Social capital I: measurement and associations with economic mobility

Measuring social capital has proven to be more challenging than measuring other forms of capital, such as financial or human capital. Data from online social networking platforms offer a path to solving this problem. The new measures of social capital constructed here provide a rich picture of how social capital varies across areas in the United States. Different notions of social capital—connectedness across socioeconomic lines, the cohesiveness of a community and civic engagement—exhibit highly different spatial patterns. Many communities are rich in one form of social capital but poor in others.

Distinguishing these forms of social capital is important because some types of social capital are more correlated with certain outcomes than others. For instance, economic connectedness (EC)—the share of high-SES friends among low-SES people—is strongly associated with upward income mobility, whereas other forms of social capital are not. Areas with higher EC have large positive causal effects on children’s prospects for upward mobility. We caution, however, that this finding does not imply that EC is the best or most important measure of social capital in general. EC may be the best predictor of economic mobility because mobility is essentially a measure of the degree to which individuals can increase their own SES, making it natural that links to higher-SES individuals are related to that outcome. This is consistent with hypotheses that bridging capital is useful specifically for getting ahead (rather than simply getting by). For other outcomes, other social capital indices that we construct here may be stronger predictors. For example, differences in life expectancy among individuals with low income across counties are more strongly predicted by network cohesiveness measures (clustering coefficients and support ratios) than EC (Supplementary Fig. 11 and Supplementary Information C.3).

Our analysis raises three sets of questions for future research. First, it would be useful to conduct systematic studies of the forms of social capital that matter for other outcomes; for example, to determine which forms of social capital matter for health behaviours or the formation of political preferences. The publicly available statistics constructed here can be used to study many such questions.

Second, it would be valuable to build on the methods developed here and construct analogous measures of social capital beyond the United States, either using social network data or other sources of network information such as financial transactions or mobile phone data. Although many of the lessons obtained from our analysis of the United States are likely to generalize more broadly, international comparisons would enrich our understanding of social capital and its determinants.

Finally, it would be useful to directly study whether efforts to increase economic connectdness can increase intergenerational income mobility. Doing so requires an understanding of the determinants of EC and potential interventions to increase it. We address these questions in the companion paper, in which we study why economic connectedness varies with SES and how we can increase connectedness among individuals with low SES.