Intersectional inequalities in social ties.

Social networks are shaped by complex, intersecting identities that drive our connection preferences. These preferences create tie inequalities: Systematic differences in the number of links members of different groups accumulate. Understanding tie inequalities is critical because they contribute to disparities in social capital, with downstream consequences for access to opportunities and resources. Previous research has examined the impact of single-dimensional identities on tie disparities, but when multiple identities intersect, network disadvantages accumulate nonlinearly, disproportionately harming individuals belonging to several disadvantaged groups. However, how multidimensional connection preferences affect network dynamics and amplify or mitigate differences in ties remains unknown. Using a network model, we characterize the effects of multidimensional preferences and attribute correlation. We find that correlation creates counterintuitive tie inequalities unobservable in one-dimensional systems. We calibrate the model with high school friendship data and derive closed-form expressions for tie inequalities, which reproduce the empirical patterns with remarkable accuracy. Our findings have substantial implications for addressing intersectional inequalities in social networks.