NPO Financial Statement Quality: An Empirical Analysis Based on Benford’s Law

Abstract

In order to assess the accuracy of the figures reported in NPOs' financial statements, I perform a digital analysis on Belgian non-profit organizations' financial statements for accounting years 2007 up to 2012. Specifically, I compare observed frequencies for digits in the second-from-the-left position with expected frequencies based on Benford's Law. Results based on the full sample indicate that observed frequencies strongly conform to Benford's Law (and thus suggest a high degree of accuracy of the figures reported in NPOs' financial statements). Nevertheless, I note statistically significant deviations from Benford's Law (both for the entire distribution and at the individual digit level). The largest deviation is noted for zeroes in the second position (i.e., a significantly positive deviation), which can be explained based on humans' reliance upon so-called cognitive reference points. Considering different sub-samples, I note that observed deviations from Benford's Law are largest for the smallest non-profits and those non-profits that rely most heavily on grants and/or donations.Résumé Afin d'évaluer l'exactitude des chiffres indiqués dans les états financiers des organisations à but non lucratif, j'effectue une analyse numérique des états financiers des OBNL belges pour les exercices 2007 à 2012. Plus précisément, je compare les fréquences observées des chiffres en deuxième position en partant de la gauche avec les fréquences attendues sur la base de la loi de Benford. Les résultats établis sur l'ensemble de l'échantillon indiquent que les fréquences observées sont fortement conformes à la loi de Benford (et semblent donc indiquer un degré élevé d'exactitude des chiffres déclarés dans les états financiers des OBNL). Néanmoins, je constate des écarts significatifs du point de vue statistique par rapport à la loi de Benford (tant pour toute la répartition qu'au niveau des chiffres individuels). Le & Tom Van Caneghem tom.vancaneghem@kuleuven.be 1 KU Leuven, plus grand écart se reconnaît par des zéros en deuxième position (soit un écart positif significatif), qui peut s'expliquer par le recours des humains à ce que l'on appelle les repères cognitifs. Compte tenu des sous-échantillons différents, je remarque que les écarts observés par rapport à la loi de Benford sont plus grands pour les petites organisations à but non lucratif et pour celles qui dépendent le plus des subventions ou des dons. Zusammenfassung Mittels einer digitalen Analyse der Jahresabschlüsse belgischer gemeinnütziger Organisationen für die Geschäftsjahre 2007 bis 2012 prüfe ich die Richtigkeit der in den Jahresabschlüssen dieser Organisationen ausgewiesenen Zahlen. Ich vergleiche insbesondere die beobachtete Häufigkeit der Ziffern an der zweiten Stelle von links, wobei für die erwartete Häufigkeit das Benfordsche Gesetz angewandt wird. Die Ergebnisse aus der gesamten Stichprobe zeigen, dass die beobachtete Häufigkeit dem Benfordschen Gesetz starkentspricht (und somit auf ein hohes Maß an Richtigkeit der in den Jahresabschlüssen der gemeinn...

Abiding by the Law? Using Benford’s Law to Examine the Accuracy of Nonprofit Financial Reports

“…Dang and Owens (2019) do not only test organizational age but also find that larger British organizations have larger deviations. Van Caneghem’s (2016) analysis of Belgian nonprofits finds the opposite, using inferential statistics for the Benford distribution of the second digit. Using data on Ugandan nonprofits, Dang et al (2019) found a U-shaped relationship between organizational age and MAD, with young and old organizations less compliant than the middle-aged.…”

“…To our knowledge, this article is the first academic study to apply Benford analysis to U.S. nonprofit data. We have located only two articles (Van Caneghem, 2016: Belgian nonprofit organizations; Dang, Burger, & Owens, 2019: Ugandan non-governmental organizations [NGOs]) and one working paper (Dang & Owens, 2019: UK charities) that applied Benford analysis to nonprofit financial data in other countries.…”

“…Van Caneghem (2016) analyzed financial reporting of Belgian nonprofits, applying a variant of Benford analysis that considers the frequency distribution of the second digit. He found that deviations were higher for small organizations and those heavily reliant on grants and donations.…”

“…First-two-digit analyses are preferred, but our sample size (number of records per organization) argues against their use here. For samples smaller than 300, Nigrini (2012) concluded (p. 20): “The first digit test (with all its flaws) should be used.” Van Caneghem (2016) used second-digit analysis because sources he cites suggested that second digits can more reliably detect made-up data (Diekmann, 2007), but he argues that many anomalies were due to roundoff error, a less intentional error.…”

The Truth about Honesty in the Nonprofit Sector

“…Nonprofits typically rely upon volunteers to fill relevant financial accountability roles in the organization (Jang, Wang, and Yoshioka 2016), including the role of treasurer. A significant body of literature has pointed to this factor as a potential cause of inaccuracies in nonprofit financial records (see Frumkin and Kim 2001;Trussel 2003;van Caneghem 2016). It is also possible that such misrepresentations may result from intentional accounting manipulation.…”