Effects of environmental regulation on actual and expected profitability

Ecological Economics 112 (2015) 129–140

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Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon

Analysis

Effects of environmental regulation on actual and expected profitability☆ Dylan G. Rassier a,⁎,1, Dietrich Earnhart b a b

U.S. Department of Commerce, Bureau of Economic Analysis, Washington, DC 20230, United States University of Kansas, Department of Economics, Lawrence, KS 66045, United States

a r t i c l e

i n f o

Article history: Received 14 December 2012 Received in revised form 6 February 2015 Accepted 12 February 2015 Available online xxxx Keywords: Porter hypothesis Regulation Firm performance Regulated industries Chemical industry

a b s t r a c t The Porter hypothesis asserts that properly designed environmental regulation motivates firms to innovate, which ultimately improves profitability. In this study, we test empirically the Porter hypothesis and the competing hypothesis that regulation undermines profitability (“costly regulation hypothesis”). In particular, we estimate the effect of clean water regulation, as reflected in the stringency of firm-specific effluent limits for two regulated pollutants, on the profitability of chemical manufacturing firms. As our primary contribution, we contrast the effect of clean water regulation on actual profitability outcomes and its effects on investors' expectations of profitability. Our results for actual profitability are consistent with the Porter hypothesis, while our results for expected profitability are consistent with the costly regulation hypothesis. Thus, our empirical results demonstrate that investors do not appear to value the positive effect of tighter clean water regulation on actual profitability. Published by Elsevier B.V.

1. Introduction Opposing theoretical arguments exist regarding the effect of environmental regulation on profitability. Porter and van der Linde (1995) assert that properly designed environmental regulation motivates firms to innovate, which ultimately improves profitability. As long as firms perceive their production processes and products as elements in a dynamic setting rather than a static setting, firms seize regulation as an opportunity to invest in technologies and techniques that not only minimize strains on the environment but also maximize the efficiency of production processes and/or improve the quality of products. The result is lower costs and/or higher revenues. This argument has become known as the Porter hypothesis. The Porter hypothesis contradicts conventional wisdom, as articulated by studies such as Palmer et al. (1995). While critics of the Porter hypothesis concede that regulation does sometimes lead to cost savings or quality improvements and that firms do not always operate as

☆ This manuscript was developed under STAR Research Assistance Agreement No.R82882801-0 awarded by the U.S. Environmental Protection Agency (EPA). The EPA does not endorse any products or commercial services mentioned in this manuscript. ⁎ Corresponding author. E-mail addresses: [email protected] (D.G. Rassier), [email protected] (D. Earnhart). 1 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.

http://dx.doi.org/10.1016/j.ecolecon.2015.02.011 0921-8009/Published by Elsevier B.V.

efficiently as might be possible, the critics reject the notion that firms systematically operate inefficiently, arguing that firms voluntarily seek opportunities to improve profitability regardless of regulation. In particular, critics claim that environmental regulation generally undermines firms' abilities to pursue opportunities to improve profitability. We identify this opposing argument as the “costly regulation” hypothesis. In this study, we test these two hypotheses by jointly assessing the effects of environmental regulation on two different aspects of profitability: actual profitability and expected profitability. Three previous studies explore one of these two aspects (Rassier and Earnhart, 2010a, 2010b, 2011), but no previous study explores the two aspects jointly. To capture actual profitability, we use an accounting-based measure of profitability, return on sales (i.e., profits divided by sales), which reflects results reported in a firm's financial statements. To capture expected profitability, we use a market-based measure of financial performance, Tobin's q (i.e., market value divided by replacement costs). This measure reflects investors' current expectations of profitability according to the discounted present value of a firm's future stream of profits, as demonstrated in the dividend discount model, which is based on efficient market theory. As our measure of environmental regulation, we use permitted wastewater discharge limits for two regulated pollutants — biochemical oxygen demand (BOD) and total suspended solids (TSS) — that are imposed on individual facilities according to statelevel and industry-level criteria pursuant to the Clean Water Act (hereafter “clean water regulation”). While not necessarily one of the primary factors driving profitability, permitted discharge limits may have the potential to meaningfully

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influence profitability. Permitted discharge limits are commonly cited directly or indirectly as a risk factor in chemical firms' annual reports.2 As important, pollution abatement and control expenditures are substantial. As a share of U.S. gross domestic product, total expenditures for pollution abatement and control were approximately 1.8% from the mid-1970s to the mid-1980s. Water pollution expenditures represent a sizable portion of overall expenditures. Specifically, in the chemical manufacturing sector, operating costs for water pollution abatement and control are a relatively large component of overall operating costs for pollution abatement and control. The share was approximately 30% in 1999 and 2005.3 Our analysis in this study contributes to the economic literature that studies the effects of environmental regulation on various aspects of firms such as innovation, financial performance, employment, productivity, investment and location decisions, and costs. In particular, our analysis in the present study builds on Rassier and Earnhart (2010a, 2010b, 2011). These previous studies examine the effect of environmental regulation on one, but not both, of the financial performance measures used in the present study. Moreover, relative to the present study, these previous studies use different data frequencies, panel estimators, regressor sets, and parameterizations of environmental regulation. Our primary contribution in this study is three-fold: (1) present a side-by-side comparison of the effects of environmental regulation on the return on sales and Tobin's q, (2) link these two outcomes by identifying return on sales as a measure of actual profitability and interpreting market value in Tobin's q as investors' current expectations of future profitability, and (3) employ behavioral finance theory for explaining the differences between the two sets of estimation results based on this identification of return on sales and interpretation of market value. Thus, the value added of our current study is not the exploration of new outcomes but the joint assessment of the two related outcomes. To strengthen our analysis, our study utilizes a panel data set. Thus, we are able to control more completely for heterogeneity across firms and exploit both inter-firm and intra-firm variation. Our empirical results indicate that tighter clean water regulation (i.e., lower permitted discharge limits for BOD and TSS) generates higher returns on sales for chemical firms. In particular, a 10% decrease in an average firm's permitted discharge limit increases the average firm's return on sales by approximately 2%. In contrast, tighter clean water regulation reduces Tobin's q for chemical firms. A 10% reduction in the average firm's permitted discharge limit prompts a decrease of approximately 0.0076% in the average firm's Tobin's q ratio, which reflects a decrease in market value of approximately $1.8 million. Our results for actual profitability are consistent with the Porter hypothesis, while our results for expected profitability are consistent with the costly regulation hypothesis. In particular, investors in chemical firms do not appear to value the positive effect of tighter clean water regulation on actual profitability. Instead, investors appear to expect a negative effect from tighter regulation. The difference in the effects of clean water regulation on actual profitability and Tobin's q are inconsistent with the Porter hypothesis and efficient market theory. In order to explore and interpret these differences more thoroughly, we employ insight from the behavioral finance literature, which uses “irrationalities” to explain investors' decisions.

2. The Effects of Environmental Regulation on Profitability This section briefly describes the theories that seek to explain the influence of environmental regulation on profitability and the empirical studies that explore this influence. 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. Bureau of Economic Analysis and the U.S. Census Bureau.

