Site uncertainty, allocation uncertainty, and superfund liability valuation

Journal of Accounting and Public Policy 17 (1998) 331±366

Site uncertainty, allocation uncertainty, and superfund liability valuation Katherine Campbell a,*, Stephan E. Sefcik b, Naomi S. Soderstrom c a

Department of Accounting, University of Connecticut, School of Business Administration, 368 Fair®eld Road, U-41A Storrs, CT 06269-2041, USA b Department of Accounting, University of Washington, College of Business Administration, Box 353200 Seattle, WA 98195-3200, USA c Department of Accounting, University of Colorado at Denver, School of Business Administration, Box 173364 Denver, CO 86217-3364, USA

Abstract The amount and timing of a ®rm's ultimate ®nancial obligation for contingent liabilities is uncertain and subject to the outcome of future events. We decompose uncertainty about Superfund contingent liabilities into two sources: (1) uncertainty regarding site clean-up cost (site uncertainty); and (2) uncertainty regarding allocation of total site-clean-up cost across multiple parties associated with the site (allocation uncertainty). We hypothesize that when a ®rm's contingent Superfund liabilities are subject to relatively more site and allocation uncertainty, these liabilities will be viewed as relatively risky. This risk will a€ect the ®rm's cost of capital. Thus, market valuation of contingent Superfund liabilities will be negatively a€ected. To empirically test our hypotheses we employ a cross-sectional model of the relation between ®rm market value and book value of assets, book value of liabilities, and a contingent Superfund liability proxy interacted with proxies for our uncertainty constructs. We ®nd di€erential results across industries. In the chemical industry, both site and allocation uncertainty are associated with di€erential valuation of contingent Superfund liabilities. The greater the uncertainty, the more negatively the contingent Superfund liability is valued. Results are insigni®cant, however, in the paper and machinery industries. Our results provide evidence, at least in the most heavily involved industry, that site-level information of a non®nancial nature can be relevant to ®nancial statement users. This is consistent with accounting regulators' incorporation of site-level Superfund enforcement data in

*

Corresponding author. Tel.: 1 860 486 4413; fax: 1 860 486 4838.

0278-4254/98/$ ± see front matter Ó 1998 Elsevier Science Inc. All rights reserved. PII: S 0 2 7 8 - 4 2 5 4 ( 9 8 ) 1 0 0 0 9 - 1

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guidance regarding ®nancial reporting for contingent Superfund liabilities. The concepts of site and allocation uncertainty, however, may provide a useful way for organizing and evaluating alternative site-level data when considering ®nancial reporting alternatives. Ó 1998 Elsevier Science Inc. All rights reserved.

1. Introduction Superfund 1 legislation has exposed entities identi®ed with contaminated sites to substantial contingent liabilities for environmental clean-up costs. Barth and McNichols (1994) have demonstrated that ®rm value is associated with the existence of contingent Superfund liabilities. However, additional research is needed on the di€erential valuation of these contingencies. Among Barth and McNichols (1994, p. 210) ®ndings is the somewhat counter-intuitive result that the number of Superfund sites with which a ®rm is identi®ed explains the stock market's assessment of ®rms' Superfund liability at least as well as more sophisticated dollar denominated cost estimates. This ®nding raises as many questions as it answers. Barth and McNichols (1994, p. 204) themselves interpret this result as suggesting ``. . .either that the market's assessment of a company's environmental liability is crude, or that our costbased proxies are noisy. The uncertainty surrounding estimation of individual company liabilities for Superfund clean-up costs makes both explanations plausible''. Superfund sites di€er in characteristics such as extent and type of contamination, available remediation alternatives, and number of parties associated with the site. Intuitively, one would not expect equivalent stock market valuation implications for association with sites di€ering in these dimensions. While Barth and McNichols (1994, pp. 185±193) provide descriptive analysis of Superfund site characteristics and an analysis of the relation between site characteristics and estimated clean-up costs, their analysis does not attempt to investigate valuation implications of di€erences in site characteristics. Thus, their study provides only a base-line analysis of the valuation of contingent Superfund liabilities. In this paper we extend the analysis of contingent

1 The Superfund program is the result of legislation including the Comprehensive Environmental Response, Compensation and Liability Act of 1980 (USC, 1980) and its subsequent reauthorizations including the Superfund Amendments and Reauthorization Act of 1986 (USC, 1986). Background on the provisions of this legislation is described in the Superfund Remediation and Enforcement Process section of this paper.

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Superfund valuation by investigating factors associated with di€erential valuation of these contingencies. There is substantial uncertainty surrounding estimation of individual company Superfund liabilities. We investigate the role this uncertainty plays in di€erential valuation of contingent Superfund liabilities. To analyze the sources of uncertainty a€ecting estimation of contingent Superfund liabilities, we consider institutional features of the Superfund process in the context of the accrual accounting framework. We suggest that uncertainty surrounding the amount and timing of a ®rm's liability for Superfund site clean-up costs can be viewed as originating from two major sources: (1) uncertainty regarding the cost of cleaning up each Superfund site (site uncertainty); and (2) uncertainty regarding allocation of clean-up costs across multiple parties associated with a site (allocation uncertainty). We develop proxies for these two types of uncertainty and empirically investigate implications of each for contingent Superfund liability valuation. In addition, we investigate valuation implications of industry aliation. Our analysis moves beyond the question of whether or not contingent Superfund liabilities are valued by the market to investigating which speci®c dimensions are important in valuation. Results of our study demonstrate that our proxies for both site and allocation uncertainty are, in certain contexts, associated with di€erential valuation of contingent Superfund liabilities. The greater the uncertainty, the more negatively the contingent Superfund liability is valued. Further, the value relevance of contingent Superfund liabilities, as well as the impact of site and allocation uncertainty, di€ers across industry aliations of a€ected ®rms. Results are consistent with hypotheses in the chemical industry, but generally insigni®cant in machinery and paper industries. Despite increasing regulatory attention, accounting for contingent Superfund liabilities remains a contentious issue. In this setting, investigation of stock market valuation has the potential to provide useful insights regarding ®nancial statement accruals and disclosures related to contingent Superfund liabilities. While results of Barth and McNichols (1994, p. 199) suggest that the market assesses a greater expected environmental liability than ®rms currently accrue, their results can be extended. Further, their results provide little insight regarding di€erential valuation of contingent Superfund liabilities. Results of our study do, however, address di€erential valuation and have implications for ®nancial reporting. As discussed later, regulators have focused guidance regarding disclosure for contingent Superfund liabilities on disclosure of information related to milestones in the enforcement process. This approach relates ®rm-speci®c ®nancial reporting to site-level events that are largely non-®nancial in character. Prior research has not investigated the implications of such an approach. Our results demonstrate that site-level and

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non-®nancial information is associated with the way in which contingent Superfund liabilities are valued and can be relevant to ®nancial statement users. This result is consistent with the tact taken by regulators, which is discussed later, in linking ®rm reporting to site-level data. The amount of available site-level data, however, is potentially overwhelming, and it is unclear whether a systematic approach has been taken by accounting regulators in evaluating alternative data and its potential role in ®nancial reporting for environmental liabilities. Our analysis provides concepts (i.e., site and allocation uncertainty) that may form a useful framework for organizing and evaluating site-level data when considering ®nancial reporting alternatives. Such a framework could potentially play a role in improving the quality of disclosure and ®nancial reporting for contingent environmental liabilities. Section 2 provides background on the Superfund remediation and enforcement processes. This is followed by a discussion of predicted relations and hypotheses. Empirical methods and results are then described, followed by concluding remarks and some suggestions for further research.

2. Background 2.1. The superfund program and remediation processes The Superfund program was created in 1980 when Congress enacted the Comprehensive Environmental Response, Compensation and Liability Act (USC, 1980). A trust fund, primarily ®nanced through taxes on the chemical and petroleum industries, was created to ®nance site clean-up (remediation) when viable Potentially Responsible Parties (PRPs) could not be found or were unwilling to take appropriate action (Acton, 1989, p. 7). The legislation also provided the Environmental Protection Agency (EPA) authority to negotiate settlements, order PRPs to take clean-up actions, and sue PRPs to repay costs of clean-up when trust funds have been used. Provisions of the legislation create a broad scope for implementation of the Act. First, the Act a€ects a large and diverse group of parties. PRPs may include individuals, partnerships, corporations, and governmental entities linked to a Superfund site. PRPs may include those who have owned or operated hazardous waste sites either during or subsequent to the pollution, as well as those generating, transporting, or disposing of hazardous substances (Acton, 1989, p. 1). Second, liability imposed by the Act has been interpreted by the courts as retroactive, strict, and joint and several (Dixon 1994, p. 3). Thus, imposition of responsibility for clean-up costs may be based on factors other than degree of culpability, such as ability to pay. Substantial litigation has

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arisen between a€ected parties regarding apportionment of clean-up cost liability for multi-party sites (i.e., between governmental entities and PRPs, between PRPs at multi-party sites, and between PRPs and their insurers) (Dixon, 1994, pp. 6±9). Third, the cost of clean-up for Superfund sites is substantial. Clean-up of identi®ed sites is estimated to cost US ®rms over $300 billion and the total clean-up cost is projected by some to eventually reach $1 trillion. 2 Superfund legislation exposes ®rms to potential liabilities that are signi®cant in magnitude. The original Superfund program has repeatedly been reauthorized. The most signi®cant Superfund amendment was made by the Superfund Amendments and Reauthorization Act of 1986 (SARA) which became e€ective on 1 January 1987 (USC, 1986). SARA increased the Superfund Trust Fund to $8:5 billion, imposed a general environmental tax on corporations to fund the Trust (Acton, 1989, p. 8), and expanded enforcement authority, particularly for settlement and cost-recovery actions (Stimson et al. (1993), p. 279). By expanding enforcement authority and providing additional settlement mechanisms, SARA provided some teeth to the Superfund program. Remediation of a Superfund site involves a series of actions beginning with a preliminary assessment/site inspection to determine whether a proposed site poses a potential hazard sucient to warrant further investigation. The site is then assigned a hazard score. The hazard ranking process considers a number of site characteristics including the seriousness of contamination to ground, water, and air (EPA, 1984, p. 2). Sites are numerically ranked according to potential hazard posed to the environment and public health. Based on this ranking, sites are added to the National Priorities List (NPL) (Stimson et al., 1993, p. 261). Remediation investigations and feasibility studies (RI/FS) are then conducted to assess site characteristics, the extent and the nature of contamination, potential risks posed, and relative merits of alternative remediation techniques. Following a public comment period, a speci®c remediation plan is chosen by the EPA and outlined in a Record of Decision (ROD). Subsequent to issuance of a ROD, a ®nal decision is made regarding the speci®c plan, and a remediation design including engineering plans and speci®cations is prepared for the site. Once this process is completed, action at the site begins in accordance with the remediation design plan. Sites continue to be monitored following completion of remediation activities to ensure e€ectiveness of the remediation response and ongoing maintenance (Stimson et al. (1993), pp. 262±268).

