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Detecting Evidence Of Non-Compliance In Self-Reported Pollution Emissions Data: An Application Of Benford'S Law

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  • Dumas, Christopher F.
  • Devine, John H.

Abstract

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.

Suggested Citation

  • Dumas, Christopher F. & Devine, John H., 2000. "Detecting Evidence Of Non-Compliance In Self-Reported Pollution Emissions Data: An Application Of Benford'S Law," 2000 Annual meeting, July 30-August 2, Tampa, FL 21740, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea00:21740
    DOI: 10.22004/ag.econ.21740
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    File URL: https://ageconsearch.umn.edu/record/21740/files/sp00du02.pdf
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    References listed on IDEAS

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    1. Ayres, Robert U & Kneese, Allen V, 1969. "Production , Consumption, and Externalities," American Economic Review, American Economic Association, vol. 59(3), pages 282-297, June.
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    Cited by:

    1. Scott Marchi & James Hamilton, 2006. "Assessing the Accuracy of Self-Reported Data: an Evaluation of the Toxics Release Inventory," Journal of Risk and Uncertainty, Springer, vol. 32(1), pages 57-76, January.

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