2.1. Opposing Theoretical Arguments Porter and van der Linde (1995) argue that properly designed and implemented environmental regulation ultimately improves profitability. In particular, environmental regulation removes the organizational inertia that impedes innovation. Once this inertia is removed, firms improve their resource productivity. Thus, firms seize regulation as an opportunity to develop and employ technologies and techniques that improve the efficiency of production processes and/or the quality of products. The former improvement decreases production costs and the latter improvement increases revenues.4 This argument represents the thrust of the “Porter hypothesis”. From an economic point of view, the Porter hypothesis contradicts conventional wisdom. Consistent with this conflict, some economists question the validity of the Porter hypothesis. In particular, Palmer et al. (1995) reject Porter and van der Linde's (1995) broad assertion that environmental regulation removes organizational inertia by providing firms with information and incentives that competitive markets somehow systematically fail to provide. Instead, Palmer et al. (1995) posit that firms in general voluntarily seek profit-increasing opportunities regardless of regulation. This general claim aside, Palmer et al. (1995) concede that tighter environmental regulation may sometimes lead to cost savings because firms do not always operate efficiently or may lead to quality improvements because firms do not always fully appreciate market opportunities. Rather than a catalyst, environmental regulation generally serves only to constrain firms' abilities to pursue profit-increasing opportunities. As one specific consequence, firms facing more stringent regulation incur higher treatment costs (hereafter the “costly regulation hypothesis”). Additional studies explore the theoretical feasibility of the Porter hypothesis. In general, the studies rely on market failures to achieve outcomes predicted by the Porter hypothesis (Lanoie et al., 2011). Simpson and Bradford (1996) build a model that supports the Porter hypothesis when firms operate in imperfectly competitive markets. Ambec and Barla (2002) and Gabel and Sinclair-Desgagné (2002) demonstrate the validity of the Porter hypothesis in the presence of systematic organizational failures. Jaffe et al. (2005) and Mohr (2002) obtain theoretical outcomes consistent with the Porter hypothesis in cases of knowledge spillovers. King (1999, 2000) and King and Lenox (2002) explore particular aspects of the organizational behavior underlying the Porter hypothesis. Finally, Xepapadeas and de Zeeuw (1999) use a model of vintage capital to show that an emissions tax negatively affects profits. 2.2. Empirical Literature Innovation is the first outcome in a series of three outcomes through which environmental regulation ultimately improves profitability according to the Porter hypothesis. The second outcome is measurable cost savings or revenue enhancements (i.e., “innovation offsets”). The third outcome is improved financial performance. While no empirical study comprehensively assesses all three outcomes, several studies empirically examine one of the three outcomes or related outcomes. Thus, we identify the studies in four sets. The first set of studies explores the effect of environmental regulation on innovation (Arimura et al., 2007; Brunnermeier and Cohen, 2003; Burtraw, 2000; Gray and Shadbegian, 1998, 2003; Jaffe and Palmer, 1997; Johnstone and Labonne, 2006; Lanoie et al., 2011; Nelson et al., 1993; Popp, 2010). A second set of studies looks at price premiums and costs, which can be roughly interpreted as innovation offsets (Ambec and Barla, 2006; Bjorner et al., 2004; Gray, 1987; Hazilla and Kopp, 1990; Jorgenson and Wilcoxen, 1990; Roe et al., 2001; Teisl et al., 2002). A third set of 4 In Porter and van der Linde's (1995, page 101) own words, properly designed regulation can lead to greater profitability because it induces “innovation offsets [that] can exceed the costs of compliance”.

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studies explores the effect of environmental regulation on financial performance (Alpay et al., 2002; Brännlund et al., 1995; Lanoie et al., 2011; Rassier and Earnhart, 2010a, 2010b, 2011). A fourth set of studies assesses the effects of environmental regulation on outcomes that are related to innovation, innovation offsets, or financial performance; these outcomes include productivity, competitiveness, employment, and location (Alpay et al., 2002; Barbera and McConnell, 1990; Berman and Bui, 2001; Dufour et al., 1998; Gollop and Roberts, 1983; Gray, 1987; Gray and Shadbegian, 1998, 2003; Greenstone, 2002; Greenstone et al., 2012; Lanoie et al., 2008; Managi et al., 2005). More detailed reviews of these sets of studies are available in Ambec and Barla (2006) and Ambec and Lanoie (2008). We briefly discuss the third set of studies that explore the effect of environmental regulation on financial performance, which are most closely related to our study. Brännlund et al. (1995) use partially simulated data to study the effect of environmental regulation on the profits of Swedish pulp and paper firms. The authors conclude that most firms in their sample are unaffected by environmental regulation yet some firms experience lower profits with regulation. Brännlund et al. (1995) do not examine either actual profits or investors' expectations of profits. Alpay et al. (2002) examine the effect of pollution regulation on the profitability of Mexican and U.S. food industries. The authors find that U.S. pollution regulation has no impact on profitability, yet Mexico's rising environmental standards reduce profitability. Alpay et al. (2002) do not examine investors' expectations of profitability. Lanoie et al. (2011) use OECD data to test the “weak”, “narrow”, and “strong” versions of the Porter hypothesis as identified by Jaffe and Palmer (1997). The authors find a negative effect of environmental regulation on business performance, which is measured qualitatively based on survey data rather than measured quantitatively based on income statement data. Most closely related to the present study, Rassier and Earnhart (2010a, 2010b, 2011) examine the effect of environmental regulation on profitability or Tobin's q for U.S. chemical manufacturers. The present study examines both of these financial performance measures. The three previous studies yield mixed results using data frequencies, panel estimators, regressor sets, and parameterizations of the environmental regulation factor that differ from the present study. Rassier and Earnhart (2010a) apply a random effect estimator to both annual and quarterly data in order to capture the effect of contemporaneous permitted discharge limits on the return on sales. Thus, the study examines the effect of environmental regulation on an accounting-based measure of financial performance. The authors find that tighter clean water regulation reduces the return on sales, which is robust to type of data frequency: annual and quarterly. Rassier and Earnhart (2011) apply a fixed effects estimator to quarterly data in order to capture the effect of contemporaneous and lagged permitted discharge limits on the return on sales. Thus, the study assesses the short-run and longrun implications of environmental regulation on financial performance. In contrast to Rassier and Earnhart (2010a), Rassier and Earnhart (2011) find that tighter clean water regulation improves return on sales in both the short run and long run, with a stronger effect in the long run. Rassier and Earnhart (2010b) apply both a fixed effects estimator and random effects estimator to annual data in order to capture the effect of contemporaneous permitted discharge limits on Tobin's q. Thus, the study examines the effect of environmental regulation on a market-based measure of financial performance. Rassier and Earnhart (2010b) also decompose Tobin's q into its constituent components and then estimate each component separately to confirm that the link from clean water regulation to Tobin's q stems from changes in market value rather than replacement costs, since the latter link has no theoretical support. The authors find that tighter clean water regulation lowers Tobin's q, which is driven by changes in market value. In contrast to the present study, none of these previous related studies (1) compare the effects of clean water regulation on return on sales and Tobin's q and (2) link the two outcomes by interpreting the market value component in Tobin's q as investors' current expectation of future profitability.

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Other related empirical studies provide conflicting evidence regarding the effects of firms' environmental performance on financial performance (Barth and McNichols, 1994; Darnall et al., 2007; Dasgupta et al., 2001; Filbeck and Gorman, 2004; Gupta and Goldar, 2005; Khanna and Damon, 1999; King and Lenox, 2001; Konar and Cohen, 2001; Russo and Fouts, 1997). Each of the studies focuses on a single measure of financial performance: accounting-based financial performance or market-based financial performance. Accounting-based measures reflect actual financial performance outcomes, while market-based measures reflect investors' expectations of financial performance. We use these studies to identify meaningful factors to explain both actual profitability and investors' expectations of profitability. 3. Actual Profitability and Investors' Expectations of Profitability We use the noted empirical studies to construct an econometric model to estimate the effects of clean water regulation on two aspects of profitability for publicly owned chemical manufacturing firms: actual profitability and investors' expectations of profitability. We capture actual profitability with an accounting-based measure of financial performance; we capture investors' expectations of profitability with a market-based measure of financial performance. Accounting-based financial performance is an internal measure that indicates how effectively a firm utilizes resources to generate profits according to results reported in a firm's financial statements. Thus, accounting-based performance captures actual profitability. In this way, accounting-based performance reflects an ex post rate of return. Market-based financial performance is an external measure that captures the market value of a firm, which arguably reflects investors' current expectations of the discounted present value of the firm's future stream of profits. At least conceptually, given the operation of efficient capital markets, market value — as reflected in stock prices — provides the best available unbiased estimate of the discounted present value of a future stream of profits as demonstrated by Fama (1970). Thus, market-based performance reflects an ex ante expected rate of return to the extent that it captures investors' current expectations of future profitability. Both aspects of profitability are important for our examination of the effect of clean water regulation. We use the estimated effect of clean water regulation on actual profitability as our benchmark since rational investors would use the results from a similar analysis of the estimated effect of clean water regulation on actual profitability in order to inform their investment decisions. In this way, the estimated effect on actual profitability should be reflected in the estimated effect of clean water regulation on investors' expectations of profitability. 3.1. Actual Profitability We select return on sales as our accounting-based measure of actual profitability. This measure is used to evaluate operational efficiency, reflecting how much profit is earned per dollar of sales. By definition, return on sales, denoted ROS, is the ratio of a firm's profits before interest and taxes, denoted Π, to the firm's sales, denoted S: ROS ¼ Π=S:

ð1Þ

We possess panel data on the ROS for firm f at time t, denoted as ROSft. In order to accommodate and exploit the panel structure of these data, we employ a fixed effects estimator.5 Appendix A explains our choice of this panel data estimator. To estimate return on sales, we use a linear specification in which ROSft is a function of these factors: (1) clean water regulation, denoted Rft; (2) relevant variables other 5 Similar to our study, Greenstone et al. (2012) employ a fixed effects estimator when exploiting plant-level panel data in order to estimate the effect of clean air regulation on productivity.

