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Showing 1 – 50 of 114 results.
Curated
Simple Crosstabs

Midlife in the United States (MIDUS 3), 2013-2014 (ICPSR 36346)

Released/updated on: 2019-04-30
Geographic coverage: Contiguous United States
Time period: 2013-05-01--2014-11-01

In 1995-1996, the MacArthur Midlife Research Network carried out a national survey of over 7,000 Americans aged 25 to 74 [ICPSR 2760]. The purpose of the study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. The study was innovative for its broad scientific scope, its diverse samples (which included siblings of the main sample respondents and a national sample of twin pairs), and its creative use of in-depth assessments in key areas (e.g. daily diary of stressful experiences [ICPSR 3725] and cognitive functioning [ICPSR 3596]) on a subset of participants. A detailed description of the study and findings generated by it are available at: http://www.midus.wisc.edu

With support from the National Institute on Aging, a follow-up of the original Midlife Development in the United States (MIDUS) sample was conducted in 2004 (MIDUS 2 [ICPSR 4652]). The daily stress and cognitive functioning projects were repeated and expanded at MIDUS 2; in addition the protocol was expanded to include biomarkers and neuroscience.

In 2013 a third wave (MIDUS 3) of survey data was collected on longitudinal participants. Data collection for this follow-up wave largely repeated baseline assessments (e.g., phone interview and extensive self-administered questionnaire), with additional questions in selected areas such as economic recession experiences. Cognitive functioning data were also collected at the same time, while data collection for the daily diary, biomarker, and neuroscience projects commenced in 2017.

MIDUS also maintains a Colectica portal, which allows users to interact with variables across waves and create customized subsets. Registration is required.

The following results may be significantly less relevant compared to results above.
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Simple Crosstabs

Contraceptive Needs and Services in the United States, 1994-2016 (ICPSR 38891)

Released/updated on: 2024-01-23
Geographic coverage: United States
Time period: 1994-01-01--2016-12-31

These data come from surveillance activities conducted by the Guttmacher Institute over several decades, collecting or compiling data for the period 1994 through 2016. These activities track the numbers of women who have a potential demand for contraceptive care (because they are of reproductive age, sexually active and not seeking to become pregnant), the subset of these women who likely need public support for care (because of their family income level or their age), the numbers of women who receive contraceptive services from publicly funded clinics, and the numbers of clinics providing publicly supported contraceptive services. These efforts have been conducted periodically, typically about every five years, but at times the intervals between efforts were shorter or longer than five years. The most recent data were collected or compiled for 2015 (women served) and 2016 (women with potential demand for services).

This release includes two separate datasets. Dataset 1, "Need for contraceptive services," provides county-level aggregate data for 6 different years (1995, 2000, 2002, 2006, 2010, and 2016). For each county, the data represent estimates of the number of women who have a potential demand for contraceptive services and the number who likely need public support for care, both in total, and according to key socio-demographic characteristics. Dataset 2, "Clinics providing contraceptive services and women served," provides county-level aggregate data for six different years (1994, 1997, 2001, 2006, 2010, and 2015). For each county, the data represent the number of publicly funded clinics according to clinic type and funding status and the number of female contraceptive patients served at those clinics.

Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, 2012 (ICPSR 35020)

Released/updated on: 2014-04-16
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2012 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, 2013 (ICPSR 36119)

Released/updated on: 2015-05-07
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2013 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, 2015 (ICPSR 36791)

Released/updated on: 2017-05-22
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2015 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, 2014 (ICPSR 36395)

Released/updated on: 2016-03-24
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2014 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, United States, 2016 (ICPSR 37062)

Released/updated on: 2018-06-29
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2016 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, United States, 2023 (ICPSR 39302)

Released/updated on: 2026-06-29
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2023 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, United States, 2017 (ICPSR 37844)

Released/updated on: 2022-10-05
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2017 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, United States, 2018 (ICPSR 37855)

Released/updated on: 2022-10-05
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2018 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, United States, 2022 (ICPSR 39067)

Released/updated on: 2024-07-29
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2022 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, United States, 2019 (ICPSR 38784)

Released/updated on: 2023-09-28
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2019 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, United States, 2020 (ICPSR 38792)

Released/updated on: 2023-12-11
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2020 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, United States, 2021 (ICPSR 38800)

Released/updated on: 2023-12-12
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2021 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, 2011 (ICPSR 34584)

Released/updated on: 2013-05-02
Geographic coverage: United States
The Uniform Crime Reporting Program Data, Police Employee Data, 2011 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record included in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated
Simple Crosstabs

National ZIP Code Crosswalk, [United States], 1990-2020 (ICPSR 39431)

Released/updated on: 2025-11-10
Geographic coverage: United States
Time period: 1990-01-01--2020-12-31

ZIP Codes are administrative codes generated by the United States Postal Service (USPS) that refer to the geographic area covered by a specific set of mail delivery routes. The U.S. Census Bureau calculates and distributes aggregated social, economic, and demographic information for the population associated with "ZIP Code Tabulation Areas" (ZCTAs), which are roughly analogous to ZIP Codes and serve as identifiers for specific neighborhoods and communities. These aggregated census data, however, are unable to account for changes in ZIP Code boundaries that occur between decennial censuses, leading to measurement error and missing data problems for scholars who attempt to use the aggregated ZCTA data. The purpose of this crosswalk file is to allow researchers to overcome this limitation, enabling them to appropriately link spatial reference information (ZIP Codes) with characteristics of the populations to which they refer.

