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REPUTATIONAL PUNISHMENT OF ENVIRONMENTAL VIOLATIONS
IN CANADA
by
Emilia Ganslandt
Thesis submitted in partial fulfillment of the
requirements for the Degree of
Bachelor of Arts with
Honours in Economics
Acadia University
April, 2020
© Copyright by Emilia Ganslandt, 2020
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This thesis by Emilia Ganslandt
is accepted in its present form by the
Department of Economics
as satisfying the thesis requirements for the degree of
Bachelor of Arts with Honours
Approved by the Thesis Supervisor
__________________________ ____________________
Dr. Andrew Davis Date
Approved by the Head of the Department
__________________________ ____________________
Dr. Burc Kayahan Date
Approved by the Honours Committee
__________________________ ____________________
Dr. Joseph Hayes Date
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I, Emilia Ganslandt, grant permission to the University Librarian at Acadia University to
reproduce, loan or distribute copies of my thesis in microform, paper or electronic formats on a
non-profit basis. I, however, retain the copyright in my thesis.
_________________________________ Signature of Author
_________________________________ Date
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Acknowledgements I would like to thank my supervisor Dr. Andrew Davis for his support and patience during the
development of this thesis. I would further like to thank Dr. Justin Beaudoin for his helpful input
and assistance, and Dr. Stephen Maclean for helping me compile the data I needed. Thank you to
my family for proofreading and providing emotional support, not only during this process but
throughout my degree. Lastly, I would like to thank the entire Department of Economics and
Environmental & Sustainability Studies for believing in me and providing me with this
opportunity.
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Table of Contents
ACKNOWLEDGEMENTS ....................................................................................................... VII
LIST OF TABLES ....................................................................................................................... XI
LIST OF FIGURES .................................................................................................................. XIII
ABSTRACT ................................................................................................................................. XV
CHAPTER 1: INTRODUCTION .................................................................................................. 1
CHAPTER 2: LITERATURE REVIEW ..................................................................................... 7
2.1REPUTATIONAL PUNISHMENT IN CANADA ............................................................................. 7
2.2 REPUTATIONAL PUNISHMENT IN THE UNITED STATES AND ABROAD ................................. 11
2.3EVENT STUDY METHODOLOGY ............................................................................................ 14
2.4IMPACT OF SOCIAL MEDIA .................................................................................................... 19
CHAPTER 3: DATASET INFORMATION .............................................................................. 23
3.1 ENVIRONMENTAL VIOLATIONS ............................................................................................. 23
3.2 STOCK MARKET DATA .......................................................................................................... 28
3.3 SOCIAL MEDIA DATA ............................................................................................................ 31
3.4 MARKET CAP DATA .............................................................................................................. 37
CHAPTER 4: EMPIRICAL METHOD ..................................................................................... 39
CHAPTER 5: RESULTS ............................................................................................................. 45
5.1 REGRESSION RESULTS USING FINE SIZE AS AN INDEPENDENT VARIABLE ........................... 45
5.1.1 The Fine Variable.......................................................................................................... 49
5.1.2 Reputational Punishment............................................................................................... 52
5.2 REGRESSION RESULTS INCLUDING SOCIAL MEDIA AS AN INDEPENDENT VARIABLE. ......... 53
5.2.1 Social media variable.................................................................................................... 57
5.3 REGRESSION RESULTS USING RELATIVE FINE SIZE AS AN INDEPENDENT VARIABLE .......... 58
5.3.1 Relative Fine Variable................................................................................................... 61
CHAPTER 6: CONCLUSION .................................................................................................... 63
APPENDIX A: REGRESSION RESULTS DAY -30 TO -1 ......................................................... 67
APPENDIX B: REGRESSION RESULTS DAY -1 TO 0 ............................................................. 68
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APPENDIX C: REGRESSION RESULTS DAY 0 TO 30 ............................................................ 69
APPENDIX D: REGRESSION RESULTS DAY -30 TO -1 WITH SOCIAL MEDIA ................ 70
APPENDIX E: REGRESSION RESULTS DAY -1 TO 0 WITH SOCIAL MEDIA .................... 71
APPENDIX F: REGRESSION RESULTS DAY 0 TO 30 WITH SOCIAL MEDIA ................... 72
APPENDIX G: REGRESSION RESULTS DAY -30 TO -1 WITH RELATIVE FINE ............... 73
APPENDIX H: REGRESSION RESULTS DAY -1 TO 0 WITH RELATIVE FINE ................... 74
APPENDIX I: REGRESSION RESULTS DAY 0 TO 30 WITH RELATIVE FINE ................... 75
APPENDIX J: DATASET INFORMATION ................................................................................. 76
APPENDIX K: CHANGES IN STOCK RETURNS FOR FIRMS DAY -30 TO -1 ..................... 77
APPENDIX L: CHANGES IN STOCK RETURNS FOR FIRMS DAY -1 TO 0 ......................... 78
APPENDIX M: CHANGES IN STOCK RETURNS FOR FIRMS DAY 0 TO 30 ....................... 79
APPENDIX N: CHANGES IN INDEXES DAY -30 TO -1 .......................................................... 80
APPENDIX O: CHANGES IN INDEXES -1 TO 0 ....................................................................... 81
APPENDIX P: CHANGES IN INDEXES DAY 0 TO 30 ............................................................. 82
APPENDIX Q: SOCIAL MEDIA DATA FOR SAMPLE ............................................................ 83
APPENDIX R: ABNORMAL RETURNS DAY -30 TO -1 .......................................................... 84
APPENDIX S: ABNORMAL RETURNS DAY -1 TO 0 .............................................................. 85
APPENDIX T: ABNORMAL RETURNS DAY 0 TO 30 ............................................................. 86
APPENDIX U: RELATIVE FINE DATA ..................................................................................... 87
GLOSSARY .................................................................................................................................. 89
REFERENCES .............................................................................................................................. 91
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List of Tables TABLE 1: ENVIRONMENTAL VIOLATIONS SAMPLE ......................................................................................27
TABLE 2: SOCIAL MEDIA VARIABLE ...........................................................................................................33
TABLE 3: ABNORMAL RETURN REGRESSION FOR DAY -30 TO -1 ................................................................45
TABLE 4: ABNORMAL RETURN REGRESSION FOR DAY -1 TO 0 ...................................................................46
TABLE 5: ABNORMAL RETURN REGRESSION FOR DAY 0 TO 30 ..................................................................48
TABLE 6: ABNORMAL RETURN REGRESSION FOR DAY -30 TO -1 WITH SOCIAL MEDIA .............................53
TABLE 7: ABNORMAL RETURN REGRESSION FOR DAY -1 TO 0 WITH SOCIAL MEDIA ................................54
TABLE 8: ABNORMAL RETURN REGRESSION FOR DAY 0 TO 30 WITH SOCIAL MEDIA ................................56
TABLE 9: ABNORMAL RETURN REGRESSION FOR DAY -30 TO -1 WITH RELATIVE FINE SIZE ....................58
TABLE 10: ABNORMAL RETURN REGRESSION FOR DAY -1 TO 0 WITH RELATIVE FINE SIZE ......................59
TABLE 11: ABNORMAL RETURN REGRESSION FOR DAY 0 TO 30 WITH RELATIVE FINE SIZE .....................60
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List of Figures FIGURE 1: NUMBER OF ENVIRONMENTAL VIOLATIONS PER YEAR IN THE SAMPLE ...................................26
FIGURE 2: AVERAGE FINE SIZE PER YEAR ...................................................................................................30
FIGURE 3: NUMBER OF NEWS ARTICLES BASED ON SOURCE ......................................................................32
FIGURE 4: AVERAGE FACEBOOK INTERACTIONS IN SAMPLE ......................................................................35
FIGURE 5: AVERAGE FACEBOOK INTERACTIONS BY YEAR FOR ALL NOTIFICATIONS ................................36
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Abstract
This thesis examines to what extent market-imposed sanctions, i.e. “reputational
penalties”, impose significant costs on firms that violate environmental regulations in Canada.
Determining the presence and size of reputational penalties is important for policy implications
as it can be used to determine whether legal fines are adequately punishing firms that violate
environmental regulations and, in combination with market-imposed sanctions, become a
sufficient deterrent of corporate behavior causing negative externalities on the environment.
The empirical method used to determine the changes in public attitudes following an
environmental violation is the standard event study methodology. The dependent variable is the
abnormal returns on shares of the company, or the difference between the security’s expected
return and its actual return. The event for this study was the release of the enforcement
notification (i.e. the notification of the fine that the firm has to pay). After applying inclusion
criteria 28 cases of environmental violations between 2010- 2019 were noted.
The results suggest that there is some negative reputational effect on the day of the
notification. However, the results also suggest that there may be a positive effect of the event for
higher fines. This, somewhat unexpected result from a theoretical point of view, may be the
result of the fine being smaller than expected by the market or, alternatively, that the market
appreciates that uncertainty is removed, along with the risk of a drawn out and costly legal case.
Based on the results, the legal penalties in Canada appear to be too low and failing to impose the
intended and adequate cost on the firm. Therefore, the main policy recommendation is to
increase the explicit costs imposed on firms that violate environmental regulations.
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CHAPTER 1: INTRODUCTION
Environmental degradation and climate change have in the last decades become popular
and prominent topics of discussion in academia, media, politics and everyday life. Corporations
have become increasingly attuned to environmental concerns as well, with terms like “green”,
“sustainable”, and “environmentally responsible” commonly being used in business language
today. Corporations have also become increasingly involved in both local, national, and
international environmental politics.1 However, at the same time 100 companies are responsible
for 71 percent of the greenhouse gas emissions released since the late 1980s.2
In the 1960s and 70s, when environmentalism was increasingly becoming a topic of
discussion in North American society, environmental ideas were often viewed by business
leaders as hostile and suspicious.3 There seemed to be a clear trade-off between corporate
profitability and environmental protection, and the two were often believed to be mutually
exclusive.4 In the 1980s, the severity of environmental degradation was underlined by a number
of major industrial incidents.5 This significantly impacted the corporate sector and started to
pivot the relationship between environmental standards and corporations.6 Environmental
objectives became increasingly integrated into business operations and strategies, and some
1 Falkner, R. (2017) 2 Riley, T. (2017, July 10) 3 ibid 4 ibid 5 ibid 6 Falkner, R. (2017)
2
corporations started to become increasingly involved in environmental politics.7 During the
1970s, 80s and 90s North American society saw a major shift towards firms becoming more
involved in caring for their shareholders and the market as a whole. These measures include the
Clean Air Act of 1970 and the sulfur dioxide trading program established under the Clean Air
Act Amendments (CAAA).8 Another turning point came in 1987 with the Montreal Protocol that
successfully reduced the depletion of the ozone layer. This was one of the first international
environmental protocols to attract support from the industries that had caused the issue to begin
with.9
In the last decades a diverse set of business strategies, approaches, and interests have
evolved in response to growing environmental concerns. Some corporations argue that
environmental regulations are a burden to their operations and therefore oppose their
implementation.10 Others are opposed to the restrictions that environmental standards and
regulations impose on their economic freedom.11 However, there is also a significant number of
companies and corporate leaders which have started to try and reconcile environmental standards
with economic development.12 Prior to the 2020 World Economic Forum, the organization
announced the “New Davos Manifest” which focused on firms’ social and environmental
responsibilities.13 The manifest states that:
7 Falkner, R. (2017) 8 ibid 9 Sunstein, C. (2007) 10 Falkner, R. (2017) 11 ibid 12 ibid 13 Schwab, K. (2019, December 1)
3
A company is more than an economic unit generating wealth. It fulfils human and societal aspirations as part of the broader social system. Performance must be measured not only on the return to shareholders, but also on how it achieves its environmental, social and good governance objectives. Executive remuneration should reflect stakeholder responsibility.14
This new manifesto was released because political leaders, economists, policy makers, and
activists around the world agreed that the current form of capitalism was not sustainable.15 It
represents a shift in societal values and the role that people believe firms have in society. Given
this change in nature, it is not unexpected that firms’ environmental practices could also impact
its reputation and its shareholders. Environmental investments, awards, and green certification
could all have impact on public attitudes and stock prices.16 Sustainable operations and corporate
behavior that avoids harm to the environment give companies access to more favorable
financing, e.g. through green bonds and Environmental, Social, and Governance (ESG) funds.17
Furthermore, being sustainable and environmentally friendly may give the firm a competitive
advantage as it may have better access to certain markets and would be able to differentiate its
products.18 This suggests that the market is concerned with environmental practices and news
about a firm protecting or harming the environment affect its reputation and thus its stock prices.
