IDEAS home Printed from https://ideas.repec.org/a/aea/aejpol/v17y2025i2p162-93.html
   My bibliography  Save this article

A Machine Learning Approach to Analyze and Support Anticorruption Policy

Author

Listed:
  • Elliott Ash
  • Sergio Galletta
  • Tommaso Giommoni

Abstract

Can machine learning support better governance? This study uses a tree-based, gradient-boosted classifier to predict corruption in Brazilian municipalities using budget data as predictors. The trained model offers a predictive measure of corruption, which we validate through replication and extension of previous corruption studies. Our policy simulations show that machine learning can significantly enhance corruption detection: Compared to random audits, a machine-guided targeted policy could detect almost twice as many corrupt municipalities for the same audit rate.

Suggested Citation

  • Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2025. "A Machine Learning Approach to Analyze and Support Anticorruption Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 17(2), pages 162-193, May.
  • Handle: RePEc:aea:aejpol:v:17:y:2025:i:2:p:162-93
    DOI: 10.1257/pol.20210618
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/pol.20210618
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.

    File URL: https://www.aeaweb.org/articles/materials/22879
    Download Restriction: no

    File URL: https://www.aeaweb.org/articles/materials/22880
    Download Restriction: no

    File URL: https://libkey.io/10.1257/pol.20210618?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    2. Oriana Bandiera & Andrea Prat & Stephen Hansen & Raffaella Sadun, 2020. "CEO Behavior and Firm Performance," Journal of Political Economy, University of Chicago Press, vol. 128(4), pages 1325-1369.
    3. Edward L. Glaeser & Andrew Hillis & Scott Duke Kominers & Michael Luca, 2016. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy," American Economic Review, American Economic Association, vol. 106(5), pages 114-118, May.
    4. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    5. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    6. Ashesh Rambachan & Jon Kleinberg & Sendhil Mullainathan & Jens Ludwig, 2020. "An Economic Approach to Regulating Algorithms," NBER Working Papers 27111, National Bureau of Economic Research, Inc.
    7. Eric Avis & Claudio Ferraz & Frederico Finan, 2018. "Do Government Audits Reduce Corruption? Estimating the Impacts of Exposing Corrupt Politicians," Journal of Political Economy, University of Chicago Press, vol. 126(5), pages 1912-1964.
    8. Fernanda Brollo & Tommaso Nannicini & Roberto Perotti & Guido Tabellini, 2013. "The Political Resource Curse," American Economic Review, American Economic Association, vol. 103(5), pages 1759-1796, August.
    9. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    10. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters, in: Essays in the Economics of Crime and Punishment, pages 1-54, National Bureau of Economic Research, Inc.
    11. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    12. Philine Widmer & Cl'ementine Abed Meraim & Sergio Galletta & Elliott Ash, 2022. "Media Slant is Contagious," Papers 2202.07269, arXiv.org, revised Feb 2025.
    13. Gianmarco Daniele & Tommaso Giommoni, 2019. "Corruption under Austerity," BAFFI CAREFIN Working Papers 19131, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    14. Andreas Kyriacou & Leonel Muinelo-Gallo & Oriol Roca-Sagalés, 2015. "Construction corrupts: empirical evidence from a panel of 42 countries," Public Choice, Springer, vol. 165(1), pages 123-145, October.
    15. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    16. Hessami, Zohal, 2014. "Political corruption, public procurement, and budget composition: Theory and evidence from OECD countries," European Journal of Political Economy, Elsevier, vol. 34(C), pages 372-389.
    17. Emanuele Colonnelli & Jorge Gallego & Mounu Prem, 2022. "What predicts corruption?," Chapters, in: Paolo Buonanno & Paolo Vanin & Juan Vargas (ed.), A Modern Guide to the Economics of Crime, chapter 16, pages 345-373, Edward Elgar Publishing.
    18. Cavalcanti, Francisco & Daniele, Gianmarco & Galletta, Sergio, 2018. "Popularity shocks and political selection," Journal of Public Economics, Elsevier, vol. 165(C), pages 201-216.
    19. Claudio Ferraz & Frederico Finan, 2008. "Exposing Corrupt Politicians: The Effects of Brazil's Publicly Released Audits on Electoral Outcomes," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 123(2), pages 703-745.
    20. Juliano Assuncao & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," Working Papers tecipa-631, University of Toronto, Department of Economics.
    21. Eduarda Machoski & Jevuks Matheus Araujo, 2020. "Corruption in public health and its effects on the economic growth of Brazilian municipalities," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 669-687, July.
    22. Zamboni, Yves & Litschig, Stephan, 2018. "Audit risk and rent extraction: Evidence from a randomized evaluation in Brazil," Journal of Development Economics, Elsevier, vol. 134(C), pages 133-149.
    23. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
    24. Benjamin A. Olken, 2007. "Monitoring Corruption: Evidence from a Field Experiment in Indonesia," Journal of Political Economy, University of Chicago Press, vol. 115(2), pages 200-249.
    25. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    26. Decio Coviello & Stefano Gagliarducci, 2017. "Tenure in Office and Public Procurement," American Economic Journal: Economic Policy, American Economic Association, vol. 9(3), pages 59-105, August.
