Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/312792 
Title (translated): 
Gestión de calidad y productividad laboral de las empresas en el Perú: Un diseño no experimental y técnicas de machine learning causal
Year of Publication: 
2024
Citation: 
[Journal:] Estudios de Economía [ISSN:] 0718-5286 [Volume:] 51 [Issue:] 1 [Year:] 2024 [Pages:] 117-158
Publisher: 
Universidad de Chile, Departamento de Economía, Santiago de Chile
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.
Subjects: 
Labor Productivity
Quality Management
Machine Learning
JEL: 
J24
L15
P42
Creative Commons License: 
cc-by-nc-sa Logo
Document Type: 
Article

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