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

Mario Tello; Daniel Tello

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

Autores

Mario Tello Universidad Nacional Mayor de San Marcos https://orcid.org/0000-0001-7929-0116
Daniel Tello University of Virginia

v. 51 n. 1 (2024): June

Resumo

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.

Palavras-chave:

Labor Productivity, Quality Management, Machine Learning

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Como Citar

Tello, M., & Tello, D. (2024). Quality managment and labor productivity of formal companies in Perú: A non – experimental design and causal machine learning techniques. Estudios De Economía, 51(1), pp. 117–158. Recuperado de https://revistas.uchile.cl/index.php/EDE/article/view/75138 (Original work published 28º de junho de 2024)