How do stock markets react to industrial accidents

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Feb 15, 2006 - examine stock market reactions to 76 petro&chemical accidents on a sample of worldwide ... As one of the major technological risks that face modern so& .... of three emerging countries: the former Czech Republic, South Africa and South Korea. ..... pointed out an economic effect on energy futures markets.
How do stock markets react to industrial accidents? The case of chemical and oil industry Gunther Capelle-Blancardy

Marie-Aude Lagunaz

February 15, 2006

Abstract Recent studies have debated the stock market’s role as an enforcer of environmental regulation. Oil and chemical companies are insured against most direct cost of an accident, but they cannot insure against increased regulation oversight, and damage to reputation. We examine stock market reactions to 76 petro-chemical accidents on a sample of worldwide publicly traded …rms from 1987-2005. Speci…cally, we …nd that the market penalty is related to media coverage and human harm, but unrelated to environmental harm. This result appears to reinforce the need to establish liability for damage to nature. JEL Classi…cation: G14, G22, Q5 Keywords: Technological Risk, Event Study, Environmental Liability, Insurance

Financial support from the French Ministry of Ecology and Sustainable Development is greatfully acknowledged. We thank Paul Lanoie, Sandrine Lardic, Georges Prat, and Sandrine Spaeter for helpful comments, as particpants at the AFFI International Conference 2005 and Second Toulouse-Montreal Conference on the Law, Economics and Management of Larges-Scale Technological Risks 2005. The usual disclaimer applies. y EconomiX Université Paris X & CES Université Paris I - Panthéon Sorbonne - CNRS, MSE - 106-112 bd de l’hôpital 75013 Paris, 00 33 1 44 07 82 70 z Agence de l’Environnement et de la Maîtrise de l’Energie & CES Université Paris I - Panthéon Sorbonne CNRS, MSE - 106-112 bd de l’hôpital 75013 Paris, 00 33 1 44 07 82 71

1

Introduction

Central to the debate over the need for government regulation of technological risks is the incentive that the private market provides to comply with safety standards, or innovate in order to prevent accidents (and reach optimal level of precaution). This incentive comes from the cost imposed upon …rms responsible for accidents. As one of the major technological risks that face modern societies, explosions in chemical plants may generate negative externalities on health and ecosystems while a¤ecting directly revenues of …rms in disrupting their production process.1 Inspections are far too infrequent and penalties too small to be considered a reasonable deterrent (Cohen (1999)). Moreover, despite the adoption of the Directive of the European Union on environmental liability (European Parliament and the Council of Europe (2004)), based upon the polluter-payer principle, the amendment of environmental liability is still a controversial issue for most European Union member states. Unlike the United States, in the events of disasters in one of their production facilities, western European companies bear direct costs (property damage losses, business interruption) but aren’t charged with the clean up of ecosystems.2 Using a sample of 76 worldwide accidents that occured in petro-chemical plants between 1987 and 2005, this paper attempts to quantify the costs that chemical and oil companies incur due to technological accidents. The potential costs include those due to loss of life and equipment, tort liability, increased regulation oversight. We begin by examining the losses incurred by shareholders when a major accident occurs. Oil and chemical companies carry insurance against many of the costs of an accident. Nevertheless, the market for incidental pollution insurance is still limited, as environmental risks do not meet all the necessary conditions to be insurable. Moreover, safety standards and subsequent cost that insurers impose on …rms to avoid moral hazard are often prohibitive. Consequently, property losses and business interruption, and third party claims (for personal and property damages) are likely to be met by either the oil companies themselves or to be covered by an oil industry mutual.3 Hence, the decline in …rm value comes from those losses that are uninsured and from the increased insurance costs that might result as insurance companies update their information about the safety of a particular plant or of the industry in general. Insurance does not fully cover income losses due to negative demand shift (Sprecher and Perl (1983)), reinforcement of safety standards, regulatory compliance costs, pollution control and abatement expenditures, and penalties and …nes from enforcement actions, and damage to reputation. Aside from …nes, authorities can demand a de…nitive or temporary shut down of plants. Moreover, …rms faced with major technological accidents spend money on marketing and advertising, in order to enhance a green image towards consumers and environmental interest groups. Finally, even if these future cash ‡ows are expected to remain relatively una¤ected, analysts may view the earnings of …rms riskier than previously expected. Consequently, market capitalization can be a¤ected by portfolio rebalancing caused by a shift in systematic risk (Baginski, Corbett and Ortega, 1991). (long time lags etc.) In such a context of strong uncertainty about the direct cost incurred by …rms following hazardous accidents, investors’response to industrial accidents is of particular interest. Stock market prices can be used to value all expected uninsured future costs. We …nd that petro-chemical’ shareholders su¤er a statistically signi…cant wealth loss when a plant experience a serious accident. Moreover, we build several indicators on the media, human economic and environmental e¤ects of accidents. Whereas 23% of accidents in our sample do result in a toxic release, we show that equity value losses are related to media coverage and fatalities, and unrelated to incidental pollution. This result may be due to the legal context, in that, unlike the United States, most of the national public authorities have not established environmental liability for damage to nature. On the other hand, human harm and media coverage approximate future costs arising from litigation and insurance. Section 2 details previous …ndings. Section 3 describes our data and key indicators. Section 4 and 5 provide an overview of the methodology and discusses full sample results. Hypothesis 1 A serious chemical accident occurs every 86 minutes in the United States, where each year, chemical accidents kill 23 employees and injure 1,000 according to the National Environmental Law Centre, and the Wharton Centre for Risk and Management and Decision. 2 For more details, see Spaeter (2004) who studies how environmental liability regimes may a¤ect prevention behavior. 3 Captive company is an insurance company entirely controlled by some parent company that sells insurance services mainly or even exclusively to its parent company. They were created in the USA in the 20s to cover new risks as medical malpractice, product-related risks and environmental risks, in a context of little and very expensive insurance products available, see Porat and Powers (1995) for more details.

and related results are discussed in Section 6. The paper concludes with a summary and policy recommendations.

