Is CSR creating shareholder value? - AU...
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Is CSR creating shareholder value? An empirical examination of the effect of FTSE4Good Global Index membership Jesper Andersen Drescher, 300545 M.Sc. Finance & International Business Supervisor: Stefan Hirth, Department of Economics, Aarhus University Business and Social Sciences March 2015 Number of characters: 129,301
Transcript of Is CSR creating shareholder value? - AU...
Is CSR creating shareholder
value?
An empirical examination of the effect of
FTSE4Good Global Index membership
Jesper Andersen Drescher, 300545
M.Sc. Finance & International Business
Supervisor: Stefan Hirth,
Department of Economics,
Aarhus University Business and Social Sciences March 2015 Number of characters: 129,301
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Abstract
In an article published in 1970 Milton Friedman claimed that CSR is a misappropriation of shareholder
wealth, through which managers impose a tax on shareholders by diverting corporate earnings towards
causes at their own discretion (Friedman, 1970). The focus on CSR and the related concept of Socially Re-
sponsible Investing has, however, only increased with the years that have passed since Friedman’s original
article, and by the beginning of 2014 more than one in every sixth dollar under management in the US was
invested according to sustainable, responsible and impact investing practices (US SIF, 2014).
This thesis sets out to answer this apparent paradox through the following research question: “Do compa-
nies’ CSR initiatives create or diminish shareholder value?”. This is a question which has been the focal point
of several empirical studies throughout the last 40 years, however, while there seems to be a small over-
weight of studies in favor of a positive relationship no general consensus has yet been reached regarding
the answer to this question.
An empirical analysis of inclusions and exclusions from the FTSE4Good Global Index is used to answer the
research question, and two different methodologies are used for testing this data; the event study meth-
odology and the difference in difference methodology. The event study methodology has already been
used extensively in research on the research question, while the use of the difference in difference meth-
odology is quite novel. The use of the difference in difference methodology therefore contributes to the
current body of empirical research on this relationship.
The results of the empirical analysis are mixed. While the event study finds little evidence of any relation-
ship, the results of the difference in difference study indicate a small decrease in financial performance for
the included companies during the first year of inclusion into the FTSE4Good Global Index. This decrease is,
however, quickly reversed and there is some statistical evidence of the socially responsible companies out-
performing the control group in later years. These results supports the argument that while the costs of
CSR activities are often incurred in the short run, the rewards are first received in a more long-term per-
spective (Eccles & Serafeim, 2013).
In the end it is concluded that the relationship between shareholder value and CSR is perhaps too complex
to allow for any simple inferences to be made regarding the direction of the relationship. Proponents of
strategic CSR view CSR as a strategic concept, which is highly dependent upon a multitude of situational
contingencies (Carroll & Shabana, 2010). Therefore, as with every other area of strategy, CSR can be a
source of value creation if it is well planned, relevant and properly communicated to the relevant
stakeholders. If this is not the case, CSR just might end up as a waste of shareholders’ money as Friedman
(1970) suggests.
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Table of Contents
1. Introduction ....................................................................................................................................... 1
1.1. Problem statement ................................................................................................................................ 2
1.2. Delimitation ........................................................................................................................................... 3
2. Methodology and structure ................................................................................................................ 3
3. Theory ............................................................................................................................................... 5
3.1. CSR ......................................................................................................................................................... 5
3.1.1. Defining CSR .................................................................................................................................. 6
3.1.1.1. Stakeholder Theory ............................................................................................................... 7
3.1.1.2. Carroll’s Pyramid of CSR ........................................................................................................ 8
3.1.1.3. Triple Bottom Line ............................................................................................................... 11
3.1.1.4. This thesis’ understanding of CSR ....................................................................................... 11
3.1.2. Motivations for engaging in CSR activities .................................................................................. 14
3.1.2.1. Externally focused motivations ........................................................................................... 14
3.1.2.2. Internally focused motivations ............................................................................................ 15
3.1.3. The relationship between CSR and CFP ...................................................................................... 16
3.1.3.1. The use of the event study methodology ............................................................................ 17
3.1.3.2. The use of other methodologies .......................................................................................... 19
3.2. Socially Responsible Investing and SRI indices .................................................................................... 21
3.2.1. The FTSE4Good Index .................................................................................................................. 23
3.3. Formulation of hypotheses ................................................................................................................. 25
4. Data analysis .................................................................................................................................... 26
4.1. Data description .................................................................................................................................. 26
4.2. Event study .......................................................................................................................................... 27
4.2.1. Data and data treatment ............................................................................................................. 28
4.2.1.1. Confounding events............................................................................................................. 28
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4.2.1.2. Market model estimation .................................................................................................... 29
4.2.2. Descriptive statistics .................................................................................................................... 30
4.2.3. Test statistics ............................................................................................................................... 31
4.2.3.1. Parametric tests .................................................................................................................. 32
4.2.3.2. Non-parametric tests .......................................................................................................... 34
4.2.4. Results ......................................................................................................................................... 36
4.3. Difference in difference study ............................................................................................................. 37
4.3.1. Theory of the difference in difference methodology .................................................................. 38
