Abstract

I.
Introduction
The U.S. Environmental Protection Agency (EPA hereafter) define environmental liabilities as “legal obligations to make future expenditures due to the past or ongoing manufacture, use, release, or threaten release of a particular substance, or other activities that adversely affect the environment" (EPA 1996). Both anecdotal evidence and empirical studies suggest that environmental liabilities are staggering. For instance, according to a study by the United Nations, the annual global environmental expenses are around US$6 trillion in 2010, which amounts to 11% of the global GDP. Barth and McNichols (1994) estimate firms' environmental liabilities under Superfund and find that the average amount of unrecognized environmental liabilities is roughly 28.6% of the market valuation of equity. 1 We study whether and how firms' environmental liabilities affect the level and structure of their financial liabilities. Despite the sheer magnitude of corporate environmental liabilities and the drastically increasing attention that environmental issues have received, the capital structure implications of corporate environmental liabilities remain largely unexplored. In essence we ask: While companies keep most of environmental liabilities as off-balance-sheet liabilities, do they treat environmental liabilities as a substitute for financial on-balance-sheet liabilities? 2 Further, do companies' observed financial leverage ratios and debt structure vary according to their environmental liabilities? Environmental liabilities are legally binding obligations and share important characteristics of financial debt. Firms with substantial environmental liabilities make regular and legally mandated outlays to achieve and maintain compliance with environmental laws and regulations. Similar to interest expense on financial debt, environment-related expenses and investments are tax-deductible. Failure to demonstrate compliance may lead to substantial non-compliance induced environmental liabilities and can even trigger bankruptcy. 3 Based on these debt-like features and building on the trade- off theory of capital structure, we expect environmental liabilities to be negatively related to the use of financial debt for four reasons. First, environmental outlays reduce taxable income, thereby lowering firms' marginal tax rates, which are defined as the present value of the tax obligation from earning an extra dollar today. DeAngelo and Masulis (1980) argue that each firm has an optimal amount of total tax deductions and that non-debt tax shields (NDTS) substitute for interest tax deductions. Thus, to the extent that environment-related outlays are an important source of tax savings, firms with higher environmental liabilities should have weaker tax incentives to use financial debt, all else equal. Second, environmental outlays reduce the amount of cash flows available to service financial debt obligations, thus lower firms’ capacity for financial debt. Third, environmental liabilities increase firms' business risk. Environmental liabilities imposed through violations are unpredictable, substantial, and highly impactful (Barth and McNichols 1994; White 2002; Clarkson et al. 2011; Schneider 2011; Li, Simunic, and Ye 2014). Clarkson et al. (2011) and Flammer (2013) show that poor environmental performance contributes significantly to firms' future operational risk and poor financial performance. Facing increasingly more stringent environmental regulations, firms with high environmental liabilities are inevitably subject to more uncertainties arising from market demand due to damaged reputation, operating costs associated with waste management and future compliance, future litigation and enforcement activities, and the cost of external financing (e.g., Gormley and Matsa 2011; Chava 2014; Li, Simunic, and Ye 2014), all of which would increase firms' likelihood of financial distress and bankruptcy, thereby reducing their use of financial debt. Finally, supply side factors may also play a role. Environmentally sensitive lending has increased markedly over the last two decades. A large number of banks, which account for about 80% of the global lending volume, have adopted the Equator Principles (http://www.equator-principles.com/) and agreed to consider social and environmental issues in their lending decisions (Chava 2014). 4 Chava (2014) also argues that when lending to firms with environmental concerns, lenders face multiple risks, which include borrowers' credit risk caused by environmental regulations and compliance, litigation risk under lender liability laws, and reputation risk arising from association with polluting firms. 5 Accordingly, he documents that lenders charge a significantly higher interest rate on the bank loans issued to firms with higher environmental liabilities and avoid lending to firms with severe environmental concerns. We thus expect that firms' environmental liabilities hamper credit supply, especially that from large and highly visible banks. In sum, the preceding four arguments all suggest that firms with higher environmental liabilities should have lower debt ratios, all other things being equal. We quantify environmental liabilities using non-financial measures provided by the EPA's Toxic Release Inventory (TRI) program, which collects data on release and management of over 650 toxic chemicals by thousands of facilities in the United States. The database is by far the most comprehensive database about waste production and releases by operating facilities in the United States. We compute a firm's total waste produced (in pounds) in all facilities. To account for the differences in toxicity across chemicals, in calculating the total waste produced, the amounts of individual chemicals produced are weighted by their respective toxicity defined by the EPA. Our main proxy for environmental liabilities is then defined at the firm-year level as the natural log of toxicity weighted production waste scaled by total sales. Using a large panel of US firms that are covered by both the Compustat database and the TRI program over the period of 1992-2013, we find that firms with higher environmental liabilities maintain lower financial leverage ratios. A one-standard deviation increase (0.175) in environmental liabilities decreases the leverage ratio by 7.5% from its mean value (0.255). This economic impact on the leverage ratio is similar to that of the market-to-book ratio, which has been shown by previous studies (e.g., Rajan and Zingales 1995) as one of the most important determinants of capital structure. The negative relation between environmental liabilities and financial leverage is robust to alternative model specifications and different variable definitions. Furthermore, we perform several tests to mitigate endogeneity concerns caused by omitted variables and reverse causality. Among these tests, we employ an instrumental variable approach using two instrumental variables, i.e., (1) the weighted-average physical distance between EPA regional office and the firm's facility, and (2) the weighted average unemployment rate of the state in which a firm's facility operates. The main results still hold, supporting a causal relation between environmental liability and firms' capital structure. To further substantiate our main findings, we employ several alternative measures of environmental liabilities. The first one is a dollarized measure of environmental liabilities capturing the financial expenses incurred by firms in complying with environmental laws and regulations. It is obtained by estimating the functional relation between the amount of toxic production-related waste and the sum of the cost of goods sold and capital expenditure. The second measure is from the Enforcement and Compliance Historical Online (ECHO) database that provides information about “compliance and enforcement information for approximately 800,000 EPA-regulated facilities”. From the ECHO database, we calculate the total cost imposed by the EPA or courts on firms for violating environmental regulations. Moreover, we rely on four alternative environmental data sources, namely, KLD Research & Analytics (KLD), Newsweek Green Rankings, Thomson Reuters Asset4, and Trucost to construct the environmental liability measures that capture not only toxic chemicals generated, but also various potential environmental damage costs and poor environmental policies and reputations. Our findings are robust to these alternative measures of environmental liabilities although sample size varies drastically. Further analysis shows that the negative effect of environmental liabilities on financial leverage varies across firms and over time. In particular, the effect is more pronounced for firms with greater visibility, i.e., larger firms and firms covered by more analysts. We also find that the effect becomes stronger after the Financial Accounting Standards Board (FASB) enacted FIN 47 in 2005, which improves corporate environmental disclosure and increases recognition of environmental liabilities. In addition, firms that rely more heavily on principal customers and those with greater community concerns are found to exhibit a stronger substitution between environmental liabilities and financial liabilities. Finally, we find that firms with higher environmental liabilities have a lower fraction of bank debt in total debt after controlling for the difference in financial leverage across firms. This finding is consistent with banks being more concerned about reputation risk and litigation risk in their lending decisions because their identities are more easily identified than other lenders, such as corporate bond holders (e.g., Chava 2014). Our paper contributes to the corporate social responsibility (CSR hereafter) literature that examines the relation between CSR and various corporate policies. 6 In particular, we present empirical evidence on the effects of environmental liabilities on capital structure. Prior studies have documented that CSR or corporate environmental performance affects the absolute levels of the cost of equity and the cost of debt (e.g., Bauer and Hann 2010;  Schneider 2011; El Ghoul et al. 2011; Dhaliwal et al. 2011; Chava 2014; El Ghoul et al. 2014). We complement these studies by investigating the leverage ratio, which is determined by firms' choice between debt and equity and captures the relative importance of the effects of environmental liabilities on the cost of debt and the cost of equity. In addition, to the best of our knowledge, we are among the first to empirically show that corporate environmental liabilities influence the fraction of bank debt in total debt. Furthermore, while a number of studies suggest that companies can do well (in terms of firm value and operating performance) by doing good for the environment and society (e.g., Orlitzky, Schmidt, and Rynes 2003, Guenster et al. 2011), few studies have examined the specific channels through which being socially responsible brings economic benefits. Our findings imply that being environmentally responsible can enhance firms' debt capacity and improve the availability of bank credit. Our analysis also contributes to the capital structure literature. By showing environmental liabilities as an important determinant of capital structure, our study adds to the stakeholder theory of capital structure and reveals the importance of society stakeholders in influencing firms' financing decisions. 7 Moreover, while the bulk of capital structure research has focused on explaining the leverage choice as it is reported on the balance sheet, several studies have shown that off-balance-sheet liabilities, such as operating leases and pension liabilities, work as substitutes for on-balance-sheet financial debt (e.g., Graham, Lemmon, and Schallheim 1998; Shivdasani and Stefanesco 2011). We extend this literature by documenting the substituting effect of environmental liabilities on financial debt. Finally, our results help explain why firms appear to be underleveraged from a tax perspective (Graham 2000) - firms' debt policy should be less conservative in their capital structure decisions than has been previously thought, once environmental liabilities are accounted for. The remainder of the paper is organized as follows. Section II discusses the important features of environmental liabilities and reviews the relevant literature briefly. We discuss our sample, variables, and summary statistics in Section III. Section IV presents our main empirical results regarding the effect of environmental liabilities on financial leverage. Section V reports additional analysis. Section VI concludes.
II.
Institutional Features of Environmental Liabilities and the Literature Review
A. Debt Features of Environmental Liabilities Environmental liabilities, according to the EPA's definition outlined in Section I, are the legal obligations governed by various environmental laws or regulations at federal, state and local levels, or even by the common law. Given their diverse sources, environmental liabilities can be further divided into six broad categories: (1) obligations to achieve and maintain compliance with environmental regulations and laws; (2) obligations imposed by pollution remediation or clean up laws; (3) obligations to pay civil and criminal fines and penalties for noncompliance; (4) obligations to compensate private parties for injury to human health, property and economic activity; (5) obligations to pay punitive damages for remiss conduct; (6) obligations to compensate governments for damages to natural resources (Barth and McNichols 1994; EPA 1996). Unlike the first category of environmental liabilities, categories (2)-(5) are conditional on firms' failure to demonstrate compliance and are often associated with accidents or uncontrollable events. These categories of liabilities are enforced either by public agencies or through private parties' suits. Though occasionally imposed, non-compliance induced environmental liabilities also entail a series of regular outlays over a long period of time until the violations are entirely corrected. 8 Environmental liabilities have several key characteristics in common with financial debt. First, to achieve and maintain compliance with environmental laws and regulations, companies incur environmental operating expenses for environmental management activities, which are necessary for properly treating, recycling, recovering, and disposing pollutants prior to direct releases into the environment. In addition, capital expenditures are often made to purchase pollution control equipment and achieve pollution prevention through technology innovation, raw material substitution, or process modification. 9 These environmental outlays are regularly incurred on a long-term basis because environmental standards are constantly revised and new obligations are continuously imposed, and also because it usually takes years for firms to fully comply with new environmental regulations or laws. Thus, environmental liabilities are legally binding obligations that result in substantial and continuous outlays on a regular basis. 10 Second, environmental outlays are tax deductible, much like interest expenses on financial debt. Third, failure to comply with environmental obligations can lead to substantial penalties, compliance costs, operation shutdowns, or even bankruptcy. The possible claimants include governments at different levels, communities, and individuals. In terms of priority, the claims are junior to secured debt but senior to unsecured debt (e.g., Rogers 2005). However there are also important differences between environmental liabilities and financial debt. For instance, unlike financial debt, most environmental liabilities have no pre-specified maturity date. The principal value of environmental liabilities may fluctuate drastically in response to the development of environmental technologies and the changes in environmental regulations over time, and thus is nearly impossible to precisely estimate in dollar terms. In addition, firms have the flexibility to incur more environmental outlays in high marginal tax states of the world to maximize the tax benefits, a feature not present with financial debt. As a result, environmental liabilities and financial debt are unlikely to be perfect substitutes.
B.