Most ZIP Codes do not change boundaries in a decade, but a large enough percentage do as to create a problem with missing or mis-specified data. Boundary changes typically involve one or more of the following three processes, although a small number of cases do not conform to these typologies: (1) two or more existing ZIP Codes are combined to create a single surviving ZIP Code, (2) an existing ZIP Code is divided into multiple resulting ZIP Codes, and (3) boundaries between two or more existing ZIP Codes are altered.

Each of these types of changes alters the geographic area that a ZIP Code refers to, and as such, the spatial unit identified by the ZIP Code includes a different population, with a different array of characteristics. By linking the spatial units associated with ZIP Codes as these boundary changes are enacted, the research team can both prevent the loss of observations due to missing data, and more accurately measure social, demographic, and economic characteristics associated with each ZIP Code.

This data set identifies changes in ZIP Code boundaries between 1990 and 2020, and provides numeric codes that cluster the ZIP Codes into the smallest geographic unit, or group of ZIP Codes, that are consistent across a decade: 1990 - 2000, 2000 - 2010, and 2010 - 2020. This "crosswalk" covers the contiguous United States, Alaska, Hawaii, and the District of Columbia. Since much administrative data is available with ZIP Code as the smallest identifiable geography, ZIP Codes are often used to embed observations from administrative data (patients, businesses, survey respondents, etc.) within their social, demographic, and economic contexts. However, ZIP Code boundaries change over time, resulting in measurement error (matching observations to the wrong contextual unit) or missing data (due to an observation reporting a ZIP Code that did not exist at the beginning of the observational period). These data were collected, and the crosswalk created, in an attempt to resolve these data quality issues.

Curated

Urban Growth in America: Philadelphia, 1774-1930 (ICPSR 56)

Released/updated on: 2008-03-25
Geographic coverage: United States, Philadelphia, Pennsylvania
Time period: 1774-01-01--1930-01-01
This study contains aggregate economic, political, and social data for the city of Philadelphia in the period 1774-1930. Data are provided for occupational categories in 1774 and 1860 (Parts 1 and 3), the place of birth of the city inhabitants in 1860 (File 2), and for workers aged 10 and over in 1930, tabulated by ward and industry group (Part 4).
Curated

Geographies of Urban Crime in Nashville, Tennessee, Portland, Oregon, and Tucson, Arizona, 1998-2002 (ICPSR 4547)

Released/updated on: 2006-08-31
Geographic coverage: Oregon, Portland, United States, Tennessee, Tucson, Nashville, Arizona
Time period: 1998-01-01--2002-01-01
This research involved the exploration of how the geographies of different crimes intersect with the geographies of social, economic, and demographic characteristics in Nashville, Tennessee, Portland, Oregon, and Tucson, Arizona. Violent crime data were collected from all three cities for the years 1998 through 2002. The data were geo-coded and then aggregated to block groups and census tracts. The data include variables on 28 different crimes, numerous demographic variables taken from the 2000 Census, and several land use variables.
Curated

Spatial Analysis of Crime in Appalachia [United States], 1977-1996 (ICPSR 3260)

Released/updated on: 2006-03-30
Geographic coverage: United States
Time period: 1977-01-01--1996-01-01
This research project was designed to demonstrate the contributions that Geographic Information Systems (GIS) and spatial analysis procedures can make to the study of crime patterns in a largely nonmetropolitan region of the United States. The project examined the extent to which the relationship between various structural factors and crime varied across metropolitan and nonmetropolitan locations in Appalachia over time. To investigate the spatial patterns of crime, a georeferenced dataset was compiled at the county level for each of the 399 counties comprising the Appalachian region. The data came from numerous secondary data sources, including the Federal Bureau of Investigation's Uniform Crime Reports, the Decennial Census of the United States, the Department of Agriculture, and the Appalachian Regional Commission. Data were gathered on the demographic distribution, change, and composition of each county, as well as other socioeconomic indicators. The dependent variables were index crime rates derived from the Uniform Crime Reports, with separate variables for violent and property crimes. These data were integrated into a GIS database in order to enhance the research with respect to: (1) data integration and visualization, (2) exploratory spatial analysis, and (3) confirmatory spatial analysis and statistical modeling. Part 1 contains variables for Appalachian subregions, Beale county codes, distress codes, number of families and households, population size, racial and age composition of population, dependency ratio, population growth, number of births and deaths, net migration, education, household composition, median family income, male and female employment status, and mobility. Part 2 variables include county identifiers plus numbers of total index crimes, violent index crimes, property index crimes, homicides, rapes, robberies, assaults, burglaries, larcenies, and motor vehicle thefts annually from 1977 to 1996.
Curated