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than clean water regulation, denoted Xft; and (3) a stochastic error term, denoted εft.6 Thus, we estimate the following equation: ROSft ¼ α 0 þ α X X ft þ α R Rft þ εft :

ð2Þ

Firms are aware of future clean water regulatory stringency before it becomes binding because permits are prepared months, if not years, in advance. Thus, clean water regulation should affect a firm's actual profitability without a lag. Therefore, we focus exclusively on the contemporaneous effect of clean water regulation on current actual profitability. While we interpret only the fixed effects estimates of Eq. (2), Appendix A reports additional econometric results based on a pooled OLS estimator and a random effects estimator; see Appendix Tables A.1 and A.2, respectively.

is efficient, arbitrage removes any divergence between the stock price and intrinsic value, implying that P0 = W0. Since the firm pays all earnings as dividends, we merely replace Dt with Pt so that P0 depends on profits. Since Pt represents future profits, Pt must reflect investors' expectations of future profits.9 As with actual profitability, we possess panel data on Tobin's q for firm f in time period t, denoted as qft. In order to accommodate and exploit the panel structure of these data, we again employ a fixed effects estimator. Appendix A explains our choice of this panel data estimator. We estimate Tobin's q using a semi-log specification in which the natural log of Tobin's q is a function of the same factors used to explain return on sales: (1) clean water regulation, (2) relevant variables other than clean water regulation, and (3) a stochastic error term, denoted μft.10 The following equation depicts this functional relationship:

3.2. Investors' Expectations of Profitability We select Tobin's q as our measure of investors' expectations of future profitability. By definition, Tobin's q, denoted q, is the ratio of a firm's market value, denoted V, to the replacement costs of the firm's assets, denoted A: q ¼ V=A:

ð3Þ

Given this construction, Tobin's q can be used to evaluate the value of the firm as given by markets against the firm's value as reflected in its assets. By scaling market value by the replacement costs of a firm's assets, Tobin's q facilitates comparisons across firms of different sizes. Based on an assumption of efficient capital markets, market value measures the discounted present value of future profit streams as determined by investors, as does Tobin's q by extension.7 Since the future profit streams are not yet realized, market value and Tobin's q must depend on investors' expectations of future profitability. As long as actual profitability may depend on environmental regulation, then investors' expectations of profitability should also depend on environmental regulation since market value reflects expectations of future profitability. According to efficient market theory, investors should be able to process all publicly available information, which includes information on environmental regulation. Strong evidence supports this semi-strong form of the efficient market hypothesis, as described by Bodie et al. (2008).8 In order to connect investors' cash flows to profits, we construct a simple dividend discount model, which explains the determination of a firm's stock price and thus market value (Bodie et al., 2008). Assume that a firm pays all its earnings (i.e., profits) as dividends. If no earnings are reinvested into the firm, then the capacity of the firm to generate additional earnings, and thus dividends, through an expanded capital stock does not grow. Thus, the firm is only able to maintain the same flow of constant dividends. To identify the stock price, we define the following notation: Pt denotes the stock price of the firm in period t, Wt denotes the intrinsic value of the firm in period t, Dt denotes the dividends paid by the firm in period t, Πt denotes the profits (i.e., earnings) generated by the firm in period t, and κ denotes the investor's required rate of return (i.e., market capitalization rate). When the firm maintains the same dividend flow, the intrinsic value of the firm in time period 0, t W0, equals the following: W0 = ∫∞ t = 1Dt/[(1 + κ)e ]. If the stock market 6 Khanna and Damon (1999) conclude that linear specifications are best for estimating rates of return. The presence of negative return on sales prevents the use of semi-log and log-linear specifications. 7 Konar and Cohen (2001) use Tobin's q as their primary measure of firm-level financial performance. 8 As the best evidence, Busse and Green (2002) and Patell and Wolfson (1984) provide studies of intraday prices that indicate rapid responses to new information.

  ln qft ¼ β0 þ βX X ft þ βR Rft þ μ ft :

ð4Þ

Similar to actual profitability reflected in Eq. (2), we interpret only the fixed effects estimates of Eq. (4); nevertheless, Appendix A reports additional econometric results based on a pooled OLS estimator and a random effects estimator; see Appendix Tables A.1 and A.2, respectively.11 4. Data For our empirical analysis, we gather data on clean water regulation and financial performance for chemical manufacturers operating during the sample period of January 1995 to June 2001. 4.1. Data on Clean Water Regulation As our measure of clean water regulation, we use the permitted wastewater discharge limits that are imposed by the Environmental Protection Agency (EPA) or authorized state regulatory agencies on major chemical manufacturing facilities regulated as point sources within the National Pollutant Discharge Elimination System (NPDES) Program.12 The NPDES program is authorized by the Clean Water Act to control water pollution by issuing to facilities permits that specify discharge limits. These facility-specific discharge limits strongly represent the primary means of controlling water pollution from point sources. Thus, any link from clean water regulation to profitability should stem from the stringency of discharge limits. A link may be expected since chemical manufacturing firms' annual reports commonly identify 9 This simple model ignores a firm's ability to reinvest earnings. A slightly more refined model, the constant growth dividend discount model, incorporates earnings reinvestment and dividend growth. Regardless of reinvestment, the implications are the same: future profits are not yet realized so the stock price depends on investors' expectations of future profits. 10 Hirsch and Seaks (1993) demonstrate that semi-log specifications are best for estimating Tobin's q. We do not explore a log–log specification since some of the regressors take zero values. We do not explore a linear specification since it does not facilitate a clean decomposition of Tobin's q into its constituent components, which is described below. 11 By employing a semi-log specification, we are able to decompose Tobin's q cleanly into its two constituent components — market value and replacement costs — and separately regress each component on the same set of explanatory factors used to estimate Tobin's q. In this way, we are able to confirm that regulation truly affects the market value of a firm and, thus, investors' current expectations of future profitability, rather than affecting replacement costs. This confirmation is important because none of the theoretical arguments claim that regulation should affect replacement costs, which is used here as a scaling factor. 12 Major facilities are distinguished from minor facilities by the EPA as those with a significant effect on receiving water or a high major rating code. Only minimal information is available on minor facilities.

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discharge limits and other forms of environmental regulation as operating risk factors.13 4.1.1. Permitted Discharge Limit Determination Permit writers consider two standards to determine a permitted discharge limit: (1) the state water quality-based standard and (2) the Effluent Limitation Guideline standard. After a limit is determined under each standard, the more stringent limit is written into the permit. The state water quality-based standard is designed to confirm that the facility's discharges do not cause the water body's ambient concentration of the relevant pollutant to exceed the state's acceptable level. Effluent Limitation Guideline standards establish a uniform upper bound on limits within a given industry across the entire United States. All of the facilities in our sample operate in industries covered by Effluent Limitation Guidelines. Thus, state-level or industry-level criteria dictate the establishment of facility-specific discharge limits.14 State water quality standards differ across water bodies and time. Moreover, the discharge limits identified by any given state water quality-based standard differ across facilities and time since the assimilative capacity of water bodies differs across location and time. To illustrate the spatial variation, a facility located on a water body given greater protection and/or containing higher background pollution faces a tighter discharge limit than a facility located elsewhere. Likewise, effluent limits differ across facilities because Effluent Limitation Guidelines differ across industries and limits differ across time to the extent that Effluent Limitation Guidelines change over time.

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to construct a regulatory measure to focus on variation in clean water regulation generally rather than a specific pollutant or specific discharge basis, we combine the potential four permitted discharge limits into a single composite permitted discharge limit. We construct the composite permitted discharge limit for each facility in each month. First, we divide each of the potential monthly permitted discharge limits — LBQ, LBC, LTQ, and LTC — by the average permitted discharge limit across all populated months and facilities, denoted L BC

TQ

13 As examples, we review the 2001 annual reports of four representative chemical firms: Dow Chemical, E.I. Du Pont de Nemours, Rohm & Haas, and Mississippi Chemical. All four reports refer generally to compliance with environmental regulation or permitted limit levels as a risk factor. In addition, the Mississippi Chemical report cites capital expenditures for environmental compliance, and the Dow Chemical and E.I. Du Pont de Nemours reports cite the status of lawsuits generated as a result of alleged unpermitted releases of wastewater. 14 We confirmed this point with Deborah Nagle, an official in the EPA Water Permits Division (May 10, 2008). 15 Previous empirical studies that examine the effect of permitted effluent 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).

