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Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures

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  • Firpo, Sergio

    (Insper, São Paulo)

Abstract

This paper presents semiparametric estimators of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. Distributional impacts of a treatment are calculated as differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called “Inequality Treatment Effects” (ITE). The estimation procedure involves a first non-parametric step in which the probability of receiving treatment given covariates, the propensity-score, is estimated. In the second step weighted sample versions of inequality measures are computed using weights based on the estimated propensity-score. Root-N consistency, asymptotic normality, semiparametric efficiency and validity of inference based on the bootstrap are shown for the semiparametric estimators proposed. In addition of being easily implementable and computationally simple, results from a Monte Carlo exercise reveal that its good relative performance in small samples is robust to changes in the distribution of latent selection variables. Finally, as an illustration of the method, we apply the estimator to a real data set collected for the evaluation of a job training program, using several popular inequality measures to capture distributional impacts of the program.

Suggested Citation

  • Firpo, Sergio, 2010. "Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures," IZA Discussion Papers 4841, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp4841
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    Cited by:

    1. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    2. Donald, Stephen G. & Hsu, Yu-Chin, 2014. "Estimation and inference for distribution functions and quantile functions in treatment effect models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 383-397.
    3. Toru Kitagawa & Aleksey Tetenov, 2021. "Equality-Minded Treatment Choice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 561-574, March.
    4. Vincent A. Hildebrand & María Noel Pi Alperin & Philippe Van Kerm, 2017. "Measuring and Accounting for the Deprivation Gap of Portuguese Immigrants in Luxembourg," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(2), pages 288-309, June.
    5. Fernando Rios-Avila, 2019. "Recentered Influence Functions in Stata: Methods for Analyzing the Determinants of Poverty and Inequality," Economics Working Paper Archive wp_927, Levy Economics Institute.
    6. Christian Ahlin & Hyeok Jeong, 2021. "A conditional Gini: measure, estimation, and application," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(2), pages 363-384, June.
    7. Maier, Michael, 2011. "Tests for distributional treatment effects under unconfoundedness," Economics Letters, Elsevier, vol. 110(1), pages 49-51, January.
    8. Ghosh, Pallab Kumar, 2014. "The contribution of human capital variables to changes in the wage distribution function," Labour Economics, Elsevier, vol. 28(C), pages 58-69.
    9. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    10. Sergio P. Firpo & Nicole M. Fortin & Thomas Lemieux, 2018. "Decomposing Wage Distributions Using Recentered Influence Function Regressions," Econometrics, MDPI, vol. 6(2), pages 1-40, May.
    11. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 1, pages 1-102, Elsevier.
    12. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    13. Akanksha Negi, 2020. "Doubly weighted M-estimation for nonrandom assignment and missing outcomes," Papers 2011.11485, arXiv.org.
    14. Ferreira, Francisco H. G. & Firpo, Sergio & Galvao, Antonio F., 2017. "Estimation and Inference for Actual and Counterfactual Growth Incidence Curves," IZA Discussion Papers 10473, Institute of Labor Economics (IZA).
    15. Ying-Ying Lee, 2015. "Efficient propensity score regression estimators of multi-valued treatment effects for the treated," Economics Series Working Papers 738, University of Oxford, Department of Economics.
    16. García, A., 2016. "Oaxaca-Blinder Type Counterfactual Decomposition Methods for Duration Outcomes," Documentos de Trabajo 014186, Universidad del Rosario.
    17. Rumeng Cui & Zhong Ma & Longfeng Wang, 2022. "Allocation of Decision Rights and CSR Disclosure: Evidence from Listed Business Groups in China," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    18. Cañón Salazar Carlos Iván, 2016. "Distributional Policy Effects with Many Treatment Outcomes," Working Papers 2016-01, Banco de México.
    19. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2018. "Covariate Distribution Balance via Propensity Scores," Papers 1810.01370, arXiv.org, revised Apr 2020.
    20. Mariko Hatase & Mototsugu Shintani & Tomoyoshi Yabu, 2013. "Great earthquakes, exchange rate volatility and government interventions," Vanderbilt University Department of Economics Working Papers 13-00007, Vanderbilt University Department of Economics.
    21. Kim, Ju Hyun & Park, Byoung G., 2018. "Weak convergence of local quantile treatment effect processes," Economics Letters, Elsevier, vol. 162(C), pages 49-52.
    22. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

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    More about this item

    Keywords

    inequality measures; treatment effects; semiparametric efficiency; reweighting estimator;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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