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Electronic Theses, Treatises and Dissertations The Graduate School

2005

An Investigation of Financial AssuranceMechanisms for Environmental LiabilitiesWendy D. Habegger

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THE FLORIDA STATE UNIVERSITY

COLLEGE OF BUSINESS

AN INVESTIGATION OF FINANCIAL ASSURANCE MECHANISMS FOR ENVIRONMENTAL LIABILITIES

By

WENDY D. HABEGGER

A Dissertation submitted to the Department of Finance

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Degree Awarded: Spring Semester, 2005

ii

The members of the Committee approve the Dissertation of Wendy D. Habegger defended on April 4, 2005.

_________________________ Pamela P. Peterson-Drake

Co-chair Directing Dissertation _________________________ Patrick F. Maroney Outside Committee Member _________________________ Gary A. Benesh Co-chair Directing Dissertation _________________________ Pamela K. Coats Committee Member

Approved: __________________________________ E. Joe Nosari, Interim Dean, College of Business The Office of Graduate Studies has verified and approved the above named committee members.

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I dedicate my dissertation to my savior Jesus Christ. I would not have completed the degree without His love, mercy, help, and direction. I also dedicate my dissertation to my parents, Karen and Kenneth Habegger. It is a privilege to have you as my parents and I could not have asked for better. Thank you for your unconditional love, encouragement, and financial support.

O sing unto the LORD a new song: sing unto the LORD, all the earth.

Sing unto the LORD, bless his name; shew forth his salvation from day to day. Declare his glory among the heathen, his wonders among all people.

For the LORD is great, and greatly to be praised: he is to be feared above all gods. For all the gods of the nations are idols: but the LORD made the heavens.

Honour and majesty are before him: strength and beauty are in his sanctuary. Give unto the LORD, O ye kindreds of the people, give unto the LORD glory and strength.

Give unto the LORD the glory due unto his name: bring an offering, and come into his courts. O worship the LORD in the beauty of holiness: fear before him, all the earth.

Say among the heathen that the LORD reigneth: the world also shall be established that it shall not be moved: he shall judge the people righteously.

Let the heavens rejoice, and let the earth be glad; let the sea roar, and the fullness thereof. Let the field be joyful, and all that is therein: then shall all the trees of the wood rejoice

Before the LORD: for he cometh, for he cometh to judge the earth: he shall judge the world with righteousness, and the people with his truth.

Psalm 96, KJV

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ACKNOWLEDGEMENTS

During the course of my studies, I had help along the way. I thank my committee members Dr. Gary Benesh, Dr. Pamela Coats, Dr. Patrick Maroney, and Dr. Pamela Peterson-Drake for their assistance and patience. I thank those Florida State University faculty members who were not on my committee but were supportive, Dr. Donald Nast and Dr. William Christiansen. I would like to thank Dr. Raid Amin from the University of West Florida, and Dr. Elton Scott, formerly from FSU for their guidance with my statistical interpretations. However, any errors of fact or interpretation are my responsibility. Finally, Dr. William Whitaker, thank you for the firm nudge to get my Ph.D. Linwood, thanks for the ear, the shoulder, the hugs, and the yeast rolls. You are next. Melita, thanks for being the best office mate. I appreciate all your prayers and support. You both are always in my heart and my prayers. To my most precious and faithful Boxer Maddie, you were there with me through it all. I would have been so lonely without your unconditional love and affection. Finally, to all the others, thank you for your prayers and continued support and interest in my life.

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TABLE OF CONTENTS List of Tables ........................................................................................................Page vi List of Figures ........................................................................................................Page viii Abstract ..............................................................................................................Page ix 1. Introduction and Purpose ..........................................................................................Page 1 Environmental Liabilities and Financial Assurance...............................................Page 2 The Focus ........................................................................................................Page 5 The Contribution ...................................................................................................Page 6 The Findings ........................................................................................................Page 7 Outline and Summary of the Dissertation .............................................................Page 7 2. Review of the Literature .............................................................................................Page 8 What are Financial Assurance Requirements?.....................................................Page 8 What is the state of financial assurance today in the United States?...................Page 14 Reasonableness and Adequacy of Current Requirements...................................Page 23 Problems and Issues.............................................................................................Page 25 3. Analysis of Current Financial Assurance Guidelines.................................................Page 29 Review of the EPA’s Standards ............................................................................Page 29 Methods for Determining Company Health and Bankruptcy Prediction ...............Page 33 Data ........................................................................................................Page 37 Methodology ........................................................................................................Page 39 Results ........................................................................................................Page 41 Robustness Check ................................................................................................Page 49 Summary ........................................................................................................Page 51 4. Tests of financial assurance effectiveness: a sensitivity analysis .............................Page 53 Purpose for Sensitivity Analysis ............................................................................Page 53 Data ........................................................................................................Page 54 Methodology ........................................................................................................Page 54 Results ........................................................................................................Page 55 Summary ........................................................................................................Page 57 5. Conclusion ........................................................................................................Page 58 Summary of Findings ............................................................................................Page 58 Conclusions ........................................................................................................Page 60 Further Research ..................................................................................................Page 61 APPENDIX ........................................................................................................Page 105 A Major Environmental Catastrophes ................................................................Page 105 REFERENCES ........................................................................................................Page 108 BIOGRAPHICAL SKETCH ............................................................................................Page 116

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LIST OF TABLES Table 2.1: Time line of the major environmental laws ................................................... Page 62 Table 3.1: State versus federal regulations ................................................................... Page 63 Table 3.2: Comparison of methods and models ............................................................ Page 66 Table 3.3: Panel A: Classification results for the EPA’s financial tests, 1985-1999..................................................................................................... Page 67 Table 3.3: Panel B: Classification accuracy rates for the EPA’s financial tests by year, 1985-1999....................................................................................... Page 68 Table 3.4: Classification accuracy rates for the EPA’s financial tests by industry, 1985-1999..................................................................................................... Page 70 Table 3.5: Panel A: Classification results for Grice and Ingram (2001), 1985-1999 .... Page 72 Table 3.5: Panel B: Classification accuracy rates for Grice and Ingram (2001) by year, 1985-1999....................................................................................... Page 73 Table 3.6: Classification accuracy rates for Grice and Ingram (2001) by industry, 1985-1999...................................................................................................... Page 74 Table 3.7: Panel A: Classification results for bond ratings, 1985-1999........................ Page 75 Table 3.7: Panel B: Classification accuracy rates for bond ratings by year, 1985-1999 ....................................................................................................... Page 76 Table 3.8: Classification accuracy rates for bond ratings by industry, 1985-1999 ........ Page 77 Table 3.9: Panel A: Classification results auditor opinion, 1985-1999 ......................... Page 78 Table 3.9: Panel B: Classification accuracy rates for auditor opinion by year, 1985-1999....................................................................................................... Page 79 Table 3.10: Classification accuracy rates for auditor opinion by industry, 1985-1999.................................................................................................... Page 80 Table 3.11: Panel A: Classification results for the Altman Z-Score Model for publicly traded firms, 1985-1999 ................................................................ Page 81

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Table 3.11: Classification accuracy rates for the Altman Z-Score Model for publicly traded firms by year, 1985-1999.................................................... Page 82 Table 3.12: Classification accuracy rates for the Altman Z-Score Model for publicly traded firms by industry, 1985-1999.............................................. Page 83 Table 3.13: Panel A: Classification results for the Altman Z-Score Model for privately held firms, 1985-1999 .................................................................. Page 84 Table 3.13: Panel B: Classification accuracy rates for the Altman Z-Score Model for privately held firms by year, 1985-1999 ................................................ Page 85 Table 3.14: Classification accuracy rates for the Altman Z-Score Model for privately held firms by industry, 1985-1999........................................... Page 86 Table 3.15: Summary of classification rates for methods including logistic results, 1985-1999.................................................................................................... Page 88 Table 3.16: Summary of classification rates by method, 1985-1999............................. Page 89 Table 3.17: Summary of classification rates for each method by industry, 1985-1999..................................................................................................... Page 91 Table 3.18: Summary of overall classification rates by method, 1985-1999 ................. Page 92 Table 4.1: Distribution of error rates for the EPA’s financial tests using varying levels of PP&E for closure costs, 1985-1999................................... Page 93 Table 4.2: Distribution of classification rates for the EPA’s financial tests using varying levels of PP&E for closure costs by industry, 1985-1999 ...... Page 94 Table 4.3: Panel A Mean and median financial measures for firms subject to the EPA’s financial tests, 1985-1999............................................................ Page 101 Table 4.3: Panel B: Mean and median financial measures for firms subject to the EPA’s financial tests by industry, 1985-1999 ......................................... Page 102 Table A1: Summary of articles from trade and popular press ....................................... Page 107

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LIST OF FIGURES Figure 3.1: The proportion of bankrupt firm/years by year prior to bankruptcy for firms from 1985-1999 ............................................................................. Page 64 Figure 3.2: Proportion of bankrupt firm/years by industry for firms from 1985-1999...... Page 65 Figure 3.3: Classification error rates for the EPA's financial tests from 1985-1999 ....... Page 69 Figure 3.4: Classification error rates for the EPA's financial tests by industry ............... Page 71

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ABSTRACT

Firms are now required to disclose environmental activities and obligations. Prior, presumably

viable firms failed to include such obligations on financials. Firms in bankruptcy are often successful in

discharging their environmentally liabilities often at great cost to the public. The purpose of this

dissertation is to examine existing financial tests companies use to assure the Environmental Protection

Agency that they can satisfy their environmental obligations. Passing these tests allows firms to continue

engaging in potentially hazardous lines of business without actually allocating the necessary funds. I

examine the ability of the tests to detect firms that eventually go bankrupt. I compare the performance of

the tests to several methods used to predict bankruptcy such as the Altman Z-Score models, Grice and

Ingram’s definition of distress, bond ratings, and auditor opinion. I also test the sensitivity of the financial

tests to varying cost of closure.

1

CHAPTER 1 INTRODUCTION AND PURPOSE

Many firms may have undisclosed obligations for mitigation and reclamation of environmental

hazards. These mitigation and reclamation obligations may be short or long term and vary in amount.

For example, in the case of Safety-Kleen, the cost of mitigation for one landfill located in the state of

South Carolina is almost $1 billion, whereas its assets prior to the 2001 bankruptcy filing were

approximately $3 billion.1 Firms can have substantial liabilities that have the potential of becoming

catastrophic. Thus, there is incentive to manage the environmental liability to keep it from becoming an

environmental catastrophe. In Appendix A, I detail several of the major environmental catastrophes to

illustrate the importance of environmental liability mitigation and management.

Given that some of these catastrophic events are still fresh in the public’s mind, there is

increased concern about the risk the public bears for firms who fail to meet their environmental

obligations. In the popular media, such as The Wall Street Journal, The Associated Press, and state and

local newspapers, and trade publications, such as Platt’s Oilgram and Platt’s Coal Outlook, one finds

numerous articles about current environmental liability management issues and the associated costs

borne by the taxpayer [Table A1].

In light of the recent corporate scandals and bankruptcies of large firms (such as Enron,

WorldCom, Global Crossing, and Sunbeam) and coupled with the current economic downturn, the public

is increasingly concerned with firm transparency and viability. Aside from the issue of undisclosed off-

balance sheet liabilities, firms have successfully discharged environmentally related liabilities by filing for

bankruptcy protection. In this dissertation, I examine a variety of methods used to classify bankrupt firms.

Specifically, I compare the Environmental Protection Agency’s (EPA) financial tests ability to detect a

firm’s loss of viability with other methods available from the financial distress and bankruptcy prediction

literature.

Accurate assessment of firm viability is important to minimize both business and social costs.

Firms incorrectly assessed as lacking viability when in fact a firm is financially viable, these firms must

seek third party indemnification for their liabilities. Thus, the firm incurs additional business costs. Firms

lacking financial viability that are misclassified continue operations as usual and expose the public to the

potential social cost of funding the cleanup should the distressed firm default.

1 Report by Disclosure Incorporated.

2

When a firm defaults on its environmental obligation, the creditors and taxpayers ultimately bear

the burden of the cost of cleanup.2 In general, one assumes a firm will continue indefinitely and be viable

in the process by generating enough resources to maintain operations—the basic definition of “going

concern.” When an auditor issues a going concern modified opinion for a firm, this indicates that the

auditor believes the firm may struggle to remain a going concern in the next fiscal year. The definition of

“financial distress” may vary but it implies a firm continues to lack the ability to maintain going concern

status; however, the firm may still recover. Failing recovery, a firm may file for bankruptcy and reorganize

or it may liquidate.

To mitigate the costs of the environmental cleanup and hold the appropriate parties responsible,

regulators make additional requirements of firms that lose their going concern status. Regulators require

those firms to obtain an alternate form of assurance or risk losing their operating permit. However, at

times, regulators do not have much warning about a firm’s pending default.3

In this dissertation, I examine one of the existing financial mechanisms used to assure regulators

that companies will be able to satisfy their environmental obligations.4 For example, an entity that creates

and maintains a landfill must assure both state and federal regulators it has the financial means—either

through internal (that is financial wherewithal) or external (such as insurance) mechanisms—to cover the

costs of cleaning up and closing the landfill. In difficult economic environments, the need for financial

assurance is great. Yet external mechanisms are often expensive for entities to obtain. If companies

meet the criteria, they prefer to provide internal assurance.

1.1 Environmental Liabilities and Financial Assurance

1.1.1 What is financial assurance? Financial assurance is the demonstration of the ability to

fund costs associated with environmental liabilities. Such costs include closure obligations, post-closure

obligations, and corrective actions taken by an owner, operator, user, and anyone deemed a potentially

responsible party as defined and codified by The Code of Federal Regulations (CFR). Facilities are

required to prove they are financially viable to fund the following activities:

2 In reference to Safety-Kleen in South Carolina, the estimated cost is approximately $24.61 per person in

South Carolina for the Safety-Kleen issue alone. This figure is a crude estimate based on the $1 billion cost for a future repair at the landfill divided by the number of residents in the state (4,063,011) as reported by the U.S. Census Bureau at the time Safety-Kleen filed for bankruptcy protection in 2001. Although this estimate does not appear to be costly, this estimate is only for one repair at one site. It does not include estimates for other repairs, other sites, other types of hazards, or other hazards and sites owned by other companies. One can easily calculate the potential burden on the taxpayer if South Carolina has more than one site of concern. Estimating the total possible environmental liability and the potential burden for the taxpayers of each state is beyond the scope of this dissertation. 3 As reported in The Tampa Tribune on March 17, 2001, the Florida Department of Environmental

Protection took emergency control of the phosphate-mining site and phosphogypsum stacks abandoned by Mulberry Phosphates. A copy of the article is at http://www.fluoridealert.org/phosphate-industry.htm. 4 These mechanisms, directly or modified, are also used for state and local governments, as well as other

municipalities. The focus of this dissertation is on companies, though much of this research is applicable to other entities. Throughout the paper, I imply the terms “company,” “state and local governments,” and “municipalities” in the terms “entity” and “owner or operator.”

3

1) maintain the site during the life of the business and comply with the EPA and related state

regulatory agency guidelines,

2) properly close the site when the business is complete,

3) restore the site to a reasonable condition as dictated by the EPA and state authorities, and

4) maintain the site after closure, and cover any costs arising from unexpected contamination,

injuries, or problems before, during, and after closure.

If a firm, new or existing, cannot provide internal financial assurance then the firm must seek

alternative external financial assurance. Inability to obtain financial assurance will leave the firm without

the necessary permits and licenses needed to operate.

1.1.2 Why is financial assurance important? The purpose of financial assurance is to

internalize all environmental liability costs to the potentially responsible party. By requiring the

responsible party to take financial responsibility for all liabilities, this mitigates the social cost borne by

taxpayers. Enforcement may be in the form of the tort laws of strict liability and joint and several liability.

When discussing the issue of liability, it is necessary to distinguish negligence from strict liability

and joint and several liability from joint liability.5 Under negligence, proof is required that the responsible

party failed to use adequate precaution and any injury sustained was the direct result of the pollution or

the unsafe work environment caused directly cause by the lack of precaution. Strict liability is

independent of negligence; therefore, the responsible party for the hazardous activity may be liable for

the potential hazard, regardless if the responsible party used adequate precaution. Negligence in

environmental cases is often difficult to prove. Therefore, in environmental cases, the state regulatory

agencies prefer the use of strict liability. It removes the burden of proving negligence, and potential

environmental hazards can be considered “unusually dangerous activities” [Cross and Miller (2001) page

277].

Joint and several liability usually applies to torts, and it means either one or all of the involved

potentially responsible parties may be responsible for the entire liability; no one is safe from the

responsibility. In a sense, it is a situation where the regulators attempt to get what they can from

whomever. If they did not get it all in the first round, then they may find other potentially responsible

parties and attempt to recover it from them as well. For joint liability, which is usually applicable in

contract situations, it may be an all or nothing situation. Thus, if it happens to one responsible party, then

it happens to all. If the state regulatory agency holds one responsible party accountable, then the agency

must hold all responsible parties accountable. Likewise, if the state regulatory agency pardons one party,

then they pardon all. Potentially responsible parties tend to use joint liability against each other for

sharing the liability and for a fairer distribution of the obligation. The state regulatory agencies tend to

5 Further discussion on types of liability can be found in Cross and Miller (2001), Chapters 12 and 25.

4

focus on the use of strict liability and joint and several liability when instigating litigation and cost recovery

from responsible parties.

To accomplish the goal of internalizing the liability, the EPA requires firms involved in potentially

hazardous lines of business to apply for permits and/or licenses. These permits give the firm permission

to perform the necessary activities related to the line of business with the potential hazard. The permits

may also predefine acceptable limits of pollution. To receive these permits, firms must meet all

guidelines required by the governing regulatory agencies. Specifically, they must show proof of financial

assurance.6 Demonstration of financial assurance contractually binds the permitee to fulfill the obligation

for which it is providing assurance.

Several financial assurance mechanisms exist. Each potential hazard can require the use of a

single mechanism or, in some cases, a combination of mechanisms. Mechanisms may include

insurance, trust funds, corporate parent guarantees, or financial tests. Many of these mechanisms

subject the responsible party of the facility to increased monitoring by state and federal regulators and,

often, an additional third-party financial provider. Misuse of assurance mechanisms can lead to the

taxpayers bearing the costs.7 In other words, a distributional concern exists as the burden shifts to the

U.S. taxpayers. The most controversial of these mechanisms, the financial test, is the focus of

investigation in this dissertation. Financial tests are the least expensive mechanism because they only

require the firm to provide financial statements attesting to the firm’s viability should it face an

environmental claim. It is a promise that binds the firm to the obligation; however, it provides no tangible

guarantee that funds will be available when needed.

Firms that are not financially viable can find third-party assurance but at a higher cost, which the

firm may not be able to afford [Boyd (2001a)]. A firm claiming it cannot afford the mechanism may be

financially beyond its means. It becomes an environmental concern. Firms may attempt to negotiate for

a temporary relaxation in the requirements. However, these relaxed requirements do not always comply

with federal law.8 In some cases, firms that negotiated a temporary reprieve filed for bankruptcy shortly

thereafter.9

6 In many cases, firms must answer to more than one regulatory agency. The many environmental acts

have established regulatory agencies relevant to the specific issue they address, while the EPA monitors all environmental hazards. For example, licensees of nuclear power reactors are also responsible to the Nuclear Regulatory Commission (NRC). The Underground Injection Control (UIC) program director for each state monitors underground injection wells for the EPA. The Office of Surface Mining Reclamation and Enforcement (OSMRE) monitors mining operations. The United States Coast Guard regulates water transportation of hazardous materials. For a review of the major regulatory acts and the statutes governing compliance and enforcement through criminal prosecution, see Lachenmayr, Lockner, Olson, and Wolpert (1998). 7 At a Florida Department of Environmental Protection (DEP) mining reclamation meeting in September

2002, the attorney for the DEP, Mr. John Alden, explained some common misuses of financial assurance mechanisms. Misuses can include approval by a representative of the company who is not an officer and, therefore, does not legally bind the company; providing fraudulent financial statements; and attempting to use one mechanism to cover sites not specified by the mechanism. 8As reported in The Greenwire on December 18, 2002, Dow Chemical Company and Michigan’s

Department of Environmental Quality came to an agreement to reduce Dow’s financial assurance costs and clean-up liability for dioxin contamination in the soil by allowing Dow to increase the amount they

5

1.2 The Focus

In this dissertation, I examine the ability of the financial tests to classify a firm according to its

financial viability. Specifically, I focus on analyzing the financial fundamentals that make up these tests.

Among the questions I address are the following:

• Are these financial tests effective in assuring that financial resources exist to fund the

cleanup of environmental accidents?10

That is, can these tests detect when a firm will go

bankrupt?

• Do the financial tests foster cost internalization, or do they hinder those responsible from

taking responsibility? That is, are these tests effective in guaranteeing the necessary

funds are available for environmental cleanups?

Whereas other studies focus on the environmental, economic, legal, and ethical effects

concerning environmental contamination, in this dissertation I investigate the financial precursor to these

issues. Specifically, I address the viability of firms to fulfill their legal financial obligation for the

maintenance, closure, and any corrective actions directly related to site operations. Other studies are

reactive studies because they are often ex-post analysis of the liability realization. They examine the

after effects on the environment and its inhabitants and the economic welfare and social costs [Riering

1992, Boyd 1993]. Other studies focus on policy-making, risk sharing, and the legal debate between

bankruptcy law and environmental law [Van ‘T Veld 1997, McGraw 1998].11

Recently, environmental law,

disclosure, and the contractual commitment of the permitee are receiving increased attention.12

This

study is a reactive study in the sense that my interest in this topic was peaked after reading about the

many firms attempting to discharge their environmental liabilities. I realized that if these firms are

successful in their bid to dismiss their obligations, then I as a taxpayer am ultimately bearing the cost. I

focus on the EPA’s financial tests applicability for social cost mitigation.

dump into the soil at no added cost to Dow. This reduction of liability means the reduction of standards. This reduction in the state standard violates the federal guidelines, so the City of Midland, Michigan, and several environmental groups have filed a lawsuit to stop the approval of the proposal. 9 Mulberry Phosphates filed for bankruptcy in 2001 and abandoned the mining site and the

phosphogypsum stacks for the State of Florida to clean up at the cost of $125 million (as of April 3, 2003). 10

Deterrence is important in some types of environmental hazards. For example, if a phosphate processing company files for bankruptcy and is unable to fund the cost of pumping the acidic water for its phosphogypsum stacks, a significant probability exists that there will be a hazardous spill into the state’s water supply. In this case, funds are necessary to deter an environmental hazard. 11

The difficulty here lays in the fact the courts must interpret congressional intent. No consensus exists among the courts. For a review of cases, see Hill (1998), Spracker and Barnette (1994), Bloom (1995), and C. Barth (1994). 12

As reported in The Ohio Law Letter in October 2002, the SEC petitioned to require full disclosure of environmental liabilities in corporate filings. This petition came in light of several major bankrupt corporations with serious environmental liabilities and on the heels of the 1998 EPA report that found 74 percent of firms fail to report environmental liability litigation in excess of $100,000. Not reporting such liabilities violates the SEC regulation S-K and can warrant criminal charges.

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1.3 The Contribution

The importance of this dissertation is in its contribution to the overall well being of the

environment and society. In light of the anecdotal evidence of firms attempting to avoid their

environmental obligation and the public seeking improved disclosure requirements from the Securities

and Exchange Commission (SEC) and the Financial Accounting Standards Board (FASB), it is logical

that the next step is to look at the adequacy of the EPA’s financial tests.13

The EPA’s financial tests use

well-known financial analysis techniques and methods. When applied, the financial tests should be able

to provide regulators with guidance about a firm’s viability. Specifically, these tests should let regulators

know when a firm’s viability may be waning. Early and more accurate detection by the EPA creates

proactive enforcement of compliance, mitigates costs, and lessens the financial burden on the taxpayer.

This dissertation ties together the areas of finance, accounting, insurance, government

regulation, the environment, financial distress, and bankruptcy under the umbrella of risk management.

This topic is timely and significant, as it affects everyone. It is a serious study of the real-world

application of risk management techniques and whether these techniques accomplish their designed

purpose. Failure of risk management techniques in this arena can lead to environmental catastrophes

that no amount of money can restore. Diligent monitoring and revision are not just necessary—they are

vital to our survival.

Because these financial tests dictate whether firms may continue to engage in potentially

hazardous lines of business, there are four potential outcomes. The first two are what we hope for; non-

bankrupt firms receive permits and continue operating while firms that are quickly approaching

bankruptcy must find an alternative means of financial assurance. The other two potential outcomes may

result in additional unexpected costs. Firms quickly approaching bankruptcy that continue operations,

without providing an alternate form of assurance create potential social costs on the environment and

taxpayers if they default on their obligations. Non-bankrupt firms that must find alternate financial

assurance incur additional business costs that may be unnecessary.

These financial tests are comprised of financial ratios. We know from prior research that ratios

can be consistent indicators of distress [Beaver (1966, 1968), Altman (1968, 1977, 1993), Ohlson (1980),

Scott (1981), and Grice and Ingram (2001)]. I compare the classification accuracy of the EPA’s financial

tests and other commonly used bankruptcy prediction methods.14

My reason for doing so is to search for

alternative methods that may have better classification accuracy than the EPA’s financial tests. If such

methods exist, then perhaps the EPA might consider selecting one of these for future use. I also conduct

sensitivity analyses for differing levels of closure costs that represent the environmental liabilities. My

purpose for doing so is to determine how sensitive the tests are to varying closure costs.

13

The disclosure requirements are the Securities and Exchange Commission (SEC) Regulation S-K items 101, 103, and 303. The new accounting change is the implementation of Financial Accounting Board Standard (FASB) Statement Number 143, effective June 2002. 14

I test the null hypothesis that there is no difference in the classification of bankruptcy between bankrupt and non-bankrupt firms. Type I error indicates a bankrupt firm passes the financial tests. Type II error indicates a non-bankrupt firm fails the financial tests.

7

1.4 The Findings

I find the EPA’s financial tests are able to classify over 90 percent of the bankrupt observations

and over 60 percent of the non-bankrupt observations correctly. Annually, the results remain consistent.

The tests do classify the firms more accurately in some industries than in others. When compared to

other methods, the classification ability of the EPA’s financial tests is most comparable and consistent

with the Altman’s Z-Score methods.

I find the financial tests are somewhat sensitive to varying closure costs. As closure costs

increase, there is an insignificant decrease in type I error [the number of bankrupt observations that fail

the financial tests]. As expected, misclassification of non-bankrupt observations increases as closure

costs increase.

1.5 Outline and Summary

In Chapter 2, I describe financial assurance tests for the major environmental liabilities and

provide a review of the environmental liability literature. The major environmental liabilities are as follows:

nuclear power reactors; above and below surface mining; injection wells; municipal solid waste landfill

facilities (MSWLF) and hazardous waste treatment, storage, and disposal facilities (TSDF); underground

storage tanks (UST); and transportation of hazardous materials in water bound vessels. I explain the

types of financial assurance available to firms with environmental liabilities and discuss problems and

issues.

In Chapter 3, I provide a review of the distress and bankruptcy prediction literature and analyze

the classification accuracy of the EPA’s financial tests. I also compare the EPA’s classification rates with

various methods for detecting bankruptcy: Grice and Ingram’s (2001) definition of financial distress, bond

ratings, and auditor opinion, Altman’s Z-Scores for publicly traded and privately held firms. In Chapter 4, I

test the sensitivity of the financial tests to detecting bankruptcy with varying levels of closure costs.

Finally, in Chapter 5, I summarize my results and offer avenues for future research.

8

CHAPTER 2 REVIEW OF THE LITERATURE

2.1 What Are the Financial Assurance Requirements?

2.1.1 Development of guidelines. Financial assurance guidelines have evolved over the past

30 years from the major environmental laws and their amendments. These laws are the reaction to

negative events caused by a lack of protection for the environment and its inhabitants. I devote this

section to the introduction of some major laws and briefly explain their purpose.15

I provide a list of some

of the environmental laws over the past 65 years in Table 2.1. Many of the earlier laws listed in Table 2.1

have been amended and are subsumed in the present laws.

Congress and state legislatures establish environmental requirements by passing laws that limit

negative human impact on the environment. These statutes are included in the federal or state code of

laws respective to the legislature that passed the law. State statutes only apply to the state, whereas

federal statutes apply to all states. Regulatory entities generate rules to implement the statutes. Local

governments also pass statutes, called ordinances, rules, or orders, to govern issues not addressed by

federal or state laws. The statutes are applicable in the region governed by the local government.

The federal government relies on the state and local governments and regulatory agencies to

implement and to enforce the federal statutes within their specific states and regions. Often, the states

regulate the general law and allow more localized governmental units to interpret implementation and

execution of the law, as long as it meets the state and federal standards. For example, the federal

government regulates landfills, and the state is responsible for the management of the landfills and

compliance with using the landfills. The local government in the area in which the landfill resides may

determine the method of waste pick up and disposal.

The National Environmental Policy Act of 1969 (NEPA) established the Council on Environmental

Quality that reports annually to Congress on the state of all environmental affairs in the United States.16

15

For an extensive survey on the evolution of environmental policy from an economics perspective, please see Cropper and Oates (1992). 16

42 U.S.C. § 4321-4347, and may be found at http://ceq.eh.doe.gov/nepa/regs/nepa/nepaeqia.htm.

9

The Council investigates all aspects of quality management for the environment, both current and

forward-looking trends. In other words, the Council is the keeper of the environment, monitoring the

effects of the current and future applications of laws and compliance. It provides guidelines for the

federal government’s role and responsibility for the environment. An all encompassing law with its

amendments, the Resource Conservation and Recovery Act of 1976 (RCRA) gives the EPA complete

control over all things related to hazardous waste, barring historical and abandoned waste sites. In those

cases, Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA)

covers those sites.17

RCRA provides objectives for the rigorous treatment of hazardous waste throughout

the waste’s existence.

