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

Author

Listed:
  • Pavel Vidal

    (Pontificia Universidad Javeriana)

  • Lya Paola Sierra-Suárez

    (Pontificia Universidad Javeriana)

  • Julieth Cerón

    (Pontificia Universidad Javeriana)

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.

Suggested Citation

  • Pavel Vidal & Lya Paola Sierra-Suárez & Julieth Cerón, 2024. "Indicator for the Regional Labor Market Using Machine Learning Techniques: Application to Colombian Cities," Revista de Economía del Rosario, Universidad del Rosario, vol. 27(1), pages 1-31.
    • Handle: RePEc:col:000151:022164
    DOI: 10.12804/revistas.urosario.edu.co/e
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    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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