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The Effects of Enforcement on Corporate Environmental Performance: The Role of Perceived Fairness

  • Dietrich Earnhart EMAIL logo , Donna Ramirez Harrington and Robert Glicksman
Published/Copyright: January 24, 2020
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Review of Law & Economics
From the journal Review of Law & Economics Volume 17 Issue 1

Abstract

Several empirical studies explore the effects of regulatory enforcement on environmental behavior and performance. Within this literature, extremely little empirical research examines the role of fairness, which we interpret broadly to include multiple dimensions, e. g. similar treatment of similarly situated regulated entities. Our study empirically examines the effect of perceived enforcement fairness on the extent of compliance with wastewater limits imposed on chemical manufacturing facilities regulated under the Clean Water Act. Our study also explores the influence of perceived fairness on the effectiveness of enforcement efforts – government inspections and enforcement actions – at inducing better compliance. For our analysis, we use a subjective measure of the degree of “fair treatment” of regulated facilities by environmental regulators, as perceived by facilities and reported as survey responses. Results reveal that a more (perceived) fair enforcement approach raises compliance, but only under limited enforcement conditions; in most instances, perceived more fair enforcement lowers compliance. As important, results show that greater perceived fairness improves the effectiveness of federal inspections and informal enforcement, but undermines the effectiveness of state inspections and formal non-penalty enforcement.

Keywords: environmental performance; compliance; enforcement; wastewater

Funding source: Independent Research Fund Denmark

Award Identifier / Grant number: DFF – 4180-00147

Funding statement: This work was supported by the U.S. Environmental Protection Agency (Funder Id: http://doi.org/10.13039/100000139, Grant Number: STAR Research Assistance Agreement No. R-82882801).

Acknowledgements

The research described in this article was conducted as part of a larger project financed by the U.S. Environmental Protection Agency (EPA) pursuant to STAR Research Assistance Agreement No. R-82882801-0. This article has not been formally reviewed by EPA. The views expressed in this article are solely those of Robert Glicksman and Dietrich Earnhart. EPA does not endorse any products or commercial services mentioned in this manuscript. The authors thank Donald Haider-Markel and Tatsui Ebihara for their participation in the EPA STAR grant research project. The authors also thank Chris Drahozal, Joel Mintz, and Cliff Rechtschaffen for their very helpful insight. Dietrich Earnhart thanks Dylan Rassier, J. Mark Leonard, and Trisha Shrum for their valuable research assistance.

Appendix

A Incomplete response to survey of chemical manufacturing facilities

This appendix assesses the incomplete response to our original survey of chemical manufacturing facilities. Given the survey’s non-response rate of 73 %, the potential for sample selection bias is a valid concern. As the initial assessment of this concern, we compare the original sample of 1,003 survey recipients to the 267 facilities that completed our survey. Based on this comparison, we find no systematic state or regional bias in survey participation. As two examples, only the Midwest region is slightly over-represented in the response group, and only the Northeast region is slightly under-represented. However, these differences are small. Moreover, across most of the states, the difference between representation in the set of survey recipients and representation in the response group averages less than two percent. In contrast, our initial assessment reveals some difference in the participation of major facilities versus minor facilities. In the sample of survey recipients, 69 % of facilities are minor facilities and 31 % are major facilities. In the group of survey respondents, major facilities are somewhat over-represented at 39 %, which proves statistically significant.

As a stronger assessment, we test for sample selection bias by assessing whether any relevant factors appear to affect a facility’s decision to complete our survey after being contacted. For this assessment, we use a probit estimator to capture the relationship between the binary decision whether or not to complete our survey and a set of explanatory factors, including major versus minor status, inspections, enforcement actions, and EPA region. This assessment reveals a bias in a single dimension: major facilities were more likely to respond to the survey than were minor facilities. Put differently, the analysis indicates that only the distinction between minor and major facilities proves important for explaining whether or not a contacted facility completed our survey. The analysis demonstrates that neither the preceding history of inspections nor the preceding enforcement actions against a particular facility explains whether or not a contacted facility responded to the survey. Moreover, the analysis demonstrates that the decision to respond is not explained by the EPA region in which a particular facility resides.