2

See Van Voorst (1993, pp. 63,64.)

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At any point in the remediation process, however, it can be decided that something was not properly assessed or technically correct, and steps can be repeated (Acton, 1989, p. 13). There are many cases where an approach has been selected based on RI/FS work, only to ®nd that it is unworkable when remedial action actually begins (Acton, 1989, pp. 13,14). At this point further RI/FS work may be performed, a new set of alternative approaches considered, and a new ROD issued. In addition, the EPA often divides sites into operable units. When this approach is taken, di€erent parts of a single site (i.e., operable units) may be moving through the remediation process on di€erent time schedules (Acton, 1989, p. 15). Thus, the remediation process at any individual site can be complex and time consuming. 2.2. Superfund enforcement process and settlements The Superfund enforcement process comprises actions the EPA may take to compel responsible parties to conduct or fund site remediation. The enforcement process for remedial actions involves ®ve major e€orts (EPA, 1998, pp. 16, 26): (1) identi®cation and noti®cation of PRPs; (2) encouragement of PRPs to perform remedial work as the work to be done is identi®ed; (3) negotiation of an enforcement agreement with PRPs if the PRPs are believed to be willing and capable of performing the work; 3 (4) exercise of EPA's authority to direct PRPs to perform removal or remedial action via a unilateral administrative order or through a lawsuit to compel performance; and/or (5) performance of the remedial work by the EPA and potential ®ling of suit against PRP(s) to recover money spent by the EPA. 4 The EPA actively promotes settlements with PRPs (Stimson et al. (1993), p. 280). Settlement has become the preferred method of resolving allocation of liability for clean-up costs, particularly after SARA's expansion of settlement mechanisms. Over the course of site remediation, multiple settlements are typically entered. The EPA may separately negotiate with multiple PRPs at each stage of the process. Thus, several settlements may be entered to fund the initial site investigation. As the remediation process unfolds, additional settlements may be entered with the same and/or additional parties. It is important to note that neither site clean-up nor explicit settlement with the federal

3 Agreements may be entered in court as judicial consent decrees or may be in the form of administrative orders signed outside of court. Both types of agreements are enforceable in court and the EPA oversees actions conducted by the PRPs under both types (EPA, 1998, pp. 3,4). 4 Various EPA publications (e.g. EPA, 1998) describe these technical and administrative processes in detail.

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government may fully de®ne a ®rm's liability. Neither of these actions precludes subsequent related legal actions or claims involving other parties (e.g., other PRPs, insurance companies, concerned citizen groups, etc.) (Acton, 1989, pp. 53,54). Even after remediation of a site has been completed, responsibility for signi®cant maintenance and monitoring costs may be required for an inde®nite period into the future (Stimson et al., 1993, p. 268). Nevertheless, settlement activity at a site represents a meaningful degree of closure or momentum in the clean-up process at the site, as well as consensus and cooperation amongst PRPs and the EPA in ®rst-stage allocation of clean-up responsibilities. 3. Hypothesis development Authoritative guidance regarding accounting for contingent liabilities is provided by Statement of Financial Accounting Standards (SFAS) No. 5 Paragraph 8 (FASB, 1975), which requires that a liability be accrued if a contingent loss is ``probable'', and the amount of loss can be ``reasonably estimated''. This standard, however, allows for substantial managerial discretion in its application. Financial statement information regarding contingent Superfund liabilities has historically been highly varied (Price Waterhouse, 1992, p. 10, and 1994, p. 9; Northcut, 1995, p. 23; Mitchell, 1994, p. 21). As a consequence of both the magnitude of environmental liabilities and variation in accounting practice, recent regulatory scrutiny of ®nancial reporting practices for environmental liabilities has increased. The Financial Accounting Standards Board FASB, issued Emerging Issues Task Force 93-5, ``Accounting for Environmental Liabilities'' (FASB, 1993) on accounting for environmental liabilities and the FASB continues to be interested in the issue of environmental liability reporting. Also in 1993, the United States Securities and Exchange Commission (SEC) issued Sta€ Accounting Bulletin No. 92 (SEC, 1993) which addresses accounting treatment and disclosure of loss contingencies with an emphasis on environmental matters. The SEC has also entered an information sharing arrangement with the EPA as an aid to enforcement with respect to Superfund contingent liability reporting. In 1996 the American Institute of Certi®ed Public Accountants (AICPA) issued Statement of Position No. 96-1, ``Environmental Remediation Liabilities'' (AICPA, 1996), which provides benchmarks tied to environmental regulatory processes to aid in the application of SFAS No. 5 in the context of contingent environmental liabilities. Despite this regulatory attention, accounting for Superfund liabilities remains a contentious issue. Estimation of contingent Superfund liability lies at the heart of issues surrounding ®nancial statement disclosure and accrual practices. The ability of ®rms to reasonably estimate contingent Superfund liabilities is hindered by substantial sources of uncertainty that are inherent in

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the institutional context of Superfund. Through a series of surveys, Price Waterhouse (1991, 1992, ) has solicited information about corporate America's views and accounting practices related to environmental issues. The 1992 and 1994 surveys asked participants to rank the importance of a variety of factors in estimating site remediation costs. The nature of the site is consistently ranked as the most important factor in estimating clean-up costs. Survey results indicate other important factors are: uncertainty regarding remediation methods and technology; extent of regulatory involvement; past experience; and the number and viability of other PRPs (Price Waterhouse, 1992, p. 14; 1994, p. 13). The factors reported in the Price Waterhouse surveys relate to two basic challenges underlying estimation of ®rm-speci®c Superfund contingent liability. First, the cost of cleaning-up a Superfund site is, in itself, dicult to estimate. RODs describe a remediation action and present an initial cost estimate for site clean-up. 5 Unfortunately, RODs are often vague in their expression of remedies, and the actual cost of clean-up can substantially deviate from ROD estimates. Church and Nakamura (1993), for example, present case studies of sites where actual clean-up costs were less then the ROD estimates (p. 103) as well as cases where costs substantially exceeded estimates (p. 61). We refer to uncertainty regarding the total cost of site cleanup as site uncertainty. Even if the cost of cleaning up a site could be known with certainty, the share of the total cost that any individual ®rm will ultimately pay would still be dicult to assess. Multiple PRPs are typically identi®ed with Superfund sites. The joint and several nature of Superfund liability creates substantial uncertainty regarding the allocation of clean-up costs at multi-party sites. 6 One

5 Regulators and accounting policy makers have used various milestones in the Superfund process as indicators for the probable existence and estimability of site clean-up costs. For example, SEC Sta€ Accounting Bulletin 92 indicates that even early in the enforcement process, perhaps prior to a RI/FS study, at least a minimum liability can be estimated (SEC, 1993, pp 3,4 Price Waterhouse, 1994, p. 12). Issuance of a ROD indicates the selection of a speci®c remediation strategy, and thus resolves an important variable in facilitating a best estimate of clean-up costs (Price Waterhouse, 1994, p. 11). But, although RODs provide some estimate of clean-up costs, many regard these estimates as highly uncertain. Acton (1989, p. 39) suggests several reasons for uncertainty regarding ROD costs estimates including: (1) RODs are issued before the details of remedial actions are developed during the remedial design phase; (2) during remedial design and remedial action phases more is learned about the site that can a€ect the remedy undertaken and its costs; (3) at the ROD stage technologies and performance standards for site remediation are not agreed upon and the remediation approach can be respeci®ed in the future; and (4) there is little experience to date to calibrate the accuracy of ROD cost estimates. 6 Even in a single-party site (i.e., one at which only one PRP has been identi®ed), some allocation uncertainty would likely be present since the PRP could seek recoveries from multiple third parties including insurers and PRPs not identi®ed by the government.

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could argue that even after a ROD cost estimate is available, a PRP's contingent Superfund liability remains inestimable (and thus not subject to accrual under SFAS No. 5) since the total cost of site remediation provides no information about how much any individual ®rm will eventually pay. Ultimately, cost allocation is negotiated and evolves over time (through agreements and legal proceedings between PRPs and government entities, among PRPs identi®ed with a site, and between PRPs and other third parties). We refer to uncertainty regarding the allocation of total site clean-up costs across PRPs as allocation uncertainty. Because site and allocation uncertainty surrounding any estimate of Superfund liability are likely to be both large and varied across sites and ®rms, this uncertainty is particularly important to valuation of these contingencies. The role of parameter uncertainty has been investigated in the context of security valuation where the uncertainty is described as creating ``estimation risk'' (Barry and Brown, 1985, p. 407; Botosan, 1997, p. 325; Clarkson and Thompson, 1990, p. 431; Coles and Loewenstein, 1988, p. 347). The intuition underlying this literature is that securities are perceived as relatively more risky when relatively little information about them is available. This risk is due to greater uncertainty surrounding the exact parameters of their return distributions. Parameter uncertainty enters investors' estimates of beta whenever parameter estimation error is correlated with realized market returns. Clarkson and Thompson (1990, pp. 441±444) provide an empirical evidence consistent with estimation risk being re¯ected in security returns. While our analysis is focused on uncertainty surrounding contingent liabilities that occur within the ®rm (i.e., at a level below that of parameters entering models of security returns), this type of uncertainty is analogous to estimation risk. The parameter uncertainty entering investors' estimates of beta is analogous to site and allocation uncertainty in the context of contingent Superfund liabilities. Thus, we hypothesize that, ceteris paribus, when there is relatively more uncertainty regarding a ®rm's liability for Superfund clean-up costs, the ®rm's contingent Superfund liabilities will be regarded as more risky. This incremental relative risk will increase the ®rm's cost of capital and be negatively re¯ected in ®rm value. Speci®cally, we hypothesize that when substantial site and/or allocation uncertainty exists, contingent Superfund liabilities will be more negatively associated with the market value of equity. In alternative form, the hypotheses are: H1: Ceteris paribus, site uncertainty is associated with a more negative relation between contingent Superfund liabilities and ®rm value. H2: Ceteris paribus, allocation uncertainty is associated with a more negative relation between contingent Superfund liabilities and ®rm value. Operationalization of these hypotheses requires controlling for the expected value of cash ¯ows related to the contingent Superfund liability (i.e., the

