Difference-in-differences analysis with repeated cross-sectional survey data.

Difference-in-differences (DiD) approach is one of the most widely used approaches for evaluating policy effects. However, traditional DiD methods may not recover the population-level average treatment effect on the treated (ATT) in the absence of population-level panel data, particularly when the composition of units in the treatment group changes over time. In this work, we address the following two challenges when applying DiD methods with repeated cross-sectional (RCS) survey data: (1) heterogeneous compositions of study samples across different time points, and (2) availability of data for only a sample of the population. We introduce a policy-relevant target estimand and establish its identification conditions. We then propose a new weighting approach that incorporates both estimated propensity scores and given survey weights. We establish the theoretical properties of the proposed method and examine its finite-sample performance through simulations. Finally, we apply our proposed method to a real-world data application, estimating the effect of a beverage tax on adolescent soda consumption in Philadelphia.