Can machine learning support better governance? In the context of Brazilian municipalities, 2001-2012, we have access to detailed accounts of local budgets and audit data on the associated fiscal corruption. Using the budget variables as predictors, we train a tree-based gradient-boosted classifier to predict the presence of corruption in held-out test data. The trained model, when applied to new data, provides a prediction-based measure of corruption which can be used for new empirical analysis or to support policy responses. We validate the empirical usefulness of this measure by replicating, and extending, some previous empirical evidence on corruption issues in Brazil. We then explore how the predictions can be used to support policies toward corruption. Our policy simulations show that, relative to the status quo policy of random audits, a targeted policy guided by the machine predictions could detect more than twice as many corrupt municipalities for the same audit rate.
Elliott Ash is Assistant Professor of Law, Economics, and Data Science at ETH Zurich's Center for Law & Economics, Switzerland. Elliott's research and teaching focus on empirical analysis of the law and legal system using techniques from applied micro-econometrics, natural language processing, and machine learning. Prior to joining ETH, Elliott was Assistant Professor of Economics at University of Warwick, and before that a Postdoctoral Research Associate at Princeton University’s Center for the study of Democratic Politics. He received a Ph.D. in economics and J.D. from Columbia University, a B.A. in economics, government, and philosophy from University of Texas at Austin, and an LL.M. in international criminal law from University of Amsterdam.