Social capital I: measurement and associations with economic mobility

There are many theories for why economic connectedness could have a positive causal effect on upward income mobility. For example, economic mobility might be facilitated by connections to people who can shape aspirations or provide access to information and job opportunities. This interpretation is consistent with the argument that bridging capital—a concept that encompasses EC—is particularly valuable for ‘getting ahead’. However, there are also many alternative explanations for the correlation between EC and mobility that do not rely on a causal effect of connectedness on mobility. We evaluate three such possibilities in turn—reverse causality, selection effects, and omitted variables—with the broader aim of better understanding the channels through which connectedness and mobility are related.

Reverse causality

The first alternative explanation for the correlation between connectedness and mobility we consider is reverse causality, whereby greater economic mobility could lead to greater EC. Specifically, in our baseline analysis, we correlated rates of upward income mobility with EC measured among adults. Because friendships and SES are measured in adulthood, economic connectedness may itself be influenced by rates of intergenerational mobility. For example, in places with high upward mobility, many children from low-SES families have high incomes as adults and may retain friendships with individuals who remain at a low SES. This would lead to high-mobility areas having a high rate of friendships among people with different SES in adulthood, even in the absence of any effect of economic connectedness on mobility.

To assess the importance of reverse causality, we examine the association between economic mobility and childhood EC, on the basis of childhood friendships and parental SES. Because childhood friendships are made before people start working, they cannot be directly influenced by rates of economic mobility. We measure childhood EC using two sources of data, each of which has benefits and drawbacks (Methods: ‘Measuring connectedness’). The first is based on the high school friends and parental SES of individuals in our primary Facebook analysis sample. The second uses data from Instagram for individuals aged 13–17 years in 2022, measuring parental SES based on the teenagers’ residential ZIP codes and mobile phone models.

The correlation between upward mobility and childhood EC across counties remains high with both of these measures: 0.44 using parental SES in the Facebook data and 0.62 using the Instagram data (Extended Data Table 2). Since upward mobility remains strongly correlated with childhood EC, any causal effects of mobility on connectedness must account for, at most, a small share of the correlation between the two variables.

Causal effects of place versus selection

A second potential non-causal explanation for the link between economic connectedness and mobility is selection. Specifically, one might be concerned that the types of families who live in high-EC areas may inherently have higher rates of mobility (for example, because they have more education or wealth), independent of where they live. For example, the types of low-income families who choose to live in high-EC areas may have demographic characteristics or make other choices that increase their children’s rates of upward mobility even in the absence of any causal effect of EC on outcomes.

One of the most salient forms of residential sorting in the United States is segregation by race and ethnicity. Such segregation could lead to a correlation between EC and mobility. For example, areas with larger Black populations tend to have lower levels of EC (Supplementary Table 3). Because Black Americans have lower rates of upward mobility than white Americans—which could be due to factors such as discrimination that are unrelated to differences in EC—differences in racial composition across neighbourhoods could induce a spurious association between EC and mobility when pooling across races.

The simplest way of assessing the importance of differences by race would be to replicate our baseline correlations conditioning on race, for instance by correlating upward mobility and connectedness among Black individuals. As a feasible alternative in the absence of individual-level data on race, we focus on counties or ZIP codes where most of the residents are of the same race (based on publicly available data from the Census). We then correlate race-specific measures of economic mobility with EC (pooling all racial groups) within these areas.

Extended Data Table 3 reports the results of this analysis. Column 1 shows that the correlation between upward mobility for white individuals and overall EC is 0.68 in counties where at least 80% of residents are white (which have a mean white share of 90%). The correlation is similar (0.69) in counties where at least 90% of residents are white, and the mean white share is 95% (column 2). Columns 3 and 4 show that results are similar at the ZIP-code level. In ZIP codes where at least 90% of residents are white, the correlation between upward mobility and EC is 0.69. Columns 5 and 6 show similarly strong correlations between upward mobility for Black people and EC in ZIP codes in which residents are predominantly Black. Columns 7 and 8 show smaller (although not statistically distinguishable) correlations between upward mobility for Hispanic people and EC in the few ZIP codes in which residents are predominantly Hispanic. Note that we can only perform this analysis at the ZIP-code level for Hispanic and Black individuals because there are very few counties that have more than 80% Black or Hispanic residents.

The results in Extended Data Table 3 show that economic connectedness remains highly correlated with economic mobility even conditional on race, which implies that segregation by race is unlikely to be the primary driver of the observed correlation between EC and mobility overall. Relationships between mobility and other measures of social capital also remain similar when restricting the sample to areas in which one race forms an overwhelming share of the population (Supplementary Fig. 5).