Thus, based on our analysis, it appears that a sample selection bias exists in only a single dimension: the distinction between a major facility and a minor facility. This single distinction proves irrelevant for our final sample of analysis since it includes onlymajor facilities.

As one last form of sample selection assessment, we incorporate information on wastewater limits and discharges, for which data are publicly available only for major facilities, for both survey respondents and non-respondents. Consistent with our final sample of analysis, our last form of assessment focuses exclusively on major facilities. Using Two-Sample Means T-tests, we demonstrate that the sample of survey respondents and the sample of survey non-respondent facilities generated extremely similar discharge ratios for the time period covered by the survey instrument: January, 1999, to March, 2003. This analysis considers separately the two most prominent wastewater pollutants: Total Suspended Solids (TSS) and Biological Oxygen Demand (BOD). For the TSS discharge ratio, both of the sample means equal 0.267 and the t-test p-value is 0.969. For the BOD discharge ratio, the two sample means are nearly identical – 0.261 and 0.256 – and the t-test p-value is 0.616.

For all these reasons, our study does not correct for any potential sample selection bias. This lack of correction is consistent with prominently published studies of environmental management practices (Anton et al., 2004; Arimura et al., 2008).

B Focus on major facilities

Our exclusive focus on major facilities obviously constrains the generalizability of our results. Still, our analysis remains strongly policy relevant since the EPA focuses its regulatory efforts on major facilities (Earnhart, 2004a, 2004b, 2009; Earnhart and Segerson, 2012). Moreover, major facilities vary among themselves with respect to several dimensions, such as their environmental management practices and product mix, as reflected in the standard deviation values reported in Table 1. Therefore, our empirical analysis exploits variation even within a sample of major facilities. In addition, major facilities represented 21 % of the chemical manufacturing facilities regulated under the NPDES program in 2001. Given their relative size, we suspect that major facilities were responsible for the bulk of wastewater discharges from this sector during the sample period. (The EPA does not systematically maintain data on discharges from minor facilities for any sector. The data coverage of the EPA Discharge Monitoring Report (DMR) Pollutant Loading Tool, which is designed to provide data on discharge loadings, confirms this point; see http://cfpub.epa.gov/dmr. Thus, we are not able to substantiate our assertion.)

Finally, all previous studies of U.S. wastewater discharges exclusively examine major facilities (e. g. Earnhart, 2004a; Shimshack and Ward, 2005, 2009; Earnhart and Segerson, 2012). Nevertheless, we acknowledge that major and minor facilities may respond differently to perceived enforcement fairness. Due to our data constraints, we cannot assess this possibility. Fortunately, our analysis of the full sample of surveyed facilities reveals no statistical link from major/minor classification to perceived enforcement fairness. (This conclusion is robust across multiple econometric estimators, regressor sets, and sub-sample time periods.) Therefore, this concern does not seem relevant, at least not in our sample. In addition, we acknowledge that major and minor facilities may face different variation in enforcement fairness. If true, the extent of enforcement fairness is correlated with the NPDES major/minor classification. This correlation is not problematic since we measure enforcement fairness, at least its perception, and focus exclusively on major facilities.

C Relevance and validity of instruments

This appendix assesses the relevance and validity of the instruments used in our instrumental variables estimation. Appendix Tables 5 and 6 display the first stage estimates for the TSS and BOD samples, respectively. To assess the relevance of our instruments, we primarily test under-identification in the first stage of estimation. Based on both the Angrist-Pischke χ2 Test statistics and the Anderson Canonical Correlation Lagrange Multiplier Test statistics, we reject the null hypothesis of under-identification given p-values of 0.008 and 0.016, respectively, for the TSS regression sample and p-values of 0.0004 and 0.007, respectively, for the BOD regression sample. Moreover, we conduct a Partial F-test based on the coefficients estimated for the instruments used in the first stage. The test statistics demonstrate that the instruments appear relevant. For the TSS sample, the Partial F-test statistic equals 2.34 (p = 0.036). Therefore, the instruments are jointly significant at the 5 % level. One of the instruments proves individually significant at the 5 % level. Lagged enforcement is associated with perceived enforcement fairness. For the BOD sample, the Partial F-test statistic equals 2.48 (p = 0.076). Therefore, the instruments are jointly significant but only at the 8 % level. Again, one of the instruments proves significant: lagged enforcement is associated with perceived enforcement fairness.

