Is conditional conservatism a source of deviations of financial statements from Benford's Law?

PurposeIn this paper, the authors examine the association between conditional conservatism and deviations of the first digits of financial statement items from what are expected by Benford's Law.Design/methodology/approachThis research uses data of companies listed on the London Stock Exchange. The authors measure deviations of first digits from Benford's Law following Amiram et al. (2015) and firm-year conditional conservatism following previous studies (Basu, 1997; Khan and Watts, 2009; García Lara et al., 2016). The authors use multiple regressions to provide evidence for their hypothesis.FindingsThe results show that conditional conservatism is positively associated with deviations from Benford's Law. The findings are robust across different measures of deviations and conditional conservatism. Also, the authors find that the relationship between deviations from Benford's Law and conditional conservatism is more pronounced for firms with debt issuance, and for leveraged firms facing financial distress. Next, the authors’ analyses confirm previous evidence by showing that the first digits of financial statement items of UK listed companies conform to Benford's Law at the firm-specific level and the market level, and deviations of income statements are larger than those of balance sheets and cash flow statements.Research limitations/implicationsThe research makes significant contributions to the literature. First, this is the first study that provides empirical evidence suggesting that conditional conservatism may be a source of deviations from Benford’s Law. Second, the authors provide evidence confirming previous US findings (e.g. Amiram et al., 2015) showing that the distributions of first digits of financial statement items of UK listed companies also conform to Benford's Law.Practical implicationsThe authors’ findings have implications for auditors. Auditors should be aware of “false positive” for material misstatements when using Benford's Law as a risk assessment procedure. While both conditional conservatism and earnings management are related to deviations from Benford's Law, conservatism-related biases could indicate less audit risks.Originality/valueThe authors provide new and original evidence suggesting that conditional conservatism is related to deviations from Benford's Law.

Benford's Law Test Method in Tax Audit Planning

The purpose of this research is to test whether the Benford’s Law test method can be used in tax audit planning. This research uses data on tax invoices of periodic VAT Tax Return registered in local tax office. The research was carried out by going through the identification and extraction stages of the first and second digits. The results show that Benford's Law can be used as an alternative method to indicate an improper periodic tax invoice. In addition, Benford's Law can also provide an assessment of which sectors and areas have indications of improper periodic tax invoice compared to other sectors and areas. Indications from this assessment can be used as a focus in planning tax invoice audit.

Are COVID-19 data reliable? The case of the European Union

Previous studies have used Benford's distribution to assess whether there is misreporting of COVID-19 cases and deaths. Data inaccuracies provide false information to the media, undermine global response and hinder the preventive measures taken by countries worldwide. In this study, we analyze daily new cases and deaths from all the countries of the European Union and estimate the conformance to Benford's distribution. For each country, two statistical tests and two measures of deviations are calculated to determine whether the reported statistics comply with the expected distribution. Four country-level developmental indexes are also included, the GDP per capita, health expenditures, the Universal Health Coverage index, and full vaccination rate. Regression analysis is implemented to show whether the deviation from Benford's distribution is affected by the aforementioned indexes. The findings indicate that only three countries were in line with the expected distribution, Bulgaria, Croatia, and Romania. For daily cases, Denmark, Greece, and Ireland, showed the greatest deviation from Benford's distribution, and for deaths, Malta, Cyprus, Greece, Italy, and Luxemburg had the highest deviation from Benford's law. Furthermore, it was found that the vaccination rate is positively associated with deviation from Benford's distribution. These results suggest that overall official data provided by authorities are not confirming Benford's law, yet this approach acts as a preliminary tool for data verification. More extensive studies should be made with a more thorough investigation of countries that showed the greatest deviation.

Applying Benford’s Law to Monitor Death Registration Data: A Management Tool for the COVID-19 Pandemic

In Spain, the COVID-19 pandemic has impacted the various regions of the country differently. The availability of reliable and up-to-date information has proved to be fundamental for the management of this health crisis. However, especially during the first wave of the pandemic (February–August 2020), the disparity in the recording criteria and in the timing of providing these figures to the central government created controversy and confusion regarding the real dimension of the pandemic. It is therefore necessary to have objective and homogeneous criteria at the national level to guide health managers in the correct recording and evaluation of the magnitude of the pandemic. Within this context, we propose using Benford’s Law as an auditing tool to monitor the reliability of the number of daily COVID-related deaths to identify possible deviations from the expected trend.

Stylometry and Numerals Usage: Benford’s Law and Beyond

We suggest two approaches to the statistical analysis of texts, both based on the study of numerals occurrence in literary texts. The first approach is related to Benford’s Law and the analysis of the frequency distribution of various leading digits of numerals contained in the text. In coherent literary texts, the share of the leading digit 1 is even larger than prescribed by Benford’s Law and can reach 50 percent. The frequencies of occurrence of the digit 1, as well as, to a lesser extent, the digits 2 and 3, are usually a characteristic the author’s style feature, manifested in all (sufficiently long) literary texts of any author. This approach is convenient for testing whether a group of texts has common authorship: the latter is dubious if the frequency distributions are sufficiently different. The second approach is the extension of the first one and requires the study of the frequency distribution of numerals themselves (not their leading digits). The approach yields non-trivial information about the author, stylistic and genre peculiarities of the texts and is suited for the advanced stylometric analysis. The proposed approaches are illustrated by examples of computer analysis of the literary texts in English and Russian.

A data transformation process for using Benford’s Law with bounded data

Benford’s Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford’s Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford’s Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford’s Law. This paper implements a transformation procedure which allows for assessment data to be compared to Benford’s Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.

Was there any widespread fraud in 2020 presidential election? What does Benford's Law say?

Fair elections free of any interference are integral tenets of any functioning democracy, and widespread election fraud is undoubtedly a serious threat to a free republic. While instances of electoral fraud are much more prevalent in countries with illiberal democracies, the U.S has recently faced such an accusation. Although he was unable to provide any concrete evidence, the former U.S. President Donald Trump accused his opponent, Joe Biden, now president, of electoral fraud after the presidential election. Fortunately, election forensics are often successful in investigating the validity of such fraud allegations. In this paper, I applied Benford’s law, a rule that should stand up to any large set of natural numbers, such as un-tampered electoral data. Using this law and basic statistical analysis of votes of U.S. counties for candidates of the two major parties, I completed a forensic analysis to investigate Mr. Trump’s allegation. My comprehensive investigation does not find any evidence supporting his allegation.

Benford’s Law for Telemetry Data of Wildlife

Benford’s law (BL) specifies the expected digit distributions of data in social sciences, such as demographic or financial data. We focused on the first-digit distribution and hypothesized that it would apply to data on locations of animals freely moving in a natural habitat. We believe that animal movement in natural habitats may differ with respect to BL from movement in more restricted areas (e.g., game preserve). To verify the BL-hypothesis for natural habitats, during 2015–2018, we collected telemetry data of twenty individuals of wild red deer from an alpine region of Austria. For each animal, we recorded the distances between successive position records. Collecting these data for each animal in weekly logbooks resulted in 1132 samples of size 65 on average. The weekly logbook data displayed a BL-like distribution of the leading digits. However, the data did not follow BL perfectly; for 9% (99) of the 1132 weekly logbooks, the chi-square test refuted the BL-hypothesis. A Monte Carlo simulation confirmed that this deviation from BL could not be explained by spurious tests, where a deviation from BL occurred by chance.