,

TC

L , L , and L , for BOD quantity, BOD concentration, TSS quantity, and TSS concentration, respectively.16 The resulting monthly scaled perBQ

mitted discharge limits are denoted as follows: LBQ S ¼ LBQ =L BC

TQ

, LBCS ¼

TC

LBC =L , LTQS ¼ LTQ =L , and LTCS ¼ LTC =L . By dividing each type of discharge limit by the pollutant-specific and basis-specific average, the scaled discharge limits simply represent ratios: the stringency of a particular facility's limit during a specific month against a particular pollutant (TSS or BOD) measured on a specific basis (quantity or concentration) relative to the sample's average for this particular pollutant and specific basis. Since the ratios are unit-less, we are able to combine the monthly scaled effluent limits between the two bases — quantity and concentration — by averaging the scaled permitted discharge limits between the two bases for each pollutant. The resulting averages are denoted as fol    BS TS lows: L ¼ LBQ S þ LBCS =2 and L ¼ LTQ S þ LTCS =2, for BOD and TSS, respectively. As with the scaled monthly limits, these resulting BS

4.1.2. Constructing a Regulatory Measure We construct a measure of clean water regulation using monthly permitted discharge limits for two regulated pollutants — biochemical oxygen demand (BOD) and total suspended solids (TSS) — as provided by the EPA's Permit Compliance System (PCS) database. BOD and TSS provide a good generalization for other regulated pollutants for three reasons. First, they are both conventional pollutants, which receive the bulk of regulatory scrutiny. Second, all previous studies of wastewater discharges examine BOD, TSS, or both (e.g., Earnhart, 2004; Laplante and Rilstone, 1996). Third, TSS is the most prevalent pollutant in our sample, and BOD is the second most prevalent. Permitted discharge limits are based on either the quantity or the concentration of pollutants. Quantity-based limits identify the absolute amount of pollutant discharges allowed (e.g., pounds per day). Concentration-based limits identify the amount of pollution allowed to be discharged relative to the volume of treated wastewater (e.g., milligrams per liter).15 A major chemical manufacturing facility may face either quantity-based limits or concentration-based limits or both quantity-based and concentration-based limits. We retain information for both quantity-based limits and concentration-based limits because we do not know whether one discharge basis is more restrictive than the other. Given the inclusion of two pollutants with two possible discharge bases, we assess potentially four different permitted discharge limits for each facility and month: BOD quantity-based limit, denoted LBQ, BOD concentration-based limit, denoted LBC, TSS quantity-based limit, denoted LTQ, and TSS concentration-based limit, denoted LTC. In order

BQ

TS

pollutant-specific averages — L and L — represent ratios: the stringency of a particular facility's limit during a specific month against a particular pollutant (TSS or BOD) relative to the sample's average for this particular pollutant. Since the ratios are again unit-less, we are able to combine the pollutant-specific limit measures — TSS and BOD — by averaging the pollutant-specific limit measures between the two pollutants. The resulting average represents our single composite limit, denoted LS, which provides a unit-less measure of clean water regulation by comparing the stringency of limits at one facility in a given month relative to the stringency of limits at all facilities in the sample. Thus, a lower “relative” permitted discharge limit indicates more stringent clean water regulation. This scaled composite limit measure allows our analysis to assess the effects of clean water regulation generally without reference to a specific pollutant or particular basis. To justify the use of a single composite limit, we estimate financial performance using the four separate permitted discharge limits as regressors and then conduct Wald tests to assess the joint equality of the coefficients associated with the four individual scaled permitted discharge limits. The Wald test statistics fail to reject the joint null hypothesis of equal coefficients, implying that our chosen approach is statistically legitimate. In other words, use of the four individual limit measures generates coefficient magnitudes that are statistically similar to those generated by the single composite measure. By focusing on the single composite limit measure, we are able to tighten our estimates of the effects of clean water regulation on actual and expected profitability. Moreover, the use of individual pollutant-basis-specific stringency measures demands that we replace missing values with acceptable replacement values in those cases where one or more of the limit types is not relevant, e.g., a facility faces only TSS limits but not BOD limits. While protocols exist for replacing missing values, these protocols introduce their own set of issues. The composite index avoids these issues by simply averaging across the relevant limit ratios. While our choice to employ a single composite measure of regulatory stringency dominates the use of four individual pollutant-basisspecific measures, we acknowledge studies that use individual

BQ

16 Averages across all populated months and facilities are as follows: L = 805 lb per BC TQ TC day, L = 27.8 mg per liter, L = 1199 lb per day, and L = 38.6 mg per liter.

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measures. Most recently, Greenstone et al. (2012) use county-level attainment and non-attainment designations for four individual pollutants regulated under the U.S. Clean Air Act — carbon monoxide, tropospheric ozone, sulfur dioxide, and total suspended particulates — to study the effect of clean air regulation on productivity. In this case, regulatory stringency may be specific to a given pollutant. Thus, a county can be in attainment regarding one or more pollutants yet out of attainment regarding one or more other pollutants. Since both a composite regulatory measure and individual regulatory measures may be informative for capturing regulatory stringency, Greenstone et al. (2012) explore both types of measures and interpret both sets of empirical results. However, Greenstone et al. (2012) emphasize caveats related to the interpretation of results for the individual regulatory measures. Consequently, the authors base their primary conclusions on the results generated by using the composite regulatory measure.17 In addition to the use of individual measures of regulatory stringency, we acknowledge alternative constructions of composite measures. Most recently, Horváthová (2012) combines individual measures of multiple pollutant-specific emission quantities into a single environmental performance index by normalizing each emitted amount of pollution according to the reporting threshold offered by the European Pollutant Release and Transfer Register (EPRTR) before summing the normalized levels. This highly useful construction is not available in our context since the NPDES system constraining wastewater discharges from U.S. point sources does not provide reporting thresholds. As important, our study seeks to measure regulatory stringency by exploiting data on effluent limits, while Horváthová (2012) seeks to measure environmental performance by exploiting data on emissions. One would not expect reporting thresholds on effluent limits since the limits themselves represent legal thresholds. Of lesser importance, the reporting thresholds embedded within EPRTR apply only to quantity terms. Yet, for our construction of a composite measure, we must normalize between pollution measured in quantity terms and pollution measured in concentration terms. Other similar studies also construct composite indices that combine multiple pollutant measures (Freedman and Jaggi, 1992; Telle, 2006; Wagner, 2005; Wagner et al., 2002). Freedman and Jaggi (1992) offer an approach that is utilized by later studies. Their approach normalizes the pollutant-specific measures by dividing each pollutant-specific quantity of emissions by the “best value” found in the sample used for analysis. For nearly all pollutants, the best value is the minimum value. In contrast, our approach uses the average value found in our sample. These two different approaches generate identical results as long as the ratio of average value and minimum value is equal across all parameters addressed in the index construction. In the case of Freedman and Jaggi (1992), the index combines multiple pollutants; in our case, the index combines both multiple pollutants and multiple bases. For simplicity, consider the case of combining TSS discharges in quantity terms and BOD discharges in quantity terms. The two index approaches generate identical results as long as the TSS discharge averageto-minimum ratio equals the BOD discharge average-to-minimum ratio. Of course, these ratios most likely are not identical across pollutants and bases. In these cases, our approach, relative to the Freedman and Jaggi (1992) approach, places greater emphasis on the pollutant or basis (i.e., quantity versus concentration) where the mean-tominimum ratio is smaller so that the weighting factor (i.e., inverse of the scaling factor) is greater. Still, we respectfully claim that use of a

17 Greenstone et al. (2012) identify two caveats. First, each pollutant-specific effect reflects a change in the single pollutant's regulatory designation while holding fixed the other pollutants' regulatory designations. Second, an emitter of multiple pollutants experiences larger productivity effects based on the composite regulatory measure than based on the individual regulatory measures if the emitter's county is designated as non-attainment for multiple pollutants. In other words, the use of individual regulatory measures confounds interpretation of the overall empirical results.

