Identification and estimation of interventions using changes in inequality measures
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/6685 |
Resumo: | 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 PinheiroEscolas::EESP2010-06-16T22:01:38Z2010-06-16T22:01:38Z2010-06-16http://hdl.handle.net/10438/6685This 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.engTextos para Discussão;214Inequality measuresTreatment effectsSemiparametric efficiencyReweighting estimatorEconomiaEconomiaIdentification and estimation of interventions using changes in inequality measuresinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlereponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessORIGINALTD 214 - Sergio Firpo.pdfTD 214 - Sergio Firpo.pdfapplication/pdf501596https://repositorio.fgv.br/bitstreams/70f8f002-962e-4e7f-acc8-7d93d8b35e3d/download7cae2814332e30a6ccab7fc6e90b3e26MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84712https://repositorio.fgv.br/bitstreams/a09292dd-5cb1-49fb-ac3d-488c284ae62a/download4dea6f7333914d9740702a2deb2db217MD52TEXTTD 214 - Sergio Firpo.pdf.txtTD 214 - Sergio Firpo.pdf.txtExtracted 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dc.title.eng.fl_str_mv |
Identification and estimation of interventions using changes in inequality measures |
title |
Identification and estimation of interventions using changes in inequality measures |
spellingShingle |
Identification and estimation of interventions using changes in inequality measures Firpo, Sergio Pinheiro Inequality measures Treatment effects Semiparametric efficiency Reweighting estimator Economia Economia |
title_short |
Identification and estimation of interventions using changes in inequality measures |
title_full |
Identification and estimation of interventions using changes in inequality measures |
title_fullStr |
Identification and estimation of interventions using changes in inequality measures |
title_full_unstemmed |
Identification and estimation of interventions using changes in inequality measures |
title_sort |
Identification and estimation of interventions using changes in inequality measures |
author |
Firpo, Sergio Pinheiro |
author_facet |
Firpo, Sergio Pinheiro |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EESP |
dc.contributor.author.fl_str_mv |
Firpo, Sergio Pinheiro |
dc.subject.por.fl_str_mv |
Inequality measures |
topic |
Inequality measures Treatment effects Semiparametric efficiency Reweighting estimator Economia Economia |
dc.subject.eng.fl_str_mv |
Treatment effects Semiparametric efficiency Reweighting estimator |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Economia |
description |
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. |
publishDate |
2010 |
dc.date.accessioned.fl_str_mv |
2010-06-16T22:01:38Z |
dc.date.available.fl_str_mv |
2010-06-16T22:01:38Z |
dc.date.issued.fl_str_mv |
2010-06-16 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
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article |
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dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10438/6685 |
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http://hdl.handle.net/10438/6685 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.por.fl_str_mv |
Textos para Discussão;214 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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reponame:Repositório Institucional do FGV (FGV Repositório Digital) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
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