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expected cost of clean-up). Disentangling the expected value of the liability (amean concept) from the uncertainty surrounding it (a variance concept) is challenging. We develop an approach to control for expected value in Section 4.3. 4. Empirical methods 4.1. Sample selection To investigate the value-relevance of site and allocation uncertainty, a sample of ®rms with Superfund liabilities was obtained. Identi®cation as a PRP, an exogenous indicator of Superfund involvement, was used as an initial screen for sample inclusion. Due to the demanding data collection process, an industry limitation was imposed to constrain overall sample size. In order to obtain an adequate sample size and increases generalizabilty of results of this study, however, multiple industries likely to be involved with Superfund liability were considered for inclusion. Two dimensions of involvement were explicitly considered: (1) number of ®rms (within industry) designated as PRPs; and (2) number of ®rms (PRPs) associated with a large number 7 of sites. Based on di€erences across these two dimensions, three industries experiencing Superfund clean-up liability involvement were selected for sample inclusion: chemicals, paper products, and machinery. Tables 1±4 provide descriptive statistics for the total sample and individual industry partitions. Table 1 reports the number of ®rms for which required data are available. Tables 2 and 3 report data by ®rm-year observation while Table 4 summarizes data by site. The chemical industry was selected based upon the large number of companies named as PRPs (approximately 20% of Compustat ®rms), the frequency with which ®rms are associated with a large number (more than 10) of sites (189 ®rm-year observations), and use of this industry in prior literature examining the valuation and disclosure of environmental liabilities (Barth and McNichols, 1994, p. 187; Mitchell, 1994, p. 9; Northcut, 1995, p. 12). The paper products industry was selected as another industry with a substantial involvement in Superfund sites (approximately 25% of Compustat ®rms) despite the smaller overall industry size (only 138 versus 488 ®rms for the chemical industry), but a relatively low number of ®rms designated as PRPs at more than 10 sites (37 ®rm-year observations).

7

More than ten sites is arbitrarily selected as a large number of sites. This number was used in descriptive statistics presented in Barth and McNichols (1994, p. 187), and based on industry-level descriptive analyses in our study (Table 3), appears to be a reasonable cut-o€ (median number of sites per ®rm across the three industries is 2).

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Table 1 Sample: Number of ®rms with available data Data type Panel A: Total sample Compustat data Site aliation ROD data Settlements Final sample

Number of ®rms with available data 1987 1988 1989 1990 1991 1992 1993 Total observations 423 178 119 165 167

431 186 139 173 166

444 192 149 186 166

465 198 158 193 168

478 204 174 200 169

505 210 180 208 174

486 210 182 210 165

Panel B: Chemical ®rms Compustat data Site aliation ROD data Settlements Final sample

71 85 62 76 70

73 88 74 81 72

76 89 78 86 75

77 92 80 87 75

76 94 83 94 74

78 96 84 94 76

75 96 86 96 73

515

Panel C: Paper ®rms Compustat data Site aliation ROD data Settlements Final sample

28 26 18 25 28

28 27 21 25 28

27 30 23 28 27

28 31 25 33 28

28 32 26 33 28

28 33 26 33 28

29 33 26 33 28

195

Panel D: Machinery ®rms Compustat data 324 Site aliation 67 ROD data 39 Settlements 64 Final sample 69

330 71 44 67 66

341 73 48 72 64

360 75 53 73 65

374 78 665 73 67

399 81 70 81 70

382 81 70 81 64

465

1,175

The smaller size of the industry and the related low total number of ®rms designated as PRPs has resulted in its non-inclusion in some previous studies (e.g., Northcut, 1995). Finally, the machinery industry was selected based on attributes it shares with each of the other two industries. The machinery industry is comparable to the chemical industry in size (532 versus 488 chemical ®rms) and number of ®rms identi®ed as PRPs (approximately 19% of Compustat ®rms versus approximately 20% for the chemical industry), yet the number of individual ®rms associated with more than 10 sites is low (16 ®rmyear observations) and more comparable to the paper industry. Thus the three industries, although all involved with Superfund liability, di€er across dimensions of involvement. Requirements for sample inclusion are summarized as follows: 1. Classi®cation in the chemical, paper and related products, or machinery industry (primary SIC code 2800, 2400 and 2600, or 3500);

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Table 2 Descriptive statistics: Financial data 1987±1993. All values (except number of observations) in millions Mean

Median

Minimum

Maximum Firm-years

Panel A: Total sample 186 ®rms Sales ($) Assets ($) Liabilities ($) Net Income ($) BVE ($) Shares

3,241.29 3,368.30 1,618.15 153.67 1,300.49 68.53

1,193.68 0.21 1,097.40 0.25 484.39 0.21 37.61 )7,987.00 409.23 )519.73 30.50 0.10

69,018.00 92,473.00 49,590.00 6,020.00 42,832.00 1,253.94

1,174 1,175 1,175 1,174 1,169 1,175

Panel B: Chemical ®rms 79 ®rms Sales ($) Assets ($) Liabilities ($) Net Income ($) BVE ($) Shares

3,275.97 3,318.30 1,487.60 249.68 1,325.65 87.99

1,389.69 1,374.99 570.18 84.49 567.55 36.75

1.07 1.97 0.47 )163.70 )519.73 1.13

39,709.00 38,870.00 17,419.00 2,487.00 16,502.00 1,253.94

514 515 515 514 509 515

Panel C: Paper ®rms 30 ®rms Sales ($) Assets ($) Liabilities ($) Net Income ($) BVE ($) shares

3,673.94 3,782.36 1,750.81 160.09 1,492.98 63.12

2,422.99 3,644.84 1,251.38 89.08 1,044.20 58.27

157.95 83.91 30.47 )319.20 )155.70 4.50

14,020.00 16,631.00 7,753.00 1,308.00 6,599.00 227.49

195 195 195 195 195 195

Panel D: Machinery ®rms 77 ®rms Sales ($) Assets ($) Liabilities ($) Net Income ($) BVE ($) Shares

3,021.52 3,250.03 1,707.10 44.84 1,192.22 49.25

496.88 0.21 418.76 0.25 184.09 0.21 8.50 )7,987.00 155.54 )501.40 13.67 0.10

69,018.00 92,473.00 49,590.00 6,020.00 42,832.00 597.05

465 465 465 465 465 465

BVE ˆ Book value of equity.

2. Identi®cation as a PRP by the EPA and inclusion in the SETS PRP database; 3. Association as a PRP with at least one site for which at least one settlement is reported for the period 1987 to 1993; and 4. Financial data availability on Compustat. Our sample period includes the seven year period 1987±1993. Although the original Superfund legislation (CERCLA) was enacted in 1980, there was an inevitable lag in identi®cation of sites and implementation of remediation and enforcement processes. In the period before enactment of SARA, there was little experience with remediation and enforcement. Methods of site evaluation were developed and initial site data collected, but mechanisms for enforcement

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Table 3 Descriptive statistics: Site data by ®rm-year observation 1987±1993 Mean

Median

Minimum

Maximum

Firm-Years

Panel A: Total sample 186 ®rms No. of Sites No. of PRPs Hazard Score No. of Settlements Settlements ($M) RODs ($M)

4.99 2 242.57 233 41.50 45.18 4.84 2 71.62 22.47 92.01 40.90

0 0 0 0 0 0

36 1,509 71.79 38 832.61 809.63

1,175 1,175 1,175 1,175 1,175 1,045

Panel B: Chemical ®rms 79 ®rms No. of Sites No. of PRPs Hazard Score No. of Settlements Settlements ($M) RODs ($M)

6.63 3 210.89 189 40.53 43.50 6.40 3 96.53 35.96 130.26 63.53

0 0 0 0 0 0

36 1,509 71.79 38 832.61 809.63

515 515 515 515 515 475

Panel C: Paper ®rms 30 ®rms No. of Sites No. of PRPs Hazard Score No. of Settlements Settlements ($M) RODs ($M)

5.53 3 272.84 271.10 42.08 46.44 5.41 3 81.60 27.98 94.59 83.03

0 0 0 0 0 0

21 965 63.28 20 495.05 446.56

195 195 195 195 195 165

Panel D: Machinery ®rms 77 ®rms No. of Sites No. of PRPs Hazard Score No. of Settlements Settlements ($M) RODs ($M)

2.95 1 264.95 263.40 42.33 45.91 2.89 1 39.85 9.79 46.09 29.93

0 0 0 0 0 0

34 1,509 66.74 33 563.65 595.22

465 465 465 465 465 405

$M ˆ Millions of dollars.

and settlement tools were limited. Since we investigate di€erential valuation of contingent Superfund liabilities on the basis of site-level information, we limit the years under examination to the post-SARA period (i.e., years after 1986). 4.2. Data EPA settlement data are available by site rather than by PRP. Thus, while settlement type, date, and amount are available for each Superfund site, tracing a settlement to individual PRPs is dicult if not impossible. Factors contributing to this diculty in associating precise settlement amounts with individual PRPs include the following: (1) EPA treats the negotiation process

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Table 4 Descriptive statistics: Superfund site data 1987±1993 Mean

Median

Minimum

Maximum

Panel A: Total sample 270 sites a No. of PRPs Hazard Score RODS ($M) Settlements ($M) Acreage

99.88 41.78 12.78 10.08 548.20

35.00 42.04 3.92 2.00 27.00

1.00 0.00 0.00 0.00 0.25

1,509.00 73.67 236.17 219.36 50,600.00

Panel B: Chemical ®rms 192 sites No. of PRPs Hazard Score RODS ($M) Settlements ($M) Acreage

45.62 41.30 10.50 7.95 493.67

15.00 40.70 2.40 1.42 27.00

1.00 0.00 0.00 0.00 0.50

1,509.00 73.67 123.57 126.85 9,336.00

Panel C: Paper ®rms 104 sites No. of PRPs Hazard Score RODS ($M) Settlements ($M) Acreage

103.16 41.93 10.78 8.57 301.02

55.00 39.99 2.98 1.61 35.00

2.00 0.00 0.00 0.00 0.25

1,509.00 73.67 147.43 162.57 7,680.00

Panel D: Machinery ®rms 126 sites No. of PRPs Hazard Score RODS ($M) Settelements ($M) Acreage

151.31 42.15 16.25 12.98 734.61

74.50 42.49 6.43 4.02 20.00

1.00 0.00 0.00 0.00 0.25

1,509.00 70.80 236.17 219.35 50,600.00

a The number of sites for the total smaple (270) does not equal the sum of the number of sites reported for the three industries reported individually (192 + 104 + 126). This is a consequence of the association of multiple PRPs with individual sites. This table reports a site as pertaining to a particular industry if at least one of its PRPs is a sample ®rm in that industry. Thus, to the extent that sample ®rms in di€erent industries have common sites, the number of sites for the total sample does not equal the sum of sites related to individual industries. $M ˆ Millions of dollars.

with con®dentiality in order to facilitate settlements; (2) PRPs often coalesce and settle as groups, through attorneys or via trusts, thus making the portion of a settlement related to a particular PRP dicult to identify; and (3) the joint and several nature of the Superfund liabilities may allow PRPs to seek recovery of settlement costs from other PRPs. Two sets of data were used to compile Superfund settlements for each ®rm in the sample: (1) the Superfund Enforcement Tracking System (SETS) (1994 update); and (2) the EPA Program-To-Date Settlements Report (10/28/94). SETS is a database maintained by VISTA Environmental Information, Inc.

