TITLE

EXPLORATORY RESEARCH APPLYING BENFORD'S LAW TO SELECTED BALANCES IN THE FINANCIAL STATEMENTS OF STATE GOVERNMENTS

AUTHOR(S)
Johnson, Gary G.; Weggenmann, Jennifer
PUB. DATE
September 2013
SOURCE
Academy of Accounting & Financial Studies Journal;2013, Vol. 17 Issue 3, p31
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This research study applies Benford's Law to selected balances in the Comprehensive Annual Financial Reports of the fifty states of the United States. The balances identified for study include: Total General Revenues of the Primary Government, Total Fund Balance of the General Fund, and Total Fund Balance of the Governmental Funds. These balances were selected because they are the most critical for governmental financial health and because these balances are most often used as benchmarks in financial analysis. Three years of data were collected, yielding 150 data points for each balance. Data in the first digit location were analyzed using Audit Command Language's (ACL) Benford's Law programming for identifying biased data. Actual occurrences of each data point were compared to expected amounts. ACL generated Z-statistics were analyzed for each comparison. In two instances a statistically significant variance was found between the actual and expected rates of occurrence: number 1 in the Total Governmental Funds and the number 8 in the Total Fund Balance of the General Fund. An overall analysis of the data using the authors' own rendition of the Mean Absolute Deviation model developed by Drake & Nigrini (2000) and revised by Nigrini (2011) shows "nonconformity" to Benford's distribution for the Total Fund Balance of the Governmental Funds; "acceptable conformity" for the Total General Revenues of the Primary Government; and "acceptable conformity" for the Total Fund Balance of the General Fund. Nonconformity suggests that further investigation may be needed, whereas acceptable conformity suggests that the balance is likely not biased and should be accepted without further analysis. This research demonstrates that Benford's Law is effective in detecting data bias in smaller data sets. Further, this study introduces a refined mean absolute deviation model which the authors' believe is better suited to, and more effective for, small data sets.
ACCESSION #
92564682

 

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