nach oben

Empirical Economics

Erschienen in:

04.09.2018

Leveraged enforcement under the EPA’s High Priority Violation Policy

verfasst von: Lirong Liu, Zhou Yang

Erschienen in: Empirical Economics | Ausgabe 3/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In the environmental enforcement literature, leveraged enforcement is an important theoretical model in which firms with current violations are targeted with intensive inspections and sanctions in the future. This paper examines the effects of leveraged enforcement on facility compliance and the differential deterrence effects of enforcement. We focus on the Environmental Protection Agency’s High Priority Violation (HPV) Policy, which represents leveraged enforcement in the regulations of air pollution in the USA. We estimate dynamic panel data models using 8755 major manufacturing facilities in the USA from 2001 to 2010. Our results suggest that being classified as an HPV facility can have significant and positive effects on compliance. We also find general deterrence effects such that a given facility’s compliance rate rises with fines on HPV facilities within the same state. However, the deterrence effects of enforcement differ by HPV status. HPV facilities on average are less responsive to additional enforcement on itself and other facilities.

Vorheriger Artikel Hysteresis losses in the Preisach framework

Nächster Artikel The effect of military spending on income inequality: evidence from NATO countries

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

In air pollution regulation, a source is considered to be major if its potential to emit is above certain thresholds established by various regulatory programs. In general, a minor source refers to any stationary source that is not major. Permits are required for all major sources but not for minor sources. The synthetic minor source is sometimes referred to as a conditional major source. For such sources, federally enforceable limitations or conditions are required to ensure that its potential to emit is below the major source thresholds.

Manufacturing facilities are identified using NAICS code 31–33.

According to the EPA, “A facility is federally reportable if its emission classification is ‘major’ or ‘synthetic minor,’ or it is subject to NSPS or NESHAP requirements and its source-level compliance status is not equal to ‘no applicable state regulation’” (EPA, AFS document, August 2012).

When the accuracy of the data is tested explicitly, results are mixed. Telle (2013) recently raises concerns about the reliability of self-reported data. Other studies that test on the validity of self-reported data do not reject the accuracy of the data (Laplante and Rilstone 1996; Shimshack and Ward 2005).

Gray and Shimshack (2011) provide a more detailed discussion of reserve causality and endogeneity problems in measuring the deterrence effects of enforcement. The authors also consider using proxy variables as an approach to address endogeneity.

Hausman test suggests HPV status is endogenous.

Shimshack and Ward (2005) include both fines on the specific facility and fines on all facilities in their regression model without using instruments. Inspection rates on other facilities are used as instruments for contemporaneous inspection on the specific facility. We decide to treat fines and inspections the same way for two main reasons. First, we believe that both fines and inspections on other facilities can affect a facility’s compliance and thus should be included as explanatory variables directly. Second, in Gray and Shimshack (2011), the authors cite Shimshack and Ward (2005) as an example when discussing the approach of using proxy variables for addressing reverse causality. Thus, we decide to follow Gray and Shimshack (2011) discussion and include fines and inspections on other facilities.

We have also tried adding more lags, which leads to a substantial loss of observations without increasing explanatory power.

We use yearly firm-level data because the annual compliance rate provides a continuous measure, which allows one to utilize a linear dynamic panel data model to address the endogeneity problem discussed earlier.

We also consider including HPV status as a dummy variable where HPV takes the value of one if a facility is on HPV list for at least one month in a year. We present the results in Appendix 1. The findings are similar.

It would be better to use a more disaggregated level of data; however, such data are not available.

These variables can partially address the omitted variable problem discussed in Gray and Shimshack (2011).

The level equation is first differenced and higher-order lags (lags 5 and earlier) are used as instruments for the differenced lagged dependent variables.

The estimated long-run (direct) effect using our main specification (4) can be calculated as follows: 0.2554/(1 − 0.8712 + 0.11 − 0.0406) = 1.29. This means additional 4 months on the HPV list in the previous year lead to about five extra months of compliance in the long run.

The average number of inspections on a HPV facility is about three times per year.

To calculate the combined effect, we replace the continuous HPV variable with the dummy variable so we can determine whether the combined effect evaluated at the sample means is still positive. Based on the results reported in Appendix 1, the combined effect is still positive.

Usually more polluting industries are expected to have a lower compliance rate compared to other industries. However, this does not necessarily contradict with our finding. The context in this paper is manufacturing facility compliance in counties with different number of manufacturing establishments. It could be that although facilities in counties with more manufacturing establishments have better compliance, when comparing with other industries within the same county, manufacturing facilities still have a lower compliance rate.

We also considered a short sample period from 2001 to 2008 and adding political variables. Results are very similar to our baseline results.

