Abstract
This article presents data on social capital at the United States’ county-level. Following Rupasingha et al. (2006), the social capital index captures the common factor among density measures of 10 different types of associations, voter turnout rates, U.S. decennial census participation rates, and the number of non-profit organizations. Based on Knack (2003), we create associational densities measures as a proxy for both bridging and bonding social capital. Including data on income inequality, racial diversity, minority group size, average household income, educational attainment, the ratio of a family household, the size of migration population, and female labor market participation rates, the data covers 3,104 U.S. counties for both 2009 and 2014. This paper includes descriptive statistics and figures. This data article is associated with the article “Race, Inequality, and Social Capital in the U.S. Counties.”
Keywords: Racial diversity, Income inequality, Social capital, U.S. counties
Specifications Table
| Subject | Sociology and Political Science |
| Specific subject area | Racial diversity, income inequality, social capital in the U.S. counties |
| Type of data | Comma-separated values, tables, figures |
| How data were acquired | The original data are from the websites of the Northeast Regional Center for Rural Development at Penn State University, the American Community Survey of the U.S. Census Bureau, and the U.S. Department of Agriculture. |
| Data format | Comma Separated values & Analysed |
| Parameters for data collection | All U.S. counties for both 2009 and 2014 |
| Description of data collection | The Northeast Regional Center for Rural Development at Penn State University provides raw data on the social capital index. Based on the two most recent social capital data, in both 2009 and 2014, which share the same component measures, other county-level data were added from the American Community Survey of the U.S. Census Bureau and the U.S. Department of Agriculture. |
| Data source location | There are three primary data sources: Northeast Regional Center for Rural Development at Penn State University, the U.S. Census Bureau's American Community Survey, and the Economic Research Service of the United States Department of Agriculture. All variables were separately downloaded and merged. |
| Data accessibility | Repository name: Mendeley Data |
| Data identification number: 10.17632/ps8mtmtmvv.2 | |
| Direct URL to data: https://data.mendeley.com/datasets/ps8mtmtmvv/2 | |
| Related research article | Mi-son Kim, Dongkyu Kim, and Natasha Altema McNeely, “Race, Inequality, and Social Capital in the U.S. Counties” |
| https://doi.org/10.1080/03623319.2020.1799178 |
Value of the Data
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Social scientists who are interested in the dynamics created by income inequality, racial diversity, and social capital in the U.S. Counties can easily utilize the dataset.
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This dataset also provides other county-level covariates that can be utilized by social science and humanities research.
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This dataset provides the most comprehensive measure of social capital of U.S. Counties for two time periods.
1. Data Description
This Data in Brief article is associated with the article “Race, Inequality, and Social Capital in the U.S. Counties.” [2] The data provided in this article were constructed to understand the variations of social capital across U.S. counties by examining the interaction between income inequality and ethnic diversity. Although the concept of social capital has been much debated, it can be largely defined as intangible social assets that individuals can utilize or enjoy by engaging with others. In that regard, Putnam [6] defines the concept as “networks, norms, and trust that enable participants to act together more effectively to pursue shared objectives.” Following Rupasingha et al. [7], the social capital index measures the extent to which individuals engage with others at the county-level.
The social capital index measures the common factor among four different types of variables: (1) the associational density of 10 different types of organizations (civic organizations, bowling centers, golf clubs, fitness centers, sports organizations, religious organizations, political organizations, labor unions, business organizations, and professional organizations), (2) the turnout rates for the previous presidential elections, (3) the response rate to the Census Bureau's decennial census, and (4) the number of non-profit organizations. The data are provided by the Northeast Regional Center for Rural Development at Penn State University. The index data has been updated four times since 1990. As the index has adopted a new associational typology for the 2000s data points, we only included data with a consistent typology. Thus, we have a social capital index for both 2009 and 2014, the two most recent data points. Table 1 reports all component measures’ summary statistics for each year, while Fig. 1 displays each county's average scores on the map.
Table 1.