Financial Accounting for Environmental Liabilities
The Financial Accounting Standards Board (FASB) established FAS 5/FIN 14 in 1975 as the underlying framework for the accounting and disclosure of environmental liabilities. FAS 5/FIN 14 requires that environmental costs must be "reasonably estimable" in order to be accrued. Moreover, FAS 5 pertains only to losses or asset impairments up to the end of the accounting period. Future commitments, such as modifying production processes to prevent or reduce pollution, generally are not considered (Roberts 1995). Firms are also allowed to avoid the accrual of environmental liabilities if they can demonstrate that the liabilities do not exceed the low recognition threshold specified by FAS 5. Thus, several third-party investigations conducted in the 1990s reveal that FAS 5/FIN 14 results in disparate and inconsistent accounting practices and widespread underreporting of environmental liabilities (Lee and Trabucchi 2008). FAS 143 was issued in 2003 by FASB to directly address the accounting of environmental liabilities. It mandates that environmental liabilities should be accounted for using the fair-value cost estimation methodology, which considers multiple cost estimate scenarios and probabilities instead of using the minimum allowed under FAS 5. FIN 47 further clarified FAS 143 and became effective in 2005. It requires the recognition of the fair value of environmental liabilities in the period in which they are incurred, even if the liabilities are conditional on future events such as the timing and method of settlement of the obligations. The effect of FIN 47 has been the immediate recognition of previously unrecorded environmental liabilities and the reevaluation of previously recorded liabilities (Lee and Trabucchi 2008). Despite positive changes in accounting standards in recent years, most of the environmental liabilities are off the books over our sample period (1992-2013) because most companies have not established rigorous procedures that consistently identify, measure, and report environmental liabilities associated with properties and facilities owned and operated by them. Our empirical analysis thus mainly relies on non-financial measures that are supposed to be highly correlated with the magnitude of environmental liabilities.
C.
A Brief Review of Relevant Literature
Our paper is closely related to recent studies examining how corporate social responsibility impacts companies' costs of financing. Bauer and Hann (2010) document that environmental performance affects the solvency of borrowing firm and that environmental concerns are associated with a higher cost of debt financing and lower credit ratings. Schneider (2011) finds that corporate environmental performance negatively affects bond yields in the paper and pulp and the chemical industries. El Ghoul et al. (2014) find that firms with higher corporate environmental responsibility have cheaper equity financing. Dhaliwal et al. (2011) show that firms with superior CSR performance are more likely to voluntarily disclose their CSR strategies, thereby reducing information asymmetry and enjoying a lower cost of equity capital. Cheng, Ioannou, Serafeim (2014) document that firms with better CSR performance face significantly lower capital constraints through enhanced stakeholder engagement and increased transparency. Chava (2014) shows that firms with more environmental concerns have higher costs of equity and debt capital. While these recent studies have documented that CSR or corporate environmental performance affects both the cost of equity and the cost of debt, by looking at the effects of environmental liabilities on the financial leverage ratio, we essentially investigate the choice between debt and equity and evaluate the cost of equity relative to the cost of debt, instead of trying to evaluate the absolute cost of equity or debt. Our study also complements the literature on off-balance-sheet liabilities. Several studies show that firms have sizable liabilities that are not recorded on the balance sheet, and that these liabilities are negatively related to the amount of on-balance-sheet debt used by firms. For example, Graham, Lemmon, and Schallheim (1998) study operating leases as an off-balance-sheet liability and find that firms with low marginal tax rates use more operating leases and have lower levels of financial debt. Shivdasani and Stefanesco (2011) consider pension liabilities and show that one percentage point increase in the pension liability to total assets ratio is associated with a 0.36 percentage point decrease in the financial leverage ratio. We are among the first to provide evidence that environmental liabilities also have a partial substitution effect on financial debt. Another closely related strand of the capital structure literature aims to understand why firms appear to be underleveraged from a tax perspective. Graham (2000) shows that firms are generally very conservative in their capital structure. The typical firm in his study could double tax benefit by issuing more financial debt until the marginal tax benefit begins to decline.  This conservatism is often thought to be puzzling because tax benefits are a highly important determinant of financing decisions to most CFOs surveyed by Graham and Harvey (2001). To explain the debt conservatism puzzle, recent studies have suggested a number of factors, which include supply side constraints faced by lenders (Faulkender and Petersen 2006), distress risk premia (Almeida and Philippon 2007), depreciation and tax credits (Schallheim and Wells 2006), employee stock option expenses (Graham, Lang, and Shackelford 2004), the tax shield provided by defined benefit pension contributions (Shivdasani and Stefanesco 2011), the CEO and governance characteristics (Strebulaev and Yang 2013), and the risk of losing intellectual property to rivals (Klasa et al. 2014). As discussed in Section II.A, environmental outlays are substantial, provide a source of valuable tax shields, and may be timed to lower the marginal tax rate and tax benefits for financial debt. Once environmental liabilities are taken into account, firms should be less conservative in their choice of leverage than what they report on the balance sheet. Thus, our findings complement the existing explanation for debt conservatism.
III.
Data, Variables, Models, and Summary Statistics
A.
Data and Sample
Our sample consists of all firms that are included in the Compustat database and have at least one facility that reports to the EPA’s TRI program at any point between 1992 and 2013. To measure environmental liabilities, we mainly reply on toxic waste data reported by firms under the TRI program. Financial data are from the Compustat Industrial Annual files. The amount of bank debt is collected from the Capital IQ database. Data on stock prices and returns are retrieved from the Center for Research in Security Prices (CRSP) files. Dollar values are converted into 2005 constant dollars using the GDP deflator. We start the sample period in 1992 since the Pollution Prevention Act (PPA), which requires firms to provide detailed information about facility-level waste management and source reduction activities, took effect in 1991 and we use one-year lagged value of environmental liability measures in regressions so that they are available when firms make financing decisions in a given year. Following standard practice, we exclude financial, insurance, and real estate companies (SIC codes 6000–6900) because their capital structures are likely to be significantly different from the industrial companies in our sample. 11 Also discarded are firms with missing values for environmental liability and for the variables employed in our regression analysis. These restrictions lead to an unbalanced panel that consists of 18,070 firm-year observations from 1,698 firms. To the best of our knowledge, the size of our sample is larger than those in previous studies that use the EPA's TRI data. 12
B.