International Data Base, February 1990 (ICPSR 8490)

Released/updated on: 1992-02-16
Geographic coverage: Global
This dataset contains information from tables of demographic, economic and social data for the countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence.
Curated

Historical, Demographic, Economic, and Social Data: The United States, 1790-2002 (ICPSR 2896)

Released/updated on: 2010-05-21
Geographic coverage: United States
Time period: 1790-01-01--2002-01-01
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.
Curated

Census Tract Data, 1940: Elizabeth Mullen Bogue File (ICPSR 2930)

Released/updated on: 2006-01-12
Geographic coverage: Atlantic City, Milwaukee, Oklahoma City, Akron, Detroit, Indiana, Berkeley, Cincinnati, Austin, Oakland, Cambridge, New York City, Columbus (Ohio), Syracuse, Memphis, Buffalo, Boston, Pittsburgh, Camden, Providence, Seattle, Savannah, Macon, Kentucky, Yonkers, Clifton, Nashville, California, Kansas, Pennsylvania, Iowa, Elizabeth, New Haven, Illinois, Texas, Connecticut, Portland (Oregon), Georgia, Virginia, Maryland, Indianapolis, Richmond, Oregon, Duluth, Flint, United States, Oklahoma, Tennessee, Kansas City (Kansas), Louisville, Alabama, Cleveland, Washington, Dayton, Superior, Minneapolis, Atlanta, Pawtucket, Massachusetts, Colorado, Missouri, New Orleans, Denver, Dallas, St. Louis, Wisconsin, Des Moines, Augusta, District of Columbia, Rhode Island, Chicago, St. Paul, Rochester (New York), Passaic, Minnesota, New York (state), Birmingham, New Jersey, Michigan, San Francisco, Baltimore, Paterson, Jersey City, Long Beach, Ohio, Los Angeles, Toledo, Hartford, Trenton, Philadelphia, Houston
The 1940 Census Tract files were originally created by keypunching the data from the printed publications prepared by the Bureau of the Census. The work was done under the direction of Dr. Donald Bogue, whose wife, Elizabeth Mullen Bogue, completed much of the data work. Subsequently, the punchcards were converted to data files and transferred to the National Archive and Records Administration (NARA). ICPSR received copies of these files from NARA and converted the binary block length records to ASCII format.
Curated

Census Tract Data, 1950: Elizabeth Mullen Bogue File (ICPSR 2931)

Released/updated on: 2006-01-12
Geographic coverage: North Carolina, Milwaukee, Indiana, Kalamazoo, Berkeley, Fort Worth, Cincinnati, Austin, Spokane, San Jose, San Diego, Columbus (Ohio), Syracuse, Springfield (Massachusetts), Boston, Providence, Seattle, Kentucky, Nashville, California, Florida, New Haven, Illinois, Connecticut, Georgia, Virginia, Maryland, Norfolk, Duluth, Flint, United States, Oklahoma, Kansas City (Kansas), Louisville, Washington, Rome (New York), Wichita, Pawtucket, Massachusetts, Missouri, New Orleans, Denver, Dallas, St. Louis, Wisconsin, Augusta, Rochester (New York), Passaic, Chicopee, Birmingham, Michigan, Baltimore, Paterson, Louisiana, Toledo, Philadelphia, Oklahoma City, Akron, Greensboro, Detroit, Utica, Bridgeport, Memphis, Buffalo, Pittsburgh, Chattanooga, Sacramento, Clifton, Kansas, Pennsylvania, Texas, Portland (Oregon), Durham, Portsmouth, Indianapolis, Richmond, Oregon, Holyoke, Tennessee, Alabama, Cleveland, Dayton, Nebraska, Superior, Omaha, Tacoma, Colorado, District of Columbia, Rhode Island, Chicago, Minnesota, New York (state), New Jersey, Miami, Ohio, Hartford, Trenton, Houston
The 1950 Census Tract files were originally created by keypunching the data from the printed publications prepared by the Bureau of the Census. The work was done under the direction of Dr. Donald Bogue, whose wife, Elizabeth Mullen Bogue, completed much of the data work. Subsequently, the punchcards were converted to data files and transferred to the National Archive and Records Administration (NARA). ICPSR received copies of these files from NARA and converted the binary block-length records to ASCII format.
Curated