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that can be accessed through LEXIS/NEXIS. SETS includes a Federal Superfund Potentially Responsible Parties component that may be searched by site or PRP name. PRPs (®rms) and sites were linked by this database. EPA's Program-To-Date Settlements Report details a chronology of cost recovery and response settlements by site. Each site typically has multiple settlements spanning a period of years. These settlements represent both cost recovery settlements and agreements to perform remedial actions. When agreement is for performance of a response action, the settlement amount represents an estimate of the cost of the agreed upon response. Settlements associated with each ®rm in the sample were determined by: (1) searching the SETS PRP database by company name to identify Superfund sites for which each company is identi®ed as a PRP; and (2) compiling, by ®rm, settlements listed on the Settlements Report for all sites with which each ®rm was identi®ed as a PRP. The SETS database (1994 update) was used to collect additional data including PRP notice dates, number of PRPs at each site, and site location by EPA region. Additional site information was obtained from other EPA databases. ROD cost estimates for each site were obtained from the EPA's ROD database (1995). Site hazard ranking score (HRS), NPL rank, and the number of acres at each site were obtained from the National Priority List (NPL) database (1996). 4.3. Research design and proxies We structure our analysis to base our tests of hypotheses on a model that extends the basic model relating market value of common equity to book value of assets, liabilities and Superfund liabilities employed by Barth and McNichols (1994, p. 193). 8 This basic model with predicted signs is described by the following relation: MVE ˆ b0 ‡ b1 BVA ‡ b2 BVL ‡ b3 SITES ‡ e; ‡

ÿ

ÿ

…1†

where: MVE is the market value of common stock, BVA is the book value of total assets, BVL the book value of total liabilities, SITES is the number of Superfund sites at which ®rm is identi®ed as a PRP, and e is the disturbance term. In this model the number of Superfund sites at which a ®rm is identi®ed as a PRP is used as a proxy for contingent Superfund liabilities. Barth and McNichols (1994, p. 196) use this measure as well as several 8

Examples of other studies using a related valuation approach include: Barth (1991); Barth et al. (1991); Barth et al. (1990); Beaver et al. (1989); Harris and Ohlson (1987); Landsman (1986).

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dollar-denominated estimates of environmental liabilities as alternative proxies for Superfund liabilities. Results of their valuation analyses are consistent with market participant assessment of an environmental liability in excess of that recognized in the ®nancial statements. Additionally, in their analysis the number of sites has at least as much explanatory power as other cost-based proxies that are substantially more cumbersome to generate. Barth and McNichols (1994, p. 198) have demonstrated this proxy to be both correlated with cost-based environmental liability and value relevant. This proxy thus serves not only as an indicator of the existence of contingent Superfund liabilities, but also as a control for the magnitude of the contingencies. We use this model as a benchmark for assessing valuation of contingent Superfund liabilities to facilitate comparison of results of our study with those of prior research. (e.g., Barth and McNichols, 1994). We hypothesize di€erential valuation of contingent Superfund liabilities. Speci®cally, we hypothesize that two separate uncertainty constructs (site and allocation uncertainty) a€ect valuation. To the extent that either type of uncertainty is relatively high, contingent Superfund liabilities will be viewed as more risky, and thus more negatively re¯ected in market value. To test these hypotheses we introduce two multiplicative dummy variables into the above described valuation model as follows: MVE ˆ b0 ‡ b1 BVA ‡ b2 BVL ‡ b3 SITES ‡

ÿ

ÿ

‡ b4 HISITE  SITES ‡ b5 HIALLOC  SITES ‡ e; ÿ

ÿ

…2†

where: HISITE ˆ 1 if the ®rm is associated with sites where site uncertainty is high, 0 otherwise; HIALLOC ˆ 1 if the ®rm is associated with sites where allocation uncertainty is high, 0 otherwise; and other variables are as previously de®ned. To develop proxies for site and allocation uncertainty constructs, we look to both the institutional framework of Superfund legislation and accounting practice. When site contamination is more extensive, toxic, and/or complex, estimates of clean-up costs are inherently more uncertain. When site contamination is more complex, for example, remediation technology choice is often less clear, modi®cations to remediation plans as work progresses are more likely, remediation is more likely to require a lengthy process, and the success (and ultimate cost) of remediation alternatives is more dicult to predict. Responses to Price Waterhouse (1992, 1994) surveys of corporate attitudes toward environmental issues are consistent with a relation between the nature of site contamination and uncertainty of cost estimates. These survey results indicate that the most important factors a€ecting estimation of remediation costs are: (1) nature of the site; and (2) uncertainty regarding remediation methods and technologies (1992, p. 14; 1994, p. 13). Thus, we base our proxy

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for site uncertainty on the EPA's site hazard rating score (HRS). 9 This score is assigned by the EPA and is speci®cally designed to indicate environmental risk based on site conditions. Multiple site factors are evaluated and grouped into categories (ground water, surface water, and air in the original system), each of which is then incorporated into the overall score. The risk assessment focus of the hazard ranking system however, results in a relatively high overall score even if the risk of pollution migration, human health, and/or environmental damage at a site is high in only one of the three categories. This is considered an important requirement for hazard scoring since some extremely dangerous sites might pose risks only through one of the three media re¯ected in the score (EPA, 1984, p. 4; EPA, 1990, p. 2). 10 Thus, as a proxy for site uncertainty the hazard score has two very desirable characteristics. First, it re¯ects the nature and risks of site contamination and thus re¯ects both of the characteristics respondents to the Price Waterhouse (1992, p. 14; 1994, p. 13) surveys cited as being most important in estimating remediation costs. Second, because it was designed for environmental risk assessment, this score re¯ects complexity of contamination which is likely to be correlated with complexity of clean-up cost estimation. Thus, while the risk considered by the EPA in developing this measure is not directly analogous to the notion of uncertainty for which we seek a proxy, we expect this measure to be related to uncertainty surrounding clean-up cost

9

A variety of alternative proxies, of course, exist. NPL rank, for example, re¯ects site characteristics as well as potential information about visibility and regulatory attention focused on individual sites. Usefulness of NPL rank as an alternative proxy for site uncertainty is constrained because it is an ordinal ranking rather than an integer value. Of the 270 sites in our sample, 241 are on the National Priorities List. For these sites, the Pearson product-moment correlation between NPL rank (ranging from 1 to 1202) and HRS (ranging from 1 to 100), is 0.85. ROD cost estimates are another potential proxy for site uncertainty. But, cost estimate magnitude may not re¯ect much information about the uncertainty surrounding the estimate. ROD cost estimates, may be, for example, a function of the size of a site rather than the complexity of clean-up. We use ROD cost estimates as a proxy for the expected cost of clean-up when estimating our site uncertainty proxy. 10 The original HRS was revised in response to SARA, e€ective 1990. The revised system retained the original structure and three categories of migration pathways, but a fourth pathway (soil exposure) was added. The range of possible scores (0±100) was not changed. Sites scored under the original HRS system are not re-scored. The EPA conducted extensive evaluations of the di€erences between the two scoring systems. Based on an analysis of 110 test sites scored with both the original and revised systems, the EPA decided not to change the cut-o€ score for NPL placement. Thus, although after 1990 sites have been scored under the revised HRS, this is not particularly problematic for our analysis. Very few sites in our sample were scored under the revised HRS. Further, both the overall method and the summary values assigned by the revised HRS are comparable to those assigned under the original system. (EPA, 1990, p. 11).