2

Previous …ndings of Market Incentives for Environmental compliance

Several studies have examined incentives provided by the private market for environmental regulatory compliance and precautionary behaviour in hazardous industries (chemicals, oil, primary metals, and paper). Earlier studies rely on the assumption that shareholders may incorporate damage to reputation, when they value the expected pro…tability and the risk pro…le of …rms engaged in hazardous activities. They all …nd an adverse e¤ect of environmental incidents and technological accidents on the equity value of …rms involved. Klassen and MacLaughin (1996)) examine a sample of oil spill, gaz leak and other incidental pollution over the period 1985-1991 and …nd a signi…cant abnormal return loss of 1.5% on average. Muoghalu, Robison and Glasckock (1990) examine hazardous waste management lawsuits over the period 1977-1986. They …nd abnormal losses of 1.2% on average at the time of the announcement of the lawsuit but no signi…cant abnormal returns were found at the time of the announcement of settlements. Among 128 initial lawsuits, 68 involve petrochemical industries. Abnormal returns due to lawsuits …lings concerning petrochemical …rms are much lower (-0.62%) than for other industries. By constrast with Muoghalu, Robison and Glasckock (1990), Lanoie and Laplante (1994), using a sample of 47 events involving Canadian …rms between 1982 and 1991, show that the stock value declined only on the day of the announcement of suit settlements resulting in …nes (about -2%). According to the authors, the fact that Canadian authorities are more concilient explain this result. Bradinath and Bolster (1996)) examine reactions to EPA judicial action between 1972-1991. They …nd a signi…cant decline of 0.43% during the week of settlement, although the market penalty is unrelated to …ne size. Karpo¤, Lott, Rankine (1998), Karpo¤, Lott, Wehrly (1998), Karpo¤, Lott, Wehrly (2005) also estimate the consequences on …rm equity value of environmental violations. They show evidence of equity value losses the day the charges are …led. Unlike Muoghalu, Robison and Glasckock (1990), they also show evidence of signi…cant losses the day of settlements for environmental violations. Karpo¤, Lott, Wehrly (2005) examine a sample of 148 legal penalties sizes reported in the Wall Street Journal. They show evidence that market value losses on companies that are found to be violating environmental laws are similar in magnitude to the legal penalties imposed (…nes and clean up activities). The fact that …rms that violate environmental regulations typically do not impose direct harms on their customers, employees, suppliers, or other stakeholders may explain this result. Jones and Rubin (2001) examine residual losses associated with environmental harm in the power electric and oil industry, between 1970 and 1992. Firms were involved in events that produce ill will, but do not a¤ect the quality of the …rms’…nal products nor break implicit labor or supply contracts. Their study also concludes that …rms face reputation penalty only when adverse events harm customers or suppliers. The most important petro-chemical accidents for publicly traded companies are the Bhopal chemical explosion on December 4, 1984 in India, the Exxon-Valdez oil spill on March 24, 1989. Salinger (1986) shows evidence of a 27% reduction in the market value of Union Carbide in December 1984, consecutive to the Bhopal explosion. This corresponds to an abnormal return equal to -31.5%. White (1996) studies the Exxon-Valdez oil spill. Exxon shareholders incur a signi…cant equity value loss of -19% for the …rst 120 days following the accident. These two catastrophes raised unprecedented, long-lasting lawsuits, due to the time needed to assess damage caused to the ecosystem. Several of these studies (Jones and Robin (2001), Karpo¤, Lott, and Wehrly (2005)) show that market value losses re‡ect …rm’s legal penalties, and that reputational penalties are, on average, negligible. Since these studies concern only north american publicly traded …rms, our sample of worldwide publicly traded …rms (Japan and European Union member states) is especially relevant.

3

Data Construction

Studying the response of investors and journalists to news about the accidents involved collecting information on accidents, stock prices and print media coverage. The sample includes 76 accidents caused by 43 di¤erent chemical and oil publicly traded companies between 1987 and 2005. All explosions were reported by English print media.4 The research was carried out using two keywords: "explosion" and "chemical plant", and excludes all accidents reported by newspapers before the 1st of January of 1990.5 Using print media as a source allowed us to identify names of companies responsible for accidents that public authorities don’t disclose according to a principle of commercial con…dentiality that holds in almost every country except in the USA. By the nature of accidents, there isn’t any discrepancy between the date of accident and that of their announcement by print media. Only one accident entered in this category, and was made public more than 20 days after the chemical spill occurred. Using the two keywords, we started with191 explosions in chemical plants since 1987. Of those, 115 accidents were removed, or 60% of the initial sample, because lots of them don’t involve publicly traded companies, otherwise illegal …reworks factories or state-owned petrochemical companies. Datastream that covers more than 75% of publicly traded companies in the world was used to isolate a sample of 43 publicly traded companies responsible for the 76 remaining accidents. Of those, These …rms all belong to the 30 more important chemical and oil …rms in the world, in terms of their 2003 sales, with 25 percent of accidents involving companies of the the oil sector. Moreover, the shares of reponsible companies are quoted on the stock markets of six developed countries: France, Germany, Japan, Spain, United Kingdom, The United States of America; and of three emerging countries: the former Czech Republic, South Africa and South Korea. The full sample also includes three oil spills: Exxon-Valdez the march 24th of 1989, in North Alaska, the Erika oil spill, on the December 12th of 1999 in the French eastern coast, the british Brent oil spill of BP the 6th of June of 1993. Print Media coverage Obviously, our sample understates and represents only partially all chemical explosions that occurred in the 90s. Moreover, in order to be exhaustive, our research would have included two additional major technological risks: …res and chemical spills. But, it also shows strong evidence of the low level of media coverage of environmental incidents and pollution records. According to Hamilton (1995), of the 450 …rms reporting TRI releases that had useable stock price data from the New York or American exchanges, only 55 companies received media coverage mentioning their TRI releases during 1989. Though these …rms accounted for only 11 of the companies in the publicly traded …rms in the regression sample, their TRI data accounted for approximately 38 of the submission form and 71 of the total emissions. The media focus on …rms accounting for a large fraction of the pollution. It also shows evidence that while the paper industry is more likely to receive media coverage, production facilities in the chemical or primary metals industries, are less likely to have their TRI releases reported by print media. According to him, companies involved in production of pollution-intensive goods might be less newsworthy, because readers already associated these …rms with pollution. Environmental and human harms Accidents are mainly characterised by the uncertainty they generate, and result in a strong heterogeneity in terms of their coverage by media. We focused our attention on indirect speeches by local populations, authorities, unions, company spokesmen, environmentalist interest groups, citizen groups, …re brigade and police. We could summarize information into three indicators of the environmental, human and media e¤ects of accidents. Estimations of material damages, business interruption losses and insurance coverage are kept con…dential by insurers and companies 4 Lexis-Nexis database covers all major newspapers, and publications in the world, written in English, and in local languages. 5 Notwithstanding, media also reported two accidents, Hoescht chemical explosion in 1987, and Exxon-Valdez in 1989, which occurred before this date.

