The standard economic view suggests that people will commit an action if its expected benefits outweigh its costs. But before people weigh the costs and benefits of an action, what affects whether they think of the action in the first place? We argue that actions are more likely to enter into consideration when they are cognitively accessible. We describe three psychological parameters that influence accessibility: automatic assumptions, identity, and perceptions of privacy. These parameters make it possible to identify new interventions for behavior change.

This study estimates the effects of means-tested housing programs on labor supply using data from a randomized housing voucher wait-list lottery in Chicago. Economic theory is ambiguous about the expected sign of any labor supply response. We find that among working-age, able-bodied adults, housing voucher use reduces labor force participation by around 4 percentage points (6 percent) and quarterly earnings by $329 (10 percent), and increases Temporary Assistance for Needy Families program participation by around 2 percentage points (15 percent). We find no evidence that the housing-specific mechanisms hypothesized to promote work, such as neighborhood quality or residential stability, are important empirically.

Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of “machine learning” are particularly useful for addressing these prediction problems. We use an example from health policy to illustrate the large potential social welfare gains from improved prediction.

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.

We examine long-term neighborhood effects on low-income families using data from the Moving to Opportunity (MTO) randomized housing-mobility experiment. This experiment offered to some public-housing families but not to others the chance to move to less-disadvantaged neighborhoods. We show that ten to 15 years after baseline, MTO: (i) improves adult physical and mental health; (ii) has no detectable effect on economic outcomes or youth schooling or physical health; and (iii) has mixed results by gender on other youth outcomes, with girls doing better on some measures and boys doing worse. Despite the somewhat mixed pattern of impacts on traditional behavioral outcomes, MTO moves substantially improve adult subjective well-being.

Improving academic outcomes for economically disadvantaged students has proven challenging, particularly for children at older ages. We present two large-scale randomized controlled trials of a high-dosage tutoring program delivered to secondary school students in Chicago. One innovation is to use paraprofessional tutors to hold down cost, thereby increasing scalability. Participating in math tutoring increases math test scores by 0.18 to 0.40 standard deviations, and increases math and nonmath course grades. These effects persist into future years. The data are consistent with increased personalization of instruction as a mechanism. The benefit-cost ratio is comparable to many successful early childhood programs.