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estimates and thus appropriate as a proxy for our underlying site uncertainty construct. The joint and several nature of Superfund liability, and the involvement of multiple parties at most sites create a complex environment in which to assess individual ®rm exposure to Superfund clean-up obligations. At the most basic level, the number of PRPs associated with a site might be expected to be correlated with the extent of uncertainty surrounding allocation of responsibility for clean-up costs. Many factors, however, make this a less than desirable proxy. First, although many PRPs might be identi®ed with a site, some may have made minimal contributions to the contamination and/or may have minimal capacity to pay. The relations between PRPs and their strategies for resolving Superfund liabilities may be at least as important in assessing allocation uncertainty as their number or relative ®nancial strength. These complexities are consistent with Barth and McNichol's (1994, p. 196) result that explanatory power was una€ected by the basis upon which ROD cost estimates were allocated across PRPs in their valuation analysis (i.e., total number of PRPs, number of Compustat PRPs, relative size of PRPs, etc.). Thus, we look to the institutional environment of Superfund liability in order to identify a proxy for allocation uncertainty. The EPA's preferred mechanism for funding Superfund remediation is settlement (Acton, 1989, pp. 42, 58,59). Among the major features of the 1986 Act reauthorizing Superfund was its provisions to enhance settlement as an enforcement mechanism. Although settlement with the government may not be the endpoint in allocation of clean-up responsibility among parties (i.e., PRPs can seek restitution for settled amounts from other PRPs and from third parties such as insurers), it nevertheless represents a signi®cant step in the process of cost apportionment. If PRPs are entering settlement agreements regarding a Superfund site, some initial responsibility for clean-up is being assigned. Responsibility for payment in accordance with settlement agreements is well speci®ed, legally enforceable, and in general not subject to substantial uncertainty (EPA, 1998, pp. 24,25). Subsequent litigation may shift these costs among di€erent Superfund stakeholders, but the ability to recoup costs through subsequent litigation and negotiation remains highly speculative, uncertain, and in the future. Thus, there is an inverse relation between the extent of settlement activity at a site and allocation uncertainty. We base our proxy for allocation uncertainty on the cumulative dollar amount of settlements at each Superfund site. Our formal hypotheses posit two separate uncertainty constructs. To investigate the validity of this decomposition, we explore the settlement phenomenon at the ®rm level. In particular, we investigate the relation of settlement activity at sites (which we associate with allocation uncertainty)

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with physical characteristics of sites (which we associate with site uncertainty), and overall Superfund regulatory activity after controlling for ®rm and enforcement characteristics. We estimate the following descriptive model at the ®rm-level: SETTLE ˆ b0 ‡ b1 ROD ‡ b2 PRP ‡ b3 AGE ‡ b4 PROSR ‡ b5 SETR ‡ b6 PUBR ‡ b7 SITES ‡ b8 HRS ‡ b9 ASSETS ‡ b10 IND1 ‡ b11 IND2 ‡ Rbi YRi ‡ e;

…3†

where: SETTLE ˆ Cumulative dollars of settlements averaged across sites, ROD ˆ Cumulative dollars of ROD cost estimates averaged across sites, PRP ˆ Average number of PRPs per site, AGE ˆ Number of years ®rm has been PRP averaged across sites, PROSR ˆ percentage of ®rm's sites that are in regions with a Prosecutorial Enforcement Approach (regions 2 and 5), SETR ˆ percentage of ®rm's sites that are in regions with a Settlement-Oriented Enforcement Approach (regions 3 and 10), 0 otherwise, PUBR ˆ percentage of ®rm's sites that are in regions with a Public Works Enforcement Approach (region 4), SITES ˆ Number of sites with which ®rm is associated, HRS ˆ Hazard Rating Score averaged across sites, ASSETS ˆ Total Assets, IND1 ˆ Dummy Variable for Paper Industry, IND2 ˆ Dummy Variable for Machinery Industry, YRi ˆ Dummy Variable for Year (i ˆ 1988±1993), e ˆ disturbance term. Empirical results from this settlement model (Eq. (3)) support the development of two separate uncertainty constructs. The explanatory value of the model is quite good with an adjusted R2 of 0.88 (see Table 5). Results suggest that settlements are positively associated with ROD cost estimates, average number or PRPs at sites, average site age, number of sites with which a ®rm is identi®ed, and total assets. 11 The positive relation between settlements and both ROD cost estimates and age of Superfund sites is intuitive since, like settlements, these variables are associated with activity

11 The Hausman (1978) test was used as a model speci®cation check. Results of this test, which is based on testing for contemporaneous correlation between the regressors and the error term, provide no evidence of model misspeci®cation.

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Table 5 Descriptive ordinary least squares regression results for settlements (total sample) Eq. (3) SETTLE ˆ b0 + b1 ROD + b2 PRP + b3 AGE + b4 PROSR + Pb5 SETR + b6 PUBR + b7 SITES + b8 HRS + b9 ASSETS + b10 IND1 + b11 IND2 + bi YRi + e Coecient

t-statistic

Intercept ROD PRP AGE PROSR SETR PUBR SITES HRS ASSETS IND1 IND2 YR88 YR89 YR90 YR91 YR92 YR93

)53.413 0.075 0.029 2.286 )4.724 )6.384 )12.134 9.026 )0.057 0.719 15.824 12.301 2.747 19.246 15.367 19.357 38.972 47.779

)8.59  21.41  4.74  3.74  )1.56  )1.23 )2.38  6.28  )0.54 3.51  3.51  4.74  0.59 4.31  3.04  3.74  7.45  8.32 

Adjusted R2 n F Statistic (p-value)

0.88 1,020 430.91 (p < 0.0001)

SETTLE is the Cumulative dollars of settlements averaged across sites, ROD is the Cumulative dollars of RODs averaged across sites, PRP is the Average number of PRPs per site, AGE is the Number of years ®rm has been a PRP averaged across sites, PROSR ˆ 1 if ®rm has a site in a prosecutorial enforcement region (Regions 2 and 5), 0 otherwise, SETR ˆ 1 if ®rm has a site in a settlement enforcement region (Regions 3 and 10), 0 otherwise, PUBR ˆ 1 if ®rm has a site in a public works enforcement region (Region 4), 0 otherwise, SITES is the number of sites with which ®rm is associated, HRS is the Hazard Rating Score averged across sites, ASSETS is total assets, IND1 is dummy variable for paper industry, IND2 is dummy variable for machinery industry, YRi is dummy variable for year (i ˆ 1988±1993).  p < 0.10 two-tailed,  p < 0.05 two-tailed,  p < 0.01 two-tailed.

at sites. Older sites and ones where RODs have been issued have moved further through the Superfund process and settlements are thus more likely. We found no association between settlements and site hazard level. This result suggests that factors other than site contamination characteristics determine site settlement activity and supports our use of site hazard levels and site settlement activity as proxies for two distinct constructs.

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An intuitive result is the positive association between the number of sites with which a ®rm is associated and settlement. Interestingly, the distribution of a ®rm's sites across EPA regions has di€erent associations with settlement activity. 12 The percentage of a ®rm's sites in the region classi®ed by Church and Nakamura (1993, p. 13) as having a public works enforcement approach (Region 4) is negatively associated with settlement relative to regions with no clear enforcement approach. A public works approach is one in which site clean-up is undertaken with Superfund trust money, and only after the site is remediated are PRPs pursued to recover these costs. (Church and Nakamura, 1993, p. 12). Thus, the more negative association with settlement when a ®rm has sites in this region is consistent with the enforcement strategy. Similarly, the percentage of sites in regions classi®ed by Church and Nakamura (1993, p. 13) as having with a prosecutorial approach (Regions 2 and 5) is negatively associated with settlement activity. The percentage of sites in regions identi®ed with an accommodation approach that promotes settlement (Regions 3 and 10) does not have a signi®cantly di€erent association with settlement than regions without a clearly de®ned enforcement approach. These results are consistent with use of settlement as the preferred enforcement mechanism, except when a particular region adopts an approach that is clearly counter to settlement (i.e., prosecutorial or public works). Additionally, these results are consistent with interpretation of settlement as an important indicator of regulatory enforcement activity at Superfund sites. Development of proxies that capture uncertainty without also re¯ecting information about the expected value of contingent Superfund liabilities is challenging. Data constraints inhibit the use of proxies based on explicit indicators of variance. One potential proxy for uncertainty would be the range of cost estimates reported in RODs. Unfortunately, while some RODs report a range of cost estimates, most do not. Our proxies are based on measures that do not explicitly relate to variance, and thus potentially re¯ect both expected value (mean) of costs as well as uncertainty (variance) of the estimates. It would not be surprising for either site hazard score or the dollar amount of settlements at a site to be correlated with both the absolute magnitude of cleanup cost estimates and the uncertainty surrounding those estimates. In order to disentangle these e€ects, we analyze the relation between the basis for each of our uncertainty proxies (HRS and cumulative dollar amount of settlements) 12 For administrative purposes, the EPA is divided into ten geographically de®ned regions (see, for example, the discussion in Church and Nakamura, 1993). Church and Nakamura (1993, p. 13) describe three distinct regional enforcement approaches: ``prosecutorial'' (regions 2 and 5), ``accomodation'' (regions 3 and 10), and ``public works'' where the government undertakes remediation activities using Superfund trust funds and seeks recovery from PRPs after the fact (region 4). Church and Nakamura (1993) do not assign regions 1,6,7,8, and 9 a distinct enforcement approach.

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and ROD cost estimates. We use ROD cost estimates as a proxy for the expected value of site clean-up costs. ROD cost estimates are ocially recognized by the EPA in its enforcement actions and are the only widely available estimates of expected Superfund site clean-up costs. In order to separate the expected value dimension of the variables upon which we base our uncertainty proxies from their variance dimension, we separately regress each proposed proxy on the dollar magnitude of ROD cleanup cost estimates using the following models: HRS ˆ

b0 ‡ b1 ROD ‡ e1 ; ‡=ÿ ‡

SETTLE ˆ

b0 ‡ b1 ROD ‡ e2 ; ‡=ÿ ‡

…4† …5†

where HRS is the EPA assigned site hazard rating score; SETTLE is the dollar amount of settlements per acre; and ROD is the dollar amount of ROD estimates of site clean-up cost per acre. In each regression, the dollar denominated variables (ROD cost estimates and settlements) are scaled by the number of acres at the site. In this way we control for the absolute physical size of the site. The error term of each regression represents the amount of variation contained in the proposed uncertainty proxy variable that is unexplained by the expected value of site clean-up cost (ROD). Results of estimating the model using HRS as the dependent variable are reported on Panel A of Table 6. For each of the seven years estimated, the adjusted R-squared is approximately zero, and in all but one year (1987), the coecient on the ROD variable is insigni®cantly di€erent from zero. This suggests that there is no signi®cant relation between site hazard score and ROD cost estimates. The information contained in HRS appears distinct from that of ROD cost estimates. This result is consistent with our contention that site HRS score re¯ects the uncertainty surrounding clean-up cost estimates to a much greater extent than the magnitude of expected clean-up cost, per se. Thus, we interpret this result as supporting our use of HRS as an uncertainty proxy. Site uncertainty is expected to be higher when the HRS score is large. When the total dollar value of settlements per acre is used as the dependent variable, results are strikingly di€erent (Panel B of Table 6). The adjusted Rsquared is between 3% and 35% for each year the regression is estimated. Further, the coecient on ROD is positive and signi®cant in each year. This result suggests that, as expected, there is a positive relation between cumulative settlement amounts and the expected value of site clean-up costs. Thus, in order to capture the information in the settlement variable that is unrelated to expected value of clean-up costs, we use the residuals from this regression as the basis of our allocation uncertainty proxy. Allocation uncertainty is expected to be larger when the absolute value of the residual from estimation of Eq. (5) is large.

