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A selection correction method for heterogeneous treatment effects in staggered adoption settings

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  • Park, Minchul

Abstract

This paper proposes an application of Heckman’s (1979) selection correction approach to estimate heterogeneous treatment effects in staggered adoption settings. With a selection model for treatment timing, selection correction terms in counterfactual trends are derived, and the identified counterfactual trends are plugged into the difference-in-differences parameters. This approach allows never-treated potential outcomes to be generated by an interactive fixed effects model, which permits non-parallel trends of the never-treated outcomes. Unlike most methods based on interactive fixed effects models, the proposed method does not require a large number of pre-treatment periods. The empirical relevance of the proposed method is illustrated by estimating the short-term effects of first childbirth timing on women’s labor market outcomes.

Suggested Citation

  • Park, Minchul, 2025. "A selection correction method for heterogeneous treatment effects in staggered adoption settings," Economics Letters, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:ecolet:v:254:y:2025:i:c:s0165176525003271
    DOI: 10.1016/j.econlet.2025.112490
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    References listed on IDEAS

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    1. Clément de Chaisemartin & Xavier D'Haultfœuille, 2020. "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects," American Economic Review, American Economic Association, vol. 110(9), pages 2964-2996, September.
    2. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    3. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
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    5. Jane Herr, 2016. "Measuring the effect of the timing of first birth on wages," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(1), pages 39-72, January.
    6. Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
    7. Nikolay Angelov & Per Johansson & Myoung‐jae Lee, 2019. "Practical causal analysis for the treatment timing effect on doubly censored duration: effect of fertility on work span," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1561-1585, October.
    8. Callaway, Brantly & Karami, Sonia, 2023. "Treatment effects in interactive fixed effects models with a small number of time periods," Journal of Econometrics, Elsevier, vol. 233(1), pages 184-208.
    9. Matteo Picchio & Claudia Pigini & Stefano Staffolani & Alina Verashchagina, 2021. "If not now, when? The timing of childbirth and labor market outcomes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 663-685, September.
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    11. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
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    13. Kaibo Gao & Zhongjing Tian, 2023. "The Effect of Motherhood on Wages: are women’s wage penalties due to lack of career aspirations?," Applied Economics, Taylor & Francis Journals, vol. 55(54), pages 6410-6426, November.
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    Keywords

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

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