Research

How does city growth respond to catastrophe? I propose a model in which local entrepreneurs have a comparative advantage in starting local businesses and business creation benefits from business activity. The model predicts that cities stagnate following a reduction in local human capital but, consistent with established facts about city resilience, recover after the destruction of local physical capital. I test for the predicted effects of a shock to human capital using U.S. city populations after the 1918 Influenza Pandemic, which killed 0.5% of residents in the largest U.S. cities. Instrumenting for Flu incidence with local weather at the peak of the epidemic, I show that cities with high influenza mortality had persistently low population levels and growth rates, with estimates implying that a 10% increase in Flu incidence caused a 13% reduction in 2010 population. A calibrated version of the model successfully rationalizes both the direction and magnitude of the observed effects.

Recent research has demonstrated that social networks play a key role in the decision to start a firm. However, little is known about how founders form their networks. Using large-scale social network data from Facebook linked to small business founders and their firms, we present 3 facts about founder networks. First, the rate at which a successful founder befriends other entrepreneurs spikes in the year that the founder started their firm and remains elevated in the 2 years after. Second, friendships with entrepreneurs formed recently are the most predictive of contemporaneous firm formation rates. Third, a substantial share of connections between founders are formed between entrepreneurs that start their businesses at similar times in the same or related industries. Taken together, these facts suggest that aspiring founders engage in substantial endogenous network formation with similar entrepreneurs, but may not do so in a way that maximizes their long-term chances of successfully founding a firm. Using a model of network formation decisions estimated with quasi-experimental variation in exposure to other entrepreneurs, we evaluate potential explanations for the observed networking patterns and discuss the implications for policies and institutions seeking to promote valuable new connections.

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. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of students with high SES across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Thus, socioeconomic integration can increase economic connectedness in communities in which friending bias is low. By contrast, when friending bias is high, increasing cross-SES interactions among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure and friending bias for each ZIP (postal) code, high school and college in the United States at https://www.socialcapital.org.