Preprints

2026

1. SocArXiv

   

   Social Capital in the United Kingdom: Evidence from Six Billion Friendships

   Tom Harris, Shankar Iyer, Tom Rutter, Drew Johnston, and 9 more authors

   2026

   Abstract HTML PDF Data

   Media Coverage: The Economist, The Financial Times, The Guardian 1, The Guardian 2, The Independent, The Standard, The Times, Stanford 3-Minute Thesis Final

   We construct and publicly release measures of social capital for the United Kingdom using Facebook data covering 20.5 million adults. Low-socioeconomic-status (SES) individuals have far fewer high-SES friends than high-SES individuals do. Areas with more cross-SES friendships and more long ties—friendships between people with no mutual connections—display higher intergenerational mobility, even controlling for other area-level characteristics. Most of the difference in connectedness to high-SES individuals between low- and high-SES individuals is due to differences in friending bias—the tendency of people to befriend others similar to themselves even conditional on the possible friends they are exposed to. Individuals with a greater share of high-SES friends, more close friends, and more mutually supported friendships report higher life satisfaction, greater interpersonal trust, stronger perceived social support, and lower feelings of isolation and disconnection, even after controlling for household income and demographics.

2. arXiv

   

   Differential Privacy for Network Connectedness Indices

   Tom A. Rutter, Yuxin Liu, and M. Amin Rahimian

   2026

   Abstract HTML PDF Code

   Researchers increasingly use data on social and economic networks to study a range of social science questions, but releasing statistics derived from networks can raise significant privacy concerns. We show how to release network connectedness indices that quantify assortative mixing across node attributes under edge-adjacent differential privacy. Standard privacy techniques perform poorly in this setting both because connectedness indices have high global sensitivity and because a single node’s attribute can potentially be an input to connectedness in thousands of cells, leading to poor composition. Our method, which is straightforward to apply, first adds noise to node attributes, then analytically debiases downstream statistics, and finally applies a second layer of noise to protect the presence or absence of individual edges. We prove consistency and asymptotic normality of our estimators for both discrete and continuous labels and show our method works well in simulations and on real networks with as few as 200 nodes collected by social scientists.

3. arXiv

   

   Social Network Structure, Wealth, and Wealth Inequality Across Cultures

   Eleanor A. Power, Monique Borgerhoff Mulder, Samuel Bowles, Matthew O. Jackson, and 56 more authors

   2026

   Abstract HTML PDF Code

   Despite theory tying wealth inequality to social structure, empirical evidence has been limited to a few studies based on online social media data. This study uses a very different type of data, expands the global coverage to very different types of societies, and investigates new questions. In particular, we collect data from 3500 sharing units (households) in 46 communities across the globe, representing considerable human social and cultural diversity. In each, we analyze the relationship between people’s material wealth and the structure of social networks: borrowing money, sharing food, working together, socializing, etc. In almost all communities, a sharing unit’s material wealth is positively associated with the number of other sharing units it both helps and is helped by. A sharing unit’s wealth is also associated with the relative wealth of the sharing units to which it is linked—a form of economic homophily. Notably, communities with greater wealth inequality are also characterized by a network structure in which poorer sharing units are less well connected to wealthier ones. We augment our unique cross-cultural data with other community-level environmental, institutional, and economic attributes, opening new avenues for future research into the co-determination of wealth and social networks.

Publications

2024

1. QJE

   

   The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data

   Raj Chetty, John N Friedman, Michael Stepner, and The Opportunity Insights Team

   The Quarterly Journal of Economics, 2024

   Abstract HTML PDF Appendix Data

   Media Coverage: NPR, Washington Post 1, Washington Post 2, Washington Post 3

   We build a publicly available database that tracks economic activity in the United States at a granular level in real time using anonymized data from private companies. We report weekly statistics on consumer spending, business revenues, job postings, and employment rates disaggregated by county, sector, and income group. Using the publicly available data, we show how the COVID-19 pandemic affected the economy by analyzing heterogeneity in its effects across subgroups. High-income individuals reduced spending sharply in March 2020, particularly in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent, dense areas. Those businesses laid off many of their employees, leading to widespread job losses, especially among low-wage workers in such areas. High-wage workers experienced a V-shaped recession that lasted a few weeks, whereas low-wage workers experienced much larger, more persistent job losses. Even though consumer spending and job postings had recovered fully by December 2021, employment rates in low-wage jobs remained depressed in areas that were initially hard hit, indicating that the temporary fall in labor demand led to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the effects of fiscal stimulus policies designed to stem the downward spiral in economic activity. Cash stimulus payments led to sharp increases in spending early in the pandemic, but much smaller responses later in the pandemic, especially for high-income households. Real-time estimates of marginal propensities to consume provided better forecasts of the impacts of subsequent rounds of stimulus payments than historical estimates. Overall, our findings suggest that fiscal policies can stem secondary declines in consumer spending and job losses, but cannot restore full employment when the initial shock to consumer spending arises from health concerns. More broadly, our analysis demonstrates how public statistics constructed from private sector data can support many research and real-time policy analyses, providing a new tool for empirical macroeconomics.

2022

1. Nature

   

   Social Capital I: Measurement and Associations with Economic Mobility

   Raj Chetty, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, and 18 more authors

   Nature, 2022

   Abstract HTML PDF Appendix Data

   Media Coverage: Axios, Brookings, CBS, Economist 1, Economist 2, Nature, Nature Podcast, New Scientist, New York Times 1, New York Times 2, New York Times 3, NPR, The Verge, Washington Post

   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 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 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.

2. Nature

   

   Social Capital II: Determinants of Economic Connectedness

   Raj Chetty, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, and 18 more authors

   Nature, 2022

   Abstract HTML PDF Appendix Data

   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.

2021

1. Economic Journal

   

   Meta-Analysis of Present-Bias Estimation using Convex Time Budgets

   Taisuke Imai, Tom A Rutter, and Colin F Camerer

   The Economic Journal, 2021

   Abstract HTML PDF Appendix Code

   We examine 220 estimates of the present-bias parameter from 28 articles using the Convex Time Budget protocol. The literature shows that people are on average present-biased, but estimates exhibit substantial heterogeneity across studies. There is evidence of modest selective reporting in the direction of over-reporting present bias. The primary source of heterogeneity is the type of reward, either monetary or non-monetary, but this effect is weakened after correcting for selective reporting. In studies using monetary rewards, the delay until the issue of the reward associated with the current time period influences estimates of the present-bias parameter.

2. JEPS

   

   The Effectiveness of a Neighbor-to-Neighbor Get-Out-the-Vote Program: Evidence from the 2017 Virginia State Elections

   Cassandra Handan-Nader, Daniel E Ho, Alison Morantz, and Tom A Rutter

   Journal of Experimental Political Science, 2021

   Abstract HTML PDF Code

   We analyze the results of a neighbor-to-neighbor, grassroots get-out-the-vote (GOTV) drive in Virginia, in which unpaid volunteers were encouraged to contact at least three nearby registered voters who were likely co-partisans yet relatively unlikely to vote in the 2017 state election. To measure the campaign’s effectiveness, we used a pairwise randomization design whereby each volunteer was assigned to one randomly selected member of the most geographically proximate pair of voters. Because some volunteers unexpectedly signed up to participate outside their home districts, we analyze the volunteers who adhered to the original hyper-local program design separately from those who did not. We find that the volunteers in the original program design drove a statistically significant 2.3% increase in turnout, which was concentrated in the first voter pair assigned to each volunteer. We discuss implications for the study and design of future GOTV efforts.