Productivity and Selection of Human Capital with Machine Learning
利用警察招聘和教师任期决策数据，证明使用机器学习预测工人生产率能带来显著的社会福利提升，对政策制定者和人力资源管理者有参考价值。
Economists have become increasingly interested in studying the nature of production functions in social policy applications, with the goal of improving productivity. Traditionally models have assumed workers are homogenous inputs. However, in practice, substantial variability in productivity means the marginal productivity of labor depends substantially on which new workers are hired--which requires not an estimate of a causal effect, but rather a prediction. We demonstrate that there can be large social welfare gains from using machine learning tools to predict worker productivity, using data from two important applications - police hiring and teacher tenure decisions.