I propose a model of skill-replacing routine-biased technological change (SR-RBTC). In this model, technology substitutes for the use of skill in routine tasks, in contrast to standard RBTC models, which assume that technology replaces the workers themselves. The SR-RBTC model explains three key trends that are inconsistent with standard RBTC models: (1) why specifically middle wages declined even though workers in routine occupations are dispersed across the entire bottom half of the wage distribution, (2) why middle wages stopped declining while technological change continued, and (3) why there is no substantial decline in the average wage of workers in routine occupations. I derive two new testable predictions from the model: a decrease in the return to skill and a decrease in skill level in routine occupations. I use an interactive fixed-effects model to confirm both predictions. Since SR-RBTC violates the ignorability assumption required by standard decomposition methods, I introduce a “skewness decomposition” to show that SR-RBTC is the main driver of bottom-half inequality trends.

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.