Download this Paper Open PDF in Browser

Identification and Inference in Nonlinear Difference-in-Differences Models

63 Pages Posted: 15 Sep 2002  

Susan Athey

Stanford Graduate School of Business

Guido W. Imbens

Stanford Graduate School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: September 2002

Abstract

This paper develops an alternative approach to the widely used Difference-In-Difference (DID) method for evaluating the effects of policy changes. In contrast to the standard approach, we introduce a nonlinear model that permits changes over time in the effect of unobservables (e.g., there may be a time trend in the level of wages as well as the returns to skill in the labor market). Further, our assumptions are independent of the scaling of the outcome. Our approach provides an estimate of the entire counterfactual distribution of outcomes that would have been experienced by the treatment group in the absence of the treatment, and likewise for the untreated group in the presence of the treatment. Thus, it enables the evaluation of policy interventions according to criteria such as a mean-variance tradeoff. We provide conditions under which the model is nonparametrically identified and propose an estimator. We consider extensions to allow for covariates and discrete dependent variables. We also analyze inference, showing that our estimator is root-N consistent and asymptotically normal. Finally, we consider an application.

Suggested Citation

Athey, Susan and Imbens, Guido W., Identification and Inference in Nonlinear Difference-in-Differences Models (September 2002). NBER Working Paper No. t0280. Available at SSRN: https://ssrn.com/abstract=330300

Susan Carleton Athey (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Guido W. Imbens

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Paper statistics

Downloads
41
Abstract Views
947