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04-09-2018

Leveraged enforcement under the EPA’s High Priority Violation Policy

Authors: Lirong Liu, Zhou Yang

Published in: Empirical Economics | Issue 3/2020

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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.

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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.

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Title: Leveraged enforcement under the EPA’s High Priority Violation Policy
Authors: Lirong Liu
Zhou Yang
Publication date: 04-09-2018
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 3/2020
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-018-1555-z

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