Census Tract Data, 1960: Elizabeth Mullen Bogue File (ICPSR 2932)

Released/updated on: 2006-01-12
Geographic coverage: Milwaukee, Indiana, Kalamazoo, Cincinnati, Austin, Spokane, San Jose, Syracuse, Springfield (Massachusetts), Providence, Seattle, St. Petersburg, Bethlehem, Nashville, California, Laredo, Fresno, Beaumont, Texarkana, Illinois, Newark, Georgia, Little Rock, Maryland, Norfolk, Oklahoma, Louisville, Arkansas, Washington, Albany (New York), Fall River, Pawtucket, Missouri, Winston-Salem, Davenport, Scranton, Dallas, Wisconsin, Nevada, Des Moines, Schenectady, Muskegon, Lawrence, St. Paul, Hawaii, Rochester (New York), Sioux City, Birmingham, Michigan, Baltimore, Paterson, New Mexico, Orlando, Canton, Philadelphia, Steubenville, Atlantic City, Akron, Topeka, Greensboro, Detroit, Charlotte, High Point, Erie, Waterloo, Bakersfield, Odessa, Abilene, Worchester, Jacksonville, Buffalo, Chattanooga, Stamford, Sacramento, Baton Rouge, Clifton, Kansas, Pennsylvania, Iowa, Texas, Fort Wayne, Indianapolis, Richmond, Holyoke, Newport News, Alabama, Nebraska, Shreveport, Superior, Omaha, Texas City, West Virginia, Elyria, Minneapolis, Youngstown, Columbia (South Carolina), Colorado, Honolulu, Phoenix, Portland (Maine), Gary, District of Columbia, Wilkes-Barre, Lancaster, Monroe, Minnesota, New Jersey, Miami, Brockton, San Francisco, Charleston (South Carolina), Lowell, Ohio, South Bend, Waco, North Carolina, Johnstown, Fort Worth, San Diego, Lincoln, Arizona, Springfield (Ohio), Boston, San Bernardino, Savannah, Macon, Montgomery, Kentucky, Florida, Hampton, Delaware, Troy, New Haven, Connecticut, Rockford, Virginia, Duluth, Flint, United States, Grand Rapids, South Carolina, Muncie, Rome (New York), Wichita, New Britain, Massachusetts, New Orleans, Denver, Salt Lake City, Harrisburg, St. Louis, Saginaw, Lubbock, Corpus Christi, Augusta, San Angelo, Allentown, Raleigh, San Antonio, Passaic, Chicopee, Pittsfield, Mobile, Gadsden, Louisiana, Toledo, Colorado Springs, Evansville, Oklahoma City, Tucson, Albuquerque, Columbus (Georgia), Utica, Tyler, Lexington, Bridgeport, Wichita Falls, Peoria, Memphis, Ogden, Pittsburgh, El Paso, Pueblo, Greenville, Haverhill, Lansing, Tulsa, Green Bay, Lorain, Hazleton, Tampa, Durham, Portsmouth, Oregon, Madison, Jackson (Michigan), York, Ann Arbor, Tennessee, Maine, Weirton, Altoona, Cleveland, Dayton, Decatur, Tacoma, Atlanta, Lima, Hamilton, Fort Smith, Middletown, Wilmington (Delaware), Rhode Island, Chicago, Waterbury, Kansas City (Missouri), New York (state), Wheeling, Santa Barbara, Galveston, Reading, Jersey City, Springfield (Missouri), Norwalk, Long Beach, New Hampshire, Easton, Manchester, Binghamton, Los Angeles, Hartford, Trenton, Stockton, Houston, New Bedford
The 1960 Census Tract files were originally created by keypunching the data from the printed publications prepared by the Bureau of the Census. The work was done under the direction of Dr. Donald Bogue, whose wife, Elizabeth Mullen Bogue, completed much of the data work. Subsequently, the punchcards were converted to data files and transferred to the National Archive and Records Administration (NARA). ICPSR received copies of these files from NARA and converted the binary block-length records to ASCII format.
Curated

Census Tract Data, 1970: Elizabeth Mullen Bogue File (ICPSR 2933)

