Emanuele Colonnelli (University of Chicago Booth School of Business), Jorge A. Gallego (New York University (NYU), Faculty of Arts and Science, Wilf Family Department of Politics), & Mounu Prem (Universidad del Rosario) have posted What Predicts Corruption? on SSRN. Here is the abstract:
Using rich micro data from Brazil, we show that multiple popular machine learning models display extremely high levels of performance in predicting municipality-level corruption in public spending. Measures of private sector activity, financial development, and human capital are the strongest predictors of corruption, while public sector and political features play a secondary role. Our findings have implications for the design and cost-effectiveness of various anti-corruption policies.