involved in the accidents.6 To tackle the inherent limitations in the disclosure of economic e¤ects, we considered the shut down of plants, which followed 24,2% of the explosions studied, as a proxy for serious material damages. The media coverage7 and human harm are also introduced to approximate total cost of accidents. According to Borenstein and Zimmerman (1987), the number of deaths and serious injuries approximates future liability claims, for which many …rms are insured. Environmental harm as measured by a toxic release, approximates social cost. Table I reports descriptive statistics on the economic, environmental and human e¤ects of accidents. 8 .24.2% of accidents resulted in at least one death or a serious injury, whereas the median number of fatalities or serious injuries is 1, and the average number is 2,9. The median number of articles reporting accidents for the …rst 9 days is 9 5 (the average number is 28). For 27,1 % of accidents, there were a toxic release. We show evidence that the seriousness of accidents measured in terms of faltalities is strongly linked to their media coverage, whereas the environmental is poorly and negatively correlated to the media coverage. 9

4

Methodology

The …rst part of this research examines the e¤ect of an accident on the equity value of the oil and chemical …rms. To examine share price behavior surrounding explosions in chemical plants, we performed a daily event study following Fama, Fisher, Jensen and Roll (1969) as implemented by MacKinlay (1997). The change in equity value associated with an explosion in a chemical plant is taken as an unbiased estimate of the …nancial consequences of the accident. Speci…cally, the market model is applied to describe the behavior of asset returns ans separate out changes in value caused by overall market e¤ects from those changes caused by the accident itself. The normal relation between the returns to a given stock and the market is given by:

Rjt =

j

+

j Rmt

+ "jt

Rmt , and is equal to of the Sharpe-Litner capital asset pricing model. The term j Rmt is the portion of the return to security j on day t that is due to marketwide factors. The parameter j measures that part of the average daily return on the stock that is not due to market movements. Lastly, "jt measures that part of the change in the value of …rm’s j stock on day t that is not due to either movements in the market or to the …rm’s average daily return. On the day of an event (here explosion in a chemical plant or in an oil re…nery), the deviation in an individual stock’s daily return from what is expected based on equation (1) , that is, the prediction error, is taken as an unibiased estimate of the …nancial e¤ects of the event. Let AR stand for this abnormal return or prediction error:

ARjt = Rjt

cj

where cj and cj are respectively, the estimates of

cRmt j

j

and

j.

6 Only the economic consequences of 23 accidents are reported by media.The losses of three accidents were reported by media as low, three others were unknown, and 16 were considered as serious. The insurance coverage of major accidents is often made public: as is the case of Total and Shell (November 1992) which are members of the Oil Insurance LTD (a mutual insurer), and of German giant chemical companies (Hoescht, Bayer) within the Gerling Allgemeine Versicherungs AG which covers liability, property and business interruption. 7 We considered as rare occurences of major accidents which were ignored or occulted by printed media. 8 Note that we focus exclusively our attention on accidents for which the systematic risk of the corresponding responsible puclicly traded companies could be estimated over a period of estimation of 180 days previous to each accident’s date. 9 The correlation matrix in Table I shows evidence of a positive coe¢ cient of 0.262, whereas the coe¢ cient of correlation between environment harm and media coverage is negative and equal to -0.223.

The average abnormal daily return for all accidents in the sample is calculated along with two measures of its statistical signi…cance. The …rst measure of signi…cance agregates into a single portfolio the abnormal returns of all chemical and oil companies experiencing an accident for the day of each …rm’s explosion. It then uses the daily variance of returns on this portfolio to calculate a t-statistic. The second signi…cance test calculates a t-statistic for each …rm’s abnormal return for each accident-day. The sum of these individual t-statistics follows a distribution that is asymptotically normal with mean zero and variance equal to the number of observations. The z-statistic for the average is then the sum of the individual t-statistics divided by the square root of the number of observations. This test is attributes less weight to observations of …rms with a high variance in returns and is therefore less sensitive to distortions from very noisy observations. Finally, to examine the total loss in return from explosions, we must look at the cumulative abnormal returns starting with the accident date. They are appropriate tests of permanence of the abnormal return from explosions.

5

Market Value E¤ects of Accidents Announcement

Studies on the …nancial e¤ects of catastrophic events usually focus attention on a speci…c and notable event they chose to study separately (White (1996), Salinger (1992), Baginski, Corbett and Ortega (1991)). The evidence we provide in our study is twofold. We show evidence of average equity losses associated with the full sample of accidents, and also show evidence of the individual e¤ect of major accidents on equity value. In Table I that reports descriptive statistics on the economic, environmental and human e¤ects of accidents, note that we focus exclusively our attention on the 67 accidents for which the systematic risk of companies could be estimated over a period of 180 days beginning ten days prior to the accident date. Table II, III and IV detail average abnormal returns and cumulated abnormal returns for these accidents. Full Sample Results On average, shareholders su¤er signi…cant losses (cumulative abnormal returns) of 0,86% the day of accidents, and of 1,15% for the …rst 5 days following accidents, associated with Student statistics of -3,88 and -2,87 respectively. This result is consistent with the e¢ cient hypothesis, according to which news are totally absorbed in the stock price during the …rst days of accidents. The percentage of …rms experiencing negative abnormal returns after the accident is high, 73 percent. Moreover, we also test if the price pressure we observe is associated with a signi…cant shift in the volume of stocks. Moreover, the price pressure we observe for Total and BP following the explosion is not associated with abnormally trading volume of stocks.. Trading volumes following accidents are compared with their average over the estimation period of 190 days prior to the explosion (controlled by their standard deviation over the same period). Abnormal trading volumes of stocks are not statistically signi…cant. We remove in sample C (in table II), 55 accidents that didn’t provoke a signi…cant drop in the equity value of each corresponding …rms at the 5 % level. In table II (sample C), we show that, on average, investor’s reactions are still instantaneous, but also tend to persist over a period of 40 days much longer than for the full sample of …rms. Shareholder su¤er an equity loss of 1,99% on average the day of accidents at the one percent level. The cumulative returns for the …rst day is -2,95% and for the …rst forty days is -6,59% (t-stat = -4,79 and -2,44 respectively). In sample C, two explosions are catastrophic events while the ten others were reported to have provoked either important economic losses, or serious human harm. Sample D also includes 7 accidents which provoked a signi…cant drop in the equity value of each corresponding …rms at the 10% level. In sample B, these 12 accidents are removed from the initial sample. We still …nd signi…cant cumulated average abnormal returns for the remaining 55 …rms of -2,43% for the …rst day the day at the one percent level.