Released/updated on: 2006-01-12
Geographic coverage: Milwaukee, Biloxi, Indiana, Kalamazoo, Austin, Spokane, Lewiston, Columbus (Ohio), Syracuse, Colonial Heights, Racine, Kenosha, Bryan, Danbury, Providence, Bethlehem, Nashville, Laredo, Knoxville, Mississippi, Beaumont, Midland, Texarkana, Illinois, Denison, Georgia, Little Rock, Maryland, Idaho, Port Arthur, Oklahoma, Arkansas, Washington, Albany (New York), Pawtucket, Bay City, Missouri, Winston-Salem, Scranton, Dallas, Wisconsin, Sioux Falls, Nevada, Des Moines, Muskegon, Lawrence, Bloomington, Hawaii, Normal, Michigan, Baltimore, New Mexico, Orlando, Lacrosse, Canton, Rochester (Minnesota), Atlantic City, Akron, Topeka, Greensboro, Charlotte, High Point, Harlingen, Erie, Waterloo, Charleston (West Virginia), Odessa, Abilene, Bristol, Worchester, Terre Haute, Provo, Jacksonville, Buffalo, Chattanooga, Baton Rouge, Oshkosh, Kansas, Great Falls, Pennsylvania, Iowa, Texas, Fort Wayne, Indianapolis, Richmond, Newport News, St. Joseph, Lafayette (Indiana), Lynchburg, Roanoke, Columbia (Missouri), Nebraska, Shreveport, Superior, Texas City, Warren, West Virginia, Amarillo, Youngstown, Columbia (South Carolina), Colorado, Honolulu, Phoenix, Cedar Rapids, Portland (Maine), District of Columbia, Fayetteville, Boise City, Wilkes-Barre, Salem (Oregon), South Dakota, Lancaster, Monroe, Minnesota, New Jersey, Brockton, Charleston (South Carolina), Lowell, Ohio, South Bend, Waco, North Carolina, Johnstown, Fort Worth, Orange, Utah, San Benito, Lincoln, Arizona, Las Vegas, Springfield (Ohio), Montana, Savannah, Macon, Kentucky, Florida, Hampton, Delaware, Gainesville, Connecticut, Rockford, Virginia, Gulfport, Duluth, Flint, United States, Grand Rapids, Kansas City (Kansas), South Carolina, Muncie, Rome (New York), Tallahassee, Wichita, Nashua, New Britain, Massachusetts, New Orleans, Denver, Salt Lake City, Harrisburg, St. Louis, Saginaw, Lubbock, Corpus Christi, Augusta, San Angelo, Allentown, Raleigh, San Antonio, Springfield (Illinois), Pittsfield, Reno, Louisiana, Toledo, Colorado Springs, Pensacola, Leominster, Albuquerque, Brownsville, Champaign-Urbana, College Station, Utica, Tyler, Lexington, Bridgeport, Billings, Petersburg, Peoria, Memphis, Ogden, Pittsburgh, El Paso, Pueblo, Greenville, Auburn, Haverhill, Lansing, Meriden, Lawton, Tulsa, Green Bay, Pine Bluff, West Palm Beach, Hazleton, Eugene, Tampa, Durham, Hollywood (Florida), Oregon, Madison, Mansfield, Jackson (Michigan), York, Ann Arbor, Tennessee, Maine, Altoona, Cleveland, Dayton, Orem, Decatur, Tacoma, Atlanta, Lima, Hamilton, Fort Smith, Middletown, Sherman, Wilmington (Delaware), Rhode Island, Fitchburg, Fort Lauderdale, Kansas City (Missouri), New York (state), Anderson, Galveston, Lake Charles, Reading, Springfield (Missouri), New Hampshire, Easton, Manchester, Hartford, Trenton, Asheville, Houston, Appleton
The 1970 Census Tract files were originally created by keypunching the data from the printed publications prepared by the Bureau of the Census. The work was done under the direction of Dr. Donald Bogue, whose wife, Elizabeth Mullen Bogue, completed much of the data work. Subsequently, the punchcards were converted to data files and transferred to the National Archive and Records Administration (NARA). ICPSR received copies of these files from NARA and converted the binary block-length records to ASCII format.
Curated

Great Plains Population and Environment Data: Agricultural Data, 1870-1997 [United States] (ICPSR 4254)

Released/updated on: 2005-06-22
Geographic coverage: Montana, United States, Wyoming, Oklahoma, South Dakota, Minnesota, Kansas, Nebraska, Iowa, New Mexico, Texas, Colorado, North Dakota
Time period: 1870-01-01--1997-01-01
The data in this series of studies were assembled by an interdisciplinary research team led by Myron Gutmann of the University of Michigan between 1995 and 2004, as part of a research project funded by the National Institute of Child Health and Human Development (Grant Number R01HD033554 to the University of Michigan). The goal of the project was to amass information about approximately 500 counties in 12 states of the Great Plains of the United States, and then to analyze those data in order to understand the relationships between population and environment that existed between the years of about 1870 and 2000. The data distributed here are all data about counties. They fall into four broad categories: about the counties, about agriculture, about demographic and social conditions, and about the environment. The information about counties (name, area, identification code, and whether the project classified the county as part of the Great Plains in a given year) is embedded in each of the other data files, so that there will be three series of data (agriculture, demographic and social conditions, and environment), containing individual data files for each year for which data are available. The United States Census of Agriculture has been conducted since 1850 on a regular schedule that was decennial until 1920, and more frequently thereafter (every five years from 1925 to 1950, then in 1954, 1959, 1964, 1978, and every five years since 1982). The agricultural data included in this collection consist of a single data file for each agricultural census year between 1870 and 1997 that includes selected material compiled as part of the United States Agricultural Census. The county-level agricultural data produced by the United States government as part of the census constitute a consistent series of measures of changing agriculture and land use.
Curated

