doi: 10.1038/s41586-022-04996-4. Epub 2022 Aug 1.
Social capital I: measurement and associations with economic mobility
Raj Chetty # 1, Matthew O Jackson # 2, Theresa Kuchler # 3, Johannes Stroebel # 4, Nathaniel Hendren 5, Robert B Fluegge 6, Sara Gong 7, Federico Gonzalez 6, Armelle Grondin 6, Matthew Jacob 6, Drew Johnston 6, Martin Koenen 6, Eduardo Laguna-Muggenburg 8, Florian Mudekereza 6, Tom Rutter 6, Nicolaj Thor 6, Wilbur Townsend 6, Ruby Zhang 6, Mike Bailey 9, Pablo Barberá 9, Monica Bhole 9, Nils Wernerfelt 9
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- PMID:35915342
- PMCID: PMC9352590
- DOI: 10.1038/s41586-022-04996-4
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Social capital I: measurement and associations with economic mobility
Raj Chetty et al. Nature.2022 Aug.
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Abstract
Social capital-the strength of an individual's social network and community-has been identified as a potential determinant of outcomes ranging from education to health1-8. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers9, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES-which we term economic connectedness-is among the strongest predictors of upward income mobility identified to date10,11. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality12-14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org .
© 2022. The Author(s).
Conflict of interest statement
In 2018, T.K. and J.S. received an unrestricted gift from Facebook to NYU Stern. Opportunity Insights receives core funding from the Chan Zuckerberg Foundation (CZI). CZI is a separate entity from Meta, and CZI funding to Opportunity Insights was not used for this research. M. Bailey, P.B., M. Bhole and N.W. are employees of Meta Platforms. T.K., J.S., S.G. and F.M. are contract affiliates through Meta’s contract with PRO Unlimited. F.G., A.G., M.J., D.J., M.K., T.R., N.T, W.T. and R.Z. are contract affiliates through Meta’s contract with Harvard University. Meta Platforms did not dispute or influence any findings or conclusions during their collaboration on this research. This work was produced under an agreement between Meta and Harvard University specifying that Harvard shall own all intellectual property rights, titles and interests (subject to the restrictions of any journal or publisher of the resulting publication(s)).
Figures
a, The mean SES rank of individuals’ friends versus their own SES percentile ranks. The series in green circles is calculated using the entire friendship network for each individual. The series in orange squares is constructed using each individual’s ten closest friends based on the frequency of public interactions such as likes, tags, wall posts and comments. SES is constructed by combining information on 22 variables to predict median household incomes in individuals’ residential block groups and then ranking individuals relative to others in the same birth cohort (Methods: ‘Variable definitions’).b, Comparison of estimates of homophily in the Facebook data and the Add Health survey. The series in purple squares plots the mean parental income rank of children’s friends against their own parents’ income percentile rank in the Add Health data. The series in green circles presents the analogous relationship in the Facebook data using our SES proxies, restricting the sample to individuals born in 1989–1994 and using their five closest friends from high school to match the Add Health sample as closely as possible (Supplementary Information A.5.2). For each series, we report slopes estimated from a linear regression on the plotted points, with heteroskedasticity-robust standard errors in parentheses.
a, County-level map of EC, defined as twice the share of friends with above-median SES among people with below-median SES.b, ZIP-code-level map of EC in Los Angeles.c, County-level map of average clustering, defined as the share of an individual’s friend pairs who are friends with each other.d, ZIP-code-level map of average clustering in Los Angeles.e, County-level map of volunteering rates, defined as the percentage of individuals who are members of volunteering or activism groups as classified by Facebook.f, ZIP-code-level map of volunteering rates in Los Angeles. We omit counties and ZIP codes where statistics are estimated on fewer than 100 Facebook users with below-median SES. These maps must be viewed in colour to be interpretable. Analogous maps for all ZIP codes in the United States are available athttps://www.socialcapital.org . Extended Data Fig. 1 presents county-level maps of other social capital measures. Maps were made with the QGIS software package.
