Multidimensional poverty In Brazil

Detalhes bibliográficos
Autor(a) principal: Todeschini, Caroline
Data de Publicação: 2016
Outros Autores: Bezerra Baço, Fernanda Mendes
Tipo de documento: Artigo
Idioma: por
Título da fonte: Informe Gepec (Online)
Texto Completo: https://e-revista.unioeste.br/index.php/gepec/article/view/12818
Resumo: Poverty and socioeconomic development go in opposite directions, the greater a population poverty rate is, the lower is the level of socioeconomic development. This paper seeks to set poverty to capture vulnerabilities. The objective of this paper is to create a multidimensional poverty indicator for the Brazilian metropolitan areas of in the year 2011, using data from the PNAD (National Survey by Household Sampling) and as a methodology, Fuzzy Sets theory (TFS). Poverty is divided into three dimensions: income, education and household infrastructure, and the education showed the largest number of poor, followed by income and the infrastructure, in that order. The poorest metropolitan areas are located in the North and Northeast Brazil, supporting the 2010 Demographic Census information held by the Brazilian Institute of Geography and Statistics (IBGE), which says that 60% of the Brazilian poor people lived in the Northeast that year.
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spelling Multidimensional poverty In BrazilPobreza multidimensional no Brasil: uma análise das regiões metropolitanasPobreza multidimensionalRegiões metropolitanasTeoria Fuzzy Sets.Poverty and socioeconomic development go in opposite directions, the greater a population poverty rate is, the lower is the level of socioeconomic development. This paper seeks to set poverty to capture vulnerabilities. The objective of this paper is to create a multidimensional poverty indicator for the Brazilian metropolitan areas of in the year 2011, using data from the PNAD (National Survey by Household Sampling) and as a methodology, Fuzzy Sets theory (TFS). Poverty is divided into three dimensions: income, education and household infrastructure, and the education showed the largest number of poor, followed by income and the infrastructure, in that order. The poorest metropolitan areas are located in the North and Northeast Brazil, supporting the 2010 Demographic Census information held by the Brazilian Institute of Geography and Statistics (IBGE), which says that 60% of the Brazilian poor people lived in the Northeast that year.Pobreza e desenvolvimento socioeconômico caminham em direções opostas, quanto maior é a taxa de pobreza de uma população, menor é o nível de desenvolvimento socioeconômico. Neste trabalho procura-se definir pobreza de modo a captar vulnerabilidades. O objetivo do presente trabalho é criar um indicador de pobreza multidimensional para as regiões metropolitanas do Brasil para o ano de 2011, utilizando os dados da PNAD (Pesquisa Nacional por Amostra de Domicílios) e, como metodologia, a Teoria Fuzzy Sets (TFS). A pobreza é dividida em três dimensões: renda, escolaridade e infraestrutura domiciliar, sendo que a escolaridade foi a que apresentou maior número de pobres, seguida pela renda e pela infraestrutura, nessa ordem. As regiões metropolitanas mais pobres estão localizadas no Norte e Nordeste brasileiro, respaldando as informações do Censo Demográfico 2010, realizado pelo Instituto Brasileiro de Geografia e Estatística (IBGE), de que 60% dos pobres do Brasil moravam na Região Nordeste naquele ano.EdUnioeste2016-02-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://e-revista.unioeste.br/index.php/gepec/article/view/1281810.48075/igepec.v19i2.12818Informe GEPEC; v. 19 n. 2 (2015); 57-711679-415X1676-067010.48075/igepec.v19i2reponame:Informe Gepec (Online)instname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEporhttps://e-revista.unioeste.br/index.php/gepec/article/view/12818/9489Todeschini, CarolineBezerra Baço, Fernanda Mendesinfo:eu-repo/semantics/openAccess2023-05-16T22:12:51Zoai:ojs.e-revista.unioeste.br:article/12818Revistahttps://e-revista.unioeste.br/index.php/gepecPUBhttps://e-revista.unioeste.br/index.php/gepec/oairevista.gepec@gmail.com1679-415X1676-0670opendoar:2023-05-16T22:12:51Informe Gepec (Online) - Universidade Estadual do Oeste do Paraná (UNIOESTE)false
dc.title.none.fl_str_mv Multidimensional poverty In Brazil
Pobreza multidimensional no Brasil: uma análise das regiões metropolitanas
title Multidimensional poverty In Brazil
spellingShingle Multidimensional poverty In Brazil
Todeschini, Caroline
Pobreza multidimensional
Regiões metropolitanas
Teoria Fuzzy Sets.
title_short Multidimensional poverty In Brazil
title_full Multidimensional poverty In Brazil
title_fullStr Multidimensional poverty In Brazil
title_full_unstemmed Multidimensional poverty In Brazil
title_sort Multidimensional poverty In Brazil
author Todeschini, Caroline
author_facet Todeschini, Caroline
Bezerra Baço, Fernanda Mendes
author_role author
author2 Bezerra Baço, Fernanda Mendes
author2_role author
dc.contributor.author.fl_str_mv Todeschini, Caroline
Bezerra Baço, Fernanda Mendes
dc.subject.por.fl_str_mv Pobreza multidimensional
Regiões metropolitanas
Teoria Fuzzy Sets.
topic Pobreza multidimensional
Regiões metropolitanas
Teoria Fuzzy Sets.
description Poverty and socioeconomic development go in opposite directions, the greater a population poverty rate is, the lower is the level of socioeconomic development. This paper seeks to set poverty to capture vulnerabilities. The objective of this paper is to create a multidimensional poverty indicator for the Brazilian metropolitan areas of in the year 2011, using data from the PNAD (National Survey by Household Sampling) and as a methodology, Fuzzy Sets theory (TFS). Poverty is divided into three dimensions: income, education and household infrastructure, and the education showed the largest number of poor, followed by income and the infrastructure, in that order. The poorest metropolitan areas are located in the North and Northeast Brazil, supporting the 2010 Demographic Census information held by the Brazilian Institute of Geography and Statistics (IBGE), which says that 60% of the Brazilian poor people lived in the Northeast that year.
publishDate 2016
dc.date.none.fl_str_mv 2016-02-08
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://e-revista.unioeste.br/index.php/gepec/article/view/12818
10.48075/igepec.v19i2.12818
url https://e-revista.unioeste.br/index.php/gepec/article/view/12818
identifier_str_mv 10.48075/igepec.v19i2.12818
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://e-revista.unioeste.br/index.php/gepec/article/view/12818/9489
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv EdUnioeste
publisher.none.fl_str_mv EdUnioeste
dc.source.none.fl_str_mv Informe GEPEC; v. 19 n. 2 (2015); 57-71
1679-415X
1676-0670
10.48075/igepec.v19i2
reponame:Informe Gepec (Online)
instname:Universidade Estadual do Oeste do Paraná (UNIOESTE)
instacron:UNIOESTE
instname_str Universidade Estadual do Oeste do Paraná (UNIOESTE)
instacron_str UNIOESTE
institution UNIOESTE
reponame_str Informe Gepec (Online)
collection Informe Gepec (Online)
repository.name.fl_str_mv Informe Gepec (Online) - Universidade Estadual do Oeste do Paraná (UNIOESTE)
repository.mail.fl_str_mv revista.gepec@gmail.com
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