Know Laws on Toxic Emissions*

SHAMEEK KONAR

Department of Economics, Vanderbilt Uni ̈ersity, Nash ̈ille, Tennessee 37203

AND

MARK A. COHEN

Owen Graduate School of Management, Vanderbilt Uni ̈ersity, Nash ̈ille, Tennessee 37203

Received September 12, 1995

There is growing academic and policy-level interest in the use of information as quasiregulatory mechanisms, such as toxic release inventory TRI and ‘‘green labels.’’ Mandatory Ž .

disclosure requirements have been touted as ‘‘market-based incentives’’ that may affect firm

behavior. We provide new evidence on the effectiveness of disclosure requirements by

examining firm behavior in response to disclosures of TRI emissions. We find that firms with

the largest stock price decline on the day this information became public subsequently

reduced emissions more than their industry peers. This is consistent with the view that

financial markets may provide strong incentives for firms to change their environmental

behavior. Q 1997 Academic Press

I. INTRODUCTION

Information remedies have recently been touted as powerful supplements 

alternatives to direct command-and-control regulation. When President Clint

and Vice President Gore recently issued their ‘‘Reinventing Environmental Reg

lation’’ report 4 , they continually stressed the ‘‘power of information’’ in bringi

w x

about significant change in environmental quality. Among their highest prior

‘‘action items’’ were the establishment of a ‘‘public access program’’ to make 

EPA data and publications available through the internet, and a new center f

environmental information and statistics to ensure that data is available to t

public. This recent interest in the use of information as a quasi-regulatory mech

nism follows several recent government programs that provide information 

interested members of the public in an attempt to affect firm behavior indirec

through consumer, public, or community pressure. Examples of recently enact

information remedies include mandatory disclosure of toxic chemical emissio

Ž . which is the subject of the current paper , securities regulations requiring disc

* This paper is based on Chap. 2 of Shameek Konar’s Ph.D. dissertation at Vanderbilt. Support fr

the Dean’s Fund for Summer Research, Owen Graduate School of Management is gratefully ackno

edged. Helpful comments were received from J. S. Butler, several anonymous referees, and semi

participants at Vanderbilt and at University of California at Davis. We also thank Scott Fenn and Pe

Chines at the Investor Responsibility Research Center IRRC for generously supplying us with th

Ž .

data and assistance. All views expressed are those of the authors and not necessarily those of IRRC 

members of its staff.

109

0095-0696r97 $25

Copyright Q 1997 by Academic P

All rights of reproduction in any form reser
gy g 

hazards? If consumers, community groups, or investors care about a firm’s em

sions, providing more firm-specific environmental information may cause co

sumers to adjust their purchase decisions, community groups to pressure firms 

reduce pollution beyond that required by federal laws, or investors to change th

portfolios. Thus, mandatory disclosure requirements might be viewed as a form 

‘‘market-based incentive’’ for firms to change their behavior. In theory, each fir

will independently weigh the costs of public disclosure of ‘‘bad’’ informati

against the costs of taking actions that will put the firm in a more favorable lig

Firms will voluntarily go beyond any legally mandated regulatory standard if it is 

their interest to do so. This will only work, however, if the ‘‘public’’ cares enou

about the information being released to ‘‘punish’’ firms that are bad actors.

Before information remedies are used more frequently as regulatory mech

nisms, we need to understand how they work and what effect they have on fir

behavior. This paper provides some initial answers to these questions by examini

firm behavior in response to disclosures that they were among the ‘‘worst polluter

in the United States. In 1986, Congress passed the Emergency Planning a

Community Right-to-Know Act, requiring manufacturing establishments with 10 

more employees in SIC codes 20 through 39 with certain threshold sizes 

Ž

chemical emissions to publicly disclose the quantity and type of toxic chemic

.

released into the environment. The first reports were due to EPA no later th

July 1, 1988 for toxic emissions in the calendar year 1987.3 Data from these repo

have been referred to as the ‘‘toxic release inventory’’ TRI . According to a rece

Ž .

paper by Cohen et al. 7 , firms with lower TRI emissions outperformed th

w x

industry competitors in the stock market between 1987 and 1989. The first pub

disclosure of TRI data occurred on June 19, 1989. According to Hamilton 1

w 

publicly traded firms whose TRI releases were first reported on that date expe

enced statistically significant negative abnormal market returns. The implication 

this drop in stock price is that investors updated their expectation of futu

pollution-related expenditures or liabilities e.g., probability of accidents, likeliho

Ž

of exposure under other regulatory programs such as Superfund , which wou

.

reduce the future firm profitability. We test this hypothesis by examining how firm

responded to this negative stock price information. In particular, we ask whether 

not significant stock price reductions translate into significant reductions in to

emissions.

The paper is organized as follows: Section II briefly reviews the theoretic

foundations for our analysis. In particular, it reviews why new information mig

act as a regulatory mechanism and how this interacts with the stock mark

Section III describes the data and empirical methods, while Section IV presen

the main results. Section V presents some confirmatory evidence that firm

1 See, for example, ‘‘Accounting and Disclosures Relating to Loss Contingencies, Staff Account

Bulletin No. 92,’’ 58 Fed. Reg. 32,843 June 8, 1993 , and Wallace 18 . Ž . w x 2 See, e.g., OECD 16 . w x 3 See 42 U.S.C.A. 11023.
AN EFFECTIVE REGULATORY INSTRUMENT?

