The Optimal Number of Tax Audits: Evidence from Italy

40 Pages Posted: 5 May 2022

See all articles by Alessandro Santoro

Alessandro Santoro

Università degli Studi di Milano-Bicocca - Center for Interdisciplinary Studies in Economics, Psychology & Social Sciences (CISEPS); Università degli Studi di Milano-Bicocca - Department of Economics, Management and Statistics (DEMS)

Paolo Berta

University of Milano-Bicocca; University of Milan - Bicocca

Daniele Spinelli

Department of Statistics and Quantitative Methods University of Milano-Bicocca

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Abstract

Tax audits are the main tool adopted by tax administrations to collect taxes. Their optimal number depends on two parameters, i.e. the enforcement elasticity of tax revenue with respect to the audit effort and the sum of private compliance costs and public administrative costs entailed by audits. In turn, the enforcement elasticity critically depends on audit selection criteria actually chosen by tax authorities. In this paper, we apply a machine learning approach to Italian data and we provide evidence that, in 2010 and 2011, audited taxpayers are those whose reporting behaviour in between the report year and the audit year has deviated from the business cycle. We use these audit criteria to match audited taxpayers to non-audited ones and we obtain an estimate of the enforcement elasticity that allows us to characterize the optimal number of tax audits as a function of the ratio between private compliance and public administrative costs.

Keywords: Optimal Tax Administration, Enforcement Elasticity of Tax Revenue, Machine Learning

Suggested Citation

Santoro, Alessandro and Berta, Paolo and Spinelli, Daniele, The Optimal Number of Tax Audits: Evidence from Italy. Available at SSRN: https://ssrn.com/abstract=4101077 or http://dx.doi.org/10.2139/ssrn.4101077

Alessandro Santoro (Contact Author)

Università degli Studi di Milano-Bicocca - Center for Interdisciplinary Studies in Economics, Psychology & Social Sciences (CISEPS) ( email )

Piazza dell'Ateneo Nuovo, 1
Milano, 20126
Italy

Università degli Studi di Milano-Bicocca - Department of Economics, Management and Statistics (DEMS) ( email )

Piazza dell'Ateneo Nuovo, 1
Milan, 20126
Italy

Paolo Berta

University of Milano-Bicocca ( email )

Piazza dell’Ateneo Nuovo 1, 20126 Milano
Milano, 20126
Italy

University of Milan - Bicocca ( email )

Via Bicocca degli Arcimboldi, 8
Milano, Milano 20126
Italy

Daniele Spinelli

Department of Statistics and Quantitative Methods University of Milano-Bicocca ( email )

Piazza dell’Ateneo Nuovo 1, 20126 Milano
Milano, 20126
Italy

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