Social capital I: measurement and associations with economic mobility

Rates of upward income mobility—children’s chances of rising up the income distribution conditional on growing up in low-income families—vary substantially across areas in the United States. A large body of literature has sought to understand and explain these differences. A widely discussed hypothesis, based on indirect proxies and ethnographic evidence, is that differences in economic mobility across areas may be related to differences in social capital.

In this section, we study this hypothesis by analysing the associations between the measures of social capital constructed above and economic mobility across areas. We obtain statistics on intergenerational income mobility and other related outcomes, such as high school graduation rates and teenage birth rates, from the publicly available Opportunity Atlas, which constructs these statistics on the basis of Census and tax data covering all children born in the United States between 1978 and 1983. We focus on correlations between upward mobility and social capital across areas rather than individuals because area-level variation is arguably more likely to be driven by institutional, policy-relevant factors than individual-level variation. Furthermore, we have precise measures of economic mobility (constructed using tax data) at the area level. At the individual level, estimates of income mobility using Facebook data have greater measurement error, which could inflate correlations between one’s own outcomes and friends’ SES.

We begin by examining correlations between social capital and economic mobility across counties and then turn to a more granular ZIP-code-level analysis.

County-level correlations

Figure 3a reports univariate correlations (weighted by the number of children with below-national-median parental income) across counties between each measure of social capital constructed above and upward income mobility (Extended Data Table 2). We define upward income mobility in each county as the average income percentile rank in adulthood of children who grew up in that county with parents at the 25th percentile of the national parental household income distribution. EC is strongly positively correlated with upward income mobility (correlation = 0.65, s.e. = 0.04), whereas all the other measures of social capital are not strongly related to mobility.

Figure 4 shows the relationship between EC and mobility non-parametrically by presenting a scatter plot of upward income mobility versus EC for the 200 most populous counties. Children who grow up in counties where low-SES individuals have more high-SES friends tend to have much higher rates of upward mobility. As an example, low-SES individuals have a much larger share of high-SES friends in Minneapolis (49%, corresponding to an EC of 0.98) compared with Indianapolis (32%, EC of 0.65). Correspondingly, children who grow up in low-income families have much higher incomes in adulthood in Minneapolis than in Indianapolis. In Minneapolis, children reach the 43rd percentile of the household income distribution on average at age 35 years (roughly US$34,300 in 2015), compared with the 34th percentile ($24,700) in Indianapolis.

On average, an increase in EC of 0.5 units (equivalent to raising the share of high-SES friends among low-SES people from 25% to 50%, and approximately equal to the difference in EC between the 10th and 90th percentile counties) is associated with an 8.2 percentile increase in children’s incomes in adulthood. This is a large difference: for context, note that children with high-income (above-median) parents end up 17 percentiles higher in the household income distribution on average than children with low-income (below-median) parents (Extended Data Fig. 2). There are similarly strong associations between EC and many other outcomes related to social mobility, such as high school completion rates and teenage birth rates (Extended Data Fig. 3).

Returning to Fig. 3a, other measures of connectedness across groups—between non-English and English speakers or between younger and older individuals—are less strongly associated with upward mobility. Communities with greater connectedness across groups in general do not necessarily have higher levels of upward mobility. Instead, connections across class lines are what appear to matter.

Measures of network cohesion (for example, clustering and support ratios) also do not strongly correlate with observational measures of upward income mobility. This is because there are many areas that exhibit highly cohesive networks—and thus might be thought of as tightly knit communities—but that nevertheless have low levels of EC and correspondingly low levels of upward mobility. A potential explanation for this pattern is that although those communities have strong social connections among their predominantly low-income residents (bonding social capital), they are not well connected to individuals from higher-SES backgrounds who can provide the types of resources, opportunities and information needed to rise economically (bridging social capital).