a, County-level univariate correlations of upward income mobility with social capital measures. Extended Data Table 2 lists the correlation coefficients plotted here.b, Estimates from a multivariable regression of upward income mobility on all variables ina together, standardizing the outcome and dependent variables to have a mean of zero and a standard deviation of one. Upward income mobility is obtained from the Opportunity Atlas and is measured as the predicted household income rank in adulthood for children in the 1978–1983 birth cohorts with parents at the 25th percentile of the national income distribution. Economic connectedness (EC) is twice the share of above-median-SES friends among below-median-SES people. Language connectedness is the share of friends who set their Facebook language to English among users who do not set their language to English, divided by the national share of users who set their language to English. Age connectedness is the share of friends who are aged 35–44 years among users who are aged 25– 34 years, divided by the national share of users aged 35-44 years. Clustering is the share of an individual’s friend pairs who are also friends with each other, averaged over all individuals in the county. Support ratio is the share of friendships between people in the county with at least one other mutual friend in the county. Spectral homophily is the second largest eigenvalue of the row-stochasticized network adjacency matrix, a measure of the extent to which the county-level friendship network is fragmented into separate groups. The Penn State index is an index of participation in civic organizations and other measures of civic engagement. Civic organizations is the number of civic organizations with Facebook pages per 1,000 Facebook users in the county. Volunteering rate is the percentage of Facebook users in the county who are members of volunteering or activism groups. All correlations and regressions are weighted by the number of children in each county whose parents have below-national-median income. Intervals represent 95% confidence intervals calculated using standard errors clustered by commuting zone.
Scatter plot of upward income mobility against economic connectedness (EC) for the 200 most populous US counties. EC is defined as twice the share of above-median-SES friends among below-median-SES individuals living in the county. Upward income mobility is obtained from the Opportunity Atlas and is measured as the predicted household income rank in adulthood for children in the 1978–1983 birth cohorts with parents at the 25th percentile of the national income distribution. We report a slope estimated using an ordinary least squares (OLS) regression on the 200 largest US counties by population, with standard errors clustered by commuting zone in parentheses. We also report the population-weighted correlation between upward mobility and EC across both the 200 largest counties as well as all counties, with standard errors (clustered by commuting zone) in parentheses. The correlations and regression are weighted by the number of children in each county whose parents have below-national-median income.
a, County-level univariate correlations of upward income mobility with economic connectedness (EC) and other county characteristics obtained from external datasets (see Supplementary Information A.5 for details). Upward income mobility is obtained from the Opportunity Atlas and is measured as the predicted household (HH) income rank in adulthood for children in the 1978–1983 birth cohorts with parents at the 25th percentile of the national income distribution. Income segregation is defined using a Theil (entropy) index. Racial segregation is defined using Theil's H-index across four groups (white, Black, Hispanic, other). See Supplementary Information A.5.1 for details. The Gini coefficient is defined as the raw Gini coefficient estimated using tax data minus the income share of the top 1% to obtain a measure of inequality among the bottom 99% in each county. The rest of the variables are all obtained from the Opportunity Atlas. Test scores are measured in third grade, which includes children who are 8 to 9 years old.b, Estimates from a single multivariable regression of upward mobility on a subset of variables froma, with both the outcome and dependent variables standardized to have a mean of zero and a standard deviation of one. The variables used inb are the seven variables froma that have the largest univariate correlations with upward mobility (except the share of households above the poverty line, which is highly correlated with median household incomes), which include all of the strongest predictors of mobility identified in prior work. All correlations and regressions are weighted by the number of children in each county whose parents have below-national-median income. Intervals represent 95% confidence intervals calculated using standard errors clustered by commuting zone.
Scatter plot of economic connectedness (EC) against median household income (based on the 2014–2018 ACS) by ZIP code. EC is defined as twice the share of above-median-SES friends among below-median-SES individuals. The points are coloured by the level of upward income mobility for children who grew up in the ZIP code. Upward income mobility is obtained from the Opportunity Atlas and is measured as the predicted household income rank in adulthood for children in the 1978–1983 birth cohorts with parents at the 25th percentile of the national income distribution.
This figure presents county-level maps analogous to those in Fig. 2 for other measures of social capital. These maps must be viewed in color to be interpretable. Age connectedness (Panel A) is the average share of friends who are 35 to 44 among users who are 25 to 34, normalized by the share of individuals who are 35 to 44. Language connectedness (Panel B) is the average share of friends who set their Facebook language to English among individuals who do not set their Facebook language to English, normalized by the share of people who set their Facebook language to English. Support ratio (Panel C) is the share of friendships between people in the county who have at least one other friend in the county in common. Spectral homophily (Panel D) is the second largest eigenvalue of the row-stochasticized network adjacency matrix, a measure of the extent to which the county-level friendship network is fragmented into separate groups. Civic organizations (Panel E) is the number of civic organizations with Facebook pages per 1,000 Facebook users in the county. See the Economic connectedness, Cohesiveness, and Civic engagement sections of Main Text and Methods for details on the definitions and construction of these measures.
