🤖 AI Summary
This study addresses the limitation of traditional approaches that estimate social contextual spillover effects along a single dimension, thereby neglecting the multidimensional interaction between overlap content and duration as well as their endogenous heterogeneity. The authors propose a multidimensional causal treatment framework that conceptualizes spillover effects as the joint causal influence of both observable and unobservable characteristics shared across life courses, innovatively decomposing them into combinations of content and duration. To identify heterogeneous causal effects, they develop a Cumulative Fixed Effects (CFE) method leveraging three-wave individual-level panel data. Simulations demonstrate that CFE remains unbiased even under highly nonlinear data-generating processes, effectively overcoming the constraints of conventional fixed effects models and accurately capturing diverse spillover mechanisms across multiple contextual settings.
📝 Abstract
Social scientists often ask about the effect of increasing one's duration of exposure to a social context on one's outcomes, i.e. the overlap effect. Past studies adopted a unidimensional treatment effect framework to estimate the effect of overlap, imposing important restrictions. In this paper, we propose a new causal framework of multidimensional treatments where the overlap effects include both the duration and the content of overlap, under which, for instance, the grandparent overlap effect is defined as the union of all causal effects of a grandparent's observed and unobserved characteristics (i.e., the content) on the grandchild across their shared life course (i.e., the duration). The multidimensional framework allows for a more flexible and context rich approach to effect heterogeneity, where unobserved contextual characteristics play two roles as unobserved confounders and as integral components of overlap effects -- overlap effects in this framework are not easily estimated with conventional fixed effects estimation. Hence, we develop a new cumulative fixed effects (CFE) approach that can estimate a range of interesting heterogeneous causal overlap effects from three-wave individual panel data. We show that the CFE approach is unbiased even in highly non-linear simulations, and we discuss assumptions and extensions.