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 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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Documentation

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.

Section 2

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

This dataset includes all individuals from the 1930 US census.

This dataset includes all households from the 1930 US census.

This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

This crosswalk consists of individuals matched between the 1920 and 1930 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.

The Veterans’ Grandchildren Mortality Plus sample consists of the records of more than 35,700 total grandchildrenboth male and female in nearly equal numbers,about 28,000 of which survived to age 45,who were born after the war to 16,791 children of 2,825 veterans,and contains an oversample of ex-POW veterans.The primary purpose of the project was to explore how grandfathers’ trauma affects the longevity and overweight of descendants. The dataset contains birth and death dates of grandchildren, census information on their parents' household, select socioeconomic and education information from the 1930 and 1940 census, and height and weight information from WWII draft cards for the grandsons. This multigenerational dataset can be used for researching the intergenerational transmission of longevity, overweight and socioeconomic status and the sex-specific pathways of this transmission and for testing mechanical linkage algorithms. Researchers built on a previously collected NIA-funded project containing census and death information of children of ex-POW and non-POW veterans (“Early Indicators, Intergenerational Processes, and Aging,” NIA grant P01AG10120, PI: Costa). The Veterans’ Grandchildren Mortality Plus data set contains the newly collected records of the veterans’ grandchildren, as well as the previously collected data of the veterans and their children.

PLURAL (Place-level urban-rural indices) is a framework to create continuous classifications of "rurality" or "urbanness" based on the spatial configuration of populated places. PLURAL makes use of the concept of "remoteness" to characterize the level of spatial isolation of a populated place with respect to its neighbors. There are two implementations of PLURAL, including (a) PLURAL-1, based on distances to the nearest places of user-specified population classes, and (b) PLURAL-2, based on neighborhood characterization derived from spatial networks. PLURAL requires simplistic input data, i.e., the coordinates (x,y) and population p of populated places (villages, towns, cities) in a given point in time. Due to its simplistic input, the PLURAL rural-urban classification scheme can be applied to historical data, as well as to data from data-scarce settings. Using the PLURAL framework, we created place-level rural-urban indices for the conterminous United States from 1930 to 2018. Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. We developed a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. Based on these methods, we constructed indices of urbanness for 30,000 places in the United States from 1930 to 2018. We call these indices the place-level urban-rural index (PLURAL), enabling long-term, fine-grained analyses of urban and rural change in the United States. The method paper has been peer-reviewed and is published in "Landscape and Urban Planning". The PLURAL indices from 1930 to 2018 are available as CSV files, and as point-based geospatial vector data (.SHP). Moreover, we provide animated GIF files illustrating the spatio-temporal variation of the different variants of the PLURAL indices, illustrating the dynamics of the rural-urban continuum in the United States from 1930 to 2018. Apply the PLURAL rural-urban classification to your own data: Python code is fully open source and available at https://github.com/johannesuhl/plural. Data sources: Place-level population counts (1980-2010) and place locations 1930 - 2018 were obtained from IPUMS NHGIS, (University of Minnesota, www.nhgis.org; Manson et al. 2022). Place-level population counts 1930-1970 were digitized from historical census records (U.S. Census Bureau 1942, 1964). References: Uhl, J.H., Hunter, L.M., Leyk, S., Connor, D.S., Nieves, J.J., Hester, C., Talbot, C. and Gutmann, M., 2023. Place-level urban–rural indices for the United States from 1930 to 2018. Landscape and Urban Planning, 236, p.104762. DOI: https://doi.org/10.1016/j.landurbplan.2023.104762 Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 16.0 [dataset]. Minneapolis, MN: IPUMS. 2021. http://doi.org/10.18128/D050.V16.0 U.S. Census Bureau (1942). U.S. Census of Population: 1940. Vol. I, Number of Inhabitants. U.S. Government Printing Office, Washington, D.C. U.S. Census Bureau (1964). U.S. Census of Population: 1960. Vol. I, Characteristics of the Population. Part I, United States Summary. U.S. Government Printing Office, Washington, D.C.

Japan Population Census: Female: Age 65 to 69 Years data was reported at 4,984,205.000 Person in 2015. This records an increase from the previous number of 4,288,399.000 Person for 2010. Japan Population Census: Female: Age 65 to 69 Years data is updated yearly, averaging 1,476,220.500 Person from Dec 1920 (Median) to 2015, with 20 observations. The data reached an all-time high of 4,984,205.000 Person in 2015 and a record low of 678,637.000 Person in 1930. Japan Population Census: Female: Age 65 to 69 Years data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G002: Population: Annual.

This data collection contains information relating to the historical censuses of the United States that make up the Integrated Public Use Microdata Series (IPUMS) disseminated through the Minnesota Population Center at the University of Minnesota. Drawn from original census enumeration forms, the data collections in this series include samples of the American population taken from the censuses of 1850 to 1990 (excluding 1890 and 1930). Data files comprise both individual and household records and include information on a broad range of population characteristics, including fertility, nuptiality, life-course transitions, immigration, internal migration, labor-force participation, occupational structure, education, ethnicity, and household composition. Also available is IPUMS-International, a preliminary database describing 48 million persons in six countries: Colombia, France, Kenya, Mexico, United States, and Vietnam. Information about the IPUMS-International samples and variables, and other supporting documentation, are available on the IPUMS website, but researchers must apply for access to the data. (Source: ICPSR, retrieved 06/29/2011)

Nepal Population Census data was reported at 29,164,578.000 Person in 2021. This records an increase from the previous number of 26,494,504.000 Person for 2011. Nepal Population Census data is updated yearly, averaging 10,484,489.500 Person from Dec 1911 (Median) to 2021, with 12 observations. The data reached an all-time high of 29,164,578.000 Person in 2021 and a record low of 5,532,574.000 Person in 1930. Nepal Population Census data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Nepal – Table NP.G001: Population Census.