While words like “green”, “sustainable”, “triple bottom line”, and “eco-friendly” seem to
be more common in both everyday conversations and in business reports, this is only one side of
the story. During the last few decades’ environmental violations have risen as well. Between
14 Schwab, K. (2019, December 2) 15 Schwab, K. (2019, December 1) 16 Lundgren, T., & Olsson, R. (2010) 17 Ambec, S., & Lanoie, P. (2008). 18 ibid
4
1991 and 2009, larger environmental fines (> $75,000) and penalties issued in Canada totaled
$1.4 million on average.19 However, in the last decade the average size of large fines in Canada
has steadily increased and in 2015 it exceeded $3.2 million.20 This trend peaked in 2017, when
this number was $32 million.21 In 2015, 17 larger fines were issued in Canada averaging
$230,000 each. By 2017, the average value of the 28 large fines issued was $1.15 million each.22
Ignoring the Volkswagen fine, the average fine is still higher in 2017 than 2015 ($637,000 and
$230,000 respectively).23 In 2018, the number of issued larger fines and penalties increased to
34, however, the total fine amount decreased to $15.7 million.24
The question then becomes: if firms that behave responsible and environmentally-
friendly thereby improving their reputation, do firms that commit environmental violations harm
their reputation? The aim of this study is to investigate whether firms that pollute harm their
reputation, and what impact this has on their stock prices. This thesis will specifically focus on
Canadian firms between 2010 and 2019. This study is relevant, first and foremost, because of its
policy implications. According to optimal penalty theory, as discussed by Becker (1968), the
optimal expected total penalty for an illegal activity equals the activity’s total social cost. Since
total penalties are made up of the explicit costs imposed by the legal system and reputational
punishments, a reversed causality relationship should exist between these two costs. This would
mean that, in order for the optimal penalty theory to hold true, a violation causing a certain social
19 Berkley Canada. (2019). 20 ibid 21 ibid 22 Berkley Canada. (2019). 23 ibid 24 ibid
5
cost should either result in smaller legal fines and larger reputational punishment or larger legal
fines and smaller reputational punishment. In real life, however, the fines may not adequately
punish the firms for their violation and the reputational punishment may be smaller than optimal.
Measuring the degree of reputational punishment can be used to determine whether legal fines
are adequate to punish firms that violate environmental regulations if one strives for the optimal
penalty to hold true. This study is also important, as to the best of my knowledge, no recent
studies have been conducted on the topic in Canada [and the most recent, previous study from
1994 reflecting conditions during an earlier era].
The latest influential cross-industry study done on reputational punishment in Canada by
Lanoie and Laplante (1994) used data from the late 1980s and early 1990s. In the three decades
since, the environmental impact of Canadian consumers has skyrocketed. Each Canadian
produces, on average, 22 tons of greenhouse gases. This is almost three times the G20 average.25
However, while Canadians are still emitting more greenhouse gases, people are also becoming
more aware and concerned about environmental issues. In September 2019, hundreds of
thousands of Canadians joined the “Fridays for the Future” climate strikes seen across the
country. Over 315,000 people joined in Montreal alone with the majority of the people being
children and youth.26 However, it is not just school children that are becoming more concerned
with climate change and environmental degradation. In a poll of voters conducted before the
Canadian federal election in the fall of 2019, one out of 10 Canadians identified climate change
as a key issue and a determining factor in how they planned on voting.27 This demonstrated
25 The Canadian Press. (2018, November 14)26 Murphy, J. (2019, September 28) 27 Shah, M. (2019, October 9)
6
Canadians’ concern about anthropogenic impacts on the Earth. Another survey conducted at the
end of 2019 showed that 76 percent of the survey respondents believed that Canada needed to do
more to mitigate and adapt to climate change and 71 percent believed the country needed to
become a global leader in climate change action.28 However, the impact of these actions was less
optimistic. Half of the respondents reported that they did not believe Canada could significantly
reduce its emissions and 58 percent believed that environmental actions would cause economic
hardships for citizens.29 This same pessimism, on the other hand, does not appear to translate to
citizens actions with 18 percent of Canadians engaging in some form of voluntary or unpaid
action to help protect and conserve the environment.30
Given the increasing concern about environmental degradation and climate change, there
is reason to believe that the market may react differently to environmental news than it did in the
early 1990s. This study will not only attempt to analyze whether firms that pollute harm their
reputation but it will also provide newer data which can be used to determine whether the
reputational effect has changed in the last few decades. This makes this study both important
from a policy perspective and as a tool to analyze if fines are of appropriate size to effectively
punish violating firms as well as from a market behavior perspective as it can be used to
determine whether Canadian’s growing concern for the environment translates into harsher
punishment of firms that violate environmental regulations.
28 Russell, A. (2020, March 11) 29 ibid 30 Statistics Canada (2015)
7
CHAPTER 2: LITERATURE REVIEW
2.1 Reputational Punishment in Canada
According to Klein and Leffler (1981), certain types of wrongdoings can be punished
through reputation because the firm internalizes this cost. Previous research suggests that
reputational costs are high for financial misrepresentation,31 false advertisement,32 lack of safety
procedures,33 punitive damages lawsuits,34 product recalls,35 and private fraud.36 Consistent with
these results, firms should be motivated to comply with environmental regulations to protect
their reputation. Drowning and Kimball (1996) argue that managers adhere to environmental
violations out of interest for the firms’ image. Cohen (1992) argues that customers’ perceptions
of the quality or safety of a firms’ products may be negatively affected by environmental
violations. The idea that firms could be punished by customers, suppliers, or government
agencies is further supported by several other studies (see Henriques & Sadorsky (1996), Decker
(2003), and Zerbe (1996)). Similarly, other studies suggest that shareholders can encourage
companies to comply with environmental regulations through punishments in the stock market.
While several papers have been written and published on reputational punishment for
environmental violations in the United States, the topic is far less explored in Canada. Laplante
and Lanoie (1994) investigated public reaction to environmental violations in Canada between
1983 and 1991, arguing that firms do suffer from lower stock prices than expected on the day
31 Karpoff, J. M., Lee, D. S., & Martin, G. S. (2008) 32 Peltzman, S. (1981) 33 Mitchell, M. L., & Maloney, M. T. (1989) 34 Karpoff, J. M., & Lott, Jr, J. R. (1999) 35 Jarrell, G., & Peltzman, S. (1985) 36 Karpoff, J. M., & Lott Jr, J. R. (1993)
8
that environmental suit settlements were announced. This contrasts with research using firms
from the United States which saw negative impacts on the day of announced lawsuits but not suit
settlements.37 According to Laplante and Lanoie, this could be the result of Canada’s more
conciliatory approach to environmental violations, supporting the view that the enforcement of
environmental legislation in Canada is laxer, hence less effective, than in the United States.
To obtain their results, Laplante and Lanoie developed a theoretical model that is tested
using an event study approach. The authors obtain data on 47 events related to publicly traded
firms operating in Canada through Canadian print media (including the Globe and Mail and the
Financial Post). Upon collection, the events were also divided into subcategories based on the
type of announcement made. In terms of the event study methodology, the authors analyze the
public reaction to environmental violations using the Capital Asset Pricing Model (CAPM)
version of the standard event-study methodology, assuming that this methodology sufficiently
captures the changes in public attitudes following the environmental violation. To achieve a
baseline and establish the average abnormal return prior to the event, the authors use data for 210
days before the announcement of the violation.
The authors conclude that no significant abnormal returns arise after the announcement of
lawsuits or violations, which according to Laplante and Lanoie may be the result of stakeholders
not believing that the Canadian government will actually pursue a punishment that will force the
firm to comply with environmental standards. This is supported by the literature, which suggests
that Canadian lawsuits are commonly drawn out and if fines are issued they are relatively low.
37 Karpoff, Lott, and Wehrly (2005)
9
The average penalty under the Quebec Environmental Quality Act between 1984-1988 was only
$667.16.38 Suit settlements that had the same media coverage (id est events that are presented in
a feature article), however, did cause abnormal losses on the day of the event. More specifically,
cases with the same media coverage saw abnormal losses of 1.65% while Canadian cases with
the same media exposure saw 2% losses on the day of public announcement. For the four cases
in which the authors have both a lawsuit announcement and a suit settlement announcement, an
average abnormal loss of 2.7% is observed on the day of the suit settlement while no losses are
observed on the day the lawsuit was announced. This, according to the authors, suggests that
fines come as a surprise to Canadian stakeholders, including shareholders, which does not appear
to be the case in the United States where firms experience abnormal losses on the day the lawsuit
was announced but not the day of the suit settlement.
In another article, Lanoie and Ambec (2008), the authors argue that being more
environmentally sustainable could also be beneficial for firms. They state that “customers may
be aware of a company’s environmental performance through its offer of green products, but
they are less likely to be familiar with its environmental performance as measured by its
emissions into water or the atmosphere”39, underlying the potential issue of asymmetric
information in the violation of environmental regulations. This could impact the size of the
abnormal returns. It further supports the idea that the announcement of an environmental
violation comes as news to the market, i.e. revelation of new information. However, according to
the authors, firms could benefit by reducing their environmental impact as it may improve the
38 Hétu, J. C. (1989) 39 Lanoie and Ambec (2008), pg.47
10
image or prestige of a firm. They write that “reducing pollution and other environmental impacts
may improve the overall image or prestige of a company, and thus increase customers’ loyalty or
support sales efforts”40. Lanoie and Ambec (2008) further highlight a number of alternatives in
which a firm can benefit from improved environmental standards including increased revenues,
differentiating products, and cost reductions.
