We show that social network exposure to COVID-19 cases shapes individuals' beliefs and behaviors concerning the coronavirus. We use de-identified data from Facebook to document that individuals with friends in areas with worse COVID-19 outbreaks reduce their mobility more than otherwise similar individuals with friends in less affected areas. The effects are quantitatively large and long-lasting: a one standard deviation increase in friend-exposure to COVID-19 cases in March 2020 results in a 1.2 percentage point increase in the probability of staying home on a given day through at least the end of May 2020. As the pandemic progresses and the characteristics of individuals with the highest friend-exposure vary changes in friend-exposure continue to drive changes in social distancing behavior, ruling out many unobserved effects as drivers of our results. We also show that individuals with higher friend-exposure to COVID-19 are more likely to publicly post in support of social distancing measures and less likely to be members of groups advocating to "reopen" the economy. These findings suggest that friends can influence individuals' beliefs about the risks of the disease and thereby induce them to engage in mitigating public health behavior.

By enabling smaller loans, broader geographic reach, and less human bias in decision-making, process automation may reduce racial disparities in access to financial services. We find evidence for all three channels using a setting where private lenders faced no credit risk but decided who to serve: the Paycheck Protection Program (PPP), which provided loans to small businesses during COVID-19. Black-owned firms disproportionately obtained their PPP loans from fintech lenders, especially in areas with high racial animus. After traditional banks automate their loan application processes, their PPP lending to Black-owned businesses increases. Our findings cannot be fully explained by racial differences in loan application behaviors, pre-existing banking relationships, contemporaneous firm performance, or fraud rates.

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.

We review the recent literature on the determinants and effects of housing market expectations. We begin by providing an overview of existing surveys that elicit housing market expectations, and discuss how those surveys may be expanded in the future. We then document a number of facts about time-series and cross-sectional patterns of housing market expectations in these survey data, before summarizing research that has studied how individuals form these expectations. Housing market expectations are strongly influenced by recently observed house price changes, by personally or locally observed house price changes, by house price changes observed in a person's social network, and by current home ownership status. Similarly, experienced house price volatility affects expectations uncertainty. We also summarize recent work that documents how differences in housing market expectations translate into differences in individuals' housing market behaviors, including their home purchasing and mortgage financing decisions. Finally, we highlight research on how expectations affect aggregate outcomes in the housing market.

We use social network data from Facebook to show that institutional investors are more likely to invest in firms from regions to which they have stronger social ties. This effect of social proximity on investment behavior is distinct from the effect of geographic proximity. Social connections have the largest influence on investments of small investors with concentrated holdings as well as on investments in firms with a low market capitalization and little analyst coverage. We also find that the response of investment decisions to social connectedness affects equilibrium capital market outcomes: firms in locations with stronger social ties to places with substantial institutional capital have higher institutional ownership, higher valuations, and higher liquidity. These effects of social proximity to capital on capital market outcomes are largest for small firms with little analyst coverage. We find no evidence that investors generate differential returns from investments in locations to which they are socially connected. Our results suggest that the social structure of regions affects firms' access to capital and contributes to geographic differences in economic outcomes.

We study the nature of peer effects in the market for new cell phones. Our analysis builds on de-identified data from Facebook that combine information on social networks with information on users' cell phone models. To identify peer effects, we use variation in friends' new phone acquisitions resulting from random phone losses and carrier-specific contract terms. A new phone purchase by a friend has a substantial positive and long-term effect on an individual's own demand for phones of the same brand, most of which is concentrated on the particular model purchased by the friend. We provide evidence that social learning contributes substantially to the observed peer effects. While peer effects increase the overall demand for cell phones, a friend's purchase of a new phone of a particular brand can reduce individuals' own demand for phones from competing brands---in particular those running on a different operating system. We discuss the implications of these findings for the nature of firm competition. We also find that stronger peer effects are exerted by more price-sensitive individuals. This positive correlation suggests that the elasticity of aggregate demand is substantially larger than the elasticity of individual demand. Through this channel, peer effects reduce firms' markups and, in many models, contribute to higher consumer surplus and more efficient resource allocation.