Congress expanded federal authority for the EPA to respond to toxic releases with the advent of

CERCLA (or Superfund) and its amendments. A key provision of CERCLA is the improvement of the

National Contingency Plan. This plan outlines the procedures for such releases. It also establishes the

National Priorities List (NPL) that contains contaminated sites that are of concern, and for which the EPA

collects taxes from the offending industries to fund the cleanup of necessary sites.18

Congress amended CERCLA with the Superfund Amendments and Reauthorization Act of 1986

(SARA), making several changes and additions to the program. In particular, SARA supports the

investigation and the implementation of new techniques for the management and maintenance of

hazardous waste and requires continuity between state and federal regulations. One of the most

important provisions is the updating of the Hazard Ranking System (HRS). The HRS is a ranking system

that provides regulators with a list of criteria used to calculate a score. This score tells the regulators the

potential threat the particular site is to the environment. SARA provides improvements to the HRS for a

more appropriate score with respect to the level of waste sites contain.19

Other laws regulate specific types of potential damage to the environment. For example,

o air emissions from all mobile sources are regulated according to the Clean Air Act of 1990

(CAA),20

o federal control of all things pesticide related is regulated by the Federal Insecticide, Fungicide

and Rodenticide Act of 1972 (FIFRA), 21

o pesticide regulation is expanded with the Food Quality Protection Act of 1996 (FQPA) and

the FIFRA and Federal Food, Drug, and Cosmetic Act (FFDCA),22

o drinking water quality is regulated by the Safe Drinking Water Act of 1974 (SDWA),23

17

42 U.S.C. § 321, and may be found at http://www.epa.gov/region5/defs/html/rcra.htm and http://www4.law.cornell.edu/uscode/42/ch82.html. 18

42 U.S.C. § 9601, and may be found at http://www.epa.gov/superfund/action/law/cercla.htm and http://www4.law.cornell.edu/uscode/42/ch103.html. 19

42 U.S.C. § 9601, and may be found at http://www.epa.gov/superfund/action/law/sara.htm and http://www4.law.cornell.edu/uscode/42/ch103.html. 20

42 U.S.C. § 7401, and may be found at http://www.epa.gov/region5/defs/html/caa.htm and http://www.epa.gov/oar/caa/contents.html. 21

7 U.S.C. § 135, and may be found at http://www.epa.gov/region5/defs/html/fifra.htm and http://www4.law.cornell.edu/uscode/7/ch6.html.

10

o all manner of chemicals are regulated under the Toxic Substances Control Act of 1976

(TSCA),24

and

o the Clean Water Act of 1977 regulates pollutants discharged into the United States waters

(CWA), which amends the Federal Water Pollution Control Act Amendments of 1972.25

These laws provide guidance to the EPA and state regulatory agencies on business compliance

and regulation. Any business activity that may produce an environmental hazard requires some form of

financial assurance. The hazards include the licensing and decommissioning of nuclear power plants;

surface and underground mining and reclamation; plugging and abandoning injection wells; waste

management landfill facilities; hazardous waste treatment, storage, and disposal facilities; and

underground storage tanks for hazardous materials.26

I discuss these types of hazards in the next

sections. For the analyses in this dissertation, I examine the hazards as a whole, meaning I do not treat

hazards uniquely.

2.1.2 Federal and state guidelines. Before discussing the guidelines for the hazards, I

address preemption. The states have the main responsibility to monitor and to enforce compliance,

provided they have the capability to do so. In cases where the state lacks the necessary laws, personnel,

equipment, or overall ability to implement the federal laws—for example, if the state does not have an

EPA office or Regional Administrator or a state regulatory agency—then federal law is the precedent. If

the state has the state-level framework and the federal and state laws are concurrent, then federal law

preempts state law [Cross and Miller (2000), Copeland (1997)].

Although states have some leeway from the federal government, the federal government may

intervene in the activities within a state at any time it sees fit if a federal law exists. However, if a federal

law does not exist but a state law does, then state law may preempt federal law. The degree of federal

government intervention depends upon the varying level of preemption [Meltz (1999)]. Preemption may

be general, in which the federal government may intervene at will, or it may be highly specific in that the

state may or may not be given certain variances or waivers for certain types of environmental activities.

The ability to receive the waiver depends upon a multitude of factors, such as the type of hazard, the

accessibility to treatment facilities, and the financial viability of the firm responsible for the hazard.

RCRA Section 3009 ensures states that have implemented federal programs may impose stricter

regulations than the federal programs require. If the federal program tightens the regulations, the states

must likewise tighten their regulations. In other words, the state may be more rigorous than federal

requirements, but it cannot be more lenient. Thus, the states may require additional or more stringent

financial assurance requirements. In states that do not have approved programs implemented, the EPA

22

Public Law 104-170, August 3, 1996, and may be found at http://www.epa.gov/opppsps1/fqpa/ and http://www4.law.cornell.edu/uscode/21/ch9.html. 23

42 U.S.C. § 300f, and may be found at http://www.epa.gov/region5/defs/html/sdwa.htm and http://www4.law.cornell.edu/uscode/42/300f.html. 24

15 U.S.C. § 2601, and may be found at http://www.epa.gov/region5/defs/html/tsca.htm and http://www4.law.cornell.edu/uscode/15/ch53.html. 25

33 U.S.C. § 1251, and may be found at http://www.epa.gov/region5/water/cwa.htm and http://www4.law.cornell.edu/uscode/33/ch26.html.

11

Regional Director evaluates the equivalency of the state-required mechanism to the federal-required

mechanism. The next section describes the guidelines for firms that may engage in the following types of

business activities that pose potential environmental hazards:

(a) nuclear power reactors,

(b) above and below surface mining,

(c) municipal solid waste landfill facilities (MSWLF) and hazardous waste treatment, storage,

and disposal facilities (TSDF),

(d) underground storage tanks (UST), and

(e) the transportation of hazardous waste across bodies of water.

2.1.2.1 Nuclear power reactors. The regulatory agency for the licensing and decommissioning

of nuclear power reactors is the Nuclear Regulatory Commission (NRC).27

The NRC defines two types of

nuclear reactors: power and non-power. Power nuclear reactors are commercial reactors used to

generate electricity. Non-power reactors are those used in research, testing, and training by non-profit

organizations. These types of entities are beyond the scope of this dissertation, as I focus on those firms

that are for-profit institutions. The NRC evaluates the financial qualifications of an applicant for a nuclear

power reactor license at various stages of business and for several reasons. Evaluations of applicants’

financials occur for the following:

• initial licensing,

• prior to being sold, acquired, or restructured because the license to operate transfers to

the buyer,

• license renewal for nuclear power reactors that are not electric utilities, and

• if the NRC suspects the firm is no longer a going concern.

Financial qualifications refer to an applicant’s ability to meet the necessary requirements as

dictated by the NRC and the EPA to acquire the operating license and comply with environmental

regulations while in operation. Financial assurance is different from financial qualification in that financial

assurance refers to the applicant’s ability to cover costs associated with hazard-related incidents and

accidents during operation and costs related to the decommissioning of the plant. Both are related; if an

applicant shows proof of financial assurance, then the financial qualifications follow. However, satisfying

the financial qualifications does not imply financial assurance is satisfied, as financial assurance is

accident related and financial qualifications relate to the day-to-day operations.

Nuclear power reactors that are electric utilities are not required to resubmit financial information

upon license renewal. The NRC argues that because electric utilities are highly regulated, they must

already be financially viable to operate the plant safely. Therefore, electric power reactors are not

required to submit proof of financial qualifications upon renewing the operating license. Nuclear power

reactors that are non-electric utilities and non-power reactors must always submit financial information.

Owners and operators of non-power reactors are typically private, state, federally operated nonprofit

26

I use the terms owners, operators, applicants, and licensees interchangeably. 27

10 CFR Parts 30 and 50.

12

educational institutions, or research institutions. The financial information for these types of institutions is

harder to obtain. Therefore, increased and regular scrutiny of their financials is necessary.

The NRC likewise monitors current license holders through the trade press and popular media. If

negative information exists about a license holder, the NRC reserves the right to investigate the firm’s

financials.28

The NRC provides flexible, case-by-case analysis of the financial assurance requirements

for each applicant and may provide some variances or waivers to these requirements. The mechanisms

available are specific to the type of nuclear entity: qualified nuclear entities, unqualified nuclear entities,

and all others. Qualified and unqualified nuclear entities may use any of the mechanisms discussed in

this dissertation. Those in the “others” category are limited, as they may not be as financially viable or

transparent.

2.1.2.2 Above and below surface mining. The Office of Surface Mining Reclamation and

Enforcement (OSMRE) is the regulating authority for surface and underground mining operations and

reclamation.29

To receive a permit to mine, companies must conduct mining operations according to the

permit and provide the required financial assurance for any incidents that occur during operation, closure,

and reclamation. A dilemma with financial assurance mechanisms is the lack of a long-term mechanism.

Often, events occur that are not included and accounted for at the beginning or during the course of

operations. When reclamation begins, unexpected environmental needs may arise for which the firm

lacks the financial assurance, and the firm may not have deep pockets to cover the costs from another

funding source.

As mentioned in the anecdotal evidence in Table A.1., Appendix A, one such unforeseen

environmental need with mining is the need for continual treatment of acid or toxic mine drainage (AMD).

AMD is a toxic byproduct of the mining activity. Currently, mining companies have been using

performance bonds (surety bonds, self-bonds, cash bonds, negotiable federal or state bonds, and

negotiable certificates of deposit) to cover the funds needed to complete the reclamation plan, without

considering the need for AMD treatment. When re-estimating financial assurance, often the affected

firms forfeit the bonds in lieu of reestablishing new bonds for higher amounts that the firm cannot afford.

Forfeiting bonds increases regulator scrutiny as the next step after bond forfeiture is often filing for

bankruptcy protection against all creditors—and especially the environmental liability.

2.1.2.3 Injection wells. The Director or Regional Administrator for the EPA office within the

specified state or region is responsible for regulating injection wells. The EPA classifies injection wells

based on the type of material injected into the ground and the depth of the injection. The office regulates

the investigation of actual and potential aquifers, injection procedures, and monitoring of the injected

waste.

Owners and operators of injection wells are limited to using only procedures specified by the EPA

for the injection process and must frequently monitor the wells for contamination. Prior to beginning the

process, they must demonstrate financial assurance for any possible contamination that may result

28

Federal Register, Volume 67, Number 107, June 4, 2002, p. 38427-38431.

13

during operations and for the eventual plugging and abandonment of the well. In cases where the

Underground Injection Control (UIC) program is state administered, the state may have more regulations

and mechanisms than the EPA requires. These state regulations may be more stringent as long as they

satisfy the EPA guidelines.

2.1.2.4 Municipal solid waste landfill facilities (MSWLF), hazardous waste treatment,

storage, and disposal facilities (TSDF). A state’s division of solid and hazardous waste management,

under RCRA, regulates waste disposal landfills and facilities. The term “landfill” applies to all facilities

that participate in land disposal activities. Subtitle C classification is for those that generate hazardous

waste in large quantities, such as corporations, schools, and hospitals. Subtitle D classification is for

those that generate a small quantity of hazardous waste, such as households and small businesses.30

Facilities may acquire multiple types of permits to operate. These permits, classified as standard

or special, depend upon the purpose of the facility and the degree of permission needed to perform an

activity.31

For example, facilities that store the hazardous waste may only receive a standard permit,

whereas a facility that burns waste would receive a special permit because the facility must maintain the

proper equipment to conduct such activities. The facility must provide for safety precautions related to

the changing chemical composition of the waste during burning and provide for proper disposal after the

activity is complete.

2.1.2.5 Underground storage tanks. The EPA Office of Underground Storage Tanks (OUST)

is responsible for regulating all manner of underground storage tanks, petroleum-based products, and

any hazardous material such as polychlorinated biphenyls (PCBs).32

The term “underground storage

tank” means either a lone tank or a system of tanks and associated components that are underground.

Some types of tanks are exempt from federal regulations, although some state and local

governments may include these tanks in their regulations, as the states may have more stringent

regulations than federally required. Currently, 29 states and the District of Columbia and Puerto Rico are

implementing the approved UST programs. Some tanks may only be required to meet federal regulations

in the event they become involved in a hazardous cleanup process. The UST programs provide clear

guidelines for owners as to the proper construction of the tank, the appropriate application of the tank,

maintenance, and disposal upon completion of use of the tank. The basis for financial assurance

standards for underground storage tanks is the number of tanks in the various stages of the life of the

tank.

29

30 C.F.R. Parts 700 – 800 and Federal Register, Volume 67, Number 96, May 17, 2002, p. 35070-35073. 30

ICF Consulting Group’s report entitled Analysis of Subtitle C and D Financial Tests with subsections disseminated from July 14, 1995, to December 9, 1997. 31

U.S. Department of Energy, Office of Environmental Policy and Assistance, RCRA Information Brief, DOE/EH-413/9715, September 1997, p. 1-4. 32

40 CFR 280, and may be found at http://www.access.gpo.gov/nara/cfr/cfrhtml_00/Title_40/40cfr280_00.html.

14

2.1.2.6 Transportation of hazardous materials in water bound vessels. The EPA regulates

domestic marine facilities, whereas the U.S. Coast Guard regulates domestic water bound vessels.33

The

International Convention for the Prevention of Pollution from Ships (MARPOL) Convention and

international treaties regulate foreign water bound vessels.34

According to regulations, the water

transportation industry must exercise extreme caution with the maintenance of all vessels, the fueling of

vessels, and the types of leaks or discharges from the vessels. Operators and owners of water-related

facilities and water bound vessels must apply for general permits as to the nature of their existence and

other permits pertaining to the cargo, purpose, and destination.

2.2 What is the state of financial assurance today in the United States?

2.2.1 Measuring environment liability. Environmental liabilities encompass a variety of

levels and types of liabilities. In this dissertation, I do not categorize liabilities. That is, I focus on total

liabilities as opposed to it’s the individual parts.35

When I refer to “liabilities,” I intend it to mean any

environmentally related obligation.

The difficulty in measuring environmental liabilities is undisputed in the literature. The difficulty

lies in quantifying the total cost. Because the total cost encompasses those known and unknown costs,

the estimates are, from the onset, inexact. Even the known costs—those related to site closure,

remediation, reclamation, and post-closure treatment—are inexact, as they are historical costs. However,

they are less inaccurate than the unknown costs. This inaccuracy is not due to lack of rigorous study.

The costs relate more to property damage and health-related claims. The extent of the damage is

unknown. For example, as mentioned in Appendix A, Table A.1, the mining operations in Pennsylvania

were unaware when they began mining decades earlier that a byproduct of the operation is the

production of acid mine drainage (AMD). AMD is extremely toxic and severely contaminates the land,

water, and, in turn, the people inhabiting the nearby land and using the nearby sources of water. The

total cost for this environmental liability is unknown. The only costs the state of Pennsylvania can

estimate are the costs for closure, cleanup, and maintenance of the AMD.

Often, both certain and uncertain costs must be estimated on a case-by-case basis if no set

standard or precedence is followed within an industry. These case-by-case estimations may include

scientifically based decision techniques, contingent scenario evaluation, and a variety of economic

valuation techniques.36

Hence, the literature provides a variety of estimation methods for these illusive

33

33 CFR 138, 151-158 and 40 CFR 263-265 and 279, and may be found at http://www.access.gpo.gov/nara/cfr/cfrhtml_00/Title_33/33cfr138_00.html, http://www.access.gpo.gov/nara/cfr/cfrhtml_00/Title_33/33cfr153_00.html, http://www.access.gpo.gov/nara/cfr/cfrhtml_00/Title_40/40cfr263_00.html, and http://www.access.gpo.gov/nara/cfr/cfrhtml_00/Title_40/40cfr279_00.html. 34

http://www.imo.org/Conventions/contents.asp?doc_id=678&topic_id=258. 35

For an explanation of the variety of categories, please see Valuing Potential Environmental Liabilities for Managerial Decision-Making: A Review of Available Techniques (1996), p 17. 36

For an introduction to and application of valuation techniques, please see Valuing Potential Environmental Liabilities for Managerial Decision-Making: A Review of Available Techniques (1996), p 22.

15

unknown costs; however, the techniques provide no guarantees of accuracy or consensus in the

estimates. Likewise, for the techniques themselves, there is no consensus on accuracy or acceptance by

those using them.

Academicians use market valuation models to evaluate a firm’s value. In these models, we input

proxies for the characteristics. These characteristics include whether or not a firm has an environmental

liability and accurately discloses this information [Landsman (1986); Beaver, Eger, Ryan, Wolfson (1989);

Harris and Ohlson (1987, 1990); Barth, Beaver, Stinson (1991); Barth (1991, 1994); Barth and McNichols

(1994); Nelson (1996); Barth, Beaver, and Landsman (1996); Campbell, Sefcik, and Soderstrom (2001)].

A byproduct of these characteristics is whether the information is believable by market participants [Barth

and McNichols (1994), Harris and Ohlson (1987, 1990), Campbell, Sefcik, and Soderstrom (2001)]. If a

liability is significantly greater than what the market perceived, the market will devalue the firm

accordingly. The above researchers show that honest disclosure ultimately is good for the firm.

Other methods used for measuring environmental damages are the hedonic pricing method, the

travel cost method, and the contingent valuation method. These methods attempt to estimate the use

and non-use values of the environment. The hedonic pricing method is often used to estimate the

change in value due to the presence or lack of an environmental characteristic [Hite, Chern, Hitzhusen,

and Randall (2000); Buschena, Anderson, and Leonard (2001); Ready, Berger, and Blomquist (1997);

Thayer, Murdoch, and Beron (1999)]. For example, a house’s price depends upon its location to a

landfill. Typically, regression analysis generates such prices or values.

Although the hedonic pricing method applies an existing pricing scheme within the market for

valuation purposes, the travel cost method does not. Instead, it estimates the value of items for which a

pricing scheme does not exist [Smith (1997), Fix and Loomis (1998)]. In other words, it attempts to value

the enjoyment of scenic natural areas, such as lakes, parks, beaches, and forests. Those who enjoy

these areas pay real costs for their enjoyment in the form of time, money, and opportunity cost. The

method of valuation is in the form of cost-benefit and impact analyses.

The contingent valuation method is used to estimate the value of environmental services and the

surrounding environment [Coller and Harrison (1995); Cummings, Harrison, and Rutstrom (1995);

Cummings and Harrison (1994); and Harrison and Lesley (1994)]. Of the three, this method is the most

controversial, as it does not depend on a pricing system. Instead, it depends on a measure of social

benefit or social opportunity cost that researchers extract from opinion surveys.

Selecting the most appropriate model or method is difficult, as they may be more appropriate for

some environmental hazards and not others. Likewise, the use of more than one model or method may

be appropriate for robustness sake; however, if the model or method is incomplete or computationally

expensive, then the purpose of the model or method is defeated. Although no consensus exists on which

valuation model or method is the best, these are concentrated efforts to estimate the liabilities for

accurate reporting.

2.2.2 Methods companies use to provide financial assurance for environment liabilities

16

2.2.2.1 Internal assurance versus external assurance. The financial assurance mechanisms

available are common for most potential environmental hazards. Although the potential environmental

hazards are different in nature, the outcome is the same. Regardless of the material, contamination is

contamination. The EPA requires mechanisms but allows for some deviation by allowing state and local

governments to apply rules that are more stringent and increase regulation requirements.

The mechanisms described below guarantee funds for closure, post-closure, and corrective

action costs. This means funds must be available for the closing, maintaining and monitoring after the

closing, and any potential accident that may occur during operation and after closure, including bodily

injury, property damage, or any other liability. Because many potential accidents can occur, it can be

difficult to quantify the funds necessary for assuring against all the possibilities. Likewise, it would be

difficult to afford the amount of assurance necessary for every conceivable accident. The purpose is to

guarantee the EPA will have access to the funds when necessary and that the appropriate PRP will pay.

If an owner or operator has deep pockets, it often provides internal assurance with self-

assurance mechanisms, such as self-assurance/self-insurance, trust funds, or internal bonds. Those that

do not have deep pockets or prefer third-party mechanisms select external assurance mechanisms.

Owners or operators who may have enough capital to provide self-assurance may prefer third-party

mechanisms to signal to the public their stability or to be more transparent. Usually, third-party

mechanisms require due diligence for qualification. Environmental due diligence means an applicant

must pass a regulator’s and/or creditor’s risk screening. The risk screening is a costly procedure. The

property under question is examined, and the potential and highly probable risks associated with the

property and its use, are assessed. Likewise, the applicant for the mechanism must pass an inspection

of its financials.

Most mechanisms have similarities, such as:

• cost estimates must be updated annually,

• mechanisms must be reevaluated with respect to these new cost estimates for their

appropriateness,

• adjustment of the necessary funds to reflect the new estimates must be made available,

• regulators must be informed of a change in funding and/or mechanism, and

• new mechanisms or additional funds must be in place within four months of the end of

the fiscal year.

2.2.2.2 External assurance

2.2.2.2.1 Trust funds. Two trust fund methods are available: a prepayment fund or an

external sinking fund. The prepayment method requires complete advanced funding in a trust fund,

escrow account, government fund, certificate of deposit, or government securities. Well-known organized

trust funds include the Superfund, Abandoned Mine Reclamation Fund, Leaking Underground Storage

Fund, Environmental Response Fund, and Oil Spill Liability Trust Fund. Unlike the trust funds initiated by

individual firms, federal and state industry-specific taxes fund the organized trust funds.

17

With an external sinking fund, annual payments are made until it is completely funded which must

be prior to the expiration of the permit or upon closure, whichever occurs first. Similar to the prepayment

fund, the external sinking fund may take on any of the forms listed above. According to the regulations,

the payment depends upon the current closure cost estimate minus the current value of the trust fund

divided by the number of years remaining in the pay-in period.

2.2.2.2.2 Surety methods: bonds, letter of credit, or line of credit. A surety

mechanism is one that provides a performance guarantee, meaning a firm that holds this type of

mechanism promises to close, clean up, and/or maintain the expressly named site. To satisfy regulators,

surety mechanisms:

• do not expire, unless otherwise noted in advance by the issuer of the mechanism,

• certify the surety will pay the full face value to the designated recipient upon the holder’s

default,

• remain in effect until regulators revoke the permit, and

• issued by surety companies approved under the U.S. Department of the Treasury

Circular 570 and the EPA.

Surety bonds, also called payment bonds, performance bonds, or financial guarantee bonds,

certify the surety company will provide the available funds when the owner or operator defaults on their

responsibilities. Similarly, parent corporations or other third parties may issue letters or lines of credit.

The assurance providers perform the same function as the surety company, meaning they guarantee the

availability of funds upon the mechanism holder’s default.

2.2.2.2.3 Insurance. Insurance policies are available for a variety of environmental

liabilities. The insurance policy contains provisions that guarantee funds will be available in the event a

claim arises. Like the surety mechanism, insurance policies remain in affect unless the policyholder

defaults on the premium payment or regulators verify closure of the site according to the indicated

guidelines. External and internal insurance are popular mechanisms for insuring closure costs and

insuring against liability claims. Internal insurance, or self-insurance, I discuss below.

2.2.2.3 Internal assurance

2.2.2.3.1 Statement or letter of intent. Typically, those who provide self-, parent, or

corporate guarantees, and those who are federal, state, or local government licensees use this

mechanism. The statement or letter of intent includes the guaranteeing entity’s obligation to provide the

funds when needed. It also denotes the estimated cost of closure and indicates the source of guaranteed

funds to cover the closure costs in the event the company or subsidiary defaults. This mechanism is

especially tenuous when a company is providing a self-guarantee. If the company fails, then the funds

are not available to meet the guarantee. Similarly, parent firms have been known to divest a failing

subsidiary, leaving regulators without recourse for reimbursement [Ringleb and Wiggins (1990) and

MacMinn and Brockett (1995)].

18

2.2.2.3.2 Self-, parent, or corporate guarantee. Passing one of two possible financial

test options is required for self-, parent, grandparent, sibling, or any other corporate guarantee.37

Companies that use the financial tests to provide self-guarantees (e.g., self-bonding or self-insuring)

must:

• provide a letter from a company manager, typically the chief financial officer, attesting to

the company’s compliance with environmental rules,

• provide the firm’s independently audited financial statements for the current fiscal year,

and

• verify the current fiscal financial statements pass the financial test.

The financial tests vary slightly according to the type of hazard. Below is a list of requirements for

passing the financial tests by each type of hazard:

2.2.2.3.2.1 Nuclear power reactor licenses. To provide self-guarantees for nuclear power reactor

licenses, companies must satisfy all the following requirements: Tangible net worth of at least $10 million

or at least 10 times the total current closure cost estimate for all facilities. They must also have at least

90 percent of total assets located in the United States or at least 10 times the current closure cost

estimate for all facilities. For those companies without bond ratings, the company must have a ratio of

cash flow divided by total liabilities greater than 0.15 and a ratio of total liabilities divided by net worth less

than 1.5. For those companies with bond ratings, the most current bond issue rated at A or higher by

Standard and Poor’s or Moody’s. Finally, the company must have at least one class of equity securities.

For those providing parent or other corporate guarantees, all the following must be satisfied. Two

of the following three ratios: a ratio of total liabilities to net worth less than 2.0; a ratio of the sum of net

income plus depreciation, depletion, and amortization to total liabilities greater than 0.1; and a ratio of

current assets to current liabilities greater than 1.5. The company must have net working capital at least

six times the sum of the current closure cost estimate for all facilities. Likewise, tangible net worth must

be at least $10 million and at least six times the sum of the current closure cost estimate for all facilities.

At least 90 percent of total assets located in the United States or at least six times the current closure

cost estimate for all facilities.

Otherwise, the parent or other company must satisfy the following requirements. The most

current bond issue rated at A or higher by Standard and Poor’s or Moody’s. The company must have net

working capital at least six times the sum of the current closure cost estimate for all facilities. Tangible

net worth must be at least $10 million and at least six times the sum of the current closure cost estimate

for all facilities. At least 90 percent of total assets must be located in the United States or at least six

times the current closure cost estimate for all facilities.

2.2.2.3.2.2 Nuclear non-power reactor licenses. Although examination of owners and

operators of non-power reactors is beyond the scope of this study, those entities may obtain parent or

37

A corporate parent must own at least 50 percent of the voting stock of the firm or subsidiary for which it is providing the guarantee. A corporate grandparent owns over 50 percent of a firm through a subsidiary, and a corporate sibling is a firm that shares the same corporate parent.

19

corporate guarantees that may not be from non-profit organizations. To provide parent and other

corporate guarantees for nuclear non-power reactor licenses, companies must satisfy the following

financial test:38

Two of the following three ratios: a ratio of total liabilities to net worth less than 2.0; a ratio of the

sum of net income plus depreciation, depletion, and amortization to total liabilities greater than 0.1; and a

ratio of current assets to current liabilities greater than 1.5. Net working capital must be at least six times

the sum of the current closure cost estimate for all facilities. Tangible net worth must be at least $10

million and at least six times the sum of the current closure cost estimate for all facilities. At least 90

percent of total assets located in the United States or at least six times the current closure cost estimate

for all facilities.

Otherwise, the parent or other company must satisfy the following requirements. For those

companies with bond ratings, the most current bond issue rated at BBB by Standard and Poor’s or Baa

by Moody’s. Net working capital must be at least six times the sum of the current closure cost estimate

for all facilities. Tangible net worth must be at least $10 million and at least six times the sum of the

current closure cost estimate for all facilities. At least 90 percent of total assets located in the United

States or at least six times the current closure cost estimate for all facilities.

According to the regulations, each nuclear power license applicant presents proof of financial

assurance and a decommissioning funding plan. Liability coverage estimates and cost estimates are

available in the decommissioning funding plan. These estimates depend on the type of power used; the

type of waste produced; all costs related to the servicing of the site, equipment, and waste; the number of

reactors at a site; and the number of incidents occurring at the site.39

These cost estimates determine the

amount and the type of financial protection and financial assurance required. “Financial protection” is the

terminology used by the NRC when discussing liability coverage. Financial protection is an additional

requirement beyond the financial assurance amounts and applies to liability claims and legal costs.40

In addition to the financial assurance and liability coverage requirements for owners and

operators, any non-regular activities performed at the site require liability coverage. Non-regular activities

are any activities beyond the normal activities of the site. For example, subcontractors at sites with

functioning nuclear reactors must obtain permits and carry liability coverage. Likewise, sites that use

plutonium or uranium or create fuel must carry additional coverage.

2.2.2.3.2.3 Mining licenses. Bonds were the most popular mechanism for obtaining

assurances for mining operations; however, given the multiple bond defaults, as mentioned in Appendix

A, Table A.1 and by Boyd (2001a), some states have denied mining companies the option to use the

bonding mechanism. A firm may obtain a surety or collateral bond from a third party. The firm may also

38

10 CFR 30 and 10 CFR 30. 39

NRC report NUREG - 1307, "Report on Waste Burial Charges." 10 CFR 30.35 and 10 CFR Part 50.75, and may be found at http://www.access.gpo.gov/nara/cfr/waisidx_03/10cfr30_03.html. 40

The amount of liability coverage is the base amount of liability coverage times the maximum kilowatt power level times the population factor. For more details, see 10 CFR 140.12 Section B.