Alberini A, Austin D (1999) Strict liability as a deterrent in toxic waste management. J Environ Econ Manag 38(1):20–48CrossRef

Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297CrossRef

Deily ME, Gray WB (2007) Agency structure and firm culture: OSHA, EPA, and the steel industry. J Law Econ Organ 23(3):685–709CrossRef

Earnhart D (2004a) Panel data analysis of regulatory factors shaping environmental performance. Rev Econ Stat 86(1):391–401CrossRef

Earnhart D (2004b) Regulatory factors shaping environmental performance at publicly owned treatment plants. J Environ Econ Manag 48(1):655–681CrossRef

Earnhart D (2004c) The effects of community characteristics on polluter compliance levels. Land Econ 80(3):408–432CrossRef

Earnhart D (2009) The influence of facility characteristics and permit conditions on the effectiveness of environmental regulatory deterrence. J Regul Econ 36:247–273CrossRef

Eckert H (2004) Inspections, warnings, and compliance: the case of petroleum storage regulation. J Environ Econ Manag 47(2):232–259CrossRef

Environmental Protection Agency (1999) The timely and appropriate (T&A) enforcement response to High Priority Violations (HPVs), June 23

Environmental Protection Agency (2009) EPA oversight and policy for high priority violations of Clean Air Act need improvement, report no. 10-P-0007, October

Friesen L (2003) Targeting enforcement to improve compliance with environmental regulations. J Environ Econ Manag 46(1):72–85CrossRef

Gilpatric SM, Vossler CA, Mckee M (2011) Regulatory enforcement with competitive endogenous audit mechanisms. Rand J Econ 42(2):292–312CrossRef

Gilpatric SM, Vossler CA, Liu L (2015) Using competition to stimulate regulatory compliance: a tournament-based dynamic targeting mechanism. J Econ Behav Organ 119(C):182–196CrossRef

Gray WB, Deily ME (1996) Compliance and enforcement: air pollution regulation in the U.S. steel industry. J Environ Econ Manag 31(1):96–111CrossRef

Gray WB, Shadbegian RJ (2005) When and why do plants comply? Paper mills in the 1980s. Law Policy 27(2):238–261CrossRef

Gray WB, Shimshack JP (2011) The effectiveness of environmental monitoring and enforcement: a review of the empirical evidence. Rev Environ Econ Policy 5(1):3–24CrossRef

Hanna RN, Oliva P (2010) The impact of inspections on plant-level air emissions. B E J Econ Anal Policy 10(1):1–33

Harford JD (1991) Measurement error and state-dependent pollution control enforcement. J Environ Econ Manag 21(1):67–81CrossRef

Harford JD, Harrington W (1991) A reconsideration of enforcement leverage when penalties are restricted. J Public Econ 45(3):391–395CrossRef

Harrington W (1988) Enforcement leverage when penalties are restricted. Journal of Public Economics 37(1):29–53CrossRef

Helland E (1998) The enforcement of pollution control laws: inspections, violations, and self-reporting. Rev Econ Stat 80(1):141–153CrossRef

Keohane N, Mansur E, Voynov A (2009) Averting enforcement: evidence from new source review. J Econ Manag Strategy 18(1):75–104CrossRef

Langpap C, Shimshack J (2010) Private citizen suits and public enforcement: substitutes or complements? J Environ Econ Manag 59(3):235–249CrossRef

Laplante B, Rilstone P (1996) Environmental inspections and emissions of the pulp and paper industry in Quebec. J Environ Econ Manag 31(1):19–36CrossRef

Lim J (2016) The impact of monitoring and enforcement on air pollutant emissions. J Regul Econ 49(2):203–222CrossRef

Liu L, Neilson WS (2013) Enforcement leverage with fixed inspection capacity. Strateg Behav Environ 3(4):305–328CrossRef

Magat WA, Viscusi WK (1990) Effectiveness of the EPA’s regulatory enforcement: the case of industrial effluent standards. J Law Econ 33(2):331–360CrossRef

Nadeau LW (1997) EPA effectiveness at reducing the duration of plant-level noncompliance. J Environ Econ Manag 34(1):54–78CrossRef

Nyborg K, Telle K (2006) Firms’ compliance to environmental regulation: is there really a paradox? Environ Resour Econ 35(1):1–18CrossRef

Raymond M (1999) Enforcement leverage when penalties are restricted: a reconsideration under asymmetric information. J Public Econ 73(2):289–295CrossRef

Russell CS (1990) Game models for structuring monitoring and enforcement systems. Nat Resour Model 4(2):143–173CrossRef

Shimshack JP, Ward MB (2005) Regulator reputation, enforcement, and environmental compliance. J Environ Econ Manag 50(3):519–540CrossRef

Shimshack JP, Ward MB (2008) Enforcement and over-compliance. J Environ Econ Manag 55(1):90–105CrossRef

Stafford SL (2002) The effect of punishment on firm compliance with hazardous waste regulations. J Environ Econ Manag 44(2):290–308CrossRef

Stafford SL (2008) Self-policing in a targeted enforcement regime. South Econ J 74(4):934–951

Telle K (2013) Monitoring and enforcement of environmental regulations: lessons from a natural field experiment in Norway. J Public Econ 99(C):24–34CrossRef

Titel: Leveraged enforcement under the EPA’s High Priority Violation Policy
verfasst von: Lirong Liu
Zhou Yang
Publikationsdatum: 04.09.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 3/2020
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-018-1555-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 3/2020

The role of temporal dependence in factor selection and forecasting oil prices

Convergence of consumption patterns in the European Union

Realized volatility and jump testing in the Japanese electricity spot market

Correction to: On the distribution of links in financial networks: structural heterogeneity and functional form

Network analysis to measure academic performance in economics

How do banking crises affect bilateral exports?