Financial Constraints and Corporate Environmental Policies Taehyun Kim Chung-Ang University This paper documents evidence that financial constraints increase firms’ toxic emissions given that firms actively trade off abatement costs against potential legal liabilities. Exploring three quasi-natural experiments in which firms’ financial resources are likely exogenously affected, we find that relaxing financial constraints reduces U.S. public firms’ toxic releases. The effects of financial constraints on toxic releases are amplified when regulatory enforcement and external monitoring weaken. Overall, our evidence highlights the real effects of financial constraints in the form of environmental pollution, which is a costly negative externality imposed on society and public health. (JEL G32, G38, K32, Q50) Received March 25, 2019; editorial decision February 19, 2021 by Editor Wei Jiang. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. In modern production processes, firms often generate byproducts that have adverse impacts on the environment and public health. In 2015 alone, U.S. firms produced 27.24 billion pounds of toxic chemicals generated in productionrelated processes. Researchers have documented costly adverse outcomes from exposure to pollutants and toxicants, including infant mortality and neurodevelopment disorders, lower education attainment, reduced labor force participation, and lower earnings in later life (for a review, see Currie et al. 2014). A better understanding of firms’ environmental decisions and how they We are grateful for the thoughtful comments from two anonymous referees and the guidance of the editor, Wei Jiang. We thank Kee-Hong Bae, Paulo Fulghieri, Nandini Gupta, April Knill, and Paul Schultz and seminar participants at the KCMI-KAFA Symposium, the University of Notre Dame, the 2017 Wabash River Finance Conference, the 2017 HKUST Finance Symposium, and the 2018 Western Finance Association Conference for valuable comments and suggestions. We also thank Andriy Bodnaruk, Gerald Hoberg, Tim Loughran, Max Maksimovic, and Bill McDonald for making their textual financial-constraint measures available to us; Dong Lou for sharing mutual fund flow-induced trade data; Justin Mohr, David Yeh, and Pete Pietraszewski for assistance with the data; and Tim Antisdel from the EPA for answering questions about the TRI Program. Send correspondence to Qiping Xu, qipingxu@illinois.edu. The Review of Financial Studies 35 (2022) 576–635 © The Author(s) 2021. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. doi:10.1093/rfs/hhab056 Advance Access publication May 5, 2021 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 576 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Qiping Xu University of Illinois Urbana-Champaign Financial Constraints and Corporate Environmental Policies 1 In 2005, the last year for which we can access official data, U.S. manufacturers spent over $26.57 billion on pollution abatement, which is approximately 1% of the manufacturing sector’s shipment value, or more than 20% of total capital expenditure. 2 Karpoff, Lott, and Wehrly (2005) examine the size of losses in market value in companies that violate environmental regulations and show that the losses are similar in magnitude to the legal liabilities imposed. 577 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 577 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 connect to financial market frictions and regulatory settings will inform more fruitful discussions of environmental protections. Environmental abatement is exorbitantly expensive because it requires substantial inputs of energy, labor, contracted services, and raw materials in a process deeply integrated into every aspect of corporate decisionmaking.1 Under the current U.S. regulatory regime, firms are required by law to partially internalize environmental costs by allocating resources for environmental protection. The U.S. Environmental Protection Agency (EPA) works with federal, state, and local authorities to ensure compliance with environmental regulations by enforcing penalties and sanctions upon confirmation of violations. We derive firms’ optimal environmental decisions within a value-maximizing net present value (NPV) framework, under which firms actively trade off the present value of abatement expenditures against the present value of expected legal liabilities. Conditional on production output, the total volume of toxic releases captures the pollution emission intensity, measured as pollution emitted per unit of output (Copeland and Taylor 2003; Shapiro and Walker 2018). Abatement expenditures reduce pollution emission intensity and consequently firms’ expected legal liabilities. The optimal environmental abatement expenditures presuppose that the marginal cost of abatement equals the marginal reduction in expected legal liabilities (Shapira and Zingales 2017).2 In this paper, we apply this fundamental NPV framework to examine how financial frictions, in particular financial constraints, affect corporate environmental policies. As financial constraints unveil and drive up the cost of financing, the marginal cost of environmental abatement increases correspondingly. Holding other factors constant, financial constraints reduce firms’ abatement activities and consequently increase total toxic releases. Exploring the Toxics Release Inventory (TRI) establishment-level microdata from the EPA for the period running from 1990 through 2014, we first show economically large and statistically significant correlations between volumes of toxic chemicals released and two text-based financial-constraint measures for public firms in the United States recently developed by Hoberg and Maksimovic (2014) and Bodnaruk, Loughran, and McDonald (2015). These text-based measures extract qualitative information about financial constraints from corporate disclosure documents and interpret this vast information source using well-defined algorithms. Our results are robust to controls for production level, overall capital expenditures, firm financial characteristics, commonly used accounting-based financial-constraint The Review of Financial Studies / v 35 n 2 2022 578 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 578 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 measures, and alternative categorizations of toxic releases under various EPA regulations. In terms of economic magnitude, a one-standard-deviation increase in the textual financial-constraint measure is associated with a 4% increase in total toxic releases for an average establishment in the sample. To establish the causal impact of financial constraints on firms’ environmental policies, we explore three quasi-natural experiments to generate plausibly exogenous shocks to firms’ available financial resources. In the first experiment, we use the 2004 American Jobs Creation Act (AJCA), which created a positive cashflow windfall by lowering the repatriation tax rate for firms that repatriated foreign earnings previously held by foreign subsidiaries (Faulkender and Petersen 2012). In the second experiment, we exploit the collateral value of firms’ real estate assets, where higher collateral value reduces lending frictions and therefore facilitates financing through higher debt capacity (Chaney, Sraer, and Thesmar 2012). In the third experiment, we use mutual fund flow-induced price pressure (FIPP), where large inflows generate temporary price appreciations and induce seasoned equity offerings (SEOs) (Lou 2012; Khan, Kogan, and Serafeim 2012). Given the exogenous nature of these shocks, they are unrelated to firm fundamentals and hence unlikely to be associated with firms’ environmental policies other than through financing costs. We find consistent results across the three experiments that relaxing financial constraints reduces discharges of toxic chemicals. These three experiments result in total toxic release reductions ranging from 8% to 18%, with the average effect being approximately 14%. After establishing causality, we turn to establishing the link between toxic releases and legal liabilities, and investigate the impact of toxic releases on firm value. We study legal liabilities by compiling information on administrative, civil, and judicial cases filed by government agencies under various environment statues. Our results reveal that higher total toxic releases make government agency investigations more likely, increase the likelihood of legal liabilities imposed consequently, and make legal liabilities (including federal and local penalties, compliance and recovery costs, and the costs of supplemental environmental projects) costlier. Legal enforcement activities represent additional operating costs, reducing firms’ net income. Our analysis shows that greater toxic releases predict worse operating performance (lower returns on assets (ROA) and smaller profit margins) as well as lower market valuation (Tobin’s q). The reported evidence substantiates the underlying tradeoffs between abatement spending and potential legal liabilities faced by firms when making environmental decisions. Pollution is a costly negative externality imposed on society and public health. The natural environment is a public good given that clean air, water, and land are shared by all. The absence of clearly defined private ownership determines market failure with environmental abatement, because firms that pollute do not bear the full costs associated with pollution under the current U.S. environmental regulatory scheme. Therefore, the marginal costs of Financial Constraints and Corporate Environmental Policies 579 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 579 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 environmental abatement that firms incur are significantly lower than the marginal social cost, and this difference drives a wedge between the level of abatement that is optimal for firms and the level preferred by society. Reduction in pollution abatement, driven by financial constraints, thus generates further resource misallocation from society’s point of view. We conduct a back-of-the-envelope calculation to show the additional costs that were imposed on society by firms’ abatement reduction when driven by financial constraints, using the average effect identified across our three quasinatural experiments (14%). The EPA prospective report on the Clean Air Act (CAA, 1990–2020) estimates that every dollar by which firms cut back on environmental abatement generates a welfare loss of $60 to the public and society. We apply a linear extrapolation assuming that abatement expenditures are cut by 14%, representing a $7.14 million reduction for an average firm-year in our sample. For toxic chemicals governed by the CAA alone, this assumption imposes an additional $428 (=$7.14 million*60) million cost on society. This estimate translates to around an $8 billion additional welfare loss to society for all establishments during our sample period. The total welfare costs across all other environmental regulations and statues and across all other firms and establishments in the United States would be significantly higher. Our estimates indicate that financial constraints exacerbate the costly negative externalities of environmental pollution. Lastly, we explore a number of cross-sectional variations in the inner workings of financial constraints and corporate environmental policies. In our first set of cross-sectional tests, we focus on heterogeneity in regulatory environments. If a certain geographic region is designated as a “nonattainment” zone by the EPA, environmental laws and regulations mandate enhanced monitoring and have costly ramifications. Our empirical analysis confirms that more stringent regulations in nonattainment counties increase legal liabilities through a higher likelihood of legal actions and more costly enforcement resolutions. We show that regulatory strictness affects firms’ ongoing environmental abatement as well as the sensitivity of toxic releases to financial constraints. Establishments located in nonattainment areas on average reduce toxic releases by about 30% relative to their intrafirm peers located in attainment areas. Furthermore, as financial constraints increase the marginal cost of abatement expenditures, firms shift financial resources to nonattainment areas where additional toxic releases might incur large regulatory penalties and further reduce abatement in attainment areas with looser environmental regulations. In our second set of cross-sectional tests, we study polluter sizes where large polluters are typically the focus of EPA monitoring and enforcement activities. We find that small polluters are much more responsive to financial constraints, whereas large polluters display lower sensitivity given the presence of higher expected legal liabilities. Overall, the managers in our sample are opportunistic: they are keenly aware of the costs and benefits associated with environmental abatement and strategically choose when and where to pollute. The Review of Financial Studies / v 35 n 2 2022 3 See Margolis, Elfenbein, and Walsh (2009), Bénabou and Tirole (2010), and Kitzmueller and Shimshack (2012) for a comprehensive review of the literature. 580 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 580 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Our paper contributes to the literature on the real effects of financial constraints. Our evidence complements and extends earlier work focusing on outcomes within the scope of a firm, such as investment and employment activities (e.g., Baker, Stein, and Wurgler 2003; Almeida and Campello 2007; Campello, Graham, and Harvey 2010; Chaney, Sraer, and Thesmar 2012; Chodorow-Reich 2013). In contrast, our paper focuses on environmental pollution as the key outcome variable, which by its nature is a costly negative externality imposed on society and public health. We demonstrate that firms carefully evaluate the private costs and benefits associated with the implementation of environmental policies. As a result, financial constraints amplify the negative externality of firms’ abatement decisions and impose additional costs on society. In addition, our paper relates to the discussion of how financial market frictions affect corporate environmental activities (e.g., Masulis and Reza 2015; Cheng, Hong, and Shue 2016; Shapira and Zingales 2017; Fernando, Sharfman, and Uysal 2017; Starks, Venkat, and Zhu 2017; Akey and Appel 2021). Our paper complements the work on corporate environmental decisions.3 The vast majority of these studies exploit firm-level variation using the Kinder, Lydenberg, and Domini (KLD) ratings, which cannot precisely capture intrafirm resource allocations for environmental protection. In comparison, we focus on establishment-level toxic releases and utilize a set of well-defined and high-quality performance metrics using a large panel. Having such granular data available enables us to exploit establishment-level variation to gauge the effects of important factors, such as regulatory mechanisms, which is essential for a better understanding of firms’ environmental decisions. Our paper also contributes to the debate over “doing well by doing good” versus “doing good by doing well.” The formal view juxtaposes corporate environmental policies and corporate financial performance. The idea is that when firms act as “responsible” partners with the environment and other nonfinancial stakeholders, their bottom lines benefit (e.g., Baron 2001; Hong and Kacperczyk 2009; Edmans 2011; Eccles, Ioannou, and Serafeim 2014; Dimson, KarakasĖ§, and Li 2015; Dunn, Fitzgibbons, and Pomorski 2017, Lins, Servaes, and Tamayo 2017; Hoepner et al. 2018; Albuquerque, Koskinen, and Zhang 2019). Implementing environmental protection is costly, however, and “doing good” may imply sacrificing shareholder value (Barber, Morse, and Yasuda 2021). We show that, especially when regulatory forces are weak, actions that can be construed as “doing well” seem to dominate the motives underlying “doing good” (Hong, Kubik, and Scheinkman 2012; Cohn and Wardlaw 2016; Andersen 2017). Financial Constraints and Corporate Environmental Policies 1. Institutional Background and Data 4 For a complete list of laws and executive orders, please see https://www.epa.gov/laws-regulations. 5 The EPA provides sector-by-sector environmental statutes and regulations (https://www.epa.gov/regulatory- information-sector). 581 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 581 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 1.1 Institutional background Born amid elevated concern about environmental pollution, the EPA was founded in 1970 to consolidate a variety of federal research, monitoring, standard-setting, and enforcement activities into one agency. A number of laws serve as the EPA’s foundation for protecting the environment and public health, with several presidential executive orders also playing a central role. Congress authorizes the EPA to develop and enact regulations as well as to explain the critical details regarding the steps that are necessary to implement environmental laws. Panel A in Figure 1 illustrates how EPA rules regulate some products used and produced in the manufacturing process.4 For instance, the CAA focuses on hazardous air pollutants, including lead, sulfur oxides, and nitrogen oxides, and chemicals capable of harming the stratospheric ozone layer, such as halons and methyl chloroform; the Clean Water Act (CWA) focuses on sources of water contamination, such as fertilizers and pesticides, and other naturally occurring chemicals and minerals (i.e., radon, uranium); and the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA), commonly known as the Superfund, was enacted to fund efforts to clean up abandoned or uncontrolled hazardous waste sites. Other government agencies also coordinate with the EPA on environmental regulatory issues. For example, the Occupational Safety and Health Administration (OSHA) has separate enforcement standards to ensure workplace safety and health, targeting known or suspected carcinogens, such as vinyl chloride, benzene, and formaldehyde. A manufacturing facility typically uses multiple chemicals and emits toxic chemicals through many types of media, such as air, water, and land, so it needs to abide by multiple federal and local regulations. Facilities across industries might have industry-specific guidelines based on the nature of the production process.5 In addition, state and local environmental regulations take overlapping forms with the federal ones. The EPA works closely with state and local authorities to ensure compliance with environmental regulations through conducting routine inspections and investigations, and enforcing penalties and sanctions on confirmation of violations. These agencies have the authority to pursue civil administrative (nonjudicial enforcement) actions directing responsible entities to come into compliance or clean up a site, either with or without penalties. For entities that have failed to comply with regulatory requirements or administrative orders, civil trials and penalties are sought by filing charges through the Department of Justice. For the most serious violations that are committed knowingly, criminal actions are pursued where The Review of Financial Studies / v 35 n 2 2022 A C Figure 1 Environmental Protection Agency Panel A showcases several Environmental Protection Agency (EPA) regulations that govern toxic releases in the United States. Panel B illustrates EPA waste management guidelines, with disposal or other releases being the least preferred method. Panel C presents abatement expenditure categories from the 2005 Pollution Abatement Costs and Expenditures (PACE) survey (the most recent available) conducted by the U.S. Census Bureau in the manufacturing sector. Source: United States Environmental Protection Agency. court convictions can result in fines or even incarceration. In summary, legal enforcement actions can conclude with settlements through administrative or judicial actions, civil and criminal penalties, or injunctive relief (requirements to perform designated actions). In addition, supplemental environmental projects, where the violator voluntarily agrees to provide tangible environmental or public health benefits to affected communities or the environment, also can be involved in an enforcement settlement. Under the current U.S. environmental regulatory system, legal liabilities represent an important factor that firms consider in their environmental decisions. Of the 197,476 investigations initiated by government agencies 582 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 582 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 B Financial Constraints and Corporate Environmental Policies 6 Coal prices differ by rank and grade. According to the U.S. Energy Information Administration, in 2019 the average annual sale price of coal per ton (2,000 pounds) ranged from $19.86 for lignite coal to $58.93 for bituminous coal to $102.22 for anthracite coal (https://www.eia.gov/coal/annual/). As one might expect, lignite and bituminous coal generate much more sulfur dioxide and smog than anthracite coal. 7 The definition excludes the use of these materials as fuel substitutes or for energy production (National Recycling Coalition 1995). 