    27. Félix J. López-Iturriaga & Iván Pastor Sanz, 2018. "Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(3), pages 975-998, December.
    28. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    29. Juliano Assunção & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," NBER Working Papers 25636, National Bureau of Economic Research, Inc.
    30. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    31. Christopher R. Knittel & Samuel Stolper, 2019. "Using Machine Learning to Target Treatment: The Case of Household Energy Use," NBER Working Papers 26531, National Bureau of Economic Research, Inc.
    32. Gustavo J. Bobonis & Luis R. Cámara Fuertes & Rainer Schwabe, 2016. "Monitoring Corruptible Politicians," American Economic Review, American Economic Association, vol. 106(8), pages 2371-2405, August.
    33. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
    34. Ilaria Angelis & Guido Blasio & Lucia Rizzica, 2020. "Lost in Corruption. Evidence from EU Funding to Southern Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(2), pages 355-377, July.
    35. Stevenson, Megan T. & Doleac, Jennifer, 2019. "Algorithmic Risk Assessment in the Hands of Humans," IZA Discussion Papers 12853, Institute of Labor Economics (IZA).
    36. Mauro, Paolo, 1998. "Corruption and the composition of government expenditure," Journal of Public Economics, Elsevier, vol. 69(2), pages 263-279, June.
    37. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    38. Jonah E. Rockoff & Brian A. Jacob & Thomas J. Kane & Douglas O. Staiger, 2011. "Can You Recognize an Effective Teacher When You Recruit One?," Education Finance and Policy, MIT Press, vol. 6(1), pages 43-74, January.
    39. Pietro Battiston & Simona Gamba & Alessandro Santoro, 2020. "Optimizing Tax Administration Policies with Machine Learning," Working Papers 436, University of Milano-Bicocca, Department of Economics, revised Mar 2020.
    40. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    41. Timothy G. Conley & Francesco Decarolis, 2016. "Detecting Bidders Groups in Collusive Auctions," American Economic Journal: Microeconomics, American Economic Association, vol. 8(2), pages 1-38, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    2. Gianmarco Daniele & Tommaso Giommoni, 2019. "Corruption under Austerity," BAFFI CAREFIN Working Papers 19131, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    3. 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).
    4. 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.
    5. Colonnelli, Emanuele & Lagaras, Spyridon & Ponticelli, Jacopo & Prem, Mounu & Tsoutsoura, Margarita, 2022. "Revealing corruption: Firm and worker level evidence from Brazil," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1097-1119.
    6. Britto, Diogo G.C. & Fiorin, Stefano, 2020. "Corruption and legislature size: Evidence from Brazil," European Journal of Political Economy, Elsevier, vol. 65(C).
    7. Maximiliano Lauletta & Martín A. Rossi & Christian A. Ruzzier, 2022. "Audits and Government Hiring Practices," Economica, London School of Economics and Political Science, vol. 89(353), pages 214-227, January.
    8. Kendall D. Funk & Erica Owen, 2020. "Consequences of an Anti‐Corruption Experiment for Local Government Performance in Brazil," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 444-468, March.
    9. Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    10. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
    11. Tsur, Yacov, 2022. "Political tenure, term limits and corruption," European Journal of Political Economy, Elsevier, vol. 74(C).
    12. Morelli, Massimo & Giommoni, Tommaso & Nicolò, Antonio, 2020. "Corruption and Extremism," CEPR Discussion Papers 14634, C.E.P.R. Discussion Papers.
      • Attila Gaspar & Tommaso Giommoni & Massimo Morelli & Antonio Nicolò, 2021. "Corruption and Extremism," BAFFI CAREFIN Working Papers 21163, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    13. Maximiliano Lauletta & Martín Rossi & Christian Ruzzier, 2021. "Playing Whac-A-Mole in the Fight against Corruption: Evidence from Random Audits in Brazil," Working Papers 157, Universidad de San Andres, Departamento de Economia, revised Sep 2021.
    14. Galletta, Sergio, 2017. "Law enforcement, municipal budgets and spillover effects: Evidence from a quasi-experiment in Italy," Journal of Urban Economics, Elsevier, vol. 101(C), pages 90-105.
    15. Eric Avis & Claudio Ferraz & Frederico Finan, 2018. "Do Government Audits Reduce Corruption? Estimating the Impacts of Exposing Corrupt Politicians," Journal of Political Economy, University of Chicago Press, vol. 126(5), pages 1912-1964.
    16. Gallego, J & Prem, M & Vargas, J. F., 2022. "Predicting Politicians Misconduct: Evidence From Colombia," Documentos de Trabajo 20504, Universidad del Rosario.
    17. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
    18. Gallego, J & Prem, M & Vargas, J. F, 2020. "Corruption in the times of pandemia," Documentos de Trabajo 18178, Universidad del Rosario.
    19. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    20. Giommoni, Tommaso, 2021. "Exposure to corruption and political participation: Evidence from Italian municipalities," European Journal of Political Economy, Elsevier, vol. 68(C).

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • H70 - Public Economics - - State and Local Government; Intergovernmental Relations - - - General
    • H83 - Public Economics - - Miscellaneous Issues - - - Public Administration
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • O17 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aejpol:v:17:y:2025:i:2:p:162-93. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.