DETECTING EVIDENCE OF NON-COMPLIANCE IN SELF-REPORTED POLLUTION EMISSIONS DATA: AN APPLICATION OF BENFORD'S LAW

The paper introduces Digital Frequency Analysis (DFA) based on Benford's Law as a new technique for detecting non-compliance in self-reported pollution emissions data. Public accounting firms are currently adopting DFA to detect fraud in financial data. We argue that DFA can be employed by environmental regulators to detect fraud in self-reported pollution emissions data. The theory of Benford's Law is reviewed, and statistical justifications for its potentially widespread applicability are presented. Several common DFA tests are described and applied to North Carolina air pollution emissions data in an empirical example.


Issue Date:
2000
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/21740
Total Pages:
44
JEL Codes:
Q25; Q28
Series Statement:
Selected Paper




 Record created 2017-04-01, last modified 2017-04-26

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