8 Energy recovery is often associated with electricity generation, although it can also offset fossil fuels used at industrial sites. 583 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 583 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 during the period of 1990–2014, 111,808 resulted in an average legal liability of $6.75 million per case. When applicable, the average penalty amounted to $270,000, with an average cost of recovery (to stabilize and/or clean up Superfund sites) of $21 million, compliance costs (the sum of injunctive relief and the physical or nonphysical costs of returning to compliance) of $14 million, and supplemental environmental project costs of $842 thousand. Panel B of Figure 1 presents the so-called “Waste Management Hierarchy,” which intuitively outlines EPA’s waste management guidelines. Source reduction involves maximizing or reducing the use of natural resources at the beginning of an industrial process, thereby reducing the waste produced by the process. Source reduction is the EPA’s preferred method of waste management. Consider a typical coal-fired plant. To minimize its environmental impact, the plant can use higher-grade (or cleaner) coal that reduces pollutants from the beginning, but doing so significantly increases production costs.6 Recycling involves a series of activities through which discarded materials are collected, sorted, processed, and converted into raw materials and used in the production of new products.7 Energy recovery is the process of generating energy from the combustion of wastes, including at waste-to-energy combustion facilities and landfill-gas-to-energy facilities.8 Treatment involves the use of various processes, such as incineration or oxidation, to alter the properties or composition of hazardous materials. Direct disposal is the least preferred method of waste management. Unsurprisingly, direct disposal is often the least costly method from a firm’s perspective. The tension between environmental protection and a firm’s bottom line lies precisely in the fact that the EPApreferred waste management methods are more costly, while less preferred methods are more harmful to the environment. In panel C of Figure 1, we decompose the cost categories for abatement expenditures according to the 2005 EPA Pollution Abatement Costs and Expenditures (PACE) survey summary for the manufacturing sector. Pollution abatement operating costs amounted to $20,677.6 million in 2005 across all industries, of which $2,848.4 million (14%) was attributed to depreciation. All new capital expenditures amounted to $128,325.2 million, of which only $5,907.8 million (4.6%) was attributed to pollution abatement capital expenditures. In contrast, expenditures associated with energy, contract work, labor, and materials and supplies make up the vast majority of abatement The Review of Financial Studies / v 35 n 2 2022 1.2 Data Our main source of data is the establishment-level TRI program administered by the EPA. Any facility in the United States that falls within a TRIreportable industry sector, has ten or more employees, and cross a certain threshold in manufactured or processed TRI-listed chemicals is required 9 The data come from PACE: 2005, published by the U.S. Census Bureau and available from https://www.census.gov/prod/2008pubs/ma200-05.pdf. 10 The only comprehensive establishment-level data information available on waste management activities is the Pollution Abatement Costs and Expenditures (PACE) survey, which records abatement costs and expenditures for the manufacturing sector in the United States. The PACE survey was conducted annually between 1973 and 1994 (with the exception of 1987) but was discontinued after 1994 by the U.S. Census Bureau. In 1999, a single PACE survey was conducted, but it differed in many ways from previous surveys, making longitudinal analysis difficult. The last available PACE survey was administered in 2005. The PACE survey does not overlap with the vast majority of the TRI sample period (1990–2014), and inconsistency in the data across vintages makes the survey unsuitable for our study, which heavily relies on time-series variation. 11 Ambient air pollution is measured by EPA pollution monitors that take hourly and/or daily readings. The choice and management of monitoring location is not subject to local authorities. 584 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 584 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 costs.9 This simple decomposition emphasizes that environmental policies not only are a sideshow to regular corporate capital investment but also run much deeper along many dimensions of operations in modern corporations. Understanding the impact of financial constraints on corporate environmental policies is important in its own right and deserves careful investigation. In all our empirical analyses, we control for firms’ overall capital expenditures to emphasize impacts that extend beyond the regular investment channel.10 In some of our empirical tests, we use a county’s attainment or nonattainment status to identify regulatory strictness. Under the Clean Air Act Amendments of 1977 (1977 CAAA), each year every county is classified by the EPA as attainment or nonattainment of the national standards for criterion pollutants. The threshold for excessive pollution is applied uniformly across the United States.11 In any given year, some counties generate pollution that cross these thresholds while others do not. Figure 2 presents the September 2017 version of the nonattainment map from the EPA. The EPA applies both mandatory and discretionary sanctions to nonattainment areas. For example, the EPA can impose a mandatory sanction for highway funding through the Federal Highway Administration. Discretionary sanctions mandate that local plants emitting a pollutant must adopt “lowest achievable emission rates” (LAER) technologies, which requires the installation of the cleanest available technologies regardless of the costs. Furthermore, if any new plants plan to locate in a nonattainment county, the EPA forces them to reduce their releases from another polluting source within the county. In contrast, for designated “attainment” areas, large polluters are required only to use the “best available control technology” (BACT), which is significantly less costly than LAER technology. In summary, nonattainment status results in more stringent regulations to reduce toxic releases without regard to cost (Becker and Henderson 2000; Walker 2013). Financial Constraints and Corporate Environmental Policies to report information about the release of such toxins. Section 313 of the Emergency Planning and Community Right-to-Know Act (EPCRA) created the TRI Program, and specifies that chemicals covered by the TRI Program cause one or more of the following: (1) cancer or other chronic human health effects, (2) significant adverse acute human health effects, and (3) significant adverse environmental effects. The current TRI toxic chemical list contains over 600 individually listed chemicals and chemical categories. This long list is compiled for the numerous environmental categories, including air pollution, clean energy, acid rain, hazardous waste, and safe drinking water. These topics correspond to over 40 environmental laws and presidential EOs where each focuses on a subset of the chemicals and compound categories, with potential overlap across topics. Section 1101 of Title 18 of the U.S. Code makes it a criminal offense to falsify information given to the U.S. Government (including intentionally falsifying records maintained for inspection). Section 325(c) authorizes civil and administrative penalties for noncompliance with TRI reporting requirements. The EPA also conducts an extensive quality analysis of TRI reporting data 585 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 585 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Figure 2 EPA nonattainment status This figure displays a map of counties designated as “nonattainment” zones for National Ambient Air Quality Standards (NAAQS) pollutants as of September 2017. The EPA publishes attainment status for each county in its Green Book publication each year. Source: https://www.epa.gov/green-book The Review of Financial Studies / v 35 n 2 2022 12 Please refer to Barnatchez, Crane, and Decker (2017) for a detailed comparison between these two data sets. Notice that we exclude establishments with fewer than 10 employees, which is the size class where NETS have large imputation rates, as documented by Barnatchez, Crane, and Decker (2017). 586 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 586 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 and provides analytical support for enforcement efforts led by its Office of Enforcement and Compliance Assurance (OECA). The EPA first identifies TRI forms containing potential errors and contacts the facilities that submitted them. If errors are confirmed, the facilities must then submit corrected reports. We cross-check the EPA’s list of priority pollutants under several EPA regulations (published through the Code of Federal Regulations (CFR) Title 40) with the TRI list. For example, of 33 hazardous air pollutants listed in CFR Title 40 61.01 under CAA section 122(b), 29 are included on the TRI list. Regarding hazardous waste treatment, storage, and disposal facilities (CFR title 264.94), 12 of the 14 priority chemicals are included on the TRI list. Under the CWA (CFR 40 401.15), 54 of 65 toxic pollutants are listed by the TRI. The comparison confirms that the TRI list contains chemicals that are particularly important with respect to their environmental and public health impact as well as regulatory concerns. We obtain information pertaining to government agency investigations and enforcement activities through the EPA’s comprehensive Enforcement and Compliance History Online (ECHO) database. For each investigation started by the EPA or state and local agencies, ECHO provides exact filing dates, detailed violation information, milestone dates, and final enforcement actions settled. It reports the dollar amounts for federal and local penalties assessed, compliance actions, cost recovery, and supplemental environmental projects. We aggregate across all these items to evaluate the total legal liability for each case. We extract facility information from the National Establishment Time-Series (NETS) database produced by Walls & Associates, which is a continuous annual compilation of different vintages of the Dun & Bradstreet (D&B) Million Dollar Directory database. The organizational structure of the NETS database shares many similarities with that of the Longitude Business Database (LDB) maintained by the U.S. Census Bureau.12 We draw firm-level accounting information from the Compustat database and stock market information from the Center for Research in Security Prices (CRSP) database. We also use flow-induced price pressure (FIPP) from Lou (2012), the metropolitan statistical area (MSA)-level Home Price Index (HPI) from the Federal Housing Finance Agency (FHFA), MSA-level local housing supply elasticity (Saiz 2010), and the 30-year U.S. fixed mortgage rate to identify the causal link between financial constraints and toxic releases. To analyze the effects of the AJCA, we hand-collect data from firms’ 10-K filings from 2001 through 2007 and determine whether a firm mentioned the AJCA in its 10-K filings and whether the firm repatriated foreign earnings under the AJCA. For seasoned equity issuance, we follow the literature (Khan, Kogan, and Serafeim 2012) and obtain data from the SDC database. We retain only Financial Constraints and Corporate Environmental Policies 1.3 Financial constraint measures Financial constraints are difficult to measure (Farre-Mensa and Ljungqvist 2016). In our empirical analysis, we made a conscious choice to avoid using accounting-based measures of financial constraints because they tend to be highly correlated with production levels, which is a key factor in determining the volume of total toxic releases. Instead, we rely mainly on two text-based financial-constraint measures developed by Hoberg and Maksimovic (2014) and Bodnaruk, Loughran, and McDonald (2015). To construct the financial-constraint measure, Bodnaruk, Loughran, and McDonald (2015) first define several words that describe financial constraints. They classify a firm-year as more constrained if the list of financial constraint words, such as “required,” “obligations,” “requirements,” “permitted,” “comply,” and “imposed,” occur more often.13 Equipped with such a dictionary, they search the entire 10-K archive and use a simple “bagof-words” approach to delineate the “tone” of management discussion and 13 See Bodnaruk, Loughran, and McDonald (2015) for detailed examples using the New York Times’ 10-K filed on February 26, 2008, where the constraining count was provoked by discussions concerning debt, legal issues, and employees. In its 10-K, the company notes that 47% of its workers are unionized (“As a result, we are required to negotiate the wages, salaries, benefits, staffing levels . . .”); the document also includes discussions of credit agencies (“To maintain our investment-grade ratings, the credit rating agencies require us to meet certain financial performance ratios”); a mandatory contract with a major paper supplier (“The contract requires us to purchase annually the lesser of a fixed number of tons . . .”); obligations (“The Company would have to perform the obligations of the National Edition printers under the equipment and debt guarantees if the National Edition printers defaulted under the terms of their equipment leases or debt agreements”); and underfunded defined benefit pension plans (“As of December 30, 2007, our postretirement obligation was approximately $229 million, representing the unfunded status of our postretirement plans”) (all constraint words in italics). 587 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 587 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 common stock issues traded on the NYSE, the AMEX, or NASDAQ, and exclude real estate investment trusts (REITs), American Depository Receipts (ADRs), utilities (SIC codes 4910–4939), and secondary offerings in which no new shares are issued. Lastly, we use the expected default frequency (EDF) provided by Moody’s Analytics to measure the probability that a firm will default (fail to make scheduled debt payments) over the next year. Without common and consistent linking keys connecting the EPA TRI report, NETS, and Compustat/CRSP databases, linking these databases poses a challenge. We first link the EPA TRI report with the NETS database at the facility level, using a link file provided by the EPA with facility-level D&B numbers (also known as “DUNS numbers”). In the second step, we link EPA TRI parent company information with the Compustat /CRSP databases using a historical name-matching algorithm. It is crucial to use historical name information, which is time-varying to plant openings, closings, and ownership changes. We obtain historical company names from CRSP, supplemented by historical name and address information obtained from 10K, 10-Q, and 8-K filings using the SEC Analytical Package provided by the Wharton Research Data Service (WRDS). Please refer to Appendix Section A.1 for a detailed description of our company name-matching process. The Review of Financial Studies / v 35 n 2 2022 1.4 Summary statistics Our final sample includes 8,294 establishments operating in 1,544 U.S. public firms over the sample period running from 1990 through 2014. A total of 92,803 establishment-year observations is included. In Table 1, we present summary statistics for the firm-level observations in our sample (panel A) and compare these with statistics for all Compustat nonfinancial firms during the sample period (panel B). Establishment-level summary statistics for the key variables 14 Not every firm completes the liquidity and capitalization resource subsections in the MD&A section. Hoberg and Maksimovic (2014) show that these firms are generally healthy firms that have few liquidity issues to disclose. 588 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 588 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 disclosure. They validate their measure by its predictive power over events, such as future dividend omissions, pension underfunding, and other related events generally described as syndromes of financial constraints. In contrast, many accounting-based financial-constraint measures have no predictive power. Hoberg and Maksimovic (2014) take a different approach. They focus on mandated disclosures regarding each firm’s liquidity as well as a discussion of the financing source each firm intends to use. Based on disclosures in the Management’s Discussion and Analysis (MD&A) section of the 10-K,14 Hoberg and Maksimovic (2014) evaluate financial constraints by counting instances when a firm was constrained from raising capital. In our empirical analysis, we focus on the debt-market constraint measure, which describes a firm’s intention to issue debt to solve its liquidity problems. We first cross-check our text-based measures against a set of commonly used accounting-based variables: interest coverage, credit rating, expected default frequency (EDF), leverage, a dividend dummy, and the payout ratio. Please refer to Table A.1 in the appendix for variable definitions. These variables have been commonly used in the literature to approximate financial constraints. We regress our two text-based measures on these accounting-based variables and report the results in Table A.2 in the appendix for the full Compustat sample (panel A) and for our EPA sample firms (panel B). We include firm fixed effects to capture the correlation within each firm. In addition, we include several financial characteristics: log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q, which served as controls in our main tests later. Across both panels, we see a strong correlation between the textual-FC measures and the accounting-based variables that line up in the expected direction: more financially constrained firms show lower interest coverage, are less likely to pay dividends, and show lower payout ratios. In addition, more financially constrained firms tend to have worse credit ratings and higher leverage, and are closer to default as indexed by higher EDF. The evidence shows that our text-based measures do match the more traditional and tangible accounting-based measures well, and capture financial constraints in an intuitive manner. In Section 2 we also conduct robustness checks on our baseline results with these accounting-based measures. Financial Constraints and Corporate Environmental Policies Table 1 Summary statistics A. Sample firm characteristics Mean Median SD N 11,421 8,788 18,519 18,513 17,993 18,513 17,448 17,817 9,350 16,806 18,460 18,519 18,482 18,149 18,504 N Text FC HM debt Assets(mil) Cash/assets CAPEX/PPE Tangible Tobin’s q Interest coverage Rating EDF Leverage Dividend dummy Payout ratio ROA Profit margin 0.70 0.02 6,027.26 0.09 0.21 0.33 1.57 22.63 9.70 2.36 0.41 0.65 0.18 0.04 0.09 0.69 0.01 838.56 0.04 0.17 0.29 1.32 6.70 10.00 0.24 0.39 1.00 0.10 0.05 0.08 0.20 0.06 25,989.75 0.11 0.16 0.18 0.81 64.03 3.67 6.50 0.27 0.48 0.26 0.09 0.10 B. Compustat firm characteristics Mean Median SD 89,738 74,296 190,436 190,243 168,289 190,093 167,213 157,697 34,737 147,733 186,742 206,371 188,336 174,653 174,581 0.69 0.00 2,504.21 0.20 0.45 0.30 2.95 3.36 10.70 6.20 0.37 0.31 0.11 −0.24 −1.62 0.67 −0.01 99.31 0.09 0.21 0.21 1.46 3.75 11.00 0.84 0.31 0.00 0.00 0.02 0.05 0.19 0.06 14,609.50 0.25 0.79 0.27 5.29 174.48 3.82 10.58 0.34 0.46 0.28 0.91 8.77 C. Establishment characteristics N Mean Median Total release CAA release CWA release CERCLA release OSHA release Health effects release No health effects release Air release Water release RSEI hazard log(total release) log(CAA release) log(CWA release) log(CERCLE release) log(OSHA release) log(health effects release) log(no health effects release) log(air release) log(water release) log(RSEI hazard) log(sales) Pr(investigation)% Pr(legal_liab>0)% Legal liabilities log(legal_liab) 92,803 92,803 92,803 92,803 92,803 88,290 36,402 91,178 91,178 86,934 92,803 79,266 85,512 89,420 53,999 88,290 36,402 91,178 91,178 86,934 92,790 92,803 92,803 92,803 92,803 114.90 59.33 77.72 101.54 10.15 80.19 17.71 53.06 3.79 13,925.70 1.67 1.18 1.36 1.53 -0.28 2.31 1.51 1.96 0.25 16.49 3.74 4.32 2.92 725,511 0.32 7.86 2.75 4.46 6.30 0.01 6.47 1.79 3.82 0.00 10.99 2.06 1.73 1.79 1.96 0.13 2.01 1.03 1.57 0.00 16.21 3.72 0.00 0.00 0.00 0.00 P25 P75 0.55 −0.02 250.11 0.01 0.11 0.19 1.05 3.30 7.00 0.09 0.22 0.00 0.00 0.01 0.04 0.82 0.05 3,116.86 0.12 0.25 0.43 1.80 14.51 13.00 1.00 0.58 1.00 0.24 0.09 0.13 P25 P75 0.55 −0.04 17.09 0.02 0.10 0.08 1.05 −0.92 8.00 0.17 0.03 