To improve public services, public sector managers must encourage reticent civil servants to enact effective reforms. We show through a randomized controlled trial that school principals, i.e., school mangers, can act as leaders to improve Instructional Management (0.3SD) and student learning...

We study the allocation and productivity consequences of training production line supervisors in soft skills via a randomized controlled trial. Consistent with standard practice for training investments within firms, we asked middle managers -- who sit above supervisors in the hierarchy -- to...

This paper studies the impact of adult prosecution on recidivism and employment trajectories for adolescent, first-time felony defendants. We use extensive linked Criminal Justice Admin- istrative Record System and socio-economic data from Wayne County, Michigan (Detroit). Using the discrete age...

Poverty reduction programs modeled on BRAC's graduation approach build up both tangible productive assets and intangible psychosocial assets such as self-confidence and the aspiration for upward mobility. The goal of this paper is to better understand how psychosocial factors operate and shape...

Difference-in-Difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher's toolkit. A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time. Despite this, no...

Hospitals play a key role in patient outcomes and spending, but efforts to improve their quality are hindered because we do not know whether hospital quality indicators are causal or biased. We evaluate the validity of commonly used quality indicators, such as mortality, readmissions, inpatient...

We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher's estimator is sharp zero consistent, meaning that the researcher concludes...

Motivated by a recent literature on the double-descent phenomenon in machine learning, we consider highly over-parameterized models in causal inference, including synthetic control with many control units. In such models, there may be so many free parameters that the model fits the training data...

We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the unobserved confounders. We focus on a novel,...