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Unpacking gender and race segregation along occupational skills and socio-economic status in Brazil
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Orientador(es)
Resumo(s)
The occupational specialization of social groups is closely tied to gender, racial and class identities, segmenting the labour market into perceived White/Black and male/female roles and skill sets. Using data from 100 million formal workers in Brazil (2003–2019), we examine patterns of occupational segmentation across 426 occupations, identifying distinct skill demands and socio-economic statuses linked to race/skin colour and gender. Classifications of ‘male’ or ‘female’ occupations are shaped by required skills, whereas distinctions between ‘White’ and ‘Black’ occupations reflect socio-economic status and historical inequalities. Women and men are segmented by gender-associated skill sets, such as engineering versus caregiving skills. Within these skill sets, strong hierarchical segregation persists, with Black individuals disproportionately concentrated in positions of lower socio-economic status. Despite recent socio-economic changes, occupational specialization patterns have remained stable. Our findings highlight that the strong association between race and lower-status occupations must be addressed for a more inclusive society.
Descrição
Cardoso, B-H. F., Souza, L., Pinheiro, F. L., Bohn, L., & Hartmann, D. (2025). Unpacking gender and race segregation along occupational skills and socio-economic status in Brazil. Nature human behaviour, 9(11), 2261–2270. https://doi.org/10.1038/s41562-025-02272-9 --- %ABS4% --- B.-H.F.C. and L.S. are grateful for the financial support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; finance code 001), and D.H. is grateful for the support from CNPq (406943/ 2021-4 and 315441/2021-6). B.-H.F.C. and D.H. thank the Ministry of Economics of Brazil for access to the 2003–2019 RAIS database (Processo SEI number 19965.106679/2021-1). F.L.P. acknowledges funding support from Fundação para a Ciência e a Tecnologia (FCT), under the project UIDB/04152, Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. D.H. thanks C. Jara-Figueroa, M. Kaltenberg and C. Hidalgo for their insights on occupational networks in Brazil and the many fruitful discussions held at the MIT Media Lab in 2014–2016. All authors thank C. Scalon, E. Catela, G. Moura, K. de Souza Silva, participants at the SASE Rio conference 2023, and members of Necode-UFSC group meetings for valuable comments. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.
Palavras-chave
skills segregation intersectionality Social Psychology Experimental and Cognitive Psychology Behavioral Neuroscience SDG 5 - Gender Equality SDG 11 - Sustainable Cities and Communities SDG 16 - Peace, Justice and Strong Institutions