Measuring Environmental Liabilities
As the current securities regulations and accounting standards leave firms considerable discretion in their environmental reporting, the environmental disclosure made by firms are often incomplete and inaccurate (Barth, McNichols, and Wilson 1997; Schneider 2011). We thus follow prior studies and rely on nonfinancial pollution measures from the TRI program to assess firms' environmental liabilities. 13 Compared with environmental performance metrics created by major social raters, the TRI program offers more objective and verifiable measures of corporate environmental performance, because it is under rigorous monitoring by EPA and contains very few subjective assessments. 14 In addition, the TRI data is less subject to selection biases as firms' environmental reporting is legally mandated. The TRI program was established by the Section 313 Emergency Planning and Community Right-to-Know Act (EPCRA) of 1987 and has been expanded to contain information on over 650 toxic chemicals from more than 20,000 U.S. industrial facilities. Each year, facilities are required to report to TRI the quantities of chemicals disposed or released into the environment (air, water, and land) and those that are treated, recycled, and recovered. 15 As our study mainly focuses on the on-going and future environmental liabilities caused by continuous production activities, chemicals released into the environment as a result of one-time accidents or catastrophic events are excluded when calculating the facility-level waste. Unreported robustness checks show that including releases caused by one-time accidents or catastrophic events has no material impact on our results. We assume that firms are liable for the total amount of toxic waste, which is defined as the sum of toxic chemicals that are released, treated, recycled, and recovered. Environmental management activities, which include treatment, recovering, and recycling, reflect firms' on-going expenditures for compliance with current environmental laws and regulations, and for meeting self-set environmental performance standards. They are long-term and recurring obligations so long as a firm's production continues to generate any environmentally unfriendly substances. More importantly, the quantity of chemicals released are positively associated with firms' expenditures for compliance with future stringent environmental laws and regulations, the probability of future litigations, enforcement actions, and remediation activities arising from past and on-going environmental contamination as well as serious damages to human health, property, and economic activities. De Franco, Li, and Zhou (2013) show that the TRI's toxic emission data constitutes an important source of non-financial information for analysts to assess firms' future environmental risk exposure and financial prospect. Specifically, we first calculate the facility-level production-related waste by summing up all the chemical waste in a given year. To account for the inherent heterogeneity of chemicals, we multiply the mass of each chemical by its toxicity, which is taken from the EPA's Risk-Screening Environmental indicator (RSEI) model. EPA provides two toxicity weights based on the exposure path: the inhalation toxicity weight and the oral toxicity weight. We follow EPA's methodology and use the inhalation toxicity weight for releases (transfers) to fugitive air, stack air, and off-site incinerations. The oral toxicity weight is used for releases (transfers) to direct water and publicly-owned treatment works (POTWs). If the path through which a chemical is released (transferred) is unknown, the higher of the two toxicity weights is used. For chemicals that have no assigned toxicity by EPA, we set toxicity equal to zero. We then aggregate the toxicity-weighted amounts of waste across facilities owned by the firm to obtain the firm-level toxicity-weighted waste. The aggregation procedure gives rise to many extreme values because of the enormous variation in the toxicity of TRI chemicals, which spans seven orders of magnitude on a pound-for-pound basis. For instance, less-toxic chemicals, such as Formic acid and Ethylene, have toxicity less than 0.5, while the most toxic chemicals, such as, Asbestos and Thorium dioxide, have toxicity equal to 1,000,000. We then log-transform the firm-level toxicity-weighted waste to mitigate the effect of skewness and achieve normality (e.g., Berrone and Gomez-Mejia 2009; Delmas and Toffel 2008), and scale it using total sales to control for production scale. The resulting measure ( EnvLiab ) is our key variable of interest in the regression analysis, with higher values indicating higher environmental liabilities. We acknowledge that EnvLiab offers an incomplete picture of a firm's environmental liabilities since it primarily focuses on toxic production-related waste. This limitation would work against finding a significant relation between environmental liabilities and capital structure. In Section IV.C, we show that our results are robust to alternative definitions and measures of environmental liabilities, which capture various aspects of environmental liabilities.
C.
Other Variables
We measure capital structure using the leverage ratio ( Leverage ), which is defined as the ratio of total debt over total assets. The fraction of bank debt in total debt, BD/D , is computed as the amount of bank debt divided by total debt. In our regression analysis, we control for a set of control variables that have been shown by previous studies as important determinants of financing decisions (e.g., Frank and Goyal 2003; Chang, Hilary, and Dasgupta 2006). Specifically, we include the natural log of total assets ( Ln ( Assets )) to control for firm size. To account for the effect of a firm's life cycle on its financial leverage, we employ the natural log of firm age, Ln( Firm Age ), which is defined as the number of years elapsed since a firm enters the CRSP database. We use the market-to-book ratio ( M/B ) to proxy for growth opportunities and use EBITDA/Assets to capture profitability. As companies having more tangible assets are expected to support more debt as these assets can be pledged as collateral, we include the net Property, Plant, and Equipment scaled by total assets ( PPE/Assets ) to account for asset tangibility. Research and development expenses scaled by assets ( R&D/Assets ) can proxy for a variety of company characteristics, such as the uniqueness of the product (Titman 1984), information asymmetry, or growth potential. We also include an R&D indicator variable ( RNDD ) that equals one if R&D expenses are missing, and zero otherwise.  To control for the risk and financial constraints faced by companies, we include a dividend payout indicator ( Dividend Payer ), Altman's (1968) unleveraged Z-score , and earnings volatility (σ(Earnings)). We expect that the incentive to take on debt increases with the company's marginal tax rate because of the tax deductibility of interest expenses. Thus, we include the corporate marginal tax rate ( MTR ) based on the operating income after interest expenses according to Graham (1996). Faulkender and Petersen (2006) document that companies having access to public bond markets, as measured by having a debt rating, take on more debt.  We thus include a debt rating indicator variable ( Debt Rating ), which equals one if a company has a debt rating assigned by Standard & Poor's, and zero otherwise, to capture companies' access to corporate bond markets. Chang, Dasgupta, and Hilary (2006) show that companies with greater analyst coverage have lower leverage. Hence, we control for analyst following by including the maximum number of analysts that make annual earnings forecasts any month over a 12-month period ( NbrAnal ). D. Table I reports the descriptive statistics for all firms in our sample. We winsorize the variables at the top and bottom 1% of their distributions. This approach reduces the impact of extreme observations by assigning the cutoff values to those that are beyond the cutoff points. Our results (untabulated) are qualitatively the same when we truncate (rather than winsorize) the distributions. [Insert Table I here] The distributions of raw waste and toxicity-weighted waste are highly skewed. An average (a median) firm in our sample generates 13.6 (0.4) million pounds of waste. The mean (median) value of toxicity-weighted waste is 43.3 billion (378.8 million). Our main measure of environmental liabilities ( EnvLiab ), after log transformation and being scaled by sales, has the mean value equal to 0.063 and has the standard deviation equal to 0.175. The average (median) firm in our sample has leverage equal to 0.255 (0.245).  Table I also reports summary statistics for the control variables described above. Since our sampling approach and variable construction criteria follow the literature, in the interest of brevity, we omit discussion of the descriptive statistics for control variables. Appendix A tabulates the distribution of firms across industries classified using the two-digit SIC code. The highest fraction of firms is from Electrical Equipment industries (SIC = 36), followed by Chemicals and Allied Products (SIC = 28), Industrial and Commercial Machinery and Computer Equipment (SIC = 35), and Transportation Equipment (SIC = 37). Overall, the firm presence exhibits a large variation across industries. As environmental liability measures tend to be industry-specific (Clarkson, Li, and Richardson 2004), we include two-digit SIC industry fixed effects in our regression analysis to control for heterogeneity along the industry dimension. IV.