0.00 0.00 −0.15 −0.09 0.81 0.03 627.35 0.29 0.44 0.47 2.49 11.63 14.00 6.04 0.61 1.00 0.10 0.07 0.12 SD P25 P75 345.22 177.48 241.47 302.64 33.60 233.94 47.86 154.98 21.24 64,764.91 3.22 3.38 3.24 3.29 3.56 1.97 1.53 1.92 0.90 5.02 1.37 20.34 16.84 30,227,928 1.92 0.75 0.07 0.25 0.44 0.00 0.56 0.13 0.13 0.00 0.39 −0.28 −0.84 −0.66 −0.46 −2.14 0.44 0.12 0.12 0.00 12.87 2.77 0.00 0.00 0.00 0.00 48.18 24.85 30.41 40.69 2.24 38.21 12.10 26.51 0.00 644.14 3.88 3.55 3.59 3.79 2.38 3.67 2.57 3.31 0.00 20.28 4.65 0.00 0.00 0.00 0.00 589 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 589 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Text FC HM debt Assets(mil) Cash/assets CAPEX/PPE Tangible Tobin’s q Interest coverage Rating EDF Leverage Dividend dummy Payout ratio ROA Profit margin The Review of Financial Studies / v 35 n 2 2022 Table 1 (Continued) D. Correlation of various toxic release measures Total release CAA CWA CERCLE Total release CAA CWA CERCLE OSHA 1 0.904 0.935 0.971 0.736 1 0.878 0.910 0.796 1 0.932 0.765 1 0.747 OSHA 1 Figure 3 Toxic releases time series This figure presents the time series of toxic releases for establishments held by U.S. public firms in our sample for a period running from 1990 through 2014. We include the total toxic release volumes (in thousands of tons) and toxic releases under the Clean Air Act (CAA) (in thousands of tons). are presented in panel C. Compared with firms in the overall Compustat universe during the same period, our median asset size is $838.56 million, while the Compustat median asset size is $99.31 million. Our firms also have more tangible assets (the Compustat median tangible ratio is 21%, while for our sample it is 29%) and less cash (Compustat median cash-to-asset ratio is 9%, while our sample median is only 4%). The differences are mainly driven by the fact that our sample overweights the manufacturing sector. Figure 3 presents the time-series plot of aggregate toxic releases of our sample establishments by year.15 The total volume of toxic releases declines over time, and toxic releases under the CAA account for over half of total toxic releases, 15 A major expansion in the industries required to report toxic releases occurred in 1998. Seven new industry sectors were required to report to TRI, including metal mining, coal mining, electric utilities, chemical wholesale distributors, petroleum bulk storage and terminals, hazardous waste management facilities, and solvent recovery facilities. To populate this figure with data, we remove some new sectors that were added to the TRI program in 1998 to keep the number of sectors constant throughout the time period. In subsequent empirical analysis, 590 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 590 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Panel A presents firm-level summary statistics for our sample of U.S. public firms during the 1990–2014 period. Panel B presents summary statistics for Compustat nonfinancial firms during the 1990–2014 period. Panel C provides summary statistics for establishment-level data. Panel D presents the correlation matrix of toxic release volumes administered under various EPA regulations. Financial Constraints and Corporate Environmental Policies however, we include those sectors. A number of smaller expansions in the reporting requirements were made between 2000 and 2014. Most of the expansion was related to newly added carcinogenic toxins based on the National Toxicology Program (NTP) in their Report on Carcinogens (ROC). Using the gradual introduction of newly identified carcinogens as events that increase corporate liabilities, Gormley and Matsa (2011) explore managerial responses. 591 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 591 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 displaying a proportional decreasing trend. Some important factors behind the decline in toxic releases include more stringent environmental regulations and the higher pollution tax that firms are paying (Henderson 1996; Levinson 2009; Shapiro and Walker 2018), migration of heavily polluting industries to other countries (Copeland and Taylor 2004), and introduction of greener technologies (Levinson 2015). Because of the above time-series attributes, we include year fixed effects in all of our specifications. In Table A.3 in the appendix, we summarize total toxic releases using the Fama-French 48-industry classification. As one might expect, the chemicals, construction materials, steel, machinery, and auto industries have the largest number of facilities discharging toxic chemicals, followed by the consumer products, food products, rubber and plastic petroleum, and public utility industries. Another feature of the data is that, for certain industries, such as precious metal, metal mining, and utilities, the average emissions per establishment are much higher than for other industries. While the TRI program includes over 600 chemicals, most establishments emit several chemicals on the list. The average establishment-year emits around five distinct chemicals, with the median being three. Over 25% of our sample establishments release only one chemical, and establishments in the 99th percentile release 28 chemicals. The number of chemicals emitted within each establishment is highly persistent in our sample. The correlation between the number of chemicals reported in year t and those reported in year t-1 is 0.973 for all establishments, 0.971 for establishments emitting more than one chemical, and 0.967 for establishments emitting more than three chemicals. We further manually check a randomly selected 10% of our sample establishments to confirm that establishments tend to emit a consistent list of chemicals without drastic changes in composition over time. In addition, chemicals emitted across industries and establishments overlap only to a certain degree. In Table A.4 in the appendix, we first list the top-30 chemicals ranked by the percentage of establishments emitting these chemicals. Even the most commonly observed chemicals, such as toluene, xylene, and ammonia, are emitted by only around 20% of sample establishments. In addition, the top chemicals ranked by aggregated volume differ from the list of chemicals ranked by coverage of establishments, indicating that volume and coverage do not correspond in a one-to-one manner. Furthermore, in Table A.5 in the appendix, we tabulate the top-five chemicals ranked by aggregated volume across the Fama-French 48 industries, and the top-five chemicals vary largely across industries. These summary statistics highlight the need to account for establishment or industry characteristics when analyzing the data. The Review of Financial Studies / v 35 n 2 2022 2. Baseline In this section, we describe our baseline ordinary least squares (OLS) regression model that relates firms’ total toxic releases to financial-constraint measures. The purpose of the correlation analysis is to establish some empirical regularities and benchmark cases. The baseline regression is as follows: Toxic Releasesi,c,t = α +βFinancial Constraintsc,t−1 +γ Firm Controlsc,t−1 +κEstablishment Controli,c,t +FEs+i,c,t , (1) 592 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 592 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Given the importance of the TRI list in capturing firms’ overall negative environmental and public health impacts, we use total toxic releases of all TRI chemicals as our key outcome variable, with an equal weight assigned to each chemical. Considering the complex regulatory structures and the large number of specific regulations involved, total toxic releases provide the most comprehensive coverage with respect to both establishments on record and chemicals on the list, capturing the variation in establishments’ overall environmental footprint. Given that detailed establishment-level abatement expenditures are not available, we rely on total toxic releases to infer firms’ abatement spending, assuming total toxic releases are a decreasing function of abatement spending conditional on the volume of production. We cross-check our total TRI releases with toxic releases administrated under the most relevant environmental regulations, namely, the CAA, the CWA, the CERCLA, and the OSHA. Panel C of Table 1 present summary statistics at the establishment level. The average total TRI toxic releases per establishment-year is approximately 115 tons, and the average releases per establishment-year based on the CAA, CWA, and CERCLA definitions range from 60 to 102 tons. Chemicals associated with health effects account for a majority of the total releases, whereas a smaller fraction of establishments emit chemicals associated with no health effects and the volumes are generally lower. Consistent with Figure 3, air releases account for a major part of the total toxic releases. Among our sample establishments, 4.3% have been subjects of investigations initiated by government agencies, with 2.92% of these cases leading to positive legal liabilities. The average legal liability is around $24.8 million conditional on cases with positive legal liabilities and $725,511 across all sample observations. Both toxic releases and legal liabilities are highly skewed. In our analysis, we use the natural logarithm of toxic releases (in tons) to address skewness in the data. Panel D tabulates the correlation across toxic releases under various EPA regulations. Across various regulations, the release amounts are highly correlated with the total TRI toxic releases, with the correlation ranging above 0.9 for the CAA, the CWA, and the CERCLA. Financial Constraints and Corporate Environmental Policies Table 2 Total toxic releases Text FC (1) (2) (3) 0.221∗∗∗ 0.195∗∗∗ 0.160∗∗ (0.070) (0.065) log(assets) 0.042 (0.039) −0.063 (0.352) 0.043 (0.119) −0.344 (0.387) 0.057 (0.036) −0.001 (0.029) 51,413 .83 Yes Yes No No 0.030 (0.039) −0.135 (0.343) 0.063 (0.114) −0.374 (0.378) 0.063∗ (0.035) 0.002 (0.027) 51,155 .84 Yes No Yes No 0.031 (0.033) 0.193 (0.297) −0.060 (0.110) −0.199 (0.296) 0.057∗ (0.033) 0.022 (0.028) 51,361 .84 Yes No No Yes Cash/assets CAPEX/PPE Tangible Tobin’s q log(sales) Observations Adj. R-squared Establishment FE Year FE State-year FE Industry-year FE 0.654∗ (0.360) 0.039 (0.039) 0.194 (0.296) 0.008 (0.130) 0.012 (0.369) 0.082∗∗ (0.032) 0.010 (0.038) 36,562 .86 Yes Yes No No (5) 0.631∗∗ (0.312) 0.030 (0.039) 0.116 (0.295) 0.002 (0.124) 0.003 (0.364) 0.093∗∗∗ (0.033) 0.017 (0.034) 36,422 .86 Yes No Yes No (6) 0.635∗∗ (0.303) 0.006 (0.039) 0.303 (0.292) −0.085 (0.124) 0.148 (0.315) 0.075∗∗ (0.035) 0.027 (0.034) 36,511 .86 Yes No No Yes This table presents results of OLS regressions of total toxic releases (measured by tons in logarithm) on two textbased financial-constraint measures. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log (sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. where i denotes an establishment, c denotes a firm, and t denotes a year. Firm controls include log(asset), Cash/assets, Tangible, and Tobin’s q, capturing several aspects of firms’ growth and financial positions. We also include total capital investment, CAPEX/PPE, to control for the impact of overall capital investment on our outcome variables. Establishment-level control is log(sales) to account for production scale. Table 2 presents estimates deriving from regressing financial constraints on total toxic releases (measured by tons in logarithm) as our main outcome variable. The regressions for columns 1 through 3 include the financialconstraint measure Text FC, which is the text-based measure developed in Bodnaruk, Loughran, and McDonald (2015). The regressions for columns 4 through 6 include the measure HM debt, which is the debt-market constraint measure taken from Hoberg and Maksimovic (2014). We hypothesize the coefficient β to be positive, that is, the more financially constrained firms are likely to release more toxins. Standard errors are clustered at the firm level. We impose establishment fixed effects to account for time-invariant unobservable establishment attributes that might affect total toxic releases. Across both textual measures and all specifications, financial constraints show significantly positive effect on firms’ toxic releases. The key coefficient of 0.221 (column 1) implies that a one-standard-deviation (0.20) increase in Text FC is associated with approximately a 4.4% (0.2*0.22) increase in the log tons of 593 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 593 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 (0.073) HM debt (4) The Review of Financial Studies / v 35 n 2 2022 16 In a log-liner model LogY = α +β ∗X + , a one-unit increase in X leads to an expected increase in LogY of βĖ i i i units. For small values of βĖ , we use the approximation eβ ≈ 1+ βĖ for a quick interpretation of the coefficients, finding that 100*βĖ is the expected percentage change in Y for a unit increase in X. 17 The RSEI hazard score includes about 400 of the TRI chemicals and chemical categories. However, an RSEI hazard score is calculated according to toxicity weights solely based on human health effects associated with long-term exposure to chemicals, while short-term exposure and ecological effects are not considered. 18 The volume of chemicals released through other disposal media, including underground and landfills, is zero for the majority of our sample, leaving too few observations for analysis. 594 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 594 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 total toxic releases.16 Similarly, the results reported in column 4 of Panel panel A in Table 2 show that a one-standard-deviation (0.06) increase in HM Debt is associated with a 4% (0.6*0.65) increase in total toxic releases. All of the point estimates across regression specifications are of similar economic magnitudes at around 4%. Appendix Table A.6 in the appendix presents estimates of regressing financial constraints on toxic chemical releases categorized under various EPA regulations. We include emissions under the CAA, the CWA, and the CERCLA to examine robustness across disposal methods. We also include emissions covered under the OSHA to demonstrate robustness against emissions that represent the highest known threats to work place safety. The coefficients of the two text-based financial-constraint measures remain quantitatively similar across categories of toxic chemical emissions. To further understand which chemicals are driving the variation of total toxic releases, we group the TRI chemicals by their per-plant or economywide magnitudes, relative human health effects, and physical properties, and examine the relationship between various toxic releases and firms’ financial constraints. For per-plant or economy-wide magnitudes, we rank chemicals according to their volume either within establishments or in our full sample. For relative human health effects, we consider whether certain chemicals are associated with human health effects identified by the EPA, and include the EPA’s Risk-Screening Environmental Indicators (RSEI) hazard score which uses relative toxicity weights that describe each chemical’s toxicity relative to that of other chemicals.17 For physical properties, we consider toxic chemicals simultaneously released through the air and water.18 We first group chemicals by their per-plant magnitudes and conduct our baseline tests. In Table A.7 in the appendix, we present regression estimates with the outcome variables being the volume of the number one (panel A) and the top-three (panel B) chemicals ranked within establishments. For each panel, we further separate our sample establishments based on the number of chemicals released to better understand which groups are giving the identifying power. The results presented in Table A.7 in the appendix demonstrate fairly consistent elasticity across establishments that emit fewer versus those that emit more chemicals. Taking panel A with the outcome variable being the volume of the number one chemical within establishments as an example, the coefficient estimates for Text FC is 0.220 for groups that emit one or two chemicals, 0.193 Financial Constraints and Corporate Environmental Policies 595 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 595 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 for groups that emit more than two chemicals, and 0.190 for our full sample, and all these results are statistically significant at the conventional level and within close proximity to each other. A similar pattern is observed in panel B with the outcome variable being the volume of the top-three chemicals. Next, we group chemicals by their economywide magnitudes and examine toxic releases by the top-five, the top-30, and the rest of the TRI chemicals according to aggregated volume (the top-30 chemicals are listed in Table A.4 in the appendix). in the appendix. As shown in Table A.4 in the appendix, no individual chemical can provide comprehensive coverage across our sample establishments given the differences in production processes across establishments and industries. When we focus only on the top-five chemicals by aggregated volume, our sample size is reduced sharply in columns 1 and 2 of panel C. Both coefficient estimates are positive but nonsignificant, and the lack of statistical power can result from a significantly small sample size. In contrast, when we expand our chemical list to the top-30 chemicals and report the results in columns 3 and 4, or to the remaining chemicals on the TRI list, we observe results showing effects close to that of our baseline results reported in Table 2. Overall, Table A.7 in the appendix demonstrates that the effects of financial constraints on total toxic releases are driven mainly by the top chemicals within establishments rather than by a few top chemicals ranked by aggregated volume, and similar effects are observed across establishments that emit varying numbers of chemicals. Last, we group chemicals by their relative health effects and physical properties and present the results in Table A.8 in the appendix. The results reported in panel A show that chemicals associated with adverse human health impacts are the key drivers of the effects of financial constraints on total toxic releases (columns 1 and 2), and the documented effects are more profound if we weight chemicals by their relative toxicity (columns 3 and 4). In contrast, we do not observe a similar effect for chemicals associated with no health effects (columns 5 and 6). The different results reported in columns 1 and 2, on the one hand, and columns 5 and 6, on the other hand, can be explained by the fact that chemicals are selected into the TRI program based on their adverse acute health effects or environmental effects. Chemicals with health impacts are the major component of total toxic releases. TRI chemicals with negative health effects, which comprise 77% of the TRI list, account for roughly 70% (80.2/114.9 tons) of the total release volume based on summary statistics listed in panel C of Table 1. On the other hand, fewer than 40% of sample establishments emit chemicals associated with no health effects, and the smaller sample size could contribute to the lack of statistical power associated with the results reported in columns 5 and 6. The results presented in panel B show that air releases are the main component within total releases driving the correlation with firms’ financial constraints, and chemicals released through water are observed in a much smaller fraction of the sample and do not display a similar correlation. Overall, the results reported in Table A.8 in the appendix show that the effects of The Review of Financial Studies / v 35 n 2 2022 3. Identification: Three experiments In Section 2 we report a positive correlation between firms’ toxic releases and financial-constraint measures. It remains challenging, however, to identify the causal impact of financial constraints on environmental policies. The key concern lies in omitted variable issues. For example, some unobservables might affect firms’ financial health and environmental decisions, which could bias the OLS coefficients in either direction. Alternatively, there is a reverse-causality concern: poorer environmental performance is associated with worse financial performance (Margolis, Elfenbein, and Walsh 2009) and higher financing costs (Chava 2014), which likely makes firms more financially constrained. To 596 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 596 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 financial constraints on total toxic releases concentrate on chemicals associated with adverse human health effects, highlighting the negative externality they impose on public health. In Table A.9 in the appendix, we rerun our baseline results with several accounting-based financial-constraint measures as the variable of interest: interest coverage, credit rating, EDF, leverage, a dividend dummy, and the payout ratio using the same specification as that used to derive the results reported in Table 2. More financially constrained firms, that