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Identification and estimation of interventions using changes in inequality measures


  • Firpo, Sergio Pinheiro


This paper presents semiparametric estimators of changes in inequality measures of a dependent variable distribution taking into account the possible changes on the distributions of covariates. When we do not impose parametric assumptions on the conditional distribution of the dependent variable given covariates, this problem becomes equivalent to estimation of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. The distributional impacts of a treatment will be calculated as differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called here 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. Using the inverse probability weighting method to estimate parameters of the marginal distribution of potential outcomes, in the second step weighted sample versions of inequality measures are computed. Root-N consistency, asymptotic normality and semiparametric efficiency are shown for the semiparametric estimators proposed. A Monte Carlo exercise is performed to investigate the behavior in finite samples of the estimator derived in the paper. We also apply our method to the evaluation of a job training program.

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  • Firpo, Sergio Pinheiro, 2010. "Identification and estimation of interventions using changes in inequality measures," Textos para discussão 214, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
  • Handle: RePEc:fgv:eesptd:214

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    1. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2006. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," American Economic Review, American Economic Association, vol. 96(4), pages 988-1012, September.
    2. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    3. Christian Schluter & Mark Trede, 2003. "Local versus Global Assessment of Mobility," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(4), pages 1313-1335, November.
    4. Tarozzi, Alessandro, 2007. "Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 314-336, July.
    5. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2002. "Removing the Veil of Ignorance in Assessing the Distributional Impacts of Social Policies," NBER Working Papers 8840, National Bureau of Economic Research, Inc.
    6. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," IZA Discussion Papers 767, Institute for the Study of Labor (IZA).
    7. Cowell, F.A., 2000. "Measurement of inequality," Handbook of Income Distribution,in: A.B. Atkinson & F. Bourguignon (ed.), Handbook of Income Distribution, edition 1, volume 1, chapter 2, pages 87-166 Elsevier.
    8. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 141-173.
    9. Champernowne,D. G. & Cowell,F. A., 1999. "Economic Inequality and Income Distribution," Cambridge Books, Cambridge University Press, number 9780521589598, March.
    10. Card, David & Sullivan, Daniel G, 1988. "Measuring the Effect of Subsidized Training Programs on Movements in and out of Employment," Econometrica, Econometric Society, vol. 56(3), pages 497-530, May.
    11. Aakvik, Arild & Heckman, James J. & Vytlacil, Edward J., 2005. "Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 15-51.
    12. Alberto Abadie & Joshua D. Angrist & Guido W. Imbens, 1998. "Instrumental Variables Estimation of Quantile Treatment Effects," NBER Technical Working Papers 0229, National Bureau of Economic Research, Inc.
    13. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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    Cited by:

    1. 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.

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