Social Connectedness, Network Embedded Inequality, and Disparities in Education Outcomes

• In Event: Excellence in Education Research: Early Career Scholars and Their Work

Abstract

Resources and information within social networks are often accumulated and exchanged in a manner that impacts education processes and outcomes. However, it has been challenging to examine the effects of social networks on education inequality at scale, partly due to the absence of high-quality social network measures. Utilizing big data based on anonymized information from the universe of over 200 million active US Facebook users, this study develops three sets of measures of county-level social network characteristics: 1) network heterogeneity; 2) network-embedded socioeconomic resources; and 3) network-embedded inequality structures. Linking these measures to county-level data on student academic performance and disparities, this study examines four research questions: 1) Are counties with similar levels of socioeconomic and education resources more connected, thus reinforcing inequality of resource distribution? 2) How is county network heterogeneity associated with county average student academic performance and performance disparities across class, gender, and racial groups? 3) How are county embedded social network resources associated with county average student academic performance and disparities? 4) How are county embedded network inequality structures associated with county average student performance disparities? Preliminary findings show that counties with smaller differences in SES indices and student average performance scores are more intensively connected. Moreover, as county network geographical heterogeneity increases, county-level student performance and racial disparity in performance also increases. These results point out that embedded resources in heterogeneous social networks could play a role in reproducing education inequality.

Author

• Ran Liu, University of Wisconsin - Madison