To assess the validity of our instruments, we primarily utilize the Sargan-Hansen Test of Overidentifying Restrictions. For both the TSS and BOD regression samples, the Sargan-Hansen Test statistic fails to reject the null hypothesis of valid orthogonality conditions given p-values of 0.118 and 0.765, respectively. Moreover, we conduct weak instrument robust inference tests: the Anderson-Rubin Wald Test and the Stock-Wright Lagrange Multiplier Test. These tests also assess whether the overidentifying restrictions are valid. Unlike the tests offered by Stock and Yogo (2005) and Staiger and Stock (1997), these tests do not rely on the F-statistic exceeding a specific value (22.3 or 13.91, respectively). Instead, these tests are robust to the presence of weak instruments, i. e. each test statistic has the correct size even when the instruments are weak, and robust to accidental exclusion of relevant instruments (Dufour and Taamouti, 2005). For both regression samples, based on both tests, the statistics fail to reject the null hypothesis of valid orthogonality conditions, given p-values of 0.272 and 0.177, respectively, for the TSS sample and p-values of 0.884 and 0.686, respectively, for the BOD sample.

This assessment uses the relevant regressors in levels. If our assessment considers the endogeneity of the lagged dependent variable, then we use the regressors in first-differences. This alternative assessment generates test statistics that comparably support our conclusions that the chosen instruments appear both relevant and not invalid.

All of these points notwithstanding, we acknowledge that our instruments may prove weak. In this case, our ability to test properly the exogeneity of the primary regressor – perceived enforcement fairness – is similarly weak.

Table 5:

First stage 1 estimation of perceived enforcement fairness: Based on TSS sample.

VariableCoeffp-value
Instruments
Lagged Immunity Audit Policy0.220.22
Lagged Privilege Audit Policy0.0310.89
Lagged Immunity and Privilege Audit Policy−0.0840.40
Lagged Enforcement0.300.02
Control Factors
Audits−0.0060.16
Lagged TSS Discharge Ratio0.520.01
Year 20010.020.78
Region 10.970.01
Region 20.730.07
Region 30.910.02
Region 40.880.01
Region 51.110.01
Region 60.800.03
State Inspections0.160.18
Federal Inspections0.760.63
Formal Non-penalty Enforcement−0.640.38
Informal Enforcement0.340.54
Penalty Enforcement0.0090.71
Firm Environmental Employees−0.0280.20
Organic Chemical Sector−0.120.27
Inorganic Chemical Sector−0.230.07
Firm Ownership Structure0.0780.61
Treatment Technology0.550.02
Local Community Pressure0.170.29
Constant−0.960.04
Tests
Exogeneity Tests
Wu-Hausman Exogeneity Test0.5250.470
Durbin-Wu-Hausman Exogeneity Test0.6270.428
Partial F-test of Instruments2.340.036
Underidentification Tests
Angrist-Pischke χ2 Test17.470.008
Anderson Canonical Correlation Lagrange Multiplier15.580.016
Sargan-Hansen Test of Overidentifying Restrictions8.780.118
Weak Instrument Robust Inference Tests
Anderson-Rubin Wald Test1.280.272
Stock-Wright Lagrange Multiplier Test8.940.177

  1. p-values lying at or below 0.10 shown in bold.