The key issue being addressed in this paper is the extent to which publi

provided environmental information acts as an alternative tool in the place 

direct environmental regulation. In order to be a successful policy alternative, t

provision of information to the public must create an incentive structure th

pressures firms to improve their environmental performance. If the provision 

this information negatively impacts the financial performance of the firm, it w

provide a strong incentive to the firm to become a better environmental actor. W

hypothesize that the change or the lack thereof , in the financial performance 

Ž .

the firm as a result of the provision of this new information will provide incentiv

that will affect the attitude of the firm toward environmental performance.

Financial performance of firms may be measured in a number of ways; the tw

basic classes of measures of financial performance are accounting-based measur

and stock market-based measures. This study uses stock market rather th

accounting measures to estimate financial performance. The stock market-bas

approach has a number of attractive aspects. This approach has strong theoretic

and empirical foundations in the financial markets literature. The efficient marke

hypothesis see, e.g., Fama 8, 9 predicts that in a well-functioning capital mark

Ž w x.

security prices provide the best available unbiased estimates of the value of 

company’s assets. That is, the company’s stock price fully reflects the prese

discounted value of all future cash flows due to all of the tangible and intangib

assets of the firm.4

Providing new environmental performance information to the public may ha

significant implications for the expected future cash flows of the firm. The

implications will be discussed in detail later in the section. As a result of this n

information investors re-evaluate the firm’s expected future cash flows and ul

mately trade the firm’s securities in the stock market, thus affecting both sto

prices and market returns. This information is incorporated into the security pric

through the creation of new arbitrage opportunities. If the new information th

has been released indicates an increase in the expected future cash flows, then t

firm’s stock is currently undervalued in the market. Traders and investors aware 

these arbitrage opportunities will take advantage and buy this stock which in tu

increases the demand for the security and drives its price up. Thus, this n

information will affect financial performance as reflected by security prices.

Stock market-based measures of financial performance are forward looki

because they incorporate future performance of the firm. Future performance 

important in this case because most of the impacts resulting from the curre

environmental performance of a firm are felt in the future. For example, increas

in expected future liability and future investments in pollution abatement capi

stock may be required in order to reduce pollution. On the other hand, accounti

4 The finance literature provides ample support for the efficient markets hypothesis. A review pa

by Fama 9 provides an excellent background to the theoretical and empirical evidence available a

w x

concludes that markets in general adjust to publicly available information.
g pppg p

expectations versus public announcements is important in the case of TRI em

sions, since the public knew that TRI emissions were going to become public w

before they were actually released in 1989. Indeed, the law requiring disclosure w

passed in 1986, and required that firms report their 1987 emissions to EPA. It w

not until June 1989 that the first disclosure of these 1987 emission levels actua

took place. Thus, by the time the first announcement was made in 1989, the mark

may have already anticipated some of these future announcements, and T

emissions may have been incorporated into existing stock prices. As a result, w

would expect the market to react more in cases when the market did not anticipa

a firm to be a high polluter and thus did not anticipate a significant reduction 

firm value following the TRI announcement.5

Why would TRI announcements reduce firm value? One possibility is th

investors use TRI emissions as a signal of the firm’s productive efficiency. A fir

that has higher emissions per dollar revenue than its competitors may be wasti

resources that ultimately end up in the air or water. Thus, a disclosure of high T

emissions may be seen by investors as an indicator of poor management practic

and increased risk of spills or accidents. Another possibility is that there is som

form of pressure from an interested ‘‘stakeholder.’’ Community pressure to redu

emissions is likely to have an effect on any firm that has a significant stake in t

community. Private parties either individually or through an environmental grou

Ž 

may sue firms under various theories of tort actions or government-defined right 

action. ‘‘Green consumers’’ may decide to boycott products of high polluting firm

or otherwise look for alternatives.6 Finally, the government may step up enforc

ment actions against high polluters. Even though TRI emissions are perfectly leg

the government might target these firms for inspections in other areas and t

result might be increased penalties andror the cost of new pollution abateme

equipment.7

Any or all of these external pressures may be placed on a firm forced to ma

new environmental disclosures and may result in costly expenditures to clean-

pollution or prevent future pollution. Although it is possible that pollution abat

ment or prevention will ultimately save some firms money e.g., through lower r

Ž

material purchases to offset the original capital expenditure, it is also possible th

.

these activities will only result in a net drain on firm profits. Thus, firms high 

the TRI list can be expected to spend resources to ‘‘catch up’’ with their compe

5 A related question is whether or not firms anticipated the importance of TRI disclosure as soon 

Congress passed the law or while it was being debated , and reduced their emissions prior to 1987, 

Ž .

