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“Effective regulatory stringency” and firms’ profitability: the effects of effluent limits and government monitoring

Journal of Regulatory Economics volume 50, pages 111–145 (2016)Cite this article

Abstract

The Porter hypothesis asserts that properly designed environmental regulation motivates firms to innovate, which ultimately improves profitability. Specifically, the Porter hypothesis posits that more stringent environmental regulation, i.e., greater regulatory stringency, leads to greater profitability. In contrast, the conventional “costly regulation” hypothesis posits that greater regulatory stringency weakens profitability mostly by driving up abatement costs. This study empirically tests these two hypotheses. Of course, regulatory stringency is difficult to measure. More important, regulatory stringency as codified in legislated acts and promulgated rules does not necessarily reflect regulatory stringency in practice, which we deem as “effective regulatory stringency”. Measurement of “effective regulatory stringency” is even more challenging. With this challenge in mind, we divide “effective regulatory stringency” into its two constituent components—(1) legal requirements and (2) regulatory scrutiny—the latter representing government efforts to ensure compliance with the legal requirements. For our analysis, we examine legal requirements in the form of facility-specific effluent limits and regulatory scrutiny in the form of government monitoring inspections. For our empirical analysis, we analyze the U.S. Clean Water Act under which the U.S. Environmental Protection Agency imposes numeric wastewater discharge limits on permitted facilities and conducts wastewater-related inspections. As its primary contribution, our study separately examines the effects of legal requirements and regulatory scrutiny on firm-level profitability in order to appreciate the influence of “effective regulatory stringency”.

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Notes

  1. Some studies explore companies’ desires to present a “green” image to consumers (e.g., Arora and Cason 1996) or assess management’s concerns over corporate image more generally (e.g., Downing and Kimball 1982). Other studies explore local community pressure (e.g., Henriques and Sadorsky 1996; Dasgupta et al. 2000). And some studies explore the role of stochastic discharge patterns (Bandyopadhyay and Horowitz 2006) or the possibility of zero marginal abatement costs (McClelland and Horowitz 1999).

  2. As examples, see the 2001 annual reports of four representative chemical firms: Dow Chemical, E.I. Du Pont de Nemours, Rohm & Haas, and Mississippi Chemical.

  3. The statistics cited here represent the most recent pollution abatement costs and expenditures published by the U.S. Census Bureau.

  4. Our study differs from Brunnermeier and Cohen (2003) since the two studies explore two different outcomes, which are both worthy of exploration. While profitability is a broader measure of the influence of regulatory stringency on firms’ performance and, thus, needed to test the full extent of the Porter hypothesis, we acknowledge that our broader measure does not permit a direct connection between regulatory stringency and innovation, which is important for the Porter hypothesis.

  5. We claim that we improve upon Brunnermeier and Cohen (2003) in two additional ways by (1) examining firm-level outcomes rather than sectoral outcomes and (2) providing a conceptual framework to guide our empirical analysis.

  6. Previous empirical studies that examine the effect of discharge limits on wastewater discharges examine exclusively quantity-based limits (e.g., Brännlund et al. 1995) or examine exclusively concentration-based limits (e.g., Bandyopadhyay and Horowitz 2006; Earnhart 2007).

  7. For the classification of each facility, the EPA calculates a major rating with points assigned on the basis of toxic pollution potential, flow type, conventional pollutant load, public health impact, and water quality impact; the EPA classifies any discharger with a point total of 80 or more as a “major facility”. The EPA also classifies any facility generating a “significant” effect on the receiving water body as a “major facility”.

  8. The chemical industry is not necessarily representative of all industrial sectors. Indeed, its unique attributes contribute to our interest in studying it.

  9. The depicted framework draws heavily upon the framework provided by Earnhart (2010) and Gray and Shadbegian (2005), while reflecting upon frameworks provided by Bluffstone (1999), Barla (2007), and Dasgupta et al. (2000).

  10. However, firms may be unable to anticipate fully future permitted discharge limit levels or firms may not be able to respond to even an anticipated limit before the limit becomes legally binding. Specifically, firms may need more time to plan and implement any response to tighter limits. Regardless of the reason, the connection from limit stringency to profitability might involve a lag. To assess this possibility empirically, we estimate profitability using an alternative regressor set that additionally includes a lagged discharge limit structure: 1-quarter lag, 2-quarter lag, 3-quarter lag, and 4-quarter lag. This lag structure follows Lanoie et al. (2008). Estimation results from this alternative specification support our claim that discharge limits affect a firm’s profitability without a lag. Specifically, the lagged limit coefficients do not prove statistically significant when considered jointly using a Wald Test. Moreover, no individual coefficient proves significant.

