Powers, and in total it lost over 600 thousand people as a direct result of the war. With the collapse of the Austro-Hungarian and Russian empires after the war, Romania gained almost double it's territory, which caused the population to soar to 18.1 million in 1930. The population then decreases by 1941 and again by 1948, as Romania seceded territory to neighboring countries and lost approximately half a million people during the Second World War. From 1948 onwards the population begins to grow again, reaching it's peak at 23.5 million people in 1990.

Like many other Eastern European countries, there was very limited freedom of movement from Romania during the Cold War, and communist rule was difficult for the Romanian people. The Romanian Revolution in 1989 ended communist rule in the country, Romania transitioned to a free-market society and movement from the country was allowed. Since then the population has fallen each year as more and more Romanians move abroad in search of work and opportunities. The population is expected to fall to 19.2 million in 2020, which is over 4 million fewer people than it had in 1990.

Prior to 1829, the area of modern day Greece was largely under the control of the Ottoman Empire. In 1821, the Greeks declared their independence from the Ottomans, and achieved it within 8 years through the Greek War of Independence. The Independent Kingdom of Greece was established in 1829 and made up the southern half of present-day, mainland Greece, along with some Mediterranean islands. Over the next century, Greece's borders would expand and readjust drastically, through a number of conflicts and diplomatic agreements; therefore the population of Greece within those political borders** was much lower than the population in what would be today's borders. As there were large communities of ethnic Greeks living in neighboring countries during this time, particularly in Turkey, and the data presented here does not show the full extent of the First World War, Spanish Flu Pandemic and Greko-Turkish War on these Greek populations. While it is difficult to separate the fatalities from each of these events, it is estimated that between 500,000 and 900,000 ethnic Greeks died at the hands of the Ottomans between the years 1914 and 1923, and approximately 150,000 died due to the 1918 flu pandemic. These years also saw the exchange of up to one million Orthodox Christians from Turkey to Greece, and several hundred thousand Muslims from Greece to Turkey; this exchange is one reason why Greece's total population did not change drastically, despite the genocide, displacement and demographic upheaval of the 1910s and 1920s. Greece in WWII A new Hellenic Republic was established in 1924, which saw a decade of peace and modernization in Greece, however this was short lived. The Greek monarchy was reintroduced in 1935, and the prime minister, Ioannis Metaxas, headed a totalitarian government that remained in place until the Second World War. Metaxas tried to maintain Greek neutrality as the war began, however Italy's invasion of the Balkans made this impossible, and the Italian army tried invading Greece via Albania in 1940. The outnumbered and lesser-equipped Greek forces were able to hold off the Italian invasion and then push them backwards into Albania, marking the first Allied victory in the war. Following a series of Italian failures, Greece was eventually overrun when Hitler launched a German and Bulgarian invasion in April 1941, taking Athens within three weeks. Germany's involvement in Greece meant that Hitler's planned invasion of the Soviet Union was delayed, and Hitler cited this as the reason for it's failure (although most historians disagree with this). Over the course of the war approximately eight to eleven percent of the Greek population died due to fighting, extermination, starvation and disease; including over eighty percent of Greece's Jewish population in the Holocaust. Following the liberation of Greece in 1944, the country was then plunged into a civil war (the first major conflict of the Cold War), which lasted until 1949, and saw the British and American-supported government fight with Greek communists for control of the country. The government eventually defeated the Soviet-supported communist forces, and established American influence in the Aegean and Balkans throughout the Cold War. Post-war Greece From the 1950s until the 1970s, the Marshall Plan, industrialization and an emerging Tourism sector helped the Greek economy to boom, with one of the strongest growth rates in the world. Apart from the military coup, which ruled from 1967 to 1974, Greece remained relatively peaceful, prosperous and stable throughout the second half of the twentieth century. The population reached 11.2 million in the early 2000s, before going into decline for the past fifteen years. This decline came about due to a negative net migration rate and slowing birth rate, ultimately facilitated by the global financial crisis of 2007 and 2008; many Greeks left the country in search of work elsewhere, and the economic troubles have impacted the financial incentives that were previously available for families with many children. While the financial crisis was a global event, Greece was arguably the hardest-hit nation during the crisis, and suffered the longest recession of any advanced economy. The financial crisis has had a consequential impact on the Greek population, which has dropped by 800,000 in 15 years, and the average age has increased significantly, as thousands of young people migrate in search of employment.