first year of disclosure. Unfortunately, no data exist to test this hypothesis. 6 Although there is an obvious problem of collective action in sustaining such a boycott, seve

boycotts such as the tuna Ž . rdolphin controversy have affected firm behavior. As Makower 13, 

w

103]106 argues by way of examples, a relatively small number of individuals can generate a signific

x

amount of media attention and bad publicity for a firm. In addition, market research has provided so

evidence of a green consumer market segment; see Roper 17 . w x 7 Indeed, EPA recently implemented a strategy of targeting enforcement activities at ‘‘high ri

firms and industries based on TRI and other data 19, pp. 1 w x ]3 .
pgg pg

firm value in subsequent years.8

Increased pressure due to new information about environmental hazards may 

focused either on an entire industry or within specific firms in an industry. In som

cases, consumers may look at alternative product lines that are less polluting 

otherwise change their aggregate consumption habits to reduce the demand for 

entire product category. In this case, the negative impact following environmen

disclosures is industry-wide. Corresponding to this effect, however, might be 

positive impact on other industries whose products now look relatively mo

favorable. The extent to which this new information is likely to affect industry-wi

valuation will depend on the availability of close substitutes, the price elasticity 

demand for that product, and other ‘‘elasticity’’ factors related to the demand f

green products. Similarly, new information on environmental hazards may ha

firm-specific effects as some firms within an industry now look more or le

favorable than others in that same industry. Even if aggregate demand for 

product line is not affected or even positively affected by this new disclosu

Ž .

differential emission rates across firms may create new winners and losers in th

industry.

III. EMPIRICAL METHOD

Since we are interested in the effect of new information on subsequent fir

behavior, we identified the first public announcement of TRI data, June 19, 198

On that date, the EPA released TRI data for the calendar year 1987. Subseque

information has shown that 1987 data are particularly unreliable, and most stud

of actual TRI emissions start with 1988 as the base year. However, since we a

primarily interested in the effect of stock price movements on subsequent fir

behavior, we begin with the June 19, 1989 announcement date}even though t

data released on that date was subsequently found to be unreliable. What matte

most is the fact that on June 19, 1989, the data was thought to be reasonab

accurate, and it was relied upon by various stakeholders.

Because of the time lag between actual emissions and public availability of T

data, we begin with 1989 emissions as the base year for our analysis. Since we a

interested in what firms did following the TRI reports made in 1989 for 19

Ž

emissions , we take actual 1989 emissions as the base year. That is the starti

.

8 Although at first glance the stock price hit might be considered a ‘‘sunk cost,’’ there are ma

reasons why management would view it otherwise. The stock price hit might be thought of as 

indicator of serious problems in the firm that will require expensive changes in production processes 

impose future liability, or that are simply an indicator of bad management. Reduced firm value mi

also increase the future cost of capital or cause the firm to become a potential takeover target. Fina

the fact that the market reacted in this manner to TRI disclosure tells management that this is now 

important criteria to investors, and thus managers might take this as new information for them to use 

deciding the relative tradeoff between pollution and profits. Regardless of the direction of causation, 

expect to observe firms that took a significant stock price hit to reduce their TRI emissions 

subsequent periods.
pgp

1991]1992.

Although the initial release of data included all U.S. facilities, the EPA did n

provide this information in a format conducive to firm-level comparisons. Instea

several environmental groups compiled this information to arrive at the ‘‘wors

corporate polluters. In other cases, the public was informed of a particu

company’s emissions when a newspaper reporter identified a particular facility 

company as being a high polluter. Some of these announcements took pla

subsequent to the initial TRI publication date of June 19. In order to determi

which firms received publicity in the media as a result of the announcement of t

TRI, we searched both the Wall Street Journal Index and the LEXIS]NEX

database for all mentions of toxic chemicals or TRI in 1989.

This search yielded 363 facilities and companies that were mentioned in t

print media as being emitters of toxic substances in 1989. A majority of the

media reports were around the dates of release of two reports. The first report w

dated June 19, 1989 the same day on which the EPA made the TRI databa

Ž

available to the public , and compiled by the Natural Resources Defense Coun

.

w x 14 NRDC . This publication reported the release of carcinogenic chemicals Ž. 

subset of the TRI chemicals , and was often the impetus for media repo

.

implicating various firms as emitters of carcinogens. The second publication w

issued by the National Wildlife Federation 15 NWF on August 10, 1989. Unli

w x Ž .

the NRDC report, which looked at carcinogens, the NWF report dealt w

aggregate releases of all toxic substances as listed under section 313 of t

EPCRA. Thus, companies receiving media attention around this time were t

largest toxic chemical polluters.

Of the firms and facilities receiving media coverage in 1989, 53% were me

tioned after the NRDC report between June 19 and June 21, 1989; 29% follow

the NWF report on August 10 and August 11, 1989; and the remaining 18% we

first mentioned in the media on other dates that year. Approximately 59% of t

facilities or plants referred to by the media were operated by publicly traded firm

the rest were owned either by private corporations or were single facilities. Aft

accounting for multiple mentions to the same parent company, 192 publicly trad

firms were found in the sample.9 Stock price data for 130 of these firms we

available from the Center for Research in Security Prices CRSP data tapes a

Ž .

formed the core of our study.10

9 Hamilton 11, Table 3 reports on the determinants of media coverage. He finds a higher likeliho

w x

of media coverage among firms with larger emissions, more TRI reports, and more employees. Fir

with many facilities spread out over the country were less likely to be reported on than those with lar

concentrated facilities. Finally, firms in the chemical and primary metals industry were less likely to 

reported on even though they had large emissions, presumably because they were already expected 

have high TRI emissions, whereas firms in the pulp and paper industry whose emissions were hig

Ž

than expected were more likely to be reported on. . 10 These 130 firms represent all NYSE and AMEX companies with complete stock price d

available on CRSP out of the 192 publicly traded firms with TRI-related media reports in 1989. We 

not include the NASDAQ companies in our data set since many of them are not as frequently trad

and are less suitable for a study of daily stock price returns.
y y g g

risk coefficients betas which are then used to calculate the prediction errors 

Ž .

the event day. These prediction errors, also known as ‘‘abnormal returns’’ Ž . m , a

it

firm specific returns that remain after controlling for the movement of the mark

on the event day and have an expected value of zero under the market model:

R s a q b R q m , m ; N 0, s2 Ž . . it i i mt it it

Whether an event has a significant effect on a firm or not may be analyzed 

examining the statistical significance of the abnormal returns. In this case t

provision of new information to the public about the environmental performan

of the firm may impact the future financial performance of the firm which wou

be reflected in the firm specific abnormal returns accruing to the firm on the d

of the event.