20

issue a self-bond, or the firm’s parent may do so. In order for a firm to issue a self-bond or for the parent

to provide a bond, the following criteria must be satisfied:41

The firm must be in operation for at least five consecutive years. The current bond issuance

rates at least an A by Standard and Poor's or Moody’s. The firm’s tangible net worth must be at least $10

million. It may satisfy the following ratios: A ratio of total liabilities to net worth of no more than 2.5 and a

ratio of current assets to current liabilities of at least 1.2. Total assets in the United States must total at

least $20 million. The firm’s ratio of total liabilities to net worth should be no more than 2.5, and its ratio of

current assets to current liabilities of at least 1.2.

Similar to the nuclear regulations, mining companies must provide for liability coverage in

addition to financial assurance. Insurance is available to cover the required amounts of $300,000 for

each incident and annual total coverage of $500,000.42

2.2.2.3.2.4 Injection wells: Class I and II. The EPA and state DEP offices regulate five

classes of injection wells. Classes III-IV are not explicitly discussed in this section, as they are directly

related to mining operations, nuclear operations, and operations that have since been banned by the

EPA. The wells discussed in this section are specific to hazardous waste and oil and gas wells.

Applicants providing Class I hazardous waste injection well self-guarantees must satisfy the following

financial test:43

For applicants without bond ratings, two of the following ratios must be satisfied: a ratio of total

liabilities to net worth less than 2.0; a ratio of the sum of net income plus depreciation, depletion, and

amortization to total liabilities greater than 0.1; or a ratio of current assets to current liabilities greater than

1.5. The applicant must have tangible net worth must be at least $10 million and at least six times the

sum of the current closure estimate. The applicant must also have net working capital must be at least

six times the sum of the current closure estimate. At least 90 percent of total assets located in the United

States or at least six times the current closure cost estimate for all facilities.

Alternatively, the applicant may satisfy all the following criteria:

Have a current bond rating of at least BBB by Standard and Poor’s or at least Baa by Moody’s.

The applicant must have net working capital at least six times the sum of the current closure cost

estimate for all facilities. Likewise, tangible net worth must be at least $10 million and at least six times

the sum of the current closure cost estimate for all facilities. At least 90 percent of the applicant’s total

assets located in the United States or at least six times the current closure cost estimate for all facilities.

41

30CFR800.23, and may be found at http://a257.g.akamaitech.net/7/257/2422/14mar20010800/edocket.access.gpo.gov/cfr_2002/julqtr/30cfr800.23.htm. 42

30CFR800.60, and may be found at http://a257.g.akamaitech.net/7/257/2422/14mar20010800/edocket.access.gpo.gov/cfr_2002/julqtr/30cfr800.60.htm. 43

40CFR144, and may be found at http://a257.g.akamaitech.net/7/257/2422/14mar20010800/edocket.access.gpo.gov/cfr_2002/julqtr/40cfr144.63.htm.

21

Applicants providing Class II oil and gas related injection well self- or parent guarantees must

satisfy the following financial test:44

The firm has been in operation for at least five consecutive years and has at least two sites, with

one site having five functional years. The firm has a respectable history of cleanup. All of the following

ratios must be satisfied: A ratio of total liabilities to net worth less than 2.0, net income plus depreciation,

depletion, and amortization to total liabilities must be greater than 0.1, and the current ratio must be

greater than 1.5. Likewise, current liabilities to net worth must be less than 1.0, current assets minus

current liabilities to total assets must be greater than –0.10, and revenues minus expenses greater than

zero. Have tangible net worth of at least $1 million.

Alternatively, the applicant may satisfy the following criteria:

The firm has been in operation for at least five consecutive years and has at least two sites, with

one site having five functional years. The firm has a respectable history of cleanup. The most current

bond issue rated at least BBB by Standard and Poor’s or at least Baa by Moody’s. Have tangible net

worth of at least $1 million.

For injection wells, the financial assurance requirements vary as the type of injection well varies.

Whereas all classes require proof of coverage for plugging and abandonment costs, others require more

financial assurance to protect against surface, soil, and water contamination. For example, in the state of

Colorado, an owner of a Class II oil and gas injection well may obtain “blanket” coverage for $25,000 for

wells in irrigated and dry land areas. Likewise, the liability requirements differ according to the proximity

to highly populated areas. For example, in Colorado, the general liability coverage requirement is

$500,000 per occurrence for low populated areas and $1 million of coverage for highly populated areas.45

2.2.2.3.2.5 MSWLF and TSDF. Those entities providing self-, parent, or other corporate

guarantees for solid waste landfills and waste treatment, storage, and disposal facilities must satisfy the

following requirements:46

The most current bond issue rated at least BBB by Standard and Poor’s or at least Baa by

Moody’s; or one of the following two ratios: a ratio of total liabilities to net worth less than 2.0 or a ratio of

the sum of net income plus depreciation, depletion, and amortization, minus $10 million, to total liabilities

greater than 0.1. Net working capital must be at least six times the sum of the current closure cost

estimate for all facilities plus $10 million. They must have tangible net worth of at least $10 million and

greater than the sum of the current closure cost estimate plus $10 million for all facilities. At least 90

percent of total assets located in the United States or at least six times the current closure cost estimate

for all facilities.

44

http://www.epa.gov/R5water/uic/forms/ffrdooc2.pdf. 45

40CFR144.63, and may be found at http://a257.g.akamaitech.net/7/257/2422/14mar20010800/edocket.access.gpo.gov/cfr_2002/julqtr/40cfr144.63.htm.Colorado’s requirements are at http://oil-gas.State.co.us/RR%20Asps/700-ser.htm. 46

40CFR258.74, and may be found at http://a257.g.akamaitech.net/7/257/2422/14mar20010800/edocket.access.gpo.gov/cfr_2002/julqtr/40cfr258.74.htm.

22

Local government standards vary slightly, as do standards for landfills that manage toxic waste

and are on the Superfund list. For MSWLFs and TSDFs, liability coverage varies for the type of accident

that may occur on the site. Accident types are dependent upon their longevity. For those accidents that

are spontaneous, the minimum liability coverage requirement is $1 million per event with a $3 million total

for all events. Accidents that occur over the long run, such as leaks that cause contamination and health

issues, the minimum liability coverage is $3 million per event with a $6 million total for all events. The

states do allow for the accumulation of types of liability. This allowance in no way diminishes the

responsibility of the PRP.

2.2.2.3.2.6 Underground storage tanks. Those entities providing self-, parent, or other

corporate guarantees for underground storage tanks must satisfy the following requirements:

The entities must have tangible net worth of at least $10 million and at least 10 times the total of

all costs related to the sum of the underground storage tanks. Likewise, they must have at least a rating

of 4A or 5A from Dun and Bradstreet. In addition, the entities must receive an unqualified opinion from an

independent auditor.

Alternatively, the following criteria may be satisfied:

Have proof of the required liability coverage. The applicant must receive an unqualified opinion

from an independent auditor. These financial assurance requirements may appear slim compared to the

other hazards. However, the underground storage tanks are also included in the assurance requirements

for MSWLF and TSDF. The requirements vary depending on the number of tanks and net worth. Many

classifications of the types and number of tanks exist, thus giving the owners and operators the flexibility

to either increase or decrease the numbers of tanks on the site based on the affordability of carrying

them. Coverage can range from $500,000 to as much as $2 million.

2.2.2.3.2.7 Transportation of hazardous materials in water bound vessels. Vessels

transporting hazardous materials across bodies of water are responsible under OPA and CERCLA.47

The following assurance is required based upon the vessel type and tonnage: The vessel operator must

have working capital at least equal to the total carrying estimates. Net worth should be at least 10 times

the total carrying estimates. Total assets must be in the U.S. and are greater than global liabilities.

Further, certain requirements exist as to the number of guarantors for this industry: No more than four

insurers may simultaneously provide insurance for the vessel or provide a financial warranty. No more

than ten sureties may simultaneously provide a surety bond.

OPA and CERCLA each dictate liability coverage requirements. Under OPA, liability coverage

for water traveling vessels spans $500,000 to $10 million, depending on the tank status (non-tank versus

tank) and tonnage, or $600 to $1,200 per gross ton, depending on the tank status. Under CERCLA, the

liability coverage spans $500,000 to $5 million, depending on the type of material carried, or $300 per

gross ton.

Multiple insurers for an aquatic vessel are acceptable if coverage is vertical as opposed to

horizontal. Horizontal coverage implies layering coverage for the liability, meaning insurers share some

47

33 CFR 138, 33 CFR Part 154, and Federal Register, Volume 61, March 7, 1996, p. 9274.

23

portions. This form is not appropriate, as the overlapping portion is often the cause of litigation as each

insurer argues its portion is the lesser. Vertical coverage implies dividing liabilities by percentage or by

type between insurers, according to each insurer’s ability and willingness to cover the liability. Because

potentially responsible parties may be subject to joint and several liability, this means that the insurers are

likewise subject. They may either be responsible for their agreed upon percentages or for the entire

liability.

2.2.2.3.3 Multiple mechanisms for multiple facilities. Using a combination of certain

allowable mechanisms enables owners and operators to guarantee all necessary funds. If an owner

operates more than one site, the mechanism(s) must cover the sum of needed funds for all the sites.

Mechanisms are not transferable or usable for multiple sites. The following examples are acceptable

combinations of mechanisms:

• Have a surety mechanism (bond or letter of credit) with a trust fund or payment

guarantee.

• Have a single trust fund with any other mechanism.

• Have multiple trust funds.

Self- and parent guarantees are individual mechanisms. The may not be combined together or with any

other mechanism or combination of mechanisms. Parent companies combine the financial statements of

all subsidiaries with their own financial statements. Therefore referencing the same financial information

repeatedly, resulting in an overestimation of viability.

2.3 Reasonableness and Adequacy of Current Requirements

Often it is difficult for the EPA to hold polluters responsible and force internalizing environmental

costs because firms ultimately have the option to abandon the site and attempt to have the liability

discharged in bankruptcy court. If the EPA’s requirements are too difficult to meet and maintain, then the

EPA runs the risk of the polluter abandoning its responsibility and passing the financial burden and

contamination on to the government, which in turn passes these costs on to the public. The EPA has

struggled since inception to maintain the delicate balance of imposing strict regulations with provisions

flexible enough to keep the polluter from completely abandoning its responsibility. These flexible

provisions are evident in the above-mentioned financial assurance mechanisms, the credit

enhancements, and the economic enforcement models from which the EPA’s standards were developed.

In each of these areas, the EPA provides means for a potentially responsible party to pay at least some

portion of its responsibility if it cannot fund the entire cost. In light of the bankruptcy law versus

environmental law debate, the EPA cannot afford to take an all-or-nothing stance. The EPA must

consider and accept something-instead-of-nothing. It is preferable to have a potentially responsible party

voluntarily contribute than risk the bankruptcy court discharging the entire liability.

24

Furthermore, the EPA is open to revising regulations when the current standards are in need of

revision.48

The high assurance requirements provide a barrier to entry for new firms with potentially

hazardous processes. However, the EPA’s purpose is not to punish those firms that are already in

existence but to help them take responsibility for their actions. The assurance requirements must be

harsh enough to deter yet flexible enough to keep the polluting party active in the process.

To facilitate the implementation of the EPA's initiatives and to help responsible parties to take as

much responsibility as possible, the EPA provides a variety of methods. The EPA takes great effort to

understand and consider its federal needs and the needs of the businesses and surrounding

communities. The EPA attempts to find a balance between all participating parties for the overall welfare

of everyone involved. Most of the concerns of the businesses and surrounding communities are financial

in nature. The ability of a firm to pay a liability depends upon the size of the firm and the availability of the

necessary resources by both the firm and the state. The EPA attempts to provide compromises for such

situations without reducing the need for compliance.

Larger businesses often have deeper pockets than smaller ones and thus are more likely to meet

the EPA’s criteria without hardship. Smaller businesses often have difficulty meeting the EPA’s criteria

and are often in need of additional guarantees for fulfillment of environmental obligations. Other credit

enhancements available are grants and guaranteed loans, bond banks, state revolving funds, bond pools,

interest rate subsidies, senior and subordinate debt structuring, cross-collateralization, small business

administration surety bond programs, and tax incentive programs. Although these credit enhancements

may be somewhat restrictive and have limitations, they provide opportunities for compliance when small

businesses could not otherwise meet the minimum requirements.

Several sources are available for small businesses to acquire credit enhancements thus enabling

small businesses to meet the financial assurance requirements through external parties. These external

parties provide the necessary financial assistance the smaller firms need. For example, the following

programs aid small businesses by finding or providing financial assistance: the Environmental Finance

Program, the Office of Small Business Ombudsman, and the National Small Business Financial

Assistance Workgroup.49

48

In 1996, the EPA commissioned an intensive study performed by the ICF Corporation to analyze the financial tests for Subtitles C and D specifically for TSDFs and MSWLFs 49

Furthermore, the EPA issues many environmental program grants, loans, and other incentives to states as incentives for the states to implement the EPA’s initiatives and programs. Although federal law requires these states to comply with environmental laws, often the states may not have the funds, personnel, or sites necessary to implement the programs on their own. Thus, the EPA provides grants, can require state contributions, or provide for reimbursement to states after implementing the EPA’s initiatives [Environmental Program Grants: State, Interstate, and Local Agencies. The Federal Register, Volume 66, Number 6, January 9, 2001, p. 1725-1748.] Examples of such grants and loans are the Pollution Prevention Program, the Partnership Program, and. the Department of Housing and Urban Development (HUD). In particular, HUD provides community redevelopment loans specifically designed to have lower interest rates than regular loans for the redevelopment of brownfields. Tax incentive programs offer a variety of tax credits, specific to the state of residence, for pollution reduction. These tax credits can be in the form of reduced property or sales taxes, which are helpful to current and future corporate residents.

25

Given the dynamic state of the market, testing the reasonableness and the adequacy of the EPA

requirements is necessary. For example, with the current changes in accounting standards, it is

necessary to examine if the effectiveness of tests still holds and if any changes exist in the types and

quantity of firms experiencing financial difficulty. Therefore, the EPA standards warrant revisiting. Given

hindsight, researchers can say what “should have been” instituted to maximize the internalization of the

negative externalities by responsible parties. Many companies in hazardous lines of business existed

prior to certain EPA regulations. The EPA implemented regulations that may no longer be adequate

given the current market. Likewise, the EPA does not have complete foresight to determine future

regulations. However, with changing accounting regulations requiring greater transparency, perhaps we

can see how the current requirements are holding up in the midst of the changing scenery.

2.4 Problems and Issues.

Liability measurement and compliance enforcement are the foremost problems. Liability

measurement is dynamic because every conceivable potential hazard and claim is unknown. Because

we have no definitive form of estimation, how can we determine if the polluter can bear the financial

responsibility? Estimation, compliance, and monitoring can be costly, time consuming, and difficult.

Coupled with the inconsistent rulings from judges because of the unresolved ambiguity between

bankruptcy law and environmental law, polluters often emerge free from responsibility. The tendency for

judges to rule pro-bankruptcy externalizes the liabilities to the taxpayers. The tide is slowly changing in

favor of environmental accountability.

Several studies on liability rules and assurance mechanisms provide evidence that complete

compliance may never be achieved, regardless of the implementation of environmental laws and

attempts to force compliance [Boyd (1993, 2001a, 2001b), Shavell (1982, 1984), Gerard (2000), Ferreira

and Suslick (2001), Larson (1996), Menell (1991)]. These studies show that individual risk and shared

risk remain with the use of financial assurance mechanisms. Specifically, with insurance and bonds, the

amounts are not adequate to fund the liability claims. Instead, the firms are finding it more cost effective

to relax their level of caution, thus allowing claims against the policy, and to forfeit the bonds instead of

providing funds for the entire liability. Several of the studies mentioned above observe that the

mechanisms are devoid of controls for moral hazard and the likelihood of default.

Contrary to these studies, others find insurance as an adequate, low-cost mechanism for

enforcing compliance, as those that benefit most from using the legal system are the lawyers [Freeman

and Kunreuther (1996), Heyes (1998), Katzman (1988)]. Furthermore, states have a greater chance of

recovering the costs under the use of insurance as opposed to the law. Because the liability is joint and

several, the states may receive cost recovery from a variety of insurance providers without the extra legal

transaction costs. However, this is assuming the insurers pay the claims without objection. Freeman and

Kunreuther (1996) compare studies conducted by the RAND Institute for Civil Justice in 1983, 1991, and

1993. They report the money available for cleanup after investigation, litigation, and other transaction

costs is greater for those with insurance policies than those without insurance policies. In all the years,

26

they consistently find that for those that lack insurance policies, the money paid for environmental costs is

40 percent of the award money. For those with insurance policies, approximately 70 to 80 percent of the

funds are available for environmental costs. The differential amount is due the plaintiffs and their

attorneys.

Research shows that to date, the laws and mechanisms cannot force the polluter to pay, some

researchers propose the use of a capital market mechanism to aid in compliance [Van ‘T Veld (1997),

Segerson (1997)]. Van ‘T Veld (1997) provides an innovative theory for the “judgment proof problem.”

The judgment proof problem coincides with bankruptcy as it implies firms cannot be held liable for

environmental liabilities greater than a firm’s net worth, and under bankruptcy protection, these liabilities

can be discharged. Given this protection, a firm is unlikely to exercise caution and risk reduction for

environmental liabilities while conducting its line of business. Van ‘T Veld suggests firms be subjected to

market mechanisms in conjunction with the financial assurance mechanisms to improve their exercise of

caution. The purpose of a market mechanism is to increase scrutiny by both regulators and the market,

thus increasing transparency. This mechanism should likewise increase the firm’s caution level and

compliance. Some market mechanisms include a limit on firm size and requiring customers of the

environmentally laden firms to share in the liability.

Similarly, Segerson (1997) focuses on an extension of the latter mechanism with respect to real

estate. Because real estate is a commodity, the contamination present in the land is a factor in its current

and future value. If the land is highly contaminated, then the land will not sell, or the new owner will

assume the environmental responsibility that comes with the land. However, a drawback of market

mechanism is that it only applies to those that function regularly in the market. This means that the one

time participant is less likely to comply whereas the repeat participant will want to comply with market

mechanisms. It appears that a combination of both market and financial assurance mechanisms are

necessary in order to accommodate all types of market participants.

Delving further into the legal debate, the research [Riering (1992), Hill (1998), Boyd (2001a)]

addresses the inconsistencies between legal judgments concerning bankruptcy law and environmental

law. The research provides an explanation as to the purpose behind the rulings, taking into consideration

each law and illustrating the inconsistencies with anecdotal evidence.50

The core of the discussion is the

lack of legislator foresight to consider the intersection of the two laws. Because congressional intent is

unclear, the courts must interpret it for each lawsuit. Based on the precedent of bankruptcy law as

recorded in Article I of the U.S. Constitution, many judges rule accordingly. However, environmental

laws, as a recent development, have evolved because of environmental disasters and an increased

awareness for a safe, clean environment.

50

In the case of Penn Terra Limited v. Department of Environmental Resources, Commonwealth of Pennsylvania, 733 F.2d 267 (3d. Cir. 1984), the court ruled pro-environmental law. In the case of Ohio v. Kovacs, 717 F.2d 984 (6th. Cir. 1983), the court ruled pro-bankruptcy law. In the case of The United States v. Whiz co, Inc., 841 F.2d 147 (6th. Cir. 1988), the court compromised but the ruling can be interpreted as more pro-bankruptcy law than pro-environmental law. In the case of the United States v. LTV Corporation (In re Chateaugay), 944 F.2d 997 (2d. Cir. 1991), the court ruled with a compromise that appears to more equitable between the two laws.

27

The Bankruptcy Reform Act (BRA) of 1979 enables corporations in distress to reorganize as

opposed to liquidate. The BRA directly competes with RCRA and CERCLA. Whereas the BRA provides

distressed firms with a strategic business decision to protect cash flows and reorganize, RCRA and

CERCLA attempt to lay claim to these cash flows for the liabilities the firms are attempting to discharge

[Depree and Jude (1995)].51

Congress attempted to ease this conflict by further specifying environmental

debts that were not dischargeable debts. However, given various interpretations, some environmental

obligations are not included as non-dischargeable debts. The key is in the classification of the liability. If

the liability is a non-monetary judgment, then the environmental claims are not dischargeable. It then

comes under the jurisdiction of “police and regulatory” powers. However, if the environmental liability is a

monetary judgment, then the claim is dischargeable and protection provided under the U.S. bankruptcy

code.52

Thus, polluters can avoid incurring environmental liability costs, regardless of the cause of

bankruptcy. Recovery of funds is difficult, especially when parent firms can spin off their liability-laden

subsidiary or the subsidiary can transfer assets back to the parent prior to filing for bankruptcy [Ringleb

and Wiggins (1990), MacMinn and Brockett (1995)]. Because court rulings are not consistent towards

one law over the other, it may be in a firm’s best interest to file for bankruptcy protection if a high

probability exists that the judge will rule in favor of the precedence set by bankruptcy law.

A characteristic that follows the firms involved in environmentally related areas or those firms that

have a history of environmental claims against them is risk overhang. Gron and Winton (2001) describe

risk overhang as the risk that remains with a firm that influences future business. Whereas their study

focuses on the financial services industry (non-life insurance companies) and the “credit crunches”

spanning the mid to late 1980s and into the early 1990s, the same holds true for firms involved with

environmental liabilities, both those providing insurance and those needing insurance. Relating their

discussion to firms that incur environmental liabilities, the firms experiencing environmental liabilities may

experience a decline in reputation and a decline in future business opportunities, depending upon the

liability. For example, when the Exxon Valdez accident occurred, Exxon Corporation reacted immediately

to the incident and instituted cleanup efforts in less than 24 hours. However, Pennsylvania DEP was not

that fortunate when LTV Steel abandoned several mines, leaving the acid mine drainage to the state.

Because this liability lingers for both the underwriter and for the firm committing the liability, minimizing

risk overhang is a priority.

Complete compliance may appear hopeless in this imperfect world; nevertheless, improved

compliance is the focus. If the EPA must accept some rather than none from responsible parties, then

researchers should attempt to study incremental steps for improving compliance. If future compliance

improves, regardless if the mechanism is market or non-market based, then the burden borne by

taxpayers is reduced and environmental costs are mitigated, hence the purpose of this dissertation.

51

Liquidation and reorganization, the supporting under-wire framework of the BRA, lifts and separates the firms from the sagging weight of environmental debts. The bankruptcy law acts as securing straps, maintaining the firm in its current upright position in the midst of legal jostling. It buffers the firm by providing padding against claimants and in turn allows environmental obligations to become dischargeable claims.

28

Not until the laws are more concrete, determination of responsibility is more transparent,

enforcement is faster and easier, and responsible parties are accountable, can the EPA adequately fulfill

its intentions. In the meantime, increased monitoring of the responsible parties is necessary, along with

regular review of the standards ensuring compliance. Hence, the goal of this study is to look at the

standards because they are often lost in the more glamorous debates of the legal and liability issues.

52

11 U.S.C. § 362 (a).

29

CHAPTER 3 ANALYSIS OF CURRENT FINANCIAL ASSURANCE GUIDELINES

The purpose of financial tests is two-fold: First, they offer assurance to the public that funds may

be available to satisfy a firm’s financial obligation to mitigate damage to the environment from business

activities. Second, they provide businesses with an affordable mechanism to fulfill the assurances

required by law.

The natural concern that follows is what happens when firms are no longer able to pass the

financial test criteria. Because the financial tests do not guarantee the assured funds, do the financial

tests provide the state regulatory agencies with early enough detection of a firm’s waning viability to

obtain an alternate financial assurance mechanism? Are these financial tests adequate to ensure most

companies will be able to satisfy their potential obligations? Anecdotal evidence, as described in Table

A.1., suggests the financial tests may not be sufficient to protect the interests of the public.53

In this

chapter, I examine the financial tests and assess their effectiveness in identifying companies that

eventually go bankrupt.

3.1 Review of the EPA’s Standards

The EPA uses a variety of detection methods and enforcement models to analyze a firm’s

financial situation and other issues that may influence compliance and enforcement. These various

methods and models are often used together to provide the EPA with a more comprehensive view of a

firm. The ultimate goal in using these methods and models is to protect the environment, taxpayers, and

good corporate citizens. Because examination of the EPA’s compliance and enforcement models is

beyond the scope of this dissertation, I choose to focus on EPA’s financial tests. These financial tests

provide financial requirements and guidance for permit acquisition and renewal.54

Any firm wanting to obtain or update a current working permit from a state’s environmental

regulatory agency may subject itself to the EPA’s financial tests. These tests require a firm to meet and

maintain a minimum level of financial viability to acquire the needed permits.

53

For example, the State of Pennsylvania no longer accepts performance bonds for assurance from the mining industry in light of the multiple mining forfeitures. Likewise, the State of Florida is currently restructuring the financial tests given the abandonment of phosphogypsum stacks by Mulberry Phosphates. 54

In 1982, the EPA implemented the financial standards and recorded them in 40 CFR 264.143.

30

A firm may use either financial test #1 or financial test #2, provided the firm meets the necessary

criteria for that test [40 CFR 264.143]. The requirements for financial tests are stringent because the firm

providing the guarantee is not required to have additional backers or monitors other than the

corresponding regulatory agency. The financial tests are as follows:55

Financial Test #1

o Two of the following three ratios: a ratio of total liabilities to net worth less than 2.0; a ratio of

the sum of net income plus depreciation, depletion, and amortization to total liabilities greater

than 0.1; and a ratio of current assets to current liabilities greater than 1.5.

o Have tangible net worth of at least $10 million.

o Have tangible net worth and net working capital each at least six times the current closure

cost estimate for all facilities.

o At least 90 percent of the total assets located in the U.S. or at least six times the current

closure cost estimate for the total of all facilities.

Financial Test #2

o Have tangible net worth of at least $10 million.

o Have tangible net worth of at least six times the current closure cost estimate for all facilities.

o At least 90 percent of the total assets located in the U.S. or at least six times the current

closure cost estimate for the total of all facilities.

o Have a current rating of at least BBB by Standard and Poor’s or at least Baa by Moody’s.

The EPA uses the above requirements as a benchmark for determining passing and failing firms.

If a firm meets all the criteria for either of the two tests, then it passes the test. If it fails any one of the

criteria, then it fails the test and must obtain an alternate form of financial assurance in the form of a third

party mechanism. What the EPA does not have is a set benchmark for the number of passing and failing

firms. Clearly, to maximize environmental and taxpayer safety, the optimal situation would be for all

financially viable firms to pass the tests and for all firms that lack financial viability to fail the tests early on

so they may obtain an alternate financial assurance mechanism. However, this situation does not always

happen. For firms with waning approaching bankruptcy, early detection is not necessarily financially

beneficial for them, and they may attempt to prolong detection. For example, early detection of a good

corporate citizen may cause this firm to seek alternate financial mechanisms it may not be able to afford

due to the increase in default risk. Likewise, the third party providing this alternate mechanism may be

reluctant to do so [Eanes and Price (2000)]. Early detection of a bad corporate citizen may cause this

firm to abandon the liability sooner rather than later [Melcer (2003)].

55

Some hazards require a minimum operation requirement in order to apply internal assurance standards. For example, a mining applicant must be operational for at least five years. The applicant can be a separate entity or in the form of a joint venture and must have fixed assets in the United States worth at least $20 million, have a ratio of total liabilities to net worth of no more than 2.5, and have a ratio of current assets to current liabilities of at least 1.2.

31

The EPA commissioned consultants to perform analyses to assess the adequacy of the financial

tests in detecting default.56

The EPA determined the financial tests offer an acceptable balance between

providing an affordable financial assurance mechanism and adequately screening financial viability.

According to the EPA’s assessment, the financial tests provide tolerable assurance at “low public and

private costs."57

In the analysis, the total risk for default and non-recovery by any bankrupt firm,

regardless of net worth or industry, is only 2.274 percent of the entire bankrupt group. This number

implies the failure and misclassification risk of a bankrupt firm completely defaulting on its environmental

obligation is very small. The EPA assumes they are able to receive some cost recovery from the

offending firm.

Consultants conducted the above analyses in 1981 and 1987 and only for landfills and disposal

treatment facilities. The assumptions for the 1987 analysis were an improvement over the assumptions

for the 1981 analysis in that the 1987 analysis applied conservative assumptions about default and

bankruptcy rates to admittedly incomplete data. Based upon admittedly incomplete data, the EPA

concluded few firms approach default, and these firms may still obtain alternative assurance

mechanisms. Presumably, the restitution of some funds is available from firms that survive bankruptcy.

The EPA concludes the financial tests continue to provide adequate financial assurance with little default

risk. This means the EPA applies a potentially outdated conclusion based upon incomplete data, a small

sample from one hazard, and assumes a fund restitution rate of at least 20 percent to the current

situation, without regard for the ever-present option to abandon.

One interpretation of the EPA’s benchmark is that it provides a test that does not hinder viable

companies but requires firms approaching bankruptcy to find more binding alternative financial assurance

mechanisms. In other words, the financial tests attempt to maximize productivity and social benefit and

to minimize the social costs of default. The EPA does not give specific measurements of what constitutes

an unacceptable level of default. Instead, the EPA relies on the guidance provided by the financial test

criteria, prior studies, and the annual analyses of the financial statements by regulators. If a firm passes

the tests, it receives its permit; if the firm fails, it fails to receive its permit.