Great Plains Population and Environment Data: Social and Demographic Data, 1870-2000 [United States] (ICPSR 4296)

Released/updated on: 2007-02-07
Geographic coverage: Montana, United States, Wyoming, New Mexico, Oklahoma, Texas, Colorado, South Dakota, Kansas, North Dakota, Nebraska
Time period: 1870-01-01--2000-01-01

The social and demographic data included in this collection consist of a single data file for each decennial year between 1870 and 2000, covering 10 of the 12 Great Plains states. Information on a variety of social and demographic topics was gathered to historically characterize populations living in counties within the United States Great Plains, in terms of: (1) urban, rural, and total population, (2) vital statistics, (3) net migration, (4) age and sex, (5) nativity and ancestry, (6) education and literacy, (7) religion, (8) industry, and (9) housing and other characteristics. These data include selected material compiled as part of the United States population census. The United States Census of Population and Housing has been conducted since 1790 on a regular schedule that is decennial. The county-level social and demographic data produced by the United States government as a result constitute a consistent series of measures capturing changes in the United States population's size, composition, and other characteristics. A subset of the variables available from the short and long-form survey questionnaires of the United States Census of Population and Housing (as compiled for counties) were extracted from previously existing digital files. Besides the decennial census of the population, county-level data were drawn from an assortment of existing digital files as well as sources that were manually digitized. Other data include compilations of county-level information gathered from various federal agencies and private organizations as well as the agriculture and economic censuses. Supplementing these compilations are manually digitized consumer market data, religious data, and vital statistics, including information about births, deaths, marriage, and divorce.

Curated

Uniform Crime Reporting Program Data [United States]: Police Employee (LEOKA) Data, 2007 (ICPSR 25104)

Released/updated on: 2009-04-14
Geographic coverage: United States
Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated

Uniform Crime Reporting Program Data [United States]: Police Employee (LEOKA) Data, 2009 (ICPSR 30765)

Released/updated on: 2011-07-27
Geographic coverage: United States
Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated

Uniform Crime Reporting Program Data [United States]: Police Employee (LEOKA) Data, 2008 (ICPSR 27646)

Released/updated on: 2010-06-21
Geographic coverage: United States
Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.
Curated

Historical, Demographic, Economic, and Social Data: The United States, 1790-1970 (ICPSR 3)

Released/updated on: 2005-12-22
Geographic coverage: United States
Time period: 1790-01-01--1970-01-01
Detailed county and state-level ecological or descriptive data for the United States for the years 1790 to 1970 are contained in this collection. These data files contain 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 conditions. Though not complete over the full time span of this study, statistics are available on such diverse subjects as total numbers of newspapers and periodicals, total capital invested in manufacturing, total numbers of educational institutions, total number of churches, taxation by state, and land surface area in square miles.
Curated

Natality Local Area Summary Data, 1980: [United States] (ICPSR 9409)

Released/updated on: 2008-10-06
Geographic coverage: United States
This collection contains information on live births in the United States during calendar year 1980. The natality data in this file are a component of the vital statistics collection effort maintained by the federal government. Geographic variables of residence for births include the state, county, city, population, division and state subcode, Standard Metropolitan Statistical Area (SMSA), and metropolitan-nonmetropolitan county. Other variables include the race and sex of the child, the age of the mother, mother's education, place of delivery, person in attendance, and live birth order. The summary variables in the file include total number of births occurring in the country, the ratio of births to married women, the ratio of births to unmarried women, number of live births by birth weight, total number of births to United States residents, births by attendant, and place of delivery.
Curated

Census of Population and Housing, 1980 [United States]: Special Tabulations of Population 60 Years and Over (ICPSR 8533)

Released/updated on: 1992-02-16
Geographic coverage: United States
Time period: 1979-01-01--1980-01-01
These data, which correspond to tables provided in the documentation, summarize information on the United States population aged 60 years and over that was collected in the 1980 Census of Population and Housing. The tables were prepared by the Bureau of the Census at the request of the National Institute on Aging. Variables appearing in one or more of the tables are age (in single years or five-year intervals), sex, race (black/white), living arrangements (institutionalization status, household/group quarters, living in families/alone, relationship to householder, persons per room), income (source, personal level, family level, household level, poverty status), veteran status, educational attainment, urban/rural residence, marital status, nativity status, and Spanish origin. In some of the tables totals that exclude amounts allocated for missing data are provided for purposes of comparison. The variables for which non-allocated figures are included are age, race, institutionalization status, income, veterans status, educational attainment, marital status, and Spanish origin. The file contains a complete set of tables for the United States as a whole, for each of the four Census regions, and for each of the 50 States, the District of Columbia, and five territories.
Curated

