Social capital I: measurement and associations with economic mobility

Many theoretical studies have shown how connections to more educated or affluent individuals can be valuable for transferring information, shaping aspirations and providing mentorship or job referrals. Consistent with these models, empirical studies have documented that social ties to well-resourced individuals can materially affect economic and labour market outcomes. Motivated by this literature, we begin by measuring connectedness across different types of people, focusing on economic connectedness: the extent to which people with low and high SES are friends with each other.

Social scientists have measured SES using many different variables, ranging from income and wealth to educational attainment, occupation, family background, neighbourhood and consumption. To capture these varied definitions, we compute the SES for each individual in our analysis sample by combining several measures of SES, such as average incomes in the individual’s neighbourhood and self-reported educational attainment (see the ‘Privacy and ethics’ section of the Methods for a discussion of how user privacy was protected during this project). We combine these measures of SES into a single SES index using a machine-learning algorithm (Methods (‘Variable definitions’) and Supplementary Information B.1). We then calculate each individual’s percentile rank in the national SES distribution relative to others in their birth cohort. Although we do not observe individuals’ incomes directly, we show that our SES rankings are highly correlated with external, publicly available measures of income across groups (for example, ZIP codes, high schools, and colleges). We also show that using simpler measures of SES, such as median household income in an individual’s ZIP code, produces very similar results to those reported below.

Figure 1a plots the mean SES rank of individuals’ friends against their own SES ranks. There is strong homophily, whereby individuals with higher SES are friends with higher-SES people. A one percentile point increase in one’s own SES rank is associated with a 0.44 percentile point increase in the SES rank of one’s friends on average. The relationship is almost linear between the 10th and 90th percentiles of the SES distribution, with a slope of 0.41 in that range. The slope rises to 0.98 between the 90th and 100th percentiles, which shows that the highest-SES individuals tend to have particularly high-SES friends. These estimates of homophily are similar (slope of 0.46 for full range, 1.02 between the 90th and 100th percentiles) when we restrict the analysis to an individual’s ten closest friends (defined on basis of the frequency of public interactions such as likes, tags, wall posts and comments). This result shows that our estimates are not significantly affected by the strength of friendships or the number of Facebook friends that people have.

For our analyses below, it is useful to measure connections between individuals in different parts of the SES distribution. For simplicity, in our main analysis, we separate individuals into two groups on the basis of their SES: below-median and above-median SES (which we refer to as low SES and high SES, respectively, below). On average, 38.8% of the friends of below-median-SES individuals have above-median SES, whereas 70.6% of the friends of above-median-SES individuals have above-median SES. As 50% of individuals have above-median SES by definition, high-SES friends are under-represented by 22.4% (1−0.3880.5=0.224) among low-SES individuals relative to their share in the population. By contrast, high-SES friends are over-represented by 41.2% among high-SES individuals (0.7060.5−1=0.412). Note that the share of high-SES friends for low-SES and high-SES individuals averages to 54.7% rather than 50% because high-SES people have more friends than low-SES people on average (Extended Data Table 1).

If high-SES and low-SES individuals were to make friendships independent of SES (that is, there were no homophily by SES) and also were to make the same number of friends on average, then 50% of low-SES individuals’ friends would have high SES. In practice, above-median-SES individuals have 25.4% more friends than below-median-SES individuals on average (Extended Data Table 1). If high-SES people continue to make 25.4% more friends than low-SES people, but friendships were formed independent of SES, the share of high-SES friends among low-SES individuals would be 1.2541+1.254=55.6%. Relative to that benchmark, low-SES individuals make 30.2% fewer high-SES friends than they would in the absence of homophily.

We go beyond the two-group median split by examining connections between individuals in different deciles of the SES distribution. Extended Data Table 1 presents a matrix of intradecile friendship rates, which shows the likelihood of friendship formation for people from different deciles of the SES distribution. Connectedness is lower between deciles that are further apart. For instance, top-decile friends are under-represented among people in the bottom decile by 75% relative to their population share (1−0.0250.1=0.75). This value is more than three times larger than the corresponding 22.4% under-representation of above-median friends among below-median individuals.

Childhood economic connectedness

In addition to measuring economic connectedness among adults, we use parent–child linkages to analyse EC based on the childhood friendships of individuals from different family backgrounds. Social capital during individuals’ formative years may be particularly relevant for intergenerational income mobility.

We measure childhood EC by analysing homophily in friendships made in high school by parents’ SES (Methods: ‘Measuring connectedness’). Figure 1b plots the mean parental SES rank of a given individual’s five closest friends in high school against the SES rank of the individual’s own parents. There is less homophily by parental SES during childhood than by own SES in adulthood, with a slope of 0.31 instead of 0.44. Much of this difference in slopes arises from the fact that SES in adulthood among friends from high school is more similar than their parents' SES, perhaps because children who befriend each other tend to follow similar trajectories.

The series represented by squares in Fig. 1b shows analogous estimates of homophily by parental SES rank among high school students using data from Add Health, a representative survey of students that contains self-reported information on close friendships (Supplementary Information A.5.2). We obtain highly similar point estimates of homophily (slope = 0.31) by parental SES rank among high school friends in the Facebook and Add Health data. This comparison suggests that selection biases in Facebook usage or measurement error in friendship links and SES ranks do not substantially distort our estimates of homophily.