In recent years, the conclusions from the 1987 appear to conflict with anecdotal evidence and the

ever-present option to abandon. The EPA’s analysis is almost 17 years old and it may be beneficial to

conduct a study using current default rates and more complete firm data and with other hazards. The

analysis should likewise consider a firm’s option to abandon the environmental liability. Because the

option to abandon is a viable choice for a firm, there is no clear-cut way to control or account for this in

my analysis. Abandonment is an ever-present option and the financial tests do not to assess the

probability of exercising this option. However, financial analysts can value future cash flows by applying

probabilities to those cash flows based on the potential state of the economy many years in the future.

56

Subtitle C and D Corporate Financial Test Issue Paper: Performance of the financial test as a predictor of bankruptcy, April 30, 1996. ICF Consulting Group performed the analyses for the EPA. ICF’s report entitled Analysis of Subtitle C and D Financial Tests has multiple sections disseminated from July 14, 1995, to December 9, 1997.

32

Similarly, the option to abandon may be valued as a real option. A firm may always attempt to exercise

its option to abandon. However, it is up to the determination of the court if the option is exercisable in the

form of discharging the liability.

In Table A.1., I provide a brief list of firms that have attempted to exercise the option to abandon.

For these firms, the benefits of abandoning the liability outweighed the costs of obtaining an alternate

financial assurance mechanism. If a firm uses the financial tests to assure the closure costs and related

liabilities and then chooses to file for bankruptcy, then the only recourse the EPA has is to petition that

the debts related to the costs of closure and any other related environmental liability not be

dischargeable. If the firm was required to use another more binding financial assurance mechanism,

such as a trust fund or a bond, then the EPA has means to reclaim the necessary funds. The EPA’s

financial tests are mere promises that a firm will pay the necessary funds when needed. They

demonstrate this by producing financial statements that meet the existing criteria. Therefore, I perform

my analyses under the same assumptions the EPA applies: the responsible party will pay all or at least

some of the environmental obligations.

3.1.1 Variations in standards provided by states. The types of programs available to states

are compliance and incentive-based programs.58

The environmental compliance programs directly

implement the federal regulations at the state level and are mandatory.59

The state incentive-based

programs are voluntary and provide incentives for participating in environmental conservation programs.60

Compliance programs can vary from state to state, and states may impose regulations that are tighter

than what is federally required. In general, however, states are updating their statutes as needed to be

more in line with the EPA’s regulations.61

An interesting facet to the compliance programs is a variance. A variance is a temporary

allowable deviation from the current standards. Usually, the standards are relaxed so a company can

rebound from the issue that caused them to fail the existing criteria in the first place. Companies

requesting variances must apply and provide proof that their need for the temporary relaxation is, indeed,

temporary. The variances cloud the true results of the financial tests because firms may apply for short-

term relaxation to the standards.

57

Subtitle C and D Corporate Financial Test Issue Paper: Performance of the financial test as a predictor of bankruptcy, April 30, 1996. 58

A list of programs is available at http://www.epa.gov/epahome/programs.htm and http://www.epa.gov/epahome/hi-voluntary.htm. 59

Programs based on the environmental laws are at http://www.epa.gov/epahome/laws.htm. For example, the Resource Conservation and Recovery Act of 1976 guides the handling of hazardous waste in every state. 60

Examples of voluntary incentive-based programs are the Green Power Partnership, the WasteWi$e Program, and recycling. 61

Some states may lag considerably in updating and applying new EPA regulations due to the scale of implementation [Lowrance (1992)]. More focus on states taking over the responsibility of day-to-day management of environmental issues, as is evident in the Regulatory Innovations Agreement signed by the EPA and the Environmental Council of the states.

61 Most states are consistent or approaching

consistency with the EPA’s guidelines. Few states are more restrictive in their regulations.

33

Environmental variances complicate the financial test issue. Firms request variances when they

need temporary easements in the current standards. These temporary easements can be justified if the

firm is truly experiencing a temporary downturn or is in a cyclical industry. Other granted easements are

the direct result of political influence and pressure [Eisler (2004), Wright (2004), Walton (2004), Weiss

and Art (1997)]. An area for future research is to find concrete objectives and clear benchmarks to make

absolute comparisons. In my analysis, I cannot account for any variances, as they are numerous and

span the major hazards. Comparing the methods, I can only gauge which method better fits the EPA’s

goal of maximizing productivity and social benefit and minimizing social costs due to default.

3.2 Methods for Determining Viability and Bankruptcy Prediction

3.2.1 Bankruptcy prediction and financial assurance. Although the purpose of this

dissertation is to examine the classification ability, I examine bankruptcy models because those are the

basis of most of the EPA’s methods. Many of the bankruptcy prediction models focus on the prediction of

a firm filing for bankruptcy within a specified timeframe, usually two to five years. Within this window of

opportunity, a firm may rebound or liquidate. These models use financial information to gauge a firm’s

financial viability and to determine the probability a firm may become defunct within the window. These

bankruptcy models foreshadow those who may become unable to fulfill their financial assurance

obligation.62

3.2.2 Going concern. To use the financial tests, a firm is required to have financial

statements audited by an independent auditor. The auditor must provide an opinion better than “adverse”

[40 CFR 264.143 (f) (3) i-iii]. In the next section, I review the importance of the auditor opinion and the

going concern status as related to financial assurance. An auditor opinion is important because it

assures investors and creditors that the financial statements are prepared according to generally

accepted accounting principles (GAAP) and that the statements provide a fair presentation of the firm’s

status [Stice, Stice, and Skousen (2002), page 12]. The auditor opinion is a gauge of a firm’s current

condition, as well as an indicator of its immediate future. Therefore, the opinion helps regulators and

investors determine the overall viability of the firm.

An auditor opinion can range from unqualified, unqualified with clarification, qualified, no opinion,

or adverse. Opinions considered good opinions are unqualified and unqualified with clarification. They

indicate the financial statements are prepared properly and the disclosure of material information.

Occasionally, the auditor may provide further explanation. Qualified opinions signify the presence of an

inconsistency in the disclosure materials. A lack of an opinion implies the firm may no longer be a going

concern, meaning the firm may not be financially viable for the next fiscal year. An adverse opinion

specifies the financial statements were not prepared according to GAAP.

62

Often used interchangeably, the terms “financial distress” and “bankruptcy” are in fact two distinct states of firm health. Financial distress refers to a firm that owes more than the income it generates, and it must seek short-term solutions to its inability to pay its obligations. Bankruptcy refers to a firm that has formally filed for bankruptcy protection under the law.

34

Receiving an adverse opinion or no opinion is a red flag for regulators, as this means the auditor

cannot confirm the appropriateness or consistency of the financial statements and that the firm may

cease as a going concern [40 CFR 264.143 (f) (8)]. Upon the first receipt of this type of an opinion, the

state agency investigates an operator’s viability and may require other forms of financial assurance. The

goal is to act quickly to obtain alternate assurance from the responsible party before the firm defaults

because firms often file for bankruptcy soon after receiving such an opinion [Jones (1996), Tan (2002),

Geiger and Raghunandan (2002)]. Often, firms in approaching bankruptcy do not submit their financials

or produce alternate financial assurance to the auditor or the state environmental regulatory agency in a

timely fashion [Deegan and Rankin (1999)].

Financial statements drive auditor opinions because the auditor is basing the opinion on the

presented financial statements. The auditor opinion should be redundant. However, according to prior

research [McKeown, Mutchler, and Hopwood (1991); Jones (1996); Tan (2002); Geiger and

Raghunandan (2002); Deegan and Rankin (1999); Carcello, Hermanson, and Huss (1995); Weil (2001)],

this is not always the case. Therefore, comparing financial statements and auditor opinions should

provide reasons why the two might coincide or diverge. The auditor opinion offers a quick point of

reference for regulators as they determine if further scrutiny is necessary. However, if regulators rely

solely on the auditor opinion prior to further investigation, they may miss potential problems.

3.2.3 Definitions of bankruptcy and financial distress. The foundation of the financial

assurance standards rests upon a firm’s financial viability. Therefore, the definition of “health” determines

the strength of the foundation. If the criteria for soundness are too lenient and financial standards too

easily met, then the ultimate goal of the standards (i.e., assuring the necessary funds when needed)

becomes irrelevant. Likewise, if the definition is too strict, then an existing firm may find it more

convenient and cost effective to exercise its option to abandon. The appropriate definition of viability can

help the financial standards in determining the financial strength of a firm and provide a more equitable

assumption of risk.

In the literature, the definition of “bankruptcy” usually means a firm has filed for bankruptcy.

However, one can define the term “financial distress” in a variety of ways. The lack of a common

definition for “financial distress” is a universal criticism in the bankruptcy prediction and financial distress

literature. Altman (1993) provides a summary of the different terms used for “financial distress” and its

many interpretations.63

For the purpose of this dissertation, I classify the firms as either being bankrupt or

non-bankrupt. I do so because the definition of bankruptcy is clear. Either a firm is bankrupt or it is not.

3.2.4 Methods of bankruptcy prediction

63

Altman’s terms are as follows: economic failure, business failure, technical insolvency, bankrupt insolvency, technical default, legal default, bankruptcy, and legal bankruptcy. Economic failure may refer to any of the following: It may indicate the cost of capital exceeds the average return on investment, a deficit in current investments and such prospects, or the revenues generated do not meet costs. Business failure indicates inadequate business conditions. Technical insolvency is the firm’s inability to fulfill immediate responsibilities. Bankrupt insolvency indicates a firm has a negative net worth. Technical or legal default is the act of defaulting on a creditor. Formal default is the act of defaulting that

35

3.2.4.1 Multivariate analysis: Z-Score, multiple discriminant analysis (MDA). Bankruptcy

prediction had humble univariate beginnings.64

Its extension into the multivariate analysis using multiple

financial criteria simultaneously is extremely useful because it provides a multidimensional picture of firm

viability. Using only one ratio at a time for analysis provides a one-dimensional view and does not fully

explain a firm’s health. Altman (1968) expands Beaver’s work using multiple discriminant analysis (MDA)

to develop the Z-Score model to estimate a firm’s probability of bankruptcy. As the Z-Score increases,

the probability of bankruptcy decreases.

According to Altman (1968, 1993), his Z-Score model has an overall prediction accuracy in the

year prior to bankruptcy of approximately 95 percent. This statistic means correct classification occurs for

95 percent of all firms, based on their true health in the year prior to insolvency. When investigating the

type of firm, bankrupt and non-bankrupt firms independently, Altman (1968) says his model is 97 percent

accurate for non-bankrupt firms and 94 percent accurate for bankrupt firms. These statistics mean non-

bankrupt firms are correctly classified 97 percent of the time and that bankrupt firms are correctly

classified 94 percent of the time (in the year prior to bankruptcy). Type I error (misclassifying a bankrupt

firm as non-bankrupt) is 6% and type II error (misclassifying a non-bankrupt firm as bankrupt).is 3%.

The predictive accuracy of the model weakens as the number of years prior to bankruptcy

increases. Altman’s model is more accurate than Beaver’s analysis for one and two years prior to

bankruptcy, but Beaver’s model is more consistent for a longer period. Beaver’s model has 80 percent

accuracy for five years prior to bankruptcy, as opposed to Altman’s five-year model that has 36 percent

accuracy [Altman (1993)]. Other researchers expand Altman’s work by applying his Z-Score model to

various industries, spanning varying intervals of time, comparing his model with others, and applying it to

international companies and industries [Bhargava, Dubelaar, and Scott (1997), Taffler (1982)].

A few variations of the original Altman model exist, including the Altman’s Z-Score for private

firms, ZETA analysis, applications to specific industries, and applications to specific countries. For all the

variables, it is more desirable for a firm to have high ratios to drive the Z-Score higher. The original

Altman model is as follows [Altman (1968), p. 594.]:

Z = 0.012X1 + 0.014X2 + 0.033X3 + 0.0006X4 + 0.999X5,

where

leads to bankruptcy. Chapter 11 bankruptcy is corporate reorganization. Chapter 7 bankruptcy is liquidation. 64

Univariate ratio analysis using accounting data conveys information about a firm’s financial health. Because the technique involves examining one factor at a time, its applicability is relevant to the improvement of the individual criteria of the financial standards. Similarly, it is useful to examine several financial measures in conjunction with each other in the prediction of distress. Wilcox (1971) contends no theoretical basis exists to explain the use of some accounting variables over others. The most significant ratios out of the multitude tested have become the benchmarks used in current research [Beaver (1966), Wilcox (1971), Altman (1968), Ohlson (1980), and Zmijewski (1984)]. This empirical research is the basis for theoretical development that has increased in complexity since its inception [Wilcox (1971)]. Prior to the development of the theory, the data and the results drove the hypotheses, as opposed to the hypotheses driving the data analysis, as is often the case in new areas of research [Scott (1981), Sheppard (1994)].

36

Z = the Z-Score firm health indicator,

X1 = net working capital/total assets,

X2 = retained earnings/total assets,

X3 = earnings before interest and taxes/total assets,

X4 = market value of equity/book value of liabilities, and

X5 = sales/total assets.

X1 is a measure of firm liquidity. Firms in distress generally have liquidity problems. Net working

capital is defined as current assets minus current liabilities. The liquidity issue arises when the current

liabilities begin to outweigh the current assets. When divided by a firm’s total assets, the measure

provides a percentage of the firm’s total assets that are liquid. A higher ratio increases the overall Z-

Score and indicates a healthier firm.

X2 is a measure of the reinvested earnings in a firm. The greater the reinvested earnings, the

more profits the firm has at its disposal. When divided by total assets, the ratio provides the percentage

of accumulated earnings reinvested in the firm with respect to the firm’s total assets. X3 is a measure of

how effective a firm’s operations and the use of its assets. In other words, it is a relative measure of the

percentage of operating income with respect to the firm’s assets.

X4 is the reciprocal of the debt to equity ratio. It measures the market’s value of the equity of the

firm as a percentage of the firm’s liabilities. This measure reflects the ability of the firm’s assets to

manage the firm’s debts, meaning if the ratio is greater than or equal to one, then enough assets exist to

at least meet or exceed the debts. If the ratio is less than one, then the debts exceed the assets. X5 is a

measure of the capacity of a firm’s assets to create sales over a specific period. This measure illustrates

the amount of goods and services sold with respect to the firm’s assets. Altman classifies firms with Z-

Scores below 1.81 as bankrupt, between 1.81 and 2.67 as inconclusive, and above 2.67 as healthy.

The Z-Score model for private firms, adapted from Altman’s original model, replaces the market

value of equity in the fourth variable with net worth. Thus, X4 is the measure of net worth to total liabilities.

The model is:

Z’=0.717X1 + 0.847X2 + 3.107X3 + 0.420X4 + 0.998X5.

Altman classifies firms with Z-Scores less than 1.23 as bankrupt, between 1.23 and 2.90 as inconclusive,

and above 2.90 as healthy. Type I error is approximately 9 percent, and type II error is approximately 3

percent. In comparing the two Altman Z-Score models, private firms have a wider area of

inconclusiveness, but the error rates of the two models are similar.65

Several researchers find the original Altman Z-Score model lacks predictability because the

model needs updating [Grice and Ingram (2001); Begley, Ming, and Watts (1996); Boritz, Kennedy, and

Sun (2003); Russ, Peffley, and Greenfield (2004)]. Researchers cite many reasons for the loss of

65

Altman’s ZETA analysis is an updated model based on market changes since the prior models. The ZETA model addresses some of the criticisms from the past. The ZETA includes firm size, recent data over a longer time span, firms from other industries, and adjustments for changes in financial reporting standards. The ZETA has classification accuracy of 90 percent within one year of bankruptcy and

37

predictability, such as the lack of the model’s coefficients to capture the current market and its dynamic

qualities. Other reasons include the small sample size only representing firms within the manufacturing

industry limits the applicability of the model and the generalizability of the results.

Altman also assumed the firms in the sample had equal prior probabilities for bankruptcy.

Assuming equality biases the results. Equal probabilities are not necessarily representative of the

percent of business failures. Equal probabilities tend to understate type I errors because it assigns

bankrupt firms with lower probabilities of failure when they may actually be higher. Likewise, type II

errors may be overstated because higher probabilities of failure given to non-bankrupt firms when they

might actually be lower

3.2.5 Summary of bankruptcy prediction models. Distress and bankruptcy prediction

models are numerous and increasingly sophisticated. However, they cannot necessarily tell us which

firm will go bankrupt. They can give identifying characteristics. Although just because a characteristic

warns of impending distress, does not mean bankruptcy will occur, because not all distressed firms go

bankrupt. For example, if a firm can manage its gloomy characteristics, then deterioration may slow or

stop. The key is to use the warning characteristics to increase regulator scrutiny to avoid the potential

abandonment of environmental obligations.

Morris (1998) suggests these models do not convey more information than what the market

already knows, implying an underlying assumption of all the models is that all information is accurate and

fully disclosed. However, given recent disclosure scandals, the inputs into the model are not completely

accurate, resulting in the model yielding incorrect results [The Ohio Law Letter, October 2002, see also

Chapter 1 footnote 12]. The difficulty level for applying these models varies, and the most cost-effective

method is to use the simplest model that gives the most accurate predictability percentages. For the

purpose of this dissertation, I apply the simplest models with the most accurate historical percentages:

Altman’s Z-Score Model for private firms and Altman’s Z-Score Model for publicly traded firms. I compare

them with the EPA’s financial tests to determine if the current EPA financial tests are as good as the

Altman benchmarks.

3.3 Data

I select a sample of publicly traded U.S. companies from Standard and Poor’s Compustat

Primary, Secondary, and Tertiary; Annual Full Coverage; and Research Files for the fiscal years 1985-

1999. I exclude foreign firms, the financial services industry, and subsidiaries. I also remove firms whose

reason for delisting is not available. I exclude financial firms because their financial ratios are very

different from those of firms in other industries and because they are not as likely to incur the same

magnitude of environmental liabilities as other industries.66

I remove subsidiaries to avoid double

accuracy of 70 percent within five years. The ZETA model is beyond the scope of this dissertation, as certain components of the model are proprietary. 66

I am not considering financial firms that underwrite environmental liabilities as that examination is beyond the scope of this dissertation. Instead, I consider environmental liabilities directly related to closure costs and all liabilities related to pre- and post-closure. Every firm has environmental liabilities,

38

counting financial information because the parent corporation’s information includes the subsidiary’s

information. I also remove firms that do not have the magnitude of environmental liabilities that would

require them to obtain financial assurance. For example, I remove service firms that provide legal and

administrative services but I retain those firms that provide environmentally related services. I remove

firms in their start up years. Those observations downwardly bias the non-bankrupt data because of high

start up costs.

I summarize the initial sample in Figure 3.1. I classify firms as bankrupt and non-bankrupt from

1985-1999. If the firm files for bankruptcy or is in liquidation or reorganization, then it is included in the

bankrupt sub-sample. A company is bankrupt in the year it files for Chapter 11 or Chapter 7, and the firm

must have at least two years of prior data to be included in the sample. I use the data for the fiscal year

prior to bankruptcy for detection purposes.

The non-bankrupt sub-sample includes all firms that have at least two years worth of data and

are not bankrupt. The non-bankrupt sub-sample also includes the healthy years from the firms that

eventually go bankrupt. The healthy years from the bankrupt firms are the years at least three fiscal

years prior to bankruptcy. I illustrate with the following examples:

o If a firm files for bankruptcy in 1998 and its fiscal year end is after May, then the firm’s 1997

data is included in the bankrupt group because financial information for the year prior to

bankruptcy occurs in 1997. I delete the 1996 data and any data after 1997. If the firm

existed in any year prior to 1996, then those years are included in the non-bankrupt group.

o If a firm goes bankrupt in 1998 and its fiscal year end is prior to May, then the firm’s 1996

data is included in my bankrupt group because the financial information for the bankrupt year

is actually from 1997, based upon the end of the fiscal year. Therefore, the year prior to

bankruptcy is 1996. I delete the data for 1995 and any data after 1996. If the firm existed in

any year prior to 1995, then those years are included in the non-bankrupt group.

After applying all the indicated filters to the bankrupt sub-sample, I find 499 bankrupt firms from

1985 to 1999.67

There are 34,921 non-bankrupt firm/years for 4,749 firms after applying the necessary

filters. The entire sample consists of 4,749 firms with 35,420 firm/years.

I subdivide the sample by the North American Industrial Classification System (NAICS). The

NAICS code is a six-digit code that identifies company activity, sub-sector, and industry information.

but some are more urgent and dangerous than others. For example, firms in the financial services industry do not necessarily have closure costs similar to firms within the mining and manufacturing industries (gas and oil companies are included in these two industries). However, they still have environmental liabilities in the form of landfill usage, wastewater treatment, and computer and other equipment disposal, etc. Because the nature of the financial services industry is not to operate in an environmentally hazardous line of business, firms within this industry do not necessarily need to assure against closure costs and certain environmental liabilities. However, they are still liable for their use of environmental resources. 67

A firm/year is the number of years a firm exists and has valid data. For example, if one firm exists for 20 years and another firm for four years, then the sample contains two firms with 24 firm/years.

39

However, for the purpose of this study, I use the two-digit NAICS code.68

I report the classification by

industry for bankrupt and non-bankrupt firm/years in Figure 3.3. Industries I am particularly interested in

are the mining, construction, and manufacturing industries. These three industries account for almost 50

percent of the total number of bankrupt firm/years. The utilities industry has a low occurrence of bankrupt

firm/years. This result is not surprising, as the industry is highly regulated.

The focus of the analysis is on the EPA’s financial tests. I evaluate the ability of the tests to

classify the firm/years within their specific groups and with respect to the entire sample. Additionally, I

compare alternate methods ability to classify bankrupt firm/years. The alternative methods include bond

ratings, auditor opinion, and Altman’s Z-score model for publicly traded and privately held companies.

3.4 Methodology

I evaluate each method’s ability to classify a firm’s financial status given their actual group status.

For each method, I apply the method-specific financial criteria and classify the non-bankrupt and bankrupt

observations accordingly. The methods for defining a firm’s viability include the Grice and Ingram (2001)

definition of health, auditor opinion, bond ratings, and Altman’s Z-Score models for publicly traded and

privately held firms.69

I treat the classification percentages from the EPA’s financial tests as the base

case with which I compare all other results. I use annual financial data for a firm’s fiscal year to

determine the firm’s health status. For the bankrupt group, I use the financial data in the fiscal year prior

to bankruptcy.

For classification purposes, I set up my two-by-two contingency tables such that type I error is

classification of a bankrupt firm/years as passing the method (being classified as non-bankrupt) and type

II error is classification of non-bankrupt firm/years as failing the method (being classified as bankrupt).

Type I error is the most critical in the case of environmental obligations because allowing a company to

do business when it cannot meet its financial obligations has environmental, health, and public cost

consequences. Type II error implies good corporate citizens will need to use a financial assurance

mechanism other than the financial tests. This error means that for the sake of caution, a healthy firm

that is misclassified is required to obtain another mechanism at additional expense. The added expense

may be an unfair penalty the healthy firm bears. Type I and II errors relate to the social and business

costs of conducting environmentally related business.

Because type I error and type II error are group specific, I also consider classification errors with

respect to the entire sample. This provides some insight to what regulators face when conducting annual

reviews of firms. For example, when a regulator conducts her review, I assume she has no prior

knowledge of a firm’s financial status. Then after the review, the regulator forms an opinion of a firm’s

68

I consolidate the NAICS codes for wholesale trade (NAICS code 42) and retail trade (NAICS codes 44-45) into the group trade. 69

Grice and Ingram (2001) define healthy firms as those that have stock ratings of B or better or investment-grade bond ratings. Likewise, they define financially distressed firms as those whose stock ratings are below B, whose bonds do not have investment-grade ratings, or who have filed for bankruptcy. As long as the firms have at least one type of rating, I apply the definition.

40

financial status based upon the information the firm submits. Her classification of firms is with respect to

the entire sample as opposed to the individual groups.

Along with examining the classification, I test the following hypothesis:

Ho: There is no association between the method and the non-bankrupt and bankrupt groups. In

other words, the method and the groups are independent.

Ha: There is an association between the method and the groups, meaning that the method and

the groups are not independent.

The association in the hypotheses refers to whether the method and the groups are independent

or dependent. If the method is independent from the groups, then there is no link or tendency for the

classification of the groups. A link implies the method and the groups have an association or a there

exists a tendency of the method to classify the groups in a particular manner. The methods are financial

health indicators and the groups are the eventual outcome. Thus, the association or link is between the

health indicator and an observations eventual outcome.

With respect to this study, the null hypothesis implies that the EPA’s financial tests do not have a

tendency to classify firm/years in any particular manner. Thus, there is just as much chance to classify

firms as passing or failing regardless of the group. Whereas, the alternative hypothesis implies a

systematic tendency of the EPA’s financial tests to classify groups differently. Investigation of the

contingency table tells how the method tends to classify the groups differently.

From the EPA’s perspective, it is more important to mitigate social costs over business costs.

From the business perspective, it is important to mitigate business costs. I test the null on an overall,

annual, and industry basis. By overall sample, I mean the entire sample as a whole, without regard to

year or industry. I examine the classification patterns for the annual and industry classifications.

Whereas my focus is on the performance of the financial tests, I also use other common methods

(definition of distress, auditor opinion, bond ratings, and bankruptcy prediction models) so I can compare

the classification ability of the EPA’s methods with these other methods.

My sample contains two independent categories—bankrupt firm/years and non-bankrupt

firm/years. I classify the sample by each of the methods. This classification yields a two-by-two

contingency table in which the columns represent the actual health of a firm (bankrupt or non-bankrupt)

and the rows represent the outcome of the method (pass or fail). In the contingency table, I provide a

tally of the observations that occur for each category. For the overall sample, I use the Chi-square

statistic from the Chi-square test of association. The Chi-square statistic tests the difference between the

estimated observations expected in each cell with the actual observations observed in each cell. This

means that the statistic compares the non-bankrupt firm/years expected to pass the EPA’s financial tests

with those that actually do pass. If there is a significant difference between the actual and the expected,

then the Chi-square is statistically significant. For example, if the EPA’s financial tests classify more non-

bankrupt observations as passing than what actually passes, then there is a tendency for the EPA’s

financial tests to pass the non-bankrupt group. Likewise, if the EPA’s financial tests classify fewer non-

41

bankrupt observations as passing than what actually passes, then there is a tendency for the EPA’s

financial tests to fail the non-bankrupt group.

For the sample by year and industry, I use the two Fisher exact p-value from the Fisher exact test

when appropriate. The Chi-square test and the Fisher exact test have the same interpretation. I use the

Fisher exact test in lieu of the Chi-square test when one or more of my contingency squares contain an

expected frequency of less than five observations because this violates the Chi-square required minimum

of five.

In the section to follow, I discuss the within group and overall classification rates. I use logistic

regressions as a robustness check for the likelihood of classifying bankrupt firm/years. From the logistic

analysis, I assess the fit of the regression, the significance of the method, and its odds ratio. In assessing

the fit of the regression, I examine the significance of the likelihood ratio. I use the p-value from the Chi-

square statistic generated by the logistic regression. This p-value determines the ability of the

independent variable (method) to classify a firm as being bankrupt. Finally, I discuss the odds ratio for

each method.

3.5 Results

I report the classification accuracy for the methods for the sample from 1985-1999, for the

sample by year, and for the sample by industry. Classification accuracy entails the percent of firm/years

classified correctly and incorrectly within each group for each method and with respect to the entire

sample. When I refer to the entire sample, I consider the classification of each observation regardless of

its group.

Classification accuracy is an important issue given the cost of misclassification. For example, in

classifying a non-bankrupt firm as failing the financial tests, the firm bears the cost of finding alternate

financial assurance. Misclassifying firms, which are quickly approaching insolvency, incurs potential

social costs. Costs of this type of misclassification can be significant given the totality of the responsibility

for the entire environmental liability. This situation is assuming the cost of misclassification is not

symmetric. If the costs are symmetric, the cost of misclassifying a non-bankrupt firm as failing is the

same as misclassifying a bankrupt firm as passing. In view of the anecdotal evidence, specifically the

mining industry in Pennsylvania, the cost of misclassification is asymmetric with the misclassification of

bankrupt firms outweighing the misclassification of non-bankrupt firms. This is especially the case if a

bankrupt firm liquidates as opposed to reorganizing. If a firm reorganizes, the EPA has another

opportunity to recover some of the funds for cleanup and closure. Otherwise, the entire burden is on the

state and the taxpayers to supply the necessary funds.

3.5.1 EPA standards: Financial tests #1 and #2. I classify bankrupt and non-bankrupt

firm/years by pass/fail rates for the financial tests. I report the criteria for the financial tests in Table 3.2

Panel A. Applying the financial tests, I use the required capital structure ratio, profitability ratio, liquidity

ratio, and measures of credit worthiness. All these measures are readily available except estimates of

closure cost. Because closure costs are not available, I use one percent of net plant, property, and

42

equipment (PP&E) as the estimate for closure costs. I chose net PP&E because it is available on

Compustat, and it represents the cost to the company for the external structures directly related to

operation and production. This measure characterizes the assets used in the creation of the potential

environmental liability.

