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Note on Pre-Taxation Data Reported by UK FTSE-Listed Companies: Search for Compatibility with Benford’s Laws

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Abstract

Pre-taxation analysis plays a crucial role in ensuring the fairness of public revenue collection. It can also serve as a tool to reduce the risk of tax avoidance, one of the UK government’s concerns. Our report utilises pre-tax income (𝑃⁢𝐼) and total assets (𝑇⁢𝐴) data from 567 companies listed on the FTSE All-Share index, gathered from the Refinitiv EIKON database, covering 14 years, i.e., the period from 2009 to 2022. We also derive the 𝑃⁢𝐼/𝑇⁢𝐴 ratio, and distinguish between positive and negative 𝑃⁢𝐼 cases. We test the conformity of such data to Benford’s Laws, specifically studying the first significant digit (𝐹⁡𝑑), the second significant digit (𝑆⁢𝑑), and the first and second significant digits (𝐹⁡𝑆⁢𝑑). We use and justify two pertinent tests, the 𝜒2 and the Mean Absolute Deviation (MAD). We find that both tests do not lead to conclusions in complete agreement with each other—in particular, the MAD test entirely rejects the Benford’s Laws conformity of the reported financial data. From the mere accounting point of view, we conclude that the findings not only cast some doubt on the reported financial data, but also suggest that many more investigations should be considered on closely related matters. On the other hand, the study of a ratio, like 𝑃⁢𝐼/𝑇⁢𝐴, of variables that are (or are not) Benford’s Laws-compliant adds to the literature concerning whether such indirect variables should (or should not) be Benford’s Laws-compliant.
Original languageEnglish
Article number15
Number of pages17
JournalStats
Volume8
Issue number1
DOIs
Publication statusPublished - 6 Feb 2025

Bibliographical note

Copyright © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Data Access Statement

Details regarding the analyzed data can be obtained within the text. The text also explains how new data were created. The final numerical results are all reported.

Funding

MA was partially supported by the project “A better understanding of socio-economic systems using quantitative methods from physics” funded by the European Union—NextgenerationEU and the Romanian Government under the National Recovery and Resilience Plan for Romania, contract no.760034/23.05.2023, code PNRR-C9-I8-CF 255/29.11.2022, through the Romanian Ministry of Research, Innovation and Digitalization, within Component 9 and “Investment i8”.

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