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Quantile regression with group-level treatments

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  • Chen, Songnian

Abstract

To study distributional effects of group level treatments, Chetverikov et al. (2016) proposed a grouped instrumental variables quantile regression estimator, a quantile extension of the Hausman and Taylor’s 1981 instrumental variables estimator for panel data. However, their approach only allows for heterogenous distributional effects of group-level treatments that correspond to individual-level unobserved characteristics, but not group-level unobserved characteristics. In this article, we propose a quantile regression model that allows for heterogenous distributional effects of group-level treatments associated with both individual-level and group-level unobserved characteristics. We propose two-step quantile regression and instrumental variables quantile regression estimators, depending on whether the group-level treatments are correlated with the group-level unobserved characteristics. Large sample properties are presented and simulation results indicate our estimators perform well in finite samples. We apply our method to study the impact of Chinese import competition on the U.S. local wage distribution, and uncover both significant individual-level and group-level heterogeneity of the group treatment effects.

Suggested Citation

  • Chen, Songnian, 2025. "Quantile regression with group-level treatments," Journal of Econometrics, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:econom:v:251:y:2025:i:c:s0304407625001332
    DOI: 10.1016/j.jeconom.2025.106079
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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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