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Optimizing Tax Administration Policies with Machine Learning

Pietro Battiston, Simona Gamba and Alessandro Santoro

No 436, Working Papers from University of Milano-Bicocca, Department of Economics

Abstract: Tax authorities around the world are increasingly employing data mining and machine learning algorithms to predict individual behaviours. Although the traditional literature on optimal tax administration provides useful tools for ex-post evaluation of policies, it disregards the problem of which taxpayers to target. This study identifies and characterises a loss function that assigns a social cost to any prediction-based policy. We define such measure as the difference between the social welfare of a given policy and that of an ideal policy unaffected by prediction errors. We show how this loss function shares a relationship with the receiver operating characteristic curve, a standard statistical tool used to evaluate prediction performance. Subsequently, we apply our measure to predict inaccurate tax returns issued by self-employed and sole proprietorships in Italy. In our application, a random forest model provides the best prediction: we show how it can be interpreted using measures of variable importance developed in the machine learning literature.

Keywords: policy prediction problems; tax behaviour; big data; machine learning (search for similar items in EconPapers)
JEL-codes: C53 H26 H32 (search for similar items in EconPapers)
Pages: 27
Date: 2020-03, Revised 2020-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pbe
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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