The Effect of Environmental Liabilities on Financial Leverage
A. Baseline Results To study the effect of environmental liabilities on the leverage ratio, we estimate the model Leverage, (1) where the key explanatory variable, EnvLiab , is measured one year before the leverage ratio is observed. The coefficient ( β ) on EnvLiab is expected to be negative. We include the two-digit SIC industry fixed effects ( Industry ) that control for heterogeneity across industries. Also included are year fixed effects ( Year ), which account for macro-economic factors that influence corporate capital structure. 16 The standard errors of the estimated coefficients allow for clustering of observations by firm but our conclusions are not affected if we allow clustering by both firm and year.
[Insert Table II here]
Column (1) of Table II presents our baseline results obtained by estimating equations (1).   The coefficient estimate on EnvLiab is negative and statistically significant ( t -statistic = -5.4), indicating that high environmental liabilities reduce firms' financial leverage. Economically, a one-standard deviation increase (0.175) in EnvLiab decreases the leverage ratio by 0.019 (= -0.109×0.175), which amounts to roughly 7.5% of the average leverage ratio (0.255). To put it into perspective, the market- to-book ratio ( M/B ), which has been suggested by previous studies to be one of the most important determinants of financial leverage, exhibits similar economic significance. A one-standard deviation increase (0.869) in M/B decreases the leverage ratio by 0.019 (= -0.022×0.869). The other control variables generally have signs consistent with those in prior literature (e.g., Frank and Goyal 2003; Chang, Dasgupta, and Hilary 2006). For example, companies that are younger, less profitable, or followed by more analysts have lower financial leverage. In contrast, firms that have credit ratings or make less investment in R&D use more debt in their capital structure. Robustness checks (untabulated) show that qualitatively similar results are obtained if we define EnvLiab without log-transforming the firm-level toxicity-weighted waste or if we defining EnvLiab as percentile ranks of the firm-level toxicity-weighted waste scaled by sales. In columns (2)-(4) of Table II, we show that our baseline results are robust to alternative model specifications and variable definitions. In particular, in column (2) we estimate equation (1) using the Fama-MacBeth (1973) procedure that corrects standard errors for cross-sectional correlations. In column (3) we estimate equations (1) with firm fixed effects that account for unidentified time- invariant firm characteristics. In column (4) we measure financial leverage using total debt divided by the quasi-market value of assets (total assets minus book value of equity plus market value of equity). The coefficients of EnvLiab remain both economically and statistically significant. Taken together, our baseline results in Table II suggest that firms with higher environmental liabilities use less financial debt. These results are consistent with our view that the environmental liabilities lower firms' capacity for financial debt, reduce the tax benefits of debt, and increase firms' business risk.
B.
Tests for Endogeneity
We have documented a robust relation between our measure of environmental liabilities and financial leverage, but its causal interpretation remains hypothetical. Our main results are potentially subject to two types of endogeneity. The first type is omitted variable bias. While we have controlled for a standard set of variables that have been shown by previous studies to affect capital structure, the relation that we observe may be spurious if our models omit any variables that affect both capital structure and environmental liabilities. The other possible endogeneity issue is reverse causality running from capital structure decisions to environmental liabilities.  For example, firms may issue debt to finance expenditures on pollution control and prevention activities, resulting in a negative relation between current financial leverage and the total amount of toxic chemical waste. In both cases, the coefficient estimates in Tables II can be biased and inconsistent.
[Insert Table III here]
We perform several tests to alleviate endogeneity concerns, and tabulate the results in Table III. First, we explicitly control for past debt issues ( PastDebt ) and pollution control and prevention activities ( Prevention ). To measure PastDebt , we compute the volume of net debt issued over the past three years, and scale it using the book value of assets at t-1. Because firms do not disclose the amount of chemicals reduced by pollution prevention activities, we follow Doshi, Dowell, and Toffel (2013) and extrapolate the amount using the production ratio reported by the TRI database. 17 Prevention is then defined as the toxicity-weighted and log-transformed amount of waste reduced by pollution prevention activities and scaled by sales. The results reported in column (1) of Table III indicate that EnvLiab remains economically and statistically significant in the leverage regression, suggesting that our results are not driven by omitting pollution prevention activities financed by past debt issues. In column (2), we use EnvLiab t 3 , instead of EnvLiab t-1 , as the key explanatory variable, because more distantly lagged values of EnvLiab should be less correlated with current omitted firm characteristics. Similar results are obtained. In column (3) we employ an instrumental variable approach to further mitigate endogeneity concerns. Specifically, we use two instrumental variables that are likely to satisfy both the relevant and exclusion criteria. The first instrument, Ln (1+DisPro), is the log of one plus the weighted average geographical distance between EPA regional offices and a firm's facilities. We first calculate the geographical distance for each facility-EPA regional office pair, and then weight the distance using the log-transformed toxicity-weighted waste generated by each facility scaled by sales. To the extent that information asymmetries and monitoring costs increase with the physical distance from EPA regional offices, EPA regional offices would be less effective in making more remote firms reduce environmental liability, implying a positive relation between and EnvLiab . We use Coval and Moskowitz's (1999) formula to calculate the geographical distance for every facility-EPA regional office pair. The geographic coordinates of reported facilities are provided by the TRI database. The coordinates of EPA's regional offices are based on zip codes from EPA's website. The second instrument variable, Unemployment , is the average unemployment rate among all the states in which a firm's facilities operate, weighted by the log-transformed toxicity-weighted waste generated by each facility scaled by sales. 18 Kah and Kotchen (2010) document that higher state unemployment rates are associated with fewer public environmental concerns, thereby leading to higher corporate environmental liabilities. These two instrumental variables, however, are not expected to directly influence financial leverage, rather than affecting it through environmental liabilities. We take the instrumental variable approach in the framework of a two-stage least squares (2SLS) regression. The results of the first-stage regression are reported in column (1) of Appendix B. Ln(1+DisPro), and Unemployment are found to be significantly and positively related to EnvLiab (t- statistics are 4.0 and 10.0, respectively). The instruments pass the relevance test as the F -statistics from the joint test of excluded instruments are significant at the 1% level. The p -values of the over- identification tests are insignificantly different from zero, thereby confirming the validity of these instrumental variables. The results of the second-stage regression, which are reported in column (3) of Table III, reveal that the EnvLiab remains economically and statistically significant in leverage regression, implying a causal relation going from environmental liabilities to financial leverage. C. Alternative Measures of Environmental Liabilities In the section we experiment with several alternative measures of environmental liabilities that are constructed using various data sources. We first measure environmental liabilities using toxic waste that is released into the environment (air, water, and land). We also scale the toxicity-weighted and log- transformed amount of releases by sales. The resulting measure is denoted by EnvLiab_Release . Compared with waste recycled, recovered, and treated, waste released is more likely to be related to future environmental regulations, litigations, and enforcement actions. The results reported in column (1) of Table IV reveal that EnvLiab_Release is negatively and significantly related to Leverage . [Insert Table IV here] Our main measure of environmental liabilities ( EnvLiab ) is a nonfinancial measure from the TRI program of EPA. By capturing the amount of waste complemented by toxicity information, EnvLiab should be highly correlated with, but is certainly not equivalent to the actual financial expenses paid by polluters to cope with the toxic waste in the present and in the future. To address this concern, we estimate the dollar amounts of environmental liabilities in several ways as follows. First, we run the following regression to establish a functional relation between EnvLiab and the financial expenses incurred by firms in complying with environmental laws and regulations.