Catastrophic accidents The most important petro-chemical accidents for publicly traded companies are the Bhopal chemical explosion on December 4, 1984 in India, the Exxon-Valdez oil spill on March 24, 1989 and the explosion of an agrochemical plant (AZF) on September 21, 2001 in France. Salinger (1986) shows evidence of a 27% reduction in the market value of Union Carbide in December 1984, consecutive to the Bhopal explosion. This corresponds to an abnormal return equal to -31.5%. White (1996) studies the Exxon-Valdez oil spill. Surprisingly, abnormal returns are not signi…cant the …rst four days. But Exxon shareholders incur a cumulative abnormal return of -19% for the …rst 120 days following the accident. Our results are consistent with White (1996) (see Table IV). The damages were estimated to $3 billion and this catastrophe raised unprecedented, long-lasting lawsuits, due to the time needed to assess damage caused to the aquatic ecosystem. We estimate that the AZF explosion resulted in a signi…cant drop of $3 billion in Total’s market capitalization, associated with a signi…cant abnormal return of -3.74% on the explosion day. Market reaction did not last more than 3 days. The company bene…ted form $500 million of insured property damages, and a public aid from the French government of 229 million Euro. As shown in Table V, using the methodology implemented by Baginski, Corbett and Ortega (1991), we show that these two catastrophic events resulted in a shift of the …rms’corresponding systematic risk, but not in the same direction. Major and Minor accidents In this section, we carry out a suit by suit analysis of cumulative abnormal returns, and shifts in systematic risks of …rms, as reported in Table IV and V. Table IV reports cumulative abnormal returns for each accident, for the …rst one, …ve and 120 days following the accident date. Investors react to the FMC explosion in 1996 only twelve days after the accident date. The announcement of a class-action suit, on December 12, 1996, may explain this delayed reaction. The lawyer required $50,0000 per person as a compensation for the containment. We also explain that the equity loss incured by Union Carbide shareholders plant in 1991 may be explained by news that the company didn’t bene…t from any insurance coverage for its serious business interruption losses. We also …nd that two accidents which provoked fatal human harm, without leading to major economic losses, two explosions in a Japanese agrochemical plant in 1990 (Daiichi), and in a French chemical plant in April of 2001 (Rhodia), both resulted, as shown in Table V, in a signi…cant increase of the systematic risk of …rms, and a signi…cant drop in their equity returns. An explosion in a re…nery of Grangemouth, in the UK, on February 1992, which was identi…ed in 2000 as one of the most dangerous industrial plant in the world, also provoked fatal human harm, and no serious damages. Nonetheless, the company experienced a signi…cant drop in its equity value 6 days after the explosion. The model of Baginski, Corbett and Ortega (2001) corroborates only partially the results we obtained for the Panel A in Table II. Table V shows evidence of some major accidents which provoked signi…cant and positive shifts in systematic risks, without resulting in signi…cant equity value losses.. It was the case of an explosion in one of the biggest hydrogen peroxide unit in the world (the company Oxysynthèse is jointly owned by Air Liquide and Elf Aquitaine), on April 1992 in France. This explosion provoked the shut down of the plant, and compelled the company to declare force majeure with its customers. We observe a signi…cant shift in the systematic risk of the two parent companies, Air Liquide (of 0,20 at the 5% level of signi…cance) and Elf Aquitaine (of 0,24 at the 5% level of signi…cance), but no signi…cant drop in their equity returns. We also show evidence that the explosion in a plant of a French petrochemical …rm (Total) located in the USA, which provoked several deaths on July of 2001, but no important economic losses, increased signi…cantly the systematic risk of the group. The 15th of March of 1993, an explosion in a German chemical plant, which also experienced a long story of accidents, and provoked at that time important human and economic losses, lead to a signi…cant drop of the stock returns of the company the day following the accident. We show evidence that the Erika oil spill, which did not provoke important material damages to the company neither human harm, but important damages to the aquatic ecosystem, for which Total was still not liable in 2005, caused a signi…cant negative shift in the systemic risk of the company. We also show that the systematic risk of a German chemical …rm (BASF) increased (of 0,32) after a plant remained closed several weeks following an explosion in July of 1990 that caused 1 death and 4 serious injuries. The shift in the systematic risk of Exxon following an explosion on February

2003 explosion could also be explained by the deaths it provoked, event if media didn’t report any serious damages. Finally, the Albright and Wilson explosion, was the second major accident in the plant in two years, and provoked important material damages, but no signi…cant drop in the equity value of the company is observed. On contrary, some accidents could be considered as minor in terms of their media coverage or their human e¤ects, and provoked surprisingly a signi…cant and negative change in the systematic risk of …rms responsible for these accidents. The explosion in a Marathon Oil re…nery in 2003 provoked the shut down of the plant, even if damages were unknown at that time. Media only pointed out an economic e¤ect on energy futures markets. A LG Petrochemical o¢ cial spokesman reported on September 2004 that the …re caused about Won 30 million ($ 26,000) worth of damage to the unit. This minor economic loss didn’t result in a signi…cant and abnormal drop in the equity returns of the company, although its systematic risk decreased by about -1,9 after the accident. Finally, a …re in a German re…nery of Royal Dutch Shell on March 2000 gave rise to a signi…cant negative shift in its systematic risk.

6

Extensions

Media coverage In this section, we investigate whether cross-sectional di¤erences in the media coverage of accidents are related to the human and environmental harm they provoke. The dependant variable is the number of printed articles that reports accidents for the …rst 9 days after accidents. The independent variables include dummy variables (1) a dummy variable set equal to one if we have information that the accident resulted in a toxic release, (2) the number of fatalities plus serious injuries, (3) a dummy variable set equal to one if the company belongs to the chemical sector (0 if it belongs to the oil sector). Table VI reports the results of robust least squares regressions using White-corrected standard errors to correct for the presence of heteroskedasticity. The coe¢ cients for human harm (t-stat = 2,57) and environmental harm (t-stat = 1,92) are positive and statistically signi…cant. The negative coe¢ cient for the chemical sector (t-stat = -2.20) indicates that the fact of belonging to the chemical sector reduce the media coverage of technological accidents. Subsamples Analysis : a …rst approach to interpret reactions on stock markets As exposed above, a suit by suit analysis of abnormal returns …nds that theccidents with major media and human e¤ects provoked at the …rm level signi…cant shareholder losses. To corroborate empirically these preliminary results, in accordance with several studies on the e¤ects of environmental incidents on the wealth of shareholders (Lanoie, Laplante (1994), Muoghalu, Robison and Glasckock (1990)), we use several indicators to build subsamples of accidents. Lanoie, Laplante (1994) chose to isolate a sample of accidents according to the type of media coverage they received.. Table V reports our results. We …rst isolate in Panel 1 in Table III 25 accidents for which companies of the United States are involved. Direct enforcement and tort liability rules are much more strengthening in the USA than in European Union member states. A comparison between the direct costs provoked by the Exxon-Valdez Oil spill in 1989, and that of the Total-Erika in December of 1999 illustrates perfectly this point. These di¤erences may be re‡ected in the European and American stock exchanges reactions. However, cumulative abnormal returns for the sample 1 are only signi…cant at the 10% level the day of accidents and the day after. We also show that equity losses incurred by chemical …rms are signi…cant over a longer period (of 20 days), and slightly higher. We isolate in Panel 3 in Table III 15 accidents that resulted in the shut down of the plants responsible for accidents. We show that this sample of accidents results, on average, in a signi…cant negative abnormal return at the 5% level the day of accidents, and several days after at the 10% level. The Panel 6 of 37 accidents that provoked at least one serious injury (the median number of fatalities or serious injuries is of 1) provoked, on average, signi…cant cumulative abnormal returns over a period of 20 days. Since the media coverage is strongly correlated with the level of human harm, we also observe signi…cant cumulative abnormal returns for the sample 4 of 32 accidents associated with the highest media coverage (above the average coverage of 10 articles