Social capital index components.
| 2009 |
2014 |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| N | mean | SD | min | max | N | mean | SD | min | max | |
| Civic orgs | 3106 | 9.0 | 21.7 | 0 | 538 | 3139 | 8.3 | 20.5 | 0 | 546 |
| Bowling centers | 3106 | 1.4 | 3.0 | 0 | 58 | 3139 | 1.2 | 2.6 | 0 | 48 |
| Golf clubs | 3106 | 3.8 | 7.3 | 0 | 142 | 3139 | 3.6 | 7.3 | 0 | 141 |
| Fitness centers | 3106 | 9.7 | 30.1 | 0 | 738 | 3139 | 10.1 | 33.6 | 0 | 845 |
| Sport orgs | 3106 | 0.3 | 1.1 | 0 | 29 | 3139 | 0.3 | 1.3 | 0 | 37 |
| Religious orgs | 3106 | 57.7 | 123.1 | 0 | 3258 | 3139 | 58.5 | 125.2 | 0 | 3275 |
| Political orgs | 3106 | 0.7 | 3.0 | 0 | 66 | 3139 | 0.8 | 3.8 | 0 | 76 |
| Labor orgs | 3106 | 4.8 | 15.0 | 0 | 292 | 3139 | 4.5 | 14.2 | 0 | 283 |
| Business orgs | 3106 | 5.3 | 14.4 | 0 | 323 | 3139 | 5.0 | 14.0 | 0 | 290 |
| Professional orgs | 3106 | 2.1 | 8.9 | 0 | 214 | 3139 | 2.1 | 9.0 | 0 | 210 |
| Voter turnout | 3106 | 0.6 | 0.1 | 0.17 | 2.079 | 3139 | 0.7 | 0.1 | 0.35 | 1.116 |
| Census rate | 3106 | 0.7 | 0.1 | 0 | 0.95 | 3139 | 0.7 | 0.1 | 0 | 0.95 |
| NGOs | 3104 | 489.1 | 1472.6 | 1 | 41,125 | 3139 | 458.4 | 1381.6 | 0 | 37,547 |
Fig. 1.
The Social Capital Index across U.S. counties. The average scores for both 2009 and 2014. The darker the region is, the more social capital there is in each county. The decile cut-off values differentiate colors.
One of the independent variables is racial diversity. From data provided by the U.S. Census Bureau's American Community Survey (ACS), the racial diversity index was calculated as one minus the Herfindahl index of 7 ethnic groups (Non-Hispanic white, Hispanic, Black, Indian, Asian, Hawaiian, and two-more). It measures the probability that two people randomly chosen from a county belong to different ethnic groups (see, e.g., Alesina et al. 1999). Fig. 2 displays each county's average scores of diversity index on the map. Another key independent variable for the associated article is income inequality. Based on data also provided by the ACS, the variable measures the Gini index, which takes 0 for a perfectly equal distribution of income and 1 for perfectly unequal income distribution. Fig. 3 shows the geographical distribution of income inequality across the U.S. Counties. Table 2 shows the list of counties at both the top and the bottom ten ranks for these three key variables in 2014.
Fig. 2.
Racial diversity across U.S. counties. The average scores for both 2009 and 2014. The darker the region is, the more diversity there is in each county. The decile cut-off values differentiate colors.
Fig. 3.
Income inequality across U.S. counties. The average scores for both 2009 and 2014. The darker the region is, the more inequality there is in each county. The decile cut-off values differentiate colors.
Table 2.