The abnormal returns were then calculated for these firms using event stu

methodology. The firm betas were estimated using the market model

E RŽ . s a q b R , it i i mt

where R is the individual firm return for that time period, a is the constant ter

it i

b is the beta for firm i and R is the market return at time t. Daily returns for 

i mt

240 day period, beginning with 250 trading days before the event and ending 

days prior to the event, were used to estimate the betas. These betas were th

used in the market model to calculate the abnormal returns for the event perio

We calculated the abnormal returns for a 20-day window around the event d

The event day has been defined as the day the firm was first mentioned in the pr

media.11 As shown in Table I, firms that were mentioned in the media as being 

toxic chemical emitter received significant stock price reductions on the day of t

announcement. The first column is taken directly from Hamilton 11 , whi

w x

indicates that all publicly traded firms with TRI emissions received a significa

stock price reduction on the day of announcement Ž . y0.284% , although there w

no price effect on the day prior to announcement. This suggests a lack 

information leakage prior to the initial press reports on June 19, 1989. The seco

column repeats this analysis for the 130 firms in our sample that had som

firm-specific media coverage Ž . y0.299% . Once again, we find no significa

information leakage on the day prior to announcement.

The third column of Table I examines the 40 firms in our sample of 130 th

received the largest negative abnormal returns, an average of y1.324% on t

event day.12 These firms represent about one-third of all firms with media repo

11 Note that this date is not necessarily the same for each firm, as media stories appeared at vari

times throughout the year. Instead, these dates represent the first time a media story appeared ab

that company’s toxic emissions. Thus, day 0 is designated as the day the company was mentioned in 

media and all firms are aligned using their event day. 12 We dropped two other firms from our sample that had higher abnormal returns, both of wh

also had ‘‘confounding events’’ on the day of the TRI announcement. Time]Warner was involved 

significant takeover activity during this time period. LTV Corp. was involved in a $2 billion dispute o

unfunded pension liabilities following their 1986 bankruptcy. The Supreme Court agreed to hear 

case on the day of the TRI announcement. Thus, these two firms were dropped from the sample 

ensure we did not incorrectly attribute abnormal returns to TRI announcements.
y 1 0.00000767 y0.00019 y0.000331

Ž. Ž. Ž . y0.249 y0.158 y0.1623 aa a 0 y0.00284 y0.00299 y0.01324

Ž . Ž . Ž. y3.8411 y2.7787 y14.52 aa a 0]5 y0.0120 y0.00743 y0.01129

Ž . Ž. Ž . y6.029 y2.99 y2.436

Number of 436 128 40

observations

Note. First column taken directly from Hamilton 1995 . Ž . Z statistics are reported in parenthesis. a

Statistically significant at p - 0.01.

in 1989, and their average abnormal returns is four times the mean Žy1.3% vers

. 13 about y0.3% for the entire sample . The smallest abnormal return in the ‘‘TO

40’’ sample is y0.6%, twice the average for the full sample of 130 firms. Abnorm

returns are normally reported in percentage terms to control for firm size Brow

Ž

and Warner 3 . A few million dollars means virtually nothing to IBM, but plen

w x.

to a company whose market value is only $50 million. Nevertheless, these percen

age losses translate into quite significant absolute dollars, with a mean loss 

y$87 million, median loss of y$29 million, and a range of y$3.6 to y$8

million for our TOP 40 sample. We use this TOP 40 sample throughout t

remainder of the paper to examine the impact of significant negative stock pri

reactions on firm behavior. The sample selection process balances our need for 

sample of companies that had very significant stock price reductions and hen

Ž

. 

are likely to react by reducing emissions and for a reasonably large sample size

Although we refer to these firms as the TOP 40 for ease of exposition, this lab

refers to the stock price hit they took, not necessarily to the level of TRI emissio

In fact, of the TOP 40 firms with negative abnormal returns, only 11 were al

among the top 40 largest TRI emitters hereinafter referred to as LARGE

Ž

EMITTERS in terms of absolute levels of pollutants. This is consistent with o

.

earlier conjecture that many of the largest TRI emitters would not recei

significant stock price reductions upon disclosure of their emissions, since t

market was already expecting these firms to be among the largest emitters. That 

the negative stock price reaction had already been capitalized in the market val

of the largest emitters by the time actual emissions were announced in June 1989

13 The smallest abnormal returns chosen had a significance level at about 25%, indicating there w

only a 25% chance that the abnormal returns were drawn from a sample with zero mean. The mean 

y1.34% has a z-statistic of 9.23 which has a negligible probability of happening if the true mean were 

14 This paper is primarily concerned with whether or not a negative stock price effect induces fir

to reduce emissions. Note that there are various pressures exerted upon firms to reduce emissions e