Census of Population and Housing, 1980 [United States]: Summary Tape File 5, Special Tabulations of Population 60 Years and Over (ICPSR 8658)

Released/updated on: 2006-01-18
Geographic coverage: United States
Time period: 1979-01-01--1980-01-01
These data, which correspond to tables provided in the documentation, summarize information on the United States population aged 60 years and over that was collected in the 1980 Census of Population and Housing. The tables were prepared by the Bureau of the Census at the request of the National Institute on Aging. The tables are comprised of cross-tabulations of both "100 percent items" and "sample items" with age (bracketed in five year intervals from 60-64 through 90+). Race (White/Black/American Indian/Asian Pacific Islander/Spanish Origin) is a factor in all of the tables, either as race of respondent, of householder, or of family head. The file contains data for a complete set of tables for each of the 50 States, the District of Columbia and five territories, the nine Census divisions, the four Census regions, and the United States as a whole.
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IPUMS Contextual Determinants of Health (CDOH) Gender Measure: Labor Force Ratio by State, United States, 2015-2022 (ICPSR 38839)

Released/updated on: 2024-04-18
Geographic coverage: United States
Time period: 2015-01-01--2022-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Gender measures in this release include the state-level labor force ratio, which compares the proportion of men in the labor force to the proportion of women in the labor force in a given state in a given year. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

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Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Gender Measure: Paid Family Medical Leave by State, United States, 2004-2023 (ICPSR 38847)

Released/updated on: 2024-04-16
Geographic coverage: United States
Time period: 2004-01-01--2023-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Gender measures in this release include state-level paid family and medical leave, which denotes whether a state has a law that guarantees paid family and medical leave for employees. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

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Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Gender Measure: Poverty Ratio by State, United States, 2015-2023 (ICPSR 38848)

Released/updated on: 2024-04-18
Geographic coverage: United States
Time period: 2015-01-01--2023-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Gender measures in this release include the state-level poverty ratio, which compares the proportion of females living in poverty to the proportion of males living in poverty in a given state in a given year. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

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Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Gender Measure: Proportion of State Legislators Identifying as Female by State, United States, 2015-2023 (ICPSR 38849)

Released/updated on: 2024-04-16
Geographic coverage: United States
Time period: 2015-01-01--2023-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Gender measures in this release include the proportion of state legislators identifying as female, which is computed as the proportion for the state legislature as a whole and for the state house and senate legislative chambers. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

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Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Gender Measure: Earnings Ratio by State, United States, 2015-2022 (ICPSR 38850)

Released/updated on: 2024-04-30
Geographic coverage: United States
Time period: 2015-01-01--2022-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts by state or county for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons as well as women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Gender measures in this release include the state-level earnings ratio, which compares the median earnings of full-time wage and salary workers identifying as male to the median earnings of full-time wage and salary workers identifying as female in a given state in a given year. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

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Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Gender Measure: Domestic Violence Gun Ownership by State, United States, 1991-2020 (ICPSR 38851)

Released/updated on: 2023-07-17
Geographic coverage: United States
Time period: 1991-01-01--2020-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Gender measures in this release include state-level domestic violence and gun ownership, which denotes whether a state has a law that prohibits domestic violence offenders from owning firearms above and beyond federal law. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

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Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Gender Measure: Abortion Access by State, United States, 2009-2022 (ICPSR 38852)

Released/updated on: 2023-07-12
Geographic coverage: United States
Time period: 2009-01-01--2022-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Gender measures in this release include state-level abortion access, which reports the proportion of a state's females aged 15-44 who reside in counties with an abortion provider by year and month from 2009-2022. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

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Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Sexual and Gender Minority Measure: Proportion Identifying as LGBTQ by State, United States, 2021-2022 (ICPSR 38853)

Released/updated on: 2023-07-18
Geographic coverage: United States
Time period: 2021-01-01--2022-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Sexual and Gender measures in this release include the proportion of a state's population identifying as LGBTQ+ in the U.S. Census Bureau's Household Pulse Survey, Phases 3.2 (07/21/2021-10/11/2021), 3.3 (12/01/2021-02/07/2022), 3.4 (03/02/2022-05/09/2022), and 3.5 (06/01/2022-08/08/2022). To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

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Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Politics Measure: Presidential Election Results by State, United States, 1976-2020 (ICPSR 38854)

Released/updated on: 2024-04-30
Geographic coverage: United States
Time period: 1976-01-01--2020-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Politics measures in this release include the state presidential election results, which is the proportion of votes cast for the Democratic candidate or the Republican candidate in the respective presidential election. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

Curated
Partially restricted

Head Start Family and Child Experiences Survey (FACES 2014) Contextual Variables Data File, United States, 2014-2017 (ICPSR 38861)

Released/updated on: 2023-09-28
Geographic coverage: United States
Time period: 2014-01-01--2017-01-01

FACES provides national information about Head Start programs and participants. Beginning in 1997, a series of nationally representative samples of Head Start children and their families, classrooms, and programs has described the population served by Head Start; staff qualifications, credentials, and opinions; Head Start classroom practices and quality measures; and the experiences and well-being of children and families. FACES studies have included assessments that measure children's cognitive skills, social-emotional skills, and physical status; observations of classroom quality; and surveys of children's parents, teachers, center directors, and program directors.