In the next chapter, I investigate varying levels of closure costs from one percent to ten percent of

net PP&E. The net PP&E measure is quantitatively less than the EPA’s rule of thumb for assurance for

closure costs, which is at least a multiple of six times tangible net worth, working capital, and total

assets.70

The reason I select a measure smaller than what the EPA might require is for two reasons: The

first is the lack of availability of actual closure costs and the firm’s estimation method. Second, not all

firms have prohibitive closure costs, so it is unfair to apply an extreme proxy.71

Financial test #1 is an inexpensive financial assurance mechanism that is available to all firms. If

a firm cannot meet the financial criteria in financial test #1, then the firm may use financial test #2,

provided the firm has rated bonds that satisfy the minimum rating. I apply the EPA’s financial tests to the

overall sample. In Table 3.3 Panel A, I report the classification rates for the EPA’s financial tests from

1985-1999. Within the individual groups, non-bankrupt and bankrupt, the method classifies the bankrupt

firm/years more accurately than non-bankrupt years. Type I error indicates about 8 percent of the

bankrupt firm/years pass EPA financial tests, and type II error indicates almost 38 percent of the non-

bankrupt firm/years fail the EPA financial tests. Thus, 39 of the 499 bankrupt firm/years passed the

financial tests and 13,266 of the 34,921 non-bankrupt firm/years failed the financial tests. The p-value for

the Chi-square statistic is highly significant indicating that we must reject the null hypothesis that the EPA

test outcome and the actual status of firm health are independent. In other words, there is an association

between the EPA test results and the financial status of the firm.

The misclassified bankrupt firm/years comprise only about one-tenth of one percent of the entire

sample. If costs of misclassification were symmetric, then it would appear as if non-bankrupt firms

receive the brunt of the tests as the tests misclassify far more non-bankrupt firms than bankrupt firms.

The non-bankrupt firms bear additional financial burden because they must allocate additional resources

to obtain alternate financial assurance. Bankrupt firms appear to be few and if costs are symmetric, then

the cost is less than the cost to the non-bankrupt firms. The misclassification with respect to the entire

sample is approximately 38 percent.

I assume that the costs of misclassification are not symmetric. I base my assumption on several

factors. These factors include the costs of the environmental obligation, the financial tests lack of

guarantee for the obligation, and the risk of transferring the obligation to the public. The known and

70

Other states use a multiplier different from what is federally required. For some examples, see Table 3.1. 71

The Securities and Exchange Commission (SEC) does not specify a benchmark of when a liability, or in this case closure costs, becomes material. Instead, the SEC cautions against using any “rule of thumb.” The SEC requires reporting all liabilities that could potentially affect an investor’s perception of the status of the firm. Goodwin Proctor Law Advisory entitled Disclosure of potential environmental liabilities in the wake of Sarbanes-Oxley, November 2002, page 3. 17 CFR Part 211, Release Number SAB 99.

43

unknown costs associated with an environmental obligation can compound quickly and firms may not

have the necessary resources to fulfill a growing obligation [Table A.1]. The financial tests do not provide

secured funds for the obligation and this absence of security leaves the taxpayer exposed to the potential

risk of bearing the financial burden. Thus, my assumption of asymmetric costs is justified.

One could suggest the EPA’s financial tests would have better classification results if they

classified all firms as healthy; then, the only firms misclassified would be the few that were truly

unhealthy. Technically, that is what is taking place. I subject the firms to the financial tests regardless of

actual health. Then, I show in the contingency tables exactly how the financial tests performed compared

to the actual health, but the knowledge of a firm’s actual fate does not influence the financial test

classification. The purpose of the tests is to assess if a firm is financially capable of handling its

environmental obligations before they happen. Thus, firms using these tests are healthy until they prove

themselves otherwise, meaning they are healthy until they fail the financial tests or provide another

financial assurance mechanism. The EPA’s financial tests are one of several mechanisms firms can use

to provide this assurance. From the EPA’s perspective, it is more important the financial test criteria be

able to classify unhealthy firms.

On an annual basis, the classification accuracy improves through time as shown in Table 3.3

Panel B [and Figure 3.3]. The majority of all bankrupt firm/years fail the financial tests and a

preponderance of non-bankrupt firm/years fail the financial tests. This result implies that most firms

subject to the EPA’s standards are providing third-party financial assurance mechanisms. Statistical

significance exists for all years indicating the consistent existence of an association between the method

and its ability to classify the groups. I list classification rates for the EPA’s financial tests, by industry in

Table 3.4 [and Figure 3.4]. The classification rates illustrate a similar pattern as the annual classification

rates. For all industries, few firms may use the financial tests to assure closure costs. Therefore, most

firms must use third-party mechanisms.

3.5.2 Grice and Ingram (2001) and bond ratings. In this section, I examine investment

ratings. I apply Grice and Ingram’s (2001) definition of “financial distress.” They define non-distressed

firms as those that have stock ratings of B or above or investment-grade bond ratings.72

Financially

distressed firms consist of those with stock ratings below B, or bond ratings below BBB, or firms that have

filed for bankruptcy. Because not all firms have stock or bond ratings, the sample reduces to 20,627

firm/years with 20,555 non-bankrupt firm/years and 72 bankrupt firm/years.

I also examine bond ratings as the sole criteria for financial viability. Bond ratings should

coincide with the Grice and Ingram definition and the EPA’s financial tests, as the bond rating itself is the

main criteria for the EPA’s financial test #2. My motivation for examining investment ratings is to

investigate their usefulness and potential applicability within the EPA standards. The EPA already makes

use of bond ratings in its financial test #2; perhaps stock ratings might prove useful as well.

72

Compustat may not report stock ratings on firms that are new to the database or are highly volatile. Absence of stock ratings does affect the sample size because the observation must contain a stock rating or a bond rating.

44

I illustrate the overall classification rates from 1985-1999, in Table 3.5 Panel A and the annual

rates in Panel B. The type I error is 1.36 percent, and type II error is almost 61 percent. The low type I

error is due to the one bankrupt observation is incorrectly classified by the Grice and Ingram definition.

However, this method correctly classifies only about 38 percent of the non-bankrupt firm/years. The

statistically significant Chi-square statistic (p<0.0001) indicates an association exists between the Grice

and Ingram definition and the non-bankrupt and bankrupt groups.

When I consider the entire sample, the misclassified bankrupt firm/years constitute one-

hundredth of a percent of the entire sample. The misclassified non-bankrupt firm/years comprise almost

65 percent. The classification rate remains consistent on an annual basis. In the latter years of the

sample, the Fisher exact p-values indicate the existence of an association between method and the

groups. The Grice and Ingram method classifies more firm/years as distressed. This misclassification of

bankrupt firm/years implies lower social costs but the preponderance of misclassified non-bankrupt

firm/years implies higher business costs.

The Grice and Ingram method considers another aspect of firm viability (i.e., stock ratings) that

the EPA’s financial tests do not. The Grice and Ingram method misclassifies bankrupt firms at a lower

rate than the EPA’s financial tests. One could argue the EPA should incorporate stock ratings into the

financial tests for firm health consideration. The EPA considers bond ratings for those firms that have

them, so why not stock ratings as well? The inclusion of stock ratings may save the firms the additional

cost of acquiring a third-party mechanism while subjecting the firm to market scrutiny.

I find the Grice and Ingram method to have high classification accuracy for bankrupt firm/years

for each industry. I report the classification rates in Table 3.6. In all industries except the utilities

industry, I find it tends to classify bankrupt firm/years better than non-bankrupt firm/years. The utilities

industry is the only industry that has a high correct classification of non-bankrupt firm/years. This is not

surprising as the utilities industry is highly monitored and regulated. Based on these findings, the use of

both stock and bond ratings provides a fuller sense of financial health, where the use of bond ratings

alone does not (see the EPA’s financial tests and below).

The bond ratings used are the Standard and Poor’s (S&P) domestic long-term issuer credit

ratings as reported by Compustat. These ratings represent a firm’s ability to service debt obligation

longer than one year. A determinant of future health is long-term bond ratings. They are appropriate

because environmental liabilities are long-term obligations. The better able a firm is to maintain a long-

term obligation, meaning it meets the scheduled debt obligation payments, the better the perceived ability

to fulfill an environmental obligation. However, the extrapolation that long-term bond ratings assure a

company will fulfill any long-term environmental obligation is premature. Extrapolation over too long of a

period is inappropriate, considering business cycles, competition, and the potential magnitude of an

environmental liability. There is no guarantee the firm’s health will remain constant even through the life

of the bond, as is evident with the frequency in which credit rating agencies revise ratings on existing

bond issues. All the debt rating says is that at this time, the firm is able to service its long-term debt; it

does not guarantee a firm can afford an environmental liability of a significant magnitude if it arises.

45

Examining bond ratings by themselves is helpful because it is a component of the EPA’s financial

tests. I make the same argument for auditor opinion. Another reason for investigating bond ratings is

that they are also an alternative financial assurance mechanism. Firms financially able to obtain a bond

may obtain an alternative form of assurance if necessary. However, just because a firm can obtain a

bond does not mean it will if the benefit of default outweighs the cost of shouldering an environmental

cleanup.73

In using this method, I exclude firm/years that lack bond ratings. The sample for this method

contains 6,210 firm/years. The motivation behind examining the bond rating and comparing it to the

definition of health by Grice and Ingram (2001) and the EPA’s financial tests is that perhaps only the bond

rating is necessary. If this is the case, then the other criteria for financial test #2 may be redundant.

I classify firm/years as healthy if they have bond ratings in line with EPA standards—BBB and

above. Firm/years are unhealthy if they have bond ratings below BBB. The initial break down of the

bond ratings yields 181 rated as AAA, 851 rated between AA+ and AA-, 2,378 rated between A+ and A-,

2,162 rated as BBB+ to BBB-, 1,502 rated as BB+ to BB-, 1,029 rated as B+ to B-, and 107 rated CCC+

and below. Therefore, 5,572 firm/years are rated as healthy (BBB and above), and 2,638 firm/years are

rated as unhealthy (below BBB). However, I must interpret the results for the bond analysis with great

caution because there are only 11 bankrupt firm/years with bond ratings. I report the overall and annual

classification rates in Table 3.7 Panels A and B, respectively. Within the groups, the type I and II errors

are zero percent and 32 percent, respectively. The statistically significant two-sided Fisher exact p-value

(p<0.0001) indicates the existence of an association between bond ratings and firm health.

Because of the lack of bond ratings in the bankrupt firm/year group, some annual statistics

cannot be calculated and thus not reported. The sample for bond ratings is much smaller as not all firms

have bond ratings. Nevertheless, for those firms that have bonds, the method does generally classify

bankrupt firm/years as unhealthy and non-bankrupt firm/years as healthy. This result is not surprising

given the scrutiny a firm undergoes to obtain and maintain a bond. The classification accuracy for the

entire sample is approximately 68 percent.

I report the industry results in Table 3.8. Classification accuracy is variable by industry and is

similar to the annual results. Given the industries of interest, those being mining, construction, and

manufacturing, only for manufacturing is there exist an association between bond ratings and the groups.

Within manufacturing, 100 percent of the bankrupt observations and almost 70 percent of the non-

bankrupt observations are classified correctly. For construction, there are only 27 non-bankrupt

observations. I cannot calculate statistics pertaining to the overall classification but for the non-bankrupt

group, one-third of the non-bankrupt observations are correctly classified. Similarly, for the mining

industry, there is a lack of observations for bankrupt firm/years. This finding coincides with the anecdotal

evidence of states removing bonds as an option for financial assurance [Table A.1]. For the non-

bankrupt observations, almost 46 percent are classified correctly.

73

Investigation of the amount of environmental cleanup is beyond the scope of this dissertation. I mention the potential magnitude of the environmental liability is to remind the reader the firm still has the

46

3.5.3 Auditor opinion. I investigate the ability to discern a company’s health using the auditor

opinion alone because it is a component of the EPA’s financial tests. Qualified opinions may be neither

positive nor negative. Instead, qualified opinions may suggest a firm must clarify its financial position to

the auditor. Because the opinions are not transparently negative, I include qualified opinions with the

unqualified and unqualified with explanatory language opinions. I compare auditor opinion with the actual

health of the firm.

Of the 35,420 firm/years, 26,672 firm/years receive unqualified opinions, 7,684 firm/years receive

unqualified with explanatory language, 998 firm/years receive qualified opinions, 64 firm/years receive no

opinions, and only one firm/year receives an adverse opinion from 1985-1999.74

I classify unhealthy

firm/years as those with no auditor opinion or an adverse opinion. All other opinions—unqualified,

unqualified with clarification, and qualified—are healthy.

I report the overall and annual classification rates in Table 3.9 Panels A and B, respectively. For

the overall sample, the type I error is approximately 97 percent, and the type II error is less than two-

tenths of a percent. Disturbingly, but not surprisingly, this method classifies almost all firm/years,

including bankrupt firm/years, as receiving positive auditor opinions. Only 13 out of 486 of the bankrupt

firm/years are correctly classified. Based on prior research, these findings are expected [McKeown,

Mutchler, and Hopwood (1991); Jones (1996); Tan (2002); Geiger and Raghunandan (2002); Deegan

and Rankin (1999); Carcello, Hermanson, and Huss (1995); Weil (2001)].

Because the number of bankrupt firm/years is less than the number of non-bankrupt firm/years,

the non-bankrupt firm/years dominate the overall classification accuracy when the entire sample is

considered. For example, only about 1.5 percent of the total firm/years are misclassified, but as

mentioned above, almost all the bankrupt firm/years are misclassified. Thus, it appears that auditor

opinions are of little use in forecasting impending bankruptcies.

The annual classification rates remain consistent for this method - high classification accuracy for

non-bankrupt firm/years and low classification accuracy for bankrupt firm/years. Some of the two-sided

Fisher exact p-values coincide with the overall Chi-square p-value while others do not. I list the industry

classification rates in Table 3.10. Results are similar in that all industries have high classification

accuracy for non-bankrupt firm/years and low classification accuracy for bankrupt firm/years. Only for two

industries does there appear to be an association between the opinion and the firm’s status.

There are several reasons why an auditor opinion may be less dire than that conveyed by a firm’s

financials. However, it is usually the case that the auditor opinion often lags the firm’s true state.

Auditors often hesitate to issue an adverse opinion or to refrain from issuing one at all. When an auditor

issues an adverse opinion or fails to issue one, bankruptcy is often imminent. Auditors fail to issue

negative opinions for a variety of reasons. Generally, auditors are concerned with job security and fear

potential litigation. They may be overconfident of the firm’s ability to survive as they may have insider

information. They also fear contributing to the “self-fulfilling prophecy” [Grice (2000); Carcello and Neal

option to abandon, even with third-party mechanisms such as bonds.

47

(2003); Mutchler, Hopwood, and McKeown (1997); Pryor and Terza (2001); Tucker and Matsumura

(1998); Matsumura, Subramanyam, and Tucker (1997); Mutchler (1985)].

3.5.4 Altman’s Z-Score models for publicly traded and privately held firms. Although I do

not use private firms in the sample, I apply the Altman’s Z-Score models for both private and publicly

traded firms. I list them in Table 3.2 Panel B. The Altman models are well-established, recognized

benchmarks used to detect financial viability, so their inclusion is appropriate in my analyses. In addition,

although the EPA does not make use of either Altman model in the financial tests, the EPA does use the

Altman’s Z-Score Model for privately held firms for mediation and litigation. My motivation for

investigating both is to provide additional alternatives the EPA may use to strengthen its financial tests.

The privately held model provides firms with a higher threshold requirement for reaching viability

and a lower threshold requirement for insolvency. This wide range between a healthy and an unhealthy

classification, also known as the inconclusive zone, benefits firms because a Z-Score in those intervals

may not necessarily attract the attention of the EPA regulator unless other financial test results suggest

financial difficulties. If the EPA uses the Altman’s model for publicly traded firms, the inconclusive zone is

tighter, with the threshold for proving health slightly lower and the threshold for demonstrating a lack of

health being higher. A tighter inconclusive zone implies a firm will have a higher chance of falling into

one of the classification zones than with the Altman model for privately held firms. However, a

disadvantage of the Altman model for publicly traded firms is endogenous to the model. Whereas the

tighter intervals may appear to be more attractive, they may be tighter because the model design is

specific to firms that are required to meet a higher disclosure standard than a privately held firm.

Therefore, I use both models.

In applying Altman’s Z-Score models, I do so blindly, meaning I do not apply many limiting

assumptions. I do not limit my application to size or a specific industry as in Altman’s original application.

Altman restricts his sample to firms in the manufacturing industry having total assets from $1 to $25

million and his sample size contains 33 observations in each group. I apply the Altman models to all the

data in the sample that remains after removing missing data.75

My sample for the publicly traded method

contains 35,012 firm/years [34,542 non-bankrupt firm/years and 470 bankrupt firm/years]. Similarly, for

the privately held model, my sample contains 33,757 firm/years [33,414 non-bankrupt firm/years and 343

bankrupt firm/years.76

3.5.4.1 Altman’s Z-Score model for publicly traded firms. Z-Scores greater than 2.675

indicate there is a low probability of a firm going bankrupt. I treat these Z-Scores as an indication of

health. Z-Scores between 1.81 and 2.675 are inconclusive, and Z-Scores less than 1.81 indicate a firm

has a high probability of going bankrupt. I provide the Z-Score intervals in Table 3.2 Panel B.

74

Some firms receive no opinion if an auditor refuses to issue an opinion or if the firm is in the process of switching auditors. 75

The data most frequently missing, zero, or less than zero for this method is sales and market value of equity. 76

The data most frequently missing, zero, or less than zero for this method is sales and net worth as recorded by book value of equity.

48

I find healthy Z-Scores for 20,654 firm/years, inconclusive Z-Scores for 5,561 firm/years, and

unhealthy Z-Scores for 8,797 firm/years. I report the overall and the annual classification rates in Table

3.11 Panels A and B, respectively. Considering the entire sample, less than one-half percent of all

observations are misclassified bankrupt firm/years and approximately 25 percent are misclassified non-

bankrupt firm/years. Within the groups, the type I error is almost 30 percent and the type II error is almost

25 percent. The overall classification accuracy rate with this method is almost 73 percent.

In general, the Altman’s Z-Score tends to classify more non-bankrupt firm/years as healthy and

more bankrupt firms/years as unhealthy for the total sample and by year. The statistically significant Chi-

square p-value (p<0.0001) for the entire sample and the Fisher exact p-values for the samples by year

support the existence of an association between the Z-Score and the groups.

I report in Table 3.12 the industry classification rates. I find the classification accuracy of the

Altman’s Z-Score varies greatly from industry to industry. Despite the model's application to only the

manufacturing industry, there are high levels of classification accuracy for other industries. In some

industries, such as agriculture, construction, manufacturing, and transportation the percent correctly

classified for both non-bankrupt and bankrupt is greater than 60 percent. The Altman’s Z-Scores are

associated with the groups for all industries except utilities, real estate, and services.

3.5.4.2 Altman’s Z-Score model for privately held firms. I calculate the Z-Scores using the

Altman’s Z-Score model for privately held firms. Z-Scores greater than 2.90 indicate a firm has a lower

chance of becoming bankrupt in the near future. Z-Scores between 1.23 and 2.90 are inconclusive and

may indicate warning signs. Z-Scores less than 1.23 indicate a firm has a high probability of going

bankrupt in the near future. I list the Z-score intervals in Table 3.2 Panel B.

I calculate Z-Scores for 33,757 firm/years. I find non-bankrupt indicating Z-Scores for 10,901

firm/years, inconclusive Z-Scores for 15,384 firm/years, and bankrupt indicating Z-Scores for 7,472

firm/years. I report the overall and the annual classification rates in Table 3.13, panels A and B,

respectively. Within the groups, the type I error is approximately 41 percent, and the type II error is

almost 22 percent. Similar to the publicly traded method, an association exists between the privately held

method and the groups. Considering the entire sample, classification accuracy is 89 percent with less

than half of a percent of the misclassified firm/years being bankrupt firm/years.

I report the classification accuracy rates among the industries in Table 3.14. The Z-Score and

the groups are dependent for all industries except for mining, utilities, transportation, real estate, and

services. For other environmental matters related to fiduciary responsibility, the EPA makes use of the

privately held model instead of the publicly traded model. However, the EPA applies the Z-Score model

after a firm contends it cannot afford to pay for its environmental liability. Applying a prediction model

after the fact seems backwards. This application could partially explain the anecdotal evidence,

especially for firms in industries for which the method has no association.

49

3.6 Robustness check

In Table 3.15, I report the within group classification rates and their respective logistic

regressions and odds ratios. I estimate individual logistic regressions for each method and actual firm

status.77

Logistic regressions model the likelihood of a particular outcome. In this case, the logistic

models the likelihood of bankrupt observation classification by each method. The likelihood ratio, which

uses the Chi-square statistic, assesses the fit of the model. A statistically significant likelihood ratio

means the logistic regression is a better fit for the data rather than not using a model [Cody and Smith

(1997); Boehmer, Broussard, and Kallunki (2002); Allison (2001)].78

This relates to the existence of an

association between the method and the actual firm status. Similar to the contingency tables, I find that

the logistic regressions for all methods are statistically significant (p<0.0001).

Because the coefficient estimated by the logistic regression is not directly interpretable, I use it to

calculate the odds ratio. The odds ratio gives the likelihood of a bankrupt observation failing a method.

For example, for the EPA’s financial tests, the corresponding odds ratio indicates a bankrupt observation

is 19 times as likely to fail rather than pass [Cody and Smith (1997)]. To calculate the odds ratio, one

must find the ratio of the probability of classifying bankrupt observations over the probability of classifying

non-bankrupt observations. To do this, one must use the intercept and coefficient estimated by the

regression.

For example, the following logistic regression is for the EPA’s financial tests:

Y = -6.3194 + 2.9577X, where Y represents the binary firm status of non-bankrupt or bankrupt

and X is the binary indicator for passing/failing the EPA financial tests. The probability of a firm being

classified as bankrupt is PB = (e(-6.3194 + 2.9577)

/(1+ e(-6.3194 + 2.9577)

)). The probability of a firm being classified

as non-bankrupt is PNB = (e(-6.3194)

/(1+ e(-6.3194)

)). The odds ratio is the ratio of the probabilities such that

PB/PNB is about 19. The difference between the above hand calculation and the number reported in Table

3.15 is due to rounding [Cody and Smith (1997); Boehmer, Broussard, and Kallunki (2002); Allison

(2001)].79

The odds ratio for Grice and Ingram and bond rating methods indicate bankrupt observations are

more likely to fail these two methods. Their odds ratios are 45 and 48 respectively. I interpret these

results with caution because the size of the sample for Grice and Ingram and bonds are different from the

other methods. These measures are not surprising given that the samples for Grice and Ingram and

77

The models I estimate are simple linear models in the following form: Log (p / (1-p)) = α+βx where α is the intercept and β is the coefficient estimate and x is the method specific binary classification indicator. 78

There are other indicators of model fit, such as the Akaike Information Criterion and the Schwartz Criterion however, those criteria are better suited when investigating incremental adjustments to the regressions by adding one variable at a time. For the purpose of my analysis, the logistics are not incrementally changing. I estimate a logistic regression for each method. Evaluating all the methods in the same logistic regression is not appropriate because I am not investigating if a firm uses all methods simultaneously. Instead, I am investigating the use of the methods independently. 79

An alternate way to calculate the odds ratio is to use the two-by-two contingency table and calculate the following: It is the product of the ratio of failing bankrupt observations to passing bankrupt observations and the ratio of passing non-bankrupt observations to failing non-bankrupt observations.

50

bond ratings are significantly smaller than the other methods. In addition, firms that hold bonds must

meet stricter disclosure and collateral requirements. In general, firms that hold debt have to be healthy

enough to hold and to service the debt.

For auditor opinion, the odds ratio is less than one. This means a bankrupt observation is less

likely to receive a negative opinion. Of all the methods, auditor opinion is least likely to fail an

observation. The odds ratio for Altman’s Z-Score for publicly traded and privately held firms is just over

seven and five, respectively. Thus, bankrupt observations are at least five times as likely to receive a low

Z-Score with either of the Altman methods.

The pseudo R-squares provide some support for classification ability for each method but are

difficult to interpret. Because the sample size varies across methods, I can only compare the pseudo R-

squares for those methods that have similar sample size and distribution. Thus, I can only cautiously

compare the EPA’s financial tests with auditor opinion and the Altman Z-Score methods. Across the four

methods, the EPA’s financial tests have the highest pseudo R-square. I warily interpret this to indicate

that the financial tests have some classification ability. The financial tests appear to have greater

classification ability than auditor opinion and slightly better classification ability than the Altman’s Z-Score

methods. Low pseudo R-square measures suggest that classification is difficult to estimate [Allison

(2001]. In general, the robustness checks provide additional confirmation that an association exists

between the methods and the groups. Thus, the methods are able to classify the groups with some

degree of accuracy.

3.7 Summary

I provide three summary tables for the methods and their overall classification rates, Tables 3.16,

3.17, and 3.18. In Table 3.16, I report the classification rates per method. I record the frequency counts,

the percent classification within group, and percent classification with respect to the entire sample. As

before, Type I error implies an increase in potential social costs should an unhealthy firm default on its

environmental obligations. Type II error implies an added cost for the firm because it must provide proof

it is financially viable. An optimal method would be one that balances the tradeoffs with respect to total

costs.

Comparing the methods, I observe that all of the methods are able to classify the groups with

some degree of accuracy. Some methods have a tendency to classify one group better than the other

group. When considering the non-bankrupt group, the EPA’s financial tests, the Altman models, and

auditor opinion tend to classify more firm/years as passing than failing. Amongst those methods, the EPA

classifies fewer non-bankrupt firm/years as passing and more non-bankrupt firm/years as failing. This

implies that the EPA’s tests require more non-bankrupt firms to acquire an alternate form of assurance.

For the bankrupt group, the EPA’s financial tests misclassify fewer bankrupt firm/years than

auditor opinion or the Altman models. The Grice and Ingram (2001) method appears to misclassify fewer

Yet a third way to odd ratios are estimated is to take the point estimate from the regression use it as the exponent in the exponential function.

51

bankrupt firm/years than all the methods except bond ratings. I interpret these results [Grice and Ingram

and bond ratings] with caution because their sample sizes are not directly comparable with the other

methods due to the lack of bond ratings for many firms.

On an annual basis, the results are similar for all methods. The association between the method

and the group is consistent and persists annually for all methods except bond ratings and auditor opinion.

This is due to the difference in sample size and the lack of bankrupt bond data and auditor opinion for

some years. On an industry basis, the association between method and group within an industry exists

for some and not others. In Table 3.17, I report the industries and the classification rates for the methods

for which an association exists between the method and the groups.

Based on Tables 3.16, 3.17, and 3.18, I find the EPA’s financial tests have a reasonable within-

group classification accuracy rate when compared to the other methods. From the environmental

perspective, because the EPA has a type I error of almost 8 percent, there is still some room for within-

group improvement. Perhaps the EPA might consider incorporating other methods, such as stock ratings

and the Altman’s Public Z-Score as an additional screen for firms that pass the EPA rules. The financial

test’s type II error of almost 38 percent implies many firms must secure alternate forms of financial

assurance. From the business perspective, this means an additional cost to the company. Thus, firms

may need to redirect resources to fulfill the assurance requirement.

The EPA generally protects the environment and taxpayers from unhealthy firms, but it may

overly penalize healthy firms who are potentially good corporate citizens. The EPA is trying to protect the

environment against contamination but at what cost to the taxpayers? On one hand, the EPA is

attempting to minimize the potential cleanup burden on the taxpayer. On the other, if the EPA prevents a

good corporate citizen from conducting operations, then the taxpayer bears this burden as well. It seems

clear that the EPA attaches a greater cost to misclassifying an unhealthy firm than it attaches to

misclassifying a healthy firm. Nevertheless, to make a definitive statement about the nature of the

tradeoff that exists between these two types of misclassifications would necessitate estimates of the

actual costs involved in each case. To date, the EPA has not chosen to provide such estimates.

52

CHAPTER 4 TESTS OF FINANCIAL ASSURANCE EFFECTIVENESS: A SENSITIVITY ANALYSIS

4.1 Purpose for Sensitivity Analysis

In the previous chapter, I compare the ability of the EPS’s financial tests to classify bankrupt and

non-bankrupt firms with other viable alternatives. Overall, I conclude that the EPA rules appear to

perform as well or better than the other methods. In this chapter, I look at how varying the estimated

costs of closure impacts on the classification accuracy of the EPA financial tests. This sensitivity analysis

is for the EPA’s rules, as the other methods do not explicitly incorporate closure costs in the

classifications.

Closure costs encompass any liability directly related to the assets and operations. These costs

account for a possible future claim for a potential liability. This dynamic aspect of closure costs is difficult

to estimate; thus, it is open to interpretation by the firm. This is not to imply that the engineered estimate

or interpretation is not rigorous. It only means that there are uncertainties with respect to the estimate

and there is no single standard for estimating these uncertain costs. However, regardless of

interpretation and its uncertainties, the firm is obligated to cover all related costs. The magnitude of these

potential costs can be very large as discussed and illustrated in Appendix A and Table A.1. As a proxy

for the estimated costs of closure, I use a firm’s amount of net property, plant, and equipment (PP&E)

because this amount represents the tangible assets a firm uses for production purposes. This measure

represents the cost to the firm to put the assets in place that directly contribute to the creation of the

potential liability.80

These closure costs estimates only loosely proxy environmental liabilities because

such liabilities are extremely difficult to estimate. Thus, I perform a sensitivity analysis where closure

costs vary from one to ten percent of net PP&E.

If a healthy firm has a relatively low cost of closure, the firm should be able to cover this cost at

no direct expense to the state regulatory agencies or the taxpayers.

80

This dissertation does not examine the ratio of net PP&E to environmental liability because the liability is difficult to estimate.

53

If cost of closure changes, the EPA's financial tests should provide an indication when the

amount of closure costs becomes unaffordable and a firm’s health questionable. From Chapter 3, we

know the financial tests are better at classifying bankrupt firm/years as opposed to non-bankrupt

firm/years. If the increase in closure costs does not cause the firm to fail the test, then the firm may

continue operations as usual. A change in type II error rates and classification accuracy merely indicates

that state environmental regulators might consider taking a closer look at the extreme cases of the non-

bankrupt firms that fail the tests when closure costs change dramatically.

