Embedded inequality: Personal network dynamics and mental health during COVID-19

Personal networks provide crucial support during crises, yet people are embedded in different network types that structure unequal access to such resources. The current study integrates these perspectives to examine whether—and how—network turnover contributed to disparities in mental health across socioeconomic status (SES) groups during the pandemic. Using two-wave panel data from the COVID-19 Pandemic and Social Network Panel Study (2020–2021), an egocentric network study of the college population in Wuhan, we employ random forests and spectral clustering to identify 7 types of core networks based on 43 network variables (i.e., Family, Friend, Restricted, Family & Community, School & Career, Just Activity, and Homebody). We find that as local social-distancing policies tightened, respondents increasingly shifted to Family and Friend networks and withdrew from School & Career and Just Activity. Individual fixed-effect models reveal that these network turnovers have heterogeneous mental health consequences net of observed and time-invariant unobserved confounders. Moving into Family and Friend networks yields the most favorable mental health outcomes for higher-SES groups, whereas benefits are less pronounced and even reversed among lower-SES groups. This pattern is consistent with SES-based differences in social support available in these network types. The current research advances an updated machine-learning approach for identifying personal network typologies. It also shows how the pandemic laid bare unequal resources embedded in personal networks and intensified health-related social inequality, underscoring the need to theorize network effects as contingent on individuals’ social status and the contexts in which networks are formed and embedded.