Economic connectedness across areas

The Facebook dataset, which is about 3,500 times larger than the Add Health sample, offers adequate precision and information to allow us to measure EC not just at the national level but also within specific communities, such as a given neighbourhood or school. We define the level of economic connectedness in a community as the average share of above-median-SES friends among below-median-SES members of that community divided by 50% to quantify the average degree of under-representation of high-SES friends among low-SES people (an algebraic definition is provided in the Methods: ‘Measuring connectedness’). A value of 0 for EC implies that a network has no connections between low-SES and high-SES people, whereas a value of 1 implies that low-SES people have an equal number of low-SES and high-SES friends. Although we focus on economic connectedness among low-SES individuals in particular, which we refer to simply as EC, we also construct and release analogous measures of community-level economic connectedness for high-SES individuals.

Figure 2a maps EC by county in the United States. EC varies significantly across areas. Counties in the bottom decile of connectedness have EC values less than 0.58. That is, below-median-SES individuals have about 42% fewer above-median-SES friends than one would expect in the absence of homophily. Counties in the top decile have EC values of 1.05 or higher, approximately commensurate to what one would expect on the basis of random sampling of friends from the national distribution, adjusting for the fact that high-SES people make more friends, as discussed above. This geographical variation in connectedness is partially driven by differences in the share of high-SES individuals in an area and partly by differences in the rates at which low-SES individuals befriend high-SES individuals in their area. We decompose the relative contributions of these two factors, which we refer to as exposure and friending bias, in the companion paper.

EC is generally lowest in the Southeast, the Southwest and industrial cities in the Midwest. It is highest in the rural Midwest and on the East Coast. The mean standard error of the county-level EC estimates is 0.004 (Methods (‘Measuring connectedness’) and Supplementary Information B.3), which implies that nearly all of the variation in Fig. 2a reflects true differences in EC across areas rather than sampling error.

EC varies not just across counties but also across neighbourhoods within counties: 42% of the variation in EC across ZIP codes is within counties. Figure 2b illustrates this local variation by mapping EC by ZIP code (formally, ZIP code tabulation areas) in the Los Angeles metropolitan area (analogous maps for all ZIP codes in the United States are available at https://www.socialcapital.org). EC ranges from 0.62 to 1.25 between ZIP codes at the 10th and 90th percentiles of the EC distribution within the Los Angeles metro area (Los Angeles, Orange and Ventura counties). EC is lowest in the lowest-income neighbourhoods of Los Angeles, such as Watts in central Los Angeles, where EC is 0.45. EC is generally higher in higher-income areas, but there is significant variation in EC even within those areas, with some places (such as Echo Park) having relatively low EC despite having many high-SES residents.

More broadly, looking outside Los Angeles, almost none of the lowest-income ZIP codes in the United States exhibit high levels of EC. It may be that there is little scope for people with low SES to connect with individuals with higher SES if there are few such people in the vicinity, echoing Blau's observation that “persons cannot associate without having opportunities for contact”. In our analysis, this point is an empirical result rather than a mechanical consequence of contact because low-SES individuals in low-income areas could in principle befriend high-SES people outside their neighbourhoods. In practice, such connections appear to be relatively rare. However, the presence of high-SES neighbours does not guarantee that low-SES people connect with those individuals, as many higher-income neighbourhoods still have EC substantially below 1.

The spatial patterns documented above are robust to the way in which economic connectedness is measured. For example, Supplementary Table 1 shows that similar spatial patterns for EC are obtained when restricting attention to individuals' ten closest friends (correlation = 0.99 across counties). Similarly, the mean friend rank of individuals at the 25th percentile of the SES distribution, a measure that controls for differences in the SES distributions within the below-median and above-median groups, has an across-county correlation of 0.98 with our baseline EC measure. The share of top-quintile-SES friends among bottom-quintile-SES individuals in a county has a correlation of 0.74 with our baseline below- versus above-median EC measure across counties. Childhood EC also exhibits broadly similar spatial patterns. We analyse two measures of childhood EC: one constructed for Facebook users from the SES of parents of high school friends and the other constructed for a sample of current 13–17 year olds who use Instagram (Methods: ‘Measuring connectedness’). We obtain across-county correlations of 0.61 for the Facebook childhood EC measure and 0.82 for the Instagram measure with our baseline EC measure (Supplementary Table 1). The high correlation with EC measured using Instagram for recent birth cohorts suggests that differences in economic connectedness across areas are relatively stable over time, which is consistent with the high degree of serial correlation in our baseline county-level EC measure across birth cohorts (Supplementary Fig. 1).

Connectedness by other attributes

We also measure connectedness between individuals who use English as their primary language versus those who do not, and individuals between the ages of 25 and 34 years versus individuals between the ages of 35 and 44 years. Language and age connectedness exhibit different spatial patterns from EC (Extended Data Fig. 1). For example, the across-county correlation between language connectedness and EC is only 0.10 (Table 1). Hence, it is not simply that some areas exhibit high levels of connectedness across all types of individuals; rather, the degree of connectedness varies across different characteristics.