A decrease in type I error indicates more bankrupt firms are correctly classified. With higher

estimates of closure costs, firms with declining health are less likely to meet their environmental

obligations. The EPA’s financial tests should be able to classify more bankrupt firms as failing with

increasing closure costs. Intuitively, I expect to see a decrease in type I error, an in increase in type II

error, and a decrease in overall classification accuracy with the change in closure costs. Higher closure

costs for non-bankrupt firm/years implies reallocation capital. Because the tests already misclassify non-

bankrupt firms, I expect increased misclassification with rising costs.

4.2 Data

For the sensitivity analysis, I use the same sample of firm/years that I used in Chapter 3 for the

analysis of the EPA’s financial tests. Similar to the prior analysis, I examine the overall sample on an

industry basis. The focus of this analysis is on the EPA’s financial tests and the classification ability with

varying closure costs. I calculate the ten costs of closure for each observation. Costs of closure vary

from one to ten percent of a firm’s net PP&E. The EPA’s financial tests require tangible net worth, net

working capital, and total assets to be greater than six times these estimated closure costs.

4.3 Methodology

I perform classification analyses for the varying levels of closure costs. In Table 4.1, I report the

classification accuracy results for the overall sample at each level of closure costs. A firm must have

tangible net worth and/or net working capital greater than six times the closure costs, depending upon

which financial test (#1 or #2) the firm uses to pass the EPA’s closure cost requirement. All the firms

within the sample have total assets well over six times the closure costs.

Type I error is a concern for the EPA because it means that the financial tests are not detecting

some bankrupt firms. These bankrupt firms continue operations without providing a more secured form of

assurance. As a result, the state and taxpayers may incur social costs related to the firm’s potential

default. Type II error is a concern to non-bankrupt firms who may be denied permits and forced to obtain

third party assurance mechanisms. Thus, they may incur additional business costs.

I test the null hypothesis (Ho) that the EPA’s financial tests are independent of the groups. Thus,

there should be no association or tendency in classifying the groups as closure costs change. This

means that there is no difference in classification accuracy when applying the tests to the non-bankrupt

and bankrupt groups. The alternative hypothesis (Ha) is that the EPA’s financial tests are dependent

54

from the groups. In other words, there is an association or tendency in the tests classification ability with

changing closure costs.

Similar to the analysis in Chapter 3, I use the Chi-square test of association for the overall

sample and the two-sided Fisher exact p-values when necessary (for industries). The Chi-square

statistics and two-sided Fisher exact p-values indicate an association or tendency of the tests in

classifying the two groups. I reject the null hypothesis if the p-values are less than a five-percent level of

significance. In the next section, I discuss the classification rates for the financial tests for varying levels

of closure costs for the entire sample and the industries.

4.4 Results

I report the type I and II errors, classification accuracy percentages, and the Chi-square statistic

and its p-value for the sample in Table 4.1. I find type I error decreases and type II error increases as

expected. As closure costs increase from one to ten percent, the type I error decreases by roughly two

percent and type II error increases almost 20 percent. On average, the within-group classification

accuracy decreases from 77 percent (with closure costs equaling one percent) to 69 percent (with closure

costs equaling ten percent). With respect to the entire sample, the average classification accuracy

decreases from approximately 62 percent to 45 percent.

The sensitivity of the non-bankrupt group influences the decrease in overall classification

accuracy with respect to the entire sample. This is not necessarily a negative aspect of the test. It

provides clues to the financial tests’ ability to detect firms that may struggle with the increase in closure

costs. Just because a non-bankrupt firm may fail the financial tests with a higher level of closure costs

does not mean the firm will default on its environmental liability. It merely signals a problem could arise in

the future if closure costs and all other costs related to closure exceed a certain level. However, the

potentially responsible parties still have the option to abandon their liability through bankruptcy protection

if the costs become such a burden that bankruptcy is necessary.

In Table 4.2, I report my finding for the industries. The only industries for which a consistent

association exists between the EPA’s financial tests and the groups are manufacturing, trades,

transportation, and information. Agriculture, mining, and services yield an association for some lower

levels of closure costs. No association exists between the financial tests and utilities, construction, and

real estate.

Classification accuracy within the groups decreased more dramatically for the non-bankrupt

group for certain industries. For example, as closure costs increased from one to ten percent,

classification accuracy for construction, trade, information, and services fell by almost six percent. This

implies that six percent more firms, in those industries, must find an alternate form of financial assurance.

For agriculture, mining, manufacturing, and real estate, classification accuracy decreased by an average

of 14 percent. For transportation and utilities, accuracy decreased by over 20 percent.

With respect to the entire sample, the decline in overall classification accuracy is dramatic. The

industries with the greatest decline in classification accuracy are real estate, utilities, transportation, and

55

mining. On average, those industries decline by almost 38 percent. For the agriculture and services, the

declines are almost 19 and 21 percent, respectively. For construction, trades, information, and

manufacturing, the percentage decline is in the lower teens [12, 12, 13, and 14 percent respectively].

I report in Table 4.3 Panel A the mean and median financial measures for the sample and for the

sample by groups. In Panel B, I record the mean and median financial measures by industry. I include

the medians because they provide a more realistic measure because the data is highly skew. When I

compare the mean and median measures for the non-bankrupt and bankrupt groups, the statistically

significant p-values (p<0.0001) imply both the means and the medians for the groups are dissimilar to

each other.

I examine the separate components of the financial tests. I limit my discussion to financial test #1

because it contains similar financial criteria as financial test #2 and retains a fuller sample. For financial

test #1, I find the means and medians for the overall sample and the groups pass the ratio #1 (total

liabilities/net worth). This means that firms, on average, have less than twice the liabilities as net worth.

The means and medians for the non-bankrupt group pass ratio #2 (net income plus depreciation,

depletion, and amortization/total liabilities) and the bankrupt group fails. For ratio #3 (current ratio), on

average, all groups pass the liquidity requirement except the median measure for the bankrupt group.

The first component of the EPA’s financial test #1 requires firms to pass two of the three ratios. It is

possible that some bankrupt firms will pass if it meets two of the three ratio’s requirements.

Examining the second component of financial test #1, that being tangible net worth greater than

$10 million, the bankrupt group fails. For the third and fourth component (tangible net worth and net

working capital, and total assets at least six times the total current closure costs), both groups median

measures pass the requirement. Because the bankrupt group, on average, fails to have tangible net

worth greater than $10 million, it fails the EPA’s financial test #1.

I find that as closure costs increase, on average, both groups fail more components. Specifically,

the groups tend to fail the net working capital requirement. On average, firms may be able to satisfy one

of the two but not both, and the criteria require both be satisfied simultaneously. Failing the net worth

requirement also causes a firm to fail financial test #2.

I report my industry findings in Table 4.3 Panel B. I find most industry means and all industry

medians pass ratio #1. Those industries whose means fail are utilities, construction, and trade. Similar

results hold for ratio #2. For ratio #3, the median measures for mining, utilities, transportation, and real

estate fail. The component most often failed by the industries is the net working capital requirement.

Specifically, utilities, transportation, information, real estate, and services fail when closure costs are one

percent of net PP&E. Thus, these four industries, on average, have a tendency to fail the EPA’s financial

tests more frequently than the other industries.81

All industries tend to have sufficient total assets to

cover closure costs. If the closure costs exceed 10 percent, then firms that are less healthy may find it

81

The utilities industry is different from other industries with respect to passing the tests. It is a highly regulated industry and the EPA is one of many agencies with which the industry complies. These results are not surprising and should not necessarily be disturbing because of the high level of regulation in this industry.

56

difficult to meet the closure costs. These firms will need alternate sources of financial assurance or

liquidate assets if necessary to acquire a third-party mechanism or to cover a liability.

4.5. Summary

In summary, I find the EPA’s financial tests are sensitive to varying closure costs. Specifically, as

the magnitude of closure costs increases, type I errors decrease with less magnitude than type II errors

increase. This means as closure costs increase, the financial tests tend to classify a greater number of

non-bankrupt firm/years as failing and fewer bankrupt firm/years as passing. The non-bankrupt

observations may be able to afford the potential increase of the environmental obligation. However, the

financial tests tend to err on the side of environmental caution by focusing on minimizing social costs.

Thus, higher closure costs result in more of the cost burden shifting to the firms engaged in activities that

may cause damage to the environment.

Overall, almost all industries pass the first few components of the financial tests. However, they

tend to fail the working capital requirements. Failure continues with increasing closure costs. This result

means firms may not have adequate current assets after satisfying current liabilities to assure closure

costs, regardless of the proxy for closure costs. However, the firms do show they have enough total

assets to handle the closure costs. Thus, firms have enough resources although those resources may be

not be specifically allocated to cover the liabilities.

One drawback with my analysis is that I assume accurate closure costs that are revised and

timely. Whether or not this situation actually occurs is difficult to say because the liabilities themselves

are difficult to estimate, and there is no standard method for their estimation. My analysis is somewhat

one-dimensional because I am assuming closure costs are a certain percentage of a firm’s PP&E.

Despite the lack of dimensionality, firms most often fail the working capital criteria. Because the financial

tests are promises of assurance, if a company cannot fulfill these promises with its current assets, then it

must obtain outside assistance of some sort. In the next chapter, I summarize my results and propose

future research.

57

CHAPTER 5 SUMMARY AND CONCLUSION

5.1 Summary of Findings

The EPA’s financial tests provide an inexpensive internal mechanism for firms to assure the EPA

that they will fund all environmentally related costs associated with business operations and closure. To

prove financial status, a firm must meet the requirements of the financial tests. The financial criteria

within the tests measure liquidity, profitability, and the ability of a firm to handle its obligations. These

measures should reflect the firm’s financial health in a timely fashion, and regulators should be able to

interpret when a firm begins to decline.82

If a firm meets the financial requirements, it implies that the firm

has sufficient means to fund environmentally related costs from “cradle to grave.” In return, the firm

receives operating permits that allows them to begin or continue operations. With this mechanism, a firm

is not obligated to put forth any funds towards an environmental liability until a claim occurs.

82

However, this is not always the case, as firms may switch auditors prior to reporting to the EPA or may not submit the financial information to the auditor in a timely fashion [McKeown, Mutchler, and Hopwood (1991); Geiger and Ranghunandan (2001); Weil (2001)]. According to the standards, the owner or operator has 60 days to report the updated cost of closure [40 CFR 264.143 (e) (9)] and must update all financial information and submit it within 90 days of the fiscal year end [40 CFR 264.143 (f) (5)]. Further, if any indication the company needs to obtain alternative financial assurance arises, the responsible party must obtain the alternative financial assurance within 120 days of the end of the fiscal year or within 30 days of notification from the regulators that the financial tests may not be used [40 CFR 264.143 (7 & 8)].

58

If the firm fails to prove its financial viability, it must obtain another form of financial assurance the

EPA deems acceptable prior to receiving a permit. These other forms of financial assurance include trust

funds, bonds, insurance, or any other external mechanism. Because these are third party mechanisms,

there is an additional business cost related to acquiring such a mechanism. Depending upon a firm’s

risk, this mechanism can be expensive or even nonexistent. For example, the State of Pennsylvania will

no longer accept performance bonds as a means for assurance for mining operations [Table A.1]. I apply

the EPA’s financial tests to firms that may incur an environmental liability. In my sample, I have two

independent groups, those firms that are non-bankrupt and those that are one year prior to filing

bankruptcy. I use classification analysis to determine if the EPA’s financial tests are able to classify the

firms by their actual financial status. I do so to examine the EPA’s ability to detect viability prior to filing

for bankruptcy. If the financial tests do not detect a firm’s viability, then there is the possibility that a firm

may file for bankruptcy protection prior to the EPA obtaining any funds for a liability. If a firm files for

bankruptcy protection, it will attempt to have their liabilities discharged. These liabilities include

environmental obligations. Much of the anecdotal evidence suggests that firms often use bankruptcy as a

means to escape their liabilities [Table A.1, McMinn and Brockett (1995), Melcer (2003), Morse (2004),

Sissell (2004), Chang (1998, 2003), Brickley (1997), and Cieri, Ganske, and Lennox (1999)]. Costs

related to environmental liabilities can magnify quickly and discharged liabilities become the responsibility

of the state and the taxpayers.

If the financial tests accurately classify firm viability then there is a chance that the EPA may

acquire the necessary funds from a firm before a bankruptcy court discharges the liabilities. Firms that

fail the financial tests may directly pay the EPA or fund the liability through the third party mechanisms

thus, sparing the state and taxpayers from the full cost of the environmental liability. The EPA’s goal is to

mitigate social costs.

Based on my results from Chapters 3 and 4, I find that the EPA’s financial tests are able to

classify most bankrupt firm/years correctly. However, the financial tests misclassify almost 40 percent of

the non-bankrupt firms. This implies that some non-bankrupt firms may be required to provide a more

costly third party assurance mechanism. From the firm’s perspective, the additional price firms must pay

for another mechanism may be too costly relative to what the firm can afford. However, if the firm wishes

to continue business operations, then they must obtain another acceptable form of assurance. This

additional cost represents a shift in corporate resources from other areas to provide coverage for

something that was once free.

The EPA attempts to balance the tradeoffs between business and social costs. The additional

cost paid by non-bankrupt firms to obtain alternative assurance is the trade off for the financial tests

stringency in detecting unhealthy firms. When I compare the EPA’s financial tests with other methods,

some of these other methods have increased classification accuracy for either one group or the other, but

not for both. For example, the Altman methods tend to classify more non-bankrupt firm/years correctly

than the EPA’s financial tests. Similarly, the Grice and Ingram approach tends to classify more bankrupt

firm/years correctly as compared to the EPA’s financial tests. However, I cannot make a judgment as to

59

whether one method is the best as the costs of misclassification are unknown. When we know the costs,

we can better value a method’s ability to classify firm/years.

I find that the bankrupt group is not particularly sensitive to varying the costs of closure, largely

because the rules detect most bankrupt firms at the lower level of closure costs assumed originally.

However, for the non-bankrupt group, the financial tests are sensitive and appear to be a prohibitive

criterion. Because the non-bankrupt group is sensitive, this influences the overall classification accuracy.

In turn, some industries show a greater decline of classification accuracy as closure costs increase.

Assuming misclassification costs are not symmetric then the misclassification of any bankrupt firm could

result in a higher social cost than what the non-bankrupt firms pay in additional business cost [Bergman

(2004)].

For the industries of interest, the mining industry appears to be more sensitive to increases in

closure costs than both the construction and manufacturing industries. As closure costs increase from

one to ten percent, classification accuracy declines almost 30 percent for mining about 10 percent for

construction, and 13 percent for manufacturing. Given the anecdotal evidence for mining, the decline in

classification accuracy is not surprising. Mining related costs can mushroom in cost and complexity as

illustrated in Pennsylvania’s concern over acid mine drainage costs and in Florida’s perpetual

maintenance of the phosphogypsum stacks.

5.2 Conclusions

Given the EPA’s desire for social cost mitigation, I find the EPA’s tests do a good job of

classifying observations from the bankrupt group. The tests tend to misclassify almost 40 percent of the

non-bankrupt observations resulting in increased business costs for those that wish to remain

operational. To balance the cost concerns, the financial tests may benefit from incorporating components

from the other methods. Specifically, the use of an Altman’s Z-Score may help in classifying more non-

bankrupt observations correctly. This may help to balance the mitigation of social costs with the

reduction of business costs.

From the onset of this dissertation, I wished to answer the following questions:

o Are these financial tests effective in assuring that financial resources exist to fund the

cleanup of environmental accidents? That is, can these tests detect when a firm will

go bankrupt?

o Do the financial tests foster cost internalization, or does it hinder those responsible

from taking responsibility?

From my findings, I conclude that the EPA’s financial tests do generally detect when a firm is very close

to insolvency. However, the tests are not effective in assuring that financial resources exist to fund a

necessary cleanup. Detection does not imply collection. There exists a vast gap between the two

[Bergmann (2004)]. What I mean is that the EPA’s financial tests may indicate when a firm is near

insolvency, this does not imply that the state regulators will be able to secure any funding from a firm

60

unless the firm is a willing to comply.83

Research shows that in general, firms are willing to internalize the

cost for the sake of firm reputation, longevity, firm value, and riskiness [Klassen and McLaughlin (1995),

McGuire, Sundgren, and Schneeweis (1988)]. However, these financial tests do not provide an external

guarantee. When a firm fails the financial tests and subsequently provides documentation of an alternate

financial assurance mechanism does the EPA truly have a guarantee? One could interpret the EPA’s

financial tests as relying on a firm’s sense of social responsibility.

5.3 Further Research

This topic provides many avenues for future research, such as the establishment of a benchmark

for what constitutes a standard level of classification accuracy. The EPA does not have a measure to

compare classification performance. Therefore comparing the other methods against the EPA’s financial

tests is difficult. Developing means of estimating the cost of misclassification for both non-bankrupt and

bankrupt firms would facilitate comparisons across methods considerably. The individual financial test

criteria also are somewhat deficient in that they do not incorporate items such as environmental

variances, legal nuances, or political influence [Barry, Bergman, Hohmann, and Steckler, 1997].

In the last five years, the dominant focus of prediction models research has been on one type of

model—neural networks using genetic algorithms. Neural networks and genetic algorithms have been an

operational topic for some time in the fields of biology, mathematics, and computer science. Only within

the last decade have we seen financial applications [Frydman, Altman, Kao (1985); Lacher, Coats,

Sharma, and Fant (1995); Olmeda and Fernandez (1997); Shah and Murtaza, (2000); Sung, Chang, and

Lee (1999); Yang, Platt, and Platt (1999); Zhang, Hu, Patuwo, and Indro, (1999); Lee, Han, and Kwon

(1996); Jain and Nag (1997)]. Most of the research published about the use of neural networks in finance

has been by the neurocomputing, operations research, information management systems, and statistics

communities. These methods warrant investigation with respect to the financial test criteria.

With respect to changes in accounting regulations, it will be interesting to revisit this topic in the

near future. We do not yet know the full impact FAS 143 and Sarbanes-Oxley will have on environmental

liabilities [FAS 143, Alciatore, Dee, and Easton (2004)]. These new regulations require increased

transparency, thus providing regulators with more company specific information with which to make

permit granting decisions.

83

As in the case of Mulberry Phosphates, state regulators received notification twenty-four hours prior to corporate abandonment.

61

Table 2.1 Time line of the major environmental laws This information is unoriginal and is available from a variety of sources [Cross and Miller (2001), www.epa.gov]. I provide this list for the convenience to show the EPA’s evolution of legislative activity.

Law Year enacted

and/or amended

Federal Food, Drug, and Cosmetic Act 1938 Shoreline Erosion Protection Act 1965 Solid Waste Disposal Act 1965 National Environmental Policy Act 1969 The Clean Air Act 1955, 1977, 1990 The Occupational Safety Health Act 1970 Pollution Prevention Packaging Act 1970 Resource Recovery Act 1970 Lead-Based Paint Poisoning Prevention Act 1971 Coastal Zone Management Act 1972 Federal Insecticide, Fungicide, and Rodenticide Act 1947, 1972 Marine Protection, Research, and Sanctuaries Act 1972 Ocean Dumping Act 1972 Endangered Species Act 1973 The Safe Drinking Water Act 1974 Shoreline Erosion Control Demonstration Act 1974 Hazardous Materials Transportation Act 1975 The Resource Conservation and Recovery Act 1976 The Toxic Substances Control Act 1976 Federal Water Pollution Control Act or The Clean Water Act 1948, 1972, 1977 Surface Mining Control and Reclamation Act 1977 Uranium Mill-Tailings Radiation Control Act 1978 Asbestos School Hazard Detection and Control Act 1980 Comprehensive Environmental Response, Compensation, and Liability Act or Superfund

1980

Nuclear Waste Policy Act 1982 Asbestos School Hazard Abatement Act 1984 Asbestos Hazard Emergency Response Act 1986 Emergency Planning and Community Right to Know Act 1986 The Superfund Amendments and Reauthorization Act 1986 Indoor Radon Abatement Act 1988 Lead Contamination Control Act 1988 Medical Waste Tracking Act 1988 Ocean Dumping Ban Act 1988 Shore Protection Act 1988 National Environmental Education Act 1990 The Pollution Prevention Act 1990 The Oil Pollution Act 1990 The Sanitary Food Transportation Act 1990 Food Quality Protection Act 1996 Chemical Safety Information, Site Security and Fuels Regulatory Relief Act 1999

62

Table 3.1 State versus federal regulations Some state regulations differ from the federal regulations. Below is a list of states whose regulations for the hazardous and solid waste storage and treatment facilities which includes publicly and privately owned landfills and underground storage tanks varies from the federal regulations. State regulations must be as strict as the federal regulations

State Variation State regulation

Florida Tangible net worth requirement is half the federal requirement (solid waste only).

FLA 62-701.900 (5) (e)

Massachusetts

Financial tests are not available options for financial assurance mechanism as of November 2002.

310 CMR 30.900

Nevada The current ratio is absent. NAC 444.7499 Section 13

North Dakota The tangible net worth and working capital requirements are two-thirds the federal requirement.

NDAC 33-20-14

Oklahoma The current ratio is absent. OAC 252:515-27-81

Oregon The debt-to-equity ratio allows more debt and the federal requirement and bond ratings are not required.

OAR 340-094-0145 (6) (f)

Virginia Lacks the federally required debt-to-equity ratio and the current ratio is absent.

9 VAC 20-70-200

Wisconsin

Different tests are required depending upon the type of activity and the tests use additional financial ratios in addition to the federally required ones and bond ratings are incorporated.

NR 685.07 (5) (f), WAC 298.41 (4)-(7), NR 685.08 (8)

63

Figure 3.1: The proportion of bankrupt firm/years prior to bankruptcy from 1985-1999. I illustrate the proportion of bankrupt firm/years per year in the sample. My sample includes firm/year observations from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999.

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year prior to bankruptcy

Proportion of bankrupt firms

64

Figure 3.2: Proportion of bankrupt firm/years by industry from 1985-1999. I illustrate the proportion of bankrupt firm/years by industry in the sample. My sample includes firm/year observations from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999.

0.00% 1.00% 2.00% 3.00% 4.00%

Agriculture, Forestry, Fishing, and

Hunting

Mining

Utilities

Construction

Manufacturing

Wholesale and Retail Trade

Transportation and Warehousing

Information

Real Estate, Rental, and Leasing

Services

Industry

Proportion of bankrupt firms

65

Table 3.2 Comparison of methods and models The EPA uses the financial tests, in Panel A. The financial tests are the EPA’s federal guidelines for satisfying financial assurance requirements. Any firm may use financial test #1 whereas only firms with bond ratings may use financial test #2. The firms must satisfy all the criteria to pass the financial tests and receive or update permits from the EPA. The Altman’s Z-Score model for privately held firms, publicly traded firms, and the Altman’s Z-score model for publicly traded firms are in Panel B.

Panel A: Financial Tests

Financial test #1 Two of the following three ratios: A ratio of total liabilities to net worth less than 2.0, a ratio of the sum of net income plus depreciation, depletion, and amortization to total liabilities greater than 0.1, and a ratio of current assets to current liabilities greater than 1.5, and tangible net worth of at least $10 million, and tangible net worth and net working capital, both at least six times the total current closure costs for the total of all facilities, and 90% of all assets located in the United States of the total assets or at least six times the current closure costs.

Financial test #2 Tangible net worth of at least $10 million, and tangible net worth of at least six times the total current closure costs, and

90% of all assets located in the United States amounting to at least 90 percent of the total assets or at least six times the current closure cost, and (met with screening out foreign firms) and the most recent bond issuance rated at BBB or above by Standard and Poor’s or Baa or above by Moody’s.

Panel B: Other Models

Model Variations Healthy Firm Indicators Indeterminate Firm Indicators

Unhealthy Firm Indicators

Altman’s Z-Score for Publicly traded firms

Z > 2.675 1.810 < Z < 2.675 Z < 1.810

Altman’s Z-Score for Privately held firms

Z’ > 2.900 1.230 <Z’ < 2.900 Z’ < 1.230

66

Table 3.3 Panel A: Classification results for the EPA’s financial tests, 1985-1999 I classify the distribution of 35,420 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I classify firms as passing or failing the EPA’s financial tests in the year prior to bankruptcy. The cells contain the frequency of observations and I denote the percentage of the entire sample in brackets. Type I error indicates the rate at which bankrupt firm/years pass the EPA’s financial tests in the year prior to bankruptcy. Type II error indicates the rate at which non-bankrupt firm/years fail the EPA’s financial tests. I report the Chi-square statistic and corresponding p-value.

EPA’s Financial Tests

Bankrupt Non-bankrupt Total

Pass 39 [7.82%]

21,655[62.01%]

21,694

Fail 460[92.18%]

13,266[37.99%]

13,726

Total Chi-square p-value

499608.8115<0.0001

34,921 35,420

67

Table 3.3 Continued Panel B: Classification accuracy rates for the EPA’s financial tests by year, 1985-1999 I classify the distribution of 35,420 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I classify firms as passing or failing the EPA’s financial tests in the year prior to bankruptcy. I report the two-sided Fisher exact p-values in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. I denote the annual sample size by N and report the percentages of correct and incorrect classification. Correct classification occurs when non-bankrupt firm/years pass and bankrupt firm/years fail the EPA’s financial tests in the year prior to bankruptcy from 1985-1999. Incorrect classification occurs when non-bankrupt firms fail and bankrupt firm/years pass the EPA’s financial tests in the year prior to bankruptcy from 1985-1999.

Year Number of

firm/years Bankrupt correctly classified

Non-bankrupt correctly classified

Fisher exact p-

value

1985 2,382 88.89% 56.68% 0.0128 1986 2,611 96.55% 55.85% <0.0001 1987 2,631 100.00% 56.29% <0.0001 1988 2,427 100.00% 56.73% <0.0001 1989 2,312 98.08% 57.83% <0.0001 1990 2,217 97.44% 60.42% <0.0001 1991 2,230 93.33% 62.55% <0.0001 1992 2,179 82.35% 65.45% <0.0001 1993 2,312 92.59% 66.00% <0.0001 1994 2,450 88.24% 65.15% <0.0001 1995 2,540 94.74% 64.95% <0.0001 1996 2,618 78.57% 66.10% <0.0001 1997 2,489 88.64% 65.89% <0.0001 1998 2,201 80.00% 65.94% <0.0001 1999 1,821 95.24% 66.39% <0.0001

All years 35,420 92.18% 62.01% <0.0001

68

Figure 3.3: Classification error rates for the EPA’s financial tests from 1985-1999. I illustrate the distribution of classification error rates for the EPA’s financial tests from 1985-1999. Type I error indicates bankrupt firm/years misclassified as non-bankrupt firm/years. Type II error indicates non-bankrupt firm/years misclassified as bankrupt firm/years.

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

1985 1986 1987 1988 1989 19901991 1992 1993 1994 1995 1996 1997 1998 1999 All

Year

Error rates

Type I Error: Misclassified bankrupt firm/years

Type II Error: Misclassified non-bankrkupt firm/years

69

Table 3.4 Classification accuracy rates for the EPA’s financial tests by industry, 1985-1999 I use a distribution of 35,420 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I classify firm/years as passing or failing the EPA’s financial test requirement. For bankrupt firms, I use the data for the year prior to bankruptcy. I classify industries subject to EPA guidelines by two-digit North American Industrial Classification System (NAICS) code. The NAICS code identifies company activity. I consolidate the NAICS codes for wholesale trade (NAICS code 42) and retail trade (NAICS codes 44-45) into trade. I denote the industry sample size by N and report the classification accuracy rates. I report the two-sided Fisher exact p-values in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. Correct classification occurs when non-bankrupt firm/years pass and bankrupt firm/years fail the EPA’s financial tests in the year prior to bankruptcy from 1985-1999. Incorrect classification occurs when non-bankrupt firms fail and bankrupt firm/years pass the EPA’s financial tests in the year prior to bankruptcy from 1985-1999.

Industries subject to EPA guidelines

NAICS code Number of firm/years

Bankrupt correctly classified

Non-bankrupt correctly classified

Fisher exact p-value

Agriculture, Forestry, Fishing, and Hunting

11 187 85.71 % 61.67 % 0.0173

Mining 21 2,224 96.30 % 41.69 % <0.0001 Utilities 22 2,028 100.00% 55.73% 0.1964 Construction 23 359 85.71% 42.90% 0.2461 Manufacturing 31-33 20,948 91.90% 68.04% <0.0001 Wholesale and Retail Trade

42-45 4,037 92.31% 59.54% <0.0001

Transportation and Warehousing

48-49 1,077 100.00% 53.26% <0.0001

Information 51 3,931 92.98% 52.66% <0.0001 Real Estate, Rental, and Leasing

53 195 100.00% 51.30% 0.2411

Services 56 434 80.00% 54.89% 0.0086

70

Figure 3.4: Classification error rates for the EPA’s financial tests by industry. I illustrate the distribution of classification error rates for the EPA’s financial tests from 1985-1999, by industry. Type I error indicates bankrupt firm/years misclassified as non-bankrupt firm/years. Type II error indicates non-bankrupt firm/years misclassified as bankrupt firm/years.

