SNAP enrollment cycles: New insights from heterogeneous panel models with cross-sectional dependence
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AbstractThe Supplemental Nutrition Assistance Program (SNAP) has grown rapidly over the past 2 decades. A large literature relies on statelevel panel data on SNAP enrollment and implements traditional twoway fixed effects estimators to identify the impact of economic conditions on SNAP enrollment. This empirical strategy implicitly assumes slope parameter homogeneity and ignores the possibility of crosssectional dependence in the regression error terms. The latter could feasibly arise in statelevel panel data if the timevarying unobserved common shocks, such as national financial crises, have differential effects on SNAP participation across states in the United States. This study empirically evaluates the appropriateness of these two assumptions by adopting a more general common factor model, allowing for slope parameter heterogeneity and error term crosssectional dependence both separately and jointly. We find that although assuming a common slope parameter across states does not seem problematic for identification, allowing for the error term crosssectional dependence leads to a roughly 40% reduction in the estimated longrun impact of the unemployment rate on SNAP enrollment. This finding has important implications for policymaking decisionseven small biases could lead to suboptimal policy responses considering the program's size. Our counterfactual simulations support our main results, implying the importance of carefully accounting for timevarying unobserved heterogeneity when studying the cyclicality of SNAP enrollment using statelevel panel data.