Research also suggests that more severe environmental regulations may actually benefit
firms directly. Using a sample of 17 manufacturing firms located in Quebec, Lanoie et al. (2008)
found that stricter regulations led to modest gains in productivity and that industries which were
highly exposed to outside competition benefited even more from this effect. In another study,
Lanoie and Tanguay (2000) collected 50 examples of firms over an eight year period whose cost
of resources, energy, and services has decreased at the same time as they were reducing their
pollution.41,42 In Lanoie and Ambec (2008), the authors use the event-study methodology to
investigate how stock markets react to either good or bad environmental news. Based on a
sample size of 14, the authors conclude that stock markets react significantly to both types of
news, with the average abnormal returns following a bad news story being 2.22%. Furthermore,
the authors argue that the significant effect is observed within the first five days following an
event which suggests that the market incorporate the information rather quickly. However, given
the small sample size it is hard to draw any unambiguous conclusions from the data.
40 Lanoie and Ambec (2008), pg.47 41 Lanoie, P., & Tanguay, G. A. (2000) 42 Lanoie, P., & Tanguay, G. A. (1998)
11
2.2 Reputational Punishment in the United States and Abroad
The question of whether firms’ reputations are tarnished by polluting the environment is
one several researchers have investigated before and the previous section outlined some
Canadian examples. However, studies on reputational punishment has been done around the
world. Porter and Van der Linde (1995) argue that a polluting firm suffers financially in terms of
lower sales and higher costs. This is only one way in which polluting firms may be penalized by
other forces than law enforcement. Polluting firms can be punished for their environmental
violations through community pressures as well (see Konar and Cohen, 1997; Arora and Cason,
1996; and Pargal et al., 1997). Karpoff, Lott, and Wehrly (2005) investigate if market-imposed
sanctions, or “reputational penalties”, are significant for firms that violate environmental
regulations in the United States. The authors use a sample of 478 environmental violations
between 1980 and 2000 to investigate whether the announcement of environmental violations
significantly impacted the accused firms’ stock price. After running an event study the authors
find that the average abnormal stock return following the initial announcement of alleged
contamination is -1.69 percent while it is only -1.58 percent after an initial announcement that
the firm has been formally charged. These abnormal stock returns are, according to the authors,
both economically meaningful and statistically significant. The authors further argue that the
corresponding losses from the abnormal stock returns are similar in size to the legal penalties
that the firms faced.
Hamilton (1995) further supports the argument that firms are significantly impacted by
the announcement of negative environmental news. By studying the impact of the disclosure of
the 1989 Toxics Release Inventory (TRI), Hamilton found that shareholders in the TRI saw
12
significant abnormal returns following the release of the index. Specifically, the author
calculated that these abnormal returns represented an average loss of $4.1 million on the day that
the pollution incident was disclosed.43 Building on the argument that information like the TRI
can serve as a quasi-regulatory mechanism. Konar and Cohen (1997) find empirical evidence
that firms which face the largest abnormal returns following the disclosure of negative
environmental behavior are quicker to change their polluting behaviors than other firms in the
industry, supporting the idea that financial markets may incentivize firms to change their
environmental behavior.44 The negative impact of an environmental disaster or pollution
announcement has been observed in specific sectors as well. Capelle-Blancard and Laguna
(2009), for example, observed a 1.3 percent decline in the market value of petrochemical firms
following the announcement of an explosion of a refinery or chemical plant.45 Furthermore, as
expected, studies specifically investigating significant disasters like the Bhopal chemical
explosion and the Exxon-Valdez oil spill report some of the largest negative abnormal returns in
the literature.46,47
Studies on the impact that environmental news has on shareholders has not only been
studied in the United States, but around the world. Gupta and Guldar (2005) studied the impact
of environmental ratings on stock prices, arguing that the announcement of negative
environmental behavior significantly impacted firms’ stock prices, observing a negative
43 Hamilton, J. T. (1995) 44 Konar, S., & Cohen, M. A. (1997) 45 Capelle-Blancard, G., & Laguna, M. A. (2010) 46 Herbst, A. F., Marshall, J. F., & Wingender, J. (1996) 47 Salinger, M. (1992)
13
abnormal return of up to 30 percent.48 Korean firms that fail to comply with national
environmental standards and regulations also face significant abnormal losses.49 Stock prices in
Argentina, the Philippines, Chile, and Mexico are also negatively affected by bad environmental
news, going against the idea that low and middle-income countries are not incentivized to invest
in pollution control methods.50 Nakao et al. (2006) showed that Japanese firms’ financial
performance is impacted by their environmental behavior as well.51
While many authors argue that firms’ stock values are significantly impacted following
the announcement of environmental violations, the literature is still torn on this topic. Xu, Zeng,
and Tam (2011) found that the announcement of negative environmental events had only a weak
impact on stock prices.52 The authors continued to argue that the impact that the disclosure of
environmental violation events has on stock prices may be country-specific, with Chinese firms
facing lower reputational punishment than firms in other countries.53 Laplante and Lanoie
(1994), which was discussed in the previous section, found no abnormal returns following the
announcement of an environmental violation or lawsuit. Furthermore, Doonan, Lanoie, and
Laplante (2002) report survey results indicate that pollution outputs by pulp and paper mills are
not affected by negative news coverage. 54As Karpoff, Lott, and Wehrly (2005) states,
“environmental violations differ from frauds and other types of wrongdoing in that they impose
costs on parties other than those with whom the polluting firm does business”, underlining one of
48Gupta, S., & Goldar, B. (2005) 49 Mamingi, N., Dasgupta, S., Hong, J. H., & Laplante, B. (2004) 50 Dasgupta, S., Laplante, B., & Mamingi, N. (2001) 51 Nakao, Y., Amano, A., Matsumura, K., Genba, K., & Nakano, M. (2007) 52 ibid 53 Xu, X. D., Zeng, S. X., & Tam, C. M. (2012) 54 Doonan, J., Lanoie, P., & Laplante, B. (2002)
14
the arguments supporting why reputational punishments may be small or nonexistent. Another
potential reason, highlighted by Laplante and Lanoie (1994), is that shareholders do not have
confidence that the firms will actually be punished for their behavior. This may be country
specific as well. As mentioned earlier, Karpoff et al. (2005) observed significant abnormal
returns in the United States on the day that a fine or lawsuit was announced, however, this was
not consistent with the Canadian data according to Lanoie and Laplante (1994). This suggests
that shareholders have higher confidence in the legal system in the United States and have a
stronger belief that the government will follow through with its prosecution. Canadian
shareholders, on the other hand, appear to not be as confident (based on the findings of Lanoie
and Laplante (1994)) that the Canadian government will follow through with its initial
announcement of a lawsuit or fine, making the announcement of a conviction unanticipated news
to the market.
2.3 Event Study Methodology
In simple terms, the event study methodology measures changes in public attitudes. For
the purpose of this thesis stock prices will be used to determine the firms’ abnormal returns, or
the difference between the observed returns and the expected return in the absence of an event,
representing changes in public attitudes (and ultimately the freedom of the company to operate,
realize new business opportunities and its cost of capital).55 The event study methodology
assumes that the capital market response sufficiently reflects the impact that new information (an
event) will have on firms’ future expected profits (Fama et al., 1969). The reaction to the event is
measured by predicting an expected return during a set period of time -- or an “event window”--
55 Cowan, A. R. (1992)
15
and then subtracting it from the actual returns observed in the data.56 The event window depends
on the study but typically includes the day before the event, the day of the event, and a few days
after the event.57 The methodology, however, is only a useful tool for measuring changes in
public attitudes if the announcement of the event comes as news to the market.58 In a corporate
context, the methodology is useful because the magnitude of the abnormal return following an
event provides a measurement of the unanticipated impact that an event of this nature has on the
wealth of the firms’ shareholders. Ergo, the event study methodology provides a tool to better
understand corporate policy decisions. From a policy perspective, event studies can help provide
insight into the effectiveness of regulations and laws.
The methodology became a popular method in economics and accounting in 1969 when
Eugene Fama, Lawrence Fisher, Michael Jensen, and Richard Roll published their paper The
Adjustment of Stock Prices to New Information which used an event study approach to examine
the stock-split announcement effect.59 The paper was the first to investigate how quickly prices
adjusted to specific types of information.60 Since then, the event study methodology has been a
popular tool used in both business and economic studies.61 As Fama (1991) states: “in 1970 there
was little evidence on the central issues of corporate finance. Now we are overwhelmed with
results, mostly from event studies”.62 Watson and Arunachalam (2018) states in its discussion on
56 Lanoie and Ambec (2008) 57 ibid 58 Sorescu, A., Warren, N. L., & Ertekin, L. (2017) 59 Fama, E., Fisher, L., Jensen, M., & Roll, R. (1969) 60 ibid 61 Binder, J. (1998) 62 Fama (1991), pg.1600
16
the methodology that “the logic is straightforward: Financial markets capitalize the expected
value that (social) policies confer on a firm. When we observe a change in the law, if a given set
of social policies are actually beneficial to firms’ performance, the associated increase in the
firm’s expected profits should be reflected in its stock price”.63 As seen in this quote, the event
study methodology allows stock prices to serve as proxies for firms’ expected profits following a
specific event. Watson and Arunachalam (2018) further state that the event study methodology
is particularly useful in analyzing firms’ interests when public statements might be strategic or
unreliable.64 The method has, for example, been used to determine that diamond firms benefit
from conflict based on market reactions to civil disruption.65,
According to Binder (1998), there are two main reasons for using the event study
methodology. The first main reason is to examine if new information is efficiently incorporated
by the market and the second is to establish the impact that a specific event has on a firm’s
equity holders. The purpose of this thesis is to investigate the impact that announcements of
environmental violation have on a firm’s stock prices which makes the event study methodology
an appropriate choice according to Binder. 66
The event study methodology makes three overarching assumptions. First, it assumes that
the market is efficient, or at least semi-efficient. Secondly, the methodology assumes that all
63 Watson, S., & Arunachalam, R. (2018), pg.1976 64 Watson, S., & Arunachalam, R. (2018) 65 Guidolin, M., La Ferrara, E. (2007) 66 Guidolin, M., La Ferrara, E. (2010)
17
publicly available information is reflected in the stock prices.67 Thirdly, it assumes that stock
prices adjust instantaneously when new information becomes available.68 Under these
assumptions, the new informational content provided by an event makes the investors instantly
adjust their expectations of the focal firms’ expected future profits.69 These new expectations are
reflected in the firms’ stock prices which then captures the value added from the new
information that the announcement provided.70 As stated by Sorescu, Warren, and Ertekin
(2017), “This assumption of an instantaneous change in stock prices is perhaps the most
appealing feature of the event study methodology: it allows researchers to isolate, in a forward
looking manner, the expected value that the firm will derive from a corporate action that has just
been revealed to the public”.71 It is this instantaneous change that the authors underline in the
quote above which allows for the researcher to investigate the value72 of an event at the time of
the announcement even though the cash flow has not materialized yet. This gives the method a
comparative advantage over other performance metrics such as profits, ROI, and sales which are
only available at low frequencies (annually or quarterly), making it hard to isolate the impact of a
specific event.73 The short-comings of the method, however, is that the event has to be news to
the market and dating of an event can sometimes be difficult which increases the risk of the
market already being aware of the event. Furthermore, it is susceptible to exogenous changes
happening within the event window. Say for example that a study is investigating the impact of a
67 Sorescu, A., Warren, N. L., & Ertekin, L. (2017) 68 Malkiel & Fama, E. F. (1970) 69 Sorescu, A., Warren, N. L., & Ertekin, L. (2017) 70 ibid 71 Sorescu, A., Warren, N. L., & Ertekin, L. (2017), p.186 72 “measured as the sum of the incremental future cash flows expected from the corporate action, discounted to the current period” (Sorescu, Warren, and Ertekin (2017)) 73 ibid
18
lawsuit towards a firm, however, during the event window the CEO resigns. This may result in
impacts on the abnormal return that are not related to the event in question. It is, therefore,
important to choose the length of the event window with caution as a longer event window
increases the risk of these exogenous events impacting the results.
