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Sensitivity Analysis in Unconditional Quantile Effects

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  • Julian Martinez-Iriarte

Abstract

This paper proposes a framework to analyze the effects of counterfactual policies on the unconditional quantiles of an outcome variable. For a given counterfactual policy, we obtain identified sets for the effect of both marginal and global changes in the proportion of treated individuals. To conduct a sensitivity analysis, we introduce the quantile breakdown frontier, a curve that (i) indicates whether a sensitivity analysis is possible or not, and (ii) when a sensitivity analysis is possible, quantifies the amount of selection bias consistent with a given conclusion of interest across different quantiles. To illustrate our method, we perform a sensitivity analysis on the effect of unionizing low income workers on the quantiles of the distribution of (log) wages.

Suggested Citation

  • Julian Martinez-Iriarte, 2023. "Sensitivity Analysis in Unconditional Quantile Effects," Papers 2303.14298, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2303.14298
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    References listed on IDEAS

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    1. Patrick Kline & Andres Santos, 2013. "Sensitivity to missing data assumptions: Theory and an evaluation of the U.S. wage structure," Quantitative Economics, Econometric Society, vol. 4(2), pages 231-267, July.
    2. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    3. Henry S Farber & Daniel Herbst & Ilyana Kuziemko & Suresh Naidu, 2021. "Unions and Inequality over the Twentieth Century: New Evidence from Survey Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(3), pages 1325-1385.
    4. Julian Martinez-Iriarte & YiXiao Sun, 2022. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: an Unconditional MTE Approach," Working Papers 131, Red Nacional de Investigadores en Economía (RedNIE).
    5. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    6. Maximilian Kasy, 2016. "Partial Identification, Distributional Preferences, and the Welfare Ranking of Policies," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 111-131, March.
    7. Julian Martinez-Iriarte & Yixiao Sun, 2020. "Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: An Unconditional MTE Approach," Papers 2010.15864, arXiv.org, revised Aug 2024.
    8. Thomas Lemieux, 2006. "Increasing Residual Wage Inequality: Composition Effects, Noisy Data, or Rising Demand for Skill?," American Economic Review, American Economic Association, vol. 96(3), pages 461-498, June.
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    Cited by:

    1. Martinez-Iriarte, Julian & Sun, Yixiao, 2024. "Identification and estimation of unconditional policy effects of an endogenous binary treatment: An unconditional MTE approach," Journal of Econometrics, Elsevier, vol. 244(1).
    2. Martínez-Iriarte, Julián & Montes-Rojas, Gabriel & Sun, Yixiao, 2024. "Unconditional effects of general policy interventions," Journal of Econometrics, Elsevier, vol. 238(2).

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