41.11 (27.41)  0.58 (0.44) 0.00 184 0.19 (p ˆ 0.66)

1988

0.27 (3.30)  0.08 (2.79)   0.03 195 7.78 (p ˆ 0.006)

41.42 (35.33)  )0.25 ()0.58) 0.00 201 0.34 (p ˆ 0.56)

1989

0.30 (3.90)  0.10 (3.73)   0.06 218 13.94 (p < 0.001)

41.56 (39.48)  )0.26 ()0.67) 0.00 225 0.45 (p ˆ 0.50)

1990

0.41 (4.34)  0.14 (4.15)   0.07 229 17.18 (p < 0.001)

41.70 (43.14)  )0.06 ()0.15) 0.00 236 0.02 (p ˆ 0.88)

1991

0.48 (4.59)  0.31 (8.42)   0.23 236 70.82 (p < 0.001)

42.03 (45.64)  )0.12 ()0.37) 0.00 244 0.13 (p ˆ 0.72)

1992

0.52 (4.70)  0.41 (10.58)   0.32 236 111.99 (p < 0.001)

42.32 (47.05)  )0.14 ()0.45) 0.00 245 0.20 (p ˆ 0.66)

1993

HRS is the Site Hazard Score, ROD is the Total Site ROD Cost Estimate ($) per Acre, SETTLE is the Total Site Settlements ($) per Acre.  p < 0.10 two-tailed,  p < 0.05 two-tailed.

Panel B: SETTLE ˆ b0 + b1 ROD + e2 Intercept )0.20 )0.04 ()0.63) ()3.65)  ROD 0.54 0.37 (5.86)  (9.29)   Adjusted R2 0.35 0.16 n 156 179 F statistic 86.21 34.38 (p value) (p < 0.001) (p < 0.001)

Panel A: HRS ˆ b0 + b1 ROD + e1 Intercept 40.26 (26.17)  ROD 3.74 (1.66)  0.02 Adjusted R2 n 161 F statistic 5.07 (p value) (p ˆ 0.03)

1987

Coecient/(t-statistic)

Table 6 Site and allocation uncertainty proxy development regressions (ordinary least squares) Eqs. (4) and (5)

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Our research design incorporates an additional control for the role of the expected value (i.e., magnitude) of clean-up cost. Our basic proxy for contingent Superfund liabilities, SITES, has been previously demonstrated to be correlated with multiple estimates of ®rm Superfund liability (Barth and McNichols, 1994, pp. 193±204). Since we introduce our uncertainty proxies as multiplicative dummy variables on the SITES variable, the valuation model itself incorporates a control for expected magnitude of Superfund clean-up costs. While we may not be able to de®nitively disentangle mean and variance e€ects, we recognize the potential limitations this poses for interpretation of our results and conduct sensitivity analyses that investigate this issue. 13 In order to test our hypotheses regarding di€erential valuation of contingent Superfund liabilities due to site and allocation uncertainty, we operationalize Eq. (2) as follows: 14 MVE ˆ b0 ‡ b1 BVA ‡ b2 BVL ‡ b3 SITES ‡

ÿ

ÿ

‡ b4 HISITE  SITES ‡ b5 HIALLOC  SITES ‡ e ÿ

ÿ

…6†

where: HISITE ˆ 1 if the average hazard rating score of sites with which a ®rm is associated is in the top quartile (by industry, by year) of the ®rms sampled, 0 otherwise; HIALLOC ˆ 1 if the absolute value of residuals from the regression of settlements per acre on ROD costs per acre (Eq. (5)) at sties with which the ®rm is associated are in the top quartile (by industry, by year) of ®rms sampled, 0 otherwise; and other variables are as previously de®ned. Throughout the analysis, all operationalizations of the above described relations are scaled by the number of common shares outstanding and market value of equity is measured at ®scal year end. 15 In order to control 13

Use of proxies in place of unobservable variables inherently subjects estimation to measurement error. We use proxies in Eqs. (4) and (5) as well as in the valuation equation (Eq. (6)). As noted, proxy development is particularly challenging for our site and allocation uncertainty constructs. Measurement error in independent variables results in contemporaneous correlation between regressors and the disturbance term. In the presence of measurement error the ordinary least square (OLS) estimator is biased. Thus, interpretation of reported results must be made with caution. 14 This model constrains intercept terms to be constant regardless of the level of site and allocation uncertainty. As a sensitivity test we investigated the e€ect inclusion of our site and allocation uncertainty variables as independent variables exerts. Results regarding our variables of interest are generally consistent with those reported (although in some cases signi®cance is increased). 15 Values are scaled in order to control for di€erences in size as well as the e€ects of heteroskedasticity. The number of common shares outstanding is used for a number of reasons. First, this de¯ator is fairly successful in controlling for the e€ects of heteroskedasticity. Second, although there is no obvious best de¯ator, Barth et al. (1991, p. 62) argue that estimation of this valuation model in per-share form is a more appropriate adjustment for heteroskedasticity than de¯ation by book value. Finally, this de¯ator facilitates comparison of our results to those reported in related papers including Barth and McNichols (1994, pp. 198,199). Empirical results, however, are somewhat sensitive to this choice. See footnote 17.

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for inter-industry heterogeneity in ®rm characteristics and sensitivity to environmental matters, all analyses are performed with data partitioned by industry (in addition to the aggregate sample of ®rms). Firms in industries with larger numbers of PRPs might be more likely to face other environmental costs and thus have relatively more negative coecients on proxies for Superfund liabilities. However, in industries with very low numbers of PRPs, ®rms exposed to Superfund liabilities may be especially negatively valued relative to competitors. A retailer, for example, associated with only one Superfund site, might be perceived quite negatively if most other retailers were associated with none. A chemical ®rm associated with only one site, however, might be perceived quite positively because most ®rms in this industry are associated with multiple sites. On the other hand, one would not anticipate that association with sites would be di€erentially valued across industries (especially since many sites are common to PRPs in multiple industries) unless capital markets expect clean-up responsibility to be allocated based, among other things, on industry classi®cation. Thus, although plausible reasons exist for valuation di€erences across industries, it is unclear what, if any, speci®c cross-industry di€erences would be expected. Accordingly, we do not specify directional hypotheses regarding valuation di€erences across industries, but control for potential inter-industry heterogeneity by conducting all analyses partitioning data by industry aliation as well as pooled across industries.

5. Results 5.1. Results of hypothesis tests Results of estimating Eq. (1) for the period 1987±1993, which replicates Barth and McNichols (1994, p. 198), are reported in Table 7. 16 Across all sample partitions, the coecients on assets are signi®cantly positive while those on liabilities are signi®cantly negative. For the total sample, the coecient on

16

Multicollinearity is inherent in this valuation model. As one would expect, assets and liabilities per share are highly correlated in our sample. Although the presence of multicollinearity results in large variances of the parameter estimates, the OLS estimator remains unbiased. Condition indices (Belsley et al., 1980, p. 103) indicate the presence of multicollinearity, but not at an extreme level, in all reported valuation model estimations. Multicollinearity is substantially reduced by forming a linear combination of assets and liabilities (i.e., book value), and substituting this variable in the valuation regression estimations. Interpretation of reported results is not qualitatively a€ected by this model respeci®cation.

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number of sites (SITES) is negative and highly signi®cant. 17 Unlike Barth and McNichols (1994, p. 198), we estimate this equation separately by industry, however, and ®nd somewhat di€erent results. The coecient on SITES is signi®cantly negative in the total, chemical, and machinery samples, but is insigni®cant in the paper sample. These results, with the exception of the insigni®cant coecient on SITES in the paper industry, are consistent with results of Barth and McNichols (1994, pp. 198,199) who estimate this equation on a sample of Compustat ®rms pooled over the years 1982±1991. Our ®nding of an insigni®cant result in the paper industry suggests industry aliation may a€ect Superfund liability valuation. An implication of this result is that the generalizability of inferences drawn from tests in single industry or aggregated multi-industry samples may be limited. The impact of site uncertainty and allocation uncertainty on valuation of the Superfund liability proxy is analyzed by estimating Eq. (6). Empirical results are reported in Table 8. 18 The SITES coecient remains negative and signi®cant in all sample partitions except the paper industry. The coecient on the site uncertainty proxy (HISITE) is signi®cantly negative, as predicted, only in the total sample and the chemical industry. Thus, the results provide only partial support for H1 and suggest that there is a di€erence in valuation implications of site uncertainty across industries. Similarly, HIALLOC is signi®cantly negative only in the chemical industry sub-sample. This result suggests that in the chemical industry, allocation uncertainty is associated with an incremental negative association between sites and ®rm market value. Settlement activity at sites may be uncertainty-reducing regarding individual ®rm liability in this industry. 19

17 We investigated implications of di€erent de¯ators when estimating this model. As alternatives to the number of shares outstanding, we used total sales, total assets, and book value of equity to scale regression variables. Results are sensitive to the de¯ator selected. Coecients on the variable of interest (SITES) as well as assets and liabilities are a€ected. 18 Econometrically pooling data across multiple years can be problematic. For sensitivity analysis we analyzed the model testing our hypotheses several additional ways including (1) a ®xed e€ects model incorporating dummy variables for individual years; (2) a GLM model; and (3) analysis of the multi-industry sample on a year-by-year basis with coecients and t-values averaged across years. Results are robust across these alternative speci®cations. 19 We explored alternative cut-o€ points for de®ning our high uncertainty proxies. When de®ned based on a median split (rather than top quartile), the HIALLOC results are consistent with those reported. We ®nd no support, however for our site uncertainty hypothesis. In general the site uncertainty result is stronger with more stringent de®nitions of high. For example, when the proxies are de®ned as the top 10% the site uncertainty coecient is signi®cantly negative in all three industries. Thus, the general interpretation of results is robust across speci®cations, but the site uncertainty result is somewhat more sensitive to the de®nition of high (with greater signi®cance under more stringent de®nitions).

