DETECTING EVIDENCE OF NON-COMPLIANCE IN SELF-REPORTED POLLUTION EMISSIONS DATA: AN APPLICATION OF BENFORDS LAW Reportar como inadecuado




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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.

Keywords: Benford ; digital frequency analysis ; pollution monitoring ; pollution regulation ; enforcement

Subject(s): Environmental Economics and Policy

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 appears in: American Agricultural Economics Association (AAEA) > 2000 Annual meeting, July 30-August 2, Tampa, FL





Autor: Dumas, Christopher F. ; Devine, John H.

Fuente: http://ageconsearch.umn.edu/record/21740?ln=en



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