Part of the event literature focuses on specifying the conditions which need to apply in
order for an effective and sound event study to be conducted. McWilliams and McWilliams
(2000), inspired by McWilliams and Siegel (1997)74, argue that there is a step-by-step process
that should be followed when conducting an event study.75 They argue that the researcher must
identify an event which provides the market with new information and then hypothesize the
market’s reaction based on the theoretical knowledge available.76 The next step is to identify the
firms which are expected to experience shifts in stock values and the date of the specific events
studied.77 Following this, the researcher must choose an event window that is justified and then
control for other events which might impact the change in stock prices to ensure that the results
reflect changes caused by the specific event in question.78 Lastly, the abnormal returns during the
event window must be calculated and tested for statistical significance.79 McWilliams and
McWilliams (2000) end by stating: “ the researcher should always be required to specify enough
detail about how and from what source data are identified so that any other interested party
could, with enough effort, replicate the reported study”, stressing the importance of transparent
74 McWilliams, A., & Siegel, D. (1997). 75 McWilliams, T. P., & McWilliams, V. B. (2000) 76 ibid 77 ibid 78 McWilliams, T. P., & McWilliams, V. B. (2000) 79 ibid
19
information which is not always the case.80 They argue that failing to provide information of this
nature is indefensible, but it does occur.81
In terms of environmental economics, the method has been used quite extensively as
well. One of the most influential studies of reputational punishments for environmental
violations is Karpoff et al. (2005), which was discussed in the previous section on reputational
punishment in the United States and abroad. This study empirically tests the reputational
penalties in the United States following over 400 environmental events. The method has also
been used to investigate how firms’ stock prices react to bad environmental news, for example
Gupta and Goldar (2005) investigate how the stock prices of Indian firms are affected by
environmental news82 (also see Xu et al.,2012; Klassen and McLaughlin, 1996; and Takeda and
Tomozawa, 2008). However, the event study methodology has also been used to investigate how
firms’ stock prices are affected by good environmental news, for example in response to awards,
environmental investments, and good green ratings.83
2.4 Impact of Social Media
In 2019, it was estimated that one-in-three people globally, or two-thirds of people
online, used social media platforms.84 In Canada, that number is significantly higher, with 94%
of Canadian Internet users being on at least one social media platform.85 This demonstrates how
80 ibid 81 ibid 82 Gupta, S., & Goldar, B. (2005) 83 Lundgren, T., & Olsson, R. (2010) 84 Ortiz-Ospina, E. (2019, September 18) 85 CBC News. (2018, March 9).
20
social media has become an integrated part of most Canadians’ everyday life. Social media is
also becoming important for how people consume and interact with news. In 2012, the Pew
Research Center found that 34 percent of Americans under the age of 30 consumed their news
online, while only 13 percent read physical newspapers.86 During the same time the amount of
Americans consuming printed media declined from 26 percent in 2010 to only 23 percent in
2012.87 Furthermore, 75 percent of people consuming their news articles online found their
information through email or social media platforms, underlining the changing consumption of
news as well.88 However, people not only seem to consume their news through social media, but
they discuss and share news through these platforms as well.89,90 As suggested by Olmstead et al.
“if searching for news was the most important development of the last decade, sharing news may
be among the most important of the next” (2011, p. 10). According to Smith and Rainie (2010),
55 percent of Twitter users have shared links to news articles on their account. This demonstrates
how social media not only becomes a platform for news but a way of sharing, discussing, and
contributing to the news coverage of an event.
Social media is not only a forum for sharing information but it has become a commonly
utilized tool for marketing and business opportunities as well. Many studies indicate that social
media platforms can directly impact a firms’ performance as well. Paniagua and Sapena (2014)
argue that a significant social media presence can affect stock prices. For example in 2012, crude
86 Pew Research Center (2012) 87 ibid 88 Purcell, K., Rainie, L., Mitchell, A., Rosenstiel, T., & Olmstead, K. (2010) 89 Hermida, A., Fletcher, F., Korell, D., & Logan, D. (2012) 90 Purcell, K., Rainie, L., Mitchell, A., Rosenstiel, T., & Olmstead, K. (2010)
21
oil futures bounced up over $1 following multiple retweets of a Twitters user’s impersonation of
a Russian minister.91 During that same year, Google’s earnings report went viral causing the
stock exchange to temporarily stop trading in Google stock.92 A more recent example is Tesla-
founder Elan Musk’s tweet in August 2018 discussing taking Tesla private, which initially
caused a soaring stock price but ultimately resulted in a $40 million penalty.93 This demonstrates
how social media can affect a firm's operation and performance.
Historically, studies investigating social discourse have focused on a variety of media
forms including print media, audio media, and visual media.94 However, as technology has
continued to advance in the last decades, the types of media influences have become more
diverse, including social media which is now a major communication tool.95 Social media differs
from traditional media in a number of ways. Since content can be user-created, firms may have
less control of the discourse following an accident or negative event than they would have if the
event was only spread through traditional media channels.96 As Seo et al. (2013) states “due to
the diverse forms of media serving as communication channels, the influence of media on the
general public is increasing”,97 supporting the incorporation of social media into this event study
as only using printed media would neglect this changing nature of media influence.
Given the changes seen in the last decades around how information and news are shared,
this thesis will be incorporating the impact of social media on stock prices following an event as
91 The Economist. (2013, January 12). 92 Efrati, A. (2012, October 19). 93 Wayland, M. (2019, August 8). 94McLuhan, M (1964) 95 Syed-Ahmad, S.F and Murphy, J (2010) 96 Palen, L(2008) 97 Seo, S., Jang, S. S., Miao, L., Almanza, B., & Behnke, C. (2013).
22
well. According to Bastos (2014), this makes sense as journalism has fundamentally changed to
become more Internet-focused. Hermida et al. (2014) further strengthen this argument by stating
that social media is becoming central to the way that Canadians are consuming news. Given this
changing nature of media consumption, social media could impact the public perception of firms
as well. This thesis will therefore incorporate social media to determine its impact on changes in
public attitudes following an environmental violation.
While previous studies on the impact of negative environmental events on Canadian
firms have not incorporated the impact of social media on stock prices, event studies in other
fields have. Seo et al. (2013) incorporated the impact that social media had on stock prices
following food safety events, finding that high media attention resulted in greater abnormal
returns. Khatua and Khatua (2016) investigated the impact that social media had on stock prices
following the 2015 Indian Budget announcement, finding that the overall positive tweets about
the budget increased stock market indicators.98 Sinanaj, Muntermann and Cziesla (2015) argue
that a firm reputation can be negatively affected by data breach events becoming public in social
media.99 These examples show that the integration of social media in event studies is becoming
an emerging part of the literature.
98 Khatua, A., & Khatua, A. (2015). 99 Sinanaj, G., Muntermann, J., & Cziesla, T. (2010)
23
CHAPTER 3: DATASET INFORMATION
3.1 Environmental Violations
The data on environmental incidents used for this study was compiled using enforcement
notifications published by Environment and Climate Change Canada.100 The notifications are
available to the public and contain all successfully issued fines, including provincial ones. The
enforcement notifications outline the size of the fine, the violation(s) that the firm was fined
under, and details about the violation committed.
Several previous studies done on reputational punishment for environmental violations
have relied on printed media and then manually compiled their dataset using certain search terms
or by manually going through the data (see Lanoie and Laplante, and Karpoff et al.). As
mentioned above, however, the data for this study was compiled using government data provided
online. The benefit of this approach is that it reduces the risk of selective data collection as the
enforcement notifications contain all successful prosecutions during the chosen time period for
this study. Furthermore, while print media is dependent on reader engagement and may only
cover incidents that are believed to spark readers’ interest, the enforcement notifications have no
such objective and cover all infractions equally. The enforcement notifications also provide
unbiased and credible information on incidents, including when the conviction took place, what
environmental act or legislation was broken, and the size of the fine. A slight disadvantage of
using the governmental enforcement notifications to compile the data set is that it does not give
an idea of the media coverage that the incident received.
100 https://www.canada.ca/en/environment-climate-change/services/environmental-enforcement/notifications.html
24
In their study from 1994, Lanoie and Laplante argue that an incident’s media exposure is
an important part of the results produced, arguing that “event studies should be based on events
that have the same extent of coverage in the media” (pg.669). The authors even go as far as to
create sub-categories based on the level of media exposure that each incident received. This was
a straightforward grouping alternative as they used printed media to assemble their data and
therefore had an idea of the level of media exposure of each incident. However, the enforcement
notifications do not provide any insight into media exposure as it reports equally on all incidents
and therefore the events cannot be grouped based on this criterion.
In Karpoff et al. (2005) the authors also use printed media to collect the initial
information on the incident and then support this data with additional information from the
Factiva database. The authors do not include media presence in their analysis but rather group
their events based on the type of violation. The violation type will be included as a control
variable in this study as well (see Table 1 for the breakdown of different violations).
The enforcement notifications contain hundreds of successful fines from the last 14 years,
however I applied multiple criteria for which events would be included in the study. First of all, I
limited my data to only publicly traded companies. This is a requirement for the event study
methodology to be implemented as I am using stock prices and abnormal returns to measure
public attitudes. I limited this even further to only include firms that have stocks or are owned by
a company that has stocks on the Toronto Stock Exchange, excluding any foreign-listed
companies operating in Canada. This was done to reduce the impact of asymmetric information.
25
The focus of this study is reputational punishment. Given the increased degrees of separation, it
is likely that a foreign company operating in Canada or owning a Canadian company is less
affected by changes in public attitudes. The foreign company may also be more likely to have
foreign investors that are not aware of the company’s environmental violation in Canada.
Secondly, it creates a more homogenous data set as all stock prices are from the same stock
market.