Of course, there are many dimensions beyond race on which families may sort across neighbourhoods, such as their underlying human capital or their propensity to invest in their children’s education. To test for sorting on such dimensions, many of which are unobservable, one would ideally randomly assign families to low-EC and high-EC areas—thereby ensuring that families in high-EC and low-EC areas are comparable—and examine whether their children’s outcomes differ in adulthood. We approximate this experiment using quasi-experimental estimates of the causal effect of growing up for an additional year in each county in the United States on household incomes in adulthood from Chetty and Hendren. That study used variation in the age at which children move across counties to identify the causal effect of growing up in each county for children with parents at the 25th percentile of the income distribution. Under the identification assumption that the timing of moves is unrelated to children’s potential outcomes—an assumption validated in a series of experimental and quasi-experimental studies—differences in adult incomes for children who move at younger versus older ages to a given county reveal its causal effect on economic mobility.

We use Chetty and Hendren's estimates to analyse the relationship between the causal effects of counties on upward mobility and EC. We measure the causal effect of each county as the mean change in an individual’s percentile income rank from growing up from birth (for 20 years) in that county instead of the average county in the United States. Extended Data Fig. 5 presents a binned scatter plot of the causal effects of counties on upward mobility against their EC. Higher EC counties have larger causal effects on upward mobility, with a correlation of 0.44 (s.e. = 0.06) after correcting for sampling error in the causal effect estimates (Methods: ‘Correlations’). In a multivariable regression of counties' causal mobility effects on all our social capital measures, EC remains highly correlated with causal effects on mobility. By contrast, most other social capital measures do not exhibit significant associations (Supplementary Fig. 6).

The slope of the relationship shown in Extended Data Fig. 5 implies that growing up from birth in a county with 1 unit higher EC increases income in adulthood by 9.8 percentiles (a 30.7% increase relative to mean income ranks) for children of parents with low income. This estimate implies that moving at birth from the 10th to 90th percentile ZIP code in terms of EC—a move associated with an increase in EC of 0.57—would increase children’s household income in adulthood by 17.5% on average. As another benchmark, note that the average difference in EC between low- and high-SES individuals is 0.636. If low-SES children were to grow up in counties with EC comparable to the average high-SES child, their incomes would increase on average by 0.636 × 30.7 = 19.5% (equivalent to 6.23 percentiles). This increase in income would close about 37% of the current 17 percentile gap in income in adulthood between children with parents at the 25th and 75th percentiles of the income distribution.

We conclude that the correlation between EC and mobility is not driven simply by differences in the types of families who live in high EC areas. Instead, growing up in an area with higher EC causes significantly higher rates of upward mobility.

Connectedness versus other factors

Higher EC areas may generate higher levels of mobility for two reasons: either economic connectedness itself has a causal effect on mobility or high-EC places have other characteristics (for example, better schools) that generate higher levels of mobility. As a step towards distinguishing these two explanations, we compare the relative explanatory power of EC and the strongest neighbourhood-level predictors of economic mobility identified in previous work.

We begin by analysing incomes across neighbourhoods. Several studies have shown that areas with lower incomes and more highly concentrated poverty have lower rates of economic mobility. Motivated by such findings, many place-based policies use high poverty rates as a marker to identify low-opportunity neighbourhoods that are eligible for special tax credits and resources, and recent work has sought to help families move to lower-poverty neighbourhoods to improve their economic prospects.

Figure 5a shows univariate county-level correlations between upward mobility and measures of income and various other neighbourhood characteristics (results at the ZIP-code level, shown in Supplementary Fig. 4b, are similar). The share of individuals above the poverty line and median household incomes have correlations of 0.3–0.35 with upward mobility across counties. When we regress upward income mobility on both EC and measures of local income levels (poverty rates or median household incomes), connectedness remains a strong predictor of upward mobility. By contrast, measures of local income levels lose much of their predictive power at both the county and ZIP code levels (Table 2 (EC versus median income and poverty rates) and Supplementary Figs. 7 and 8).

These findings suggest that EC may be a mediator through which concentrated poverty affects upward mobility. That is, living in a lower income neighbourhood may inhibit upward mobility insofar as it reduces interaction with higher SES people, but does not appear to have a strong influence beyond its effect on EC. Figure 6 demonstrates this point more directly by presenting a scatter plot of EC against median household income by ZIP code. The dots are coloured according to the level of upward income mobility for children who grew up in low-income families in that ZIP code, with blue representing areas with higher levels of upward mobility and red representing areas with lower levels of mobility. Horizontal slices of the graph—neighbourhoods with different levels of median income but comparable levels of EC—tend to have similar levels of economic mobility. By contrast, vertical slices of the graph—areas with comparable incomes but different levels of EC—transition from low to high economic mobility as EC rises. These results imply that it is growing up in an area with high EC—rather than just around high-income people—that leads to better prospects for upward mobility.

