Unveiling the endogeneity between social-welfare and labor efficiency: Two-stage NDEA neural network approach

This paper estimates intersectoral labor efficiencies and its covariances in Brazil from 2003 to 2016 using a two-stage neural network model. It investigates whether there is a positive relationship between education and productivity. At the first stage, a static Markov chain regression provides labor quantitative efficiency (volume) and labor value efficiency (value added). The second stage runs a dynamic regression model between each estimated efficiency and the social contextual variables to unveil endogeneities from covariances among the variables set. Strong covariances are found between the efficiencies and fertility rate, suggesting that there is a relevant gap between the productive sector and worker qualification, leading to lower levels of efficiency in Brazil, as shown in the theoretical model. The economy is unable to allocate efficiently the stock of qualified workers.