sample average dominates use of a sample minimum since the average is more representative of a sample than the smallest value, which is likely to be an outlier. The approach of Freedman and Jaggi (1992) is similar to our approach in that it relies upon sample values to normalize the pollutantspecific quantities. Thus, the normalization might be sensitive to the particular distribution of quantities observed in a particular dataset. In contrast, the approach by Horváthová (2012) utilizes references that are common to any sample drawn from the EPRTR and arguably any sample of data on pollutants that are recorded in the EPRTR. Any of these concerns prove moot in two circumstances. As the first circumstance, the stringency reflected in any pollutant-specific effluent limit is sufficiently similar to the stringency reflected in the limits of all other individual pollutants across the regulated polluters within the sample. Put differently, in terms of our sample parameters, the stringency reflected in TSS discharge limits is very strongly correlated with the stringency reflected in BOD discharge limits. In this case, the stringency reflected in any composite limit index, regardless of its construction, is sufficiently captured by any one pollutant limit's stringency. Our data reveal that TSS and BOD limits are very strongly correlated (i.e., N 0.90). As the second circumstance, the effect of regulatory stringency on profitability does not vary across the various pollutant-basisspecific measures. As noted above, our empirical results support this point.18 4.1.3. Permitted Discharge Limits and the Porter Hypothesis Permitted discharge limits are a particularly useful measure of clean water regulation because they represent a performance-based standard rather than an input-based standard. The Porter hypothesis does not claim that any type of environmental regulation improves profitability. Instead, the Porter hypothesis enunciates that a well-designed regulation improves profitability. Porter and van der Linde (1995) explicitly offer performance-based standards as examples of well-designed regulation. Thus, our assessment of a performance-based standard represents a proper test of the Porter hypothesis. If permitted discharge limits depend on a firm's financial condition, facility-level wastewater discharges, or a facility's ability to control discharges (i.e., cost of compliance), we would misinterpret the effect of clean water regulation on financial performance because permitted discharge limits would not be exogenously determined. However, the process of determining permitted discharge limits indicates that permitted discharge limits are highly unlikely to depend on financial performance, wastewater discharges, or ability to control discharges. Permitted discharge limit levels are determined by Effluent Limitation Guidelines, which apply uniformly across all facilities within a particular industry, or by state water quality standards, which clearly are not based on financial performance, wastewater discharges, or ability to control discharges.19 Thus, we claim that permitted limits imposed on regulated facilities are exogenous with respect to the profitability of the firms owning the regulated facilities. However, we acknowledge that some previous studies demonstrate that environmental protection agencies, such as the 18 For a deeper discussion on the construction of indices designed to measure environmental regulatory stringency, see Brunel and Levinson (2013), especially pages 6–8 and 20–22; for a deeper discussion on the construction of indices designed to measure environmental performance, see Telle (2006), especially pages 203–205. 19 For both Eqs. (2) and (4), we do not control for firms that exit in response to changes in discharge limits, which may yield benefits for surviving firms. However, a change in discharge limits in one quarter is unlikely to cause exits that contemporaneously benefit the surviving firms. In addition, the chemical manufacturing industries are sufficiently competitive to prevent firms from extracting monopoly rents as demonstrated by statistics from the U.S. Census Bureau. In particular, the Concentration Ratios in Manufacturing, published from the 1997 Economic Census (the only year in our sample for which Economic Census statistics are available), indicates that the 4-, 8-, 20-, and 50-firm concentration ratios for the chemical manufacturing industry group are 11.9, 18.2, 32.7, and 50.8, respectively. Finally, our analysis controls for the degree of competition by including industry concentration as a regressor.

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U.S. EPA, might shape their decisions regarding regulatory stringency in response to pressure from firms and local communities. For example, Earnhart (2004) demonstrates that local community characteristics influence wastewater-related inspection decisions; these demonstrated links might reflect local community pressure. In the case of pressure from firms, these efforts to influence regulatory stringency decisions may be driven by the firms' profitability, including expected future profitability. Moreover, we acknowledge that environmental protection agencies might adjust the setting of effluent limits without explicit pressure from firms so that more profitable firms face tighter limits and less profitable firms enjoy looser limits. While these possibilities exist, we are not aware of any study that provides any supporting evidence, consistent with the legal constraint identified above. Similarly, we acknowledge that previous theoretical studies explore regulatory trading, for example, across multiple regulations or multiple mediaspecific environmental protection laws imposed on regulated facilities (Heyes and Rickman, 1999; Maxwell and Decker, 2006). This type of regulatory trading might apply to the setting of effluent limits. Again, while this possibility exists, we are not aware of any study that provides any supporting evidence, consistent with the legal constraint identified above, i.e., permit writers are not granted the discretion to perform this type of trading.

4.2. Data on Profitability and Related Explanatory Factors In addition to the data on clean water regulation, we gather data on profitability and factors that are shown by previous studies to explain profitability. We must limit our analysis to firms with publicly available financial data, which is available from Standard & Poor's Compustat Research Insight©. Research Insight© contains annual and quarterly data from financial statements for publicly owned firms traded on stock exchanges in the United States. We focus on quarterly data for two reasons. First, quarterly data are more likely than annual data to capture fluctuations in short-run financial performance. Second, fluctuations captured by quarterly data may be important for linking to month-to-month variation in wastewater discharge limits. We gather the quarterly data for the sample period: first quarter of 1995 to second quarter of 2001. Our accounting-based measure of actual profitability is return on sales as defined in Eq. (1). To construct return on sales, we use profits before interest and taxes and sales less returns and allowances (i.e., net sales) from firms' income statements. Our market-based measure of expected profitability is Tobin's q as defined in Eq. (3). To construct Tobin's q, we use a formulation based on firms' balance sheets (Konar and Cohen, 2001). For the market value of debt and equity securities, we add debt values, the par value of preferred stock,20 and the market value of common stock, as calculated by multiplying the quarter-end price by the number of shares outstanding. For the replacement costs of assets, we add net property, plant, and equipment, cash and equivalents, total receivables, and total inventories.21 Given that return on sales reflects actual profitability and Tobin's q reflects expected profitability, we include as regressors the following factors that explain profits: (1) a firm's sales growth, (2) a firm's capital intensity, (3) age of a firm's assets, (4) a firm's size, (5) a firm's current 20

The market value of preferred stock is unavailable. We acknowledge that using balance sheet data to estimate Tobin's q raises measurement concerns because active markets do not exist for used capital goods and book values based on reported depreciation may not reflect replacement costs (Konar and Cohen, 2001). However, results from a pair of studies demonstrate that these concerns appear unfounded. As the first study, Lindenberg and Ross (1981) estimate Tobin's q for firms that were required by the SEC to report replacement costs in annual reports for the period 1976 to 1985. As the subsequent study, Chung and Pruitt (1994) use accounting measures to estimate Tobin's q and compare the results to Lindenberg and Ross (1981). The second set of authors finds that their formulation of Tobin's q explains at least 96.6% of the variation in the Lindenberg and Ross (1981) formulation. 21