over for the …rst 9 days following accidents). On the other hand, we show that the sample 5 of 17 accidents that results speci…cally in a toxic release is only associated with signi…cant cumulative abnormal returns for the …rst day after accident at the 10 % level. The cross-Sectional Relation between Abnormal Returns and the Human and Environmental E¤ ects of Accidents In this section, an attempt is made to explain the variation in investor responses. We investigate whether cross-sectional di¤erences in the stock price reactions are related to human and environmental harm. We carry out an ordered logistic regression. The dependant variable is the level of con…dence associated with cumulative abnormal returns calculated over a period 3 days following accidents. 1 (respectively 2) refers to 7 accidents (respectively 12) which provoked a signi…cant abnormal return at the 10% level (respectively at the 5% level), and 0 refers to the 55 accidents which did not provoke any signi…cant abnormal returns. Several explanatory variables are introduced. On one hand, we control for the human e¤ect (the variable equals to the number of fatalities aggregated to the number of serious injuries) and the environmental harm (a dummy variable set equal to one if we have information that the accident resulted in a toxic release) of accidents. The media coverage is measured by the total number of printed articles in all news in English reporting accidents and names of responsible companies for the …rst 9 days following explosion. Table VII reports the results of robust ordered logitstic regressions using corrected standard errors to correct for the presence of heteroscedasticity. Media coverage alone captures all the explanatory power of the variables introduced. Since serious injuries and fatalities explain at the 5% level the media coverage of accidents by printed media, we exclude the media coverage in models (3) and (4) in Table VII. We therefore show that fatal human harm explains better than media coverage equity value losses. On contrary, none of these variables: the shut down of plants responsible for accidents, the fact of belonging to the chemical industry, and incidental pollution (toxic release) explain the variation in investor responses.

7

Conclusion

Firms carry insurance against many of the costs of an accident, such as equipment and business losses, and tort liability. Nevertheless, the stock market reacts negatively (and instantaneously, which con…rms the …nancial markets’overall e¢ ciency with regard to the release of new information) to explosions in chemical plants and re…neries. Shareholders of oil and chemical companies su¤er a temporary equity loss of 0.86% on average. In this respect, our study is consistent with previous results. We also examine indicators on media coverage, and human and environmental harms provoked by accidents. Whereas 23% of accidents in our sample do result in a toxic release, we show that equity value losses are related to media coverage and fatalities, and unrelated to incidental pollution. Fatal human harm and media coverage may approximate non insured future costs. In the context of strong uncertainty about environmental and third liability claims, investors are expected to anticipate costs associated with future judicial or penal actions. We conclude that such uncertainty do not throw shareholders into a panic selling. This result may be due to the fact that most of the public authorities, unlike the United States, have not established environmental liability for damage to nature.

Table I - Accident and Firm Descriptive Statistics

Variable Total Sales 2003 ª Market capitalization 31/12/2003 ª Fatalities or Serious Injuries Number of Articles in 9 days Dummy variables Toxic Release Human Effect Shut Down

N Mean Median 37 32894,08 9303 37 30466,05 5667

Standard Deviation Min Max 127 211436 22 271002

71 72

2,479 28,514

1 9

4,510 65,132

0 0

29 446

70 72 66

0,271 0,542 0,242

0 1 0

0,448 0,502 0,502

0 0 0

1 1 1

N corresponds to the number of accidents, or firms, for which information is available. The total number of firms is 41, but information about 4 firms is not available or is missing (Yara, Hosung, Albright and Wilson, and Union Carbide). ª in USD Millions

Correlation Matrix (N=70) Variable

Fatalities or Serious Injuries Number of Articles in 9 days Toxic Release Chemicals industry

Fatalities or Serious Injuries

Number of Articles in 9 days

1 0,362 -0,201 -0,255

1 0,223 -0,405

Toxic Release

Chemicals industry

1 -0,052

1

Table II - Cumulative Abnormal Returns CARt is the sample average cumulative abnormal return for the day t in event time. Event time is days relative to the accident date. CARt are computed given the market model parameters which are estimated with OLS through the period [-190; -10] in event time. CARit < 0 is the percentage of firms with negative CAR at time t. Panel A: CARt are aggregated across all accidents. Panel C (D): CARt are aggregated across accidents which cause statistically significant negative CARit, t < 10 at the 5% (10%) level. Panel B: Panel A minus Panel C.

t 0 1 2 3 4 5 15 20 40 80 120 0 1 2 3 4 5 10 15 20 40 80 120

CARt

t-stat

Panel A: 67 events -0,86 -3,88 -1,28 -4,05 -1,08 -2,80 -1,31 -2,95 -1,16 -2,34 -1,22 -2,25 -1,83 -2,06 -2,31 -2,27 -2,63 -1,85 -2,90 -1,45 -3,05 -1,25 Panel C: 12 events -1,99 -4,64 -2,95 -4,79 -3,46 -4,63 -4,56 -5,33 -4,27 -4,46 -3,97 -3,80 -5,35 -3,80 -5,62 -3,32 -5,80 -2,99 -6,59 -2,44 -5,03 -1,33 -10,02 -2,16

CARit < 0 45 49 42 41 40 37 36 41 45 42 38

(67%) (73%) (63%) (61%) (60%) (55%) (54%) (61%) (67%) (63%) (57%)

10 11 11 12 12 11 10 9 10 9 8 8

(83%) (92%) (92%) (100%) (100%) (92%) (83%) (75%) (83%) (75%) (67%) (67%)

