Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes

TL;DR

This paper develops nonparametric tests for treatment effect heterogeneity in duration outcomes, accommodating right-censoring and treatment noncompliance without relying on parametric assumptions.

Contribution

It introduces flexible, distribution-free tests for treatment effects in duration data that work under various treatment assignment mechanisms and are supported by bootstrap methods.

Findings

Tests are consistent against fixed alternatives.

They can detect local alternatives at the parametric rate.

Finite sample performance is validated through simulations.

Abstract

This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a policy 1) has zero distributional (average) effect for all subpopulations defined by covariate values, and 2) has homogeneous average effect across different subpopulations. The proposed tests are based on two-step Kaplan-Meier integrals and do not rely on parametric distributional assumptions, shape restrictions, or on restricting the potential treatment effect heterogeneity across different subpopulations. Our framework is suitable not only to exogenous treatment allocation but can also account for treatment noncompliance - an important feature in many applications. The proposed tests are consistent against fixed alternatives, and can detect nonparametric alternatives converging to the null at the parametric $n^{-1/2}$-rate, $n$ being the sample size. Critical values are computed with the assistance of a multiplier bootstrap. The finite sample properties of the proposed tests are examined by means of a Monte Carlo study and an application about the effect of labor market programs on unemployment duration. Open-source software is available for implementing all proposed tests.