Selected Paper, American Agricultural Economics Association, Annual meeting.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
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.
Bibtex:
@TechReport{,
author={Dumas, Christopher F. and Devine, John H.},
title={{Detecting Evidence Of Non-Compliance In Self-Reported Pollution Emissions Data: An Application Of Benford'S Law}},
year={2000},
institution={American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association)},
type={2000 Annual meeting, July 30-August 2, Tampa, FL},
url={https://ideas.repec.org/p/ags/aaea00/21740.html},
number={21740},
doi={},
}
Reference Type: Conference Paper
Subject Area(s): Environmental Sciences