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Mapping stratification: The industry-occupation space reveals the network structure of inequality

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  • Hartmann, Dominik
  • Jara-Figueroa, Cristian
  • Kaltenberg, Mary
  • Gala, Paulo

Abstract

Social stratification is determined not only by income, education, race, and gender, but also by an individual's job characteristics and their position in the industrial structure. Utilizing a dataset of 76.6 million Brazilian workers and methods from network science, we map the Brazilian Industry- Occupation Space (BIOS). The BIOS measures the extent to which 600 occupations co-appear in 585 industries, resulting in a complex network that shows how industrial-occupational communities provide important information on the network segmentation of society. Gender, race, education, and income are concentrated unevenly across the core-periphery structure of the BIOS. Moreover, we identify 28 industrial occupational communities from the BIOS network structure and report their contribution to total income inequality in Brazil. Finally, we quantify the relative poverty within these communities. In sum, the BIOS reveals how the coupling of industries and occupations contributes to mapping social stratification.

Suggested Citation

  • Hartmann, Dominik & Jara-Figueroa, Cristian & Kaltenberg, Mary & Gala, Paulo, 2019. "Mapping stratification: The industry-occupation space reveals the network structure of inequality," Hohenheim Discussion Papers in Business, Economics and Social Sciences 06-2019, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    • Handle: RePEc:zbw:hohdps:062019
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    Cited by:

    1. Hartmann, Dominik & Zagato, Ligia & Gala, Paulo & Pinheiro, Flavio L., 2021. "Why did some countries catch-up, while others got stuck in the middle? Stages of productive sophistication and smart industrial policies," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 1-13.
    2. Sabrina Aufiero & Giordano De Marzo & Angelica Sbardella & Andrea Zaccaria, 2023. "Mapping job complexity and skills into wages," Papers 2304.05251, arXiv.org.
    3. Auerswald, Philip & Dani, Lokesh, 2022. "Entrepreneurial opportunity and related specialization in economic ecosystems," Research Policy, Elsevier, vol. 51(9).
    4. David E. Bloom & Victoria Y. Fan & Vadim Kufenko & Osondu Ogbuoji & Klaus Prettner & Gavin Yamey, 2021. "Going beyond GDP with a parsimonious indicator: inequality-adjusted healthy lifetime income," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 19(1), pages 127-140.
    5. Diogo Ferraz & Enzo B. Mariano & Daisy Rebelatto & Dominik Hartmann, 2020. "Linking Human Development and the Financial Responsibility of Regions: Combined Index Proposals Using Methods from Data Envelopment Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 150(2), pages 439-478, July.
    6. JosŽ Luis Oreiro & Luciano Luiz Manarin & Paulo Gala, 2020. "Deindustrialization, economic complexity and exchange rate overvaluation: the case of Brazil (1998-2017)," PSL Quarterly Review, Economia civile, vol. 73(295), pages 313-341.

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    More about this item

    Keywords

    labor markets; social structure; stratification; economic sociology; wages; inequality;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • L0 - Industrial Organization - - General
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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