Ž

community pressure, consumers, cost savings , and most firms have reduced their TRI emissions o

.

time. Thus, in this study, we focus only on those firms that received a statistically significant stock pr

‘‘hit’’ and compare them to their industry peers. A broader study of why firms reduce TRI emissio

would require a different methodology and data. Instead, we focus on one of the reasons for T

emission reductions}the financial markets. 15 Additional evidence with regard to the validity of this hypothesis is discussed in Section V.
y pg pp

industries would not allow us to draw legitimate inferences on the behavior 

firms in the TOP 40. Thus, instead of comparing the TOP 40 to all publicly trad

companies, we adopt an ‘‘industry-weighted’’ approach. To construct this compa

son sample, we first determined the Standard & Poors’ industry classification f

each of the TOP 40 companies above. We obtained a list of 1500 publicly trad

firms from IRRC which also contained TRI and related environmental inform

Ž

. 16 tion , of which 942 were in industries where at least one firm reported T

emissions. Of these 942 companies, 512 were in the same industry category as o

of our TOP 40. After excluding a few companies with missing data and the TOP 4

a total of 455 companies remained in our comparison group. Finally, each firm 

the sample of 455 companies was given a weight so that we could replicate t

industry classifications of the TOP 40. For example, since the TOP 40 abnorm

returns sample included two steel companies, firms in the steel industry were giv

a weight of 1r20 for purposes of constructing the industry-weighted sample.17

Since firms vary dramatically by size and opportunities to pollute, any measur

ment of environmental performance must somehow adjust emission levels so th

firms can be compared on an equal footing. Unless we explicitly control for fir

size, any results would be extremely misleading. For example, a multi-billion dol

firm with massive emissions could reduce emissions by a small percentage a

become the firm with the largest absolute emission reductions while still being o

of the most inefficient polluters. Thus, we adopt two measures of performance: 

the absolute level of emissions per thousand dollars revenue, and 2 firm ra

Ž .

within its industry category normalized by the number of firms in the industr

Ž 

where ranks are determined by the level of emissions per dollar revenue.18

In addition to TRI data, we also obtained two other measures of environmen

performance from the IRRC dataset: oil and chemical spills and governmen

imposed fines for environmental violations. We collected this information 

16 IRRC selects this list of 1500 publicly traded firms to include both the Standard and Poors’ 

and the Fortune 500 companies, as well as the largest capitalization stocks. Thus, the firms in 

population are among the largest and most visible publicly traded companies in the United States. Sin

we are interested in the effect of stock price declines on subsequent firm behavior, this is 

appropriate population for our study. Moreover, we are interested in comparing similarly situa

companies. Thus, it is important to compare a firm that took a significant stock price hit with one t

could potentially have received the same hit in the market. It also allows us to ignore differ

underlying incentives and governance structures of privately held organizations. 17 Note that although we report the ‘‘industry-weighted’’ sample of 455 firms throughout the pap

we also compared the TOP 40 to the unweighted full sample of 873 firms with TRI data, as well as t

smaller ‘‘matched sample’’ of 80 firms. The results reported in Tables III and IV remain essentia

unchanged regardless of the comparison group. 18 An alternative approach is to examine the percentage reduction in emissions. The problem w

this measure of performance is that, empirically, percentage changes of TRI vary dramatically, a

often for reasons that have little to do with firm reaction to the market. For example, a firm mi

expand by acquiring or building a new plant or merging with an existing firm. In such a case, T

emissions might double or triple. Alternatively, a firm that closes a plant might cut its emissions by h

But that firm is also producing less output. Given the huge variation in percentages, we opted for 

measure of emissions per dollar revenue.
ppy 

likelihood and severity see, e.g., Cohen 6 . Thus, a reduction in the number a

Ž w x.

the amount of spills may indicate better environmental management techniqu

and that the firm is attempting to improve its environmental performance. T

other trend variable is the dollar value of fines per 1000 dollars revenue. Althou

government imposed penalties are certainly an indication of environmental perfo

mance, they are also partly based on discretionary andror random enforceme

Both variables are measured as the average of two different time periods, 1988]19

and 1991]1993.

The next section examines TRI emissions and the ranking of firms within th

industry category, both before and after public announcement of TRI data.

IV. RESULTS

Table II lists the TOP 40 firms that received the largest negative abnorm

returns in 1989 as a result of TRI announcements. Among these TOP 40 firm

Owens]Corning Fiberglass Corp. received the largest negative abnormal retur

Ž . y2.7% , while McDonnell Douglas Corp. received the smallest stock price 

Ž . y0.6% . Next, firms are ranked within their industry category on the basis 

pounds of TRI emissions per thousand dollars revenue. For example, in 19

Owens]Corning Fiberglass Corp. was the 7th largest TRI emitter in the Buildi

Materials industry on a per dollar basis out of the 19 firms in the Buildi

Materials industry, while in 1992 it was the 2nd largest emitter per dollar revenu