2014 Rankings of social capital, inequality, and diversity.
| Social capital: top 10 | Income Inequality: top 10 | Racial Diversity: top 10 |
|---|---|---|
| Hinsdale County, CO | Randolph County, GA | Aleutians West Census Area, AK |
| Lexington city, VA | Calhoun County, GA | Queens County, NY |
| Mineral County, CO | McMullen County, TX | Maui County, HI |
| Motley County, TX | New York County, NY | Alameda County, CA |
| Thomas County, NE | Borden County, TX | Aleutians East Borough, AK |
| Hooker County, NE | Baylor County, TX | Hawaii County, HI |
| Griggs County, ND | Orleans Parish, LA | Fort Bend County, TX |
| Grant County, NE | Corson County, SD | Kauai County, HI |
| Kiowa County, KS | Campbell County, SD | Solano County, CA |
| Smith County, KS | Eastland County, TX | Honolulu County, HI |
| Social Capital: bottom 10 | Income Inequality: bottom 10 | Racial Diversity: bottom 10 |
| Sioux County, ND | Yakutat City and Borough, AK | Tyler County, WV |
| Jim Hogg County, TX | Bristol Bay Borough, AK | Jackson County, KY |
| Webb County, TX | Spencer County, KY | Holmes County, OH |
| Hancock County, TN | Emery County, UT | Magoffin County, KY |
| Zavala County, TX | Lake of the Woods County, MN | Dickenson County, VA |
| Loving County, TX | Sublette County, WY | Osage County, MO |
| Maverick County, TX | Chattahoochee County, GA | Lincoln County, WV |
| Starr County, TX | Grant County, NE | Leslie County, KY |
| Shannon County, SD | Power County, ID | Blaine County, NE |
| Chattahoochee County, GA | Clark County, ID | Keya Paha County, NE |
We further measured two different types of social capital by utilizing ten associational density variables. Scholars in the literature suggest that social capital has two different types: bridging and bonding social capital [3], [4], [5]. According to Putnam [5], bridging social capital can be defined as an open network that crosscuts, thus bridges, the existing social cleavages while bonding social capital is an inward-looking network that fortifies existing social interests. We labeled the former as ‘Putnam-type’ and the latter ‘Olson-type’ following Knack [3]. Based on Knack [3] and Rupasingha et al. [7], we measured bridging social capital (Putnam-type) with the associational density of the first six organizations (religious organizations, civic organizations, bowling centers, fitness centers, golf clubs, and sports organization) and bonding social capital (Olson-type) with the same density of the remaining associations (business organization, labor union, political organizations, and professional organizations). Both Fig. 4 and Fig. 5 display each variable on the map respectively.
Fig. 4.
Putnam type (bridging) social capital index across U.S. counties. The average scores for both 2009 and 2014. The darker the region is, the more bridging social capital there is in each county. The decile cut-off values differentiate colors.
Fig. 5.
Olson type (bonding) social capital index across U.S. counties. The average scores for both 2009 and 2014. The darker the region is, the more boding social capital there is in each county. The decile cut-off values differentiate colors.
We included other correlates of social capital in the dataset. Following the typology provided by the U.S. Department of Agriculture, the Rural-Urban Continuum Codes (RUCC), the urban and rural variables were dummy coded by taking suburban counties as a reference category. The RUCC scheme provides nine categories that distinguish metropolitan counties by population, and nonmetropolitan counties by population and adjacency to the metro area. We utilized three categories of metropolitan counties to construct a dummy variable for urban counties while using two categories of nonmetropolitan counties that are not adjacent to the metro area to construct the rural indicative variable. It is often believed that rural areas provide a favorable environment for social capital. In the statistical estimation of the associated article, the remaining category was considered as suburban areas and omitted in the regression analysis.
All other county-level variables are compiled by utilizing the ACS database. For the income variable, we used the mean income in the past 12 months with the inflation-adjusted dollars. Then, we transformed the average household income with the natural logarithm. The dataset also has the educational attainment variable that measures the percentage of residents who have at least some college education per county. It is well known that socioeconomic status is positively associated with social capital. Because social capital would be difficult to form in a fluid county, we include the share of the non-migratory population in our dataset. From the ACS's county-to-county migration flow data, we calculated the percentage of non-movers out of the county population. In a similar vein, it is expected that the family-oriented community would provide a good environment for social capital. Thus, the dataset includes the percentage of family households out of the total number of households for each county. Lastly, we include the size of the female workforce. The theoretical explanations about how traditional gender roles affect social capital are unsettled. Following Rupasingha et al. [7], we considered this variable to test the effect of women's traditional role as housewives empirically. Table 3 presents the summary statistics for all covariates over 3139 counties for both 2009 and 2014.
Table 3.