  2. Null hypotheses for tests shown above:

  3. Exogeneity: enforcement fairness variable is exogenous

  4. Instrument relevance: instruments’ coefficients jointly equal zero

  5. Underidentification: system is under-identified

  6. Overidentifying restrictions: instrumental variables estimation is valid

  7. Weak instrument: orthogonality conditions are valid

  8. Estimation also includes lagged federal and state inspections as regressors.

Table 6:

First stage 1 estimation of perceived enforcement fairness: Based on BOD sample.

VariableCoeffp-value
Instruments
Lagged Immunity Audit Policy−0.440.13
Lagged Privilege Audit Policy0.050.86
Lagged Immunity and Privilege Audit Policy−0.230.24
Lagged Enforcement−110.400.02
Control Factors
Audits−0.0030.65
Lagged BOD Discharge Ratio0.050.23
Year 20010.020.78
Region 114.790.19
Region 244.180.05
Region 3−7.080.04
Region 4−7.770.15
Region 5−7.060.05
State Inspections0.310.10
Federal Inspections12.270.81
Formal Non-penalty Enforcement744.510.02
Informal Enforcement−82.970.05
Penalty Enforcement−0.0030.50
Firm Environmental Employees−0.100.01
Organic Chemical Sector0.080.66
Inorganic Chemical Sector−0.290.32
Firm Ownership Structure2.560.03
Treatment Technology−0.440.22
Local Community Pressure−0.720.11
Constant6.900.01
Tests
Exogeneity Tests
Wu-Hausman Exogeneity Test0.020.886
Durbin-Wu-Hausman Exogeneity Test0.040.841
Partial F-test of Instruments2.480.076
Underidentification Tests
Angrist-Pischke χ2 Test20.760.000
Anderson Canonical Correlation Lagrange Multiplier4.100.007
Sargan-Hansen Test of Overidentifying Restrictions1.150.765
Weak Instrument Robust Inference Tests
Anderson-Rubin Wald Test0.290.884
Stock-Wright Lagrange Multiplier Test2.270.686

  1. p-values lying at or below 0.10 shown in bold.

  2. Null hypotheses for tests shown above:

  3. Exogeneity: enforcement fairness variable is exogenous

  4. Instrument relevance: instruments’ coefficients jointly equal zero

  5. Underidentification: system is under-identified

  6. Overidentifying restrictions: instrumental variables estimation is valid

  7. Weak instrument: orthogonality conditions are valid

  8. Estimation excludes lagged federal and state inspections as regressors, contrary to the first-stage estimation based on the TSS sample.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/rle-2019-0012).

Published Online: 2020-01-24

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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  6. Economic Analysis of Accident Law: A New Liability Rule that Induces Socially Optimal Behaviour in Case of Limited Information
  7. To Settle or to Fight to the End? Case-level Determinants of Early Settlement of Investor-State Disputes
  8. The (Mixed) Effects of Minimum Asset Requirements When There is a Possibility of Technological Change
  9. The Effect of Constitutional Commitment to Social Security on Social Expenditure Schemes
  10. Case Note
  11. Who Bears an Employee’s Special Annual Payment?
  12. A Note on the Article by Mark Ramseyer, published in RLE 2020
https://doi.org/10.1515/rle-2019-0012
Keywords for this article
environmental performance; compliance; enforcement; wastewater

Articles in the same Issue

  1. Frontmatter
  2. Articles
  3. Equilibria Under Negligence Liability
  4. Does an Inclusive Citizenship Law Promote Economic Development?
  5. The Effects of Enforcement on Corporate Environmental Performance: The Role of Perceived Fairness
  6. Economic Analysis of Accident Law: A New Liability Rule that Induces Socially Optimal Behaviour in Case of Limited Information
  7. To Settle or to Fight to the End? Case-level Determinants of Early Settlement of Investor-State Disputes
  8. The (Mixed) Effects of Minimum Asset Requirements When There is a Possibility of Technological Change
  9. The Effect of Constitutional Commitment to Social Security on Social Expenditure Schemes
  10. Case Note
  11. Who Bears an Employee’s Special Annual Payment?
  12. A Note on the Article by Mark Ramseyer, published in RLE 2020
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