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What is a Labor Market? Classifying Workers and Jobs Using Network Theory

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  • Jamie Fogel
  • Bernardo Modenesi

Abstract

This paper develops a new data-driven approach to characterizing latent worker skill and job task heterogeneity by applying an empirical tool from network theory to large-scale Brazilian administrative data on worker--job matching. We microfound this tool using a standard equilibrium model of workers matching with jobs according to comparative advantage. Our classifications identify important dimensions of worker and job heterogeneity that standard classifications based on occupations and sectors miss. The equilibrium model based on our classifications more accurately predicts wage changes in response to the 2016 Olympics than a model based on occupations and sectors. Additionally, for a large simulated shock to demand for workers, we show that reduced form estimates of the effects of labor market shock exposure on workers' earnings are nearly 4 times larger when workers and jobs are classified using our classifications as opposed to occupations and sectors.

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  • Jamie Fogel & Bernardo Modenesi, 2023. "What is a Labor Market? Classifying Workers and Jobs Using Network Theory," Papers 2311.00777, arXiv.org.
    • Handle: RePEc:arx:papers:2311.00777
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    References listed on IDEAS

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    1. David Card, 1990. "The Impact of the Mariel Boatlift on the Miami Labor Market," ILR Review, Cornell University, ILR School, vol. 43(2), pages 245-257, January.
    2. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
    3. Efraim Benmelech & Nittai K. Bergman & Hyunseob Kim, 2022. "Strong Employers and Weak Employees: How Does Employer Concentration Affect Wages?," Journal of Human Resources, University of Wisconsin Press, vol. 57(S), pages 200-250.
    4. Schmutte, Ian M., 2014. "Free to Move? A Network Analytic Approach for Learning the Limits to Job Mobility," Labour Economics, Elsevier, vol. 29(C), pages 49-61.
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    Cited by:

    1. Jamie Fogel & Bernardo Modenesi, 2024. "Detailed Gender Wage Gap Decompositions: Controlling for Worker Unobserved Heterogeneity Using Network Theory," Papers 2405.04365, arXiv.org.

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