Finally, we examine associations between economic mobility and measures of civic engagement. The widely used Penn State index of participation in civic organizations has a correlation of 0.06 across counties with upward mobility. There are similarly weak associations of upward mobility with our measures of the density of civic organizations and volunteering rates. The difference between these findings and previous work that has found stronger associations between civic engagement and economic mobility is primarily because we weight our correlations by the number of children with below-national-median parental income. As a result, rural areas—where civic engagement is more strongly correlated with mobility—receive lower weight in our correlations (Supplementary Information C.2).

When we regress measures of upward mobility on standardized versions of all of the social capital measures together, EC remains the strongest predictor of upward mobility by a significant margin. By contrast, measures of civic engagement and network cohesiveness have coefficients near zero (Fig. 3b). Furthermore, a Lasso regression selects EC as the first social capital measure to include and places greater weight on EC than on other measures (Supplementary Fig. 2a). Moreover, the incremental R² of including EC conditional on all the other social capital measures is an order of magnitude larger than the incremental R² of including any of the other measures (Supplementary Fig. 2c).

ZIP-code-level correlations

When studying variation across ZIP codes instead of counties in the United States, we find very similar correlations between upward income mobility and social capital measures (Extended Data Table 2 and Supplementary Fig. 3). In particular, upward mobility is highly correlated (0.69) with EC across ZIP codes (Supplementary Fig. 4), but more weakly correlated with all the other social capital measures. Going from the 10th to the 90th percentile ZIP code in the United States in terms of EC is associated with an 11 percentile increase in the mean adult income rank of children growing up in low-income families. This value is comparable to the 12.6 percentile difference in mean income ranks between Black children and white children with low-income parents.

Next, we examine the association between social capital measures and upward mobility across ZIP codes within the same county to assess whether the ZIP-code-level relationships differ across counties. The ZIP-code-level correlation between EC and mobility is strongly positive within nearly all counties. By contrast, there is substantial heterogeneity in the ZIP-code-level relationships between other measures of social capital and mobility across counties. Extended Data Fig. 4a illustrates this by presenting binned scatter plots of the relationship between upward mobility and clustering coefficients by ZIP code across four cities in Ohio: Akron, Cleveland, Columbus and Youngstown. In Cleveland and Columbus, where baseline levels of clustering are relatively low, neighbourhoods with higher clustering coefficients have significantly higher levels of upward income mobility. But in Akron and Youngstown, which generally have higher levels of clustering, clustering and upward mobility are negatively correlated. Hence, it is not that clustering coefficients have no signal in predicting economic mobility; instead, their relationship with mobility varies across places, in part depending on their average levels of clustering.

The relationship between EC and mobility is much more stable across the same four cities, as shown in Extended Data Fig. 4b. The relationships between clustering coefficients and EC closely match those for mobility. In Cleveland and Columbus, clustering coefficients and EC are positively related, whereas in Akron and Youngstown, they are negatively related (Extended Data Fig. 4c). Building on these examples, we find that clustering is often positively correlated with EC and mobility when clustering is low, whereas it is often negatively correlated with both EC and mobility when levels of clustering are high. These patterns suggest that EC may mediate the relationship between other social capital measures and mobility. That is, the links between other social capital measures and mobility might run through economic connectedness.

Extended Data Fig. 4d generalizes the four examples by plotting the distributions of the correlations between upward mobility and various measures of social capital across ZIP codes within the 250 most populous counties in the United States. For EC, the distribution sharply peaks around 0.7, showing that economic connectedness and mobility are positively correlated across ZIP codes in nearly all counties. By contrast, the other social capital measures exhibit more diffuse distributions across counties. Notably, these differences are not just due to sampling error in the correlations. Adjusting for noise by calculating the reliability of the estimates and the standard deviation of the latent signal distribution produces similar conclusions (Supplementary Table 2).

To summarize, measures of social capital that are based solely on the structure of the network graph (network cohesion) or purely on individuals’ civic behaviours (civic engagement) do not have robust associations with observational measures of economic mobility across areas. Measures that combine data on networks with information on SES have stronger and more stable relationships with economic mobility.

Having established that EC stands out among social capital measures as a strong predictor of economic mobility, in the remainder of the paper we focus on understanding the source of this correlation; that is, why more economically connected areas tend to have higher rates of economic mobility.