CARt

t-stat

Panel B: 55 events -0,62 -2,43 -0,92 -2,54 -0,56 -1,28 -0,60 -1,19 -0,48 -0,85 -0,62 -1,00 -1,00 -0,99 -1,54 -1,32 -1,77 -1,09 -2,44 -1,06 -1,53 -0,55 Panel D: 19 events -2,35 -4,52 -3,24 -4,35 -3,98 -4,41 -4,98 -4,79 -4,95 -4,26 -4,73 -3,73 -5,39 -3,15 -6,20 -3,00 -8,62 -3,64 -7,09 -2,14 -7,12 -1,53 -7,76 -1,37

CARit < 0 35 38 31 29 28 26 27 31 36 34 30

(64%) (69%) (56%) (53%) (51%) (47%) (49%) (56%) (65%) (62%) (55%)

17 18 18 19 19 16 14 14 15 12 13 11

(89%) (95%) (95%) (100%) (100%) (84%) (74%) (74%) (79%) (63%) (68%) (58%)

Notes: t-stat as described in text. Statistically significant t-stat at the 5% level in bold. Statistically significant tstat at the 10% level in italic.

Table III - Cumulative Abnormal Returns by Firms DS Code

Date

CAR0

CAR1

CAR5

CAR120

DS Code

Date

CAR0

CAR1

CAR5

CAR120

HOECHST 15/11/87 0,41 2,54 3,49 -11,27 CROMPTON 01/04/96 -1,78 -0,98 -2,57 13,83 EXXON MOBIL 24/03/89 0,11 -0,49 MITSUI CHEM. 17/07/96 0,04 0,69 1,68 -25,32 -3,39 -17,41 ROYAL DUTCH 20/03/90 0,09 -0,21 -0,16 3,44 ALBRIGHT WILSON 03/10/96 0,70 1,75 5,32 -42,88 DAIICHI PHARM. 26/05/90 -2,46 -4,29 17,03 FMC 17/11/96 -0,37 -0,52 0,68 -3,91 -15,15 BASF 19/07/90 -0,88 -0,94 -2,78 -4,40 FMC 04/12/96 -0,22 -0,89 -3,00 -12,44 UNION CARBIDE 12/03/91 -4,19 5,32 WYMAN GORDON 22/12/96 -6,36 -3,41 12,07 -3,72 -10,80 -6,36 ALBRIGHT WILSON 17/06/91 -1,20 -3,59 -4,79 -25,75 DU PONT 04/04/97 -0,12 -2,11 -2,44 -9,06 DSM 13/12/91 -0,43 -0,92 MITSUI CHEM. 04/04/97 -1,66 -4,20 -5,87 -31,74 -5,18 17,15 BP 10/02/92 -2,06 -13,27 SUMITOMO 23/12/98 -0,14 -3,44 -3,42 -16,89 2,92 3,17 ELF AQUITAINE 22/04/92 0,42 1,58 0,89 10,23 BAYER 08/06/99 -1,40 -2,62 0,28 -9,06 AIR LIQUIDE 22/04/92 -0,27 -1,53 -1,20 3,56 TOTAL 12/12/99 -0,05 0,81 5,37 10,98 AKZO NOBEL 08/09/92 -0,69 -0,63 2,77 -6,39 ROYAL DUTCH 23/03/00 -1,47 -2,33 -4,84 16,59 TOTAL 09/11/92 -1,08 -1,54 0,02 6,62 BP 07/06/00 -2,48 -0,20 1,68 -3,93 LUBRIZOL 26/01/93 0,88 -0,89 -0,89 7,66 BP 10/06/00 -0,28 1,36 -0,10 -7,27 HOECHST 22/02/93 -0,96 -3,54 -16,09 HOSUNG CHEM. 24/08/00 -12,41 -14,64 -18,78 -49,92 -2,77 HOECHST 15/03/93 -1,30 -1,98 0,48 -4,51 TOTAL 03/09/00 2,93 2,95 4,16 -14,48 DOW CHEMICALS 02/05/93 0,35 0,59 0,06 11,20 SOLUTIA 12/10/00 -0,59 1,01 -1,10 6,91 MARATHON OIL 03/05/93 0,91 3,32 3,40 6,94 EXXON MOBIL 09/12/00 -2,22 -5,08 -0,45 -3,14 BP 03/06/93 0,57 0,76 0,12 -14,11 RHODIA 25/04/01 -3,29 -4,62 -7,35 -69,12 SUMITOMO 04/07/93 -2,35 -0,96 -1,82 10,30 TOTAL 14/07/01 -0,85 -0,98 -1,35 -1,13 EXXON MOBIL 02/08/93 0,28 -1,03 -1,58 -7,82 TOTAL 21/09/01 -3,21 -0,03 -3,63 -3,74 OLIN 08/04/94 -0,05 -0,93 0,94 6,82 ASAHI KASEI 12/03/02 -0,20 -3,03 -1,05 0,36 ROYAL DUTCH 27/05/94 -0,11 -0,09 1,11 -2,92 GUERBET 03/09/02 -4,72 -11,86 5,78 -3,23 BP 04/06/94 -0,90 -1,73 -1,24 -8,44 RHODIA 08/01/03 -2,95 -7,18 -3,67 -28,03 EXXON MOBIL 08/08/94 1,22 -0,84 1,46 9,78 MARATHON OIL 12/01/03 0,75 1,26 3,67 27,45 ROHM HAAS 15/10/94 -0,57 -0,92 2,40 -6,39 EXXON MOBIL 21/02/03 0,32 1,51 0,81 -12,10 CROMPTON 04/04/95 -0,78 -1,51 -1,51 -8,66 DSM 13/08/03 -0,30 -2,03 -0,48 -5,88 ASHLAND 16/08/95 -0,75 -1,13 -2,23 6,73 REPSOL YPF 14/08/03 0,53 0,48 1,71 -6,67 DU PONT 20/08/95 1,00 -0,28 -0,39 2,62 LONZA GROUP 22/02/04 -1,75 -1,79 -0,28 -2,36 LYONDELL 21/11/95 -0,80 0,12 2,17 30,39 BP 31/03/04 0,27 -0,36 3,41 9,16 AK STEEL HDG. 05/12/95 0,34 -0,69 0,74 21,47 CROMPTON 11/06/04 0,14 0,66 2,31 74,12 FMC 05/12/95 -1,13 -2,91 LG PETROCHEM. 25/08/04 -1,28 -1,41 -1,90 -22,15 -3,00 -20,05 MITSUBISHI 29/12/95 0,09 0,18 -1,36 8,18 SASOL 01/09/04 -1,60 -0,51 -1,60 -12,11 HOECHST 27/01/96 -1,02 -0,87 0,68 1,99 Notes: Statistically significant at the 5% level in bold. Statistically significant at the 10% level in italic. DS Code is the Datastream code. CARt is the sample average cumulative abnormal return for the specified day in event time. Event time is days relative to the accident date (dd/mm/yy). CARt are computed given the market model parameters which are estimated with OLS through the period [-190; -10] in event time.