is, firms with worse credit rating, shorter distance to default, higher leverage, and no or lower payouts, emit a greater volume of toxic chemicals. The coefficient estimates are statistically significant at the 1% level for credit rating and leverage, whereas the coefficient estimates of EDF, dividend dummy, and payout ratio deliver a similar message but are statistically insignificant at the conventional level. The coefficient estimate for interest coverage is positive but statistically insignificant. For a one-standard-deviation increase in financial constraints measured by these accounting-based variables, our estimates in Table A.9 in the appendix imply an increase in total toxic releases by approximately 1%-2% for leverage, dividend dummy, and payout ratio, and 3%–4% for credit rating and EDF. The estimated impact is close to the 4% economic magnitude derived using two text-based financial-constraint measures. Table A.10 in the appendix also presents the results of a horse race between the text-based and accounting-based financial-constraint measures. Including accounting-based financial-constraint measures does not change the economic magnitude or statistical significance of the textual financial-constraint measures. In addition, accounting-based financial-constraint measures do not seem to affect total toxic releases once the textual financial-constraint measures are included. In our setting, accounting-based measures of financial constraints have disadvantages because they tend to be closely correlated with production levels, which is a key factor in determining the volume of total toxic releases. This particular concern makes text-based financial-constraint measures preferable for our analysis. In summary, we recognize that no financial constraint metric is perfect. We hope that these two text-based measures capture some relevant aspects of financial constraints in an intuitive manner, and rely on quasi-natural experiments to generate causal interpretations. Financial Constraints and Corporate Environmental Policies 3.1 Experiment 1: Tax holiday The 2004 AJCA provided a temporary tax break for firms by lowering the repatriation tax rate from 35% to 5.25% for multinational U.S. firms that had earnings held by foreign subsidiaries. It was implemented to boost domestic investment and employment by incentivizing U.S. firms to bring back their stockpiles of cash that had been “trapped” overseas because of repatriation taxes due. The U.S. Department of the Treasury specified how the repatriated earnings could be spent, such as on expenditures for plants and equipment, employment, and acquisitions. To qualify for the tax break, companies need to have “domestic reinvestment plans” that outline the uses of the repatriated funds. In a nutshell, the AJCA introduced a positive cashflow windfall for firms that repatriated foreign earnings, which is well-suited to capturing the temporary and significant changes in financing cost for our sample firms located in the United States. Collecting data from 10-K statements from 2000 through 2007, we are able to identify 350 unique firms that repatriated foreign earnings under the AJCA, of which 132 are matched to our EPA-Compustat-merged sample. We exploit a differences-in-differences (DID) analysis as in Faulkender and Petersen (2012). We classify firms into three categories: (1) firms with little or no likelihood of repatriating foreign earnings, (2) firms with a reasonable likelihood of repatriating foreign earnings that choose not to repatriate such earnings under the AJCA, and (3) firms with a reasonable likelihood of repatriating foreign earnings that choose to repatriate under the AJCA. To properly account for the heterogeneous ex ante likelihood of repatriation and the actual repatriation of foreign earnings, the empirical design explicitly considers both the predicted probability of repatriation and the residual from actual repatriation. Our focus is on the residual term, which identifies differential reactions between treatment (category 3) and control firms (category 2). 597 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 597 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 establish the causal link, we need to generate an exogenous shock to financial constraints, while the shock should be unrelated to firms’ environmental abatement decisions. In this section, we exploit three quasi-natural experiments to generate an exogenous shock. These three experiments include the American Jobs Creation Act (AJCA) of 2004 (Faulkender and Petersen 2012), the collateral value of firms’ real estate assets (Chaney, Sraer, and Thesmar 2012), and mutual fund flow-induced price pressure (FIPP) (Lou 2012; Khan, Kogan, and Serafeim 2012). These experiments affect the cost of financing through cash flow windfalls from foreign income repatriation, higher debt capacity driven by collateral values, and seasoned equity issuance as a result of short-term price appreciation. These three shocks are completely independent of each other and generate exogenous shocks to firms’ financial constraints through orthogonal channels. We study toxic releases in response to these three shocks to generate causal inferences regarding how financial constraints affect toxic releases. The Review of Financial Studies / v 35 n 2 2022 Table 3 Tax holidays, AJCA (1) Residual*FC Pr(Repatriates) Residual Preinvestment profit log(assets) Cash/assets CAPX/PPE Tangible Tobin’s q log(sales) Observations Adj. R-squared Establishment FE Year FE Industry-year FE State-year FE Firm FE (3) −0.731∗∗∗ (0.243) −0.128 (0.265) 0.209∗∗ (0.099) −0.206∗∗ (0.100) 0.449 (0.325) 0.118∗∗ (0.055) 0.633∗∗ (0.321) −0.183 (0.125) 0.549 (0.373) 0.054 (0.036) 0.033 (0.037) 19,255 .91 Yes −0.500∗∗ (0.222) −0.196 (0.246) 0.147 (0.095) −0.114 (0.092) 0.750∗∗ (0.318) 0.131∗∗ (0.053) 0.381 (0.290) −0.185 (0.129) 0.581∗ (0.347) 0.036 (0.034) 0.030 (0.034) 19,170 .91 Yes Dom inv (4) 0.038∗∗∗ (0.015) −0.027 (0.017) −0.017∗∗ (0.007) −0.018∗∗∗ (0.006) 0.183∗∗∗ (0.035) −0.037∗∗∗ (0.006) 0.227∗∗∗ (0.040) 0.033∗∗ (0.016) 0.009 (0.037) 0.006 (0.004) 3,024 .32 Yes Yes Yes Yes This table presents regression estimates of the effects of the AJCA. For columns 1 through 3, we use the total amount of toxic releases (measured by tons in logarithm) at the establishment level as outcome variables. The residual is defined as the dummy variable Repatriation minus Pr(Repatriate), where Pr(Repatriate) is estimated from the cross-sectional logistic regression as in Table A.11 in the appendix. FC is defined as the proportion of years during which a firm had insufficient after-taxes earnings to fully finance capital expenditures in the four-year period prior to the AJCA (Faulkender and Petersen 2012). Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <. 05; ***p <.01. Specifically, we first estimate the probability of foreign earnings repatriation (P r(Repatriate)c,t ) based on firm characteristics prior to 2004 and present the results in Table A.11 in the appendix. Firms with fewer investment opportunities, higher unrepatriated foreign earnings, and larger tax breaks generated by the AJCA are more likely to repatriate, consistent with findings reported in previous studies. We then generate the residual term (Residualc,t = Repatriatec,t −P r(Repatriate)c,t ), which captures the effects of repatriation under the AJCA, holding firm characteristics and the probability of repatriation constant (i.e., foreign earnings in low-tax jurisdictions). The financial assumption behind the AJCA is that a significant portion of firms with profitable overseas subsidiaries are financially constrained in their domestic operations. Firms that were not constrained before the AJCA should have reached desired levels of environmental activities, and therefore the AJCA would change only the source of financing without affecting the 598 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 598 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 FC −0.544∗∗ (0.234) −0.157 (0.250) 0.150 (0.096) −0.091 (0.095) 0.790∗∗ (0.311) 0.140∗∗∗ (0.053) 0.480 (0.299) −0.136 (0.125) 0.639∗ (0.342) 0.034 (0.034) 0.022 (0.035) 19,269 .91 Yes Yes log(total release) (2) Financial Constraints and Corporate Environmental Policies 3.2 Experiment 2: The collateral channel In our second identification strategy, we exploit the collateral channel of firms’ real estate assets, which represents a major part of their tangible assets. An 19 We use the Compustat segment file to compute domestic capital expenditures and domestic R&D. 599 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 599 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 abatement level. In contrast, for financially constrained firms that previously underfunded their environmental activities, the AJCA significantly reduced the cost of internal capital for their domestic segments. Repatriating income under the AJCA enables constrained firms to fund environmental abatement activities that otherwise would have been forgone, resulting in a reduction in toxic releases in their domestic segments. We examine the effects of foreign earnings repatriation on corporate environmental policies as follows: Toxic ReleasesI,c,t = α +β1 Residualc,t ∗FCc,t +β2 Pr(Repatriate)c,t (2) +β3 Residualc,t +β4 FCc,t +γ Controls+F E +i,t , where i denotes an establishment, c denotes a firm, and t denotes a year, with firm controls including log(assets), Preinvestment earnings, CAPEX/PPE, and Cash/assets, Tangible, and Tobin’s q. The establishment control is log(sales). In this experiment, we follow Faulkender and Petersen (2012) and use FC to identify the financially constrained group, where FC is defined as the proportion of years during which a firm’s investment expenditures exceed its internal cash flow from 2000 through 2003. We choose this measure for easy replication of their findings regarding the effects of the AJCA on domestic investment, therefore lending support to our empirical settings. Our main variable of interest is Residual*FC, which captures the effects of foreign earnings repatriation on financially constrained firms. Table 3 presents the regression estimates of Equation (2). The negative coefficient for Residual*FC suggests that repatriating firms that were previously constrained (i.e., unable to fully fund abatement activities) significantly reduce toxic chemical releases after the repatriation. In terms of economic magnitude, a one-standard-deviation increase in the repatriation shock (0.27) leads to a drop of roughly 15% (0.27*0.544) in total toxic releases (based on coefficient estimates reported in column 1 with the loglinear specification) for US establishments owned by financially constrained firms, relative to their nonconstrained peers. To put these estimates in perspective, we further examine the effects of the AJCA on domestic investment and present the results in column 4 of Table 3. The dependent variable Domestic Inv is defined as the sum of domestic capital expenditures, domestic R&D, advertising expenses, and acquisitions scaled by assets.19 We find that financially constrained firms disproportionately increased domestic investment relative to unconstrained firms after foreign earnings repatriation (Faulkender and Petersen 2012). The Review of Financial Studies / v 35 n 2 2022 H P Ii,t = α +β ×Elasticityi ×Mortgage Ratet +MSA F E +Y ear F E +i,t . (3) In the second stage, we run the following regression specification: Yk,j,i,t = α +β ×RE V aluej,i,t +γ H P Ii,t + κn I nit. Condj n ×H P Ii,t +controls +k,j,i,t , (4) where k denotes an establishment, j denotes a firm, i denotes an MSA, t denotes a year, and Firm j ’s real estate value in year t (RE V aluej,i,t ) is 20 Several steps are involved in this process. First, the asset values provided by Compustat in 1993 are book values instead of market values. To generate market values in 1993, we approximate the average age of these assets using the ratio of accumulated depreciation of buildings to the historic costs of buildings (assuming a 40-year depreciation schedule) and then calculate the historical cost using the CPI before 1975 and state-level HPI after 1975. Second, after obtaining the 1993 market value, we use headquarters MSA-level HPI as the price inflater to generate real estate values for 1993–2007. Chaney, Sraer, and Thesmar (2012) collect information about real estate assets using firms’ 10-K filings and confirm that facilities are clustered near headquarters in the same MSA areas and that a major portion of corporate real estate assets are located at the headquarters. 600 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 600 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 increase in the value of real estate assets reduces external financing frictions, and has been documented to generate more debt issuance and investment activities (Chaney, Sraer, and Thesmar 2012). We therefore use this setting to identify the effects of financial constraints on environmental policies. This experiment uses variations in a firm’s real estate value driven by local real estate prices. Before 1994, Compustat provided a detailed decomposition of property, plant, and equipment (PPE), which includes three major categories of real estate assets: building, land and improvement, and construction in progress. We retrieve the market value of these three categories for firms in 1993 and then inflate their real estate value using local MSA-level Housing Price Index (HPI) data provided by the Federal Housing Finance Agency (FHFA) for 1993–2007.20 Real estate value calculated this way has the advantage of not being affected by decision to acquire additional real estate assets after 1993, which helps us to obtain a cleaner identification. One may have concerns about the potential for omitted variables associated with both HPI and firms’ environmental decisions that could contaminate the collateral channel that we try to establish. To address these endogeneity concerns, we use the MSA-level HPI instrumented by the MSA-level supply elasticity (Saiz 2010) interacted with 30-year mortgage rates in the United States (Mian and Sufi 2011; Chaney, Sraer, and Thesmar 2012). The intuition is that, as housing demand increases based on changes in the mortgage rate, home prices are less sensitive to demand in elastically supplied markets because these areas capitalize demand shocks into quantities rather than prices, whereas in areas with very inelastic supplies, demand shocks translate into prices instead of quantities (e.g., San Francisco or Boston). The first-stage specification is as follows: Financial Constraints and Corporate Environmental Policies 3.3 Experiment 3: Mutual fund flow-induced price pressure In our third experiment, we take advantage of one key observation from the mutual fund literature: when investors move capital to or away from mutual funds, the inflows and outflows force mutual fund managers to proportionally scale their existing stock positions up or down. Consequently, their trades induce price pressure that pushes stock prices up (down) with capital inflows (outflows) (Coval and Stafford 2007; Lou 2012). As FIPP slowly dissipates, a subsequent return reversal occurs. A common interpretation of the temporary price pressure is that it represents a source of “nonfundamental” shocks (Edmans, Goldstein, and Jiang 2012; Hoberg and Maksimovic 2014). Khan, Kogan, and Serafeim (2012) examine the effects of temporary price pressure resulting from flow-driven trading and find that positive price pressure induces SEOs. In other words, large inflow-induced price pressure generates exogenous positive variation in external financing that reflects equity market 601 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 601 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 calculated by inflating the starting value using local MSA-level HPI in year t (RE V aluej,i,1993 ×H P Ii,t ), normalized by the lagged PPE. The regressions include MSA-level HPI to control for the direct impact of real estate prices on toxic releases. It also includes firm-level initial conditions (Init. Cond) , which are the five quintiles of age, assets, and return on assets interacted with MSA-level HPI to control for the heterogeneous ownership decisions and their potential impact on the sensitivity that we measure. Establishment fixed effects are included in all specifications. We present the first-stage regression estimates in Table A.12 in appendix, where the interaction terms are significantly positively related to MSA HPI. Table 4, panel A, presents the regression estimates for firms’ debt issuance, overall debt level, and financial-constraint measures on the instrumented real estate value. An increase in real estate value translates into more debt issuance and higher total debt, confirming that the increase in real estate value facilitates debt issuance through the collateral channel. Higher debt capacity and consequently more debt issuance relaxes firms’ financial constraints, as shown in columns 3 and 4 of panel A, where both coefficient estimates are negative and statistically significant. We then examine the effects of higher real estate value on firms’ toxic releases and report the results in panel B of Table 4. Column 1 uses real estate value inflated by HPI, and column 2 uses real estate value inflated by the instrumented HPI. Across both specifications, higher real estate value leads to reduced toxic releases. The coefficient estimates reported in column 2 show that, for a onestandard-deviation increase (1.47) in the instrumented real estate value (RE value IV ), total toxic releases drop by around 8% (1.47*0.053) based on the log-linear specification. In column 3, we report the results of examining the effects of total debt on toxic releases. The coefficient estimate on total debt is negative and statistically significant, demonstrating that firms issuing more debt are those that reduce toxic releases. The Review of Financial Studies / v 35 n 2 2022 Table 4 The collateral channel: Real estate shock A. Real estate value, debt issuance, and financial constraints (1) Debt issuance RE value IV 0.061∗∗ 0.440∗∗∗ (0.025) (0.082) 18,941 18,237 .28 .45 Yes Yes Yes Yes Yes Yes B. Real estate value and toxic releases (1) RE value (3) Text FC −0.046∗∗ (0.018) RE value IV −0.419∗∗ (0.167) 12,691 .34 Yes Yes Yes Cash/assets CAPEX/PPE Tangible Tobin’s q log(sales) Observations Adj. R-squared Establishment FE Year FE Init characteristics −0.047 (0.054) 0.536 (0.450) 0.020 (0.144) 0.067 (0.350) −0.047 (0.045) 0.039 (0.034) 25,161 .82 Yes Yes Yes −0.219∗∗∗ (0.049) 10,523 .48 Yes Yes Yes (2) −0.053∗∗∗ (0.017) Total debt log(assets) (4) HM debt −0.117∗∗ (0.051) 0.552∗∗ (0.279) 0.030 (0.117) 0.023 (0.272) −0.079∗∗∗ (0.030) 0.064∗∗∗ (0.025) 20,565 .83 Yes Yes Yes (3) −0.299∗∗ (0.137) −0.015 (0.051) 0.274 (0.427) 0.041 (0.138) 0.283 (0.356) −0.044 (0.045) 0.039 (0.036) 25,796 .82 Yes Yes Yes This table presents regression estimates of the effects of firms’ real estate value. Firms’ real estate value is calculated as the 1993 market value inflated by the MSA-level House Price index (HPI), where MSA-level HPI is instrumented by the interaction between MSA-level local housing supply elasticity and U.S. 30-year fixed mortgage rates. In panel A, we demonstrate the effects of real estate value on new debt issuance, total debt amount, and textual financial-constraint measures, while controlling for MSA-level HPI, initial firm characteristics (the five quintiles of age, assets, and return on assets) interacted with MSA-level HPI, Tobin’s q, and cash position. In panel B, we tabulate the effects of real estate value on total toxic releases. Firm-level controls include MSA-level HPI, initial firm characteristics (the five quintiles of age, assets, and return on assets) interacted with MSA-level HPI, lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. *p <.1; **p <.05; ***p <.01. valuation, relaxing firms’ financial constraints. We explore this setting to test the associated impact on corporate environmental policies. Specifically, we use a quarterly measure of FIPP from Lou (2012): Sharesi,j,t−1 ×P erc F lowi,t ∗P SFi,t−1 F I P Pj,t = i , (5) i Sharesi,j,t−1 where Sharesi,j,t−1 is the number of firm j ’s shares held by mutual fund i at the end of quarter t −1, P erc F lowi,t denotes capital flow to mutual fund i in quarter t as a fraction of its total net assets at the end of quarter t − 602 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 602 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Observations Adj. R-squared Firm FE Year FE Controls (2) Total debt Financial Constraints and Corporate Environmental Policies 21 The DGTW data are available via http://terpconnect.umd.edu/ wermers/ftpsite/Dgtw/coverpage.htm. 603 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 603 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 1, and P SFi,t−1 captures mutual funds’ trading responses to capital flows. More details of the construction of the FIPP measure are included in Appendix Section A.2. Intuitively, FIPP measures flow-induced trading by the aggregate mutual fund industry. One key feature