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Estimation of average treatment effects using panel data when treatment effect heterogeneity depends on unobserved fixed effects

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  • Shosei Sakaguchi

Abstract

This paper proposes a new panel data approach to identify and estimate the time‐varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the linear fixed effects specification of potential outcome equations and uses exogenous variables that are correlated with the fixed effects. I apply the approach to study the impact of a mother's smoking during pregnancy on her child's birth weight.

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  • Shosei Sakaguchi, 2020. "Estimation of average treatment effects using panel data when treatment effect heterogeneity depends on unobserved fixed effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 315-327, April.
    • Handle: RePEc:wly:japmet:v:35:y:2020:i:3:p:315-327
    DOI: 10.1002/jae.2752
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    Cited by:

    1. Riccardo D'Alberto & Francesco Pagliacci & Matteo Zavalloni, 2023. "A socioeconomic impact assessment of three Italian national parks," Journal of Regional Science, Wiley Blackwell, vol. 63(1), pages 114-147, January.
    2. Sayoni Roychowdhury & Indrila Ganguly & Abhik Ghosh, 2021. "Robust Estimation of Average Treatment Effects from Panel Data," Papers 2112.13228, arXiv.org, revised Dec 2022.
    3. Valentin Verdier, 2020. "Average treatment effects for stayers with correlated random coefficient models of panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 917-939, November.

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