We study the extent to which ML techniques can be used to improve tax auditing efficiency using administrative data, without the need of randomized audits. Using Italy's population data on sole proprietorship tax returns, audits and their outcome, we develop a new approach to address the so...

Algorithms (in some form) are already widely used in the criminal justice system. We draw lessons from this experience for what is to come for the rest of society as machine learning diffuses. We find economists and other social scientists have a key role to play in shaping the impact of...

How can we get more 'output,' and of the right sort, from policing? The question has only taken on greater importance with recent, widely publicized instances of police misconduct; declines in public trust in police; and a rise in gun violence, all disproportionately concentrated in economically...

Although non-experimental studies find robust neighborhood effects on adults, such findings have been challenged by results from the Moving to Opportunity (MTO) residential mobility experiment. Using a within-study comparison design, this paper compares experimental and non-experimental...

There is growing concern that it is too difficult or costly to substantially improve the academic skills of children who are behind in school once they reach adolescence. But perhaps what we have tried in the past relies on the wrong interventions, failing to account for challenges like the...

Body-worn cameras (BWCs) are an increasingly common tool for police oversight, accountability, and transparency, yet there remains uncertainty about their impacts on policing outcomes. This paper reviews what we know about the benefits of BWCs and how those benefits compare to the costs of this...

Whether police misconduct can be prevented depends partly on whether it can be predicted. We show police misconduct is partially predictable and that estimated misconduct risk is not simply an artifact of measurement error or a proxy for officer activity. We also show many officers at risk of...

Consumer choices are increasingly mediated by algorithms, which use data on those past choices to infer consumer preferences and then curate future choice sets. Behavioral economics suggests one reason these algorithms so often fail: choices can systematically deviate from preferences. For...

While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate...

We calculate the social return on algorithmic interventions (specifically their Marginal Value of Public Funds) across multiple domains of interest to economists--regulation, criminal justice, medicine, and education. Though these algorithms are different, the results are similar and striking....