Regional or educational disparities? a counterfactual exercise

Detalhes bibliográficos
Autor(a) principal: Ferreira, Pedro Cavalcanti
Data de Publicação: 2004
Outros Autores: Salvato, Márcio Antônio, Duarte, Angelo José Mont'Alverne
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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spelling 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/plain29019http://bibliotecadigital.fgv.br:80/dspace/bitstream/10438/1013/2/1556.pdf.txt9bb1a7b560888dfc673259f42d03cb7fMD521556(2).pdf.txt1556(2).pdf.txtExtracted Texttext/plain64617http://bibliotecadigital.fgv.br:80/dspace/bitstream/10438/1013/4/1556%282%29.pdf.txt43bd6db8be1756a91234b47554a969e6MD54ORIGINAL1556(2).pdf1556(2).pdfapplication/pdf8419425http://bibliotecadigital.fgv.br:80/dspace/bitstream/10438/1013/3/1556%282%29.pdf21eaa0f0274e2e2218cc7e92ef788f60MD53THUMBNAIL1556(2).pdf.jpg1556(2).pdf.jpgGenerated Thumbnailimage/jpeg1905http://bibliotecadigital.fgv.br:80/dspace/bitstream/10438/1013/5/1556%282%29.pdf.jpgba1d05489ecb3ec90683fe78ac94fe34MD5510438/10132021-03-26 21:27:33.189oai:bibliotecadigital.fgv.br:10438/1013Repositório InstitucionalPRIhttp://bibliotecadigital.fgv.br/dspace-oai/requestopendoar:39742021-03-27T00:27:33Repositório 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.
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dc.publisher.none.fl_str_mv Fundação Getulio Vargas. Escola de Pós-graduação em Economia
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