Michael Luca of Johns Hopkins University reviews “Decisions about Decisions: Practical Reason in Ordinary Life” by Cass R. Sunstein. The Econlit abstract of this book begins: “Explores the psychological, practical, and emotional factors that inform decision-making processes.”

Is there latent demand to support Black-owned businesses? We explore this question by analyzing a new feature that made it easier to identify Black-owned restaurants on an online platform. We find that labeling restaurants as minority-owned increased customer engagement and firm performance, as measured by online traffic, calls, orders, and in-person visits. These effects were more pronounced in areas characterized by greater support for the Democratic Party and lower implicit bias against racial minorities. Labeled restaurants also see an increase in the fraction of reviews that are written by White customers. (JEL D22, J15, L25, L83, L86)

When should private companies allocate public resources? In our model, delegation is attractive when delay is costly, the impact of funds is similar across firms, and government and private objectives are aligned. We use novel firm-level survey data to measure heterogeneity in the impact of the Paycheck Protection Program and to assess whether banks targeted loans to high-impact firms. Banks did target loans to their most valuable pre-existing customers. However, we find that treatment effect heterogeneity is moderate, suggesting that delegation was likely superior from the government’s perspective to delaying loans to improve targeting.

The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to crowdsource competence by making data public and offering a reward for the best algorithm. A simple model suggests that open tournaments dominate consulting contracts when cities can tolerate risk and when there is enough labor with low opportunity costs. We also report on an inexpensive Boston-based restaurant tournament, which yielded algorithms that proved reasonably accurate when tested “out-of-sample” on hygiene inspections.

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.