Abstract
Low levels of social interaction across class lines have generated widespread concern1–4 and are associated with worse outcomes, such as lower rates of upward income mobility4–7. Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper7. 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 122-134 |
| Number of pages | 13 |
| Journal | Nature |
| Volume | 608 |
| Issue number | 7921 |
| DOIs | |
| State | Published - Aug 4 2022 |
Funding
We thank J. Friedman, M. Gentzkow, E. Glaeser, R. Putnam, B. Sacerdote, A. Shleifer, and numerous seminar participants for comments; G. Crowne, T. Harris, A. Kim, J. Sun, V. Weiss-Jung and A. Zheng for 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 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). 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 thank J. Friedman, M. Gentzkow, E. Glaeser, R. Putnam, B. Sacerdote, A. Shleifer, and numerous seminar participants for comments; G. Crowne, T. Harris, A. Kim, J. Sun, V. Weiss-Jung and A. Zheng for 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 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). 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. 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.B, P.B., M.B. and N.W. are employees of Meta Platforms. T.K., J.S., S.G. and F.M. are contract affiliates through Meta’s 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’s 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)).
ASJC Scopus subject areas
- General