All individuals rely on a fundamental set of mental capacities and functions, or bandwidth, in their economic and non-economic lives. Yet, many factors associated with poverty, such as malnutrition, alcohol consumption, or sleep deprivation, may tax this capacity. Previous research has demonstrated that such taxes often significantly alter judgments, preferences, and decision-making. A more suggestive but growing body of evidence points toward potential effects on productivity and utility. Considering the lives of the poor through the lens of bandwidth may improve our understanding of potential causes and consequences of poverty.

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.