Preventing rather than Punishing: An Early Warning Model of Malfeasance in Public Procurement

Jorge Gallego, G Rivero () and J.D. Martínez

No 16724, Documentos de Trabajo from Universidad del Rosario

Abstract: Is it possible to predict corruption and public inefficiency in public procurement? With the proliferation of e-procurement in the public sector, anti-corruption agencies and watchdog organizations in many countries currently have access to powerful sources of information. These may help anticipate which transactions become faulty and why. In this paper, we discuss the promises and challenges of using machine learning models to predict inefficiency and corruption in public procurement, both from the perspective of researchers and practitioners. We exemplify this procedure using a unique dataset characterizing more than 2 million public contracts in Colombia, and training machine learning models to predict which of them face corruption investigations or implementation inefficiencies. We use different techniques to handle the problem of class imbalance typical of these applications, report the high accuracy of our models, simulate the trade-off between precision and recall in this context, and determine which features contribute the most to the prediction of malfeasance within contracts. Our approach is useful for governments interested in exploiting large administrative datasets to improve provision of public goods and highlights some of the tradeoffs and challenges that they might face throughout this process.

Keywords: Corruption; Inefficiency; Machine Learning; Public Procurement (search for similar items in EconPapers)
JEL-codes: C53 C55 M42 O12 (search for similar items in EconPapers)
Pages: 33
Date: 2018-09-26
New Economics Papers: this item is included in nep-big and nep-law
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://repository.urosario.edu.co/bitstream/handle ... quence=4&isAllowed=y

Related works:
Journal Article: Preventing rather than punishing: An early warning model of malfeasance in public procurement (2021)
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:col:000092:016724

Access Statistics for this paper

More papers in Documentos de Trabajo from Universidad del Rosario Contact information at EDIRC.
Bibliographic data for series maintained by Facultad de Economía ().