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Bounds on distributional treatment effect parameters using panel data with an application on job displacement

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  • Callaway, Brantly
Abstract
This paper develops new techniques to bound distributional treatment effect parameters that depend on the joint distribution of potential outcomes — an object not identified by standard identifying assumptions such as selection on observables or even when treatment is randomly assigned. I show that panel data and an additional assumption on the dependence between untreated potential outcomes for the treated group over time (i) provide more identifying power for distributional treatment effect parameters than existing bounds and (ii) provide a more plausible set of conditions than existing methods that obtain point identification. I apply these bounds to study heterogeneity in the effect of job displacement during the Great Recession. Using standard techniques, I find that workers who were displaced during the Great Recession lost on average 34% of their earnings relative to their counterfactual earnings had they not been displaced. Using the methods developed in the current paper, I also show that the average effect masks substantial heterogeneity across workers.

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  • Callaway, Brantly, 2021. "Bounds on distributional treatment effect parameters using panel data with an application on job displacement," Journal of Econometrics, Elsevier, vol. 222(2), pages 861-881.
    • Handle: RePEc:eee:econom:v:222:y:2021:i:2:p:861-881
    DOI: 10.1016/j.jeconom.2020.02.005
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    Cited by:

    1. Battistin, Erich & Lamarche, Carlos & Rettore, Enrico, 2020. "Quantiles of the Gain Distribution of an Early Childhood Intervention," IZA Discussion Papers 13101, Institute of Labor Economics (IZA).
    2. Daniel Kaliski, 2023. "Identifying the impact of health insurance on subgroups with changing rates of diagnosis," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2098-2112, September.
    3. Afrouz Azadikhah Jahromi & Brantly Callaway, 2022. "Heterogeneous Effects of Job Displacement on Earnings," Empirical Economics, Springer, vol. 62(1), pages 213-245, January.
    4. Heshani Madigasekara & D. S. Poskitt & Lina Zhang & Xueyan Zhao, 2024. "Partial Identification of Distributional Treatment Effects in Panel Data using Copula Equality Assumptions," Papers 2411.04450, arXiv.org.
    5. Brantly Callaway, 2022. "Difference-in-Differences for Policy Evaluation," Papers 2203.15646, arXiv.org.

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    More about this item

    Keywords

    Joint distribution of potential outcomes; Distribution of the treatment effect; Quantile of the treatment effect; Copula stability assumption; Panel data; Job displacement;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

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