of our FIPP calculation is that, instead of using the actual transactions, we use hypothetical trades based on mutual funds’ previous quarter-end portfolio weights. The uninformed nature of the trading arises as it captures the change in a mutual fund’s positions that are mechanically induced by fund flows instead of fund managers’ discretionary trading that is potentially driven by information about firms’ fundamentals. Another advantage of the FIPP measure is that it excludes contemporaneous dollar volume in the construction, which isolates the direct impact of fund flows from the potential confounding effect of returns on dollar volume documented in Wardlaw (2020). To further validate the transitory nature of the FIPP, we trace DGTW characteristic adjusted abnormal returns (Daniel et al. 1997) for each quintile and the highest decile of FIPP from the event quarter (the portfolio-formation quarter) to 12 quarters afterward and report the results in Table A.13.21 We first note that the event quarter abnormal returns line up nicely with the magnitude of FIPP in quarter 0: quintile 1 (outflow) shows negative abnormal returns while quintile 5 (inflow sample) shows positive abnormal returns. In addition, return reversal, where the outflow sample displays positive abnormal returns and the inflow sample displays negative abnormal returns from quarters 1-12 post-event, shows that prices recover to fundamental values after around three years. This pattern is consistent with the idea that FIPP drives stock prices away from the fundamentals only temporarily (Lou 2012; Gredil, Kapadia, and Lee 2019). Decile 10, our large-inflow sample, shows the highest FIPP and abnormal returns in quarter 0, and the largest reversal post-event. Figure 4, panel A, plots DGTW abnormal returns for decile 10 and extends the event window to quarter 16. The inflow-induced price appreciation reverses around quarter 12 and does not continue the downward trend, consistent with transitory price pressure instead of potential selection bias with underlying stocks. We apply a DID analysis to examine the effects of FIPP on firms’ SEOs, financial constraints, and total toxic releases. For each specific inflow event, we pair the treatment (decile 10 of FIPP) firm with a control firm from the same industry-year and track both the treatment and control groups from three years before to three years after a large inflow shock. In particular, the control firm is chosen from those that experience large inflow shocks outside the [-3y, 3y] event window of the specific inflow shock. For example, for a treatment firm that experienced a large inflow shock in year t, we choose a control firm that experienced a large inflow shock in year t-10. In other words, a firm can serve as a “control” outside its designated [-3y, 3y] event window of being The Review of Financial Studies / v 35 n 2 2022 A Figure 4 Flow-induced price pressure Panel A presents cumulative abnormal returns (DGTW-adjusted) from quarter 1 through quarter 16 for sample firms that experience large flow-induced price pressure (FIPP), defined as the highest decile of FIPP in the event year t (but not in year t-1). Panel B shows coefficient estimates βk of the dynamic difference-in-differences analysis of the likelihood that treatment and control firms conduct seasoned equity offerings (SEOs). For every treatment firm, the control firm is defined as a firm within the same industry-year that experiences large inflow shocks outside of the [-3,3] event window. The omitted benchmark year is year t-3. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. Standard errors are clustered at the firm t+3 level: Yi,t = α + t+3 k=t−2 βk ·T reatedi ×1[Y ear = k]+ k=t−2 θk ×1[Y ear = k]+γ ·Xi,t +ηi +νt +i,t . 604 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 604 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 B Financial Constraints and Corporate Environmental Policies Table 5 Flow-induced price pressure A. FIPP, SEO, and financial constraints (1) (2) SEO dummy Text FC Post Inflow Observations Adj. R-squared Firm FE Year FE Controls −0.242∗∗∗ (0.094) 0.010 (0.007) 0.014 (0.012) 8,125 .43 Yes Yes Yes −0.058∗ (0.032) 0.001 (0.002) 0.003 (0.004) 6,989 .55 Yes Yes Yes B. FIPP, SEO, and total toxic releases (1) Inflow*Post (2) −0.167∗∗ (0.082) −0.177∗∗ (0.086) 0.252∗∗ (0.099) 0.244∗∗ (0.099) 0.089 (0.082) 0.101 (0.082) 0.114 (0.070) 0.155 (0.364) −0.117 (0.174) 0.778 (0.552) 0.163∗∗∗ (0.060) −0.002 (0.071) 10,336 .87 Yes Yes SEO*Post Inflow SEO Post log(assets) Cash/assets CAPEX/PPE Tangible Tobin’s q log(sales) Observations Adj. R-squared Establishment FE Year FE 10,558 .87 Yes Yes (3) (4) −0.213∗∗ (0.101) −0.163∗ (0.097) 0.676∗ (0.380) 0.078 (0.072) 0.595∗ (0.312) 0.088 (0.066) 0.038 (0.121) −0.194 (0.444) 0.202 (0.176) −1.589∗∗ (0.732) 0.085 (0.063) 0.067 (0.074) 8,428 .84 Yes Yes 8,656 .84 Yes Yes This table presents regression estimates of the effects of large flow-induced price pressure (FIPP), defined as the highest decile of FIPP in the event year t (but not in year t-1), on firms’ seasoned equity offerings (SEOs) and the total amount of toxic releases. For every treatment firm, the control firm is defined as a firm within the same industry-year that experiences large inflow shocks outside of the [-3,3] event window. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. “treated,” and the treatment dummy of a same firm can switch between zero and one depending on which role the firm plays at a particular point in time. This control selection and matching process is designed to address the concerns that firms experiencing large inflow shocks might differ fundamentally from firms that never experience flow shocks along major dimensions that might 605 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 605 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 0.020∗∗ (0.008) −0.012∗∗ (0.005) −0.006 (0.009) 13,868 .12 Yes Yes Yes Inflow*Post (3) HM debt The Review of Financial Studies / v 35 n 2 2022 affect investment opportunities and subsequent decisions, such as SEOs (Berger 2019). In the following DID specification, our coefficient of interest is β3 , which captures the differential changes in outcome variables between the treatment and control groups after large inflow shocks: In addition to using the usual year fixed effects to capture aggregate timeseries variation, we include firm fixed effects and establishment fixed effects to absorb time-unvarying characteristics at the firm or establishment level. Because a firm can be in the “treatment” or “control” groups at different points in time, firm or establishment fixed effects do not fully absorb the treatment dummy. Table 5, panel A, presents the effects of large inflow shocks on firms’ SEOs (column 1) and the coefficient estimate shows a 2% increase in SEO likelihood, which represents a 51.2% increase relative to the sample average (3.9%). The estimate lies within the range of 30%–84% documented in Khan, Kogan, and Serafeim (2012), confirming that managers seize the window of opportunity for equity issuance when the stock price temporarily appreciates relative to the fundamentals. Figure 4, panel B, also plots the coefficient estimates of a dynamic DID specification to formally examine pretrend and post-shock SEO behaviors. Prior to the inflow pressure, the estimated difference between the treatment and control groups is not statistically different from zero, indicating parallel pretrends. As the FIPP unfolds for the treated group from year 0, the treatment group displays a sharp increase in the likelihood of SEOs compared with the control group. The increase in SEO likelihood remains statistically positive through years 1 and 2 and reverts back in year 3, which matches the reversal in abnormal returns demonstrated in panel A. The parallel pretrends and the sharp increase in the treatment group’s SEOs from the large inflow year suggests that the FIPP is responsible for the movement. Table 5, panel A, columns 2 and 3, report that SEOs driven by the large inflow shock also reduce firms’ textual financial-constraint measures correspondingly, validating the relaxation in financial constraints post-large inflows. Table 5 reports DID regression estimates of firms’ toxic releases based on Equation (6). Columns 1 and 2 present estimates of the large inflow dummy on toxic releases, with the coefficient for the interaction term T reated ∗P ost being negative and statistically significant. Treatment establishments show significant reductions in toxic releases relative to control establishments. In terms of economic magnitude, large inflow shocks lead to an approximate 18% drop in toxic releases. To obtain the results reported in columns 3 and 4, we apply the same matching procedure as in the FIPP test to pair the SEO (treatment) and corresponding control groups. The results indicate that firms that conduct SEOs display greater toxic release reductions than control firms, 606 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 606 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Yj,t = α +β1 Postj,t +β2 Treatedj,t +β3 Post*Treatedj,t +γ Controls+F E +j,t . (6) Financial Constraints and Corporate Environmental Policies with the economic magnitude similar to that indicated in columns 1 and 2. Overall, Table 5 illustrates that short-term price appreciations driven by FIPP lead to SEOs and accompanying relaxation of financial constraints, which result in lower toxic release volumes. 4. Toxic Releases, Firm Value, and Social Costs 22 For a linear-log model Y = α +β ∗logX + , the expected change in Y associated with a p% increase in X can i i i be calculated as βĖ ∗log([100+p]/100). For small values of p, we approximate the expected change in Y using βĖ ∗p as log([100+p]/100) ≈ p%. 607 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 607 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 In this section, we examine the relationship between toxic releases, firm value, and the potential environmental costs imposed on society. In particular, we provide detailed evidence linking toxic releases to government agency enforcement actions and legal liabilities, substantiating the trade-offs faced by firms while making environmental decisions. We further extend the discussion to examine the extra costs imposed on society, highlighting the costly negative externality of additional toxic releases driven by financial constraints. We first connect total toxic releases to the likelihood of enforcement actions and the dollar amount of legal liabilities. Given that legal enforcement activities are rare events in the sample, with only about 4.3% of the observations ever experiencing investigations regarding environmental issues, we explore the cross-sectional variation within each industry-year first. We regress total toxic releases on the likelihood of an investigation being initiated against an establishment as well as the likelihood that an investigation results in positive legal liabilities (including federal and local penalties, compliance and recovery costs, and the costs of supplemental environmental projects). Panel A of Table 6, columns 1 and 3, present regression estimates obtained with a logistic model, while columns 2 and 4 present results obtained with an OLS model, all with industry-year fixed effects included while controlling for production volume (log(sales)). The results reported in panel A reveal that a greater volume of total toxic releases significantly increases the likelihood of legal investigation initiated by government agencies and eventually legal costs imposed. OLS and logistic models generate quantitatively similar estimates. We use the average effects of financial constraints on total toxic releases identified across our three quasi-natural experiments ((8%+15%+18%)/3=14%) to help quantify the magnitudes. A 14% increase in total toxic releases corresponds to an approximately 3% increase in the likelihood of investigation in the current year relative to the sample average based on the estimates reported in column 2 (=0.800*14%/4.3), and a 3% increase in the likelihood of positive enforcement costs relative to the sample average based on the estimates reported in column 4 (=0.547*14%/2.92).22 Column 5 presents the results of the log of total dollar amount of legal liabilities, with estimated elasticity of 0.062 and statistically The Review of Financial Studies / v 35 n 2 2022 Table 6 Toxic releases, legal liabilities, and firm value A. Toxic releases and legal liabilities Pr(investigation)% Pr(legal_liab>0)% (1) (2) (3) (4) log(total release) log(sales) log(legal_liab) (5) 0.800∗∗∗ (0.040) 0.601∗∗∗ (0.088) 0.793∗∗∗ (0.048) 0.249∗∗∗ (0.071) 0.547∗∗∗ (0.032) 0.315∗∗∗ (0.068) 0.062∗∗∗ (0.004) 0.038∗∗∗ (0.007) 85,096 92,746 .05 Yes OLS 78,012 92,746 .04 Yes OLS 92,746 .05 Yes OLS Observations Adj. R-squared Industry-year FE Model Yes Logit Yes Logit B. Toxic releases and firm value ROA(%) (1) Lagged.log(total release) Profit margin(%) (2) Tobin’s q (3) −0.121∗∗ (0.055) −0.751∗∗ (0.320) −0.004∗∗ (0.002) 0.656∗∗ (0.292) 10.209∗∗∗ (1.317) 8.576∗∗∗ (0.486) −8.811∗∗∗ (1.308) 0.764∗∗ (0.298) −24.402∗∗∗ (1.711) −22.458∗∗∗ (7.728) 21.739∗∗∗ (2.875) −16.266∗∗ (7.591) 44.126∗∗∗ (1.742) −0.142∗∗∗ (0.010) 1.022∗∗∗ (0.045) 0.449∗∗∗ (0.017) 0.106∗∗ (0.045) 0.030∗∗∗ (0.010) Lead.log(total release) log(assets) Cash/assets CAPEX/PPE Tangible Lagged.log(sales) Lead.log(sales) Observations Adj. R-squared Year FE Firm FE 17,558 .38 Yes Yes 17,544 .40 Yes Yes 16,906 .63 Yes Yes (4) −0.007∗∗∗ (0.002) 0.002 (0.002) −0.349∗∗∗ (0.013) 1.284∗∗∗ (0.050) 0.414∗∗∗ (0.018) 0.152∗∗∗ (0.049) −0.097∗∗∗ (0.012) 0.405∗∗∗ (0.013) 13,749 .66 Yes Yes In this table, we report the effects of total toxic releases on government agency enforcement actions as well as firms’ operating performance and value. Panel A links total toxic releases to the likelihood of investigation, the likelihood of positive legal liabilities imposed (including federal and local penalties, compliance and recovery costs, and the costs of supplemental environmental projects), and the log dollar amount of legal liabilities. Coefficient estimates of the OLS regressions and margin effects of the logistic regressions are presented in panel A. Panel B shows the effects of total toxic releases on firms’ ROA, Profit margin, and Tobin’s q. log(sales) is included in both panels to control for production volume at the establishment level. Firm-level controls used in panel B include log(assets), Cash/assets, CAPEX/PPE, and Tangible. Standard errors are clustered at the establishment level in panel A and at the firm level in panel B. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. significant at the 1% level. To confirm that our results are also robust to withinestablishment variation, we report OLS regressions with establishment and year fixed effects in Table A.14, panel A, in the appendix.23 The positive effects of toxic releases on legal liabilities that we document in Table 6, panel A, remain statistically significant across all specifications.24 23 A logistic regression specification will not suitably incorporate a large number of fixed effects. 24 In untabulated results, using the full EPA data generates estimates very similar to those reported for establishments belonging to Compustat firms, for either cross-sectional or within-establishment specifications. 608 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 608 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 1.064∗∗∗ (0.055) 0.453∗∗∗ (0.083) Financial Constraints and Corporate Environmental Policies 609 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 609 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Notice that the effects of total toxic releases on legal enforcement actions reported in panel A of Table 6 likely present a lower bound. Given the limited resources of government agencies, the complexity of the legal framework, and the time required to comply with legal requirements, it might take government agencies several years to detect, pursue, and finalize legal enforcement actions. Our data set does not provide information that allows us to precisely link toxic releases in a specific firm-year to the designated enforcement actions, making it difficult to capture the full impact on current and future legal liabilities. The results reported in Table A.14, panel B, in the appendix, reflect regressions of total toxic releases from the previous year and current year on legal liabilities in the current year, controlling for production in both years. Total toxic releases from both years display a significant positive loading on all three outcome variables, with the coefficient estimates being slightly smaller in the previous year. This analysis reflects potential delays in government agency actions. We thus interpret our results as providing informative lower-bound estimates that reflect the additional legal liabilities faced by firms when increasing their toxic releases. Next, we investigate firms’ operating performance and value. Legal enforcement activities represent additional operating costs imposed on firms, reducing net income. Panel B of Table 6 reports the results of testing for the effects of total toxic releases on firms’ ROA, profit margin, and Tobin’s q by aggregating across all establishments within each firm-year. As shown in columns 1 and 2 of panel B, the coefficient estimates for both ROA and profit margins are negative and statistically significant at the conventional level. While ROA and profit margins reflect the impact on firm value in a concurrent manner, Tobin’s q captures the present value of all future cash flows by incorporating current and future expected legal liabilities. We use Tobin’s q to refer the effects on overall firm value in column 3, where a higher volume of total toxic releases is associated with a lower Tobin’s q and is statistically significant at the 5% level. Although we focus on within-firm variations and control for other observable financial characteristics, the estimated effects of total toxic releases on firm value are not fully identified as “causal.” Omitted variables at firm-year frequency might be driving both total toxic releases and worse market performance. To further address this concern, we include the next year’s total releases in the regressions together with the lagged version and present the results of this lead-lag structure in column 4 (Cohn and Wardlaw 2016). While the coefficient for lagged total release remains stable, we do not observe statistically significant loading of the lead total releases. The evidence is consistent with a higher tonnage of toxic releases predicting lower future firm value, but not vice versa. The average firm in our sample has a book asset value of $6 billion with Tobin’s q of 1.57. Therefore, a 14% increase in total toxic releases translates into a decrease in market value of around $9.2 million The Review of Financial Studies / v 35 n 2 2022 5. Cross-Sectional Analysis In Section 3, we illustrate the causal impact of financial constraints on firms’ environmental policies. Important questions remain unanswered regarding financial constraints and environmental protection. For example, how do financial constraints interact with regulatory enforcement? In this section, we explore cross-sectional settings for the regulatory environment and external monitoring, leading to the heterogeneous impacts of financial constraints on firms’ corporate environmental policies. As discussed in Section 1.1, under the CAA the EPA applies the “nonattainment” county label when the air contains a specified amount of any of the common air pollutants for which the EPA has established a National Ambient Air Quality Standard. Designation as a nonattainment county triggers air quality planning and control requirements under which corrective actions must be taken. LAER, (supposedly) without cost consideration, is required 610 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 610 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 (14%*0.007*1.57*$6 billion) based on estimates reported in column 4 for an average firm-year. Lastly, we conduct a back-of-the-envelope calculation to gauge the additional costs imposed on society associated with discretionary toxic releases. We draw references from the EPA’s prospective reports on the Clean Air Act from 1990 through 2020, which provides a comprehensive quantification of the costs and benefits of pollution controls. The economic welfare associated with pollution abatement occurs because cleaner air leads to better health and productivity as well as savings on medical expenses, both of which are projected to more than offset expenditures for pollution abatement. The central benefits estimate exceeds the costs estimate by a factor of more than 30 to 1, and the high benefits estimate exceeds the costs estimate by 90 to 1. In other words, for every dollar that firms cut environmental abatement, there is a welfare loss of $60 to the public and society, using the middle range of the estimated benefits and costs factor. The PACE survey (2005) specifies that, on average, abatement costs account for