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

Author

Listed:
  • Gallego, J
  • Rivero, G
  • Martínez, J.D.
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.

Suggested Citation

  • Gallego, J & Rivero, G & Martínez, J.D., 2018. "Preventing rather than Punishing: An Early Warning Model of Malfeasance in Public Procurement," Documentos de Trabajo 16724, Universidad del Rosario.
    • Handle: RePEc:col:000092:016724
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    2. Gallego, Jorge & Prem, Mounu & Vargas, Juan F., 2020. "Corruption in the Times of Pandemia," Working papers 43, Red Investigadores de Economía.
    3. Gallego, Jorge & Prem, Mounu & Vargas, Juan F., 2022. "Predicting Politicians' Misconduct: Evidence from Colombia," SocArXiv 5dp8t, Center for Open Science.
    4. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    5. Frédéric Marty, 2022. "From Economic Evidence to Algorithmic Evidence: Artificial Intelligence and Blockchain: An Application to Anti-competitive Agreements," GREDEG Working Papers 2022-32, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    6. Frédéric Marty & Thierry Warin, 2023. "Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement," CIRANO Working Papers 2023s-26, CIRANO.
    7. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
    8. Belev, S. & Veterinarov, V. & Matveev, E., 2023. "Vertical collusion in public procurement: Estimation based on data for R&D composite auctions," Journal of the New Economic Association, New Economic Association, vol. 59(2), pages 36-63.
    9. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.

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    More about this item

    Keywords

    Corruption; Inefficiency; Machine Learning; Public Procurement;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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