Evaluating Conformity of Municipality Financial Data to Benford’s Distribution: An Exploratory Study

• In Event: Quality of Accounting Information

Abstract

Benford’s Law provides an expected distribution of leading digits in a set of naturally occurring numbers. One might expect that each of the leading digits, 1 to 9, would occur equally in a large set of numbers. However, the occurrence of each of the leading digits is not evenly distributed. For example, the leading digit will be 1 approximately 30% of the time, 2 about 18% of the time, and the percentage decreasing for each digit down to less than 5% for the leading digit 9 (Fewster 2009). The expected distribution has been shown to apply to many different types of data.
Benford’s Law can be used to aid in the detection of fraud in accounting data (Durtschi et al. 2004). When used for fraud detection, it is often applied to large sets of transaction data (e.g., sales, purchases, refunds, etc.). Most accounting studies have looked at for-profit financial accounting numbers or transaction data. Another area in which Benford’s Law might apply is in governmental accounting. Johnson and Weggenmann (2013) examined state government financial data and its consistency with Benford’s Law.
Many applications of Benford’s Law have been with for-profit accounting data but few studies have applied the law to governmental accounting data. Johnson and Weggenmann (2013) were among the first to use the tool with governmental financial statements. This research extends the application of Benford’s Law in the governmental sector to analyze data from municipalities. The purpose of this research is to examine the conformity of municipal financial data with Benford’s Law. A selected subset of financially troubled municipalities is examined separately to identify whether the conformity of those numbers differs from the full data set. This application of Benford’s law may provide additional information to identify characteristics of troubled municipalities so analysts can focus on areas most likely to indicate potential financial failure.

Authors

• Cheryl Langford Prachyl, University of Dallas

• Mary Fischer, University of Texas-Tyler

• Treba A Marsh, Stephen F. Austin St University