  11. Even though our choice of government monitoring inspections is consistent with the most relevant previous study, inspections represent only one component of regulatory scrutiny. Certainly, we could explore other measures of the regulatory scrutiny applied by environmental protection agencies to induce compliance with imposed discharge limits. As the most obvious dimension, we could explore the count or monetary value of any sanctions imposed for non-compliance with imposed discharge limits. However, we purposively avoid sanctions for three reasons. First, sanctions are more vulnerable to concerns of endogeneity. Second, by extracting money from firms, sanctions directly lower profits by raising costs in a way that is not related to innovation offsets, which complicates our testing of the Porter hypothesis. Third and most important, sanctions are imposed on a small proportion of permitted facilities, consistent with the reasonably strong prevalence of compliance. In contrast, inspections are conducted at facilities regularly. Therefore, we are able to garner much more variation in our sample data by focusing on inspections.

  12. Online Appendix Table A-1 provides distributions of the pollutant-basis-specific discharge limits.

  13. To construct one alternative measure of legal stringency, we divide each of the monthly discharge limits by the median discharge limit when considering all populated months and facilities. Use of this alternative measure generates estimation results nearly identical to those reported below. In particular, the Model Set C estimates offer identical statistical significance since the means and medians only scale the coefficient magnitudes. To construct a second alternative measure of legal stringency, we divide each of the monthly discharge limits by the mean discharge limit generated by averaging across firms. Use of this second alternative measure generates estimation results highly similar to those displayed below; these alternative results support conclusions identical to those reported below.

  14. We assess whether or not our chosen replacement values are the “best” values. For each pollutant-basis-specific limit, we generate an indicator that takes a value of one when we use a limit replacement; this indicator equals zero otherwise. We include these four indicators as regressors in our econometric analysis. If these indicators prove statistically significant, then our replacement values are not “best”. These indicators never prove significant in our econometric analysis.

  15. This construction involves three issues worthy of discussion. First, this regressor construction imposes the restriction that the influence of each quarter’s count of government monitoring inspections is equal across the four individual quarters. This restriction represents a joint null hypothesis of equal coefficients. We test this hypothesis by incorporating four lagged individual quarter regressors into an alternative specification and implementing a Wald test to assess the joint null hypothesis. The test statistic fails to reject the null hypothesis so use of a composite measure appears statistically appropriate. Second, the regressor makes no distinction between state and federal inspections, consistent with Brunnermeier and Cohen (2003) and most previous studies of wastewater discharges (e.g., Laplante and Rilstone 1996; Helland 1998). Third, several previous studies of wastewater discharges use a similar approach for explaining the influence of inspections on facilities’ chosen discharges (e.g., Earnhart 2004b, 2009; Laplante and Rilstone 1996; Helland 1998).

  16. Since one might expect that the relationship between environmental regulation and profitability depends on ownership structure, we do not claim that our results generalize to privately owned firms.

  17. As an alternative measure, for each firm, we calculate the proportion of facilities inspected at least once over the preceding 4-quarter period. Use of this alternative measure generates highly similar estimation results in general; however, the alternative estimates offer less evidence of a link from monitoring scrutiny to profitability especially when the specification includes an interaction term (Model C).

  18. The online appendix explores the sub-sample of firms without a primary SIC code in the chemical industries.

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Acknowledgments

This manuscript was partially developed under a STAR Research Assistance Agreement No. R-82882801-0 awarded by the U.S. Environmental Protection Agency. It has not been formally reviewed by the EPA.

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The views expressed in this document are solely those of the authors. The EPA does not endorse any products or commercial services mentioned in this manuscript. Moreover, this manuscript and the analysis herein were developed prior to and independent of the author’s employment with the Bureau of Economic Analysis (BEA). The views expressed in this manuscript are solely those of the authors and not necessarily those of the U.S. Department of Commerce or BEA.

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  1. Department of Economics, University of Kansas, 436 Snow Hall, 1460 Jayhawk Blvd, Lawrence, KS, 66045, USA

    Dietrich Earnhart

  2. U.S. Department of Commerce, Bureau of Economic Analysis, Washington, DC, USA

    Dylan G. Rassier

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  1. Dietrich Earnhart
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Correspondence to Dietrich Earnhart.

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Earnhart, D., Rassier, D.G. “Effective regulatory stringency” and firms’ profitability: the effects of effluent limits and government monitoring. J Regul Econ 50, 111–145 (2016). https://doi.org/10.1007/s11149-016-9304-8

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