Japan Population Census: Age 65 to 69 Years data was reported at 9,643,867.000 Person in 2015. This records an increase from the previous number of 8,210,173.000 Person for 2010. Japan Population Census: Age 65 to 69 Years data is updated yearly, averaging 2,793,526.000 Person from Dec 1920 (Median) to 2015, with 20 observations. The data reached an all-time high of 9,643,867.000 Person in 2015 and a record low of 1,255,830.000 Person in 1930. Japan Population Census: Age 65 to 69 Years data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G002: Population: Annual.

Nepal Population Census: Density per Sq Km data was reported at 180.000 Person in 2011. This records an increase from the previous number of 157.300 Person for 2001. Nepal Population Census: Density per Sq Km data is updated yearly, averaging 63.960 Person from Dec 1911 (Median) to 2011, with 11 observations. The data reached an all-time high of 180.000 Person in 2011 and a record low of 37.590 Person in 1930. Nepal Population Census: Density per Sq Km data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Nepal – Table NP.G001: Population Census.

The unique multigenerational data base, Uppsala Birth Cohort Multigeneration Study (UBCoS), was established in 2004 by combining existing data on a representative and well-defined cohort of 14,192 males and females born in Uppsala from 1915-1929 with information on descendants of the original cohort members obtained from routine data registers.

To date, the study has been further developed by additional data collection in school archives and records from Census 1930 and the period of follow-up extended till end of year 2010. Further data collection is currently ongoing.

The study is unique in investigating intergenerational effects as "forward in time" processes, starting at the beginning of the last century (i.e. well before any of the routine registers were in place). Intergenerational associations can be currently investigated in more than 140,000 study subjects from families spanning up to five generations, including the 14,192 original cohort members, their 22,559 children, 38,771 grandchildren and 25,471 great grandchildren.

The main research objectives are to: (i) Address questions of the extent to which and the mechanisms whereby social advantage and disadvantage are transmitted from one generation to the next, giving rise to continuity in social disadvantage both over the life cycle and across generations. (ii) Explore how early social and biological factors are transmitted from the parent generation to offspring generation(s). (iii) Integrate the understanding of broader social mechanisms with the understanding of disease specific aetiology to answer the question of how, and to what extent, health inequalities are reproduced into each new generation.

Purpose:

The aim of the study is to investigate life course and intergenerational determinants of social inequalities in health.

Number of participants: 14,192 original cohort together with >140,000 family members.

This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015.

【対象期間】昭和5年10月1日国勢調査【注】【計数出所】内閣統計局調査 / PERIOD: Population census on Oct. 1, 1930. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet]. / 公的統計: 集計データ、統計表 / official statistics: aggregate data / 集計 / Aggregation / Keywords: 人口センサス, 統計, 経済, Statistics, Economics, Censuses, 人口, Population【リソース】Fulltext

【対象期間】昭和5年10月1日国勢調査【注】【計数出所】内閣統計局調査 / PERIOD: Population census on Oct. 1, 1930. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet]. / 公的統計: 集計データ、統計表 / official statistics: aggregate data / 集計 / Aggregation / Keywords: 人口センサス, 家族生活と結婚, 統計, 経済, Statistics, Economics, Censuses, Family life and marriage, 人口, 世帯, Population, Households【リソース】Fulltext

【対象期間】昭和5年10月1日国勢調査【注】【計数出所】内閣統計局調査 / PERIOD: Population census on Oct. 1, 1930. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet]. / 公的統計: 集計データ、統計表 / official statistics: aggregate data / 集計 / Aggregation / Keywords: 人口センサス, 統計, 経済, Statistics, Economics, Censuses, 人口, Population【リソース】Fulltext

【対象期間】昭和5年10月1日国勢調査【注】【計数出所】内閣統計局調査 / PERIOD: Population census on Oct. 1, 1930. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet]. / 公的統計: 集計データ、統計表 / official statistics: aggregate data / 集計 / Aggregation / Keywords: 人口センサス, 雇用, 統計, 経済, Statistics, Economics, Censuses, Employment, 人口, 労働力, Population, Labour Force【リソース】Fulltext

【対象期間】大正14年10月1日国勢調査, 昭和5年10月1日国勢調査【注】【計数出所】内閣統計局調査 / PERIOD: Population census on Oct. 1, 1925 and Oct. 1, 1930. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet]. / 公的統計: 集計データ、統計表 / official statistics: aggregate data / 集計 / Aggregation / Keywords: 人口センサス, 家族生活と結婚, 統計, 経済, Statistics, Economics, Censuses, Family life and marriage, 人口, 世帯, Population, Households【リソース】Fulltext