Estimation and Inference of Average Treatment Effect in Percentage Points under Heterogeneity

Ying Zeng

Papers from arXiv.org

Abstract: In semi-logarithmic regressions, treatment coefficients are often interpreted as approximations of the average treatment effect (ATE) in percentage points. This paper highlights the overlooked bias of this approximation under treatment effect heterogeneity, arising from Jensen's inequality. The issue is particularly relevant for difference-in-differences designs with log-transformed outcomes and staggered treatment adoption, where treatment effects often vary across groups and periods. I propose new estimation and inference methods for the ATE in percentage points, which are applicable when treatment effects vary across and within groups. I establish the methods' large-sample properties and demonstrate their finite-sample performance through simulations, revealing substantial discrepancies between conventional and proposed measures. Two empirical applications further underscore the practical importance of these methods.

Date: 2024-08, Revised 2025-07
New Economics Papers: this item is included in nep-ecm and nep-inv
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