Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign country. Given Facebook’s scale as well as the relative representativeness of Facebook’s user body, these data provide the first comprehensive measure of friendship networks at a national level.

We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We combine anonymized data from Facebook, the largest online social network, with housing transaction data and explore both the structure and the effects of social networks. 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. Survey data show that these relationships are driven by the effects of social interactions on individuals' housing market expectations. This paper was previously circulated as "Social Networks and Housing Markets." For helpful comments, we are grateful to

Using novel survey data, we document that individuals extrapolate from recent personal experiences when forming expectations about aggregate economic outcomes. Recent locally experienced house price movements affect expectations about future U.S. house price changes and higher experienced house price volatility causes respondents to report a wider distribution over expected U.S. house price movements. When we exploit within-individual variation in employment status, we find that individuals who personally experience unemployment become more pessimistic about future nationwide unemployment. The extent of extrapolation is unrelated to how informative personal experiences are, is inconsistent with risk adjustment, and is more pronounced for less sophisticated individuals.

We use aggregated data from Facebook to show that COVID-19 was more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases as of the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population densities of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases predicts future outbreaks over and above physical proximity. These results suggest data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

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 health1–8. 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 papers9, 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 date10,11. 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 inequality12–14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org.