These rankings are not based on absolute TRI emissions because larger firm

would tend to have higher emissions and hence bias the rankings. The number 

firms in each industry is taken from the list of 1500 publicly traded firms in t

IRRC dataset. As shown in Table II, the mean ‘‘rank’’ for TOP 40 companies w

7.20 in 1989. The mean ‘‘normalized’’ rank in 1989 company rank divided 

Ž

industry size was 0.339, indicating that firms in the TOP 40 were in the top 1 . r3 

their industry in terms of TRI emissions per dollar revenue.19 The average rank 

1992 was 7.85, while the normalized rank was 0.396, indicating that these firm

became relatively ‘‘lower’’ TRI emitters following the public announcement of T

data, both in absolute and relative terms within their industries.20

19 Although these firms were among the largest TRIr$ emitters in their industry categories, th

were not necessarily the largest emitters. Only 11 of the TOP 40 firms that received significant negat

abnormal returns were also among the 40 LARGEST EMITTERS in terms of absolute pollut

volume. 20 The change in absolute ranking is not significant Ž . p - 0.31 . This is not really an appropri

measure of comparison, however, since the size of industries range from four to 53 firms. Thus, a t

unit change in ranking, for example, might indicate an important shift in relative performance in 

industry with four firms, but only a minor change in an industry with 53 firms. Thus, a more appropri

measure and the one used in subsequent empirical analyses is the normalized ranking, rank divided 

Ž .

number of firms in the industry. As shown later in Table V, the improvement in normalized fi

ranking is not statistically significant at normal levels of significance Ž . p - 0.15 .
py y 

Owens-Corning Fiberglass Corp. 1 Building Material 7r19 2r19 y5 0.6

Quanex Corp. 2 Steel 16r22 15r22 y1 y1.3

Cooper Industries Inc. 3 Machinery 14r49 18r49 q4 y0.6

Crown Cork & Seal Co. Inc. 4 Containers Metal 1 Ž . r12 3r12 q2 y3.1

Armco Inc. 5 Steel 5r22 4r22 y1 y1

Eastman Kodak Co. 6 Photographic 2r4 1r4 y1 y0.7

Thomas Industries Inc. 7 Electrical Equip. 9r53 4r53 y5 0.4

Hewlett-Packard Co. 8 Electronics Instr. 6r21 9r21 q3 y0.3

Rhone-Poulenc Rorer 9 Drugs 1r18 4r18 q3 y16.1

Coastal Corp. 10 Natural Gas 1r40 1r40 0 y0.6

General Motors Corp. 11 Auto 1r5 1r5 0 y0.6

Phelps Dodge Corp. 12 Metals 1r18 4r18 q3 y14.2

Springs Industries Inc. 13 Textiles 6r24 9r24 q3 y0.2

Scott Paper Co. 14 Household Prod. 3r9 2r9 y1 0.8

James River Corp. of Virginia 15 Paper 9r27 15r27 q6 y1.1

Illinois Tool Works Inc. 16 Manufacturing 17r37 9r37 y8 0.2

Inland Steel Industries Inc. 17 Steel 2r22 3r22 q1 y4.6

Eaton Corp. 18 Heavy Duty Truck 2r6 6r6 q4 y1.0

International Business Machines 19 Computers 9r44 5r44 y4 y0.0

Bethlehem Steel Corp. 20 Steel 15r22 12r22 y3 y1.4

Maytag Corp. 21 Household Furn. 5r6 5r6 0 y0.2

Whirlpool Corp. 22 Household Furn. 3r6 4r6 q1 y0.9

Unocal Corp. 23 Oil 3r32 7r32 q4 y1.2

Parker Hannifin Corp. 24 Manufacturing 14r37 14r37 0 y0.4

Olin Corp. 25 Chemicals 8r16 15r16 q7 y4.6

Chrysler Corp. 26 Auto 2r5 4r5 q2 y0.4

General Electric Co. 27 Electrical Equip. 12r53 21r53 q9 y0.4

Allied-Signal Inc. 28 Manufacturing 3r37 3r37 0 y3.4

Raytheon Co. 29 Aerospace 10r19 14r19 q4 y0.3

Lockheed Corp. 30 Aerospace 16r19 18r19 q2 0.0

Boeing Co. 31 Aerospace 8r19 15r19 q7 y0.5

Upjohn Co. 32 Drugs 2r18 2r18 0 y7.1

Ferro Corp. 33 Specialty Chem. 7r31 8r31 q1 y2.9

Atlantic Richfield Co. 34 Oil 20r32 11r32 y9 0.3

Westvaco Corp. 35 Paper 1r27 2r27 q1 y3.0

Mobil Corp. 36 Oil 19r32 8r32 y9 0.5

Armstrong World Industries Inc. 37 Household Furn. 2r6 2r6 0 y0.3

Union Camp Corp. 38 Paper 3r27 8r27 q5 y3.9

Federal Paper Board Co. Inc. 39 Paper 11r27 9r27 y2 1.0

McDonnell Douglas Corp. 40 Aerospace 12r19 16r19 q4 y0.2

Mean values 7.20 7.85 0.65 y1.8

Normalized industry rank

Ž . company rankrindustry size 0.339 0.396

a

Positive number indicates improvement relative to industry; rank s 1 for worst polluter. b

Negative number indicates improvement through reduced emissions.
y gp 

abnormal sample of means with

returns firms industry peers

Sample size 40 455 a TRIr$ revenue in 1989 4.382 2.318 y1.87

TRIr$ revenue in 1992 2.543 2.017 y0.53

Change in TRIr$ revenue 1989]1992 y1.84 0.17 b t test values comparing 1989 to 1992 y1.49 y0.35

within each category

Note. t statistics are reported based on one-tailed tests. a

Significant at p - 0.05. b

Significant at p - 0.10.

