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


  • Mario Tello Universidad Nacional Mayor de San Marcos
  • Daniel Tello University of Virginia


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.


Labor Productivity, Quality Management, Machine Learning