+

)

)

BVA

BVL

SITES

0.39 1175 249.71 (p < 0.0001)

16.69 (16.26)  0.80 (16.64)  )0.83 ()16.73)  )7.39 ()6.74) 

Total sample

0.32 515 81.29 (p < 0.0001)

24.88 (14.66)  1.13 (12.47)  )1.55 ()9.39)  )8.96 ()7.86) 

Chemical industry

Coecient/(t-statistic)

0.15 195 12.65 (p < 0.0001)

19.57 (5.70)  0.55 (3.92)  )0.64 ()3.73) )2.07 ()0.69)

Paper industry

0.69 465 351.50 (p < 0.0001)

9.29 (8.66)  0.93 (18.06)  )0.96 ()17.99)  )11.25 ()4.57) 

Machinery industry

MVE is the market value of equity (per share), BVA is the book value of assets (per share), BVL is the book value of liabilities (per share), SITES is the number of sites at which ®rm is identi®ed as a PRP (per share). (Reported t-values are computed using the White (1980) adjustment.)  p < 0.01 one-tailed.

Adjusted R2 n F Statistic (p value)

+/)

Intercept

Predicted sign

MVE ˆ b0 + b1 BVA + b2 BVL + b3 SITES + e

Table 7 Ordinary least square regression of market value of equity on book value of assets and liabilities and proxy for environment liability Eq. (1)

K. Campbell et al. / Journal of Accounting and Public Policy 17 (1998) 331±366 357

+

)

)

)

)

BVA

BVL

SITES

HISITE* SITES

HIALLOC* SITES

0.39 1175 150.21 (p < 0.0001)

16.85 (16.27)  0.81 (16.89)  )0.83 ()16.94)  )6.93 ()6.94)  )4.72 ()1.73)  )2.70 ()0.98)

Total sample

Coecient/(t-statistic)

0.33 515 50.62 (p < 0.0001)

25.75 (14.90)  1.12 (12.52)  )1.52 ()9.41)  )8.58 ()8.77)  )19.79 ()4.11)  )7.46 ()2.11) 

Chemical industry

0.16 195 7.63 (p < 0.0001)

19.42 (5.72)  0.56 (4.08)  )0.64 ()3.86)  )4.92 ()0.68) 0.60 (0.10) 7.55 (1.33)

Paper industry

0.69 465 210.74 (p < 0.0001)

9.20 (9.04)  0.94 (19.60)  )0.96 ()19.20)  )9.81 ()3.89)  )1.25 ()0.28) )5.10 ()1.08)

Machinery industry

MVE is the market value of equity (per share), BVA is the book value of assets (per share), BVL is the book value of liabilities (per share), SITES is the number of sites at which ®rm is identi®ed as a PRP (per share), HISITE ˆ 1 if average HRS at sites where ®rm is identi®ed as a PRP is in the top quartile of sampled ®rms, 0 otherwise, HIALLOC ˆ 1 if the absolute value of residuals from regressions of settlements per acres on ROD cost estimates per acre at sites where ®rm is identi®ed as a PRP is in the top quartile of sampled ®rms, 0 otherwise. (Reported t-values are computed using the White (1980) adjustement.)  p < 0.10 one-tailed,  p < 0.05 one-tailed,  p < 0.01 one-tailed.

Adjusted R2 n F Statistic (p value)

+/)

Intercept

Predicted sign

MVE ˆ b0 + b1 BVA + b2 BVL + b3 SITES + b4 HISITE SITES + b5 HIALLOC SITES + e

Table 8 Ordinary least squares regression of market value of equity on book value of assets and liabilities, proxy for environmental liability, and uncertainty interaction terms Eq. (6) 358 K. Campbell et al. / Journal of Accounting and Public Policy 17 (1998) 331±366

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359

Our proxies for site and allocation uncertainty are designed to be independent of information related to the magnitude of expected Superfund liabilities (i.e., a mean e€ect). Since we observe a relation between our proxy for the expected magnitude of Superfund liability (ROD cost estimates) and settlements (but not with HRS), a two-stage regression approach is used to develop our allocation uncertainty proxy (but not our site uncertainty proxy). In order to investigate the implications of our two-stage regression approach to developing the allocation uncertainty proxy, we re-estimated Eq. (6) using total dollar of settlements as the proxy for allocation uncertainty. Results (not reported) using this speci®cation are more consistent with our hypotheses. The coecients on this re-de®ned HIALLOC variable are signi®cantly negative in the chemical, paper, and total sample. This result is consistent with a mean e€ect related to the expected magnitude of Superfund liabilities playing a role in the re-de®ned HIALLOC variable which is eliminated by the two-stage proxy development approach. 5.2. Discussion of cross-industry results Although ex ante, we have no di€erential directional predictions across industries, apparent di€erences exist. As discussed above, signi®cance of coecients di€er across industries, as does the explanatory power of models. While the explanatory power of our models exhibits little variation within an industry, across industries there are rather large di€erences. In the valuation models tested, we found adjusted R-squareds of 32% in the chemical industry, 15% in paper, and 69% in machinery. Our proxy for Superfund liabilities (SITES) is associated with market value of equity in only the chemical and machinery industries. Evidence consistent with our hypotheses regarding the role of uncertainty in the di€erential valuation of Superfund liabilities is reported only in the chemical industry. These results suggest that the capital markets evaluate Superfund liabilities di€erently in di€erent industries. The signi®cance of results of our hypothesis tests in the chemical industry is consistent with its place as one of the industries most visibly a€ected by Superfund legislation. The chemical industry's sensitivity to Superfund liability was highlighted in 1994 when, as a group, the chemical industry participated in developing proposed revisions to Superfund legislation that would have imposed a tax in exchange for relief from liability for site clean-up costs (Ember, 1994, pp. 6,7). In such highly exposed industries, the bene®ts of seeking information to evaluate contingent Superfund liabilities may more consistently exceed costs than in less visibly involved industries. Thus, EPA data related to site and allocation uncertainty may be more sought after and used in valuing ®rms when industry aliation or

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other characteristics make Superfund concerns obvious. Overall, our results suggest that there are meaningful di€erences in the valuation of Superfund liabilities across industries that may have important implications for generalizability of empirical results from this and related studies. While hypothesis testing may be most powerful in highly sensitive industries, such as the chemical industry, results may not necessarily be generalizable to less visibly involved industries. In considering possible explanations for cross-industry di€erences a reexamination of descriptive statistics reported in Table 3 provides some initial insights. While the null hypothesis that site hazard level is equal in all industries cannot be rejected, site characteristics do di€er across industries. We found that chemical industry sites have the highest: (1) mean number of settlements; (2) mean cumulative dollar value of settlements; and (3) mean dollar value of ROD cost estimates. These measures relate to the construction of our allocation uncertainty proxy. The lower levels of, and variation in, settlements and ROD cost estimates may result in our HIALLOC proxy (which is determined on a within industry basis) being less successful in capturing high allocation uncertainty in these industries. Allocation uncertainty may simply be too low in these industries to have a meaningful valuation association. Further investigation of our sample reveals additional inter-industry di€erences including: (1) number of sample ®rms on a site, and (2) extent to which PRPs at sites include ®rms from more than one of the sample industries (t-statistics not reported). Chemical industry sites have more sample ®rms identi®ed as PRPs than paper and machinery sites. In addition, chemical industry sites are more likely to have PRPs that are members of the other industries sampled (approximately 56%) than are machinery (approximately 31%) or paper (approximately 25%) industry sites. While these measures are not complex, both (i.e., percentage of sites with multi-industry PRPs and mean number of sample ®rms associated with sites) re¯ect site complexity from a liability apportionment perspective. Thus, these descriptive ®ndings are consistent with the above noted inter-industry di€erences in allocation uncertainty-related factors (i.e., settlements and ROD costs estimates). It may be that in the machinery and paper industries there is a less complex liability apportionment environment overall, thus making it dicult to capture a statistically signi®cant allocation uncertainty relation with valuation. Finally, in the sample selection section we described inter-industry di€erences in Superfund involvement (i.e., number of companies named as PRPs and frequency with which ®rms are associated with a large number of sites). In our sample we found 158 (approximately 31%) ®rm-year observations in the chemical industry with more than 10 sites, but only 36 (approximately 18%) and 15 (approximately 3%) in paper and machinery industries, respec-

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361

tively. This is consistent with a more visible Superfund involvement for chemical ®rms. Much descriptive evidence is consistent with inter-industry di€erences in both the extent of Superfund involvement and pollution patterns. Unlike chemical and machinery ®rms, the environmental impacts of pulp and paper mills are largely e‚uent-related. Additionally, paper companies tend to be much more homogeneous than chemical and machinery companies (as assessed by number of SIC codes in which ®rms report signi®cant activities). Thus, while it is dicult to unambiguously interpret the di€erence in inter-industry results reported, a number of plausible explanations exist. 20 These di€erences in results are intriguing, and although not a primary topic of investigation in this analysis, may be an interesting area for future research. At a minimum, these results motivate reconsideration of the generalizability of prior research results derived from pooled multi-industry samples. 5.3. Methodological considerations Several econometric issues create challenges for interpretation of results from cross-sectional valuation models. Alternative explanations for reported results include the e€ects of correlated omitted variables and measurement error bias. Prior literature has discussed the limitations of these valuation models at some length (Barth and McNichols, 1994, pp. 206±208; Holthausen, 1994, pp. 216±219; Skinner, 1996, pp. 394±395). Sensitivity of the basic model used in our analysis to correlated omitted variables and measurement error is addressed in Barth and McNichols (1994, pp. 216±219). They (1994, pp. 193± 204) provide some evidence that the true coecient for the environmental liability proxy (i.e., the SITES variable) is negative, but their conclusions depend on several assumptions regarding measurement error in the independent variables. Their results (1994, pp. 193±204), however, are robust across alternative model speci®cations designed to mitigate correlated omitted variable concerns. Our analysis builds on their basic model (1994, pp. 193±204). As reported, our results are also robust across alternative speci®cations. While speci®cation issues a€ect our analysis, results of our hypothesis tests are less sensitive to these issues than the basic valuation model result. We examine

20 Among the plausible explanations are also methodological ones. These include measurement error related to our proxy selection and inferential issues common to balance sheet identity-based valuation models. These limitations are discussed in Section 5.3.

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di€erential valuation of contingent Superfund liabilities by using indicator variables to assess valuation across alternative sub-samples of ®rms. Unless our partitioning variables (i.e., site and allocation uncertainty proxies) are correlated with any bias in the SITES coecient, coecient bias resulting from the underlying valuation model is unlikely to explain results of our hypothesis tests. Nevertheless, interpretation of our results is subject to cautions appropriate for all cross-sectional balance sheet identity valuation model analyses.