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ratio, (6) a firm's research and development intensity, (7) a firm's market share, and (8) concentration of the industry in which a firm operates. Appendix B explains how each of these factors is connected to profitability based on the factor's use in previous studies of financial performance. 4.3. Matching Environmental and Financial Data The environmental data are recorded at the facility level with a monthly frequency. The financial data are recorded at the firm level with a quarterly frequency for firms in all industries. We match the facility-level environmental data to the firm-level financial data by year using the firm-level name, which is available from the EPA's Toxic Release Inventory (TRI) database. To resolve the frequency difference, we aggregate the environmental data across months to a quarterly frequency. To resolve the potential level difference, we average the permitted discharge limit across commonly owned facilities within a firm to the firm level. Our objective is to link the stringency of environmental regulation, as reflected in wastewater discharge limits imposed on individual facilities, to a firm's profitability. However, not all of a firm's facilities may face wastewater discharge limits. Moreover, a firm may not own only chemical manufacturing facilities. Put differently, the financial data reflect activity from all facilities, but our environmental data reflect activity only from major chemical manufacturing facilities permitted to discharge wastewater. To assess the coverage of our environmental data, for each firm we count the number of chemical manufacturing facilities permitted to discharge wastewater, as reflected in the PCS database, and we count the number of facilities recorded in the TRI database. We then calculate the ratio of these two counts: PCS count/TRI count. Across all sample years, the average ratio is 0.494, with a standard deviation of 0.354. Thus, our data capture a near majority of all TRI facilities for the average firm. Despite this relatively high average ratio, we further refine our data in order to tighten our coverage. Specifically, we focus on firms that own a greater proportion of chemical manufacturing facilities. We use the firm-level primary SIC code from Research Insight© to divide the sample into two sub-samples: one sub-sample includes only firms with a primary SIC code in the chemical manufacturing industries (i.e., SIC code begins with 28), while the other sub-sample includes firms with a primary SIC code outside the chemical industries. Chow tests reject the null hypothesis that the coefficient estimates are the same between the chemical and non-chemical samples. Based on these test results, we proceed with the sub-sample of firms with a primary SIC code in the chemical industries. 5. Results 5.1. Basic Results Our analysis seeks to estimate the effects of clean water regulation, as measured by permitted discharge limits, on two aspects of profitability: (1) actual profitability, as measured by return on sales, an accounting-based measure, and (2) investors' expectations of profitability, as measured by Tobin's q, a market-based measure. After matching the environmental and financial data and eliminating records with missing data, the sample includes an unbalanced panel of 740 observations representing 47 firms. [Appendix C explains the differences in the sample sizes between the present study and the previous related studies: Rassier and Earnhart (2010a, 2010b, 2011).] Table 1 contains descriptive statistics. Table 2 contains correlation coefficients among the independent variables. Table 3 displays results from the fixed effects estimation of return on sales, shown in Eq. (2), and of Tobin's q, shown in Eq. (4). The displayed p-values are derived from standard errors that are clustered on individual firms to correct for any unknown form of heteroskedasticity and/or serial correlation.

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Table 1 Descriptive statistics. Variable

Mean

Std. deviation

Return on sales (ratio) Tobin's q (ratio) Market value (billions of dollars) Replacement costs (billions of dollars) Sales growth (percent) Capital intensity (ratio) Age of assets (ratio) Firm size (dollars) Market share (ratio) Industry concentration (ratio) Current ratio (ratio) R&D intensity (ratio) Discharge limit (ratio) Number of observations Number of firms

0.073 1.941 23.500 8.780 0.050 0.079 0.521 22.067 0.156 0.675 1.624 0.046 1.847 740 47

0.109 2.050 43.500 12.000 0.120 0.072 0.114 1.371 0.214 0.120 0.623 0.044 2.757

For both return on sales and Tobin's q, the coefficient estimates for the traditional control variables are generally consistent with our expectations, which are discussed in Appendix B. First, consider the fixed effects estimation of return on sales. Based on our analysis, this accounting-based measure is positively related to capital intensity and the current ratio and negatively related to firm size. Coefficient estimates for sales growth, the age of assets, market share, industry concentration, and research and development intensity are not statistically significant. Second, consider the fixed effects estimation of Tobin's q. Based on our analysis, this market-based measure of investors' expectations of future profitability is positively related to firm size and research and development intensity and negatively related to industry concentration. Coefficient estimates for sales growth, capital intensity, the age of assets, market share, and the current ratio are not statistically significant. We next evaluate the effects of clean water regulation on profitability. For return on sales, the coefficient estimate for the permitted discharge limit is negative and significant at the 10% level. For Tobin's q, the coefficient estimate for the permitted discharge limit is positive and significant at the 10% level.22 Recall that lower values of the permitted discharge limit indicate more stringent regulation for our two regulated pollutants. Accordingly, a positive coefficient estimate for the permitted discharge limit indicates that more stringent regulation hinders profitability, while a negative coefficient estimate indicates that more stringent regulation improves profitability. Thus, our results for actual profitability are consistent with the Porter hypothesis, while rejecting the costly regulation hypothesis. 5.2. Interpretations Contrary to the Porter hypothesis and efficient market theory, the results for investors' expectations of profitability are inconsistent with the benchmark results linking clean water regulation to actual profitability. If the discharge limit affects actual profitability, then it should also affect investors' expectations of future profitability in a similar manner. Thus, investors in chemical firms do not appear to value the 22 Since Tobin's q is a ratio of market value to replacement costs, our analysis of this ratio is not able to assess whether any link from clean water regulation to market-based profitability stems from changes in market value only, replacement costs only, or both. To explore the Tobin's q results further, we draw upon the decomposition of Tobin's q into its constituent components — market value and replacement costs — and separately regress each component on the same set of explanatory factors used to estimate Tobin's q. Since the theoretical literature is silent on the effect of regulation on firms' replacement costs, we wish to eliminate the possibility that this effect is driving the estimated effect of regulation on Tobin's q. Based on our additional analysis, the results reveal that variation in permitted discharge limits seem to affect Tobin's q by altering market value with no meaningful effect on replacement costs.

positive effect of clean water regulation on actual profitability and instead presumably expect a negative impact from tighter regulation — an expectation consistent with the costly regulation hypothesis. Given the inconsistency in our results for actual profitability and investors' expectations of profitability, we wish to discuss underlying reasons for the differences, which contradict the Porter hypothesis and efficient market theory. While strong evidence from previous studies supports efficient market theory, other evidence rejects the theory, indicating that a firm's stock price may not accurately reflect the present value of all future payments to the stockholder.23 As an alternative to efficient market theory, behavioral finance attempts to explain investors' decisions based on a notion of irrationalities, while also highlighting the limits of arbitrage. For our purposes, we focus on irrationalities.24 Studies of behavioral finance posit that individuals make complicated investment decisions poorly or irrationally. First, investors do not always process information correctly. Thus, they do not consider the true probability distributions associated with future and uncertain rates of return. Second, even if investors were able to assess the true probability distributions of future returns, they often make inconsistent or systematically suboptimal investment decisions (Bodie et al., 2008). Taken together, individual investors possess built-in biases and misperceptions that cause stock prices to diverge from fundamental or intrinsic values, which may help us to interpret our empirical results. Consider initially the information processing errors. First, investors may place too much weight on recent experience compared to prior beliefs. We are not certain whether this error helps to explain our results since we are not aware of investors' recent experience with environmental regulation. Second, investors may overestimate the precision of their own beliefs or forecasts. If investors believe that tighter regulation is costly, then this error helps to explain the effect on Tobin's q. Third, investors may be too slow in updating their beliefs in response to new evidence. In order to interpret our results in this light, assume that investors initially believe that tighter regulation is costly overall without necessarily rejecting the notion that regulation may also prompt some innovation offsets. Our results may reflect the initial beliefs. However, we do not observe any subsequent updates in beliefs based on our analysis. Consider next the behavioral biases. First, investors may view gains differently than they do losses based on some meaningful reference points. For our interpretive purposes, assume that investors view the Effluent Limitation Guideline as their reference point and that they believe that tighter regulation is costly. Given these two assumptions, the imposition of a water quality based limit represents a loss from the perspective of the investor. Prospect theory indicates that investors are particularly averse to the possibility of even a small loss, so they need a higher return in order to compensate for this possibility. Second, individuals who make decisions that turn out badly have more regret when those decisions are unconventional. Perhaps tighter regulation represents an unconventional setting. Consequently, investing in firms facing tighter regulation is less conventional, and investors require a higher rate of return, which lowers the intrinsic value of the firm's stock. 5.3. Economic Consequences Lastly, we assess the economic consequences of our results by calculating how changes in the permitted discharge limit affect return on sales and Tobin's q. In particular, we calculate how a 10% reduction in the permitted discharge limit affects return on sales and Tobin's q. 23 Previous studies identify a set of effects that are inconsistent with efficient market theory: price-earnings effect (Basu, 1977, 1983), small firms effect (Banz, 1981), market-tobook effect (Fama and French, 1992), momentum effect (Jegadeesh and Titman, 1993), long-term reversal effect (Chopra et al., 1992; De Bondt and Thaler, 1985), and post-earnings announcement price drift effect (Ball and Brown, 1968; Battalio and Mendenhall, 2005; Bernard and Thomas, 1990; Rendleman et al., 1982). 24 Barberis and Thaler (2003) and De Bondt and Thaler (1995) provide useful surveys of the behavioral finance literature.