approximately 20% of total CAPEX. Within our sample the average CAPEX is $256 million per firm-year, and therefore the average abatement costs are about $51 million. For a 14% increase in total toxic releases driven by financial constraints, we apply a linear extrapolation assuming that abatement expenditures are cut by 14%, representing a $7.14 million reduction in abatement spending. This reduction imposes an additional $428 million ($7.14 million*60) cost on society for toxic chemicals regulated by the CAA, using the estimated costs and benefits factor of 60 from EPA prospective reports. Aggregating across all 18,519 firm-years in our sample results in a social cost of approximately $8 billion under the CAA. Total welfare costs across all other environmental regulations and statutes, and across all other firms and establishments in the United States, would be significantly higher. Financial Constraints and Corporate Environmental Policies 611 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 611 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 for major new or modified emission sources located in nonattainment areas. Additionally, any new emissions are required to be offset by an existing emission source within the same county. This set of environmental regulations generates cross-county variation in the strictness of regulatory monitoring and enforcement. For firms that are actively trading off the present value of abatement costs and legal liabilities, regulatory strictness is an important factor. While greater volumes of toxic releases increase expected legal liabilities, the effect is expected to be much stronger in nonattainment counties given stringent regulatory statutes. We test the sensitivity of expected legal liabilities across attainment and nonattainment counties and present the results in Table A.15 in the appendix using both toxic releases administered under the CAA (panel A) and total toxic releases (panel B). Coefficients for the interaction terms between toxic releases and nonattainment status are positive and statistically significant, confirming that polluting in nonattainment areas triggers higher legal liabilities, through both the likelihood of legal enforcement actions and more costly resolutions. We investigate how nonattainment status, associated with costly legal consequences, affects firms’ ongoing abatement-expenditure decisions. We start by examining average toxic releases based on nonattainment status and report the results in columns 1 and 2 of Table 7. The regression specifications include firm-year fixed effects to narrow the comparison to the same firmyear, absorbing any firm-year-level movements that might affect environmental decisions. Furthermore, we include industry fixed effects using either FamaFrench 48 industry categories or four-digit SIC codes at the establishment level to control for cross-industry differences in the production and polluting process. The reported economic magnitude is major: the nonattainment dummy is associated with a 25%–31% lower volume of toxic releases. This evidence reveals firms’ strategic behavior with respect to their ongoing environmentalabatement activities: within the same firm and industry, establishments located in attainment counties (facing weak regulations) devote significantly fewer resources to environmental protection compared with their peers in nonattaintment countries (facing strong regulations). The results confirm that nonattainment status imposes significantly higher average abatement costs on establishments located in those areas (Becker 2005). The results reported in columns 3 and 4 of Table 7 answer a different question: when firms are more financially constrained, how do establishments from counties with either attainment status manage environmental abatements? We interact the textual financial-constraint measures with the attainment status of the county where an establishment is located. To ensure that differences in other observable characteristics are not driving a differential response, we also include the interaction terms between observable characteristics and two financial constraint measures in the regressions. The coefficient estimates for the interaction terms involving nonattainment status and financial The Review of Financial Studies / v 35 n 2 2022 Table 7 Nonattainment status Nonattainment=1 (1) (2) (3) −0.306∗∗∗ −0.253∗∗∗ (0.074) (0.072) 0.159 (0.106) −0.215∗∗ (0.107) Nonattainment=1 × Text FC Nonattainment=1 × HM debt HM debt log(assets) Cash/assets CAPEX/PPE Tangible Tobin’s q log(sales) Observations Adj. R-squared Firm-year FE FF48 FE SIC4 FE Establishment FE Year FE Interaction terms 0.403∗∗∗ (0.038) 86,224 .36 Yes Yes 0.368∗∗∗ (0.033) 86,206 .41 Yes 0.017 (0.070) −0.867∗ (0.481) −0.026 (0.045) −0.499 (0.697) −0.461 (0.284) −1.684∗∗∗ (0.402) 0.066 (0.077) 0.092∗∗ (0.046) 51,023 .84 1.616 (1.564) 0.042 (0.034) 0.181 (0.236) 0.019 (0.100) -0.051 (0.262) 0.086∗∗∗ (0.029) 0.013 (0.024) 36,562 .86 Yes Yes Yes Yes Yes Yes Yes In this table, we report results pertaining to relations between total toxic releases and the nonattainment status of the county where an establishment operates. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Columns 1 and 2 include firm, year, and industry fixed effects (defined at the establishment level). Columns 3 and 4 include establishment and year fixed effects. Interaction terms between financial constraint measures and control variables are also included. Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. constraint measures indicate that the effects of financial constraints are significantly weaker in nonattainment countries. When a firm experiences financial constraints, establishments in nonattainment counties (facing strong regulations) show smaller changes in toxic releases, while their peers in attainment counties (facing weak regulations) display a sharp spike in toxic releases. Our results suggest a spillover effect driven by regulatory strictness when firms are financially constrained. Financially constrained firms reallocate abatement expenditures from attainment counties to nonattainment counties because of the higher expected legal liabilities in the latter areas. In addition to exploring nonattainment status, we explore polluters’ size as another gauge of regulatory and external monitoring forces. In a world of limited regulatory resources and fixed costs for investigating establishments, large polluters are the focus of EPA monitoring and enforcement activities— they pose the largest threat to public health and the environment and their higher visibility makes these establishments attractive targets for regulators seeking 612 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 612 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 −0.978∗∗∗ (0.376) Text FC (4) Financial Constraints and Corporate Environmental Policies Table 8 Large polluters 70th percentile (1) (2) Large polluter=1 × Text FC −0.283∗∗∗ (0.086) Large polluter=1 × HM debt Text FC HM debt log(assets) Cash/assets CAPEX/PPE Tangible Tobin’s q log(sales) Observations Adj. R-squared Year FE Establishment FE Interaction terms −0.027 (0.042) −0.283 (0.627) −0.552∗∗ (0.271) −1.658∗∗∗ (0.384) 0.084 (0.061) 0.042 (0.033) 50,774 .87 Yes Yes Yes 2.010 (1.383) 0.041 (0.039) 0.236 (0.275) −0.152 (0.115) −0.404 (0.350) 0.074∗∗ (0.034) 0.009 (0.020) 36,029 .89 Yes Yes Yes −0.247∗∗∗ (0.087) −0.697∗∗ (0.340) −0.017 (0.041) −0.455 (0.663) −0.546∗∗ (0.272) −1.717∗∗∗ (0.377) 0.074 (0.063) 0.056 (0.035) 50,867 .86 Yes Yes Yes −0.728∗ (0.398) 1.974 (1.452) 0.030 (0.038) 0.202 (0.269) −0.113 (0.110) −0.435 (0.323) 0.081∗∗ (0.033) 0.008 (0.021) 36,171 .88 Yes Yes Yes 80th percentile (5) (6) −0.283∗∗∗ (0.091) −0.917∗∗ (0.357) −0.030 (0.042) −0.514 (0.652) −0.493∗ (0.271) −1.940∗∗∗ (0.377) 0.012 (0.067) 0.061∗ (0.037) 51,003 .85 Yes Yes Yes −0.770∗ (0.411) 1.700 (1.498) 0.018 (0.037) 0.179 (0.262) −0.080 (0.108) −0.500 (0.308) 0.076∗∗ (0.032) 0.009 (0.021) 36,250 0.87 Yes Yes Yes In this table, we report the heterogeneous effects of financial constraints on total toxic releases for large and small polluters. Large polluter indicates establishments with total toxic releases above the 70th, 75th, and 80th percentiles of the sample distribution. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Interaction terms between financial constraint measures and control variables are also included. Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. to send signals to other firms and the public (Becker 2005). In addition, large polluters also face greater public scrutiny of their environmental activity and performance. For example, a number of environmental advocacy groups, public interest groups, and news media routinely scrutinize so-called “superpolluters.” We predict that smaller establishments with loose external monitoring forces will increase toxic releases to a greater extent when firms experience financial constraints than large establishments facing higher expected legal liabilities will. To derive the results reported in Table 8, we examine the impact of financial constraints on toxic releases across polluter size, where large polluters are defined as establishments with total toxic releases above the 70th, 75th, and 80th percentiles of the sample distribution. Again, the interaction terms between observable characteristics and two financial constraint measures are included in the regressions to ensure that heterogeneity in other observable characteristics is not driving a differential response. Our most robust finding reported in Table 8 is that financial constraints have a much smaller impact on large polluters, and similar differences between large and small polluters are observed across the specifications reported in Table 8. 613 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 613 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 −0.726∗∗ (0.331) −0.913∗∗ (0.401) 75th percentile (3) (4) The Review of Financial Studies / v 35 n 2 2022 6. Conclusion Exploiting several novel establishment-level data sets, we provide evidence that financial constraints have direct impacts on corporate environmental 25 While our matching-sample design can ensure that firm-level characteristics remain the same, county- or establishment-level unobservable characteristics may exist that can confound the interpretation of the interaction terms between our cross-sectional features and financial constraint measures. 614 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 614 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 For a robustness check, we conduct a matching-sample test where we create matched samples of establishments under the same parent firms but with different cross-sectional features. Our matching process ensures that the matched establishments are exposed to exactly the same firmlevel characteristics, and therefore firm-level observable or unobservable characteristics cannot drive a differential response to financial constraints. This specification compares the sensitivity of toxic releases to financial constraints between establishments in attainment counties and matched establishments located in nonattainment counties, or between large polluters and matched smaller polluters, all operated by the same firms. We present results for the robustness tests in Table A.16 in the appendix. Columns 1 and 2 include results for nonattainment status. Consistent with the results presented Table 7, establishments in nonattainment counties display less sensitivity to financial constraints than their peers located in attainment counties. Columns 3–8 include the results of robustness checks for large polluters, and these results are largely consistent with the results presented in Table 8, where large polluters display less sensitivity to financial constraints than peer smaller polluters. The coefficients reported in columns 3, 5, and 7 are statistically significant at the 5%–10% levels, and the coefficients reported in columns 4, 6, and 8 are negative but statistically nonsignificant. The decrease in statistical power in columns 4, 6, and 8 is likely caused by the sharp drop of around 50% in the sample size through the matching process.25 Overall, the cross-sectional results for nonattainment status and polluters’ size highlight firms’ active management of toxic releases: firms are keenly aware of the external regulatory environment when trading off legal liabilities and abatement expenditures to maximize value. As financial constraints increase the marginal cost of abatement expenditures, firms shift resources to establishments where additional toxic releases might incur large regulatory penalties and reduce abatement in areas with looser environmental regulations. It is important to recognize that intrafirm resource adjustment is possible because of the composition of abatement costs. Heavy equipment is not the only option for processing industrial waste and reducing toxic chemical releases. Instead, the vast majority of abatement costs are variable costs, involving factors such as labor and contract work, which can be modified across locations fairly easily. Financial Constraints and Corporate Environmental Policies A.1. Company Name Matching Process We use a string-matching algorithm to link TRI establishments to Compustat firms. The TRI database records the ultimate parent company name for each establishment every year, which can change over time following such incidents as ownership changes and parent company name changes. Compustat company information provides only the most up-to-date parent company names. To ensure matching accuracy, for each Compustat company identified by a GVKEY we obtain historical company name from CRSP, supplemented by historical name and address information from the 10K, 10-Q, and 8-K filings using the SEC Analytical Package provided by the Wharton Research Data Service. For each company name in the TRI or Compustat/CRSP data, a time stamp is generated to indicate the effective period of the identifier. We remove all punctuation marks, delete corporate designators, such as “Corporation,” “Company,” “INC,” or “LLC,” standardize the most common words to a consistent format, and then generate a similarity score between the deduplicated TRI parent names and Compustat/CRSP company names using a string-matching command in STATA.26 We further narrow potential matches to cases in which time stamps from Compustat/CRSP and the TRI coincide. This time stamp filter further reduces false positives as public companies that share the same names in different time periods will not be matched incorrectly. We use the example of Skyworks Solution, Inc., to demonstrate the importance of using historical name information in our matching process. Skyworks Solution, Inc., was formed as a result of the merger of Alpha Industries and the wireless communications division of Conexant in June 2002.27 Skyworks continued using the GVKEY 1327 after the merger in the Compustat database. From the Compustat company information, we can obtain only “Skyworks solutions” as the current company name. The CRSP historical name specifies that between January 1980 and May 2002, GVKEY 1327 corresponded to “Alpha Industries, Inc.,” which is also the parent company name presented in the TRI database. The final matches link establishments to GVKEY 1327 with historical parent 26 For instance, “United States” is simplified to “US,” “Manufacturing” to “MFG,” and “Internationals” to “INTL.” 27 http://investors.skyworksinc.com/news-releases/alpha-and-conexant-announce-plan-close-skyworks-merger- today 615 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 615 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 policies. Treating toxic chemicals generated during the manufacturing process is costly and consumes significant financial resources. Firms reduce abatement expenditures when facing financial constraints because their environmental protection costs increase correspondingly. Given that firms’ private costs of pollution abatement are in general much smaller than the social cost, the additional toxic chemicals released impose costs on the environment, society, and public health. Collaborative cross-sectional test results also illustrate that the documented impacts are amplified by weak regulatory enforcement and external monitoring. These results consistently point to the externalities of financial constraints in the form of environmental pollution. In terms of policy implications, our evidence suggests that temporal variations in toxic releases are closely tied to a firm’s financial strength. When regulatory oversight and enforcement are resource constrained, our results suggest the wisdom of adopting a nonrandom auditing policy that focuses on scenarios in which violations of environmental regulations are most likely to occur. The Review of Financial Studies / v 35 n 2 2022 names “Alpha Industries, Inc.,” during 1990 through 2001 and “Skyworks Solutions, Inc.,” from 2002 through 2014. All unique matches with similarity scores equal to 100% are kept. For cases with multiple matches and similarity scores below 100%, we drop any matches with similarity scores below 25%, which is a very conservative lower bound. We then rank all potential matches according to their similarity scores and manually check for the correct matches. In a few cases, the EPA parent information misses one or two years of parent names while all the other years display the same parent names. The parent names from other years are then filled in the missing years. The survivorship-bias-free CRSP mutual fund database provides mutual funds’ total net assets, net monthly returns, expense ratios, and other fund characteristics. Monthly fund returns are calculated as net returns plus 1/12 of annual fees and expenses. For mutual funds with multiple share classes, total net assets (TNA) are summed up across all share classes, net returns and expense ratios are weighted by TNA across all share classes, and the investment objective code of the share class with the largest TNA is used. MFLinks file is then used to merge the CRSP mutual fund database with the Thompson Financial CDA/Spectrum holding database, which contains quarterly mutual fund holdings data. To exclude all fixed-income funds, international funds, and previous metal funds, the investment objective code reported by CDA/Spectrum is limited to aggressive growth, growth, growth and income, balanced, unclassified, or missing. Since some mutual funds misreport their investment objective codes, the ratio of equity holdings is set to between 0.75 and 1.2. Additional requirements include a minimum fund size of $1 million and that the TNAs reported by CDA/Spectrum and CDA CRSP do not differ by more than a factor of two (i.e., 0.5< T N ACRSP <2). T NA The investment flow to fund i in quarter t is defined as the following: f lowi,t = T N Ai,t −T N Ai,t−1 ∗(1+RETi,t )−MGNi,t , T N Ai,t−1 (A1) where MGNi,t is the increase in TNA following fund mergers in quarter t. To estimate the merger date, we use the latest net asset value (NAV) report date of the target fund. A target fund is matched to its acquirer starting one month before its last NAV report date until five months after, and then the month in which the acquirer has the smallest absolute percentage flow is designated as the event month. Mutual funds that are initiated have inflows equal to their initial TNA, and funds that are liquidated have outflows equal to their terminal TNA. The following equation is estimated to gauge the effect of trading costs and other constraints on the degree of partial scaling: tradei,j,t = β0 +β1 f lowi,t +γ2 X +γ3 f lowi,t X +i,t . The dependent variable, tradei,j,t = sharesi,j,t splitadj sharesi,j,t−1 (A2) −1 is the percentage of trading in stock j by fund i in quarter t, with split adjustments. The key independent variable is f lowi,t , which is the capital flow to fund i in quarter t as a fraction of the fund’s TNA at the end of the previous quarter. X includes variables that capture trading costs: (a) the ownership share of mutual fund i in stock j (defined as sharesi,j,t−1 shroutj,t−1 ), and (b) the effective bid-ask spread of stock j derived from the basic market-adjusted model (Hasbrouck 2009). The portfolio-weighted average ownership share and liquidity cost are included in the X vector to examine the effects of portfolio-level constraints on managers’ decisions to invest capital inflows in their existing positions as opposed to initiating new positions. The regression specification corresponds to columns 1 and 7 of table 2 in Lou (2012) for the outflow and inflow samples, respectively. The estimated β1 is extracted as the partial scaling factors (PSF) and used as an input in the final step of the FIPP calculation. 