workingPaper
What predicts corruption?
Fecha
2019Autor
Colonnelli, Emanuele
Gallego, Jorge A.
Prem, Mounu
Gallego, Jorge A.
Institución
Resumen
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.