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Quality managment and labor productivity of formal companies in Perú: A non – experimental design and causal machine learning techniques

Author

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  • Mario Tello
  • Daniel Tello

Abstract

This paper evaluates the impacts of quality management tools on the labor productivity of companies in Peru for the period 2014-2019 based on causal Machine Learning (ML) techniques (MLC), which reduce or eliminate three potential problems: the endogeneity of the variables of interest, the existence of confusing variables (confounding) and overfitting due to the introduction of many control variables. Using the National Survey of Companies (INEI-ENE 2023), the evaluation indicates that quality control tools affect the productivity of formal companies, particularly large and medium-sized companies.

Suggested Citation

  • Mario Tello & Daniel Tello, 2024. "Quality managment and labor productivity of formal companies in Perú: A non – experimental design and causal machine learning techniques," Estudios de Economia, University of Chile, Department of Economics, vol. 51(1 Year 20), pages 117-158, June.
    • Handle: RePEc:udc:esteco:v:51:y:2024:i:1:p:117-158
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    File URL: https://estudiosdeeconomia.uchile.cl/index.php/EDE/article/view/75138/77047
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    More about this item

    Keywords

    Labor Productivity; Quality Management; Machine Learning.;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • P42 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Productive Enterprises; Factor and Product Markets; Prices

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