Regional or educational disparities? a counterfactual exercise
Autor(a) principal: | |
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Data de Publicação: | 2004 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/1013 |
Resumo: | This work investigates the impact of schooling Oil income distribution in statesjregions of Brazil. Using a semi-parametric model, discussed in DiNardo, Fortin & Lemieux (1996), we measure how much income diíferences between the Northeast and Southeast regions- the country's poorest and richest - and between the states of Ceará and São Paulo in those regions - can be explained by differences in schooling leveIs of the resident population. Using data from the National Household Survey (PNAD), we construct counterfactual densities by reweighting the distribution of the poorest region/state by the schooling profile of the richest. We conclude that: (i) more than 50% of the income di:fference is explained by the difference in schooling; (ii) the highest deciles of the income distribution gain more from an increase in schooling, closely approaching the wage distribution of the richest region/state; and (iii) an increase in schooling, holding the wage structure constant, aggravates the wage disparity in the poorest regions/ states. |
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Ferreira, Pedro CavalcantiSalvato, Márcio AntônioDuarte, Angelo José Mont'AlverneEscolas::EPGEFGV2008-05-13T15:47:05Z2008-05-13T15:47:05Z2004-03-010104-8910http://hdl.handle.net/10438/1013This work investigates the impact of schooling Oil income distribution in statesjregions of Brazil. Using a semi-parametric model, discussed in DiNardo, Fortin & Lemieux (1996), we measure how much income diíferences between the Northeast and Southeast regions- the country's poorest and richest - and between the states of Ceará and São Paulo in those regions - can be explained by differences in schooling leveIs of the resident population. Using data from the National Household Survey (PNAD), we construct counterfactual densities by reweighting the distribution of the poorest region/state by the schooling profile of the richest. We conclude that: (i) more than 50% of the income di:fference is explained by the difference in schooling; (ii) the highest deciles of the income distribution gain more from an increase in schooling, closely approaching the wage distribution of the richest region/state; and (iii) an increase in schooling, holding the wage structure constant, aggravates the wage disparity in the poorest regions/ states.engFundação Getulio Vargas. Escola de Pós-graduação em EconomiaEnsaios Econômicos;532Educação - BrasilEconomiaEducação - Brasil - Disparidades regionaisRenda - Distribuição - BrasilRegional or educational disparities? a counterfactual exerciseinfo: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/openAccessTEXT1556.pdf.txt1556.pdf.txtExtracted Texttext/plain29019https://repositorio.fgv.br/bitstreams/c4522d0f-8a51-4bb5-bf0d-a2b0fbdbfb18/download9bb1a7b560888dfc673259f42d03cb7fMD521556(2).pdf.txt1556(2).pdf.txtExtracted texttext/plain65765https://repositorio.fgv.br/bitstreams/fe864692-df47-4af3-b0ba-a2105e1d40fe/download1a6ae6349f5c11e606e7b46003f04e47MD58ORIGINAL1556(2).pdf1556(2).pdfapplication/pdf8419425https://repositorio.fgv.br/bitstreams/a873717f-e5e0-48ab-843f-ef73cff2089b/download21eaa0f0274e2e2218cc7e92ef788f60MD53THUMBNAIL1556(2).pdf.jpg1556(2).pdf.jpgGenerated Thumbnailimage/jpeg3435https://repositorio.fgv.br/bitstreams/bc41d11d-7b54-47a9-b72e-2363f84890bc/downloadecc9ad641f4ea0911f0060628a6e8328MD5910438/10132023-11-09 22:58:05.723open.accessoai:repositorio.fgv.br:10438/1013https://repositorio.fgv.brRepositório InstitucionalPRIhttp://bibliotecadigital.fgv.br/dspace-oai/requestopendoar:39742023-11-09T22:58:05Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV)false |
dc.title.eng.fl_str_mv |
Regional or educational disparities? a counterfactual exercise |
title |
Regional or educational disparities? a counterfactual exercise |
spellingShingle |
Regional or educational disparities? a counterfactual exercise Ferreira, Pedro Cavalcanti Educação - Brasil Economia Educação - Brasil - Disparidades regionais Renda - Distribuição - Brasil |
title_short |
Regional or educational disparities? a counterfactual exercise |
title_full |
Regional or educational disparities? a counterfactual exercise |
title_fullStr |
Regional or educational disparities? a counterfactual exercise |
title_full_unstemmed |
Regional or educational disparities? a counterfactual exercise |
title_sort |
Regional or educational disparities? a counterfactual exercise |
author |
Ferreira, Pedro Cavalcanti |
author_facet |
Ferreira, Pedro Cavalcanti Salvato, Márcio Antônio Duarte, Angelo José Mont'Alverne |
author_role |
author |
author2 |
Salvato, Márcio Antônio Duarte, Angelo José Mont'Alverne |
author2_role |
author author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EPGE |
dc.contributor.affiliation.none.fl_str_mv |
FGV |
dc.contributor.author.fl_str_mv |
Ferreira, Pedro Cavalcanti Salvato, Márcio Antônio Duarte, Angelo José Mont'Alverne |
dc.subject.por.fl_str_mv |
Educação - Brasil |
topic |
Educação - Brasil Economia Educação - Brasil - Disparidades regionais Renda - Distribuição - Brasil |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Educação - Brasil - Disparidades regionais Renda - Distribuição - Brasil |
description |
This work investigates the impact of schooling Oil income distribution in statesjregions of Brazil. Using a semi-parametric model, discussed in DiNardo, Fortin & Lemieux (1996), we measure how much income diíferences between the Northeast and Southeast regions- the country's poorest and richest - and between the states of Ceará and São Paulo in those regions - can be explained by differences in schooling leveIs of the resident population. Using data from the National Household Survey (PNAD), we construct counterfactual densities by reweighting the distribution of the poorest region/state by the schooling profile of the richest. We conclude that: (i) more than 50% of the income di:fference is explained by the difference in schooling; (ii) the highest deciles of the income distribution gain more from an increase in schooling, closely approaching the wage distribution of the richest region/state; and (iii) an increase in schooling, holding the wage structure constant, aggravates the wage disparity in the poorest regions/ states. |
publishDate |
2004 |
dc.date.issued.fl_str_mv |
2004-03-01 |
dc.date.accessioned.fl_str_mv |
2008-05-13T15:47:05Z |
dc.date.available.fl_str_mv |
2008-05-13T15:47:05Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10438/1013 |
dc.identifier.issn.none.fl_str_mv |
0104-8910 |
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0104-8910 |
url |
http://hdl.handle.net/10438/1013 |
dc.language.iso.fl_str_mv |
eng |
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eng |
dc.relation.ispartofseries.por.fl_str_mv |
Ensaios Econômicos;532 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Fundação Getulio Vargas. Escola de Pós-graduação em Economia |
publisher.none.fl_str_mv |
Fundação Getulio Vargas. Escola de Pós-graduação em Economia |
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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Repositório Institucional do FGV (FGV Repositório Digital) |
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