The sixth column of Table II reports the change in industry rank between 19

and 1992. A positive number indicates that the firm lowered its rank in terms 

TRIr$, and is now a relatively ‘‘cleaner’’ firm than before. Of the 40 firms show

21 lowered their rankings, 7 firms remained unchanged, and 12 increased th

relative TRI emissions. The last column shows the absolute change in T

emissions per dollar of revenue, with a negative number indicating a reduction. 

the 40 firms, 32 reduced their TRIr$ revenue, while only 8 firms increas

emissions. The average firm reduced emissions by 1.84 pounds per thousand dolla

revenue.

It is clear from Table II that the 40 firms who received the most significa

negative stock price reactions following announcement of TRI emissions signi

cantly lowered both their TRIr$ emissions and their TRIr$ ranking within th

industry. As further evidence, and to help rule out the possibility of other unknow

factors causing the TRI reductions, Table III compares industry rankings a

TRIr$ reductions of the TOP 40 firms to our industry-weighted sample of 4

companies. TRIr$ revenue for the TOP 40 firms was nearly twice that of t

industry-weighted sample in 1989 Ž . p - 0.05 . However, by 1992, the TOP 40 firm

had reduced their TRIr$ revenue nearly in half, so that the difference betwe

those firms and the industry-weighted sample was no longer statistically significa

While the TOP 40 firms reduced TRIr$ revenue by 1.84 pounds per thousa

dollars revenue,21 the industry-weighted sample had only reduced their TRI

revenue by 0.17 pounds per thousand.

Since the analysis above only compared two years of data 1989 and 1992 , w

Ž .

also compared average TRIr$ revenue over two different time periods in order 

smooth out any errors in reporting or annual fluctuations. Table IV reports on t

average TRIr$ revenue during the time periods 1988]1990 and 1991]1992.22 T

results are equally striking, with average TRIr$ revenue decreasing from 4.48 

21 As noted in Table III, this reduction from 4.38 to 2.54 pounds per thousand dollar revenue 

Ž 

statistically significant at p - 0.10. 22 Although the results shown in Table IV are based in current dollars, there is virtually no differen

once they are converted to constant dollars.
pppg

1988]1990 1992 for TRI two time periods Ž .

a TRIr$ revenue 4.48 3.23 3.93

TOP 40

Industry weighted sample Ž . n s 455 2.43 2.14 1.25

Spillsr$ revenue 4.00 3.14 1.61

TOP 40

Industry weighted sample Ž . n s 455 2.04 2.45 y0.58

Finesr$ revenue 22.4 134.8 y1.44

TOP 40 a Industry weighted sample Ž . n s 455 15.9 96.7 y2.85

Note. t statistics are reported based on two-tailed tests. a

Significant at p - 0.01.

3.23 in the TOP 40 group Ž . p - 0.01 , compared to a reduction from 2.43 to 2.

Ž . p - 0.21 for the industry-weighted sample.

Table IV also reports on two other measures of environmental performance: 

and chemical spills, and government-imposed fines.23 While the average volume 

spills per dollar sales decreased in the TOP 40 group from 4.00 to 3.14, Ž p - 0.1

it actually increased slightly in the industry-weighted sample from 2.04 to 2.

Ž

p - 0.56 ..

Trends in government-imposed fines are a little more complicated to asse

since they not only serve as a measure of firm environmental performance, but al

reflect changes in government enforcement programs. Both the TOP 40 firms a

the industry sample increase their finesr$ revenue over time, with average fin

being higher for the TOP 40 firms. However, only the increase in industry-weight

Ž . 24 fines is statistically significant p - 0.01 . The fact that fine levels increase f

both samples may be an indication of changes in EPA penalty andror enforceme

trends.25

V. CONFIRMATORY EVIDENCE ON THE ROLE OF INFORMATION

We have argued that the reason for a significant reduction in stock mark

valuation following release of TRI data is the fact that this is new information a

23 Unlike the TRI trend data which compare 1988]1990 and 1991]1992, these data comp

1988]1990 and 1991]1993. Since there is a longer lag in reporting TRI data, it is not as current as s

data and enforcement data. 24 Note that there are more than twice as many firms with increased finesr$ than decreased fines

among the industry-matched sample. This ratio is about 1.5 for the TOP 40. Using a Wilcoxon sign t

we find that the industry-matched sample has a significant number of increases at p - 0.01, wher

the TOP 40 sample does not have a statistically significant number of increases Ž . p - 0.26 . Thus, ev

if enforcement trends and fine levels are increasing for the TOP 40 group, they are increasing at a mu

lower rate than their industry peers. 25 Cohen 5 and U.S. Environmental Protection Agency 19 provide evidence of increasing monet

wx w x

penalties and enforcement trends during this time period.
noted earlier, we found that only 11 of our TOP 40 were also among the 

LARGEST EMITTERS. The 40 LARGEST EMITTERS were also larger emitte

in terms of TRIr$. Whereas the TOP 40 sample had average emissions of 4.

pounds per thousand dollars revenue in 1989, the 40 LARGEST EMITTER

averaged 12.95 pounds per thousand dollars revenue. Moreover, while the TOP 

significantly reduced their TRIr$ from 4.38 to 2.54, Ž . p - 0.10 , the LARGE

EMITTERS did not reduce TRIr$ at a statistically significant level from 12.95 

Ž

10.05, p - 0.23 . This same result carries over to the industry TRI . r$ rankin

where the normalized rank in terms of TRI Ž . r$ of the 40 LARGEST EMITTER

remained virtually unchanged 0.216 to 0.214 , while the ranking for the TOP 

Ž .

sample improved from 0.34 to 0.40 Ž . p - 0.15 . These findings are consistent w

the view that media attention and resultant stock price effects have more of 

effect on subsequent firm behavior than simply being known as the largest emitte

in the United States.

