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Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy

Author

Listed:
  • Monica Andini

    (Bank of Italy)

  • Emanuele Ciani

    (Bank of Italy)

  • Guido de Blasio

    (Bank of Italy)

  • Alessio D'Ignazio

    (Bank of Italy)

  • Viola Salvestrini

    (London School of Economics and Political Science)

Abstract

Machine Learning (ML) can be a powerful tool to inform policy decisions. Those who are treated under a programme might have different propensities to put into practice the behaviour that the policymaker wants to incentivize. ML algorithms can be used to predict the policy-compliers; that is, those who are most likely to behave in the way desired by the policymaker. When the design of the programme is tailored to target the policy-compliers, the overall effectiveness of the policy is increased. This paper proposes an application of ML targeting that uses the massive tax rebate scheme introduced in Italy in 2014.

Suggested Citation

  • Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_1158_17
    as

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    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2017/2017-1158/en_tema_1158.pdf
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    References listed on IDEAS

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    Cited by:

    1. Andreas Joseph, 2019. "Parametric inference with universal function approximators," Papers 1903.04209, arXiv.org, revised Oct 2020.
    2. Concetta Rondinelli & Roberta Zizza, 2020. "Spend today or spend tomorrow? The role of inflation expectations in consumer behaviour," Temi di discussione (Economic working papers) 1276, Bank of Italy, Economic Research and International Relations Area.
    3. Aiello, Francesco & Albanese, Giuseppe & Piselli, Paolo, 2019. "Good value for public money? The case of R&D policy," Journal of Policy Modeling, Elsevier, vol. 41(6), pages 1057-1076.
    4. 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.

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    More about this item

    Keywords

    machine learning; prediction; programme evaluation; fiscal stimulus;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • H3 - Public Economics - - Fiscal Policies and Behavior of Economic Agents

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