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 health. 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 papers, 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 date. 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 inequality. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org.

Low levels of social interaction across class lines have generated widespread concern and are associated with worse outcomes, such as lower rates of upward income mobility. Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper. 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. The other half is explained by friending bias—the tendency for people with low SES to befriend people with high SES at lower rates even conditional on exposure.

We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We use anonymized data from Facebook, the world’s largest online social network, to first explore heterogeneity in the structure of individuals’ social networks. We then exploit the rich variation in the data to analyze the effects of social interactions on housing market investments. To do this, we combine the social network information with housing transaction data. Variation in the geographic dispersion of social networks, combined with time-varying regional house price changes, induces heterogeneity in the house price experiences of different individuals’ friends. We show that individuals whose geographically distant friends experienced larger recent house price increases are more likely to transition from renting to owning. They also buy larger houses and pay more for a given house. Similarly, when homeowners’ friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price. We find that these relationships are driven by the effect of social interactions on individuals’ housing market expectations. Survey data show that individuals whose geographically distant friends experienced larger recent house price increases consider local property a more attractive investment, with bigger effects for individuals who regularly discuss such investments with their friends.

We introduce a new measure of social connectedness between U.S. county pairs, as well as between U.S. counties and foreign countries. Our measure, which we call the Social Connectedness Index (SCI), is based on the number of friendship links on Facebook, the world’s largest online social network. Within the U.S., social connectedness is strongly decreasing in geographic distance between counties. The population of counties with more geographically-dispersed social networks is richer, more educated, and has higher life expectancy. Region-pairs that are more socially connected have higher trade flows, even after controlling for geographic distance and the similarity of regions along other demographic and socioeconomic measures. Higher social connectedness is also associated with more cross-county migration and patent citations. Social connectedness between U.S. counties and foreign countries is correlated with past migration patterns, with social connectedness decaying in the time since the primary migration wave from that country. Trade with foreign countries is also strongly related to the social connectedness with those countries. These results suggest that the SCI captures an important role of social networks in facilitating economic and social interactions. Our findings highlight the potential for the SCI to mitigate the measurement challenges that pervade empirical literatures that study the role of social interactions across the social sciences.

We study the relationship between homebuyers’ beliefs about future house price changes and their mortgage leverage choices. Whether more pessimistic homebuyers choose higher or lower leverage depends on their willingness and ability to reduce the size of their housing market investments. When households primarily maximize the levered return of their property investments, more pessimistic homebuyers reduce their leverage to purchase smaller houses. On the other hand, when considerations such as family size pin down the desired property size, pessimistic homebuyers reduce their financial exposure to the housing market by making smaller downpayments to buy similarly-sized homes. To determine which scenario better describes the data, we investigate the cross-sectional relationship between house price beliefs and mortgage leverage choices in the U.S. housing market. We use plausibly exogenous variation in house price beliefs to show that more pessimistic homebuyers make smaller downpayments and choose higher leverage, in particular in states where default costs are relatively low, as well as during periods when house prices are expected to fall on average. Our results highlight the important role of heterogeneous beliefs in explaining households’ financial decisions.

Advertisers are demanding more accurate estimates of the impact of targeted advertisements, yet no study proposes an appropriate methodology to analyze the effectiveness of a targeted advertising campaign, and there is a dearth of empirical evidence on the effectiveness of targeted advertising as a whole. The targeted population is more likely to convert from advertising so the response lift between the targeted and untargeted group to the advertising is likely an overestimate of the impact of targeted advertising. We propose a difference-in-differences estimator to account for this selection bias by decomposing the impact of targeting into selection bias and treatment effects components. Using several large-scale online advertising campaigns, we test the effectiveness of targeted advertising on brand-related searches and clickthrough rates. We find that the treatment effect on the targeted group is about twice as large for brand-related searches, but naively estimating this effect without taking into account selection bias leads to an overestimation of the lift from targeting on brand-related searches by almost 1,000%.