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

Monica Andini, Emanuele Ciani, Guido de Blasio, Alessio D'Ignazio and Viola Salvestrini ()
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Viola Salvestrini: London School of Economics and Political Science

No 1158, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

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 (search for similar items in EconPapers)
JEL-codes: C5 H3 (search for similar items in EconPapers)
Date: 2017-12
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1158_17

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