where the dependent variable is the sum of operating expenses, which are measured by the cost of goods sold ( COGS ), and capital expenditure ( CAPEX ) scaled by sales. Waste is the amount of chemical j (in pounds) deflated by sales. Totally 549 chemicals ( N = 549) are included in the regression. The underlying assumption of this equation is that COGS and CAPEX contain respectively current expenses and capital expenditures related to waste management. β j captures the functional relation between environmental costs and each chemical that a firm reports. X represents the control variables included in equation (1). Firm and year fixed effects are added to account for unmodeled firm heterogeneity and unmodeled macroeconomic effects that affect operating expenses and capital expenditure. In addition, 22 by including firm fixed effects, the estimated coefficient β j captures the sensitivity of current operating expenses and capital expenditures to within-firm variations in the amount of toxic waste. The results obtained by estimating equation (2) are reported in Appendix C. To save space, the coefficients of 549 chemicals are not tabulated. The dollarized amount of environmental liabilities is then computed as ββWaste , , , for each year based on all kinds of production-related waste that a firm reports. We then re-estimate equation (1) after replacing EnvLiab with
reported in column (2) of Table IV illustrate that the negative effect of this dollarized proxy for environmental liabilities on financial leverage is significant at the 5% level. Second, we then focus on a monetized part of environmental liabilities that arises from violations of environmental regulations and laws. EPA's ECHO (Enforcement and Compliance History Online) database contains facilities' compliance records, which include the occurrence of monitoring activities, the determination of violations, enforcement actions to address violations, and the costs associated with enforcement actions between 1992 and 2013. 19 Since most facilities resolve violations within a year, environmental obligations induced by violations and enforcement actions can be viewed as short-term liabilities. We compute the dollar amount of costs associated with the enforcement actions by summing up penalties, the compliance cost, recovery cost, and the supplemental project cost, and scale the sum by sales. 20 The resulting measure, EnvLiab_ECHO , is then used in the leverage ratio regression. After removing firm-years that are not covered by the ECHO database, the number of observations for this test reduces to 2,890. Nevertheless, the results reported in column (3) of Table IV show that EnvLiab_ECHO is negatively and significantly related to Leverage . A potential concern with our analysis is that our main measure of environmental liabilities only captures liabilities associated with a set of toxic chemicals specified by the EPA, and thus is unable to provide a comprehensive picture of a firm's environmental liabilities. To address the issue, we rely on four alternative data sources, namely, Kinder, Lydenberg, and Domini (KLD) Research & Analytics ,Newsweek Green Rankings, Thomson Reuters Asset4, and Trucost. The KLD database has been widely used in prior studies of corporate social responsibility. It provides binary ratings to approximately 80 factors in seven CSR areas based on data from various sources, including company disclosures, media reports, data from government and non-government agencies, and dialogs with companies. 21 We use the ratings in the environmental concern category to measure corporate environmental liabilities. We first sum up environmental concern items to calculate raw environmental concern scores. 22 To address the concern that the number of environmental concerns varies across the sample period, we follow Deng, Kang, and Low (2013) and create an adjusted measure by dividing the raw environmental scores by the number of environmental concern indicators. The resulting measure is denoted by EnvLiab_KLD . By construction, a higher value of EnvLiab_KLD indicates a higher level of environmental liabilities. We identify 23,163 firm-year observations that are jointly covered by the KLD database and Compustat between 1992 and 2013. 23 Column (4) of Table IV presents the results obtained using EnvLiab_KLD as a proxy for environmental liabilities. Consistent with the main results using the TRI data, we find that EnvLiab_KLD is negatively and significantly associated with the leverage ratio ( t -statistic = -5.4), indicating that firms with more environmental concerns maintain lower leverage ratios. Next, we use Newsweek's green rankings complied via collaborating with three agencies (i.e., Trucost,  KLD Research & Analytics, and CorporateRegister.com) for 500 largest US companies from 2008 onwards. The green ranking is based on three components: the environmental impact score from Trucost, the green policy score derived from KLD, and the reputation score based on data collected by CorporateRegister.com.  The three component scores are converted into standardized values called Z- scores. The overall green score is calculated as the weighted sums of the three Z-scores using the weights 45%, 45%, and 10%, respectively. 24 We converted the overall green score into percentile ranks ranging from 1 (best performing) to 100 (worst performing) and denote the resulting measure by EnvLiab_Newsweek . Firms with high potential environmental damage costs and poor environmental policies and reputations are assumed to have high values of EnvLiab_Newsweek . The sample size for this test reduces to 1,680 firm-year observations that are jointly covered in the Newsweek Green Rankings and Compustat between 2009 and 2012. Column (5) of Table IV shows that the coefficient on EnvLiab_Newsweek remains negative and significant ( t -statistic = -1.9), suggesting that firms that are more environmentally responsible have higher financial leverage. Our third alternative measure of environmental liabilities is the environmental score provided by the Thomson Reuters Asset4 database, which has been widely used by prior studies (e.g., Cheng, Ioannou, Serafeim, 2014; Lys, Naughton, and Wang, 2013). Asset4 provides comprehensive data on the environmental impact of Russell 1000 firms from 2002. To the extent that a firm's environmental scores is directly related to its environmental expenditure, a high environmental score ( EnvLiab_Asset4 ) indicates higher environmental liabilities (Lys, Naughton, and Wang, 2013). After matching with the Compustat database, we obtain a sample of 6,097 firm-year observations between 2003 and 2013.  The results, reported in column (6) of Table IV, show that the estimated coefficient on EnvLiab_Asset4 is negative and significant ( t -statistic = -2.6), indicating that firms with higher level of environmental expenditures maintain low leverage ratio. Finally, we collect information on corporate environmental cost from the Trucost database. Trucost uses a unique methodology to estimate corporate environmental costs based on an input-output model, and is currently the only data source that provides the environmental cost of firms in dollar amount. We identify 6,070 firm-year observations jointly covered by the Trucost and Compustat database between 2002 and 2013. The results are reported in column (7) of Table IV. Environmental cost ( EnvLiab_Trucost ) is negatively and significantly related to leverage ratio ( t -statistic = -2.2). Taken together, our main findings are robust to using alternative measures of environmental liabilities from various data sources.
