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Machine learning in the service of policy targeting: The case of public credit guarantees

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  • Andini, Monica
  • Boldrini, Michela
  • Ciani, Emanuele
  • de Blasio, Guido
  • D'Ignazio, Alessio
  • Paladini, Andrea

Abstract

Public credit guarantees should be provided to firms that are both creditworthy and credit constrained. We use Machine Learning (ML) predictive tools to propose a targeting rule that includes both objectives. The study elaborates on the case of Italy's Guarantee Fund and demonstrates, by means of ex-post evaluation methods, that the program effectiveness can be increased by ML targeting. We discuss some of the problems in using algorithms for the implementation of public policies, such as transparency and manipulation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jeborg:v:198:y:2022:i:c:p:434-475
    DOI: 10.1016/j.jebo.2022.04.004
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    Cited by:

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    2. Emanuele Ciani & Marco Gallo & Zeno Rotondi, 2020. "Public credit guarantee and financial additionalities across SME risk classes," Temi di discussione (Economic working papers) 1265, Bank of Italy, Economic Research and International Relations Area.
    3. 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.
    4. Borrotti, Matteo & Rabasco, Michele & Santoro, Alessandro, 2023. "Using accounting information to predict aggressive tax location decisions by European groups," Economic Systems, Elsevier, vol. 47(3).
    5. Michele Rabasco & Pietro Battiston, 2023. "Predicting the deterrence effect of tax audits. A machine learning approach," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 531-556, July.
    6. 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).
    7. Thierno Bocar Diop & Lionel Védrine, 2025. "Did crop diversity criterion from CAP green payments affect both economic and environmental farm performances? Quasi-experimental evidence from France," Post-Print hal-04739921, HAL.

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    More about this item

    Keywords

    Machine learning; Program evaluation; Loan guarantees;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • H81 - Public Economics - - Miscellaneous Issues - - - Governmental Loans; Loan Guarantees; Credits; Grants; Bailouts

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