616 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 616 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 A.2. Flow-Induced Price Pressure Financial Constraints and Corporate Environmental Policies Table A.1 Variable definitions Firm Text FC HM debt Interest coverage Rating EDF Leverage Dividend dummy Payout ratio Dom inv RE value Debt issuance Total debt FIPP Inflow SEO ROA Profit margin Establishment log(total release) log(CAA release) log(CWA release) log(CERCLA release) log(OSHA release) log(health effects release) log(no health effects release) log(air release) log(water release) log(RSEI hazard) Nonattainment Pr(investigation)% Pr(legal_liab)% log(legal_liab) log(sales) Logarithm of tons of total toxic releases administered under the TRI program Logarithm of tons of toxic release administered under the Clean Air Act Logarithm of tons of toxic release administered under the Clean Water Act Logarithm of tons of toxic release administered under the Comprehensive Environmental Response, Compensation, and Liability Act Logarithm of tons of toxic release administered by the Occupational Safety and Health Administration Logarithm of tons of toxic release associated with health effects Logarithm of tons of toxic release not associated with health effects Logarithm of tons of toxic release through air Logarithm of tons of toxic release through water Logarithm of the EPA’s Risk-Screening Environmental Indicators (RSEI) hazard score A dummy that equals one if an establishment resides in a county with nonattainment status The likelihood of government agencies filing an environmental investigation against an entity The likelihood of positive legal liabilities( including federal and local penalties, compliance and recovery cost, and the costs of supplemental environmental projects) imposed on an entity Logarithm of dollar amount of legal liabilities imposed on an entity Logarithm of number of sales dollar amount (inflation adjusted) at the establishment level 617 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 617 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 log(assets) Cash/assets CAPEX/ PPE Tangible Tobin’s q Textual financial-constraint measure by Bodnaruk, Loughran, and McDonald (2015) Debt focus financial-constraint measured by Hoberg and Maksimovic (2014) log number of total assets Cash and Short-term investment/L.Assets Capital expenditures/ L.PPENT PPENT/L.Assets (Total asset + Common shares outstanding × Closing price (Fiscal year) − Common equity − Deferred taxes)/Asset Operating income before depreciation/Interest payment S&P domestic long-term issuer credit rating The probability that a company fails to make a scheduled debt payment within a year (Debt in current liabilities + Long-term debt)/ Assets A dummy that equals to one if a firm-year pays dividends (dividend payment + share repurchases)/operating income before depreciation (Domestic Capital Expenditure+Domestic R&D+Advertising expenses, and Acquisitions)/Assets 1993 market value of real estate assets inflated by the MSA-level House Price index (HPI) New debt issued /L.PPENT Total amount of debt/L.PPENT Flow-induced price pressure defined in Lou (2012) A dummy variable that equals one for firms with FIPP in the highest decile in year t (but not in year t-1) A dummy variable that equals one if a firm conducts pure seasoned equity offerings in a year Income before extraordinary items/L.Assets EBIT/sales 618 Observations Adj. R-squared Firm FE Year FE Tobin’s q Tangible CAPEX/PPE Cash/assets log(assets) HM debt Text FC (1) 13.680∗∗∗ (1.961) −6.372 (11.146) −1.953 (1.500) −1.398 (9.509) 1.166∗∗∗ (0.341) 66,673 .36 Yes Yes −18.857∗∗∗ (4.095) −78.718∗∗∗ (19.772) 16.839∗∗∗ (2.279) −2.984 (12.538) −1.708 (1.785) 9.108 (11.878) 2.221∗∗∗ (0.479) 54,554 .38 Yes Yes (2) (3) −1.033∗∗∗ (0.074) 0.381 (0.346) −0.180∗∗∗ (0.051) −0.733∗ (0.386) −0.189∗∗∗ (0.051) 17,031 .89 Yes Yes 0.808∗∗∗ (0.102) (4) 1.482∗∗∗ (0.392) −1.087∗∗∗ (0.072) 0.282 (0.353) −0.217∗∗∗ (0.056) −0.411 (0.419) −0.169∗∗∗ (0.045) 14,255 .87 Yes Yes Rating (5) −2.689∗∗∗ (0.094) −7.134∗∗∗ (0.343) −1.036∗∗∗ (0.052) 2.076∗∗∗ (0.564) −0.394∗∗∗ (0.017) 73,056 .62 Yes Yes 2.153∗∗∗ (0.222) EDF 8.335∗∗∗ (0.827) −2.513∗∗∗ (0.100) −6.670∗∗∗ (0.359) −1.047∗∗∗ (0.060) 3.102∗∗∗ (0.640) −0.383∗∗∗ (0.019) 63,668 .62 Yes Yes (6) A. Full Compustat sample (7) −0.041∗∗∗ (0.003) −0.383∗∗∗ (0.012) −0.018∗∗∗ (0.002) 0.116∗∗∗ (0.018) 0.004∗∗∗ (0.000) 79,441 .66 Yes Yes 0.102∗∗∗ (0.007) (8) 0.394∗∗∗ (0.025) −0.041∗∗∗ (0.004) −0.367∗∗∗ (0.013) −0.018∗∗∗ (0.002) 0.124∗∗∗ (0.020) 0.004∗∗∗ (0.001) 65,597 .68 Yes Yes Leverage 0.019∗∗∗ (0.004) 0.024∗ (0.014) 0.003 (0.002) −0.001 (0.019) 0.001∗∗∗ (0.000) 80,347 .63 Yes Yes −0.058∗∗∗ (0.010) (9) −0.129∗∗∗ (0.039) 0.012∗∗∗ (0.004) 0.040∗∗∗ (0.015) 0.007∗∗∗ (0.002) −0.002 (0.022) 0.002∗∗∗ (0.001) 66,265 .63 Yes Yes (10) Dividend dummy Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Interest coverage Table A.2 Textual financial constraint measures −0.175∗∗∗ (0.029) 0.006∗∗ (0.003) 0.022∗ (0.011) −0.006∗∗∗ (0.001) −0.014 (0.014) −0.001∗∗∗ (0.000) 66,127 .23 Yes Yes (12) (Continued) 0.009∗∗∗ (0.002) 0.020∗∗ (0.009) −0.006∗∗∗ (0.001) −0.013 (0.011) −0.001∗∗∗ (0.000) 80,187 .24 Yes Yes −0.022∗∗∗ (0.008) (11) Payout ratio The Review of Financial Studies / v 35 n 2 2022 Page: 618 576–635 619 Page: 619 576–635 −1.008 (2.814) 74.978∗∗∗ (19.691) 18.400∗∗ (7.692) 27.412 (19.446) 18.494∗∗∗ (3.782) 10,253 .49 Yes Yes −83.971∗∗∗ (20.895) −1.035 (3.730) 43.648∗ (25.925) 19.115∗∗ (9.636) 7.691 (28.228) 19.687∗∗∗ (4.099) 7,824 .50 Yes Yes (2) (3) −1.251∗∗∗ (0.131) 0.103 (0.587) −1.427∗∗∗ (0.263) −1.533∗∗ (0.673) −0.722∗∗∗ (0.080) 5,913 .90 Yes Yes 0.848∗∗∗ (0.134) Rating 1.669∗∗ (0.679) −1.156∗∗∗ (0.136) 0.611 (0.640) −1.792∗∗∗ (0.306) −0.966 (0.868) −0.686∗∗∗ (0.095) 4,406 .89 Yes Yes (4) (5) −1.505∗∗∗ (0.320) −2.666∗∗ (1.067) −3.451∗∗∗ (0.595) 2.122 (1.837) −1.015∗∗∗ (0.148) 10,157 .53 Yes Yes 1.227∗∗∗ (0.356) EDF 3.768∗∗ (1.763) −1.167∗∗∗ (0.369) −1.504 (1.292) −4.359∗∗∗ (0.749) 1.442 (2.243) −1.188∗∗∗ (0.168) 8,131 .53 Yes Yes (6) B. EPA sample (7) 0.016 (0.012) −0.303∗∗∗ (0.049) −0.072∗∗∗ (0.017) 0.006 (0.059) −0.023∗∗∗ (0.006) 10,657 .71 Yes Yes 0.083∗∗∗ (0.013) (8) 0.239∗∗∗ (0.056) 0.013 (0.013) −0.202∗∗∗ (0.050) −0.080∗∗∗ (0.017) 0.043 (0.070) −0.028∗∗∗ (0.006) 8,178 .73 Yes Yes Leverage 0.126∗∗∗ (0.019) 0.039 (0.072) −0.041 (0.032) −0.023 (0.082) 0.007 (0.010) 10,677 .72 Yes Yes −0.061∗∗∗ (0.022) (9) −0.211∗ (0.108) 0.102∗∗∗ (0.020) −0.033 (0.080) −0.016 (0.035) −0.061 (0.095) 0.015 (0.012) 8,191 .73 Yes Yes (10) Dividend dummy −0.018∗ (0.010) 0.056 (0.059) −0.023 (0.024) −0.060 (0.056) −0.006 (0.007) 10,664 .31 Yes Yes −0.015 (0.016) (11) −0.139∗∗ (0.064) −0.032∗∗∗ (0.012) −0.026 (0.068) −0.008 (0.027) −0.196∗∗∗ (0.074) −0.011 (0.010) 8,176 .31 Yes Yes (12) Payout ratio In this table, we report the results of examining the relationship between two textual financial-constraint measures and several accounting-based measures, including interest coverage, credit rating, expected default frequency (EDF), leverage, a dividend dummy, and the payout ratio. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. Observations Adj. R-squared Firm FE Year FE Tobin’s q Tangible CAPEX/PPE Cash/assets log(assets) HM debt −7.924∗∗ (3.179) (1) Interest coverage Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Text FC Table A.2 (Continued) Financial Constraints and Corporate Environmental Policies The Review of Financial Studies / v 35 n 2 2022 N Agriculture Food products Candy & soda Beer & liquor Tobacco products Recreation Printing and publishing Consumer goods Apparel Medical equipment Pharmaceutical products Chemicals Rubber and plastic products Textiles Construction materials Construction Steel works, etc. Fabricated products Machinery Electrical equipment Automobiles and trucks Aircraft Shipbuilding, railroad equipment Defense Precious metals Nonmetallic and industrial metal mining Coal Petroleum and natural gas Utilities Personal services Business services Computers Electronic equipment Measuring and control equipment Business supplies Shipping containers Transportation Wholesale Retail Almost nothing Total Mean 179 161.18 3,953 82.33 204 15.56 255 35.10 162 153.70 595 59.30 160 8.52 4,314 52.41 374 23.00 1,077 33.73 1,590 101.88 10,806 152.23 3,135 78.70 1,121 51.73 7,271 46.77 364 95.63 7,119 208.82 2,689 25.57 6,766 21.92 3,335 35.31 73.19 4,927 2,224 37.46 650 43.47 466 37.80 152 1,079.73 363 586.05 62 242.26 2,623 180.09 2,773 746.93 314 55.28 1,910 87.47 576 43.53 3,342 12.81 1,116 14.22 4,104 263.02 2,211 113.45 247 88.08 2,512 67.93 745 54.31 395 362.62 87,181 119.37 Median 6.67 13.66 9.26 11.50 42.38 37.71 4.48 9.95 10.21 6.01 9.12 10.25 9.55 8.60 7.04 1.45 8.66 3.87 2.98 3.60 10.88 6.91 20.20 6.30 654.31 39.67 68.17 33.79 430.55 1.01 5.90 5.00 1.55 1.37 29.58 39.50 3.89 2.23 4.86 16.22 8.42 SD 426.08 209.21 24.13 52.61 211.58 76.29 17.49 116.32 32.54 118.25 294.00 415.81 217.41 150.91 149.83 344.72 540.37 80.53 98.88 139.81 166.31 113.10 71.85 126.26 1,015.07 848.03 363.19 339.30 771.09 257.38 254.39 197.31 44.21 48.12 445.00 253.47 249.68 244.89 155.53 691.44 351.30 P25 P75 0.38 4.14 0.44 3.92 9.67 5.60 0.73 0.65 1.81 0.55 1.00 1.70 1.29 1.61 0.43 0.20 0.73 0.35 0.25 0.23 1.01 0.59 4.67 0.59 10.60 2.55 24.10 2.25 108.06 0.13 0.73 0.14 0.13 0.13 4.38 7.13 1.16 0.55 0.72 1.08 0.85 78.00 65.00 19.10 41.67 215.64 87.00 9.76 52.22 28.50 21.28 52.96 66.44 44.35 37.72 29.89 34.98 71.69 21.62 14.39 21.12 53.00 27.78 51.47 31.81 2,248.90 1,014.77 284.00 198.52 1,219.48 12.98 39.07 21.17 7.72 7.50 341.45 116.61 24.97 17.20 23.24 259.53 52.20 The table presents summary statistics indicating total toxic releases (tons) by Fama-French 48 industry classifications. Industries with fewer than 50 observations have been dropped. 620 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 620 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Table A.3 Summary statistics by industry Financial Constraints and Corporate Environmental Policies Rank By coverage of establishments 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 TOLUENE XYLENE (MIXED ISOMERS) AMMONIA LEAD COMPOUNDS ZINC COMPOUNDS METHANOL SULFURIC ACID HYDROCHLORIC ACID LEAD CERTAIN GLYCOL ETHERS COPPER 1,1,1-TRICHLOROETHANE METHYL ETHYL KETONE NICKEL CHROMIUM MANGANESE CHROMIUM COMPOUNDS COPPER COMPOUNDS MANGANESE COMPOUNDS ETHYLBENZENE ETHYLENE GLYCOL NICKEL COMPOUNDS ACETONE 1,2,4-TRIMETHYLBENZENE N-BUTYL ALCOHOL POLYCYCLIC AROMATIC COMPOUNDS BARIUM COMPOUNDS NITRATE COMPOUNDS NITRIC ACID CHLORINE Percentage (%) By aggregated volume 21.82 20.55 19.76 18.68 17.92 17.88 16.98 15.17 15.16 14.55 14.36 13.56 13.43 13.35 12.71 11.71 11.09 10.70 10.45 9.92 9.78 9.42 8.90 8.58 8.55 8.54 8.28 7.99 7.90 7.88 Tonnage HYDROCHLORIC ACID 2,076,614.00 ZINC COMPOUNDS 2,021,955.00 ARSENIC COMPOUNDS 1,252,983.00 COPPER COMPOUNDS 1,195,326.00 MANGANESE COMPOUNDS 1,137,704.00 METHANOL 1,050,810.00 NITRATE COMPOUNDS 929,225.80 AMMONIA 849,445.10 BARIUM COMPOUNDS 621,526.70 SULFURIC ACID 525,227.80 LEAD COMPOUNDS 517,826.20 TOLUENE 370,201.50 XYLENE (MIXED ISOMERS) 274,026.00 CHROMIUM COMPOUNDS 228,378.10 HYDROGEN FLUORIDE 208,405.60 ZINC (FUME OR DUST) 186,306.00 METHYL ETHYL KETONE 174,344.60 N-HEXANE 166,820.90 NITRIC ACID 161,019.40 CERTAIN GLYCOL ETHERS 153,482.60 VANADIUM COMPOUNDS 142,625.20 DICHLOROMETHANE 121,415.30 N-BUTYL ALCOHOL 119,302.40 CARBONYL SULFIDE 116,372.10 PHOSPHORIC ACID 110,929.10 ETHYLENE 108,518.60 NICKEL COMPOUNDS 108,077.50 FORMALDEHYDE 99,570.78 STYRENE 98,966.32 ACETONE 94,531.52 The table lists the top-30 chemicals ranked by aggregated volume and coverage of sample establishments. 621 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 621 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Table A.4 Top chemicals by coverage of establishments [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 622 576–635 DICHLOROMETHANE N-HEXANE AMMONIA NITRATE COMPOUNDS HYDROCHLORIC ACID 1,1,1-TRICHLOROETHANE CERTAIN GLYCOL ETHERS METHANOL ACETONE FREON 113 DICHLOROMETHANE HYDROCHLORIC ACID TOLUENE HYDROCHLORIC ACID AMMONIA ACETONITRILE MANGANESE COMPOUNDS METHYL ETHYL KETONE XYLENE (MIXED ISOMERS) LEAD COMPOUNDS CERTAIN GLYCOL ETHERS 1,1,1-TRICHLOROETHANE N-HEXANE DICHLOROMETHANE MANGANESE COMPOUNDS ZINC COMPOUNDS BARIUM COMPOUNDS AMMONIA BARIUM COMPOUNDS AMMONIA HYDROCHLORIC ACID TOLUENE FREON 113 1,1,1-TRICHLOROETHANE HYDROCHLORIC ACID CERTAIN GLYCOL ETHERS DICHLOROMETHANE METHANOL AMMONIA LEAD COMPOUNDS MANGANESE COMPOUNDS METHANOL ZINC COMPOUNDS ZINC COMPOUNDS ZINC COMPOUNDS ZINC (FUME OR DUST) MANGANESE COMPOUNDS XYLENE (MIXED ISOMERS) METHYL ETHYL KETONE XYLENE (MIXED ISOMERS) 1,1,1-TRICHLOROETHANE ARSENIC COMPOUNDS COPPER COMPOUNDS AMMONIA NITRATE COMPOUNDS HYDROCHLORIC ACID METHANOL TOLUENE METHYL ETHYL KETONE 1,1,1-TRICHLOROETHANE COPPER METHANOL METHANOL AMMONIA AMMONIA HYDROCHLORIC ACID ZINC (FUME OR DUST) 2 NITRATE COMPOUNDS NITRATE COMPOUNDS NITRATE COMPOUNDS AMMONIA AMMONIA STYRENE 1,1,1-TRICHLOROETHANE TOLUENE TOLUENE TOLUENE METHANOL AMMONIA STYRENE 1 AMMONIA N-BUTYL ALCOHOL TOLUENE HYDROCHLORIC ACID NITRATE COMPOUNDS LEAD SULFURIC ACID MANGANESE COMPOUNDS METHANOL FREON 113 ZINC COMPOUNDS METHANOL MANGANESE COMPOUNDS TOLUENE HYDROCHLORIC ACID LEAD COMPOUNDS MANGANESE COMPOUNDS NITRATE COMPOUNDS ZINC COMPOUNDS METHANOL NITRATE COMPOUNDS CERTAIN GLYCOL ETHERS MANGANESE ZINC COMPOUNDS ZINC COMPOUNDS TRICHLOROETHYLENE N-BUTYL ALCOHOL ZINC COMPOUNDS AMMONIA MANGANESE COMPOUNDS HYDROCHLORIC ACID ZINC COMPOUNDS ACETONE TOLUENE XYLENE (MIXED ISOMERS) METHYL ETHYL KETONE XYLENE (MIXED ISOMERS) ACETONE NITRATE COMPOUNDS METHANOL 3 SULFURIC ACID HYDROCHLORIC ACID CYCLOHEXANE N-HEXANE SULFURIC ACID ZINC COMPOUNDS HYDROGEN FLUORIDE ZINC COMPOUNDS SULFURIC ACID METHANOL DICHLOROMETHANE ZINC (FUME OR DUST) ZINC COMPOUNDS PROPYLENE COPPER ZINC COMPOUNDS LEAD COMPOUNDS METHANOL FORMALDEHYDE AMMONIA COPPER COMPOUNDS XYLENE (MIXED ISOMERS) STYRENE XYLENE (MIXED ISOMERS) METHYL ISOBUTYL KETONE TOLUENE MANGANESE COMPOUNDS N-HEXANE HYDROCHLORIC ACID HYDROCHLORIC ACID N-BUTYL ALCOHOL NICOTINE AND SALTS TOLUENE COPPER COMPOUNDS AMMONIA CHROMIUM COMPOUNDS DICHLORODIFLUOROMETHANE TOLUENE MANGANESE COMPOUNDS CARBON DISULFIDE 4 MANGANESE COMPOUNDS AMMONIA N-HEXANE DICHLOROMETHANE METHANOL COPPER COMPOUNDS MANGANESE COMPOUNDS HYDROCHLORIC ACID N-HEXANE 1,1,1-TRICHLOROETHANE AMMONIA N,N-DIMETHYLFORMAMIDE LEAD COMPOUNDS SULFURIC ACID FREON 113 COPPER COMPOUNDS SULFURIC ACID ETHYLENE GLYCOL XYLENE (MIXED ISOMERS) MANGANESE COMPOUNDS LEAD COMPOUNDS TOLUENE 1,1,1-TRICHLOROETHANE NITRATE COMPOUNDS N-BUTYL ALCOHOL TETRACHLOROETHYLENE STYRENE N,N-DIMETHYLFORMAMIDE BARIUM COMPOUNDS BROMOMETHANE CERTAIN GLYCOL ETHERS NITRATE COMPOUNDS XYLENE (MIXED ISOMERS) METHANOL METHYL ETHYL KETONE CERTAIN GLYCOL ETHERS CERTAIN GLYCOL ETHERS NITRATE COMPOUNDS METHANOL METHYL ETHYL KETONE 5 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 622 The table lists the top-five chemicals ranked by aggregated volume for each of the Fama-French 48 industries. Agriculture Food products Candy soda Beer liquor Tobacco products Recreation Printing and publishing Consumer goods Apparel Medical equipment Pharmaceutical products Chemicals Rubber and plastic products Textiles Construction materials Construction Steel works etc Fabricated products Machinery Electrical equipment Automobiles and trucks Aircraft Shipbuilding, railroad equipment Defense Precious metals Nonmetallic and industrial metal mining Coal Petroleum and natural gas Utilities Personal services Business services Computers Electronic equipment Measuring and control equipment Business supplies Shipping containers Transportation Wholesale Retail Almost nothing Industry Table A.5 Top chemicals by industry The Review of Financial Studies / v 35 n 2 2022 Financial Constraints and Corporate Environmental Policies (1) CAA Text FC (2) CWA (3) CERCLA 0.260∗∗∗ 0.187∗∗∗ (0.059) (0.055) 0.197∗∗∗ (0.055) HM debt log(assets) Cash/assets CAPEX/PPE Tangible Tobin’s q log(sales) Observations Adj. R-squared Year FE Establishment FE 0.040 0.062∗∗ (0.033) (0.030) −0.023 0.083 (0.283) (0.250) 0.097 0.092 (0.104) (0.098) ∗ −0.427 −0.131 (0.244) (0.250) ∗∗ 0.085 0.066∗∗ (0.035) (0.030) 0.044∗ 0.007 (0.026) (0.023) 45,143 47,360 .83 .84 Yes Yes Yes Yes 0.054∗ (0.031) −0.166 (0.269) 0.066 (0.098) −0.267 (0.256) 0.077∗∗ (0.032) 0.008 (0.024) 49,410 .84 Yes Yes (4) OSHA (5) CAA (6) CWA (7) CERCLA (8) OSHA 0.333∗∗∗ (0.077) 0.918∗∗∗ 0.546∗∗ 0.865∗∗∗ 0.749∗∗ (0.273) (0.245) (0.254) (0.352) 0.018 0.057 0.096∗∗∗ 0.069∗∗ 0.055 (0.041) (0.035) (0.034) (0.032) (0.048) −0.158 0.264 0.300 0.252 0.194 (0.315) (0.246) (0.236) (0.236) (0.305) ∗∗∗ 0.432 0.048 0.000 0.044 0.043 (0.155) (0.114) (0.103) (0.102) (0.171) 0.007 −0.109 0.102 −0.022 0.094 (0.266) (0.250) (0.270) (0.261) (0.299) ∗∗∗ ∗∗∗ ∗∗∗ 0.065 0.093 0.075 0.087 0.092∗∗ (0.043) (0.031) (0.029) (0.029) (0.043) 0.027 0.058∗∗ 0.027 0.031 0.027 (0.029) (0.027) (0.023) (0.026) (0.031) 33,143 31,627 33,359 35,275 23,411 .81 .86 .87 .87 .83 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes This table presents the results of OLS regressions of toxic releases under various EPA regulations on two textbased financial-constraint measures. The toxic release measures include the log tons of toxic releases administered under the Clean Air Act(CAA), the Clean Water Act (CWA), the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA), and the Occupational Safety and Health Act (OSHA). Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. 623 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 623 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Table A.6 Toxic releases under various EPA Regulations The Review of Financial Studies / v 35 n 2 2022 Table A.7 Chemicals grouped by per-plant and economywide magnitude Text FC HM debt Observations Adj. R-squared Year FE Establishment FE Controls # chemicals <=2 (1) (2) 0.220∗∗ (0.109) 0.342 (0.570) 22,241 16,593 .79 .83 Yes Yes Yes Yes Yes Yes # chemicals >2 (3) (4) 0.193∗∗∗ (0.062) 0.628∗ (0.330) 28,251 19,304 .87 .89 Yes Yes Yes Yes Yes Yes Full sample (5) (6) 0.190∗∗∗ (0.069) 0.562∗ (0.333) 51,390 36,546 .83 .86 Yes Yes Yes Yes Yes Yes B. Top-three chemicals ranked by per-plant magnitude Text FC HM debt Observations Adj. R-squared Year FE Establishment FE Controls # chemicals <=2 (1) (2) 0.220∗∗ (0.109) 0.342 (0.570) 22,241 16,593 .79 .83 Yes Yes Yes Yes Yes Yes # chemicals >2 (3) (4) 0.193∗∗∗ (0.062) 0.628∗ (0.330) 28,251 19,304 .87 .89 Yes Yes Yes Yes Yes Yes Full sample (5) (6) 0.190∗∗∗ (0.069) 0.562∗ (0.333) 51,390 36,546 .83 .86 Yes Yes Yes Yes Yes Yes C. Chemicals ranked by economywide magnitude Top 5 (1) Text FC 16,224 .87 Yes Yes Yes (3) 0.154∗∗∗ (0.051) 0.087 (0.086) HM debt Observations Adj. R-squared Year FE Establishment FE Controls Top 30 (2) 0.130 (0.402) 10,817 .89 Yes Yes Yes 38,516 .86 Yes Yes Yes (4) 0.486∗∗ (0.237) 27,282 .88 Yes Yes Yes Below top 30 (5) (6) 0.139∗ (0.071) 38,615 .79 Yes Yes Yes 0.591∗ (0.344) 26,943 .82 Yes Yes Yes This table presents our baseline results for the volume of the number one (panel A) and top-three (panel B) chemicals (measured by tons in logarithm) within each establishment. Columns 1 and 2 present results for establishments emitting only one or two chemicals; columns 3 and 4 present results for establishments emitting more than two chemicals; and columns 5 and 6 present results for the full sample. Panel C presents our baseline results for the volume of the number one, the top-30, and the rest of the chemicals ranked by aggregated total release in the sample. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. 624 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 624 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 A. Number one chemical ranked by per-plant magnitude Financial Constraints and Corporate Environmental Policies Health effects (1) (2) Text FC 0.192∗∗∗ (0.072) 0.563∗ HM debt Observations Adj. R-squared Year FE Establishment FE Controls (0.336) 35,632 .87 Yes Yes Yes 50,034 .85 Yes Yes Yes (1) Text FC 44,531 .84 Yes Yes Yes 0.307∗∗∗ (0.091) 49,829 .82 Yes Yes Yes B. Physical properties Air (2) 0.233∗∗∗ (0.077) HM debt Observations Adj. R-squared Year FE Establishment FE Controls A. Health effects Hazard score (3) (4) 0.759∗ (0.419) 31,126 .87 Yes Yes Yes 0.985∗∗∗ (0.343) 35,483 .86 Yes Yes Yes No health effects (5) (6) 0.138 (0.163) 0.537 (0.556) 12,789 .86 Yes Yes Yes 19,622 .84 Yes Yes Yes Water (3) (4) 0.151 (0.128) 13,698 .88 Yes Yes Yes 0.268 (0.463) 9,616 .90 Yes Yes Yes Panel A presents our baseline results based on the health effects of chemicals. Columns 1 and 2 present results for chemicals associated with adverse human health impacts; columns 3 and 4 present results for the EPA’s Risk-Screening Environmental Indicators (RSEI) hazard score with toxic chemicals weighted by their relative human health effects; and columns 5 and 6 present results for chemicals that are not associated with human health impacts. In panel B, we group chemicals by their physical properties: columns 1 and 2 present results for chemicals released through the air, and columns 3 and 4 present results for chemicals released through water. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. 