Inequality treatment effects
Autor(a) principal: | |
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Data de Publicação: | 2005 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/12192 |
Resumo: | This paper presents semiparametric estimators for treatment effects parameters when selection to treatment is based on observable characteristics. The parameters of interest in this paper are those that capture summarized distributional effects of the treatment. In particular, the focus is on the impact of the treatment calculated by differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called here inequality treatment effects. 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 reweighting method to estimate parameters of the marginal distribution of potential outcomes, in the second step weighted sample versions of inequality measures are.computed. Calculations of semiparametric effciency bounds for inequality treatment effects parameters are presented. Root-N consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound 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. |
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Firpo, Sergio PinheiroEscolas::EPGEFGV2014-10-23T10:51:09Z2014-10-23T10:51:09Z2005-05-05http://hdl.handle.net/10438/12192This paper presents semiparametric estimators for treatment effects parameters when selection to treatment is based on observable characteristics. The parameters of interest in this paper are those that capture summarized distributional effects of the treatment. In particular, the focus is on the impact of the treatment calculated by differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called here inequality treatment effects. 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 reweighting method to estimate parameters of the marginal distribution of potential outcomes, in the second step weighted sample versions of inequality measures are.computed. Calculations of semiparametric effciency bounds for inequality treatment effects parameters are presented. Root-N consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound 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.engEscola de Pós-Graduação em Economia da FGVSeminários de pesquisa econômica da EPGETodo cuidado foi dispensado para respeitar os direitos autorais deste trabalho. Entretanto, caso esta obra aqui depositada seja protegida por direitos autorais externos a esta instituição, contamos com a compreensão do autor e solicitamos que o mesmo faça contato através do Fale Conosco para que possamos tomar as providências cabíveisinfo:eu-repo/semantics/openAccessreatment Effects, Inequality Measures, Semiparametric Efficiency, Reweighting EstimatorEconomiaEconometriaInequality treatment effectsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlereponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVORIGINAL000364941.pdf000364941.pdfapplication/pdf1418671https://repositorio.fgv.br/bitstreams/62995bc3-58c0-48dd-b9d0-84085a717101/download11c6e46393c0518e424fa832546abb18MD51LICENSElicense.txtlicense.txttext/plain; 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dc.title.eng.fl_str_mv |
Inequality treatment effects |
title |
Inequality treatment effects |
spellingShingle |
Inequality treatment effects Firpo, Sergio Pinheiro reatment Effects, Inequality Measures, Semiparametric Efficiency, Reweighting Estimator Economia Econometria |
title_short |
Inequality treatment effects |
title_full |
Inequality treatment effects |
title_fullStr |
Inequality treatment effects |
title_full_unstemmed |
Inequality treatment effects |
title_sort |
Inequality treatment effects |
author |
Firpo, Sergio Pinheiro |
author_facet |
Firpo, Sergio Pinheiro |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EPGE |
dc.contributor.affiliation.none.fl_str_mv |
FGV |
dc.contributor.author.fl_str_mv |
Firpo, Sergio Pinheiro |
dc.subject.por.fl_str_mv |
reatment Effects, Inequality Measures, Semiparametric Efficiency, Reweighting Estimator |
topic |
reatment Effects, Inequality Measures, Semiparametric Efficiency, Reweighting Estimator Economia Econometria |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Econometria |
description |
This paper presents semiparametric estimators for treatment effects parameters when selection to treatment is based on observable characteristics. The parameters of interest in this paper are those that capture summarized distributional effects of the treatment. In particular, the focus is on the impact of the treatment calculated by differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called here inequality treatment effects. 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 reweighting method to estimate parameters of the marginal distribution of potential outcomes, in the second step weighted sample versions of inequality measures are.computed. Calculations of semiparametric effciency bounds for inequality treatment effects parameters are presented. Root-N consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound 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. |
publishDate |
2005 |
dc.date.issued.fl_str_mv |
2005-05-05 |
dc.date.accessioned.fl_str_mv |
2014-10-23T10:51:09Z |
dc.date.available.fl_str_mv |
2014-10-23T10:51:09Z |
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 |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10438/12192 |
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http://hdl.handle.net/10438/12192 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.por.fl_str_mv |
Seminários de pesquisa econômica da EPGE |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Escola de Pós-Graduação em Economia da FGV |
publisher.none.fl_str_mv |
Escola de Pós-Graduação em Economia da FGV |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do FGV (FGV Repositório Digital) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
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Fundação Getulio Vargas (FGV) |
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FGV |
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FGV |
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collection |
Repositório Institucional do FGV (FGV Repositório Digital) |
bitstream.url.fl_str_mv |
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