0% 10% 20% 30% 40% 50% 60% 70% 80%

Agriculture, Forestry, Fishing, and Hunting

Mining

Utilities

Construction

Manufacturing

Wholesale and Retail Trade

Transportation and Warehousing

Information

Real Estate, Rental, and Leasing

Services

Industry

Industry

Type I Error: Misclassification of bankrupt firm/years

Type II Error: Misclassification of non-bankrupt firm/years

71

Table 3.5 Panel A: Classification results for Grice and Ingram (2001), 1985-1999 I classify the distribution of 20,627 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999 by the definition of health by Grice and Ingram (2001) in the year prior to bankruptcy. The cells contain the frequency of observations and I denote the percentage of the entire sample in brackets. Type I error indicates the rate at which bankrupt firm/years pass Grice and Ingram’s definition in the year prior to bankruptcy. Type II error indicates the rate at which non-bankrupt firm/years fail Grice and Ingram’s definition. Grice and Ingram’s (2001) definition of financial distress in classifying non-distressed and distressed firms is such that non-distressed firms are those that have stock ratings of B or greater or investment grade bond ratings. Distressed firms are those whose stock ratings are below B or bonds that do not have investment grade ratings or firms that have filed for bankruptcy. I report the Chi-square statistic and corresponding p-value. I also report the two-sided Fisher exact p-values* because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5.

Grice and Ingram (2001)

Bankrupt Non-bankrupt Total

Non-distressed 1 [1.39%]

7,932[38.59 %]

7,933

Distressed 71[98.61%]

12,623[61.41 %]

12,694

Total Chi-square p-value Fisher exact p-value

7249.9510<0.0001<0.0001

20,555 20,627

72

Table 3.5 Continued Panel B: Classification accuracy rates for Grice and Ingram (2001) by year, 1985-1999 I use a distribution of 20,627 firm/years is drawn from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I apply Grice and Ingram’s (2001) definition of financial distress to the sample. Grice and Ingram’s (2001) definition of financial distress is such that non-distressed firms are those that have stock ratings of B or greater or investment grade bond ratings. Distressed firms are those whose stock ratings are below B or bonds that do not have investment grade ratings or firms that have filed for bankruptcy. I denote the overall and annual sample size by N and report the correct and incorrect classification rates. I report the percent for each type of firm, non-bankrupt and bankrupt, that are correctly classified. Correct classification occurs when non-bankrupt firm/years are classified as non-distressed and bankrupt firm/years are classified as distressed by Grice and Ingram in the year prior to bankruptcy from 1985-1999. Incorrect classification occurs when non-bankrupt firms are classified as distressed and bankrupt firm/years are classified as non-distressed by Grice and Ingram in the year prior to bankruptcy from 1985-1999. I report the two-sided Fisher exact p-values in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5.

Year N Bankrupt correctly classified

Non-bankrupt correctly classified

Fisher exact p-

value 1985 1,247 100.00% 54.74% 0.4531 1986 1,463 75.00% 46.88% 0.6275 1987 1,425 100.00% 43.12% 1.0000 1988 1,358 100.00% 42.58% 0.5111 1989 1,359 100.00% 40.46% 0.0461 1990 1,331 100.00% 40.32% 0.1535 1991 1,327 100.00% 39.53% 0.1583 1992 1,272 100.00% 32.83% 0.1794 1993 1,329 100.00% 32.33% 0.1823 1994 1,443 100.00% 33.52% 0.5543 1995 1,455 100.00% 34.18% 0.3060 1996 1,473 100.00% 34.63% 0.5556 1997 1,470 100.00% 34.65% 0.3050 1998 1,412 100.00% 35.03% 0.0022 1999 1,263 100.00% 35.28% 0.0179

All years 20,627 98.61% 38.59 % <0.0001

73

Table 3.6 Classification accuracy rates for Grice and Ingram (2001) by industry, 1985-1999 I use a distribution of 20,627 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I apply Grice and Ingram’s (2001) definition of financial distress to the sample. Grice and Ingram’s (2001) definition of financial distress is such that non-distressed firms are those that have stock ratings of B or greater or investment grade bond ratings. Distressed firms are those whose stock ratings are below B or bonds that do not have investment grade ratings or firms that have filed for bankruptcy. The North American Industrial Classification System (NAICS) code identifies company activity. I consolidate the NAICS codes for wholesale trade (NAICS code 42) and retail trade (NAICS codes 44-45) into trade and similarly, I consolidate services (NAICS codes 54 to 92). I denote the industry sample size by N and the classification rates. I report the percent for each type of firm, non-bankrupt and bankrupt, that are correctly classified. Correct classification occurs when non-bankrupt firm/years are classified as non-distressed and bankrupt firm/years are classified as distressed by Grice and Ingram in the year prior to bankruptcy from 1985-1999. Incorrect classification occurs when non-bankrupt firms are classified as distressed and bankrupt firm/years are classified as non-distressed by Grice and Ingram in the year prior to bankruptcy from 1985-1999. I report the two-sided Fisher exact p-values in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. *I indicate the lack of a statistic due to lack of bankrupt firm/years with N/A.

Industries subject to EPA guidelines

NAIC code N Bankrupt correctly

classified

Non-bankrupt correctly

classified

Fisher exact p-value

Agriculture, Forestry, Fishing, and Hunting

11 85 100.00% 28.57% 1.0000

Mining 21 1,119 100.00% 9.51% 1.0000 Utilities 22 1,840 N/A 74.95% N/A Construction 23 208 100.00% 20.29% 1.0000 Manufacturing 31-33 12,654 100.00% 35.99% <0.0001 Wholesale and Retail Trade 42-45 2,119 100.00% 40.62% 0.0013 Transportation and Warehousing

48-49 667 100.00% 31.17% 0.5561

Information 51 1,627 90.00% 44.71% 0.0497 Real Estate, Rental, and Leasing

53 119 N/A 14.29 % N/A

Services 56 189 100.00% 20.21 % 1.0000

74

Table 3.7 Panel A: Classification results for bond ratings, 1985-1999 I use a distribution of 8,210 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. Because in this analysis, I classify firm/years by Standard and Poor’s long-term domestic bond ratings, I remove those firm/years without bond ratings. Type I error indicates the rate at which bankrupt firm/years have bond ratings at least BBB or greater (investment grade bond ratings) in the year prior to bankruptcy. Type II error indicates the rate at which non-bankrupt firm/years fail to have bond ratings less than BBB. I report the Chi-square statistic and corresponding p-value. I also report the two-sided Fisher exact p-values* because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5.

Bond Ratings Bankrupt Non-bankrupt Total

At least BBB 0* [0.00%]

5,572[67.96%]

5,572

Below BBB 11[100.00%]

2,627[32.04%]

2,638

Total Chi-square p-value Fisher exact p-value

1123.2654<0.0001<0.0001

8,199 8,210

75

Table 3.7 Continued Panel B: Classification accuracy rates for bond ratings by year, 1985-1999 I use a distribution of 8,210 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I classify firm/years as being above or below a rating of BBB by Standard and Poor’s long-term domestic bond ratings. Because in this analysis, I classify firm/years by Standard and Poor’s long-term domestic bond ratings, I remove those firm/years without bond ratings. I find 5,572 firm/years with ratings above BBB and 2,638 below BBB. I report the percent for each type of firm, non-bankrupt and bankrupt, that are correctly classified. Correct classification occurs when non-bankrupt firm/years has bond ratings of at least BBB and bankrupt firm/years have bond ratings below BBB in the year prior to bankruptcy. Incorrect classification occurs if non-bankrupt firm/years have bond ratings less than BBB and bankrupt firm/years have bond ratings of at least BBB in the year prior to bankruptcy. I denote the sample size by N. I report the two-sided Fisher exact p-values in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. Year N Bankrupt

correctly classified

Non-bankrupt correctly classified

Fisher exact p-value*

1985 400 0.00% 73.50% N/A 1986** 569 100.00% 65.85% 0.3427

1987 567 0.00% 64.90% N/A 1988 521 0.00% 67.37% N/A 1989 499 0.00% 69.14% N/A 1990 475 0.00% 72.00% N/A 1991 490 0.00% 73.27% N/A 1992 453 0.00% 69.09% N/A 1993 488 0.00% 66.60% N/A 1994 558 0.00% 68.82% N/A 1995 579 0.00% 68.22% N/A 1996 618 0.00% 67.31% N/A

1997** 663 100.00% 66.31% 0.3379 1998** 683 100.00% 65.24% <0.0001 1999** 647 100.00% 66.05% 0.1163

All years 8,210 100.00% 67.96% <0.0001 *I indicate the lack of a statistic due to lack of bankrupt firm/years with N/A. **In these years, all of the bankrupt observations are correctly classified. In 1986 and 1987, there is one observation in each year. In 1998, there are seven observations and in 1999, there are two observations.

76

Table 3.8 Classification accuracy rates for bond ratings by industry, 1985-1999 I use a distribution of 8,210 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I classify firm/years as being above or below a rating of BBB by Standard and Poor’s long-term domestic bond ratings. Because in this analysis, I classify firm/years by Standard and Poor’s long-term domestic bond ratings, I remove those firm/years without bond ratings. I find 5,572 firm/years with ratings above BBB and 2,638 below BBB. The NAICS code identifies company activity. I consolidate the NAICS codes for wholesale trade (NAICS code 42) and retail trade (NAICS codes 44-45) into trade. I report the percent for each type of firm, non-bankrupt and bankrupt, that are correctly classified. Correct classification occurs when non-bankrupt firm/years has bond ratings of at least BBB and bankrupt firm/years have bond ratings below BBB in the year prior to bankruptcy. Incorrect classification occurs if non-bankrupt firm/years have bond ratings less than BBB and bankrupt firm/years have bond ratings of at least BBB in the year prior to bankruptcy. I denote the sample size by N. I report the two-sided Fisher exact p-values* in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5.

Industries subject to EPA guidelines

NAICS Code

N Bankrupt correctly

classified

Non-bankrupt correctly

classified

Fisher exact p-value*

Agriculture, Forestry, Fishing, and Hunting

11 25 0.00% 76.00% N/A

Mining 21 543 0.00% 45.67% N/A Utilities 22 1,041 0.00% 93.76% N/A Construction 23 27 0.00% 33.33% N/A Manufacturing** 31-33 4,377 100.00% 67.02% 0.0039 Wholesale and Retail Trade**

42-45 874 100.00% 61.54% 0.0575

Transportation and Warehousing**

48-49 404 100.00% 67.25% 0.3292

Information** 51 774 100.00% 65.54% 0.1196 Real Estate, Rental, and Leasing

53 54 0.00% 74.07% N/A

Services 56 91 0.00% 40.66% N/A *I indicate the lack of a statistic due to lack of bankrupt firms with N/A. **In these industries, all of the bankrupt observations are correctly classified. The manufacturing, trade, transportation, and information industries have five, three, one, and two bankrupt observations, respectively.

77

Table 3.9 Panel A: Classification results auditor opinion, 1985-1999 I classify the distribution of 35,420 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999 by auditor opinion in the year prior to bankruptcy. Correct classification occurs when non-bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion. Likewise, correct classification also occurs when bankrupt firm/years receive no opinion or an adverse opinion in the year prior to bankruptcy. Type I error indicates the rate at which bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion in the year prior to bankruptcy. Type II error indicates the rate at which non-bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion. I report the Chi-square statistic and corresponding p-value.

Auditor opinion Bankrupt Non-bankrupt Total

Unqualified, unqualified with clarification, or qualified

486[97.39%]

34868[99.85%]

35,354

Adverse or none 13[2.61%]

53[0.15%]

66

Total Chi-square p-value

499159.2223<0.0001

34,921 35,420

78

Table 3.9 Continued Panel B: Classification accuracy rates for auditor opinion by year, 1985-1999 I classify the distribution of 35,420 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999 by auditor opinion in the year prior to bankruptcy. Correct classification occurs when non-bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion. Likewise, correct classification also occurs when bankrupt firm/years receive no opinion or an adverse opinion in the year prior to bankruptcy. Type I error indicates the rate at which bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion in the year prior to bankruptcy. Type II error indicates the rate at which non-bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion. I report the two-sided Fisher exact p-values* in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. I denote the sample size by N.

Year N Bankrupt

correctly classified

Non-bankrupt correctly classified

Fisher exact p-values*

1985 2,382 0.00% 99.62% 1.0000 1986 2,611 3.45% 99.69% 0.0958 1987 2,631 0.00% 99.73% 1.0000 1988 2,427 9.09% 99.83% <0.0001 1989 2,312 7.69% 99.73% <0.0001 1990 2,217 2.56% 99.86% 0.0686 1991 2,230 10.00% 99.82% <0.0001 1992 2,179 5.88% 99.86% 0.0309 1993 2,312 0.00% 99.91% 1.0000 1994 2,450 0.00% 99.96% 1.0000 1995 2,540 0.00% 99.92% 1.0000 1996 2,618 0.00% 99.88% 1.0000 1997 2,489 0.00% 99.96% 1.0000 1998 2,201 0.00% 100.00% N/A 1999 1,821 0.00% 100.00% N/A

All years 35,420 2.61% 99.85% <0.0001 *All opinions are unqualified, unqualified with clarification, or qualified. Therefore, I could not calculate a statistic.

79

Table 3.10 Classification accuracy rates for auditor opinion by industry, 1985-1999 I classify the distribution of 35,420 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999 by auditor opinion by two-digit North American Industrial Classification System (NAICS code). The NAICS code identifies company activity. I consolidate the NAICS codes for wholesale trade (NAICS code 42) and retail trade (NAICS codes 44-45) into trade. Correct classification occurs when non-bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion. Likewise, correct classification also occurs when bankrupt firm/years receive no opinion or an adverse opinion in the year prior to bankruptcy. Type I error indicates the rate at which bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion in the year prior to bankruptcy. Type II error indicates the rate at which non-bankrupt firm/years receive an unqualified opinion, an unqualified opinion with clarification, or a qualified opinion. I report the two-sided Fisher exact p-values* in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. I denote the sample size by N

Industries subject to EPA

guidelines

NAICS code

N Bankrupt correctly

classified

Non-bankrupt correctly

classified

Fisher exact

p-value*

Agriculture, Forestry, Fishing, and Hunting

11 187 0.00% 100.00 N/A

Mining 21 2,224 3.70% 99.54% 0.1260 Utilities 22 2,028 0.00% 100.00% N/A Construction 23 359 14.29% 98.86% 0.0943 Manufacturing 31-33 20,948 2.46% 99.92% <0.0001 Wholesale and Retail Trade

42-45 4,037 3.85% 99.72% 0.0022

Transportation and Warehousing

48-49 1,077 0.00% 100.00% N/A

Information 51 3,931 1.75% 99.72% 0.1610 Real Estate, Rental, and Leasing

53 195 0.00% 100.00% N/A

Services 56 434 0.00% 100.00% N/A *All opinions are unqualified, unqualified with clarification, or qualified. Therefore, I could not calculate a statistic.

80

Table 3.11 Panel A: Classification results for the Altman Z-Score Model for publicly traded firms, 1985-1999

I use a distribution of 35,012 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. Sample attrition is the result of insufficient data. I calculate Z-scores for each firm/year using Altman’s Z-Score Model for publicly traded firms. For publicly traded firms, Z-Scores less than 1.81 indicate a higher propensity for bankruptcy. Z-Scores, between 1.81 and 2.675 are inconclusive and Z-Scores greater than 2.675 indicate a lower propensity for a firm to go bankrupt. I combine the inconclusive Z-Scores with those that indicate a lower propensity for bankruptcy into one category. Type I error indicates the rate at which bankrupt firm/years have Z-Scores at least 1.81 in the year prior to bankruptcy and type II error indicates the rate at which non-bankrupt firm/years have Z-Scores less than 1.81. I report Chi-square and corresponding p-value. I denote the sample size by N. Altman’s Z-Score Model for publicly traded firms is as follows:

Z = 0.012X1 + 0.014X2 + 0.033X3 + 0.0006X4 + 0.999X5, where Z = the Z-Score firm health indicator, X1 = working capital/total assets, X2 = retained earnings/total assets, X3 = earnings before interest and taxes/total assets, X4 = market value of equity/book value of liabilities, and X5 = sales/total assets.

Altman’s Z-Score Model for publicly traded firms

Bankrupt Non-bankrupt Total

Z ≥ 1.81 139 [29.57%]

26,076[75.49%]

26,215

Z < 1.81 331[70.43%]

8,466[24.51%]

8,797

Total Chi-square p-value

470519.6494<0.0001

34,542 35,012

81

Table 3.11 Continued Panel B: Classification accuracy rates for the Altman Z-Score Model for publicly

traded firms by year, 1985-1999 I use a distribution of 35,012 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. Sample attrition is the result of insufficient data. I calculate Z-scores for each firm/year using Altman’s Z-Score Model for publicly traded firms. For publicly traded firms, Z-Scores less than 1.81 indicate a higher propensity for bankruptcy. Z-Scores, between 1.81 and 2.675 are inconclusive and Z-Scores greater than 2.675 indicate a lower propensity for a firm to go bankrupt. I combine the inconclusive Z-Scores with those that indicate a lower propensity for bankruptcy into one category. Type I error indicates the rate at which bankrupt firm/years have Z-Scores at least 1.81 in the year prior to bankruptcy and type II error indicates the rate at which non-bankrupt firm/years have Z-Scores less than 1.81. I report the two-sided Fisher exact p-values* in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. I denote the sample size by N. Altman’s Z-Score Model for publicly traded firms is as follows:

Z = 0.012X1 + 0.014X2 + 0.033X3 + 0.0006X4 + 0.999X5, where Z = the Z-Score firm health indicator, X1 = working capital/total assets, X2 = retained earnings/total assets, X3 = earnings before interest and taxes/total assets, X4 = market value of equity/book value of liabilities, and X5 = sales/total assets.

Year N Bankrupt

correctly classified

Non-bankrupt correctly classified

Fisher exact

p-value

1985 2,356 28.57% 76.16% 0.67431986 2,580 75.00% 73.63% <0.00011987 2,597 82.35% 74.29% <0.00011988 2,397 80.00% 73.47% <0.00011989 2,283 76.00% 73.98% <0.00011990 2,197 81.58% 72.30% <0.00011991 2,208 84.62% 74.24% <0.00011992 2,158 81.25% 78.48% <0.00011993 2,283 46.15% 80.24% 0.00231994 2,424 56.25% 75.25% 0.00721995 2,513 75.68% 77.50% <0.00011996 2,579 60.71% 78.83% <0.00011997 2,456 70.73% 76.73% <0.00011998 2,178 59.26% 72.98% <0.00011999 1,803 64.10% 73.41% <0.0001

All years 35,012 70.43% 75.49% <0.0001

82

Table 3.12 Classification accuracy rates for the Altman Z-Score Model for publicly traded firms by industry, 1985-1999 I use a distribution of 35,012 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. Sample attrition is the result of insufficient data. I calculate Z-scores for each firm/year using Altman’s Z-Score Model for publicly traded firms. For publicly traded firms, Z-Scores less than 1.81 indicate a higher propensity for bankruptcy. Z-Scores, between 1.81 and 2.675 are inconclusive and Z-Scores greater than 2.675 indicate a lower propensity for a firm to go bankrupt. I combine the inconclusive Z-Scores with those that indicate a lower propensity for bankruptcy into one category. Type I error indicates the rate at which bankrupt firm/years have Z-Scores at least 1.81 in the year prior to bankruptcy and type II error indicates the rate at which non-bankrupt firm/years have Z-Scores less than 1.81. The NAICS code identifies company activity. I consolidate the NAICS codes for wholesale trade (NAICS code 42) and retail trade (NAICS codes 44-45) into trade. I report the two-sided Fisher exact p-values* in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. I denote the sample size by N. Altman’s Z-Score Model for publicly traded firms is as follows:

Z = 0.012X1 + 0.014X2 + 0.033X3 + 0.0006X4 + 0.999X5, where Z = the Z-Score firm health indicator, X1 = working capital/total assets, X2 = retained earnings/total assets, X3 = earnings before interest and taxes/total assets, X4 = market value of equity/book value of liabilities, and X5 = sales/total assets.

Industries subject to EPA guidelines

NAICS code

N Bankrupt correctly

classified

Non-bankrupt correctly

classified

Fisher exact p-value

Agriculture, Forestry, Fishing, and Hunting

11 186 71.43% 71.51% 0.0268

Mining 21 2,172 83.33% 44.65% 0.0063 Utilities 22 2,023 100.00% 17.41% 1.0000 Construction 23 359 85.71% 83.24% 0.0002 Manufacturing 31-33 20,673 76.98% 84.07% <0.0001 Wholesale and Retail Trade

42-45 4,013 46.75% 88.29% <0.0001

Transportation and Warehousing

48-49 1,077 65.00% 61.87% 0.0193

Information 51 3,886 71.70% 69.29% <0.0001 Real Estate, Rental, and Leasing

53 195 50.00% 48.19% 1.0000

Services 56 428 50.00% 74.88% 0.0572

83

Table 3.13 Panel A: Classification results for the Altman Z-Score Model for privately held firms, 1985-1999

I use a distribution of 33,757 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. Sample attrition is the result of insufficient data. I calculate Z-scores for each firm/year using Altman’s Z-Score Model for privately held firms. For privately held firms, Z-Scores less than 1.23 indicate a higher propensity for bankruptcy. Z-Scores, between 1.23 and 2.90 are inconclusive and Z-Scores greater than 2.90 indicate a lower propensity for a firm to go bankrupt. I combine the inconclusive Z-Scores with those that indicate a lower propensity for bankruptcy into one category. Type I error indicates the rate at which bankrupt firm/years have Z-Scores at least 1.23 in the year prior to bankruptcy and type II error indicates the rate at which non-bankrupt firm/years have Z-Scores less than 1.23. I report Chi-square and corresponding p-value. I denote the sample size by N. Altman’s Z-Score Model for privately held firms is as follows:

Z’ = 0.717X1 + 0.847X2 + 3.107X3 + 0.420X4 + 0.998X5, where Z’ = the Z-Score firm health indicator, X1 = working capital/total assets, X2 = retained earnings/total assets, X3 = earnings before interest and taxes/total assets, X4 = net worth/book value of liabilities, and X5 = sales/total assets.

Altman’s Z-Score Model for privately held firms

Bankrupt Non-bankrupt Total

Z ≥ 1.23 142 [41.40%]

26,143[78.24%]

26,285

Z < 1.23 201[58.60%]

7,271[21.76%]

7,472

Total Chi-square p-value

343267.3539<0.0001

33,414 33,757

84

Table 3.13 Continued Panel B: Classification accuracy rates for the Altman Z-Score Model for privately held firms

by year, 1985-1999 I use a distribution of 33,757 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. Sample attrition is the result of insufficient data. I calculate Z-scores for each firm/year using Altman’s Z-Score Model for privately held firms. For privately held firms, Z-Scores less than 1.23 indicate a higher propensity for bankruptcy. Z-Scores, between 1.23 and 2.90 are inconclusive and Z-Scores greater than 2.90 indicate a lower propensity for a firm to go bankrupt. I combine the inconclusive Z-Scores with those that indicate a lower propensity for bankruptcy into one category. Type I error indicates the rate at which bankrupt firm/years have Z-Scores at least 1.23 in the year prior to bankruptcy and type II error indicates the rate at which non-bankrupt firm/years have Z-Scores less than 1.23. I report the two-sided Fisher exact p-values* in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. I denote the sample size by N. Altman’s Z-Score Model for privately held firms is as follows:

Z’ = 0.717X1 + 0.847X2 + 3.107X3 + 0.420X4 + 0.998X5, where Z’ = the Z-Score firm health indicator, X1 = working capital/total assets, X2 = retained earnings/total assets, X3 = earnings before interest and taxes/total assets, X4 = net worth/book value of liabilities, and X5 = sales/total assets.

Year N Bankrupt

correctly classified

Non-bankrupt correctly classified

Fisher exactp-value*

1985 2,284 16.67% 80.47% 1.00001986 2,483 59.09% 77.20% 0.00031987 2,510 72.73% 78.82% <0.00011988 2,300 61.11% 78.53% 0.00031989 2,185 73.17% 79.01% <0.00011990 2,105 73.08% 78.69% <0.00011991 2,125 66.67% 78.52% <0.00011992 2,085 44.44% 81.65% 0.06631993 2,212 40.91% 80.50% 0.02571994 2,368 57.14% 76.81% 0.00661995 2,436 55.56% 78.12% 0.00021996 2,502 59.09% 78.06% 0.00021997 2,360 65.52% 76.45% <0.00011998 2,077 38.46% 75.27% 0.06091999 1,725 56.00% 75.00% 0.0016

All years 33,757 58.60% 78.24% <0.0001

85

Table 3.14 Classification accuracy rates for the Altman Z-Score Model for privately held firms by industry, 1985-1999 I use a distribution of 33,757 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. Sample attrition is the result of insufficient data. I calculate Z-scores for each firm/year using Altman’s Z-Score Model for privately held firms. For privately held firms, Z-Scores less than 1.23 indicate a higher propensity for bankruptcy. Z-Scores, between 1.23 and 2.90 are inconclusive and Z-Scores greater than 2.90 indicate a lower propensity for a firm to go bankrupt. I combine the inconclusive Z-Scores with those that indicate a lower propensity for bankruptcy into one category. Type I error indicates the rate at which bankrupt firm/years have Z-Scores at least 1.23 in the year prior to bankruptcy and type II error indicates the rate at which non-bankrupt firm/years have Z-Scores less than 1.23. The NAICS code identifies company activity. I consolidate the NAICS codes for wholesale trade (NAICS code 42) and retail trade (NAICS codes 44-45) into trade. I report the two-sided Fisher exact p-values* in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5. I denote the sample size by N. Altman’s Z-Score Model for privately held firms is as follows:

Z’ = 0.717X1 + 0.847X2 + 3.107X3 + 0.420X4 + 0.998X5, where Z’ = the Z-Score firm health indicator, X1 = working capital/total assets, X2 = retained earnings/total assets, X3 = earnings before interest and taxes/total assets, X4 = net worth/book value of liabilities, and X5 = sales/total assets.

86

Table 3.14 Continued Classification accuracy rates for the Altman Z-Score Model for privately held firms by industry, 1985-1999

Industries subject to EPA guidelines

NAICS code

N Bankrupt correctly

classified

Non-bankrupt correctly

classified

Fisher exact p-value*

Agriculture, Forestry, Fishing, and Hunting

11 184 83.33% 62.36% 0.0347

Mining 21 2,067 72.73% 43.77% 0.1350 Utilities 22 2,017 0.00% 29.85% N/A Construction 23 348 60.00% 86.59% 0.0214 Manufacturing 31-33 19,962 62.70% 86.95% <0.0001 Wholesale and Retail Trade

42-45 3,888 38.33% 91.93% <0.0001

Transportation and Warehousing

48-49 1,056 43.75% 69.04% 0.2840

Information 51 3,628 67.65% 67.33% <0.0001 Real Estate, Rental, and Leasing

53 190 50.00% 54.26% 1.0000

Services 56 417 53.85% 70.05% 0.1215 *I indicate the lack of a statistic due to lack of bankrupt firms with N/A.

87

Table 3.15 Summary of classification rates for methods including logistic results, 1985-1999 I use a distribution of 35,420 firm/years from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I present the overall classification rates for all methods. I denote the overall sample size by N and report the classification accuracy with respect to each group, non-bankrupt and bankrupt. I include Chi-square statistics and corresponding p-values. I use logistic regressions as a robustness check and use the five percent level of significance. For each method, I also include the significance of the likelihood ratio for the fit of the logistic, the p-value for the Chi-square for the method (independent variable) within the logistic regression, the odds ratio or likelihood a firm is to be classified as bankrupt, and the pseudo R-square.

*I report the two-sided Fisher exact p-values (p<0.0001) because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5.

Method N Bankrupt correctly

classified

Non-bankrupt correctly

classified

Chi-square p-value Likelihood ratio p-

value

Variable p-value

Odds ratio

Pseudo R-

square

EPA’s Financial Tests 35,420 92.18% 62.01% 608.8115 <0.0001 <0.0001 <0.0001 19.2536 0.1312

Grice and Ingram (2001) 20,627 98.61% 38.59% 41.9510 <0.0001 <0.0001 <0.0001 44.6148 0.0645

Auditor Opinion 35,420 2.61% 99.85% 159.2223 <0.0001 <0.0001 <0.0001 0.0568 0.0097

Bond Rating 8,210 100.00% 67.96% 23.2654* <0.0001 <0.0001 <0.0001 48.7793 0.0056

Altman’s Z-Score Model for publicly traded firms

35,012 70.43% 75.49% 519.6494 <0.0001 <0.0001 <0.0001 7.3346 0.0922

Altman’s Z-Score Model for privately held firms

33,757 58.60% 78.24% 267.3539 <0.0001 <0.0001 <0.0001 5.0894 0.0591

88

Table 3.16 Summary of classification rates by method, 1985-1999 In this expanded contingency table, I report the total number of observations per cell per method. I collect the observations from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. All methods classify the distribution of 35,420 firm/years except Grice and Ingram, bond ratings and the Altman models. Their sample sizes are 20,627, 8,210, 35,012, and 33,757 firm/years respectively. Beneath the total number of firm/year observations, I record the percent of within group classification and the percent classification with respect to the entire sample, respectively. Type I error indicates the rate at which bankrupt firm/years pass the EPA’s financial tests in the year prior to bankruptcy with respect to only the bankrupt group. Type II error indicates the rate at which non-bankrupt firm/years fail the EPA’s financial tests with respect to only the non-bankrupt group.

