Targeting Policy-Compliers with Machine Learning: An Application to a Tax Rebate Programme in Italy

38 Pages Posted: 8 Dec 2017

See all articles by Monica Andini

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, Department of International Development, Students

Date Written: December 5, 2017

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.

Keywords: machine learning, prediction, programme evaluation, fiscal stimulus

JEL Classification: C5, H3

Suggested Citation

Andini, Monica and Ciani, Emanuele and de Blasio, Guido and D'Ignazio, Alessio and Salvestrini, Viola, Targeting Policy-Compliers with Machine Learning: An Application to a Tax Rebate Programme in Italy (December 5, 2017). Bank of Italy Temi di Discussione (Working Paper) No. 1158, Available at SSRN: https://ssrn.com/abstract=3084031 or http://dx.doi.org/10.2139/ssrn.3084031

Monica Andini (Contact Author)

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Emanuele Ciani

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Guido De Blasio

Bank of Italy ( email )

Via Nazionale 91
00184 Roma
Italy

Alessio D'Ignazio

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Viola Salvestrini

London School of Economics and Political Science, Department of International Development, Students ( email )

London
United Kingdom

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