EconPapers    
Economics at your fingertips  
 

What Predicts Corruption?

Emanuele Colonnelli, Jorge Gallego and Mounu Prem

No fq2xb_v1, SocArXiv from Center for Open Science

Abstract: The ability to predict corruption is crucial to policy. Using rich micro-data from Brazil, we show that multiple machine learning models display high levels of performance in predicting municipality-level corruption in public spending. We then quantify which individual municipality features and groups of similar characteristics have the highest predictive power. We find that 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.

Date: 2020-12-26
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://osf.io/download/5fe3b8b7e3acd100224a5b2b/

Related works:
Chapter: What predicts corruption? (2022) Downloads
Working Paper: What predicts corruption? (2019) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:fq2xb_v1

DOI: 10.31219/osf.io/fq2xb_v1

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().

 
Page updated 2025-03-27
Handle: RePEc:osf:socarx:fq2xb_v1