Inequality and income segregation in Brazilian cities: a nationwide analysis.

Autor: de Sousa Filho JF; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.; School of Economics (PPGE), Federal University of Bahia, Salvador, Brazil., Dos Santos GF; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.; School of Economics (PPGE), Federal University of Bahia, Salvador, Brazil., Andrade RFS; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.; Institute of Physics, Federal University of Bahia, Salvador, Brazil., Paiva AS; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.; Institute of Physics, Federal University of Bahia, Salvador, Brazil., Freitas A; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil., Castro CP; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.; Institute of Physics, Federal University of Bahia, Salvador, Brazil., de Lima Friche AA; Observatory for Urban Health in Belo Horizonte (OSUBH), Federal University of Minas Gerais, Belo Horizonte, Brazil., Barber S; Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, USA., Caiaffa WT; Observatory for Urban Health in Belo Horizonte (OSUBH), Federal University of Minas Gerais, Belo Horizonte, Brazil., Barreto ML; Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil.; Institute of Public Health (ISC), Federal University of Bahia, Salvador, Brazil.
Jazyk: angličtina
Zdroj: SN social sciences [SN Soc Sci] 2022; Vol. 2 (9), pp. 191. Date of Electronic Publication: 2022 Sep 10.
DOI: 10.1007/s43545-022-00491-9
Abstrakt: 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.
Competing Interests: Conflict of interestWe declare no competing interest.
(© The Author(s) 2022.)
Databáze: MEDLINE
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