- Overview
- How to Use the Data
- Technical Details
- Download
- Loading Data into Stats Packages
- Citation and Use
Overview
The Contextual Determinants of Health (CDOH) 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, LGBTQ+ persons and women.
Unlike other IPUMS products, the CDOH data are organized into multiple categories. We currently provide measures related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. We have created measures 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). Our measures are currently available for states or counties from approximately 2015 to 2020.
The geographic and temporal availability varies by measure. Users should carefully read the documentation to understand the temporal and geographic characteristics of each measure and to properly cite each measure.
Categories of data include:
- Race and Ethnicity. The race and ethnicity data include variables representing the differences between Black and non-Hispanic white, Asian and non-Hispanic white, and Hispanic and non-Hispanic white for five county-level indicators: education, employment, income, homeownership, and residential segregation.
- Sexual and Gender Minority. The sexual and gender minority data include several policies and policy-relevant indicators by state: proportion of population who are LGBTQ+, proportion of unions, marriages, and households populated by same-sex couples.
- Gender. The gender data focuses on differences between men and women, primarily cis-men and women. Indicators include: state-level ratios of earnings, poverty rates, employment, women serving in state legislatures, and access to paid family leave.
- Politics. The political data provides variables, by state and county, related to the proportion of votes cast for the Democratic and Republican candidates in the 2020 presidential election.
How to Use the Data
Each data file contains information about states or counties, and some files also contain multiple years of data for states and counties. Each column represents a characteristic about a particular state or county.
Since all records in the data files refer to geographic units, we include codes to help users link these contextual measures with their own data. For state-level measures, we include a state FIPS code (statefips). State FIPS codes uniquely identify states and the District of Columbia in the United States. IPUMS CDOH provides text versions of state FIPS codes, and they range from 01 (Alabama) to 56 (Wyoming).
For county-level measures, we include a state and a county FIPS code (statefips and countyfips). County FIPS codes uniquely identify counties within a particular state. Similar to the state FIPS codes, IPUMS CDOH provides text versions of county FIPS codes. To merge county-level measures with external datasets, please use both the state and county FIPS codes in the join or merge command.
Technical Details
Basics
The data files are provided through the links below as comma-separated values (CSV) files within Zip archives.
Each Zip file includes a PDF file describing the content of the data file in detail. It includes a citation for the measure and additional references or data sources that informed or contributed to the creation of the measure.
Multiple Years of Data
Many of our measures are available for more than one year. For these measures, each row in the data file represents a particular combination of a geographic unit (e.g. state, county) and year.
If you open a data file that contains measures for multiple years, it will look like the following:
| State FIPS | Year | Earnings Ratio |
|---|---|---|
| 01 | 2018 | 1.20 |
| 01 | 2019 | 1.18 |
| 01 | 2020 | 1.19 |
| 02 | 2018 | 1.30 |
| 02 | 2019 | 1.27 |
| 02 | 2020 | 1.25 |
Download
Race and Ethnicity
| Measure | Temporal Frequency | Temporal Range | Description | Citation |
|---|---|---|---|---|
| Educational Inequity (by County) | Pooled over 5 years | 2006-2010 through 2018-2022 | Ratio between the proportion of people aged 25 years and older identifying as White alone, not Hispanic or Latino, with a college degree or higher and the proportion of people aged 25 years and older identifying as a different race/ethnic group with a college degree or higher. | Rachel Hardeman, Claire Kamp Dush, Wendy Manning, and David Van Riper. Racism - county - educational inequity. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-009.2024-02 |
| Employment Inequity (by County) | Pooled over 5 years | 2006-2010 through 2018-2022 | 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. | Rachel Hardeman, Claire Kamp Dush, Wendy Manning, and David Van Riper. Racism - county - employment inequity. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-010.2024-02 |
| Homeownership Inequity (by County) | Pooled over 5 years | 2006-2010 through 2018-2022 | 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. | Rachel Hardeman, Claire Kamp Dush, Wendy Manning, and David Van Riper. Racism - county - homeownership inequity. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-011.2024-02 |
| Income Inequity (by County) | Pooled over 5 years | 2006-2010 through 2018-2022 | Income inequity 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. Our privileged group 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. Our deprived group 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. | Rachel Hardeman, Claire Kamp Dush, Wendy Manning, and David Van Riper. Racism - county - income inequity. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-012.2024-02 |
| Residential Segregation - Index of Dissimilarity Inequity (by County) | Pooled over 5 years | 2006-2010 through 2018-2022 | Residential segregation measures the physical separation of population groups into different areas (i.e., neighborhoods) in a geographic unit (i.e., a county or city). We report the index of dissmilarity (D) for United States counties in our data file. The index of dissimilarity is a measure of evenness and measures the proportion of a group's population that must move so that each sub-county geographic unit in a county has the same proportion of that group as the county. We use census tracts as our sub-county geographic unit because census tracts nest within counties. | Rachel Hardeman, Claire Kamp Dush, Wendy Manning, and David Van Riper. Racism - county - residential segregation - index of dissimilarity. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-013.2024-02 |
Gender
| Measure | Temporal Frequency | Temporal Range | Description | Citation |
|---|---|---|---|---|
| Abortion Access (by State) | Monthly | 2009-2022 | The state-level abortion access measure 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. | Hyunjae Kwon, Claire Kamp Dush, Wendy Manning, and David Van Riper. Sexism - state - abortion access. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-007.2024-02 |
| Domestic Violence and Gun Ownership | Annual | 1991-2020 | The state-level domestic violence and gun ownership measure denotes whether a state has a law that prohibits domestic violence offenders from owning firearms above and beyond federal law. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Sexism - state - domestic violence and gun ownership. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2023. https://doi.org/10.18128/M130-005.2023-04 |
| Earnings Ratio | Annual | 2015-2022 | The state-level earnings ratio 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. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Sexism - state - earnings ratio. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-001.2024-02 |
| Labor Force Ratio | Annual | 2015-2022 | The state-level labor force ratio 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. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Sexism - state - labor force ratio. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-002.2024-02 |
| Paid Family and Medical Leave | Annual | 2004-2023 | The state-level paid family & medical leave measure denotes whether a state has a law that guarantees paid family & medical leave for employees. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Sexism - state - paid family & medical leave. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-004.2024-02 |
| Proportion of State Legislators Identifying as Female | Annual | 2015-2023 | The measure captures the proportion of state legislators who identify as female. We compute the proportion for the state legislature as a whole and for the state house and senate legislative chambers. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Sexism - state - proportion of state legislators identifying as female. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-006.2024-02 |
| Poverty Ratio | Annual | 2015-2023 | The state-level poverty ratio compares the proportion of females living in poverty to the proportion of males living in poverty in a given state in a given year. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Sexism - state - poverty ratio. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-003.2024-02 |
Sexual and Gender Minority
| Measure | Temporal Frequency | Temporal Range | Description | Citation |
|---|---|---|---|---|
| Proportion Identifying as LGBTQ+ (by state) | Single point in time | Late 2021 - early 2022 | The proportion of a state's population identifying 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). | Christopher Julian, Claire Kamp Dush, Wendy Manning, and David Van Riper. Cisheterosexism - state - proportion of population identifying as LGBTQ+. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2023. https://doi.org/10.18128/M130-008.2023-04 |
| Same-sex households (by county) | Single point in time | 2020 | County-level measures of the (1) proportion of same-sex unions among all unions, (2) proportion of same-sex marriages among all marriages, (3) proportion of same-sex marriages among all same-sex unions, and (4) proportion of same-sex unions among all households. These proportions were computed using data from the 2020 U.S. Decennial Census. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Cisheterosexism - county - same-sex households. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-016.2024-02 |
Politics
| Measure | Temporal Frequency | Temporal Range | Description | Citation |
|---|---|---|---|---|
| County Presidential Results | Every 4 years | 2000-2020 | The county presidential election results measure provides the proportion of votes cast for the Democratic candidate or the Republican candidate in presidential elections. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Politics - county - presidential election results. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-015.2024-02 |
| State Presidential Results | Every 4 years | 1976-2020 | The state presidential election results measure provides the proportion of votes cast for the Democratic candidate or the Republican candidate in presidential elections. | Claire Kamp Dush, Wendy Manning, and David Van Riper. Politics - state - presidential election results. IPUMS Contextual Determinants of Health. Minneapolis, MN: IPUMS. 2024. https://doi.org/10.18128/M130-014.2024-02 |
Loading Data into Stats Packages
IPUMS CDOH data files are distributed in the comma-separated value (CSV) format, with the first row of each CSV containing the variable names. This format provides users with flexibility - CSV files can be opened in popular spreadsheet software packages (e.g., Microsoft Excel or Apple's Numbers) or statistical packages (e.g., Stata, SAS, R, or SPSS). We provide basic instructions for loading CSV files into two popular statistical packages - Stata and R.
Stata
The insheet command in Stata will load an IPUMS CDOH data file. If you have downloaded the Earning Ratio measure and extracted files from the ZIP archive, you will find a CSV called state-sexism-earnings-ratio.csv. To load this CSV into Stata, you will run the following line of code:
- insheet using state-sexism-earnings-ratio.csv
You can then save the file to a Stata file (.dta), or you can analyze the data on their own.
R
There are a variety of ways to read CSV files into R data frames. Three common functions are read.csv, read_csv, and vroom. To load the state-sexism-earnings-ratio.csv using these function:
- read.csv (from base R)
- read.csv("state-sexism-earnings-ratio.csv", stringsAsFactors='FALSE')
- read_csv (from the readr package)
- read_csv("state-sexism-earnings-ratio.csv")
- vroom (from the vroom package)
- vroom("state-sexism-earnings-ratio.csv")
Citation and Use
Users of IPUMS CDOH measures must abide by the citation and use conditions.