The second restriction I made was to only include events where the fines were equal to
or larger than $100,000. This was done to ensure that the fine had non-negligible consequences
for the company. This is of relevance as many of the firms are large corporations and smaller
fines would not have imposed any real economic sanction. The fines varied significantly in size
from five incidents with fines of $100,000 to eight incidents with fines in the millions. The two
largest fines in the sample, of $3 million each, were imposed on Syncrude Canada Ltd. in 2010
and Teck Metals Ltd. in 2016 (see Figure 2 for average fine size per year). In total, after
imposing these restrictions, I was left with 28 incidents between 2010 and 2019 (see Figure 1 for
number of Environmental violations per year and Appendix J for full information on the dataset).
Most of these incidents involved companies in the energy sector (39.29%) or in the mining
industry (46.43%) (see Table 1 for more descriptive statistics).
It is worth noting that the event chosen for this study is the release of the enforcement
notification. This is the first time that the size of fine that the firm has to pay is released,
however, in most of the cases it is not the first time that the market hears about the firms’
26
environmentally harmful practices. In some of the cases, the market has been aware of the
infraction for years before the enforcement notification is released.
Figure 1: Number of Environmental Violations Per Year in the Sample
Figure 1: This graph compares the number of violations committed each year in
the sample. The sample includes 28 incidents between 2010 and 2019 (Information on the incidents can be found in Appendix J)
27
Table 1: Environmental Violations Sample
Event Classification Number:
Size of fine:
>1,000,000 28.57% (8)
500,000-999,999 7.14% (2)
100,000-499,999 64.29% (18)
Total: 100 (28)
Act Violated:
Canadian Environmental Protection Act, 1999 (CEPA)
5
Migratory Birds Convention Act, 1994 4
Pollution Prevention provisions (subsection 36(3)) of the Fisheries Act
17
Multiple acts violated 2
Total 28
Industry:
Materials 46.43% (13)
Energy 39.29% (11)
Other 14.28% (4)
Total 100 (28)
Note.—Descriptive statistics of 28 environmental violation events identified from enforcement notifications published by Environment and Climate Change Canada during the period 2010-2019. CEPA=Canadian Environmental Protection Act, 1999 (CEPA); MBCA=Migratory Birds Convention Act, 1994 (see Appendix J for entire dataset)
28
3.2 Stock Market Data
The second set of data used in this study is from the Toronto Stock Exchange. For each
incident four data points are gathered which are the stock prices from 30 days before the
incident, the day before the incident, the day of the incident, and 30 days after. This study
assumed that the event (id est the release of the enforcement notification) comes as news to the
market, however this may not be the case in the real world. Hence, the 30 days and the 1 day
before is included to control for the market already being aware of the fine size before the release
of the enforcement notification, a situation known as leakage. It also controls for other impacts
that are not related to the event. The 30 days after is chosen as this is the window used by
Laplante and Lanoie in their study. Furthermore, this thesis predicts that firms will incorporate
the new information from the enforcement notification on the initial day that the event is
announced and that this is the day on which the most impact will be observed. The data for day
30 is included to test this hypothesis and to examine if there are any persistent impact the firms’
infraction would have on its stock prices.
Many of the firms in the sample are in very volatile industries and their stock prices could
change significantly from day to day. Furthermore, given that the study looks at dates within a
two-month window there could also be other events impacting the firms’ stock prices. To control
for market trends, I used data from industry specific indexes on the same dates as the incidents
(id est day -30, -1, 0, and 30). Specifically, I used the S&P/TSX Capped Energy Index101 for the
oil and gas companies, and the S&P/TSX Capped Materials Index102 for the mineral and natural
101 S&P/TSX Capped Energy Index [^TTEN] - Toronto Stock Exchange Index 102 S&P/TSX Capped Materials Index [^TTMT] - Toronto Stock Exchange Index
29
resource companies. For the two incidents involving Canadian National Railway I used Canadian
Pacific Railway as it is the other dominant national railway company in Canada and together the
two companies capture the entire railway market. Given the duopoly nature of the Canadian
railway market, the general market effects unrelated to the incidents should be captured by the
stock price of the other railway company, hence the decision to use the Canadian Pacific Railway
data to capture market effects on the dates of the Canadian National Railway incidents. For the
environmental violation committed by Hudson’s Bay Company the S&P/TSX Capped Consumer
Discretionary Index103 was used and for the Bentall Kennedy incident the S&P/TSX Capped
Real Estate Index104 was utilized (see Appendix N, O, and P for more information on the returns
of the Indexes during the event window).
Using industry specific indexes was a deviation from previous influential studies. Lanoie
and Laplante (1994) used the CAPM model in which they calculate the expected return by
including the rate of return of the Canadian federal government 90-day Treasury Bill, historic
prices and the rate of return of the Toronto Stock market. Karpoff, Lott, and Wehrly (2005) on
the other hand use the CRSP equal-weighted index as a control for market trends in the CAPM
model in their study. While broader indexes like these can be used as a benchmark, I believe that
using the industry specific indexes provides a more accurate picture of the market trends
especially given the volatile nature of the energy and mineral markets, which makes up the
majority of my sample. Furthermore, the historic stock prices used in the CAPM may not be a
good prediction of future stock prices. The risk-free rate derived from the short-term government
103 S&P/TSX Capped Consumer Discretionary Index [^TTCD] - Toronto Stock Exchange Index 104 S&P/TSX Capped Real Estate Index [^TTRE] - Toronto Stock Exchange Index
30
securities may also change on a daily basis which would introduce volatility to this variable
which the CAPM model does not account for.
Figure 2: Average Fine Size per Year
Figure 2: This graph compares the average fine size for each year in the sample. The
sample includes 28 incidents between 2010 and 2019. The fine size is given in thousands of Canadian dollars. It is worth noting that 2010 only includes one incident
and the average fine is therefore a reflection of Syncrude Canada Ltd.’s fine of $3 million (See Appendix J for full list of events in the sample).
31
3.3 Social Media Data
As mentioned earlier, Lanoie and Laplante (1994) argued for the importance of media
exposure and the fact that the enforcement notifications did not capture media exposure was a
potential downside of the data collection method used in this thesis. To account for this, and the
changing nature of media, a social media variable will be used in the study as well. While Lanoie
and Laplante (1994) grouped the events based on if the violation was a featured article, this
variable is another measure of media presence and is based on the presence that the event had in
social media.
In this thesis, the social media platform used is Facebook. Facebook has in recent years
become a popular place for people to share and interact with news. To determine the amount of
interactions a news article had, SharedCount105 was used. This platform provided not only data
for how many people shared the news article, but also how many people interacted with those
shared links (i.e. liked, commented, or re-shared the link). For each event, I searched for the
name of the firm, “environmental fine”, and the year.106 The top news article on Google was
then used to determine the social media presence of that event. If no news article existed or was
not found on the first page of Google, the event was assumed to have 0 social media interaction.
If the incident had multiple articles published about it, the top result was used. In terms of the
news article source, 14 articles were published in national newspaper, 6 were found in local ones,
and 8 events did not have news articles published about them (see Figure 3). Some of the events
105 https://www.sharedcount.com/ 106 For example: Syncrude Canada Ltd. Environmental fine 2010
32
were published in both national and regional newspapers, however, when this was the case the
top story on Google was used to determine the social media presence.
Figure 3: Number of News Articles Based on Source
Figure 3: This graph demonstrates the source that the articles in the sample were
retrieved for. The sample includes 28 incidents between 2010 and 2019. The three categories are local, national, and no news article found (see Appendix Q for entire
social media dataset).
33
The social media variable also includes the amount of Facebook interactions that the
event notification itself received. This was done to measure the social media interactions of
events that did not have articles written about them. The Facebook interactions of the event
notification and the news article where then combined to make the social media variable.
Table 2: Social Media Variable
Social Media Average Interaction:
Size of fine:
>1,000,000 849.5
500,000-999,999 48
100,000-499,999 9.44
Act Violated:
Canadian Environmental Protection Act, 1999 (CEPA)
70.8
Migratory Birds Convention Act, 1994 656.67
Pollution Prevention provisions (subsection 36(3)) of the Fisheries Act
13.28
Multiple acts violated 2249.5
Industry:
Materials 18.38
Energy 592.64
Other 76
Note.—The average amount of interactions on Facebook (including shares, comments, and likes) for 28 environmental violation events identified from the enforcement notifications published by Environment and Climate Change Canada during the period 2010-2019 (see Appendix Q for more information about the social media dataset).
34
Table 2 summaries some of the key characteristics of the social media data. As expected,
the number of interactions increase with the size of the fine. For fines over $1 million the
average interaction is 849.5 while for fines under $500,000 this number is only 9.44 (see
Appendix Q for more information about the social media dataset). The highest number of
interactions was for Husky Oil Operations Limited’s violation in 2019 which had 4436
interactions. The events in which the firm violated multiple regulations also saw more
interactions, however, this number is driven up by the Husky Oil incidents and therefore not too
much value should be placed on this. The second highest number of interactions was for MBCA
that had an average of 656.67 interactions. In terms of industry differences, energy saw the
highest number of interactions with an average of 592.64 interactions per event. Firms in the
materials industry saw an average of 18.36 interactions, and other industries had an average of
76 interactions (See Appendix Q for full list of interactions).
There is also a significant difference in interactions between the years in the sample.
There are no interactions with either news articles or enforcement notifications before 2016.
After 2016, the average number of interactions increase for each year and in 2019 the number of
interactions is 2127 (see Figure 4). This follows the same trend as can be seen for the 74
enforcement notifications published between 2010 and 2019 (both publicly and private firms).
Besides one case in 2013 that had 65 interactions, no news article before 2016 received any
interactions and 2019 had the highest amount of interactions (see Figure 5).
35
Figure 4: Average Facebook Interactions in Sample
Figure 4: This graph compares the average amount of interaction on Facebook for each
year in the sample. The sample includes 28 incidents between 2010 and 2019. The average interactions include shares, comments, and likes of both a news article (if
found) and the enforcement notification.
36
Figure 5: Average Facebook Interactions by Year for All Notifications
Figure 5: This graph compares the amount of Facebook interactions by year for all enforcement
notifications published by the Government of Canada that contain fines larger or equal to $100,000. This includes 74 incidents between 2010 and 2019. It is worth noting that 2013 only
includes one incident that received any Facebook interactions and the average is therefore a reflection of the 65 reactions that the Kelly Cove Salmon Ltd.’s incident received.
37
3.4 Market Cap Data
The fourth set of data is the firms’ market cap data which will be used to determine the
relative fine size. The relative fine size will be used as an alternative to the fine size. This
variable will account for differences in firm sizes which may impact the abnormal returns as
well. A large fine, for example, may impact a small firm’s abnormal returns more than a large
firm as shareholders may believe that the larger firm has more reserves and a larger revenue
stream to deal with the fine. The two sets of data used to determine the relative fine size is the
size of the fine and the size of the firm. The firms’ market cap from the Toronto Stock Exchange
on the day the enforcement notification was released will be used to determine the size of the
firm. To determine the relative fine size, the size of the fine will be divided by the firm’s market
cap for the day of the event.