Although local income levels explain little of the relationship between EC and outcomes for children starting out in low-income (25th percentile) families, they do appear to mediate the relationship between connectedness and outcomes for children from high-income (75th percentile) families. We illustrate this result in Extended Data Fig. 6a. As a reference, the series in orange circles presents a county-level binned scatter plot of upward mobility against EC for individuals with low SES. This series is similar to the scatter plot in Fig. 4, except that we include all counties and group them into 20 equal-sized bins on the basis of their level of EC to show the conditional expectation of upward mobility given EC non-parametrically. Consistent with the pattern in Fig. 4, there is a strong positive slope of 18.2.

Now consider the relationship between the average income ranks in adulthood of children with parents at the 75th percentile and the share of low-SES friends that high-SES individuals have. This relationship (plotted in blue circles) is flatter than that for low-SES individuals. A 1 unit increase in cross-group connectedness—defined here as twice the share of low-SES friends among high-SES individuals—is associated with an 8.6 percentile reduction in mean income rank for children with parents at the 75th percentile. Notably, after controlling for the share of high-SES individuals in the county, greater cross-group EC remains strongly positively associated with outcomes for children with parents at the 25th percentile (as established above), but is now uncorrelated with the economic outcomes for children with parents at the 75th percentile (Extended Data Fig. 6b). A potential explanation for this pattern is that greater interaction between low-SES and high-SES households conditional on the income mix in an area benefits lower-income people without harming those with higher incomes; however, greater income mixing (integration) benefits lower-income people partly at the expense of higher-income people by redistributing public goods (for example, local public school funding) from people with higher incomes to people with lower incomes. These results raise the possibility that more economically connected communities can benefit lower-income households with limited adverse impacts on those with higher incomes, particularly if increasing cross-SES connections does not require changing the income mix or resources in an area.

Going beyond average income levels, previous research has also shown that in counties where people of different incomes or racial backgrounds live in separate neighbourhoods, levels of economic mobility are generally lower. Indices of segregation by income and race (constructed from Census data using standard methods; Supplementary Information A.5.1) have negative correlations of 0.17–0.21 with economic mobility across counties, significantly lower than the correlation of 0.65 observed with EC. Hence, using network data to directly measure interaction (rather than using residential location as a proxy) adds considerable explanatory power for understanding economic mobility. Moreover, when we regress upward mobility on both EC and segregation measures, connectedness remains a strong predictor of upward mobility. By contrast, the segregation indices lose their predictive power (Table 2 (EC versus segregation and inequality) and Supplementary Fig. 9).

Previous work has established that Black individuals living in neighbourhoods with a larger Black population have poorer educational and economic outcomes on average. We replicate these results in the odd-numbered columns of Table 2 (EC versus share of Black residents) by regressing upward income mobility for Black people and white people on the share of Black residents in an area (for both counties and ZIP codes). The corresponding even-numbered columns show that controlling for EC eliminates or even reverses the relationship between the share of Black residents and rates of upward mobility (Supplementary Fig. 10). Areas with a larger Black population tend to have lower levels of EC (Supplementary Table 3), and this relationship accounts for the negative correlation between the share of Black residents and rates of mobility.

Research has also found a strong negative correlation between income inequality within a generation (measured, for example, using the Gini coefficient) and upward mobility across generations, coined the ‘Great Gatsby curve’. Controlling for EC essentially eliminates this relationship (columns 5 and 6 of Table 2 (EC versus segregation and inequality) and Supplementary Fig. 9). Greater income inequality is associated with less EC, and that relationship largely explains the negative correlation between inequality and mobility. In short, a lack of economic connectedness may be a key reason that upward mobility is lower in areas with larger Black populations and greater inequality.

Finally, we turn to other factors that have been explored in previous work, ranging from the quality of local schools to job availability to measures of family structure. EC is more strongly correlated with upward economic mobility than almost all of those characteristics in univariate specifications (Fig. 5a). We also estimate a multivariable regression of upward mobility on EC along with other predictors that have the highest univariate correlations with mobility. In this analysis, EC is the strongest predictor of upward mobility (Fig. 5b) and has the largest incremental R² (Supplementary Fig. 2d). EC is also among the first variables—along with the share of single parents—that are chosen by a Lasso regression as predictors of economic mobility (Supplementary Fig. 2b).

In summary, places with higher levels of EC generate higher levels of economic mobility, even when controlling for the strongest neighbourhood-level predictors of economic mobility identified in prior research. Moreover, the relationships between these other neighbourhood characteristics and mobility become much weaker once we control for EC, which indicates that the links between those factors and mobility may run through their impacts on EC. These findings suggest that other observable neighbourhood characteristics do not explain why higher EC areas generate higher levels of upward mobility, calling for further focus on causal mechanisms through which economic connectedness itself may affect mobility.