V.
Further Analysis
In this section we first examine how our main results vary across time and across firms depending on various firm-specific characteristics. We then study the effect of environmental liabilities on the use of bank debt. A. Heterogeneity in the Effect of Environmental Liabilities on Financial Leverage To examine how the relation between environmental liabilities and financial leverage varies across firms and over time, we interact EnvLiab with several variables in the leverage ratio regressions. First, while generally there is a lack of consistency and accuracy in environmental reporting, the impact of environmental liabilities should be more likely to be properly recognized when firms are more visible. Moreover, firms with greater visibility are more subject to public scrutiny and have higher reputation risk and litigation risk, thus their financial policy should be more sensitive to environmental liabilities. In other words, we expect the effect of environmental liabilities to be more pronounced for firms with high visibility. We use firm size (Miller 2006) and analyst coverage (Mehran and Peristiani 2010) to measure firms' visibility. We then interact EnvLiab with Ln ( Assets ) and NbrAnal separately and report the results in columns (1) and (2) of Table V. The interaction terms are negative and significant for both regressions, consistent with our expectations. [Insert Table V here] Second, Maksimovic and Titman (1991) and Titman (1984) argue that customers may be reluctant to conduct business with suppliers with high financial leverage or financing difficulties, which weakens suppliers' ability to honour implicit contracts. To the extent that environmental liabilities lower firms' capacity for financial debt and increase firms' business risk, the effect of environmental liabilities on leverage is expected to be stronger for firms that rely heavily on large customers. Thus, we interact the fraction of sales to principal customers ( PriCus ) with EnvLiab and report the results in column (3) of Table V. 25 The interaction term is negative and significant, implying that firms with higher environmental liabilities are more likely to maintain lower leverage ratios to mitigate large customers' concerns about their financial health. Third, local communities, as an important external stakeholder group, can exert pressure on companies via voting in elections, activism within environmental nongovernment organizations, and citizen lawsuits (Delmas and Toffel 2004). Firms with higher environmental liabilities tend to face more pressure on environmental performance and experience more uncertainty about their future financial performance and operations when they have poor relationships with communities. Thus, we expect that the negative impact of EnvLiab on Leverage will be more pronounced for firms with poorer community relations. To examine how community relations affect our findings, we use the Kinder, Lydenberg, and Domini's (KLD) community rating to measure a firm's community relations ( ComCon ). 26 We then interact EnvLiab with ComCon and report the results in column (4) of Table V. The interaction term is negative and significant, suggesting that firms with poor community relations reduce their financial debt more given the increased business risk caused by environmental liabilities. Last, the FASB's FIN 47 became effective in 2005. Given that its effect has been the immediate recognition of previously unrecorded environmental liabilities and the re-evaluation of previously recorded liabilities (Lee and Trabucchi 2008), we conjecture that the effect of environmental liabilities on capital structure is stronger post-FIN 47.  To explore this possibility, we introduce the indicator variable Post_FIN47 , which equals one if the observation is in 2005 or later, and zero otherwise.  We then augment equation (1) by including Post_FIN47 and EnvLiab×Post_FIN47 and run the regression without year fixed effects.  The results are reported in column (5) of Table V. Consistent with our conjecture, the interaction term is negative and significant, suggesting that the substitution between environmental liabilities and financial liabilities becomes stronger after FIN47 improves corporate environmental disclosure and increases recognition of environmental liabilities.
B.
The Effect of Environmental Liabilities on the Use of Bank Debt
We then examine the effect of environmental liabilities on the fraction of bank debt in total debt. We expect that compared with other creditors (e.g., corporate bond holders), banks are more sensitive to borrowers' environmental liabilities, since they are more identifiable and more likely to be held responsible for environmental damages caused by borrowers.  We employ the following model to study the relation between environmental liabilities and the use of bank debt in total debt. (3) (BD / D) i,, = α + βEnvLiab where we include Leverage as a control variable as previous studies document a negative relation between the use of bank debt and leverage (Houston and James 1996; Denis and Mihov 2003). Our bank debt sample starts from 1994 as Capital IQ has been collecting detailed information on debt structure from 1994.  We exclude firm-years with missing or zero total debt.  The number of observations is reduced to 14,831. ²⁷
The results reported in column (1) of Table VI show that EnvLiab attracts a negative and statistically significant coefficient ( t -statistic = -6.8), suggesting that firms with high environmental liabilities rely less on bank debt. Economically, a one standard deviation increase (0.175) in EnvLiab reduces the ratio of bank debt to total debt by 0.038 (= -0.219×0.175), which amounts to roughly 21% of the average bank debt to total debt ratio (0.184). Consistent with previous studies (Houston and James 1996; Denis and Mihov 2003), we find that larger firms, more profitable firms, firms with higher leverage, and firms with credit ratings tend to have a lower proportion of bank financing in debt structure. Columns (2)-(4) report the results obtained using alternative specifications. In column (2) we estimate equation (3) using the Fama-MacBeth (1973) procedure that corrects standard errors for cross- sectional correlations. In column (3) equation (3) is estimated with firm fixed effects (rather than industry fixed effects). In column (4) we report the second-stage of the 2SLS regression, which involve using Ln (1+DisPro) and Unemployment as instruments for EnvLiab . The results of the first-stage regression are reported in column (2) of Appendix B. The effects of environmental liabilities remain significant across all alternative specifications, suggesting a robust and causal relation running from environmental liabilities to the use of bank debt. In untabulated tests, we interact EnvLiab with several variables described in Section V.A that proxy for firm visibility, the reliance on principal customers, and community concerns, respectively. We find that the interaction terms are statistically insignificant in the bank debt regressions, suggesting the effect of environmental liabilities on the use of bank debt does not vary significantly across the firms in our sample according to these characteristics.
Conclusions
VI.