6. Concluding remarks The amount and timing of a ®rm's ultimate ®nancial obligation for Superfund liabilities, like all contingent liabilities, is uncertain and subject to the outcome of future events. However, in the case of Superfund liabilities, at least two factors exacerbate the uncertainty surrounding estimation of individual company liability. First, measurement of the total cost of remediating any particular Superfund site is dicult. Second, the portion of site clean-up cost that any individual ®rm will ultimately bear is hard to assess. This paper empirically investigates the impact of these two major sources of uncertainty ± site uncertainty and allocation uncertainty ± on the valuation of contingent Superfund liabilities. Investigation of the nature and valuation of contingent Superfund liabilities is of interest to a€ected ®rms, investors, independent auditors, and other stakeholders for several reasons including: (1) the substantial magnitude of these liabilities; (2) the extended period over which these contingencies are resolved; and (3) the broad set of ®rms and stakeholders a€ected. In the spirit of Barth and McNichols (1994, p. 193), a basic model of the relation between market value of ®rms and book value of assets and liabilities is constructed to test the extent to which cross-sectional di€erences in market value are explained by a proxy for Superfund liabilities. However, we extend this analysis to investigate the di€erential valuation of contingent Superfund liabilities. We control for the expected value of site clean-up costs and develop proxies for site and allocation uncertainty. We ®nd that in the chemical industry, both types of uncertainty are associated with incrementally negative valuation of contingent Superfund liabilities. Since signi®cant results are reported only for the pooled and chemical industry samples, we can only interpret results as partially supporting our hypotheses. The ®nding that industry aliation does a€ect results is, in itself, interesting. While this introduces some concern regarding generalizability of empirical results from single industry and pooled samples, this result also

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provides guidance regarding samples where Superfund-related hypotheses may most powerfully be tested. The di€erence in industry results that we report is consistent with reported inter-industry di€erences in the allocation of site liabilities and relative information search costs. In industries with highly visible Superfund exposure (such as the chemical industry), the bene®t of seeking external information to evaluate contingent Superfund liabilities may more consistently exceed costs than in less uniformly sensitive industries (such as machinery). These results raise issues regarding the potential role of di€erent levels and types of disclosures for di€erentially affected industries and ®rms. We interpret our ®ndings as providing support for the value relevance of both site and allocation uncertainty, at least in highly sensitive industries. Our ®ndings suggest that: (1) site-level data contains relevant information for valuation of individual ®rm contingent Superfund liability; and (2) the concepts of site and allocation uncertainty provide a useful framework for organizing and evaluating Superfund data. These results are consistent with regulators' focus on Superfund enforcement process milestones in disclosure guidance. We provide evidence consistent with the usefulness of such site-level and non-®nancial information. While some regulatory and practitioner attention has been focused on ®nancial statement disclosure of site contamination and cost information by PRPs, little has been directed at the complexity of the cost allocation environment. As corporations and accounting policy makers continue to grapple with ®nancial statement disclosure issues related to contingent Superfund liabilities, the relation to underlying concepts of site and allocation uncertainty may be a useful structure in which to evaluate alternative disclosures and improve ®nancial reporting quality. Further research regarding implications of the transition from contingent liability to de®ned liability (the liability accrual process) should be a fruitful line of study. Ultimately the question of the impact of this transition in the context of contingent environmental liabilities will require consideration of mediating e€ects of various ®nancial statement disclosure practices during the period of transition. Such study could contribute not only to understanding the implications of accounting for environmental liabilities, but also to understanding of the process of ®nancial statement accrual of liabilities in general. Contingent Superfund liabilities provide a meaningful context in which to investigate the role of information in valuation. Superfund claims exist in a complex information environment where both external (EPA) and ®rm-provided (®nancial statement) sources of information regarding the underlying claim are available. This paper investigates the valuation implications of external EPA data on contingent Superfund claims. Future analysis can be extended to consider valuation implications

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of the interaction between external and internal (i.e., ®rm-provided) information.

Acknowledgements We have bene®ted from discussions with Mary Barth, Duane Helleloid, Dana Northcut, and comments from workshops participants at the University of Washington, the Norwegian School of Management, and the 1995 University of Washington/EPA Environmental Accounting Research Conference. A very early version of this paper (Campbell et al., 1994) was presented at the 1994 American Accounting Association Annual Meeting in New York. We would also like to thank two anonymous reviewers for their insightful comments. The ®rst author gratefully acknowledges ®nancial support of the KPMG Peat Marwick Foundation.

References Acton, J.P., 1989. Understanding Superfund: A Progress Report. The RAND Corporation, Santa Monica. American Institute of Certi®ed Public Accountants (AICPA), 1996. Statement of Position No. 96-1 (Environmental Remediation Liabilities. American Institute of Certi®ed Public Accountants, New York. Barry, C., Brown, S., 1985. Di€erential information and security market equilibrium. Journal of Financial and Quantitative Analysis 20 (4), 407±422. Barth, M.E., 1991. Relative measurement errors among alternative pension asset and liability measures. The Accounting Review 66 (3), 433±463. Barth, M.E., Beaver, W.H., Stinson, C.H., 1991. Supplemental data and the structure of thrift share prices. The Accounting Review 66 (1), 56±66. Barth, M.E., Beaver, W.H., Wolfson, M.A., 1990. Components of earnings and the structure of bank share price. Financial Analysts Journal 46 (3), 53±70. Barth, M.E., McNichols, M.F., 1994. Estimation and market valuation of environmental liabilities relating to superfund sites. Journal of Accounting Research 32 (Suppl.), 177±209. Beaver, W.H., Eger, C., Ryan, S., Wolfson, M.A., 1989. Financial reporting, supplemental disclosures, and bank share prices. Journal of Accounting Research 27 (2), 157±178. Belsley, D.A., Kuh, E., Welsch, R.E., 1980. Regression Diagnostics: Identifying In¯uential Data and Sources of Collinearity. Wiley, New York. Botosan, C., 1997. Disclosure level and the cost of equity capital. The Accounting Review 72 (3), 323±349. Campbell, K., Sefcik, S., Soderstrom, N., 1994. Superfund settlements and ®rm valuations. Collected Abstracts of the American Accounting Association Annual Meeting, 10±13 August, New York, p. 85.

K. Campbell et al. / Journal of Accounting and Public Policy 17 (1998) 331±366

365

Church, T.W., Nakamura, R.T., 1993. Cleaning Up the Mess: Implementation Strategies in Superfund. The Brookings Institution, Washington, DC. Clarkson, P., Thompson, R., 1990. The empirical estimates of beta when investors face estimation risk. The Journal of Finance 45 (2), 431±453. Coles, J.L., Loewenstein, U., 1988. General equilibrium and portfolio composition in the presence of uncertain parameters and estimation risk. Journal of Financial Economics 22 (2), 279±303. Dixon, L.S., 1994. Fixing Superfund: The E€ect of the Proposed Superfund Reform Act of 1994 on Transaction Costs. The RAND Corporation, Santa Monica. Ember, L., 1994. Superfund liability consensus sought. Chemical and Engineering News 72 (3), 6±7. Financial Accounting Standards Board (FASB), 1975. Statement of Financial Accounting Concepts No. 5, Accounting for Contingencies. Financial Accounting Standards Board, Stamford. Financial Accounting Standards Board (FASB), 1993. Emerging Issues Task Force No. 93±5, Accounting for Environmental Liabilities. Financial Accounting Standards Board, Norwalk. Harris, T.S., Ohlson, J.A., 1987. Accounting disclosures and the market's valuation of oil and gas properties. The Accounting Review 62 (4), 651±670. Hausman, J.A., 1978. Speci®cation tests in econometrics. Econometrica 46 (6), 1251±1272. Holthausen, R., 1994. Discussion of estimation and market valuation of environmental liabilities relating to superfund sites. Journal of Accounting Research 32 (Suppl.) 211±219. Landsman, W., 1986. An empirical investigation of pension fund property rights. The Accounting Review 61 (4), 662±691. Mitchell, L.B., 1994. Comparability in accounting for contingencies: the case of superfund. Working Paper, Columbia University. Northcut, D., 1995. Environmental accounting policies in ®rms subject to superfund cleanup costs. Working Paper, University of Arizona. Price Waterhouse, 1991. Environmental Accounting: The Issues, The Developing Solutions - A Survey of Corporate America's Accounting for Environmental Costs. Price Waterhouse, New York. Price Waterhouse, 1992. Accounting for Environmental Compliance: Crossroad of GAAP, Engineering, and Government - A Survey of Corporate America's Accounting for Environmental Costs. Price Waterhouse, New York. Price Waterhouse, 1994. Progress on the Environmental Challenge - A Survey of Corporate America's Environmental Accounting and Management. Price Waterhouse, New York. Skinner, D.J., 1996. Are disclosures about bank derivatives and employee stock options `value relevant'? Journal of Accounting and Economics 22 (1±3), 393±405. Stimson, J.A., Kimmel, J.J., Rollin, S.T., 1993. Guide to Environmental Laws from Premanufacture to Disposal. The Bureau of National A€airs, Washington, DC. US Congress (USC), 1980. Comprehensive Environmental Response, Compensation and Liability Act. United States Code Title 42 Pts. pp. 9601±9675. US Congress (USC), 1986. Comprehensive Environmental Response, Compensation and Liability Act. United States Code Title 42 Pts. pp. 9601±9675. US Environmental Protection Agency (EPA), 1984. Uncontrolled Hazardous Waste Site Ranking System: A Users Manual. Publication 105NCP-HRS-2-1. US Environmental Protection Agency, Washington, DC. US Environmental Protection Agency (EPA), 1990. The Revised Hazard Ranking System: Background Information. Publication 9320.7-03FS. US Environmental Protection Agency, Washington, DC. US Environmental Protection Agency (EPA), 1998. The RCRA, Superfund, and EPCRA Hotline Training Module. Publication 540-R-98-028. US Environmental Protection Agency, Washington, DC.

366

K. Campbell et al. / Journal of Accounting and Public Policy 17 (1998) 331±366

US Securities and Exchange Commission (SEC), 1993. Sta€ Accounting Bulletin No. 92. Accounting and Disclosure Relating to Loss Contingencies. US Securities and Exchange Commission, Washington, DC. Van Voorst, B., 1993. Toxic dumps: The lawyers' money pit. Time 142 (11), 63±64. White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48 (4), 817±830.