Measuring tax administrations efficiency using data envelopment analysis: evidence from 26 European countries

Athanasios Anastasiou, Charalampos Kalligosfyris and Eleni Kalamara

International Journal of Computational Economics and Econometrics, 2023, vol. 13, issue 1, 61-109

Abstract: The purpose of this paper is to assess the efficiency of tax administrations of 26 European countries, using data envelopment analysis. In particular, by applying the CCR data environment analysis (DEA) output-oriented model, the quantification of the tax administrations performance of 26 European countries is attempted in the areas of taxpayers' servicing, public revenue collection, strengthening voluntary tax compliance and targeted tax audits, the assessment of relative efficiency, the evaluation of results and the identification of fully efficient and inefficient tax administrations, in which a real improvement in their efficiency can be achieved. Subsequently, for the tax administrations that are assessed as inefficient, the reference units are identified, the missing quantity of outputs and the excess amount of inputs are estimated, in order to make them efficient and a set of possible ways of improving their operation is proposed, through specific changes.

Keywords: tax administration; efficiency; data envelopment analysis; DEA; tax compliance; tax audit; tax revenue. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=127311 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:13:y:2023:i:1:p:61-109

Access Statistics for this article

More articles in International Journal of Computational Economics and Econometrics from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().