Socioeconomic Classification of the Working-Age Brazilian Population: A Joint Latent Class Analysis Using Social Class and Asset-Based Perspectives
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.
KeywordsSocioeconomic classification Social class Asset-based approach Latent class analysis Brazil
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.
- Bartholomew, D., & Knott, M. (1999). Latent variables models and factor analysis. London: Edward Arnold.Google Scholar
- Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Hoboken: Wiley.Google Scholar
- Connelly, R., Gayle, V., & Lambert. P. S. (2016). A review of occupation-based social classifications for social survey research. Methodological Innovations, 9. doi: 10.1177/2059799116638003.
- Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, 39(B), 1–38.Google Scholar
- Erikson, R., & Goldthorpe, J. H. (1992a). The constant flux. Oxford: Clarendon Press.Google Scholar
- Filmer, D., & Pritchett, L. H. (2001). Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India. Demography, 38(1), 115–132.Google Scholar
- Friedman, D. (1957). A theory of the consumption function. Princeton: Princeton University Press.Google Scholar
- Grusky, D. B. (2001). Social Stratification: class, race, and gender in sociological perspective (2nd ed.). Boulder, Colorado: Westview Press.Google Scholar
- International Labour Office. Seventeenth international conference of labour statisticians. In Geneva, Nov 24th–Dec 3rd 2003 (Vol. ICLS/17/2003/4): ILO.Google Scholar
- Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. New York: Houghton Mifflin.Google Scholar
- Lucchini, M., & Schizzerotto, A. (2010). Unemployment risks in four EU countries: A validation study of the ESeC. In D. Rose & E. Harrison (Eds.), Social class in Europe: An introduction to the European socio-economic classification (pp. 324, Studies in European Sociology). Abingdon: Routledge.Google Scholar
- McCutcheon, A. L. (1987). Latent class analysis (Vol. 64, Quantitative Applications in the Social Sciences). Newbury Park, CA: Sage Publications Inc.Google Scholar
- McLachlan, G. J., & Basford, K. E. (1988). Mixture models. New York: Marcel Dekker.Google Scholar
- Pevalin, D., & Rose, D. (2002). The National Statistics Socio-Economic Classification: Unifying oficial and sociologial approaches to the conceptualisation and measurement of social class in the United Kingdom. Societé Contemporaines, 9, 45–46.Google Scholar
- PNAD Pesquisa Nacional por Amostra de Domicílio (2013). Instituto Brasileiro de Estatística e Geografia (IBGE). http://www.ibge.gov.br/home/estatistica/populacao/trabalhoerendimento/pnad2013/.
- Rose, D., & Harrison, E. (2010). Social class in Europe: An introduction to the European socio-economic classification. Abingdon: Routledge.Google Scholar
- Rose, D., Harrison, E., & Pevalin, D. (2010). The European socio-economic classification: A prolegomenon. In D. Rose & E. Harrison (Eds.), Social class in Europe: An introduction to the European socio-economic classification (pp. 324, Studies in European Sociology). Abingdon: Routledge.Google Scholar
- Rutstein, S. O., & Johnson, K. (2004). The DHS wealth index. DHS comparative reports. Calverton, Maryland: ORC Macro.Google Scholar
- Weber, M. (1978). Economy and society (Vol. 1). Berkeley: University of California Press.Google Scholar