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Table 2 Correlation coefficients for independent variables.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Sales growth Capital intensity Age of assets Firm size Market share Ind. concentration Current ratio R&D intensity Discharge limit

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1.00 −0.21 0.15 0.13 −0.05 −0.18 0.04 0.26 0.31

1.00 −0.03 0.07 0.01 0.07 −0.02 −0.05 −0.04

1.00 0.26 −0.06 −0.25 0.07 0.20 0.22

1.00 0.60 −0.08 −0.48 0.52 0.39

1.00 0.44 −0.29 −0.00 −0.07

1.00 −0.04 −0.39 −0.41

1.00 −0.22 −0.17

1.00 0.60

1.00

First, we consider actual profitability. The effect of a 10% reduction in the average permitted discharge limit, denoted R, causes return on sales to increase by a factor of 0.0015 as follows: ΔROS ¼ α R  %Δ  R ¼ −0:008  −0:10  1:847 ¼ 0:0015. Relative to the sample average return on sales of 0.073, this factor represents an approximate 2% increase. We consider next the effect of a 10% reduction in the average permitted discharge limit on Tobin's q. Our results indicate that investors expect a negative effect from tighter regulation. The effect of a 10% reduction in the average permitted discharge limit causes Tobin's q to decrease by 0.0076% as follows:

%Δq ¼ βR  %Δ  R ¼ 0:041  −0:10  1:847 ¼ −0:0076:

This percent change represents a $1.8 million decrease in market value, based on the average firm's market value of $23.5 billion. Thus, the effects of tighter clean water regulation on actual profitability and investors' expectations of future profitability are both statistically meaningful and economically meaningful. Our estimation of an accounting-based measure of actual profitability indicates positive effects of tighter regulation on actual profitability. Based on our estimation of a market-based measure of expected future profitability, investors appear to expect that a firm faced with a more stringent permitted discharge limit will experience an undermined ability to pursue profit-increasing activities.

Sales growth Capital intensity Age of assets Firm size Market share Industry concentration Current ratio R&D intensity Discharge limit Intercept F-test for fixed effects Adjusted R2 Number of observations Number of firms

Actual profitability: Return on sales

Expected profitability: Tobin's q

Coefficient

P-Valueb Coefficient

0.067 0.184 −0.106 −0.045 −0.175 −0.029 0.028 −0.085 −0.008 1.120 F(46,683) = 2.58 0.230 740 47

0.179 0.009 0.310 0.072 0.204 0.834 0.008 0.836 0.089 0.045 0.000

−0.034 0.476 0.392 0.294 1.219 −1.446 0.038 5.777 0.041 −6.114 F(46,683) = 30.8 0.898 740 47

In this paper, we study the effects of clean water regulation on the actual and expected profitability of publicly owned firms in the chemical manufacturing industries. As our measure of actual profitability, we focus on return on sales, which is an accounting-based measure that captures profitability as reported on a firm's financial statements. As our measure of expected profitability, we focus on Tobin's q, which is a market-based measure that captures investors' expectations of the present value of a firm's future stream of profits by constructing the ratio of market value to replacement costs. Our empirical analysis identifies a positive relationship between tighter clean water regulation and actual profitability. In contrast, investors' expectations of profitability, as captured by Tobin's q, indicate that investors in chemical firms appear to expect a negative relationship between tighter clean water regulation and profitability. Our study represents an important step toward resolving the debate regarding the effect of environmental regulation on financial performance. The Porter hypothesis asserts that properly designed environmental regulation motivates firms to innovate, which ultimately improves financial performance via lower costs and/or higher revenues. The costly regulation hypothesis argues that firms voluntarily seek opportunities to improve financial performance regardless of regulation and that firms facing more stringent regulation are forced to incur higher costs. Overall, our results for actual profitability are consistent with the Porter hypothesis, while our results for expected profitability are consistent with the costly regulation hypothesis.

Appendix A. Analysis of Panel Data

Table 3 Fixed effects estimation of actual and expected profitability. Independent variablea

6. Conclusions

P-Valueb 0.921 0.181 0.443 0.067 0.306 0.032 0.575 0.002 0.076 0.082 0.000

a In addition to firm size, we control for the average size of all facilities owned by a firm by including as a proxy the average flow of wastewater that facilities are designed to manage (“flow design capacity”), not the amount of pollution discharged by a facility. We neither report nor interpret the coefficient estimates for facility size because they prove insignificant and because results are robust to the exclusion of this factor. b P-values are derived from standard errors that are clustered on individual firms.

To estimate Eqs. (2) and (4), we draw upon an unbalanced panel data set. To control for firm-specific effects, we consider three panel estimators: pooled OLS, fixed effects, and random effects. We use standard tests to assess the empirical strength of the panel estimators: the Breusch–Pagan Lagrange multiplier test, the Hausman test for random effects, and the F-test for fixed effects. The Breusch– Pagan test indicates whether there is an uncorrelated firm-specific component of variance. If not, the random effects estimator reduces to the pooled OLS estimator. Otherwise, the random effects estimator is more appropriate because the pooled OLS estimator fails to use information about the heteroskedasticity that results from using repeated observations on the firm. The Hausman test for random effects assesses whether the random effects estimates are systematically different from the fixed effects estimates. The random effects estimates are consistent and efficient as long as the firm-specific effects are uncorrelated with the independent variables. If true, the fixed effects estimates are consistent but not efficient. Otherwise, the random effects estimates are inconsistent and the fixed effects estimates are both consistent and efficient. The F-test for fixed effects indicates whether inclusion of firm-specific intercepts is warranted. If so, the fixed effects estimates dominate the pooled OLS estimates since the latter are biased.

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Table A.1 Pooled OLS estimation of actual and expected profitability. Independent variablea

Sales growth Capital intensity Age of assets Firm size Market share Industry concentration Current ratio R&D intensity Discharge limit Intercept Breusch–Pagan test Adjusted R2 Number of observations Number of firms

Actual profitability: Return on sales

Expected profitability: Tobin's q

Coefficient

P-Valueb

Coefficient

P-Valueb

0.106 0.162 −0.090 0.009 0.047 −0.100 0.019 0.339 0.004 −0.097 χ2(1) = 24.3 0.165 740 47

0.027 0.130 0.091 0.352 0.242 0.003 0.044 0.126 0.176 0.667 0.001

0.514 −0.064 2.039 0.028 1.096 −1.348 −0.118 5.694 0.039 −0.730 χ2(1) = 2509.0 0.711 740 47

0.052 0.900 0.004 0.667 0.002 0.000 0.180 0.001 0.054 0.579 0.000

a In addition to firm size, we control for the average size of all facilities owned by a firm by including as a proxy the average flow of wastewater that facilities are designed to manage (“flow design capacity”), not the amount of pollution discharged by a facility. We neither report nor interpret the coefficient estimates for facility size because they prove insignificant and because results are robust to the exclusion of this factor. b P-values are derived from robust standard errors.

The relevant test statistics are reported in Table 3 and Appendix Tables A.1 and A.2. As shown in Appendix Table A.1, the Breusch–Pagan test statistics reveal heteroskedasticity in the pooled OLS model for both Eqs. (2) and (4). Moreover, as shown in Table 3, the reported F-test for fixed effects statistics reveal significant firm-specific effects in the fixed effects estimates for both Eqs. (2) and (4). Finally, as shown in Appendix Table A.2, the Hausman test of random effects statistics indicate that the random effects estimates do not appear consistent for either Eq. (2) or Eq. (4). Consequently, we report and interpret the fixed effects estimates as our core results, while only reporting the pooled OLS estimates and random effects estimates in Appendix Tables A.1 and A.2, respectively. To identify the effect of permitted discharge limits on profitability, the fixed effects estimator relies on intra-firm variation in permitted discharge limits. We assess the extent of intra-firm variation in permitted discharge limits by calculating firm-level coefficients of variation (i.e., standard deviation / mean) for the permitted discharge limits.

