Social capital I: measurement and associations with economic mobility

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.

Social capital II: determinants of economic connectedness

“…Following our companion paper 7 , we measure the degree of cross-class interaction-which we term economic connectedness, or EC-as the share of friends with above-median SES ('high SES') among people with below-median SES ('low SES') divided by 50%, to quantify the average degree of under-representation of high-SES friends among people with low SES. A value of 0 for EC implies that a network has no connections between people with low and high SES, whereas a value of 1 implies that people with low SES have an equal number of low-and high-SES friends.…”

“…Low levels of social interaction across class lines have generated widespread concern [1][2][3][4] and are associated with worse outcomes, such as lower rates of upward income mobility [4][5][6][7] . Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper 7 .…”

“…Low levels of social interaction across class lines have generated widespread concern [1][2][3][4] and are associated with worse outcomes, such as lower rates of upward income mobility [4][5][6][7] . Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper 7 . We show that about half of the social disconnection across socioeconomic lines-measured as the difference in the share of high-socioeconomic status (SES) friends between people with low and high SES-is explained by differences in exposure to people with high SES in groups such as schools and religious organizations.…”

“…In addition to being of potential concern in its own right, a lack of interaction between different groups of people is associated with worse economic and social outcomes [4][5][6] . For example, in our companion paper 7 , we used data on social networks from Facebook to show that communities in which people with low SES interact less with people with high SES exhibit less upward income mobility across generations.…”

“…We use data on the social networks of 70.3 million Facebook users between the ages of 25-44 years to construct and publicly release (https://www.socialcapital.org) privacy-protected measures of exposure and friending bias for each high school, college and ZIP code in the United States. As in previous research using Facebook data 7,[12][13][14][15][16] , we use social network data as a proxy for real-world friendships rather than online interactions per se; as a result, our analysis does not shed light on the effects of online social networks themselves on exposure and friending bias.…”