38
39
CHAPTER 4: EMPIRICAL METHOD
In order to determine whether firms that pollute harm their reputation and whether the
public cares about it, this thesis will use the event study methodology. As mentioned in previous
sections, this methodology is used by other influential studies on reputational punishment
including Laplante and Lanoie (1994) and Karpoff et al. (2005).
For the purpose of this thesis the event in question is the release of the enforcement
notification. An enforcement notification is released by the Government of Canada and follows a
firm being successfully fined for an environmental violation. It outlines the violation(s) that the
firm committed and the fine that the company has to pay. The release of the enforcement
notification was chosen for two reasons. The first reason is based on the assumption that
shareholders are not aware of the fine the firm will receive for its environmental violation prior
to conviction which makes the enforcement notification news to the market. This is a standard
assumption of the event study methodology, however, it may not be true in reality. To control for
potential leakage, the 30 days and the day before is included in this study. The second reason that
the enforcement notification was chosen was because it outlines the actual explicit cost that the
firm has to pay. According to Becker (1968) the optimal penalty theory states that the expected
total penalty for an illegal activity should equal the activity’s total social cost. The author further
argues that total penalties are made up of the explicit costs imposed by the legal system (in terms
of fines and required improvement to environmental standards) and reputational punishments.107
If regulators strive to achieve optimal penalty theory then smaller legal fines should result in
107 Becker (1968)
40
larger reputational punishments and larger legal fines in smaller reputational punishments.
However, this may not hold in practice. The fines may not be large enough to significantly
punish the firm for their social cost. This thesis may guide regulators that strive for optimal
penalties to determine whether legal fines are adequate to punish firms that violate
environmental regulations or if they do not impose the economic sanction intended in which case
the optimal penalty theory does not hold true.
This thesis considers three separate specifications, with three regressions run under each
specification. All three specifications will be used to determine the reputational effects of firms
violating environmental regulations. The dependent variable for all three specifications is the
abnormal return. The abnormal return is the difference between the actual return of a firm’s
security and the expected return. The expected return is based on the return of the industry
specific indexes mentioned earlier. The abnormal return specification is given as:
ARit = Rit - E(Rit) (1)
Where ARit is the abnormal return for security i on day t, Rit is the actual return for
security i on day t, and E(Rit) is the expected return for security i on day t based on the return of
the firm’s industry index.
The three specifications all try to determine the change in public attitudes following an
event. This will be done utilizing ordinary least squares (OLS). They then vary in which
independent variable are included. For each specification, three regressions will be used all of
41
which will use abnormal returns as the dependent variable. For the purpose of this thesis, the
abnormal return is the difference between the stock returns for two dates (i.e. -30 to -1, -1 to 0,
and 0 to 30), not the average daily abnormal return during those periods. The first regression is
with the abnormal return from day -30 to day -1, the second is with the abnormal return from day
-1 and 0, and the third regression is with the abnormal return from day 0 to 30. The specification
for these regressions are as follows:
ARit = β0 + β1 (size of fine)i + β2 (violation dummy)i + β3(sector dummy)i + 𝜺it (2)
As can be seen above, the independent variables for this regression specification are the
size of the fine, the violation dummy, and the sector dummy. The size of the fine is the explicit
monetary cost to the firm. This variable is included to determine how much of the abnormal
return can be explained, ceteris paribus, by the size of the fine. The violation dummy is to
control for any effects related to the specific act or regulation that the firm violated. Furthermore,
the public may view different infractions differently and the inclusion of the violation dummy
helps examine if this is the case. For this thesis, violations under the Canadian Environmental
Protection Act, 1999 (CEPA) will take the value of 1 and all other violations will take the value
of 0. The sector dummy is included to account for any impacts that are related to the industry
that the firm is in. This dummy is included as stakeholders may assume that there are inherent
risks of environmental violations in some industries and therefore not punish them as severely
based on that. For this thesis, firms in the energy sector will take a value of 1 and all other
industries will take a value of 0.
42
To determine the impact that social media has on the firms’ abnormal returns a
modification of equation 2 will be used. The dependent variable remains the same with the only
difference from the previous specification being the inclusion of social media. Again, the three
regressions will be ran using OLS. The regression specification used to determine the impact that
social media plays on changes in public attitudes towards violating firms’ is as follows:
ARit = β0 + β1 (size of fine)i + β2 (violation dummy)i + β3(sector dummy)i +
β4(social media)i +𝜺it
(3)
As seen above, the independent variables now include the size of the fine, the violation
dummy, the sector dummy, and the social media variable. The first three variables will remain
the same as in equation 2. The social media variable is based on the number of interactions that
the news of each event had. This variable is included to determine the impact that social media
has on the changes in public attitudes following the event. The variable will also be used to
determine the impact of the changing nature of media consumption. Furthermore, this variable
helps control for media exposure which Lanoie and Laplante (1994) argue is important for event
studies of this nature.
The fines in the sample vary largely in size from $100,000 to several millions, however,
the size of the firms’ fined vary greatly as well. This could potentially result in different
responses to an event as a fine would impose a greater economic sanction on a smaller firm than
a larger one. To control for differences in firm sizes, three regressions will be used where the
relative fine size is used as one of the independent variables instead of the sum of the fine. The
43
dependent variable will still be the abnormal returns. For the purpose of this thesis, the abnormal
return is the difference in stock prices from two days (id est -30 to -1, -1 to 0, and 0 to 30), not
the average daily abnormal return during those periods. The first regression is with the abnormal
return from day -30 to day -1, the second is with the abnormal return from day -1 and 0, and the
third regression is with the abnormal return from day 0 to 30. The specification for these
regressions are as follows:
ARit = β0 + β1 (relative size of fine)i + β2 (violation dummy)i + β3(sector dummy)i +
β4(social media)i +𝜺it
(4)
As seen above, the specification still includes independent variables for the violation
dummy, the sector dummy, and social media. The only independent variable that changes from
specification 3 is the fine being relative to the size of the firm. For this thesis, the size of the firm
is assumed to be the market cap from the Toronto Stock Exchange on the day of the event. The
relative fine size is then calculated by dividing the fine by the size of the firm. This variable is, as
mentioned above, included to account for potential discrepancies in abnormal returns resulting
from differences in firms’ sizes.
The results of specifications (2) - (4), and its implications, will be discussed in the
following section.
44
45
CHAPTER 5: RESULTS
This section will present the empirical results on the impact that the announcement of the
enforcement notification had on a firms’ abnormal returns. The results will be separated based on
the independent variables included and the point in time that they analyse. Variables of
importance will be further discussed at the end of each section.
5.1 Regression Results using Fine Size as an Independent Variable
Table 3:Abnormal Return Regression for Day -30 to -1
Dependent Variable:
Abnormal Return
Fine 3.68E-06 (1.21)
Sector -2.83 (-0.48)
Violation -0.75 (-0.11)
Constant 0.90 (0.21)
Observations 28
R2 0.06
Adjusted R2 -0.05 Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The fine variable is measured in dollars. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations
Table 3 outlines the results for equation 2 with abnormal returns from the day -30 to -1 as
the dependent variable (see Appendix A for full results). As can be seen above, none of the
46
variables are statistically significant at any normal level of confidence. This matches the
expected results for this regression. The R2 is also very low which may suggest that the market is
not aware of, and does not expect, the event. If little to no leakage occurs, there should not be
any significant variables in this regression as the fine has not been announced yet and therefore
should not affect the abnormal returns. It is also worth noting that the constant is positive and
very close to zero as well. This further suggests that the market is not aware of the current
situation and is not predicting the release of the enforcement notification. This aligns with my
initial assumption that the enforcement notification is news to the market.
Table 4: Abnormal Return Regression for Day -1 to 0
Dependent Variable:
Abnormal Return
Fine 8.90E-07** (2.19)
Sector -0.33 (-0.41)
Violation 0.35 (0.37)
Constant -0.46 (-0.79)
Observations 28
R2 0.17
Adjusted R2 0.07 Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The fine variable is measured in dollars. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations
47
The table above outlines the results of equation 2 using the abnormal returns from the day
of the event as the dependent variable (see Appendix B for the full results). The results show that
neither the sector nor the violation variable are of statistical significance. This suggest that the
market does not react differently based on which violation the firm broke or what sector it
belongs to. The constant variable, although not significant at the 10 percent level of confidence,
is negative which does suggest that there is some initial reputational punishment for the event
that is not explained by the fine size, the sector that the firm is in, or the violation it was
convicted for. In terms of the fine variable, it is positive and statistically significant at a 5 percent
level of confidence. The value of the variable is 8.90E-07 which suggests that, ceteris paribus,
an increase of 1.1 million in the fine size will result in a 1 percentage point increase in the
abnormal return. This was not the expected result, a priori, but it is of great importance. The
implication of this result will be discussed in detail below. It is further worth noting that the R2
increased significantly between day -30 to -1 when it was 0.06 and day -1 to 0 when it was 0.17.
This suggest that the event came as news to the market and that little leakage occurred.
48
Table 5: Abnormal Return Regression for Day 0 to 30
Dependent Variable:
Abnormal Return
Fine -2.64E-06 (-0.84)
Sector 1.99 (0.32)
Violation -10.06 (-1.38)
Constant 5.66 (1.24)
Observations 28
R2 0.10
Adjusted R2 -0.01 Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The fine variable is measured in dollars. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations
Table 5 shows the results for equation 2 using the abnormal returns from day 0 to day 30
as the dependent variable (see Appendix C for full results). As seen above, the variable for the
sector and violation are still insignificant. The fact that the sector and violation variables are not
statistically significant for this regression suggest that the public does not have different views
based on the violation or sector. The results further suggest that the event does not impact the
abnormal returns 30 days after the event as neither the fine nor the constant are of statistical
significance. This is interesting as there was some impact seen on the day of the event. While the
49
fine variable is not of statistical significance it is still of economic significance. Since the sign of
the fine variable is negative this suggest that the fine variable may have a negative effect on the
firm in the long-run. It is worth noting that the R2 has decreased again from 0.17 which was
observed in the previous regression, to 0.10 in this regression. One potential explanation for this
is the difference in window size for these regressions. For the regression using the abnormal
returns from day -1 to 0 as the dependent variable, the window observed is only one day. During
this one day, the event is probably one of the most significant impacts of the stock prices.
However, in the regression using the abnormal return from day 0 to 30 as the dependent variable,
the window is 30 days. This makes the stock prices more susceptible to exogenous changes and
volatility in price that is unrelated to the event. This makes it harder to identify the impact of the
event on the abnormal return during the 30-day window following the event than the day before
to the day of the event. This is an inherent feature of the event study methodology which
produces the most robust results when the event window is rather narrow and less influenced by
other market events.
5.1.1 The Fine Variable The purpose of this thesis is to investigate the reputational effect of the release of the
enforcement notification. However, as previously mentioned this is not the first time that the
market becomes aware of the violation committed. For many of the violations, the market has
been aware of the firms’ environmental practice for years or since the investigation of the firm
began. Therefore, there are two possible outcomes for the firm. Either the firm gets away with
the environmental discrepancy or it gets punished. If the latter is true than it could either receive
a fine or face a trial.
