Your browser doesn't support javascript.
loading
Inequality and income segregation in Brazilian cities: a nationwide analysis.
de Sousa Filho, José Firmino; Dos Santos, Gervásio F; Andrade, Roberto F Silva; Paiva, Aureliano S; Freitas, Anderson; Castro, Caio Porto; de Lima Friche, Amélia A; Barber, Sharrelle; Caiaffa, Waleska T; Barreto, Maurício L.
Afiliação
  • de Sousa Filho JF; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.
  • Dos Santos GF; School of Economics (PPGE), Federal University of Bahia, Salvador, Brazil.
  • Andrade RFS; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.
  • Paiva AS; School of Economics (PPGE), Federal University of Bahia, Salvador, Brazil.
  • Freitas A; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.
  • Castro CP; Institute of Physics, Federal University of Bahia, Salvador, Brazil.
  • de Lima Friche AA; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.
  • Barber S; Institute of Physics, Federal University of Bahia, Salvador, Brazil.
  • Caiaffa WT; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.
  • Barreto ML; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.
SN Soc Sci ; 2(9): 191, 2022.
Article em En | MEDLINE | ID: mdl-36105865
Residential segregation has brought significant challenges to cities worldwide and has important implications for health. This study aimed to assess income segregation in the 152 largest Brazilian cities in the SALURBAL Project. We identify specific socioeconomic characteristics related to residential segregation by income using the Brazilian demographic census of 2010 and calculated the income dissimilarity index (IDI) at the census tract level for each city, subsequently comparing it with Gini and other local socioeconomic variables. We evaluated our results' robustness using a bootstrap correction to the IDI to examine the consequences of using different income cut-offs in substantial urban and regional inequalities. We identified a two minimum wage cut-off as the most appropriate. We found little evidence of upward bias in the calculation of the IDI regardless of the cut-off used. Among the ten most segregated cities, nine are in the Northeast region, with Brazil's highest income inequality and poverty. Our results indicate that the Gini index and poverty are the main variables associated with residential segregation. Supplementary Information: The online version contains supplementary material available at 10.1007/s43545-022-00491-9.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies País/Região como assunto: America do sul / Brasil Idioma: En Revista: SN Soc Sci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies País/Região como assunto: America do sul / Brasil Idioma: En Revista: SN Soc Sci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil