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 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.
All manuscripts (and other items you'd like to publish) must be submitted to
phsdatacore@stanford.edu for approval prior to journal submission.
We will check your cell sizes and citations.
For more information about how to cite PHS and PHS datasets, please visit:
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This dataset was created on 2020-01-10 22:52:11.461
by merging multiple datasets together. The source datasets for this version were:
IPUMS 1930 households: This dataset includes all households from the 1930 US census.
IPUMS 1930 persons: This dataset includes all individuals from the 1930 US census.
IPUMS 1930 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.
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 1930 census data was collected in April 1930. 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 SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.
Coded variables derived from string variables are still in progress. These variables include: occupation and industry.
Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGEMARR, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, FARM, EMPSTAT, OCC1950, IND1950, MTONGUE, MARST, RACE, SEX, RELATE, CLASSWKR. 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 edite
Census Year 1930 Census Tracts. The dataset contains polygons representing CY 1930 census tracts, created as part of the D.C. Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Census tracts were identified from maps provided by the U.S. Census Bureau and the D.C. Office of Planning. The tract polygons were created by selecting street arcs from the WGIS planimetric street centerlines. Where necessary, polygons were also heads-up digitized from 1995/1999 orthophotographs. METADATA CONTENT IS IN PROCESS OF VALIDATION AND SUBJECT TO CHANGE.
1930 United States Federal Census contains records from Ambler, Montgomery, Pennsylvania, USA by Year: 1930; Census Place: Ambler, Montgomery, Pennsylvania; Page: 7A; Enumeration District: 0013; FHL microfilm: 2341814 - .
This dataset includes all households from the 1930 US census.
1930 United States Federal Census contains records from Montpelier, Washington, Vermont, USA by Ancestry.com. 1930 United States Federal Census [database on-line]. Provo, UT, USA: Ancestry.com Operations Inc, 2002.; Year: 1930; Census Place: Montpelier, Washington, Vermont; Page: 11B; Enumeration District: 0023; FHL microfilm: 2342165; Original data: United States of America, Bureau of the Census. Fifteenth Census of the United States, 1930. Washington, D.C.: National Archives and Records Administration, 1930. T626, 2,667 rolls. - .
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Census 1930. Density of the population of each municipality of the Netherlands, in the 9 groups of municipalities, the 11 provinces and the Netherlands as a whole according to the situation on 31 December 1930. The data are taken from Part 1, Tables VII, VIII and IX. Data available for: 1930 Status of the figures: The data in this table are final. Changes as of June 1, 2018: None, this table has been discontinued. When will new numbers come out? Not applicable anymore.
This dataset includes all individuals from the 1930 US census.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This is the census data collected for Austin, Texas in 1930.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This is the shapefile of the mapped 1930 census data for Austin, Texas.
1930 United States Federal Census contains records from Swanzey, Cheshire, New Hampshire, USA by Census Place: Swanzey, Cheshire, New Hampshire; Page: 7B; Enumeration District: 0029; FHL microfilm: 2341034 - .
This map depicts US Census data from the 1930 decennial census for total population and race
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The 1930 census. Division of the population of the various congregations, the groups of municipalities, the provinces and the Empire by sex and marital status.
The data are derived from Part 2, Table I.
Data available for: 1930
Status of the figures: The data in this table are final.
Changes as of 1 June 2018: None, this table has been discontinued.
When are new figures coming? No longer applicable.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
This application displays the buildings in State College borough in 1930 as polygon features. The buildings are linked to a table with the contents of the 1930 Census of State College. Click on a building to bring up information about its physical features, such as building material or number of floors, as well as its address and associated land use. If the building contained residents listed on the Census, scroll down within the info box and click on the link below "Related Tables" to bring up a list of the residents. Clicking on a resident in the list will open that resident's entry in the Census table, which includes socioeconomic information such as their name, age, nationality, marital status, and occupation. Residents can also be searched for by name in the Query box that appears on the left side of the screen. Data Sources- Scanned copies of the U.S. Census for various years (including 1920 and 1930) available from Ancestry Library Edition database.- Sanborn shapefiles were created by Bednar student interns at Penn State's Pattee/Paterno Library. They are based on the collection of PA Sanborns housed in the Maps Collection at the library.
https://lida.dataverse.lt/api/datasets/:persistentId/versions/2.3/customlicense?persistentId=hdl:21.12137/M1M4QKhttps://lida.dataverse.lt/api/datasets/:persistentId/versions/2.3/customlicense?persistentId=hdl:21.12137/M1M4QK
This dataset contains data on population by sex and age on the basis of the results of the Census Data of Latvia, which was carried out on 24 February 1930. Dataset "Latvian Population by Sex and Age in 1930 Census Data" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".
This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.
https://d-repo.ier.hit-u.ac.jp/statistical-ybhttps://d-repo.ier.hit-u.ac.jp/statistical-yb
PERIOD: Oct. 1, 1920, Oct. 1, 1930. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet].
This dataset was created by Alex Hoffman
The 3rd Population Census. In order to clarify the state of Japan’s population and households, the population census has been conducted in Japan almost every five years.More details on the "Population Census of Japan" overall including other years can be found here: https://d-infra.ier.hit-u.ac.jp/Japanese/statistical-yb/b001.html. The census introduced separate classifications for the type of occupation and the industry of occupation.
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 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.
All manuscripts (and other items you'd like to publish) must be submitted to
phsdatacore@stanford.edu for approval prior to journal submission.
We will check your cell sizes and citations.
For more information about how to cite PHS and PHS datasets, please visit:
https:/phsdocs.developerhub.io/need-help/citing-phs-data-core
This dataset was created on 2020-01-10 22:52:11.461
by merging multiple datasets together. The source datasets for this version were:
IPUMS 1930 households: This dataset includes all households from the 1930 US census.
IPUMS 1930 persons: This dataset includes all individuals from the 1930 US census.
IPUMS 1930 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.
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 1930 census data was collected in April 1930. 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 SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.
Coded variables derived from string variables are still in progress. These variables include: occupation and industry.
Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGEMARR, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, FARM, EMPSTAT, OCC1950, IND1950, MTONGUE, MARST, RACE, SEX, RELATE, CLASSWKR. 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 edite