50
As mentioned earlier, the fine variable on day 0 is positive and statistically significant.
More specifically, holding everything else constant the abnormal return increases by 1
percentage point for each increase in the fine size of 1.1 million. This result is interesting as it
suggests that the fine comes as positive news to the financial market, especially for the firms that
receive higher fines. Theoretically this should not be the case. The fact that the firm was fined
should come as bad news to the market as the alternative was that the firm got away with the
behavior. Given that the fine variable is positive, the news of the size of the fine must have a
greater positive magnitude than the negative effect coming from the firm being fined to begin
with. There are three potential explanations for these results. First of all, the market may not like
uncertainty. For many of the violations, the market has been aware of the firm being under
investigation for several years and the news that the firm has been fined may therefore be
positive news as it removes the uncertainty related to the investigation. This helps explain why
the fine may come as positive news but not why a larger fine would result in higher abnormal
returns than a lower fine.
The second potential explanation is that the fine was lower than expected. For example,
for violations convicted under CEPA (under which 5 out of 28 of the violations in the sample
were) the maximum fine for a first time offending firm is $6 million and for second or
subsequent offences the fine can be as high as $12 million.108 The highest fine under CEPA in
the sample for this thesis is the 2018 Canadian National Railway Co fine of $1,126,627. The
same goes for the Fisheries Act (under which 17 out of 28 violations in the sample were fined
108 Climate Change Canada. (2019, July 4)
51
under) and the MBCA (under which 4 out of 28 violations were fined under) where the minimum
fine for first time offenders fined on indictment is $75,000 if the firm is deemed a small revenue
corporation109 and $500,000 for a larger corporation.110,111 The maximum fine under the
Fisheries Act and the MBCA is again $6 million for first time offenders.112 ,113 For second and
subsequent offences the minimum fine is $1 million and the maximum is $12 million.114 ,115 The
largest fine under the Fisheries Act (not including events where a firm was fined under multiple
acts) in the sample is the 2016 Teck Metals Ltd. fine of $3 million. Under MBCA the largest fine
is the 2010 Syncrude Canada Ltd. fine of $3 million. This means that none of the firms fined
under CEPA, MBCA, and the Fisheries Act received the maximum fine. The positive fine
variable may therefore be the result of the fine being lower than expected. Higher fines would
suggest that the firm committed a more serious violation. Hence, firms that received higher fines
would have had even more reason to expect an even higher fine and therefore the abnormal
return is positively correlated with the size of the fine.
109 A small revenue corporation is defined as a firm which gross revenues does not exceed $5 million for the year before the fined was issued 110 Legislative Services Branch. (2020, March 11). Consolidated Federal Laws of Canada, Fisheries Act. 111 Legislative Services Branch. (2020, March 11). Consolidated Federal Laws of Canada, Migratory Birds Convention Act, 1994 112 Legislative Services Branch. (2020, March 11). Consolidated Federal Laws of Canada, Fisheries Act. 113Legislative Services Branch. (2020, March 11). Consolidated Federal Laws of Canada, Migratory Birds Convention Act, 1994 114 Legislative Services Branch. (2020, March 11). Consolidated Federal Laws of Canada, Fisheries Act. 115 Legislative Services Branch. (2020, March 11). Consolidated Federal Laws of Canada, Migratory Birds Convention Act, 1994
52
The third potential reason that the fine variable is positive is that the release of the fine
removes the threat of an expensive and drawn out legal case. This would help explain why the
abnormal returns increased with the size of the fine. The larger the fine, the greater the initial
violation was. This would mean that the firms that received larger fines would also be more
likely to have a lawsuit against them. Therefore, the news of a fine would be even more positive
to firms with larger violations as their greater risk of a lawsuit is removed. Furthermore, the fines
do not come with compliance requirements or seizure of production or profits which a lawsuit
might. This would make it even more beneficial for the firm to avoid a lawsuit.
5.1.2 Reputational Punishment
As seen in the results above, the constant is negative on day 0 which could suggest some
reputational punishment for the event. For this study, the event in question is the release of the
enforcement notification. This was chosen as it is the date when the firms’ explicit cost for their
violation becomes known, however, it is not the first time that the market may have heard about
the firms’ violation. In most of the cases, the market has been aware that the firm is under
investigation for months or years. The fact that the constant is insignificant while the firm
variable is significant could suggest that the reputational punishment is already embedded in
stock prices since the release of the information on the firm being under investigation. In other
words, the market already knows that the firm harmed the environment and violated regulations,
however, it does not know what punishment the firm will receive. Therefore, the news to the
market following the release of the enforcement notification is not that the firm violated an
environmental violation but rather the explicit cost associated with that violation. This could
explain why the fine variable is of such significance for this study.
53
5.2 Regression Results Including Social Media as an Independent Variable
Table 6: Abnormal Return Regression for Day -30 to -1 with Social Media
Dependent Variable:
Abnormal Return
Fine -2.37E-06 (-0.65)
Sector 2.33 (0.35)
Violation -10.28 (-0.36)
Media -0.001 (-0.15)
Constant 5.54 (1.17)
Observations 28
R2 0.10
Adjusted R2 -0.05
Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The fine variable is measured in dollars. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations. The social media variable is measured in number of interactions
Table 6 shows the results for equation 3 with the abnormal returns from between day -30
and -1 as the dependent variable (see Appendix D for full results). The independent variables are
the same as in equation 2 besides the addition of the social media variable. As seen above, none
of the variables are statistically or economically significant. This is expected as the purpose of
this regression was to control for leakage. If the market is unaware of the event, then no effects
54
should be observed. These results also align with the results from equation 2 using abnormal
returns from day -1 as the dependent variable (see Table 3).
Table 7: Abnormal Return Regression for Day -1 to 0 with Social Media
Dependent Variable:
Abnormal Return
Fine 1.02E-06** (2.177)
Sector -0.16 (-0.19)
Violation 0.24 (0.25)
Media -0.0003 (-0.59)
Constant -0.52 (-0.86)
Observations 28
R2 0.19
Adjusted R2 0.04 Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The fine variable is measured in dollars. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations. The social media variable is measured in number of interactions
The table above summarizes the results from equation 3 using the abnormal returns from
day -1 to day 0 (see Appendix E for full results). The sector and violation variables are still
insignificant. The constant, on the other hand, is negative which again suggests that, ceteris
paribus, there is some reputational punishment for the firms violating environmental regulations.
55
The media variable, while not significant, is negative which suggests that increased social media
awareness of the event might negatively impact a firms’ abnormal return. The R2 value has
increased from 0.10 when the dependent variable was the abnormal returns from day -1 (see
table 6) to 0.19 for this regression. This increase further supports the hypothesis that the market
is unaware of the fine being issued and suggests that little leakage has occurred. The fine
variable is both positive and statistically significant at a 5 percent level of confidence. The value
of the variable is 1.02E-06 which suggests that, holding everything else constant, an increase of
$1 million in the fine size will result in a 1 percentage point increase in the abnormal return. As
can be seen, the magnitude of the fine variable did not change much from the specification not
including the social media variable (see table 4 for results). Again this was not the expected
result but it is consistent with the results from the regression that omitted the social media
variable. As mentioned above, this could be the result of uncertainty being removed or that the
fine was not as high as expected. Furthermore, firms that received higher fines may have feared
that they would be tied up in a legal case and therefore prefer the fine.
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Table 8: Abnormal Return Regression for Day 0 to 30 with Social Media
Dependent Variable:
Abnormal Return
Fine -2.37E-06 (-0.65)
Sector 2.33 (0.35)
Violation -10.28 (-1.36)
Media -0.001 (-0.15)
Constant 5.54 (1.17)
Observations 28
R2 0.10
Adjusted R2 -0.05 Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The fine variable is measured in dollars. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations. The social media variable is measured in number of interactions
Table 8 summarizes the results from equation 3 with the dependent variable being the
abnormal return from day 0 to day 30 (see Appendix F for full results). The violation and sector
variable are still insignificant which suggest that the industry that the firm is in and the regulation
that it violated has little impact on the abnormal returns of the firms in question. The fine
variable is negative which could suggest that in the long-run a higher fine does negatively impact
the firm. The social media variable is still negative but still not significant.
57
5.2.1 Social media variable As seen in the results above, the social media variable is negative for all three
regressions. This may suggest that there is some leakage occurring that results in the social
media variable being negative for the regression using the abnormal returns from day -30 to -1 as
the dependent variable. However, the social media variable is statistically insignificant for all
three regressions and it cannot be ruled out that the effect of this variable is equal to zero. This
could be the result of either social media not having an impact on the abnormal returns of a firm
or because the sample size in this study is too small. The effect of the latter is further worsened
by the fact that the usage of Facebook has changed both in scope and nature during the sample
period. This resulted in more than half of the events having no social media presence. Hence,
further research is needed to determine social media’s impact on changes in public attitudes
following environmental violation events
While the social media variable is not of statistical significance, it could have some
economic significance. For the regression using the abnormal returns from day -1 to day 0 as the
dependent variable, the social media variable is equal to -0.0003. This would mean that, holding
everything else constant, an increase in social media interactions by approximately 3300 would
result in a decrease of abnormal returns by 1 percentage point. Furthermore, for the regression
using the abnormal return from day 0 to day 30, the social media variable equals -0.001. This
would suggest that, ceteris paribus, an increase in social media interactions by 1000 would result
in a 1 percentage point decrease in the abnormal return. This would suggest that it takes the
market a little longer to react to and incorporate the events social media presences.
58
5.3 Regression Results using Relative Fine Size as an Independent Variable
Table 9: Abnormal Return Regression for Day -30 to -1 with Relative Fine Size
Dependent Variable:
Abnormal Return
Relative Fine -0.02 (-0.13)
Sector -2.57 (-0.38)
Violation -0.54 (-0.07)
Media 0.001 (0.13)
Constant 3.49 (0.86)
Observations 28
R2 0.01
Adjusted R2 -0.17 Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The relative fine variable is the fine size divided by the firm’s market cap on the day of the event. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations. The social media variable is measured in number of interactions Table 9 summarizes the results for equation 4 with the dependent variable being the
abnormal returns for day -30 to day -1 (see Appendix G for full results). The independent
variables are the same as equation 3 besides the fine variable which has been exchanged for a
relative fine variable. The relative fine is included as a variable in the regression to control for
differences in firm sizes. None of the variables in this regression are statistically significant. This
aligns with expectations of what would happen as the event has not occurred yet and if the
59
variables were significant this would indicate that the market was somewhat aware of what was
going to happen.
