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

Kerry Ye1, Alyssa Bilinski1,2, Youjin Lee1

  • 1Department of Biostatistics, Brown University, 121 S Main St, Providence, RI 02903, USA.

Health Services & Outcomes Research Methodology
|December 5, 2025
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Summary
This summary is machine-generated.

This study introduces a new weighting method for difference-in-differences (DiD) analysis using repeated cross-sectional (RCS) data. The method accurately estimates policy effects despite changing sample compositions and limited population data.

Keywords:
Difference-in-differencesInverse probability weightingRepeated cross-sectional dataSurvey samples

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Area of Science:

  • Econometrics
  • Public Health Policy
  • Survey Methodology

Background:

  • Traditional difference-in-differences (DiD) methods struggle with policy evaluation using repeated cross-sectional (RCS) data.
  • Challenges include heterogeneous sample compositions over time and reliance on sampled, not population-level, data.
  • Accurate estimation of the average treatment effect on the treated (ATT) is often compromised.

Purpose of the Study:

  • To develop a robust method for estimating policy effects using RCS data.
  • To address limitations of traditional DiD in the presence of time-varying sample compositions.
  • To identify a policy-relevant target estimand and its identification conditions.

Main Methods:

  • Proposed a novel weighting approach combining propensity score estimation and survey weights.
  • Established theoretical properties of the new weighting method.
  • Conducted simulations to evaluate finite-sample performance.

Main Results:

  • The proposed weighting method successfully addresses challenges in DiD analysis with RCS data.
  • Demonstrated accurate estimation of the average treatment effect on the treated (ATT) under specified conditions.
  • Simulation results confirmed the method's finite-sample performance.

Conclusions:

  • The developed method provides a reliable approach for policy effect evaluation using RCS survey data.
  • Applicable in scenarios with evolving unit compositions and incomplete population data.
  • Successfully applied to estimate the impact of a beverage tax on adolescent soda consumption in Philadelphia.