Dynamic Bunching Estimation with Panel Data

Abstract

Abstract Bunching estimation of distortions in a distribution around a policy threshold provides a means of studying behavioral parameters. Standard cross-sectional bunching estimators rely on identification assumptions about heterogeneity that I show can be violated by serial dependence of the choice variable or attrition related to the threshold. I propose a bunching estimation design that exploits panel data to obtain identification from relative within-agent changes in income and to estimate new parameters. Simulations using household income data demonstrate the benefits of the panel design. An application to charitable organizations demonstrates opportunities for estimating elasticity correlates, causal effects, and extensive-margin responses.