Table 10: Abnormal Return Regression for Day -1 to 0 with Relative Fine Size
Dependent Variable:
Abnormal Return
Relative Fine 0.03 (1.22)
Sector -0.09 (-0.10)
Violation 0.58 (0.56)
Media -0.001 (-0.87)
Constant -0.02 (-0.03)
Observations 28
R2 0.08
Adjusted R2 -0.08 Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The relative fine variable is the fine size divided by the firm’s market cap on the day of the event. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations. The social media variable is measured in number of interactions The table above shows the results from equation 4 using the abnormal returns from
day -1 to day 0 as the dependent variable (see Appendix H for full results). The sector and
violation variable are statistically insignificant which aligns with the results of the previous
regressions as well. The media variable is negative, as expected, but it is not significant. The
60
relative fine is still positive but compared to the fine variable for equation 2 and 3, the variable is
no longer statistically significant. This suggest that the abnormal return is more influenced by the
explicit cost imposed by the fine than its relative size to the firm. The implications of this result
will be discussed further in a later subsection. The R2 also increased from 0.01 from regression 9
(see table 9) to 0.08 in this regression. This is a substantial increase but not as great as seen in
previous cases.
Table 11: Abnormal Return Regression for Day 0 to 30 with Relative Fine Size
Dependent Variable:
Abnormal Return
Relative Fine -0.07 (-0.43)
Sector 2.09 (0.31)
Violation -11.07 (-1.46)
Media 0.001 (0.14)
Constant 4.40 (1.04)
Observations 28
R2 0.09
Adjusted R2 -0.07 Note: The t-statistics is in brackets * p<0.1; **p<0.05; ***p<0.01 The relative fine variable is the fine size divided by the firm’s market cap on the day of the event. The sector dummy takes 1 for firms in the energy sector and 0 for other sectors. The violation dummy takes 1 for Canadian Environmental Protection Act, 1999 (CEPA) and 0 for other violations. The social media variable is measured in number of interactions
61
Table 11 summarizes the results from equation 4 with the abnormal return from day 0 to
day 30 as the dependent variable (see Appendix I for full results). Neither the sector variable nor
the violation variable are statistically significant. The media variable is positive, which may
suggest that the media effect is not persistent. The relative fine variable is, while not statistically
significant, of the expected sign. The fact that the relative fine variable is negative in may
suggest that in the long run it is more important how large the fine is in relation to the firms’ size
than in the short run. This could be because for a small firm the fine would have larger
implications for their operations than it would for a larger firm.
5.3.1 Relative Fine Variable
As seen in the regression results above, the relative fine variable is positive using the
abnormal returns from day -1 to 0 as the dependent variable and negative using the abnormal
returns from day 0 to -1. This could suggest that the fines relative size is more important in the
long run. This could be the result of a large fine impacting the operations of a smaller firm more
in the long run than it would a larger firm. However, the relative fine variable is not statistically
significant for any of the regressions. This separates it from the fine variable that was statistically
significant at the 5% level of confidence using the abnormal returns from day -1 to 0 as the
dependent variable. This could suggest that the market cares more about the explicit cost that the
fine imposes rather than its size in relation to the firm.
One potential reason for this somewhat unexpected result may be that the public and the
market have an easier time comprehending an explicit fine. Everyone knows what a $1 million
fine, for example, implies and acts the same way regardless of the size of the firm that received
the fine. Furthermore, the public may not be aware of the size of the firm at the day of the event
62
and therefore not know the size of the relative fine. This could result in the fine variable being of
statistical significance while the relative fine variable is not.
Overall, the results from this study suggests that the market cares about the explicit cost
of the fine following an environmental violation, not the relative size of the fine to the firm. The
market does not place different judgement based on the violation that the firm was fined under or
the sector which it operates in. Social media did not appear to be of statistical significance,
however, it could be of economic significance with just a few thousand interactions resulting in a
quite significant loss of abnormal returns. There appears to be some inherent reputational penalty
from the market, however, it is outweighed by the positive effect of the fine variable using the
abnormal returns from day -1 to day 0. There are a number of potential reasons for this, which
have been discussed throughout this paper, including the market being relieved that the
uncertainty is gone, and with it the threat of a long drawn out and costly legal case. Furthermore,
the fine may have been smaller than the market expected and therefore the size of the fine comes
as positive news to shareholders. Lastly, for many of the events the market would have been
aware about the firm’s environmental violation for years, however, the uncertainty was related to
if and how the firm would be penalized. Therefore, the reputational penalty may already be
embedded in the stock prices of the violating firm.
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CHAPTER 6: CONCLUSION
This thesis has investigated the changes in public attitudes, i.e. the stock market reaction,
following the news of a firm violating an environmental regulation using data on 28 Canadian
events between 2010 and 2019. This thesis uses a standard-event study methodology with the
event in question being the release of the enforcement notification which includes the monetary
fine.
This thesis used three separate specifications to investigate the changes in public attitudes
following a firm being successfully fined for an environmental violation. Under each
specification, three regressions are used to see if the results are different over time. The results
show when using the specification with abnormal return as the dependent variable and the fine
size, the sector, and the violation as the independent variables then none of the variables are of
statistical significance for the regression using data from day -30 to -1. However, the fine
variable is positive and statistically significant when using the abnormal return from day -1 to 0
as the dependent variable. More specifically, it suggests that, holding everything else constant,
for every $1.1 million that the fine size increases by the abnormal return increases by 1
percentage point. There are at least three potential explanations for this including that the
uncertainty is removed, the fine is smaller than expected, or the threat of an expensive and
drawn-out lawsuit is removed. These results do not align with Lanoie and Laplante (1994) which
found negative abnormal returns on the day that the suit settlement was announced. Karpoff et al
(2005), on the other hand, found no negative results on the day that the settlement was
announced but they did find negative abnormal returns on the day the lawsuit was announced.
64
While their study was conducted in the United States, it does highlight a limitation in this thesis.
The event chosen for this thesis was, as mentioned before, the release of the enforcement
notification, however, as Karpoff et al (2005) highlights other results may have been found if the
announcement of the violation was used as the event rather than the date of the enforcement
notification release. The reason that the fine variable was of such significance and the
reputational punishment insignificant may be because the reputational effect is already
embedded in the stock prices. Further research should be done using the announcement of the
violation rather than the enforcement notification to determine whether this hypothesis holds true
in real life. The R2 also increase significantly between the regression using data from day -30 to
day -1 and the regression with the abnormal return from day -1 to day 0 as the dependent
variable. This suggests that little leakage has occurred before the event and the enforcement
notification comes as news to the market. For the regression using the abnormal returns from day
0 to day 30 as the dependent variable, none of the variables are of statistical significance.
When including social media in the specification, the variables remain statistically
insignificant for the regression using the abnormal return from day -30 to -1 as the dependent
variable. For the regression using the abnormal return between day -1 and day 0, the fine variable
is of statistical significance and very close in magnitude to the fine variable for the earlier
specification. More specifically, an increase in the fine size by $1 million will result in an
increase of 1 percentage point in the abnormal return, holding everything else constant. For the
regression using the abnormal returns from day 0 to day 30, none of the variables are of
statistical significance.
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The last specification includes the relative fine size (the size of the fine divided by the
size of the firm) instead of the size of the firm. When changing the explicit fine size for the
relative size of the fine in comparison to the firm, the variable is no longer significant. This
suggest that the market is more reactive to the explicit fine size than the relative size of the fine.
While the relative size was expected to be of more importance, the results show that this is not
the case. This could be because explicit costs are easier for the market to understand and
incorporate. For example, a fine of $1 million feels like a large fine and would be interpreted the
same regardless of the size of the firm. Furthermore, the market may not be aware of the size of
the firm when the enforcement notification is released and therefore may not know the relative
fine size. This could help explain the results found in this thesis.
Some other limitations of this study include the fact that the sample is rather limited.
When collecting the events for this study, certain restrictions of the events were implemented.
These included the firm having to be publicly traded in the Toronto Stock Exchange, the fine
size being equal to or larger than $100,000, and the enforcement notification being released
between 2010 and 2019. This resulted in 28 events from across Canada which is on the smaller
side of an event study. This small sample size could have resulted in some variables being
interpreted as insignificant while this was not the case. Another limitation of this study is that it
only uses four points in time (i.e. 30 days before the event, the day before the event, the day of
the event, and 30 days after the event) while Lanoie and Laplante (1994) uses data from every
day from 30 days before the event and 30 days after. Including more dates in the study would
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have made it easier to determine how persistent the impact of the fine was on the firms’ stock
prices following the event. The last major limitation was related to the social media variable.
Given the small sample size to begin with and the fact that Facebook only appear to be of
importance for spreading news in the last few years of the sample, it is hard to make any robust
conclusions from this data. It was hard to determine whether the social media variable was
insignificant due to the small sample size or because Facebook interactions does not impact a
firm’s abnormal return. Furthermore, this was to the best of my knowledge the first time that
social media was incorporated into an event study analyzing environmental violations.
Therefore, further research into the impact of social media on the changes in public attitudes
following an environmental violation is needed in order to determine whether Facebook
interactions affect a firm’s abnormal returns.
In sum, this thesis has started to provide insight into a gap in the reputational punishment
literature in Canada. This is to the best of my knowledge the first study done on environmental
violations since the 1990s and therefore provides more up to date data. Given the results, it does
not appear that even though Canadians are more environmentally concerned they punish the
firms harder than they did three decades ago. Furthermore, the positive fine variable indicates
that fines may be too low to adequately punish firms for violating environmental regulations and
it is therefore recommended that fines be increased to impose larger economic sanctions and
better reflect the social cost of the violation.
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Appendix A: Regression Results Day -30 to -1
See Table 3 for discussion of results
68
Appendix B: Regression Results Day -1 to 0
See Table 4 for discussion of results
69
Appendix C: Regression Results Day 0 to 30
See Table 5 for discussion of results
70
Appendix D: Regression Results Day -30 to -1 with Social Media
See Table 6 for discussion of results
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Appendix E: Regression Results Day -1 to 0 with Social Media
See Table 7 for discussion of results
72
Appendix F: Regression Results Day 0 to 30 with Social Media
See Table 8 for discussion of results
73
Appendix G: Regression Results Day -30 to -1 with Relative Fine
See Table 9 for discussion of results
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Appendix H: Regression Results Day -1 to 0 with Relative Fine
See Table 10 for discussion of results
75
Appendix I: Regression Results Day 0 to 30 with Relative Fine
See Table 11 for discussion of results
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Appendix J: Dataset information
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Appendix K: Changes in Stock Returns for Firms Day -30 to -1
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Appendix L: Changes in Stock Returns for Firms Day -1 to 0
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Appendix M: Changes in Stock Returns for Firms Day 0 to 30
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Appendix N: Changes in Indexes Day -30 to -1
81
Appendix O: Changes in Indexes -1 to 0
82
Appendix P: Changes in Indexes Day 0 to 30
83
Appendix Q: Social Media Data for Sample
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Appendix R: Abnormal Returns Day -30 to -1
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Appendix S: Abnormal Returns Day -1 to 0
86
Appendix T: Abnormal Returns Day 0 to 30
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Appendix U: Relative Fine Data
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Glossary AR - Abnormal Return
CAAA- Clean Air Act Amendments
CAPM- Capital Assets Pricing Model
CEPA- Canadian Environmental Protection Act, 1999 (CEPA)
ESG funds - Environmental, Social, and Governance funds
MBCA- Migratory Birds Convention Act, 1994
OLS- Ordinary Least Square
TRI- Toxics Release Inventory
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