Estimating Treatment Effects in Panel Data Without Parallel Trends

TL;DR

This paper introduces a flexible method for estimating treatment effects in panel data that relaxes the parallel trends assumption, allowing for multidimensional unobservables and non-additive effects, with empirical evidence showing improved estimates.

Contribution

It develops a new framework for treatment effect estimation in panel data that overcomes limitations of the standard DID approach by accommodating complex unobserved heterogeneity.

Findings

Smaller long-run earnings losses in empirical application

Framework accounts for multidimensional unobserved factors

Provides conditions for identifying average treatment effects

Abstract

This paper proposes a novel approach for estimating treatment effects in panel data settings, addressing key limitations of the standard difference-in-differences (DID) approach. The standard approach relies on the parallel trends assumption, implicitly requiring that unobservable factors correlated with treatment assignment be unidimensional, time-invariant, and affect untreated potential outcomes in an additively separable manner. This paper introduces a more flexible framework that allows for multidimensional unobservables and non-additive separability, and provides sufficient conditions for identifying the average treatment effect on the treated. An empirical application to job displacement reveals substantially smaller long-run earnings losses compared to the standard DID approach, demonstrating the framework's ability to account for unobserved heterogeneity that manifests as differential outcome trajectories between treated and control groups.