Author
Listed:
- Lucas Ferreira Mation
- Vanessa Gapriotti Nadalin
- Cleandro Krause
Abstract
A identificação correta dos fenômenos de favelização é fundamental para a formulação de uma série de políticas públicas. Entretanto, a principal fonte oficial de informação a nível nacional, a classificação de setores subnormais disponível nos censos demográficos, não é comparável entre diferentes edições dos censos. Em particular, o Instituto Brasileiro de Geografia e Estatística (IBGE) indica no Censo 2010 que houve aprimoramento na capacidade de identificação de áreas subnormais, impossibilitando comparações diretas com o Censo 2000. Este artigo apresenta uma metodologia que permite a construção de estimativas comparáveis entre 2000 e 2010 das áreas e populações residentes em favelas. O método explora a possibilidade de parear os setores censitários de 2000 e 2010 para então reclassificar as áreas de favelas do Censo 2000 com base nas informações mais precisas dos setores subnormais do Censo 2010. Após construir uma classificação comparável, o artigo analisa a evolução do crescimento populacional e também as características socioeconômicas das favelas no Brasil e nas suas principais regiões metropolitanas. Os resultados mostram que, enquanto a comparação dos dados originais destes dois censos indica que a população residente em favelas teria crescido 75%, os dados corrigidos mostram um crescimento de apenas 8,6% entre 2000 e 2010. Também mostra-se que houve, de modo geral, melhorias nas condições socioeconômicas das favelas, chegando a exceder as melhorias observadas nas demais áreas. The correct detection of the phenomena of slums is critical to the formulation of several public policies. The main sources used in Brazil are data available from population censuses, which are not comparable over time. Accordingly, the statistics office (IBGE) alerts that changes in classification procedures improved considerably the identification of slums in the 2010 Census compared to the previous census. This article presents a methodology that enables the construction of estimates of areas and populations living in slums that are comparable between 2000 and 2010. The method explores the matching of census tracts of 2000 and 2010, and then reclassifies slum areas of 2000 based on more accurate information of slum areas of 2010. Having constructed a comparable, we analyze the growth of population in slums and their socioeconomic , both in Brazil as a whole and in the country’s main metropolitan areas. results show that while the comparison of the original data indicates that the living in slums would have grown 75%, the corrected data show an only 8,6% between 2000 and 2010. We also show that there has been a general of socioeconomic indicators in slums. Generally these improvements those observed in non-slum areas.
Suggested Citation
Lucas Ferreira Mation & Vanessa Gapriotti Nadalin & Cleandro Krause, 2014.
"Favelização no Brasil Entre 2000 e 2010: Resultados de Uma Classificação Comparável,"
Discussion Papers
2009, Instituto de Pesquisa Econômica Aplicada - IPEA.
Handle:
RePEc:ipe:ipetds:2009
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Citations
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Cited by:
- Guedes, Ricardo & Iachan, Felipe S. & Sant’Anna, Marcelo, 2023.
"Housing supply in the presence of informality,"
Regional Science and Urban Economics, Elsevier, vol. 99(C).
- Sant'Anna, Marcelo Castello Branco & Iachan, Felipe Saraiva & Guedes, Ricardo Brito, 2021.
"Housing supply in the presence of informality,"
FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE)
823, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
- Guedes, Ricardo & Iachan, Felipe & Sant'Anna, Marcelo, 2021.
"Housing Supply in the Presence of Informality,"
SocArXiv
mvhnf, Center for Open Science.
- Gomes Joice Genaro & Pedrassoli Julio Cesar, 2021.
"Survey of Slum Housing Characteristics Using Drones: An Experiment in the Alto das Pombas Community, Salvador de Bahia/Brazil,"
Quaestiones Geographicae, Sciendo, vol. 40(3), pages 169-182, September.
- Moraes, Ricardo Kalil & Wanke, Peter Fernandes & Faria, João Ricardo, 2021.
"Unveiling the endogeneity between social-welfare and labor efficiency: Two-stage NDEA neural network approach,"
Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
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