Productividad Total de los Factores y sus Determinantes en Economías con Distintas Capacidades de Innovación

Francisco Reyna Salazar; Héctor Eduardo Díaz Rodríguez; Mario Alberto Morales Sánchez

doi:10.4067/S0718-88702026000100137

Authors

Francisco Reyna Salazar Facultad de Economía, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, México
Héctor Eduardo Díaz Rodríguez Universidad Autónoma Metropolitana, Unidad Xochimilco, Ciudad de México, México
Mario Alberto Morales Sánchez Facultad de Economía, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, México

DOI:

https://doi.org/10.4067/S0718-88702026000100137

Keywords:

Innovation, Total Factor Productivity, Neural Networks

Abstract

Economic growth theory, based on the Solow model, posits that technological change is key to increasing per capita income, while Innovation theory suggests that technological change is boosted by factors that foster innovation such as human capital, infrastructure, and institutions. This study analyzes the impact of factors related to innovation capacity on the Total Factor Productivity (TFP), as a proxy of technological change, of 132 countries between 2013 and 2021, categorizing them into high, medium, and low productivity groups, using the Global Innovation Index through an artificial neural network methodology. The results show that while high-productivity countries rely on human capital and institutions, low-productivity countries benefit more from creative activities and improvements in basic infrastructure.

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