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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

    as
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    More about this item

    Keywords

    machine learning; prediction; programme evaluation; fiscal stimulus;

    JEL classification:

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

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