Evidence that the accuracy of self-reported lead emissions data improved: A puzzle and discussion
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We investigate the accuracy of facility-reported data both within and across emissions and off-site transfer inventories of lead (Pb) in time. We build on recent work using Benford’s Law to detect statistical anomalies in large data sets. Our application exploits a regulatory experiment to test for systematic changes in firm behavior triggered by the 2001 implementation of the Final Rule, a major regulatory change governing the U.S. Environmental Protection Agency’s (EPA) oversight of lead emissions. Statistical results show that the EPA’s Final Rule functioned to significantly improve the accuracy of facility-reported lead data. This finding is surprising because abatement requirements increased and both the probability of firm audit and expected penalties for misreporting apparently decreased in the post-Final Rule period. To explain this counterintuitive result we develop a reporting model for the firm. We argue that organizational investments made in response to specific requirements of the Final Rule, as well as rising public awareness of the risks of lead, may have induced firms to report more accurately.
KeywordsLead emissions Final Rule 2001 Benford’s law Toxic release inventory Self-regulation
JEL ClassificationsQ53 Q58 K32 K42
- Benford, F. (1938). The law of anomalous numbers. Proceedings of the American Philosophical Society, 78(4), 551–572.Google Scholar
- Cohen, M. A. (1999). Monitoring and enforcement of environmental policy. In H. Folmer & T. Tietenberg (Eds.), International Yearbook of Environmental and Resource Economics (Vol. III, pp. 44–106). Northampton: Edward Elgar Publishing, Inc.Google Scholar
- Dumas, C. F., & Devine, J. H. (2000). Detecting evidence of non-compliance in self-reported pollution emissions data: An application of Benford’s Law. Paper presented at American Agricultural Economics Association Meeting, Tampa, FL. Available at http://agecon.lib.umn.edu.
- Durtschi, C., Hillison, W., & Pacini, C. (2004). The effective use of Benford’s Law to assist in detecting fraud in accounting data. Journal of Forensic Accounting, V(1), 17–34.Google Scholar
- Environmental Protection Agency. (1999). Economic analysis of the Final Rule to modify reporting of persistent bioaccumulative toxic chemicals under EPCRA section 313. Economic and policy analysis branch. Washington: Office of Pollution Prevention and Toxics.Google Scholar
- Fu, D., Shi, Y. Q., & Su, Q. (2007). A generalized Benford’s Law for JPEG coefficients and its application in image forensics. Proceedings of The International Society for Optics and Photonics, 6505. Google Scholar
- Mebane Jr., W. R. (2006). Election forensics: The second-digit Benford’s Law test and recent American presidential elections. Paper presented at the Election Fraud Conference, Salt Lake City, UT.Google Scholar
- Nigrini, M. J., & Mittermaier, L. J. (1997). The use of Benford’s Law as an aid in analytical procedures. Auditing: A Journal of Practice and Theory, 16(2), 52–67.Google Scholar
- Ruetzel, S. (1994). Snitching for the common good: In search of response to the legal problems posed by environmental whistleblowers. Digital Commons@Georgia Law.Google Scholar
- Tietenberg, T., & Wheeler, D. (2001). Empowering the community: Information strategies for pollution control. In H. Folmer (Ed.), Frontiers of Environmental Economics (pp. 85–120). Northampton: Edward Elgar Publishing.Google Scholar
- Varian, H. (1972). Benford’s Law. The American Statistician, 23, 65–66.Google Scholar