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.

本文使用两阶段神经网络模型估计了巴西 2003 年至 2016 年的跨部门劳动效率及其协方差。它调查了教育和生产力之间是否存在正相关关系。第一阶段，静态马尔可夫链回归提供劳动量化效率（数量）和劳动价值效率（增值）。第二阶段在每个估计的效率和社会背景变量之间运行动态回归模型，以揭示变量集之间协方差的内生性。效率和生育率之间存在很强的协方差，表明生产部门和工人资格之间存在相关差距，导致巴西效率水平较低，如理论模型所示。