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

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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.

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Notes

  1. Available at http://www.ibge.gov.br/home/estatistica/populacao/trabalhoerendimento/pnad2013/. Access on April 2016.

  2. Available at http://www.mtecbo.gov.br/cbosite/pages/home.jsf. Access on April 2016.

  3. Erikson and Goldthorpe (2002) considered this denomination “somewhat unfortunate” (p. 33).

  4. Available at http://www.sidra.ibge.gov.br/bda/popul/default.asp?z=t&o=25&i=P. Access on July 2016.

  5. Instituto Brasileiro de Geografia e Estatística (IBGE), http://www.ibge.gov.br/home/.

  6. Latent class estimation varying the number of latent classes from one to 10 shows that in solutions with more than four latent classes, the extra latent classes can be ignored as they do not help extract unobserved heterogeneity from the large data set. Detailed results on model selection are available from the first author upon request.

  7. Detailed results on the concomitant variables in the model are given in Appendix (Table 11).

  8. Given the sample size, the sample proportion follows asymptotically a normal distribution.

  9. The comparisons between LSeCs for the other categories of Adapted ESeC are available upon request.

  10. The full table as well as the tests for the remaining pairs and for the other three ESeC classes are available upon request.

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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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  1. Universidade Católica de Minas Gerais, Av. Itaú 505, Belo Horizonte, MG, 30535-012, Brazil

    André Junqueira Caetano

  2. Instituto Universitário de Lisboa (ISCTE-IUL), BRU, Av. das Forças Armadas, 1649-026, Lisbon, Portugal

    José G. Dias

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  1. André Junqueira Caetano
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Correspondence to André Junqueira Caetano.

Appendix

Appendix

See Tables 10, 11 and 12.

Table 10 Latent class estimates—characteristics and assets of the households
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Table 11 Latent class estimates—profiling variables
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Table 12 Dichotomization of the three- and four-category wealth indicators
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Caetano, A.J., Dias, J.G. Socioeconomic Classification of the Working-Age Brazilian Population: A Joint Latent Class Analysis Using Social Class and Asset-Based Perspectives. Soc Indic Res 139, 119–146 (2018). https://doi.org/10.1007/s11205-017-1710-5

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