These results are consistent with our hypothesis that the market reacted more 

unexpected TRI disclosures than to those that were already expected to be ve

large. Further evidence is presented by Hamilton 11 in an analysis of the dol

w x

value of abnormal returns on the day of TRI disclosure. After controlling for si

of firm, he found a positive correlation between the number of existing Superfu

sites already known to the public and abnormal returns, which is consistent w

Ž .

the market already anticipating that these firms would be major polluters. He al

found little correlation between industry dummies for the five most polluti

TABLE V

Comparison of TRIr$ and Industry Rank for Top 40 Abnormal Returns and Top 40 Emitters

Top 40 firms Top 40 firms

ranked by event ranked by

day abnormal absolute TRI

returns levels

TRIr$ revenue in 1989 4.38 12.95

TRIr$ revenue in 1992 2.54 10.05

Change in TRIr$ revenue 1989]1992 y1.84 y2.90 a Ž . Ž . Ž. t statistic in parenthesis y1.49 0.77

Normalized industry rank in 1989 0.339 0.216

Ž . company rankrindustry size

Normalized industry rank in 1992 0.396 0.214

Ž . company rankrindustry size

Decline in normalized industry 0.05 y0.002

rank 1989]1992 1.03 Ž. Ž . y0.17

Ž . t statistic in parenthesis

Note. The top 40 firms ranked by absolute TRI emissions have higher TRIr$ and higher TRI

normalized industry rankings than the top 40 ranked firms ranked by abnormal returns, in both 19

and 1992 Ž . p - 0.01 . t statistics are reported based on one-tailed tests. a

Significant at p - 0.10.
pyy p

Twenty-two firms had positive abnormal returns with a t statistic at the 25% lev

or below the same criteria used in generating the TOP 40 sample of compan

Ž

with negative abnormal returns . Of these 22, we had to eliminate three that d

.

not have subsequent environmental data to compare. Thus, we constructed 

sample of 19 firms with the largest positive abnormal returns hereinafter label

Ž

POSITIVE 19.}comparable to our TOP 40 negative returns. Although the samp

size is small and we have not controlled for industry characteristics, we can make 

few tentative comparisons. Whereas the TOP 40 firms had an average normaliz

industry rank of 0.339 in 1989, the POSITIVE 19 firms were ranked 0.385. Th

the POSITIVE 19 were relatively low emitters in their industry groups compared 

the TOP 40 in 1989, although the difference is only significant at p - 0.28. 

1992, the TOP 40 group caught up with the POSITIVE 19, with the normaliz

rank being 0.396 for the TOP 40 and 0.383 for the POSITIVE 19. This is consiste

with our hypothesis that the market reacted to new information}some of whi

positively affected firms that looked more favorable than their industry peers a

more favorable than investor’s expectations.

VI. CONCLUDING REMARKS

This paper has examined firm behavior in response to a significant stock mark

reaction to new information on toxic chemical emissions. We identified all firm

with significant negative abnormal returns upon the public announcement of th

TRI emissions in 1989. Firms with the largest negative stock price effects followi

announcement of their TRI emissions were found: 1 to be among the top 1 Ž . r3 

polluting firms per dollar revenue in their industries; 2 not to be the large

Ž . Ž.

absolute TRI emitters; 3 to subsequently reduce their TRI emissions more th

Ž .

other firms in their industry including those firms with the largest TRI Ž r$ reven

prior to the disclosure of TRI levels ; 4 to also make other significant attempts 

.Ž.

improving their environmental performance by reducing the number and sever

of oil and chemical spills; and 5 to have a lower chance of receiving higher fin

Ž .

from the government in subsequent years.

We have provided a partial answer to the fundamental question concerning t

role of information in reducing the emission of environmental contaminants. N

information concerning a firm’s toxic emissions that has a significant effect 

market valuation is likely to induce that firm to significantly reduce subseque

emissions and to otherwise improve its environmental performance. Thus, in som

instances, providing information to the public may be an effective remedy 

reduce environmental externalities beyond a regulatory standard. Neverthele

several important questions are left unanswered. Are these reduced emissions t

‘‘worst’’ pollutants for these firms? Are these emissions the most cost-effecti

26 The only significant industry dummy was primary metals, which actually had a positive coefficie

Thus, the market may have been surprised that this industry was not as large a polluter as expect
ygy gpg 

emissions following a significant stock price hit realized; in other words, does 

‘‘pay’’ these firms to reduce TRI? Finally, do the social benefits of this form 

regulation exceed its costs, and does it dominate direct regulation or other forms 

market-based incentives? Answers to these questions must await further researc

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27 One of the ‘‘risks’’ associated with using information as a regulatory tool is that providing o

piece of information might stifle the flow of another type. For example, by focusing on the absol

level of TRI emissions instead of the harmfulness of emissions, some have argued that firms who app

to be the worst polluters are actually not.