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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 for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp4841
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    Cited by:

    1. 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 for the Study of Labor (IZA).
    2. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, Elsevier.
    3. 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.
    4. 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.
    5. 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.
    6. García, A., 2016. "Oaxaca-Blinder Type Counterfactual Decomposition Methods for Duration Outcomes," DOCUMENTOS DE TRABAJO 014186, UNIVERSIDAD DEL ROSARIO.
    7. Cañón Salazar Carlos Iván, 2016. "Distributional Policy Effects with Many Treatment Outcomes," Working Papers 2016-01, Banco de México.
    8. Maier, Michael, 2011. "Tests for distributional treatment effects under unconfoundedness," Economics Letters, Elsevier, vol. 110(1), pages 49-51, January.
    9. 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.
    10. 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.
    11. repec:eee:ecolet:v:162:y:2018:i:c:p:49-52 is not listed on IDEAS
    12. Toru Kitagawa & Aleksey Tetenov, 2017. "Equality-minded treatment choice," CeMMAP working papers CWP10/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. 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.

    More about this item

    Keywords

    inequality measures; treatment effects; semiparametric efficiency; reweighting estimator;

    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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