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What predicts corruption?
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Colonnelli, Emanuele
Gallego Durán, Jorge Andrés
Prem, Mounu
Gallego Durán, Jorge Andrés
Fecha
2019-02-07
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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.
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Keywords
Corruption , Machine learning , Prediction