This figure shows binned scatter plots of children’s mean SES ranks in adulthood against their own parents’ SES ranks. Each point plots the mean SES rank of children who have parents at a given percentile of the SES distribution. The series in circles is based on data from Facebook, with SES rank calculated as described in the Variable Definitions section of Methods. The series in squares is based on administrative tax data analysed in prior work, with SES ranks corresponding to household income ranks. The sample for both series is children born between 1980 and 1982. In both samples, children’s SES ranks are based on their ranks within their birth cohort among children linked to parents, while parents’ SES ranks are based on their ranks relative to other parents in the same group of parents linked to children born between 1980–82. We report a slope estimated using a linear regression for each series, with heteroskedasticity-robust standard errors in parentheses.
This figure replicates the across-county correlations shown in Fig. 3a with two different outcome variables: high school completion rates (Panel A) and teen birth rates (Panel B) for children with parents at the 25th percentile of the national income distribution. These outcome variables are obtained from the Opportunity Atlas. See notes to Fig. 3 for further details.
Panel A presents binned scatter plots of upward income mobility against the degree of clustering in networks across ZIP codes in four counties in Ohio: Summit County (Akron), Cuyahoga County (Cleveland), Franklin County (Columbus), and Mahoning County (Youngstown). Clustering is defined as the share of an individual’s friend pairs who are also friends with each other, averaged over all individuals in a ZIP code. Panel B presents analogous ZIP code-level binned scatter plots of upward mobility against economic connectedness. Panel C presents ZIP code-level binned scatter plots of economic connectedness against clustering coefficients. To construct these binned scatter plots, we group ZIP codes within each county into ten (population-weighted) bins based on the relevant social capital measure shown on the horizontal axis and plot the mean (population-weighted) level of the outcome variable against the social capital measure within each bin. Panel D presents kernel density plots of the distribution of ZIP-code-level correlations between upward mobility and several social capital measures across counties for the 250 most populous counties. To construct these distributions, we first estimate correlations between upward income mobility and the social capital measure of interest at the ZIP code level in each county, and then plot the distribution of these correlations. All correlations and distributions are weighted by the number of children whose parents earn less than the national median household income in each ZIP code and county, respectively.
This figure presents a binned scatter plot of counties’ causal effects on upward mobility against economic connectedness. The binned scatter plot is constructed in the same way as described in the notes to Extended Data Figure 4, using 20 bins of Economic Connectedness instead of 10 and weighting by the precision (inverse of standard error squared) of the causal effect estimates. Causal effects on upward mobility are the annual exposure effect estimates constructed by Chetty and Hendren by analyzing cross-county movers. These annual exposure effects are multiplied by 20 so that they can be interpreted as the causal effect of growing up in a given location from birth to age 20 on an individual’s household income percentile rank in adulthood. The slope is estimated using an OLS regression of the causal effect estimates on EC, weighting by the precision of the causal effect estimates. The signal correlation is calculated by dividing the raw (precision-weighted) correlation between the causal effects and EC by the square root of the precision-weighted reliability of the estimated causal effects.
This figure presents binned scatter plots of children’s predicted income ranks in adulthood against cross-SES connectedness by county, separately for children with low-income (25th percentile) parents and high-income (75th percentile) parents. Data on children’s outcomes are obtained from the Opportunity Atlas. We define cross-SES connectedness as the normalized share of friends for an individual in one SES group who belong to the other SES group. For below-median SES individuals, cross-SES connectedness is the same as our baseline definition of economic connectedness. Hence, the series in orange circles in Panel A is a binned scatter plot analog of Fig. 4, pooling data from all counties (see notes to Extended Data Figure 4 for details on construction of binned scatter plots). For above-median-SES individuals, cross-SES connectedness is twice the share of their friends who are low-SES. Panel B replicates Panel A, controlling for the share of high-SES individuals in each county. The series in Panel B are constructed by first residualizing predicted household income ranks and cross-SES connectedness on the share of high-SES people using univariate OLS regressions, and then constructing a binned scatter plot of the residuals after adding back the means of each variable for scaling purposes. We report estimates of the slope of each series based on OLS regressions with standard errors, clustered by commuting zone, in parentheses.
Comment in
- The social connections that shape economic prospects.Angrist N, Sacerdote B.Angrist N, et al.Nature. 2022 Aug;608(7921):37-38. doi: 10.1038/d41586-022-01843-4.Nature. 2022.PMID:35915246No abstract available.
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