Table IV – Shift in the systematic risk of firms The model estimated is: Rt = α + β1*Rmt + D + β1*Post*Rmt + ut where t = [-180; 180], Rt is the day t return, Rmt is the day t market return, D = 1 on event day (dd/mm/yy) and the day after and D = 0 otherwise, Post = 0 for days -180 to -1 and Post = 1 for post-accident period (days 0 to 180). Parameters are estimated with OLS. Date HOECHST EXXON MOBIL ROYAL DUTCH DAIICHI PHARM. BASF UNION CARBIDE ALBRIGHT WILSON DSM BP ELF AQUITAINE AIR LIQUIDE AKZO NOBEL TOTAL LUBRIZOL HOECHST HOECHST DOW CHEMICALS MARATHON OIL BP SUMITOMO EXXON MOBIL OLIN ROYAL DUTCH BP EXXON MOBIL ROHM HAAS CROMPTON ASHLAND DU PONT LYONDELL AK STEEL HDG. FMC MITSUBISHI HOECHST

15/11/87 24/03/89 20/03/90 26/05/90 19/07/90 12/03/91 17/06/91 13/12/91 10/02/92 22/04/92 22/04/92 08/09/92 09/11/92 26/01/93 22/02/93 15/03/93 02/05/93 03/05/93 03/06/93 04/07/93 02/08/93 08/04/94 27/05/94 04/06/94 08/08/94 15/10/94 04/04/95 16/08/95 20/08/95 21/11/95 05/12/95 05/12/95 29/12/95 27/01/96

α

β1

D

0,07 -0,08 0,04 0,09 -0,06 0,01 -0,13 -0,01 -0,14 0,07 0,00 0,00 0,01 -0,05 0,01 0,02 -0,02 -0,07 0,09 -0,06 0,02 0,08 -0,02 0,11 0,00 -0,01 -0,04 -0,04 0,02 -0,02 0,16 -0,04 -0,07 0,13

0,74 1,23 0,97 0,62 0,59 0,96 -0,20 0,71 1,07 0,80 0,99 0,71 0,90 0,35 1,00 1,01 1,05 0,94 0,80 0,93 0,63 0,72 1,06 1,03 0,55 0,65 0,45 0,45 1,02 0,85 0,39 0,80 1,02 0,91

0,10 -0,12 -0,04 -2,01 -0,05 -1,31 -0,64 -0,10 1,29 0,32 -0,12 0,13 -1,13 -0,22 -0,72 -1,06 0,08 1,35 0,41 -0,74 -0,43 0,05 -0,10 -0,68 -0,40 -0,27 -0,68 -0,07 0,04 0,26 -0,79 -0,82 0,24 -0,61

β2*Post 0,03 -0,28 0,02 0,22 0,32 0,19 0,11 -0,06 -0,27 0,20 0,23 0,62 0,11 0,52 -0,01 -0,08 -0,15 -0,23 0,19 -0,12 -0,30 -0,43 0,02 -0,15 0,16 0,07 -0,72 -0,07 -0,13 -0,07 0,03 -0,15 0,14 0,27

R2 0,65 0,55 0,74 0,37 0,47 0,28 0,02 0,20 0,18 0,57 0,55 0,32 0,37 0,05 0,44 0,44 0,16 0,10 0,16 0,37 0,10 0,08 0,72 0,25 0,14 0,09 0,01 0,04 0,15 0,08 0,02 0,18 0,32 0,30

DS Code CROMPTON MITSUI CHEM. ALBRIGHT WILSON FMC FMC WYMAN GORDON DU PONT MITSUI CHEM. SUMITOMO BAYER TOTAL ROYAL DUTCH BP BP HOSUNG CHEM. TOTAL SOLUTIA EXXON MOBIL RHODIA TOTAL TOTAL ASAHI KASEI GUERBET RHODIA MARATHON OIL EXXON MOBIL DSM REPSOL YPF LONZA GROUP BP CROMPTON LG PETROCHEM. SASOL

Notes: Statistically significant at the 5% level in bold. Statistically significant at the 10% level in italic.

Date 01/04/96 17/07/96 03/10/96 17/11/96 04/12/96 22/12/96 04/04/97 04/04/97 23/12/98 08/06/99 12/12/99 23/03/00 07/06/00 10/06/00 24/08/00 03/09/00 12/10/00 09/12/00 25/04/01 14/07/01 21/09/01 12/03/02 03/09/02 08/01/03 12/01/03 21/02/03 13/08/03 14/08/03 22/02/04 31/03/04 11/06/04 25/08/04 01/09/04

α

β1

D

0,00 -0,11 -0,09 -0,06 -0,07 0,16 0,02 -0,24 0,10 0,03 -0,01 0,02 0,00 0,01 -0,02 0,05 0,00 0,05 -0,14 0,03 0,06 -0,02 0,15 -0,13 -0,01 0,02 -0,05 0,03 -0,09 0,02 0,11 -0,03 0,07

0,67 0,51 0,88 0,68 0,65 0,42 0,98 1,11 1,01 0,61 1,15 1,05 0,50 0,49 0,34 0,46 0,00 -0,04 0,62 0,58 0,58 1,14 0,04 0,69 0,72 0,95 0,62 0,74 0,70 1,00 2,39 0,76 0,98

0,52 0,39 0,91 -0,12 -0,27 -3,79 -0,64 -2,20 -1,14 -0,85 0,99 -0,98 0,01 0,63 -7,59 1,44 -1,31 -0,60 -1,58 0,02 -2,13 -0,66 -1,61 -1,28 0,40 0,60 -0,75 0,06 -0,54 0,69 -1,12 0,41 -0,98

β2*Post -0,32 0,42 -0,24 -0,20 -0,12 -0,44 0,21 -0,59 0,03 0,01 -0,67 -0,46 -0,11 -0,13 0,52 0,07 0,69 0,24 0,40 0,21 0,31 -0,03 0,23 0,32 -0,27 -0,38 -0,02 0,13 -0,16 -0,01 -0,52 -0,41 -0,05

R2 0,04 0,13 0,11 0,20 0,20 0,04 0,35 0,19 0,32 0,26 0,21 0,35 0,05 0,05 0,10 0,12 0,05 0,02 0,20 0,33 0,39 0,46 0,02 0,26 0,40 0,58 0,41 0,51 0,13 0,33 0,29 0,23 0,27

