A long-standing and influential view in U.S. correctional policy is that “nothing works” when it comes to rehabilitating incarcerated individuals. We revisit this hypothesis by studying an innovative law-enforcement-led program launched in the county jail of Flint, MI: Inmate Growth Naturally and Intentionally through Education (IGNITE). We develop an instrumental variables approach to estimate the effects of IGNITE exposure, leveraging quasi-random court delays that cause individuals to spend more time in jail before and after the program’s launch. Holding time in jail fixed, we find that one additional month of IGNITE exposure reduces weekly misconduct in jail by 25% and three-month recidivism by 24%, with the recidivism effects growing over time. Surveys of staff and community members, along with administrative test-score records and within-jail text messages, suggest that cultural change and improved literacy and numeracy scores are contributing mechanisms.

Competition in health insurance markets may fail to improve health outcomes if consumers are not able to identify high quality plans. We develop and apply a novel instrumental variables framework to quantify the variation in causal mortality effects across plans and how much consumers attend to this variation. We first document large differences in the observed mortality rates of Medicare Advantage plans within local markets. We then show that when plans with high (low) mortality rates exit these markets, enrollees tend to switch to more typical plans and subsequently experience lower (higher) mortality. We derive and validate a novel "fallback condition" governing the subsequent choices of those affected by plan exits. When the fallback condition is satisfied, plan terminations can be used to estimate the relationship between observed plan mortality rates and causal mortality effects. Applying the framework, we find that mortality rates unbiasedly predict causal mortality effects. We then extend our framework to study other predictors of plan mortality effects and estimate consumer willingness to pay. Higher spending plans tend to reduce enrollee mortality, but existing quality ratings are uncorrelated with plan mortality effects. Consumers place little weight on mortality effects when choosing plans. Good insurance plans dramatically reduce mortality, and redirecting consumers to such plans could improve beneficiary health.

Conventional value-added models (VAMs) compare average test scores across schools after regression-adjusting for students’ demographic characteristics and previous scores. This article tests for VAM bias using a procedure that asks whether VAM estimates accurately predict the achievement consequences of random assignment to specific schools. Test results from admissions lotteries in Boston suggest conventional VAM estimates are biased, a finding that motivates the development of a hierarchical model describing the joint distribution of school value-added, bias, and lottery compliance. We use this model to assess the substantive importance of bias in conventional VAM estimates and to construct hybrid value-added estimates that optimally combine ordinary least squares and lottery-based estimates of VAM parameters. The hybrid estimation strategy provides a general recipe for combining nonexperimental and quasi-experimental estimates. While still biased, hybrid school value-added estimates have lower mean squared error than conventional VAM estimates. Simulations calibrated to the Boston data show that, bias notwithstanding, policy decisions based on conventional VAMs that control for lagged achievement are likely to generate substantial achievement gains. Hybrid estimates that incorporate lotteries yield further gains.

Economists often measure discrimination as disparities arising from the direct effects of group identity. We develop new tools to model and measure systemic discrimination, capturing how discrimination in other decisions indirectly contributes to disparities. A novel experimental design, the iterated audit, identifies systemic discrimination. We illustrate these new tools in two field experiments. The first experiment shows how racial discrimination can accumulate across multiple rounds of hiring through the interaction of two forces: greater discrimination against inexperienced workers, which affects the opportunity to obtain experience, and high subsequent returns to experience. The second experiment shows how gender-based differences in the language of recommendation letters can translate into systemic gender discrimination in STEM hiring. We discuss how our findings qualify previous results on direct discrimination and how our tools can be used to target policy interventions.

We develop empirical tools for studying discrimination in multi-phase systems, and apply them to the setting of foster care placement by child protective services. Leveraging the quasi-random assignment of two sets of decision-makers-initial hotline call screeners and subsequent investigators-we study how unwarranted racial disparities arise and propagate through this system. Using a sample of over 200,000 maltreatment allegations, we find that calls involving Black children are 55% more likely to result in foster care placement than calls involving white children with the same potential for future maltreatment in the home. Call screeners account for up to 19% of this unwarranted disparity, with the remainder due to investigators. Unwarranted disparity is concentrated in cases with potential for future maltreatment, suggesting that white children may be harmed by "under-placement" in high-risk situations.