The Family and Child Experiences Survey (FACES 2014) Contextual Variables Data File contains 28 contextual, community-level variables about 399 Head Start centers included in the FACES 2014 study sample. It does not contain data collected as part of the FACES 2014 study; instead, it contains information from publicly available data sources and is designed to merge with other FACES 2014 data files to enhance the understanding of Head Start center communities. The contextual variables data describe characteristics of the census tract or block group in which Head Start centers are located.

The contextual variables include three index variables constructed by research institutions, 24 demographic and socioeconomic variables derived from the American Community Survey (ACS), and a measure of rural/urban status from the U.S. Department of Agriculture. The FACES 2014 Contextual Variables Data File is intended to be used with the other FACES 2014 data files. For example, in conjunction with the other FACES 2014 data, these data could be used to:

  • describe the characteristics of neighborhoods where children attend Head Start,
  • describe how children's experiences or Head Start quality differ by neighborhood characteristics, or
  • explore associations among neighborhoods, Head Start experiences, and child and family well-being.
Curated
Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Politics Measure: Presidential Election Results by County, United States, 2000-2020 (ICPSR 39236)

Released/updated on: 2025-01-30
Geographic coverage: United States
Time period: 2000-01-01--2020-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Politics measures in this release include county-level presidential election results from 2000-2020, indicating the proportion of votes cast for the Democratic candidate or the Republican candidate in each presidential election. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

Curated
Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Sexual and Gender Minority Measure: Same-Sex Households by County, United States, 2020 (ICPSR 39237)

Released/updated on: 2025-01-30
Geographic coverage: United States

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

Sexual and Gender Minority measures in this release include county-level summary data on the proportion of same-sex households in the United States, as reported in the 2020 Decennial Census. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

Curated
Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Race and Ethnicity Measure: Educational Inequity by County, United States, 2005-2022 (ICPSR 39238)

Released/updated on: 2025-01-30
Geographic coverage: United States
Time period: 2005-01-01--2022-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Race and Ethnicity measures in this release include county-level summary data on educational inequity between racial groups in the United States from 2005-2022. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

Curated
Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Race and Ethnicity Measure: Employment Inequity by County, United States, 2005-2022 (ICPSR 39239)

Released/updated on: 2025-02-20
Geographic coverage: United States
Time period: 2005-01-01--2022-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Race and Ethnicity measure in this release is an indicator of employment inequity, which includes a ratio between the proportion of people aged 16-64, in the civilian labor force, who are employed and identify as White alone, not Hispanic or Latino and the proportion of people aged 16-64, in the civilian labor force, who are employed and identify as a different race/ethnic group (Black, Asian, and Hispanic). To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

Curated
Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Race and Ethnicity Measure: Homeownership Inequity by County, United States, 2005-2022 (ICPSR 39240)

Released/updated on: 2025-02-25
Geographic coverage: United States
Time period: 2005-01-01--2022-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Race and Ethnicity measure in this release is an indicator of homeownership inequity, which includes the ratio between the proportion of householders identifying as White alone, not Hispanic or Latino, who own (as opposed to renting) their home and the proportion of householders identifying as a different race/ethnic group who own their home. Three ratios are provided for Black, Asian, and Hispanic groups. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

Curated
Partially restricted

IPUMS Contextual Determinants of Health (CDOH) Race and Ethnicity Measure: Income Inequity by County, United States, 2005-2022 (ICPSR 39241)

Released/updated on: 2025-02-25
Geographic coverage: United States
Time period: 2005-01-01--2022-01-01

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women.

The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website.

Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020.

The Race and Ethnicity measure in this release is an indicator of income inequity which is measured using the index of concentration at the extremes (ICE). ICE is a measure of social polarization within a particular geographic unit. It shows whether people or households in a geographic unit are concentrated in privileged or deprived extremes. The privileged group in this study is the number of households with a householder identifying as White alone, not Hispanic or Latino, with an income equal to or greater than $100,000. The deprived group in this study is the number of households with a householder identifying as a different race/ethnic group (e.g., Black alone, Asian alone, Hispanic or Latino), with an income equal to or less than $25,000. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

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