Indicator for the Regional Labor Market Using Machine Learning Techniques: Application to Colombian Cities

Pavel Vidal Alejandro, Lya Sierra and Julieth Cerón
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Julieth Cerón: Pontificia Universidad Javeriana

Revista de Economía del Rosario, 2024, vol. 27, issue 1, No 3, 31 pages

Abstract: This article proposes a methodology to estimate a labor market indicator that combines economic, social, inequality, and expectation variables. Machine Learning techniques are used to select the most relevant variables. The indicator captures the traditional evolution of the employment and unemployment rates and incorporates information on gender, age, informality, productive sectors, and Google Trends data. This approach allows for a more comprehensive understanding of the labor market situation, better visibility of regional differences, and analysis of the heterogeneous impact of the pandemic and subsequent recovery. The methodology is exemplified in the Colombian cities of Cali, Medellín, Bogotá D.C., and Popayán.

Keywords: labor market indicator; machine learning; Lasso; backward stepwise selection method; principal components; Google Trends (search for similar items in EconPapers)
JEL-codes: C38 C45 C55 C88 J21 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:col:000151:022164

DOI: 10.12804/revistas.urosario.edu.co/e

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