Estimating the economic effects of US state and local fiscal policy: A synthetic control method matching-regression approach

Abstract

In this paper, we advance the empirical literature on US state and local fiscal policymaking by using the synthetic control method (SCM) to create pairwise matches for states in subsequent regression analysis of the relationships between state and local fiscal policies and several state economic outcomes. Additional contributions include the use of principal component analysis to construct broader narratives of state economic performance and to reduce the dimensionality of the characteristics used in SCM matching, while the regressions also include variables to control for post-matching economic shocks. Compared to conventional regression analysis, the SCM matching-regression approach better addresses potential endogeneity, reduces interpolation bias, and creates fiscal policy measures that better reflect policy differences. The SCM-matched regressions produce more statistically significant relationships between state and local fiscal variables and economic outcomes than do the conventional unmatched regressions, suggesting improved identification of state and local fiscal policy effects on economic outcomes. Robust relationships found include negative economic effects of the own-source revenue burden and property taxes. Consistent with the existing literature, the estimated fiscal policy effects are quantitatively small and unlikely to drive differences in state economic performance.