625 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 625 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Table A.8 Chemicals grouped by health effects and physical properties The Review of Financial Studies / v 35 n 2 2022 (1) Interest coverage 0.001 (0.000) Rating (2) (3) (4) (5) 0.009∗∗∗ (0.003) EDF 0.006 (0.007) Leverage 0.075∗∗∗ (0.029) −0.038 (0.045) Dividend dummy Payout ratio log(assets) Cash/assets CAPEX/PPE Tangible Tobin’s q log(sales) Observations Adj. R-squared Year FE Establishment FE (6) 0.011 (0.028) −0.206 (0.232) −0.031 (0.083) −0.283 (0.196) −0.053∗∗ (0.027) 0.028 (0.020) 85,096 .78 Yes Yes −0.030 (0.039) −0.666∗∗ (0.299) 0.012 (0.122) −0.275 (0.233) −0.060∗ (0.033) 0.026 (0.023) 64,674 .80 Yes Yes 0.009 (0.027) −0.052 (0.227) −0.030 (0.082) −0.293 (0.198) −0.019 (0.026) 0.021 (0.020) 80,895 .79 Yes Yes 0.010 (0.027) −0.132 (0.223) 0.007 (0.081) −0.295 (0.195) −0.038 (0.026) 0.030 (0.020) 86,134 .78 Yes Yes 0.014 (0.027) −0.157 (0.222) 0.003 (0.081) −0.300 (0.195) −0.040 (0.026) 0.030 (0.020) 86,201 .78 Yes Yes −0.031 (0.035) 0.010 (0.027) −0.149 (0.223) −0.005 (0.081) −0.307 (0.195) −0.039 (0.026) 0.030 (0.020) 86,140 .78 Yes Yes This table presents results of OLS regressions of total toxic releases (measured by tons in logarithm) on several accounting-based financial-constraint measures: interest coverage, credit rating, expected default frequency (EDF), leverage, a dividend dummy, and the payout ratio. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. 626 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 626 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Table A.9 Accounting-based financial constraint measures 627 Page: 627 576–635 50,805 .83 Yes Yes Yes 0.000 (0.000) 40,828 .84 Yes Yes Yes −0.002 (0.017) (3) 51,142 .83 Yes Yes Yes −0.000 (0.004) 0.219∗∗∗ (0.073) (4) 51,364 .83 Yes Yes Yes −0.002 (0.112) 0.221∗∗∗ (0.072) (5) 51,413 .83 Yes Yes Yes 0.032 (0.064) 0.224∗∗∗ (0.073) (6) −0.069 (0.043) 51,386 .83 Yes Yes Yes 0.220∗∗∗ (0.073) 35,945 .86 Yes Yes Yes 0.564 (0.363) 0.000 (0.000) (7) 28,504 .86 Yes Yes Yes −0.011 (0.018) 0.986∗∗ (0.417) (8) 36,237 .86 Yes Yes Yes −0.001 (0.004) 0.658∗ (0.364) (9) 36,514 .86 Yes Yes Yes −0.042 (0.119) 0.679∗ (0.359) (10) 36,562 .86 Yes Yes Yes −0.024 (0.064) 0.651∗ (0.359) (11) −0.093∗ (0.053) 36,532 .86 Yes Yes Yes 0.647∗ (0.360) (12) In this table, we report the results of testing the robustness of our baseline results against additional accounting-based financial-constraint measures: interest coverage, credit rating, expected default frequency (EDF), leverage, a dividend dummy, and the payout ratio. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. Observations Adj. R-squared Establishment FE Year FE Controls Payout ratio Dividend dummy Leverage EDF Rating Interest coverage HM debt (2) 0.349∗∗∗ (0.083) (1) 0.217∗∗∗ (0.073) Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Text FC Table A.10 Robustness: Accounting-based financial constraint measures Financial Constraints and Corporate Environmental Policies The Review of Financial Studies / v 35 n 2 2022 (1) Rep(yes/no) log(market value of assets) Tobin’s q Preinvestment earnings/BVA 0.378∗∗∗ (0.023) −0.088∗∗ (0.036) 2.829∗∗∗ (0.480) log(1+ foreign earnings (3 yr)) Foreign earnings (3 yr)>0 (1 if yes) log(1+perm reinvested earnings) Perm reinvested earnings>0 (1 if yes) Estimated repatriation tax/MVA Tax loss carryforward/MVA Observations Adj. R-squared 6,032 .13 (2) Rep(yes/no) 0.187∗∗∗ (0.041) −0.083∗ (0.049) 2.026∗∗∗ (0.729) 0.065 (0.045) 1.983∗∗∗ (0.216) 0.086 (0.069) 0.127 (0.410) 28.231 (22.683) −1.365∗∗∗ (0.510) 5,358 .27 (3) Rep consider 0.185∗∗∗ (0.020) −0.059∗∗∗ (0.023) 1.009∗∗∗ (0.294) 0.003 (0.034) 1.799∗∗∗ (0.141) 0.077 (0.052) 0.720∗∗ (0.287) 15.310 (15.497) −0.317∗∗ (0.138) 5,358 .19 This table presents the results of estimating the probability of repatriation using cross-sectional logistic regressions. For columns 1 and 2, the dependent variable is an indicator variable that takes the value of one when a firm repatriated foreign income under the AJCA in 2004 or later and zero otherwise. The independent variables are based on values in 2003 or earlier. Ordered logit model estimation results are shown in column 3. The dependent variable takes the value of two if a firm repatriated foreign earnings under the AJCA, the value of one if it discussed repatriation of foreign earnings under the AJCA in its 10-K but did not repatriate, and zero otherwise. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. 628 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 628 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Table A.11 Tax holidays (AJCA): First stage Financial Constraints and Corporate Environmental Policies MSA HPI (1) MSA supply elasticity * Mortgage rate 0.027∗∗∗ (0.005) 1 quartile elasticity * Mortgage rate 2 quartile elasticity * Mortgage rate 3 quartile elasticity * Mortgage rate Observations Adj. R-squared Year FE MSA FE 1,246 .94 Yes Yes (2) -0.062∗∗∗ (0.008) -0.046∗∗∗ (0.008) -0.014∗∗ (0.007) 1,246 .94 Yes Yes In this table we report the first stage regression estimates of MSA-level HPI on the interaction between local housing supply elasticity and U.S. 30-year fixed mortgage rates for the period running from 1993 through 2007 (Chaney, Sraer, and Thesmar 2012). For column 1, we use MSA-level local housing supply elasticity (Saiz 2010), and, for column, 2 we use quartiles of the elasticity. MSA and year fixed effects are included in all specifications and standard errors are clustered at the MSA level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. 629 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 629 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Table A.12 The collateral channel: Real estate shock first stage The Review of Financial Studies / v 35 n 2 2022 Table A.13 Flow-induced price pressure and abnormal returns DGTW-adjusted returns (%) Qtr 0 Qtr 1-12 Quintiles −5.229 -0.665 1.906 5.299 17.535 −1.112 −0.819 −0.155 0.757 2.762 0.978 1.077 0.608 −0.348 −1.128 Decile 10 Inflow 27.919 3.359 -2.551 This table reports the average FIPP, DGTW-adjusted returns for event quarter 0 (the portfolio-formation quarter), and DGTW-adjusted cumulative abnormal returns from quarter 1 through quarter 12 for each quintile and the highest decile (inflow) of the mutual fund flow sample. Table A.14 Toxic releases and legal liabilities A. Toxic releases and legal liabilities Pr(investigation)% Pr(legal_liab>0)% (1) (2) log(total release) log(sales) Observations Adj. R-squared Year FE Establishment FE 0.153∗∗∗ (0.038) −0.000 (0.150) 91,725 .15 Yes Yes 0.117∗∗∗ (0.030) −0.202 (0.132) 91,725 .11 Yes Yes 0.011∗∗∗ (0.003) −0.023 (0.014) 91,725 .10 Yes Yes B. Lagged toxic releases and legal liabilities Pr(investigation)% Pr(legal_liab>0)% (1) (2) (3) (4) Lagged.log(total release) log(total release) Lagged.log(sales) log(sales) Observations Adj. R-squared Industry-year FE Model 0.415∗∗∗ (0.120) 0.744∗∗∗ (0.118) 0.453∗∗ (0.184) −0.028 (0.177) 70,412 Yes Logit 0.313∗∗∗ (0.071) 0.561∗∗∗ (0.071) 0.602∗∗∗ (0.215) −0.018 (0.208) 77,133 .05 Yes OLS 0.339∗∗∗ (0.112) 0.542∗∗∗ (0.111) 0.297∗ (0.166) −0.062 (0.162) 64,686 Yes Logit log(legal_liab) (3) 0.238∗∗∗ (0.059) 0.373∗∗∗ (0.059) 0.396∗∗ (0.181) −0.081 (0.178) 77,133 .04 Yes OLS log(legal_liab) (5) 0.026∗∗∗ (0.006) 0.043∗∗∗ (0.006) 0.045∗∗ (0.022) −0.008 (0.021) 77,133 .05 Yes OLS Panel A reports the regression estimates of the effects of total toxic releases on government agency enforcement actions, including the likelihood of investigation, the likelihood of positive legal liabilities being imposed (including federal and local penalties, compliance and recovery costs, and the costs of supplemental environmental projects), and the log dollar amount of legal liabilities. Coefficient estimates of the OLS regressions and marginal effects of the logistic regressions are presented. log(sales) is included to control for production volume at the establishment level. Standard errors are clustered at the establishment level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. 630 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 630 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 FIPP(%) Low (outflow) 2 3 4 High (inflow) Financial Constraints and Corporate Environmental Policies A. Toxic releases under the CAA Pr(investigation)% Pr(legal_liab>0)% (1) (2) (3) (4) Nonattain*log(CAA) log(CAA) Nonattainment log(sales) Observations Adj. R-squared Industry-year FE Model 0.486∗∗∗ (0.096) 0.800∗∗∗ (0.064) 0.044 (0.354) 0.514∗∗∗ (0.093) 72,449 Yes Logit 0.497∗∗∗ (0.078) 0.541∗∗∗ (0.039) 0.900∗∗∗ (0.224) 0.675∗∗∗ (0.099) 79,202 .05 Yes OLS 0.346∗∗∗ (0.084) 0.621∗∗∗ (0.056) 0.171 (0.300) 0.281∗∗∗ (0.080) 66,108 Yes Logit 0.366∗∗∗ (0.065) 0.373∗∗∗ (0.031) 0.695∗∗∗ (0.182) 0.360∗∗∗ (0.076) 79,202 .04 Yes OLS log(legal_liab) (5) 0.037∗∗∗ (0.007) 0.045∗∗∗ (0.003) 0.067∗∗∗ (0.020) 0.043∗∗∗ (0.008) 79,202 .05 Yes OLS B. Total toxic releases Pr(investigation)% (1) (2) Nonattain*log(total release) log(total release) Nonattainment log(sales) Observations Adj. R-squared Industry-year FE Model 0.323∗∗∗ (0.081) 0.944∗∗∗ (0.057) 0.257 (0.342) 0.422∗∗∗ (0.082) 85,096 Yes Logit 0.463∗∗∗ (0.077) 0.644∗∗∗ (0.038) 0.682∗∗∗ (0.180) 0.586∗∗∗ (0.087) 92,746 .05 Yes OLS Pr(legal_liab>0)% (3) (4) 0.226∗∗∗ (0.071) 0.709∗∗∗ (0.050) 0.341 (0.288) 0.224∗∗∗ (0.071) 78,012 Yes Logit 0.354∗∗∗ (0.065) 0.428∗∗∗ (0.029) 0.506∗∗∗ (0.144) 0.303∗∗∗ (0.067) 92,746 .04 Yes OLS log(legal_liab) (5) 0.035∗∗∗ (0.007) 0.050∗∗∗ (0.003) 0.049∗∗∗ (0.015) 0.036∗∗∗ (0.007) 92,746 .05 Yes OLS In this table, we report the results obtained by examining the relationship between toxic releases and legal liabilities across attainment and nonattainment counties. Outcome variables include the likelihood of government agency investigations, the likelihood of positive legal liabilities being imposed (including federal and local penalties, compliance and recovery costs, and the costs of supplemental environmental projects), and the log dollar amount of legal liabilities. Coefficient estimates of the OLS regressions and marginal effects of the logistic regressions are presented. log(sales) is included to control for production volume at the establishment level. Standard errors are clustered at the establishment level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. 631 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 631 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Table A.15 Legal liabilities and nonattainment status The Review of Financial Studies / v 35 n 2 2022 Table A.16 Robustness: Nonattainment status and large polluters Large polluter Nonattainment status (1) Nonattainment=1 × Text FC Nonattainment=1 × HM debt Large polluter=1 × HM debt Text FC HM debt log(assets) Tobin’s q Tangible Cash/assets CAPEX/PPE log(sales) Observations Adj. R-squared Year FE Establishment FE −1.393∗ (0.770) 70th percentile (3) −0.210∗ (0.116) (4) 75th percentile (5) (6) 80th percentile (7) (8) −0.218∗ (0.114) −0.272∗∗ (0.117) −0.228 −0.078 −0.081 (0.483) (0.486) (0.491) 0.312∗∗ 0.121 0.088 0.066 (0.133) (0.088) (0.086) (0.084) 1.604∗∗ 0.696∗ 0.627∗ 0.648∗ (0.642) (0.382) (0.380) (0.376) 0.047 0.039 −0.040 −0.000 −0.039 −0.008 −0.010 0.005 (0.045) (0.053) (0.034) (0.043) (0.035) (0.044) (0.037) (0.046) 0.079∗ 0.059 0.081∗∗ 0.079∗∗ 0.077∗∗ 0.080∗∗ 0.082∗∗ 0.099∗∗∗ (0.047) (0.043) (0.034) (0.038) (0.032) (0.037) (0.033) (0.038) 0.182 0.196 −0.259 0.280 −0.107 0.400 −0.087 0.474 (0.326) (0.324) (0.251) (0.287) (0.251) (0.294) (0.255) (0.318) 0.319 0.481 −0.147 0.062 −0.183 −0.164 −0.076 −0.023 (0.359) (0.322) (0.279) (0.266) (0.298) (0.286) (0.312) (0.305) 0.226 0.156 0.168 0.198 0.170 0.138 0.067 0.041 (0.169) (0.188) (0.120) (0.132) (0.125) (0.139) (0.127) (0.137) −0.036 −0.007 −0.013 0.026 −0.010 0.022 −0.006 0.020 (0.036) (0.034) (0.022) (0.024) (0.022) (0.024) (0.021) (0.025) 21,599 14,772 34,54523,100 31,984 21,311 29,321 19,131 .86 .87 .86 .88 .86 .88 .87 .89 Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes In this table, we report results where we match establishments operated by the same parent firms but with different nonattainment status and size. Large polluter indicates establishments with total toxic releases above the 70th, 75th, and 80th percentiles of the sample distribution. Firm-level controls include lagged log(assets), Cash/assets, CAPEX/PPE, Tangible, and Tobin’s q. The establishment-level control is contemporaneous log(sales). Standard errors are clustered at the firm level. Standard errors appear in parentheses. *p <.1; **p <.05; ***p <.01. References Akey, P., and I. Appel. 2021. The limits of limited liability: Evidence from industrial pollution. Journal of Finance 76:5–55. Albuquerque, R., Y. Koskinen, and C. Zhang. 2019. Corporate social responsibility and firm risk: Theory and empirical evidence. Management Science 65:4451–69. Almeida, H., and M. Campello. 2007. Financial constraints, asset tangibility, and corporate investment. Review of Financial Studies 20:1429–60. Andersen, D. C. 2017. Do credit constraints favor dirty production? theory and plant-level evidence. Journal of Environmental Economics and Management 84:189–208. Baker, M., J. C. Stein, and J. Wurgler. 2003. When does the market matter? stock prices and the investment of equity-dependent firms. Quarterly Journal of Economics 118:969–1005. Barber, B. M., A. Morse, and A. Yasuda. 2021. Impact investing. Journal of Financial Economics 139:162–85. Barnatchez, K., L. Crane, and R. Decker. 2017. An assessment of the national establishment time series (NETS) database. Working Paper, Board of the Governor of the Federal Reserve System. 632 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 632 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Large polluter=1 × Text FC −0.298∗ (0.172) (2) Financial Constraints and Corporate Environmental Policies Baron, D. P. 2001. Private politics, corporate social responsibility, and integrated strategy. Journal of Economics & Management Strategy 10:7–45. Becker, R., and V. Henderson. 2000. Effects of air quality regulations on polluting industries. Journal of political Economy 108:379–421. Becker, R. A. 2005. Air pollution abatement costs under the clean air act: evidence from the pace survey. Journal of environmental economics and management 50:144–69. Bénabou, R., and J. Tirole. 2010. Individual and corporate social responsibility. Economica 77:1–19. Bodnaruk, A., T. Loughran, and B. McDonald. 2015. Using 10-k text to gauge financial constraints. Journal of Financial and Quantitative Analysis 50:623–46. Campello, M., J. R. Graham, and C. R. Harvey. 2010. The real effects of financial constraints: Evidence from a financial crisis. Journal of Financial Economics 97:470–87. Chaney, T., D. Sraer, and D. Thesmar. 2012. The collateral channel: How real estate shocks affect corporate investment. American Economic Review 102:2381–409. Chava, S. 2014. Environmental externalities and cost of capital. Management Science 60:2223–47. Cheng, I.-H., H. G. Hong, and K. Shue. 2016. Do managers do good with other peoples’ money? Working Paper, Dartmouth College. Chodorow-Reich, G. 2013. The employment effects of credit market disruptions: Firm-level evidence from the 2008–9 financial crisis. Quarterly Journal of Economics 129:1–59. Cohn, J. B., and M. I. Wardlaw. 2016. Financing constraints and workplace safety. Journal of Finance 71:2017–58. Copeland, B., and S. Taylor. 2003. Trade and the environment: Theory and evidence. Princeton, NJ: Princeton University Press. —– 2004. Trade, growth, and the environment. Journal of Economic literature 42:7–71. Coval, J., and E. Stafford. 2007. Asset fire sales (and purchases) in equity markets. Journal of Financial Economics 86:479–512. Currie, J., J. G. Zivin, J. Mullins, and M. Neidell. 2014. What do we know about short- and long-term effects of early-life exposure to pollution? Annual Review of Resource Economics 6:217–47. Daniel, K., M. Grinblatt, S. Titman, and R. Wermers. 1997. Measuring mutual fund performance with characteristic-based benchmarks. Journal of finance 52:1035–58. Dimson, E., O. KarakasĖ§, and X. Li. 2015. Active ownership. Review of Financial Studies 28:3225–68. Dunn, J., S. Fitzgibbons, and L. Pomorski. 2017. Assessing risk through environmental, social and governance exposures. Journal of Investment Management. Eccles, R. G., I. Ioannou, and G. Serafeim. 2014. The impact of corporate sustainability on organizational processes and performance. Management Science 60:2835–57. Edmans, A. 2011. Does the stock market fully value intangibles? employee satisfaction and equity prices. Journal of Financial Economics 101:621–640. ISSN 0304-405X. Edmans, A., I. Goldstein, and W. Jiang. 2012. The real effects of financial markets: The impact of prices on takeovers. Journal of Finance 67:933–71. Farre-Mensa, J., and A. Ljungqvist. 2016. Do measures of financial constraints measure financial constraints? Review of Financial Studies 29:271–308. Faulkender, M., and M. Petersen. 2012. Investment and capital constraints: Repatriations under the american jobs creation act. Review of Financial Studies 25:3351–88. 633 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 633 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Berger, E. 2019. Selection bias in mutual fund flow-induced fire sales: Causes and consequences. Working Paper, Cornell University. The Review of Financial Studies / v 35 n 2 2022 Fernando, C. S., M. P. Sharfman, and V. B. Uysal. 2017. Corporate environmental policy and shareholder value: Following the smart money. Journal of Financial and Quantitative Analysis 52:2023–51. Gormley, T. A., and D. A. Matsa. 2011. Growing out of trouble? corporate responses to liability risk. Review of Financial Studies 24:2781–821. Gredil, O., N. Kapadia, and J. H. Lee. 2019. Do rating agencies deserve some credit? evidence from transitory shocks to credit risk. Working Paper, Tulane University. Henderson, J. V. 1996. Effects of air quality regulation. The American Economic Review 86:789–813. Hoberg, G., and V. Maksimovic. 2014. Redefining financial constraints: A text-based analysis. Review of Financial Studies 28:1312–52. Hoepner, A. G., I. Oikonomou, Z. Sautner, L. T. Starks, and X. Zhou. 2018. ESG shareholder engagement and downside risk. Working Paper, University College Dublin. Hong, H., and M. Kacperczyk. 2009. The price of sin: The effects of social norms on markets. Journal of Financial Economics 93:15–36. Hong, H., J. D. Kubik, and J. A. Scheinkman. 2012. Financial constraints on corporate goodness. Working Paper, Columbia University. Karpoff, J. M., J. R. Lott, and E. W. Wehrly. 2005. The reputational penalties for environmental violations: Empirical evidence. Journal of Law and Economics 48:653–75. Khan, M., L. Kogan, and G. Serafeim. 2012. Mutual fund trading pressure: Firm-level stock price impact and timing of seos. Journal of Finance 67:1371–95. Kitzmueller, M., and J. Shimshack. 2012. Economic perspectives on corporate social responsibility. Journal of Economic Literature 50:51–84. Levinson, A. 2009. Technology, international trade, and pollution from us manufacturing. American economic review 99:2177–92. ———. 2015. A direct estimate of the technique effect: changes in the pollution intensity of us manufacturing, 1990–2008. Journal of the Association of Environmental and Resource Economists 2:43–56. Lins, K. V., H. Servaes, and A. Tamayo. 2017. Social capital, trust, and firm performance: The value of corporate social responsibility during the financial crisis. Journal of Finance 72:1785–824. Lou, D. 2012. A flow-based explanation for return predictability. Review of Financial Studies 25:3457–89. Margolis, J. D., H. Anger Elfenbein, and J. P. Walsh. 2009. Does it pay to be good ... and does it matter? A meta-analysis of the relationship between corporate social and financial performance. Working Paper, Harvard University. Masulis, R. W., and S. W. Reza. 2015. Agency problems of corporate philanthropy. Review of Financial Studies 28:592–636. Mian, A., and A. Sufi. 2011. House prices, home equity–based borrowing, and the us household leverage crisis. American Economic Review 101:2132–56. Saiz, A. 2010. The geographic determinants of housing supply. Quarterly Journal of Economics 125:1253–96. Shapira, R., and L. Zingales. 2017. Is pollution value-maximizing? The Dupont case. Working Paper, Law School, IDC. Shapiro, J. S., and R. Walker. 2018. Why is pollution from us manufacturing declining? the roles of environmental regulation, productivity, and trade. American Economic Review 108:3814–54. Starks, L. T., P. Venkat, and Q. Zhu. 2017. Corporate esg profiles and investor horizons. Working Paper, University of Texas at Austin. 634 [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 634 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 Hasbrouck, J. 2009. Trading costs and returns for us equities: Estimating effective costs from daily data. Journal of Finance 64:1445–77. Financial Constraints and Corporate Environmental Policies Walker, W. R. 2013. The transitional costs of sectoral reallocation: Evidence from the clean air act and the workforce. Quarterly Journal of Economics 128:1787–835. Wardlaw, M. 2020. Measuring mutual fund flow pressure as shock to stock returns. Journal of Finance 75:3221–43. [07:44 18/12/2021 RFS-OP-REVF210059.tex] Page: 635 576–635 Downloaded from https://academic.oup.com/rfs/article/35/2/576/6265483 by National University of Singapore user on 19 January 2024 635