Title: Quantile Treatment Effects in Difference in Differences Models with Panel Data

Speaker: Tong Li, Vanderbilt University

Time: November 23rd, 2018 15:00–16:30

Venue: Conference Room 106B, Zhonghui Building

About the speaker:

Tong Li is a Professor of Economics at Vanderbilt University. He received PhD in Economics at University of Southern California in 1997. Prof. Tong Li's primary research and teaching interest are microeconometrics with a focus on identification and inference of econometric models with latent variables, as well as game-theoretic models. He also studies dynamic/nonlinear panel data analysis, and empirical microeconomics focusing on empirical analysis of strategic behavior of agents with asymmetric information. His research has been supported by the National Science Foundation and the American Statistical Association Committee on Law and Justice Statistics. His work has been published in general interest journals including Econometrica, Review of Economic Studies, American Economic Journal: Microeconomics, International Economic Review, Review of Economics and Statistics, and top field journals such as the Journal of Econometrics, RAND Journal of Economics, Games and Economic Behavior. He has served as an associate editor of the Journal of Econometrics, the Journal of Applied Econometrics, the Journal of Econometric Methods, and the Journal of Economic Behavior and Organization. Since 1999, he has supervised eleven PhD. dissertations and has placed students on faculty at London School of Economics, North Carolina State University, National University of Singapore, Shanghai Jiao Tong University, Temple University, and others.

Abstract:

This paper considers identification and estimation of the Quantile Treatment Effect on the Treated (QTT) under a straightforward distributional extension of the most commonly invoked Mean Difference in Differences assumption used for identifying the Average Treatment Effect on the Treated (ATT). Identification of the QTT is more complicated than the ATT though because it depends on the unknown dependence (or copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes for the treated group. To address this issue, we introduce a new Copula Stability Assumption that says that the missing dependence is constant over time. Under this assumption and when panel data is available, the missing dependence can be recovered, and the QTT is identified. Second, we allow for identification to hold only after conditioning on covariates and provide very simple estimators based on propensity score re-weighting for this case. We use our method to estimate the effect of increasing the minimum wage on quantiles of local labor markets' unemployment rates and find significant heterogeneity.