89

Table 3.16 Continued Summary of classification rates by method, 1985-1999

Method Bankrupt Non-bankrupt

Pass

EPA Within group Entire sample GI Within group Entire sample Bond Within group Entire sample Auditor Within group Entire sample Z-Score public Within group Entire sample Z-Score private Within group Entire sample

39

[7.82%] [0.11%]

1

[1.39 %] [<0.01%]

0

[0.00%] [0.00%]

486

[97.39%] [1.37%]

139

[29.57%] [0.40%]

142

[41.40%] [0.42%]

21,655

[62.01%] [61.14%]

7932

[38.59%] [38.45%]

5,572

[67.96%] [67.87%]

34,868

[99.85%] [98.44%]

26,076

[75.49%] [74.48%]

26,143

[78.24%] [77.44%]

Fail

EPA Within group Entire sample GI Within group Entire sample Bond Within group Entire sample Auditor Within group Entire sample Z-Score public Within group Entire sample Z-Score private Within group Entire sample

460

[92.18%] [1.30%]

71

[98.61%] [0.34%]

11

[100.00%] [0.13%]

13

[2.61%] [0.04%]

331

[70.43%] [0.95%]

201

[58.60%] [0.60%]

13,266

[37.99%] [37.45%]

12,623

[61.41%] [61.20%]

2,627

[32.04%] [32.00%]

53

[0.15%] [0.15%]

8,466

[24.51%] [24.18%]

7,271

[21.76%] [21.54%]

90

Table 3.17 Summary of classification rates for each method by industry, 1985-1999 In this table, I report the within-group classification accuracy rates for each method for each industry. I collect the observations from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. All methods classify the distribution of 35,420 firm/years except Grice and Ingram, bond ratings, and the Altman models. Their sample sizes are 20,627, 8,210, 35,012, and 33,757 firm/years respectively. Although I do not report the error rates, type I error indicates the rate at which bankrupt firm/years pass the EPA’s financial tests in the year prior to bankruptcy with respect to only the bankrupt group. Type II error indicates the rate at which non-bankrupt firm/years fail the EPA’s financial tests with respect to only the non-bankrupt group. I exclude utilities and real estate because no association exists between any method and those industries. I remove services as only one method, the EPA’s financial tests, has an association with groups.

Method Agriculture Mining Construction Manufacturing Wholesale & Retail Trades

Transportation & warehousing

Information

Bankrupt Non-bankrupt

Bankrupt Non-bankrupt

Bankrupt Non-bankrupt

Bankrupt Non-bankrupt

Bankrupt Non-bankrupt

Bankrupt Non-bankrupt

Bankrupt Non-bankrupt

EPA’s financial tests

85.71 % 61.67 % 96.30 % 41.69 % 91.90% 68.04% 92.31% 59.54% 100.00% 53.26% 92.98% 52.66%

Grice and Ingram

60.00% 86.59% 62.70% 86.95% 38.33% 91.93% 67.65% 67.33%

Bond Ratings

100.00% 67.02%

Auditor Opinion

2.46% 99.92% 3.85% 99.72%

Z-Score Public

71.43% 71.51% 83.33% 44.65% 85.71% 83.24% 76.98% 84.07% 46.75% 88.29% 65.00% 61.87% 71.70% 69.29%

Z-Score Private

83.33% 62.36% 60.00% 86.59% 62.70% 86.95% 38.33% 91.93% 67.65% 67.33%

91

Table 3.18 Summary of overall classification rates by method, 1985-1999 I report the total number of observations per cell per method. I collect the observations from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. All methods classify the distribution of 35,420 firm/years except Grice and Ingram, bond ratings, and the Altman models. Their sample sizes are 20,627, 8,210, 35,012, and 33,757 firm/years respectively. Beneath the total number of firm/year observations, I record the overall classification accuracy and inaccuracy for the overall percent classification with respect to the entire sample.

Method Classification Accuracy

Classification Inaccuracy

EPA GI Bond Auditor Z-Score public Z-Score private

22,115

[62.44%]

8,003 [38.79%]

5,583

[68.00%]

34,881 [98.48%]

26,407

[74.58%]

26,344 [77.50%]

13,305[37.56%]

12,624[61.21%]

2,672[32.00%]

539[1.52%]

8,605

[25.42%]

7,413[22.50%]

92

Table 4.1 Distribution of error rates for the EPA’s financial tests using varying levels of PP&E for closure costs, 1985-1999 I use a distribution of 35,420 firm/years (34,921 non-bankrupt firm/years and 499 bankrupt firm/years) from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I classify non-bankrupt and bankrupt firm/years as passing or failing the EPA’s financial test requirement for the overall sample and annually in the year prior to bankruptcy for each level of closure costs. I estimate closure costs to vary from one- to ten-percent of a firm’s net property, plant, and equipment. These rates represent the within-group classification accuracy. I report the type I and II error rates and p-value associated with these rates. The overall classification accuracy represents the classification with respect to the entire sample. Type I error indicates the rate at which bankrupt firm/years pass the EPA’s financial tests in the year prior to bankruptcy. I report the Chi-square statistic and corresponding p-value.

Level of closure costs by percent

Bankrupt correctly

classified

Non-bankrupt correctly

classified

Overall classification

accuracy

Type I error Type II error Chi-square P-value

1% 92.18% 62.01% 62.44% 7.82% 37.99% 608.8115 <0.00012% 92.38% 60.42% 60.97% 7.62% 39.58% 570.0417 <0.00013% 92.59% 58.99% 59.46% 7.41% 41.01% 538.1618 <0.00014% 92.79% 57.49% 57.99% 7.21% 42.51% 506.6590 <0.00015% 92.99% 55.87% 56.39% 7.01% 44.13% 474.8531 <0.00016% 92.99% 53.81% 54.36% 7.01% 46.19% 432.7134 <0.00017% 93.19% 51.20% 51.79% 6.81% 48.80% 387.8263 <0.00018% 93.59% 48.56% 50.20% 6.41% 51.44% 350.1671 <0.00019% 93.99% 46.08% 46.75% 6.01% 53.92% 318.4687 <0.000110% 94.19% 44.03% 44.73% 5.81% 55.97% 292.3238 <0.0001

93

Table 4.2 Distribution of classification rates for the EPA’s financial tests using varying levels of PP&E for closure costs by industry, 1985-1999

I use a distribution of 35,420 firm/years (34,921 non-bankrupt firm/years and 499 bankrupt firm/years) from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I classify firm/years as passing or failing the EPA’s financial test requirement for each level of closure costs. I estimate closure costs to vary from one to ten percent of a firm’s net property, plant, and equipment. For bankrupt firms, I use the data for the year prior to bankruptcy. I classify industries subject to EPA guidelines by two-digit North American Industrial Classification System (NAICS) code. The NAICS code identifies company activity. I consolidate the NAICS codes for wholesale trade (NAICS code 42) and retail trade (NAICS codes 44-45) into trade. I denote the industry sample size by N, the number of non-bankrupt firm/years within the industry by Nnb, and the number of bankrupt firm/years within the industry by Nb. I report the type I and II error rates and p-value associated with these rates. Type I error indicates the rate at which bankrupt firm/years pass the EPA’s financial tests in the year prior to bankruptcy. Type II error indicates the rate at which non-bankrupt firm/years fail the EPA’s financial tests. The correct classification and error rates relate to the within-group classification. I also report the overall classification accuracy for classification with respect to the entire sample. I report the two-sided Fisher exact p-values* in lieu of the Chi-square because the Chi-square may not provide accurate statistics as 25% of the cells have expected counts less than 5.

94

Table 4.2 Continued Distribution of classification rates for the EPA’s financial tests using varying levels of PP&E for closure costs by industry, 1985-1999

Industries subject to EPA guidelines

Level of closure

cost

Bankrupt correctly classified

Non-bankrupt correctly classified

Overall classification

accuracy

Type I error

Type II error

Fisher exact

p-value

Agriculture, Forestry, Fishing, and Hunting

1% 85.71% 61.67% 62.57% 14.29% 38.33% 0.0173

NAICS Code=11 2% 85.71% 61.11% 62.03% 14.29% 38.89% 0.0187 N=187 3% 85.71% 61.11% 62.03% 14.29% 38.89% 0.0187 Nnb=180 4% 85.71% 60.00% 60.96% 14.29% 40.00% 0.0216 Nb=7 5% 85.71% 57.78% 58.82% 14.29% 42.22% 0.0447 6% 85.71% 53.33% 55.14% 14.29% 46.67% 0.0570 7% 85.71% 52.22% 53.48% 14.29% 47.78% 0.0618 8% 85.71% 50.00% 51.34% 14.29% 50.00% 0.1189 9% 85.71% 46.11% 47.61% 14.29% 53.89% 0.1316 10% 85.71% 42.78% 44.39% 14.29% 57.22% 0.2420 Mining 1% 96.30% 41.69% 42.36% 3.70% 58.31% <0.0001 NAICS Code=21 2% 96.30% 34.27% 35.03% 3.70% 65.73% 0.0003 N= 2,224 3% 96.30% 29.86% 30.67% 3.70% 70.14% 0.0012 Nnb=2,197 4% 96.30% 26.04% 26.89% 3.70% 73.96% 0.0063 Nb=27 5% 96.30% 23.30% 24.19% 3.70% 76.70% 0.0109 6% 96.30% 20.16% 21.09% 3.70% 79.84% 0.0288 7% 100.00% 17.16% 18.16% 0.00% 82.84% 0.0092 8% 100.00% 14.11% 15.15% 0.00% 85.89% 0.0252 9% 100.00% 11.70% 12.77% 0.00% 88.30% 0.0650 10% 100.00% 9.65% 10.74% 0.00% 90.35% 0.1033

95

Table 4.2 Continued Distribution of classification rates for the EPA’s financial tests using varying levels of PP&E for closure costs by industry, 1985-1999

Industries subject to EPA guidelines

Level of closure

cost

Bankrupt correctly classified

Non-bankrupt correctly classified

Overall classificati

on accuracy

Type I error

Type II error

Fisher exact

p-value

Utilities 1% 100.00% 55.73% 53.45% 0.00% 44.27% 0.1964 NAICS Code=22 2% 100.00% 52.71% 52.76% 0.00% 47.29% 0.2240 N=2,028 3% 100.00% 50.35% 50.40% 0.00% 49.65% 0.2469 Nnb=2,026 4% 100.00% 48.72% 48.77% 0.00% 51.28% 0.5001 Nb=2 5% 100.00% 46.00% 46.06% 0.00% 54.00% 0.5030 6% 100.00% 38.70% 38.76% 0.00% 61.30% 0.5255 7% 100.00% 24.93% 25.00% 0.00% 75.07% 1.0000 8% 100.00% 13.38% 13.46% 0.00% 86.62% 1.0000 9% 100.00% 5.73% 5.82% 0.00% 94.27% 1.0000 10% 100.00% 3.06% 3.16% 0.00% 96.94% 1.0000 Construction 1% 85.71% 42.90% 43.76% 14.29% 57.10% 0.2461 NAICS Code=23 2% 85.71% 42.61% 43.45% 14.29% 57.39% 0.2461 N=359 3% 85.71% 40.91% 41.78% 14.29% 59.09% 0.2489 Nnb=352 4% 85.71% 40.06% 40.95% 14.29% 59.94% 0.2518 Nb=7 5% 85.71% 39.49% 40.39% 14.29% 60.51% 0.2543 6% 85.71% 39.20% 40.11% 14.29% 60.80% 0.2557 7% 85.71% 38.07% 39.00% 14.29% 61.93% 0.2624 8% 85.71% 36.65% 37.60% 14.29% 63.35% 0.4294 9% 85.71% 33.52% 34.54% 14.29% 66.48% 0.4325 10% 85.71% 30.97% 32.03% 14.29% 69.03% 0.6806

96

Table 4.2 Continued Distribution of classification rates for the EPA’s financial tests using varying levels of PP&E for closure costs by industry, 1985-1999

Industries subject to EPA guidelines

Level of closure

cost

Bankrupt correctly classified

Non-bankrupt correctly classified

Overall classificati

on accuracy

Type I error

Type II error

Fisher exact

p-value

Manufacturing 1% 91.90% 68.04% 68.36% 8.10% 31.96% <0.0001 NAICS Code=31-33 2% 92.25% 67.20% 67.54% 7.75% 32.80% <0.0001 N=20,948 3% 92.61% 66.26% 66.62% 7.39% 33.74% <0.0001 Nnb=20,664 4% 92.96% 65.12% 65.50% 7.04% 34.88% <0.0001 Nb=284 5% 93.31% 63.74% 64.14% 6.69% 36.26% <0.0001 6% 93.31% 62.26% 62.68% 6.69% 37.74% <0.0001 7% 93.31% 60.44% 60.89% 6.69% 39.56% <0.0001 8% 93.31% 58.46% 58.94% 6.69% 41.54% <0.0001 9% 94.01% 56.40% 56.91% 5.99% 43.60% <0.0001 10% 94.37% 54.19% 54.73% 5.63% 45.81% <0.0001 Wholesale and Retail Trade

1% 92.31% 59.54% 60.16% 7.69% 40.46% <0.0001

NAICS Code=42-45 2% 92.31% 58.95% 59.60% 7.69% 41.05% <0.0001 N=4,037 3% 92.31% 57.89% 58.55% 7.69% 42.11% <0.0001 Nnb=3,959 4% 92.31% 56.48% 57.17% 7.69% 43.52% <0.0001 Nb=78 5% 92.31% 55.17% 55.88% 7.69% 44.83% <0.0001 6% 92.31% 53.73% 54.47% 7.69% 46.27% <0.0001 7% 92.31% 52.24% 53.01% 7.69% 47.76% <0.0001 8% 92.31% 50.69% 51.53% 7.69% 49.31% <0.0001 9% 92.31% 48.65% 49.52% 7.69% 51.35% <0.0001 10% 92.31% 47.26% 48.16% 7.69% 52.74% <0.0001

97

Table 4.2 Continued Distribution of classification rates for the EPA’s financial tests using varying levels of PP&E for closure costs by industry, 1985-1999

Industries subject to EPA guidelines

Level of closure

cost

Bankrupt correctly classified

Non-bankrupt correctly classified

Overall classificati

on accuracy

Type I error

Type II error

Fisher exact

p-value

Transportation and Warehousing

1% 100.00% 53.26% 54.13% 0.00% 46.74% <0.0001

NAICS Code=48-49 2% 100.00% 47.40% 48.38% 0.00% 52.60% <0.0001 N=1,077 3% 100.00% 43.71% 44.76% 0.00% 56.29% <0.0001 Nnb=1,057 4% 100.00% 40.02% 41.14% 0.00% 59.98% <0.0001 Nb=20 5% 100.00% 36.52% 36.70% 0.00% 63.48% 0.0002 6% 100.00% 31.98% 33.24% 0.00% 68.02% 0.0008 7% 100.00% 27.91% 29.25% 0.00% 72.09% 0.0020 8% 100.00% 22.71% 24.14% 0.00% 77.29% 0.0117 9% 100.00% 17.60% 19.13% 0.00% 82.40% 0.0348 10% 100.00% 14.19% 15.79% 0.00% 85.81% 0.0963 Information 1% 92.98% 51.57% 53.25% 7.02% 48.43% <0.0001 NAICS Code=51 2% 92.98% 51.57% 52.18% 7.02% 48.43% <0.0001 N= 3,931 3% 92.98% 50.28% 50.90% 7.02% 49.72% <0.0001 Nnb=3,874 4% 92.98% 49.10% 49.73% 7.02% 50.90% <0.0001 Nb=57 5% 92.98% 48.12% 48.77% 7.02% 51.88% <0.0001 6% 92.98% 46.41% 47.09% 7.02% 53.59% <0.0001 7% 92.98% 44.66% 45.36% 7.02% 55.34% <0.0001 8% 92.98% 42.67% 43.40% 7.02% 57.33% <0.0001 9% 92.98% 40.86% 41.62% 7.02% 59.14% <0.0001 10% 92.98% 39.75% 40.53% 7.02% 60.25% <0.0001

98

Table 4.2 Continued Distribution of classification rates for the EPA’s financial tests using varying levels of PP&E for closure costs by industry, 1985-1999

Industries subject to EPA guidelines

Level of closure

cost

Bankrupt correctly classified

Non-bankrupt correctly classified

Overall classificati

on accuracy

Type I error

Type II error

Fisher exact

p-value

Real Estate, Rental, and Leasing

1% 100.00% 47.67% 51.80% 0.00% 52.33% 0.4990

NAICS Code=53 2% 100.00% 47.67% 48.21% 0.00% 52.33% 0.4990 N=195 3% 100.00% 43.52% 44.11% 0.00% 56.48% 0.5071 Nnb=193 4% 100.00% 36.79% 37.44% 0.00% 63.21% 0.5345 Nb=2 5% 100.00% 26.42% 27.18% 0.00% 73.58% 1.0000 6% 100.00% 21.24% 22.06% 0.00% 78.76% 1.0000 7% 100.00% 15.54% 16.41% 0.00% 84.46% 1.0000 8% 100.00% 13.47% 14.36% 0.00% 86.53% 1.0000 9% 100.00% 10.88% 11.80% 0.00% 89.12% 1.0000 10% 100.00% 9.84% 10.77% 0.00% 90.16% 1.0000 Services 1% 80.00% 49.16% 55.76% 20.00% 50.84% 0.0339 NAICS Code=54 2% 80.00% 49.16% 50.23% 20.00% 50.84% 0.0339 N=434 3% 80.00% 46.06% 47.23% 20.00% 53.94% 0.0629 Nnb=419 4% 80.00% 42.48% 43.77% 20.00% 57.52% 0.1101 Nb=15 5% 80.00% 40.10% 41.47% 20.00% 59.90% 0.1777 6% 80.00% 38.66% 40.09% 20.00% 61.34% 0.1811 7% 80.00% 37.71% 39.17% 20.00% 62.29% 0.1865 8% 86.67% 36.28% 38.02% 13.33% 63.72% 0.0974 9% 86.67% 34.84% 36.64% 13.33% 65.16% 0.1006 10% 86.67% 32.70% 34.57% 13.33% 67.30% 0.1599

99

Table 4.3 Panel A Mean and median financial measures for firms subject to the EPA’s financial tests, 1985-1999 I use a distribution of 35,420 firm/years (34,921 non-bankrupt firm/years and 499 bankrupt firm/years) from Standard and Poor’s Compustat Annual Research, Annual Industrial, and Full Coverage files from 1985-1999. I report the mean and median measures in millions of U.S. dollars for the components of the financial measures used in the EPA’s financial test criteria for the overall sample. I include the measures for non-bankrupt and bankrupt firms and for each of the industries represented in my sample. I estimate closure costs to be one percent of a firm’s net property, plant, and equipment. These closure costs incorporate the six times multiple required by the financial tests. I test the differences in means and medians between the two groups. For the test between means, I assume unequal because I find the p-values for the F-statistics for testing the equality of variances are less than 0.0001. For the test between medians, I report the p-value from the median test.

100

Table 4.3 Panel A Mean and median financial measures for firms subject to the EPA’s financial tests, 1985-1999

Ratios Overall Bankrupt Non-bankrupt p-value Mean Median Mean Median Mean Median Means Median

Ratio #1: Total liabilities/Net worth 1.853 1.032 1.466 0.974 1.858 1.032 <0.0001 0.8218 Ratio #2: Net income plus depreciation, depletion, and amortization/Total liabilities 0.565 0.500 -0.709 -0.125 0.584 0.506 <0.0001 <0.0001 Ratio #3: Current ratio 2.808 1.923 1.891 1.047 2.821 1.937 <0.0001 <0.0001 Tangible net worth $457.06 $53.73 $7.72 $1.22 $463.48 $55.81 <0.0001 <0.0001 Net working capital $95.55 $20.16 $2.81 $0.09 $96.88 $20.92 <0.0001 <0.0001 Total assets $1206.49 $118.48 $48.16 $7.00 $1223.04 $122.75 <0.0001 <0.0001 Total liabilities $750.01 $53.67 $40.50 $5.75 $760.11 $55.76 <0.0001 <0.0001 Net PP&E $585.77 $30.45 $13.49 $1.48 $593.95 $31.70 <0.0001 <0.0001

101

Table 4.3 Continued Panel B Mean and median financial measures for firms subject to the EPA’s financial tests by industry, 1985-1999

Ratios Agriculture, Forestry, Fishing,

and Hunting Mining Utilities

Mean Median Mean Median Mean Median Ratio #1: Total liabilities/Net worth 1.909 0.858 1.063 0.864 4.680 1.878 Ratio #2: Net income plus depreciation, depletion, and amortization/Total liabilities

0.096 0.211 1.903 0.958 0.585 0.559

Ratio #3: Current ratio 3.717 1.982 2.530 1.271 1.031 0.880 Tangible net worth $166.63 $30.86 $221.81 $35.98 $1078.08 $406.90 Net working capital $76.07 $14.93 $34.90 $2.22 $-71.79 $-7.96 Total assets $396.80 $45.20 $608.77 $84.46 $3253.56 $1184.05 Total liabilities $230.17 $16.85 $386.96 $36.76 $2179.61 $777.32 Net PP&E $191.14 $12.99 $412.61 $54.91 $2344.72 $871.44

102

Table 4.3 Continued Panel B Mean and median financial measures for firms subject to the EPA’s financial test by industry, 1985-1999

Ratios Construction Manufacturing Wholesale and Retail Trade

Mean Median Mean Median Mean Median Ratio #1: Total liabilities/Net worth

3.954 1.444 1.740 0.888 2.146 1.297

Ratio #2: Net income plus depreciation, depletion, and amortization/Total liabilities

0.922 0.266 0.497 0.539 0.440 0.309

Ratio #3: Current ratio 2.101 1.557 3.176 2.220 2.373 1.791 Tangible net worth $93.40 $19.79 $434.17 $50.14 $235.51 $46.82 Net working capital $34.71 $10.21 $134.45 $30.65 $114.20 $28.32 Total assets $278.56 $52.15 $1077.81 $101.35 $631.19 $121.08 Total liabilities $185.16 $30.66 $644.26 $41.82 $395.49 $64.75 Net PP&E $76.57 $13.11 $432.61 $24.16 $225.29 $23.50

103

Table 4.3 Continued Panel B Mean and median financial measures for firms subject to the EPA’s financial tests by industry, 1985-1999

Ratios Transportation and

Warehousing Information Real Estate, Rental, and

Leasing Services

Mean Median Mean Median Mean Median Median Median Ratio #1: Total liabilities/Net worth

1.840 1.647 1.151 0.840 1.929 1.511 0.006 0.946

Ratio #2: Net income plus depreciation, depletion, and amortization/Total liabilities

0.577 0.465 0.264 0.367 1.329 0.593 0.349 0.378

Ratio #3: Current ratio 1.715 1.183 2.691 1.691 1.627 1.260 3.278 1.745 Tangible net worth $618.89 $101.81 $657.78 $40.07 $210.17 $28.75 $243.47 $33.36 Net working capital $32.65 $5.44 $45.08 $11.24 $17.44 $2.48 $6.80 $7.59 Total assets $1886.13 $276.84 $1776.65 $97.81 $681.03 $103.66 $767.70 $55.23 Total liabilities $1267.24 $161.66 $1119.64 $40.91 $470.86 $52.78 $524.23 $21.06 Net PP&E $1344.73 $139.13 $849.12 $13.78 $434.61 $54.95 $390.13 $12.27

104

APPENDIX A: MAJOR ENVIRONMENTAL CATASTROPHES

This appendix serves to provide further explanation and anecdotal evidence for the relevance,

importance, and urgency of this topic. A brief summary of three environmental catastrophes that

occurred in the United States provides illustration of the environmental and financial devastation that may

result from certain environmental obligations. Following the summaries is a list of articles from the

popular trade press and the Wall Street Journal that address current environmental issues, in particular

the financial distress and pending bankruptcies of firms with environmental obligations. These

environmental obligations not only include environmental catastrophes but also any costs related to the

environment, meaning the financial condition of the firm and potential default on the fulfillment of

environmental obligations is a concern.

Major Environmental Catastrophes

Love Canal, Three Mile Island, and the Exxon-Valdez oil spill are some of the worst and most

visible environmental catastrophes to occur in the U.S.84

The municipality of Niagara Falls in New York,

bought the Love Canal in the 1920s and used it as a landfill.85

Hooker Chemical Company (HCC), a

subsidiary of Occidental Chemical Corporation (OCC), acquired the site and operated it for approximately

13 years until its sale to the Niagara Falls School Board. During those years of operation, HCC buried

approximately 22,000 tons of hazardous chemical waste in the 20-acre site area. Complaints concerning

problems the inhabitants around the site were experiencing went unaddressed for over two decades. In

1980, the Love Canal was a national emergency site, and the people in the surrounding area were

relocated pending further investigation. Jimmy Carter appointed the Ecumenical Task Force (ETF) to test

and to document the contamination, and testing continued from 1980-1991.

The tests included testing the pollution level, testing the habitability of the land, and studying the

effects on genetics. All tests yielded similar results. The land was severely polluted and unfit for human

habitation. Furthermore, investigators found substantiation of genetic mutation. According to the New

York Department of Environmental Conservation, the state has paid $800 million in the cleanup process,

and OCC has paid $2.6 billion.

84

To date, remediation and reclamation is incomplete at these sites. Additionally, remediation requirements have been compromised, as companies have asked and been granted reductions in fulfilling the initial requirements. 85

Ecumenical Task Force of the Niagara Frontier Love Canal Collection (1988), University Archives, University Libraries, State University of New York at Buffalo: http://ublib.buffalo.edu/libraries/projects/lovecanal/.

105

On March 28, 1979, Three Mile Island Nuclear Reactor Unit Two experienced almost complete

meltdown in Harrisburg, Pennsylvania.86

The malfunction of Unit 2 was due to multiple human errors in

succession, resulting in several releases of radiation gas into the atmosphere and contaminated water

into the Susquehanna River. Studies show that while each error was avoidable and reversible at the time

of occurrence, because the undetected and uncorrected errors persisted, a partial meltdown occurred.

Taxpayers paid $700 million for the construction of Unit 2, and it was online for only 90 days before the

accident. Taxpayers were again responsible for the decommissioning costs, estimated at $433 million.

In 1996, a judge dismissed a lawsuit with over 2000 personal injury claims against the owner, General

Public Utilities, stating the plaintiffs did not prove the necessary link between the causes of any illness

and radioactive contamination.

On March 24, 1989, the Exxon-Valdez oil tanker dumped almost 11 million gallons of crude oil

into the waterway of Prince William Sound, Alaska, after running aground on Bligh Reef.87

The state of

Alaska commissioned several economic impact reports that estimated the impact of the contamination on

the tourism industry, fish and wildlife resources, and the fishing industry. Although these reports only

provide preliminary estimates, Exxon is currently in litigation, contesting additional damages assessed

because of the underestimation of the true costs of restitution.

Exxon’s fine reduced from $150 million to $25 million to reflect its cleanup efforts. Exxon divided

the $25 million paid between an environmental fund and a victim fund. Exxon is also required to pay

$100 million in restitution for the loss of fish and wildlife. It will also pay a civil settlement of $900 million

over 10 years. Various state environmental funds share the monies. Exxon paid approximately $2.3

billion in cleanup costs; however, remediation is not complete, and the wildlife is difficult to replenish.

86

This information is from the 20th Anniversary of the TMI Accident Press Packet issued by the Three

Mile Island Alert Organization. Nuclear Reaction: Why Do Americans Fear Nuclear Power? Three Mile Island - The Judge’s Ruling. PBS FRONTLINE Special Report: http://www.pbs.org/wgbh/pages/frontline/shows/reaction/readings/ 87

Exxon-Valdez Oil Spill Trustee Council, http://www.oilspill.state.ak.us/.

106

Table A.1 Summary of articles from trade and popular press

Source Date Firm State Hazard Total

Estimated Liability

Spokane Spokesman-Review

10/25/2002 Kaiser

Aluminum

Washington, California,

Rhode Island

Polychlorinated biphenyl clean up and Superfund clean up

$74 million

The St. Petersburg Times

07/06/2003 Mulberry Corporation

Florida

Abandoned phosphate mines and phosphogypsum stacks

$140 million

The Associated Press State &Local Wire

04/15/2003 Metachem Products

Delaware

Abandoned factory filled with chemicals and used equipment

$75 million

The News Tribune

01/31/2003 Asarco Mining

Washington Abandoned mines and acid mine drainage

$1 billion

Platt’s Coal Outlook and The Philadelphia Inquirer

01/02 /2003 09/26/2002

Bethlehem Steel, Beth Energy Mines, LTV Steel, and Mon View

Pennsylvania Abandoned mines, acid mine drainage, illegal dumping

$5 billion initially + $53 million annually

The Palm Beach Daily Business Review

10/29/2002 Pan Am Florida Contamination at Miami-Dade County Airport

$200 million

Greenwire 10/18/2002 Safety-Kleen South Carolina

Hazardous waste - landfill

$1 billion

The Associated Press State & Local Wire

September 6, 2002

W.R. Grace & Co

Montana Asbestos + $1 billion

The Salt Lake Tribune

June 7, 2002

Magnesium Corporation of America

Utah Mining contamination - vermiculite

$1 billion

The Associated Press State & Local Wire

May 9, 2002

Enron Texas Hazardous Waste – leaking oil pipelines

$15 million

107

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BIOGRAPHICAL SKETCH

Wendy D. Habegger is a Ph.D. student in finance at Florida State University. She obtained her

Bachelor’s of Science in Mathematics from Augusta State University in 1994, and her Master’s in

Education with a major in Mathematics from Georgia Southern University in 1995. She served as faculty

member at Georgia Southern University from 1995–1998. She is currently faculty at the University of

West Florida.