Table A.2 Random effects estimation of actual and expected profitability. Independent variablea

Sales growth Capital intensity Age of assets Firm size Market share Industry concentration Current ratio R&D intensity Discharge limit Intercept Hausman test Overall R2 Number of observations Number of firms

Actual profitability: Return on sales

Expected profitability: Tobin's q

Coefficient

P-Valueb

Coefficient

P-Valueb

0.073 0.191 −0.114 0.008 0.051 −0.105 0.022 0.294 0.004 −0.065 χ2(10) = 25.4 0.161 740 47

0.046 0.001 0.022 0.282 0.252 0.004 0.009 0.070 0.152 0.692 0.005

0.087 0.462 0.890 0.128 0.881 −1.217 −0.028 5.873 0.041 −2.741 χ2(10) = 29.5 0.649 740 47

0.433 0.004 0.000 0.001 0.004 0.000 0.325 0.000 0.013 0.001 0.001

a In addition to firm size, we control for the average size of all facilities owned by a firm by including as a proxy the average flow of wastewater that facilities are designed to manage (“flow design capacity”), not the amount of pollution discharged by a facility. We neither report nor interpret the coefficient estimates for facility size because they prove insignificant and because results are robust to the exclusion of this factor. b P-values are derived from robust standard errors.

The sample-wide mean of the coefficients of variation is 0.093 with a standard deviation of 0.116. Thus, intra-firm variation is low; nevertheless, the extent of intra-firm variation is sufficient for identification.

Appendix B. Inclusion of Control Factors in the Regression Analysis Return on sales reflects actual profitability, and Tobin's q reflects expected profitability. Thus, we include as regressors the following control factors, which previous studies use to explain profits: (1) a firm's sales growth, (2) a firm's capital intensity, (3) age of a firm's assets, (4) a firm's size, (5) a firm's current ratio, (6) a firm's research and development intensity, (7) a firm's market share, and (8) concentration of the industry in which a firm operates. Below we discuss the role of each factor. Sales growth. While robust sales growth is generally indicative of a firm's ability to compete and shield itself from cyclical market variations, there may be an optimal point beyond which further sales growth impairs a firm's flexibility and adaptability, adversely affecting profitability (Perez-Quiros and Timmerman, 2000). Thus, the ultimate effect of sales growth on profitability is ambiguous. However, empirical evidence consistently supports a positive relationship between profitability and sales growth (Konar and Cohen, 2001; Khanna and Damon, 1999; Russo and Fouts, 1997; Capon et al., 1990). We capture sales growth as the quarter-over-quarter compounded percent change in a firm's sales for the previous three years. Capital intensity. Capital intensity is defined as the amount of fixed or real capital relative to other factors of production and is generally measured as capital investments or asset stocks relative to sales, output, or labor (Capon et al., 1990). Assuming that a firm takes its capital and other factors of production as given at a point in time, capital intensity serves as a proxy for capacity utilization. Higher capital intensity indicates lower capacity utilization. If a firm's assets are idle, the firm's profits are lower. Accordingly, empirical evidence generally supports a negative relationship between profitability and capital intensity at the firm level (Khanna and Damon, 1999; Russo and Fouts, 1997; Capon et al., 1990). We measure a firm's capital intensity as the ratio of the firm's capital investments to the firm's sales. Age of assets. A firm with older assets may be less operationally efficient than a firm with new assets, which frequently embed updated technologies that lead to greater productivity. Alternatively, new equipment may be more expensive than old equipment, which yields higher depreciation. Thus, the a priori effect of the age of assets on profitability is ambiguous (Khanna and Damon, 1999). Existing empirical evidence finds a positive relationship between profitability and the age of assets (Konar and Cohen, 2001; Khanna and Damon, 1999). We capture a firm's age of assets as the ratio of net property, plant, and equipment to gross property, plant, and equipment, the difference between the two representing depreciation. Size. Profitability is expected to vary with the scale of operations, yet both economies of scale and diseconomies of scale may influence profitability. Empirical results reflect this ambiguity (Capon et al., 1990; Russo and Fouts, 1997; Konar and Cohen, 2001). Thus, the a priori effect of firm size on profitability is ambiguous. We capture a firm's size as the natural log of total assets. Current ratio. The current ratio is defined as the ratio of current assets (assets expected to be converted to cash within one year) to current liabilities (liabilities due within one year). Thus, the current ratio indicates a firm's ability to meet creditors' demands within a one-year period. If current liabilities exceed current assets (i.e., current ratio b 1) a firm may be unable to meet short-term obligations. While empirical evidence generally finds that lower levels of debt are positively related to financial results, a high current ratio may signal that a firm is not efficiently investing cash for future growth and productivity (Capon et al., 1990). Thus, the a priori effect of the current ratio on profitability is ambiguous.

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Research and development intensity. A firm that assumes the risks associated with research and development is expected to be innovative, agile, and a market leader and should reap returns from its investments. Consistent with these expectations, empirical evidence generally supports a positive relationship between profitability and research and development (Konar and Cohen, 2001; Capon et al., 1990). We capture a firm's research and development intensity as the ratio of research and development to the firm's sales.25 Market share. Economic theory asserts that monopoly power improves profitability. To capture monopoly power, empirical studies generally include a measure of market share. These studies support the predicted positive relationship (Konar and Cohen, 2001; Capon et al., 1990). We capture a firm's market share as the ratio of the firm's sales to total industry sales by 4-digit SIC industry. Industry concentration. Industry concentration reflects the extent to which a small number of firms account for a large proportion of industrial activity. Accordingly, a firm in a high-concentration industry is expected to reap higher profits than a firm in a low-concentration industry. In their extensive meta-analysis of the determinants of profitability, Capon et al. (1990) overwhelmingly conclude that higher profits accrue to firms operating in high-concentration industries. We capture industry concentration as the four-firm sales ratio that is specific to the 4-digit SIC industry in which a firm operates. Appendix C. Differences in the Samples used by the Present Study and Previous Related Studies The empirical analyses in the present study and in the previous related studies — (Rassier and Earnhart, 2010a, 2010b, 2011) — utilize annual or quarterly observations that are taken from a single database. However, the sample size varies across the studies due to missing data. Eliminating records with missing data is consistent with Konar and Cohen (2001), a prominent study linking environmental performance to measures of profitability, and studies included in the metaanalysis of Capon et al. (1990). Similar to previous studies of financial performance (e.g., Rassier and Earnhart, 2010a, 2010b, 2011; Anton et al., 2004; Russo and Fouts, 1997), we assume that the presence of missing financial data does not bias the estimation results. The present study includes a quarterly unbalanced panel of 740 observations on 47 firms. Rassier and Earnhart (2010a) use an annual unbalanced panel of 337 observations on 73 firms and a quarterly unbalanced panel of 926 observations on 59 firms. Thus, both studies use quarterly data. However, the present study includes two additional regressors (current ratio and research and development intensity). Missing quarterly data for the two additional regressors reduces the number of quarterly observations from 926 to 740 and the number of firms from 59 to 47. Rassier and Earnhart (2010b) use an annual unbalanced panel of 229 observations on 54 firms. Since the present study uses quarterly data, a comparison between Rassier and Earnhart (2010b) and the present study is not meaningful. However, compared with Rassier and Earnhart (2010a), Rassier and Earnhart (2010b) include two additional regressors (current ratio and research and development intensity), which reduces the number of annual observations from 337 to 229 and the number of firms from 73 to 54. Rassier and Earnhart (2011) use a quarterly unbalanced panel of 815 observations on 53 firms to estimate the short-run effect of environmental regulation only on the return on sales. In contrast, the present study estimates this effect on both return on sales and Tobin's q. In order to compare the effects on these two outcomes cleanly, the present study restricts the sample to observations with available data for both outcomes. Due to missing data for Tobin's q, this restriction reduces the number of quarterly observations from 815 to 740 and the number of firms from 53 25 Quarterly research and development expenditures are poorly populated in Research Insight©, so we use annual research and development expenditures.

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Table C.1 Summary of differences between the present study and previous related studies.

Dependent variables Return on sales Tobin's q Independent variables Sales growth Capital intensity Age of assets Firm size Market share Industry concentration Current ratio R&D intensity Contemporaneous Discharge Limit Lagged discharge limits

Present study

Rassier and Earnhart (2010a)

Rassier and Earnhart (2010b)

Rassier and Earnhart (2011)

1

1

2

1

2

1

2

X

X

X

X

X

X

X X X X X X X X X

X X X X X X X X X

X X X X X X X X X

X X X X X X X X X

2

X X

X X X X X X X X X

X X X X X X X X X

X X X X X X

X

X

X

Sample Number of observations Number of firms

740 47

740 47

Data frequency Annual data Quarterly data

X

X

X

X

Estimator Fixed effects Random effects

X X X X X X

337 73

926 59

X

229 54

229 54

X

X

X

X X

X

815 53

584 47

X

X

X

X

X

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