Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 108-121 |
| Number of pages | 14 |
| Journal | Nature |
| Volume | 608 |
| Issue number | 7921 |
| DOIs | |
| State | Published - Aug 4 2022 |
Funding
In 2018, T.K. and J.S. received an unrestricted gift from Facebook to NYU Stern. Opportunity Insights receives core funding from the Chan Zuckerberg Foundation (CZI). CZI is a separate entity from Meta, and CZI funding to Opportunity Insights was not used for this research. M. Bailey, P.B., M. Bhole and N.W. are employees of Meta Platforms. T.K., J.S., S.G. and F.M. are contract affiliates through Meta\u2019s contract with PRO Unlimited. F.G., A.G., M.J., D.J., M.K., T.R., N.T, W.T. and R.Z. are contract affiliates through Meta\u2019s contract with Harvard University. Meta Platforms did not dispute or influence any findings or conclusions during their collaboration on this research. This work was produced under an agreement between Meta and Harvard University specifying that Harvard shall own all intellectual property rights, titles and interests (subject to the restrictions of any journal or publisher of the resulting publication(s)). We are grateful to J. Friedman, M. Gentzkow, E. Glaeser, R. Putnam, B. Sacerdote, A. Shleifer and numerous seminar participants for helpful comments; G. Crowne, T. Harris, A. Kim, J. Sun, V. Weiss-Jung and A. Zheng for excellent research assistance; A. Hiller and S. Oppenheimer for project management and content development; S. Halvorson, R. Korzan, C. Shram and M. Wong of Darkhorse Analytics for creating the data visualization platform;\u00A0S.\u00A0Vadhan for his help in developing the differential privacy methods used in this paper; and the Meta Research Team for their support. This research was facilitated through a research consulting agreement between some of the academic authors (R.C., M.O.J., J.S., and\u00A0T.K.) and Meta Platforms. M.O.J. is an external faculty member of the Santa Fe Institute. The work was funded by the Bill & Melinda Gates Foundation, the Overdeck Family Foundation, Harvard University, and the National Science Foundation (under grants SES-1629446 and SES-2018554 issued to M.O.J. in his academic capacity at Stanford University). Opportunity Insights also receives core funding from other sponsors, including the Chan Zuckerberg Initiative, the Robert Wood Johnson Foundation and the Yagan Family Foundation. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the funders. We are grateful to J. Friedman, M. Gentzkow, E. Glaeser, R. Putnam, B. Sacerdote, A. Shleifer and numerous seminar participants for helpful comments; G. Crowne, T. Harris, A. Kim, J. Sun, V. Weiss-Jung and A. Zheng for excellent research assistance; A. Hiller and S. Oppenheimer for project management and content development; S. Halvorson, R. Korzan, C. Shram and M. Wong of Darkhorse Analytics for creating the data visualization platform; S. Vadhan for his help in developing the differential privacy methods used in this paper; and the Meta Research Team for their support. This research was facilitated through a research consulting agreement between some of the academic authors (R.C., M.O.J., J.S., and T.K.) and Meta Platforms. M.O.J. is an external faculty member of the Santa Fe Institute. The work was funded by the Bill & Melinda Gates Foundation, the Overdeck Family Foundation, Harvard University, and the National Science Foundation (under grants SES-1629446 and SES-2018554 issued to M.O.J. in his academic capacity at Stanford University). Opportunity Insights also receives core funding from other sponsors, including the Chan Zuckerberg Initiative, the Robert Wood Johnson Foundation and the Yagan Family Foundation. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the funders.
ASJC Scopus subject areas
- General