Descriptive statistics.
| Variable | N | mean | SD | Min | Max |
|---|---|---|---|---|---|
| Social capital index | 6245 | −0.007 | 1.250 | −3.925 | 9.149 |
| Racial diversity index | 6245 | 0.286 | 0.183 | 0 | 0.769 |
| Income Inequality index | 6245 | 0.436 | 0.036 | 0.207 | 0.652 |
| Urban | 6244 | 0.372 | 0.483 | 0 | 1 |
| Rural | 6244 | 0.271 | 0.444 | 0 | 1 |
| Ln(Average Household Income) | 6245 | 10.942 | 0.224 | 10.259 | 11.934 |
| Education (some college) ratio | 6245 | 0.483 | 0.109 | 0.181 | 0.886 |
| Non-migration population ratio | 6234 | 0.859 | 0.046 | 0.478 | 0.997 |
| Female workforce ratio | 6245 | 0.701 | 0.076 | 0.361 | 1 |
| Family household ratio | 6245 | 0.523 | 0.068 | 0.233 | 0.902 |
| Putnam (bridging associations) | 6245 | 1.254 | 0.653 | 0 | 6.887 |
| Olson (bonding associations) | 6245 | 0.142 | 0.151 | 0 | 2.253 |
2. Experimental Design, Materials and Methods
Data construction for the associated article was constrained by the availability of data on social capital. Given social capital data for both 2009 and 2014, all relevant variables were compiled utilizing various data sources. Table 4 provides detailed information about all variables included in the dataset, including primary sources. These raw data are publicly available. However, putting them together to create correlates of social capital at the county-level requires careful handling of the data to align both temporal and geographical units. The Federal Information Processing Standards (FIPS), a four-digit county code, were used to match data points across different data sources. Furthermore, all data provided by the U.S. Census Bureau's ACS utilize the 5-year average estimates so that the dataset contains the least amount of missing values. With the constructed dataset, the associated article examined the variations of social capital at the county-level by utilizing two-stage multilevel regression analysis with year fixed effect [1,8,9]. Researchers could easily re-use or expand our dataset to better understand the variation of social capital at the county-level.
Table 4.
Variable description and data sources.
| Variable Description | Data source |
|---|---|
| FIPS - Federal Information Processing Standard, four-digit county codes | National Institute of Standards and Technology |
| sk - Social capital index – 13 components + population data | Northeast Regional Center for Rural Development |
| 1. assn – Associational density of 10 types of organizations (per 1000 people) | |
| 1. relig (# of religious organization), 2. civic (# of civic organization), 3. bus (# of business organization, 4. pol (# of political organization), 5. prof (# of professional organization), 6. labor (# of labor unions), 7. bowl (# of bowling centers), 8. fitns (# of fitness centers), 9. golf (# of golf clubs), 10. sport (# of sports organization), & 11. pop (County population) | |
| 2. pvote – previous presidential election turnout | |
| 3. respn – US Census response rate | |
| 4. nccs – # of non-profit organizations | |
| gini – Gini coefficient | American Community Survey, US Census Bureau |
| eth_div – Ethnic diversity: 7-category diversity measure | American Community Survey, US Census Bureau |
| 1. p_white (Non-Hispanic white%), 2. p_hispanic (Hispanic%), 3. p_black (Black%), 4. p_indian (American Indians%), 5. p_asian (Asian%), 6. p_hawaiian (Pacific Islander%), 7 p_tomore (Other%) | |
| urban & rural – Dummy variables for urban and rural counties | Economic Research Service, USDA |
| fam_household –% of family household | American Community Survey – US Census Bureau |
| female_wforce –% of female labor market participation | American Community Survey – US Census Bureau |
| educ –% of people with at least some college education | American Community Survey – US Census Bureau |
| income – Average household income | American Community Survey – US Census Bureau |
| Nonmover –% of non-migratory population | American Community Survey – US Census Bureau |
| Putnam – Bridging social capital: associational density for 6 components of sk: relig, civic, bowl, fitns, sport, & golf | Authors’ calculation |
| Olson – Bonding social capital: associational density for 4 components of sk: bus, pol, prof, & labor | Authors’ calculation |
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.
Acknowledgments
The authors would like to thank the anonymous reviewers for their feedback. We would also like to thank the Center for Survey Research and Policy Analysis at the University of Texas Rio Grande Valley for providing the financial support for the open access publication.
Footnotes
Supplementary material associated with this article can be found in the online version at doi:10.1016/j.dib.2021.106717.
Appendix. Supplementary materials
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Associated Data
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