Extreme Changes in Changes

TL;DR

This paper introduces a new changes-in-changes estimator tailored for accurately assessing treatment effects at extreme quantiles, such as the lowest or highest 5%, improving analysis of subpopulations with extreme outcomes.

Contribution

The paper develops a novel CIC estimator specifically designed for extreme quantiles and provides a simple inference method, addressing limitations of existing estimators for such subpopulations.

Findings

Effective estimation of treatment effects at extreme quantiles.

Simulation studies validate the estimator's accuracy for below 5% and above 95% quantiles.

Applied to EITC reform data, revealing significant effects on infant birth weights in critical conditions.

Abstract

Policy analysts are often interested in treating the units with extreme outcomes, such as infants with extremely low birth weights. Existing changes-in-changes (CIC) estimators are tailored to middle quantiles and do not work well for such subpopulations. This paper proposes a new CIC estimator to accurately estimate treatment effects at extreme quantiles. With its asymptotic normality, we also propose a method of statistical inference, which is simple to implement. Based on simulation studies, we propose to use our extreme CIC estimator for extreme, such as below 5% and above 95%, quantiles, while the conventional CIC estimator should be used for intermediate quantiles. Applying the proposed method, we study the effects of income gains from the 1993 EITC reform on infant birth weights for those in the most critical conditions. This paper is accompanied by a Stata command.