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Social Indicators Research

, Volume 139, Issue 1, pp 119–146 | Cite as

Socioeconomic Classification of the Working-Age Brazilian Population: A Joint Latent Class Analysis Using Social Class and Asset-Based Perspectives

  • André Junqueira CaetanoEmail author
  • José G. Dias
Article

Abstract

This paper presents and applies a methodology of socioeconomic classification that integrates asset- and social class approaches. We employ data from the 2013 Brazilian National Household Survey and use latent class analysis to identify clusters and classify the working population. With regard to social class the Brazilian occupations are classified based on the European Socioeconomic Classification (ESeC) schema and an indicator of employment status. As for household wealth, we use the items related to household condition, ownership of durable goods and access to public services with the highest discriminatory power. We also make use of variables that account for the Brazilian spatial and socio-demographic heterogeneity. We found four clusters which we term latent socioeconomic stratum (LSeS). When compared we found an ordered pattern from the best-off LSeS (1) to the worst-off (4) with respect to household wealth and ESeC classes. Nevertheless, although the class composition of each LSeS reveals a distinct concentration of specific ESeC classes, all classes are present in each LSeS. Controlling for social class, differences in household wealth are more marked between LSeS than between social classes within the same LSeS. Hence, the methodology unveils the latent socioeconomic strata, reveals a class schema for each stratum and points out potential stratum fractions within them. The results were validated using variables external to the model, namely household food security status and years of schooling. The external validation revealed the same ordered pattern and the presence of stratum fractions.

Keywords

Socioeconomic classification Social class Asset-based approach Latent class analysis Brazil 

Notes

Acknowledgements

Support for this research was provided by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Grant 309272/2011-4, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Grant BEX4385/13-6, and Fundação para a Ciência e Technologia (Portugal), UID/GES/00315/2013. The authors would like to thank an anonymous referee for his/her constructive inputs.

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Universidade Católica de Minas GeraisBelo HorizonteBrazil
  2. 2.Instituto Universitário de Lisboa (ISCTE-IUL), BRULisbonPortugal

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