In recent years, environmental issues have increasing impacts on corporate decisions in light of global increasing awareness on corporate environmental impacts. Although environmental liabilities have been staggering and become an increasingly important issue for investors and corporate managers, relatively few previous studies examine the impact environmental liabilities on corporate financing decisions, capital structure decisions in particular.  The study fills this gap in the literature. Using a large sample of firms covered by the TRI database and the Compustat between 1992 and 2013, we investigate how environmental liability affect two important aspects of corporate capital structure, namely the leverage ratio and the use of bank debt. We find that firms with high environmental liability maintain low leverage ratios and have a higher fraction of bank debt in total debt. Our results are robust to a variety of tests on variable definitions, endogeneity issues, and alternative measures of environmental liabilities.
Appendix A Industry distribution

Appendix B Instrumental variable approach – First stage regressions
The sample consists of firm-years jointly covered in Compustat and the TRI program between 1992 and 2013.  EnvLiab is Ln (1 + Weighted Waste)/Sales.  Weighted Waste is obtained by multiplying the mass of each chemical by its toxicity.  DisPro is the weighted average geographical distance between EPA regional offices and a firm's facilities. Unemployment is the weighted average unemployment rate among all the states in which a firm's facilities operate.  Other variable definitions are in the legend of Table I.  Constant terms, year fixed effects, and two-digit SIC industry fixed effects are included in all regressions but their coefficients are not reported.  The t -statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent errors, which are also corrected for correlation across observations for a given firm. The symbols ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Appendix C Functional relation between environmental liabilities and financial expenses
The sample consists of firm-years jointly covered in Compustat and the TRI program between 1992 and 2013. ( COGS + CAPEX / Sales is cost of goods sold ( COGS ), and capital expenditure ( CAPEX ) scaled by sales. Waste ; is the amount of chemical j (in pounds) deflated by sales. Other variable definitions are in the legend of Table I.  Waste i, constant terms, year fixed effects, and firm fixed effects are included in all regressions but their coefficients are not reported. The t -statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent errors, which are also corrected for correlation across observations for a given firm. The symbols ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
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M/B is the market-to-book ratio defined as ( Assets + Market value of equity - Book value of equity)/Assets. EBITDA/Assets is earnings before interest, taxes, depreciation and amortization scaled by Assets. PPE/Assets is net Property, Plant, and Equipment ( PPE ) scaled by Assets. R&D/Assets is R&D expenses scaled by Assets. RNDD is a dummy variable that equals one if R&D expenses are missing, and zero otherwise. Dividend Payer is a dummy variable that equals one if company paid dividends, and zero otherwise. Z-score is (3.3 × pretax income + Sales + 1.4 × retained earnings + 1.2 × (current assets − current liabilities))/Assets. σ(Earnings) is the historical standard deviation (using available data during the previous 5 years) of the ratio of EBITDA/Assets. MTR is the simulated marginal tax rate after interest expenses obtained from John Graham's Web site. Debt Rating is a dummy variable equal to one if the firm has a debt rating assigned by Standard & Poor's and zero otherwise. NbrAnal is the maximum number of analysts making
The sample consists of firm-years jointly covered in Compustat and the TRI program between 1992 and 2013.  The dependent variable, Leverage , is the ratio of total debt over total assets. EnvLiab is Ln(1+Weighted Waste)/Sales. Weighted Waste is in millions and obtained by multiplying the mass of each chemical by its toxicity. Sales is net sales. Assets is the book value of total assets. Other explanatory variables are defined in the legend of Table I. All explanatory variables are lagged one period relative to the dependent variable. Constant terms are included in all regressions but their coefficients are not reported. In column (1), year and two-digit SIC industry fixed effects are included in the regression. Column (2) reports the results obtained using the Fama-MacBeth (1973) procedure. Column (3) includes both year and firm fixed effects. In column (4), the dependent variable ( Leverage_M ) is computed as total debt divided by the quasi-market value of assets (total assets minus book value of equity plus market value of equity).  The t- statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent errors, which are also corrected for correlation across observations for a given firm. The symbols ***, **, and * denote significance at the 1%,
The sample consists of firm-years jointly covered in Compustat and the TRI program between 1992 and 2013. The dependent variable, Leverage , is the ratio of total debt over total assets. EnvLiab is Ln(1+Weighted Waste)/Sales. Weighted Waste is obtained by multiplying the mass of each chemical by its toxicity.  PastDebt is the sum of debt
The dependent variable Leverage is the ratio of total debt over total assets. EnvLiab_Release is the toxicity-weighted and log-transformed amount of releases scaled by sales. Σ i -i β , Waste j is computed by summing up β , Waste j where Waste j is the amount of chemical j (in pounds) deflated by Sales and β j is estimated using equation (2) for chemical j that a firm reports. EnvLiab_ECHO is the summation of penalties, compliance cost, recovery cost, and supplemental project cost scaled by Sales. EnvLiab_KLD is defined as the total environmental concerns divided by the number of concerns in that year. EnvLiab_Newsweek is computed based on the green scores retrieved from Newsweek's website. EnvLiab_Asset4 is the environmental score extracted from Thomson Reuters Asset4 database. EnvLiab_Trucost is the environmental cost from the Trucost database. Other variable definitions are defined in the legend of Table I. All explanatory variables are lagged one period relative to the dependent variable. Constant terms, year fixed effects, and two-digit SIC industry fixed effects are included in all regressions but their coefficients are not reported. The t -statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent errors, which are also
The sample consists of firm-years jointly covered in Compustat and the TRI program between 1992 and 2013.  The dependent variable ( Leverage ) and control variables are defined in the legend of Table I. PriCus is the fraction of sales to all principal customers. ComCon is formed by summing up the KLD ratings on community concerns.  ComConDum is a dummy variable that equals one if ComCon is missing, and zero otherwise. Post_FIN47 is a dummy variable that equals one if the observation is in 2005 or later, and zero otherwise. All explanatory variables are lagged one period relative to the dependent variable. In columns (1)-(4), constant terms, year fixed effects, and two-digit SIC industry fixed effects are included in all regressions but their coefficients are not reported. Column (5) does not include year fixed effects. The t -statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent
The sample consists of firm-years jointly covered in Compustat and the TRI program between 1992 and 2013. BD/D is the amount of bank debt divided by total debt. EnvLiab is Ln (1 + Weighted Waste)/Sales. Other  variable Weighted Waste is obtained by multiplying the mass of each chemical by its toxicity. definitions are in the legend of Table I. All explanatory variables are lagged one period relative to the dependent variable. Constant terms, year fixed effects, and two-digit SIC industry fixed effects are included in all regressions but their coefficients are not reported. The t -statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent errors, which are also corrected for correlation across observations for a given firm. The symbols ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.