Table V - Cumulative Abnormal Returns (Sub-samples) CARt is the sample average cumulative abnormal return for the day t in event time. Event time is days relative to the accident date. CARt are computed given the market model parameters which are estimated with OLS through the period [-190; -10] in event time. CARit < 0 is the percentage of firms with negative CAR at time t.

t

CARt

t-stat

CARit < 0

Panel 1: 25 events (USA) 0 1 2 3 4 5 10 20 40 80 120

-0,56 -0,82 -0,48 -0,94 -0,91 -1,04 -1,34 -0,93 -0,77 3,27 5,35

-1,81 -1,86 -0,90 -1,52 -1,31 -1,38 -1,30 -0,65 -0,39 1,17 1,57

14 18 15 15 14 14 16 16 18 11 10

-0,32 -1,00 -0,77 -0,41 -0,16 -0,31 -1,59 -1,53 -3,58 -5,48 -8,05

-0,88 -1,93 -1,22 -0,55 -0,20 -0,35 -1,30 -0,91 -1,52 -1,65 -2,00

10 13 11 9 11 9 11 9 11 13 11

t-stat

CARit < 0

Panel 2: 44 events (Chemical) (56%) (72%) (60%) (60%) (56%) (56%) (64%) (64%) (72%) (44%) (40%)

Panel 5: 17 events (Toxic) 0 1 2 3 4 5 10 20 40 80 120

CARt

-1,16 -1,86 -1,50 -1,79 -1,70 -2,04 -2,18 -2,78 -2,80 -2,62 -3,46

-3,84 -4,32 -2,87 -2,96 -2,52 -2,77 -2,18 -2,01 -1,45 -0,97 -1,05

33 34 31 27 28 28 26 26 27 25 23

-1,25 -1,45 -1,68 -2,18 -1,85 -2,05 -2,45 -3,22 -2,28 -4,70 -5,07

-3,99 -3,26 -3,11 -3,50 -2,65 -2,69 -2,38 -2,26 -1,14 -1,68 -1,49

25 27 28 27 24 24 22 23 23 25 23

t-stat

CARit < 0

Panel 3: 15 events (Shut down) (75%) (77%) (70%) (61%) (64%) (64%) (59%) (59%) (61%) (57%) (52%)

Panel 6: 37 events(Human) (59%) (76%) (65%) (53%) (65%) (53%) (65%) (53%) (65%) (76%) (65%)

CARt

-0,79 -0,83 -1,04 -1,13 -0,91 -1,02 -1,58 -0,11 0,48 -1,50 0,98

-2,59 -1,92 -1,95 -1,83 -1,32 -1,36 -1,55 -0,08 0,25 -0,54 0,29

12 12 11 8 7 7 7 8 8 9 7

-0,33 -0,74 -0,76 -1,35 -0,97 -1,15 -1,94 -2,05 -1,89 -0,76 0,54

-1,49 -2,36 -1,97 -3,04 -1,96 -2,11 -2,63 -2,00 -1,32 -0,38 0,22

19 25 21 22 19 18 22 21 23 19 15

t-stat

CARit < 0

Panel 4: 32 events (news) (80%) (80%) (73%) (53%) (47%) (47%) (47%) (53%) (53%) (60%) (47%)

Panel 7: 33 events (< 1996) (68%) (73%) (76%) (73%) (65%) (65%) (59%) (62%) (62%) (68%) (62%)

CARt

-0,66 -0,73 -0,64 -0,65 -0,16 -0,34 -0,90 -1,23 -0,76 -1,39 -0,85

-2,47 -1,92 -1,38 -1,22 -0,27 -0,52 -1,02 -1,00 -0,45 -0,58 -0,29

18 22 19 19 15 15 18 22 22 21 19

(56%) (69%) (59%) (59%) (47%) (47%) (56%) (69%) (69%) (66%) (59%)

Panel 8: 34 events (≥ 1996) (58%) (76%) (64%) (67%) (58%) (55%) (67%) (64%) (70%) (58%) (45%)

Notes: t-stat as described in text. Statistically significant t-stat at the 5% level in bold. Statistically significant t-stat at the 10% level.

-1,38 -1,81 -1,40 -1,27 -1,34 -1,29 -0,76 -2,56 -3,36 -4,98 -6,55

-3,62 -3,33 -2,11 -1,67 -1,58 -1,39 -0,60 -1,47 -1,38 -1,45 -1,57

26 24 21 19 21 19 16 20 22 23 23

(76%) (71%) (62%) (56%) (62%) (56%) (47%) (59%) (65%) (68%) (68%)

Table VI – Determinants of Media coverage of Accidents (1) Coef Constant Toxic release Injuries Chemicals Number of obs. F Prob > F R-squared Root MSE

1,51 45,39 * 6,15 *** 70 6,32 0,0031 0,2221 59,0410

(2) Std.Err.

t P>|t|

Coef

4,85 0,31 0,76 24,53 1,85 0,07 1,84 3,34 0,00

34,32 40,58 4,93 -42,40 70 4,76 0,0046 0,3075 56,1260

Std.Err. *** * *** **

16,07 21,14 1,92 19,25

Notes: *** (** *) Statistically significant t-stat at the 1% ( 5% 10%) level.

t P>|t| 2,14 1,92 2,57 -2,20

0,04 0,06 0,01 0,03

Table VII - Ordered Logistic Regression of Abnormal Returnsª (1) Number of articles in 9 days Ser. Injuries or Fatalities Toxic Release Chemicals industry Shut Down Pseudo R2 Log pseudolikelihood Wald chi2 Prob > chi2 Number of observations

(2)

Coef.

Z-stat

0,01 *** 0,08 -0,45 0,91

2,37 1,40 -0,58 1,15

0,07 -48635805 8,92 0,06 67

Coef. 0,01 *** 0,09 0,94

0,07 -48817222 8,34 0,04 67

(3) Z-stat 2,15 1,65 1,19

Coef. 0,11 *** 0,00 0,44 0,04 -50469453 5,35 0,15 67

(3) Z-stat

Coef.

2,28 0,00 0,68

0,11 *** 0,05 0,32 0,30 0,04 -4599876 4,86 0,30 63

(4) Z-stat 2.15 0.07 0.49 0.47

Coef.

Z-stat

-0,19 0,00 0,36 0.0043 -4756652 0,44 0,93 63

-0,28 0,00 0,58

ªDependent variable in ordered logistic regression equals 0 if there was no significant Abnormal Return, 1 if the firm undergoes a significant Abnormal Return at the 10% level, 2 if the firm undergoes a significant Abnormal Return at the 5% level. *** Statistically significant at the 1 % level.

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