4.3.2. Data and data treatment ............................................................................................................. 39
4.3.3. Matching process ........................................................................................................................ 40
4.3.3.1. Propensity score matching .................................................................................................. 41
4.3.3.2. Matching procedure ............................................................................................................ 43
4.3.3.3. Evaluation of matching procedure ...................................................................................... 44
4.3.3.4. Choice of performance indicators ....................................................................................... 46
4.3.4. Statistical tests ............................................................................................................................ 46
4.3.4.1. Revenue ............................................................................................................................... 48
4.3.4.2. Operating income ................................................................................................................ 49
4.3.4.3. Operating profit margin ...................................................................................................... 49
4.3.5. Conclusion to difference in difference results ............................................................................ 50
5. Discussion ........................................................................................................................................ 52
6. Conclusion ....................................................................................................................................... 53
6.1. Suggestions for further work ............................................................................................................... 54
7. Bibliography ..................................................................................................................................... 56
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List of figures
Figure 1: Structure of this thesis ....................................................................................................................... 4
Figure 2: The Pyramid of CSR ............................................................................................................................ 9
Figure 3: The Sustainable Value Matrix ........................................................................................................... 13
Figure 4: FTSE4Good assessment model ......................................................................................................... 24
Figure 5: Changes in mean revenue for treated companies and control group ............................................. 48
Figure 6: Changes in mean operating income for treated companies and control group .............................. 49
Figure 7: Changes in mean operating profit margin (%) for treated companies and control group .............. 49
List of tables
Table 1: Studies on the relationship between CSR and financial performance .............................................. 21
Table 2: Overview of event study data process .............................................................................................. 29
Table 3: Overview of inclusions and exclusions in final sample ...................................................................... 31
Table 4: Abnormal return and test statistics of incl. and excl. from the FTSE4Good Global Index ................ 36
Table 5: Depiction of selection process ........................................................................................................... 40
Table 6: Logit model specification and results ................................................................................................ 42
Table 7: mean values of key financial metrics ................................................................................................. 45
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Abbreviations
CAR – Cumulative abnormal return
CFP – Corporate financial performance
CSR – Corporate Social Responsibility
DJSI – Dow Jones Sustainability Index
EMH – Efficient Market Hypothesis
R&D – Research and development
ROA – Return on assets
SRI – Socially Responsible Investing
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1. Introduction
In 1970 Milton Friedman wrote an article for the New York Times Magazine, in which he philosophized
about the social responsibilities of companies. In the article Friedman states that:
“There is one and only one social responsibility of business—to use its resources and engage in activities
designed to increase its profits so long as it stays within the rules of the game, which is to say, engages in
open and free competition without deception or fraud.”
- Milton Friedman, 1970, p. 126
As this quote shows, Friedman did not feel highly about the call for businesses to commit resources to the
social ills of the world. In fact, according to Friedman (1970) Corporate Social Responsibility (henceforth
referred to as CSR) is nothing but a mere misappropriation of shareholder wealth, an agency problem
through which managers divert shareholders’ money towards causes at their own discretion in order to
further their own private agendas. Expenditures on CSR were seen by Friedman as managers imposing a tax
on shareholders and redirecting these funds towards social problems in an undemocratic manner. This also
implies that Friedman did not deny the very existence of social problems, however, in accordance with
Theodore Levitt (1958) he argued that it was the role of the state to address these issues, while business
were intended to focus on the single metric of maximizing shareholder value.
Since the publication of Friedman’s article, the focus on CSR, and its many related concepts such as Stake-
holder Theory, the Triple Bottom Line and corporate sustainability have, however, only increased. Laszlo
and Zhexembayeva (2011) identifies three driving trends behind this increased focus on CSR and sustaina-
bility, namely:
declining resources inflicted by the overexploitation of natural resources.
radical transparency caused by the emergence of countless NGOs as well as the development of
the internet and social media.
increasing expectations towards business processes and products from many different stakehold-
ers including customers, governments, NGOs and also internal stakeholders such as employees and,
not least, investors.
This thesis will specifically focus on the link between CSR and the investor, the owner of the company. As
already stated, Friedman (1970) views CSR expenditures as a misappropriation of corporate funds diminish-
ing the wealth of shareholders, never the less tremendous amounts of funds are today being managed in
so-called Socially Responsible Investment funds (henceforth referred to as SRI funds), and the latest num-
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bers from 2014 suggest that assets of close to $7 trillion are under management in funds using SRI strate-
gies in the US alone (US SIF, 2014), while the number for Europe is roughly €127 billion (Vigeo, 2014). The
number of European SRI funds has furthermore increased from 159 funds in 1999 to 957 funds by 2014
(Vigeo, 2014, p. 7), and the foremost reason for this steep rise in the number of offered SRI funds seems to
be investor demands for such products (US SIF, 2014). While it might be argued that some of these inves-
tors have chosen to invest in these funds due to ethical reasons, there also seems to be a widespread belief
in parts of the investor community that a high degree of CSR activities are not necessarily destroying share-
holder value, but may in fact create value for shareholders (US SIF, 2014).
The increased focus on SRI and the link between CSR and corporate financial performance (henceforth re-
ferred to as CFP) has also lead to a new field of academic studies aimed at uncovering the performance of
SRI investments in comparison to traditional investments, as well as the relationship between CSR and CFP
at the individual firm level. It is this latter question, the nature of this relationship between CSR and share-
holder value, which will be the focal point of this thesis. Is Friedman (1970) right and is CSR actually a mis-
appropriation of corporate funds, or can companies in fact do better in financial terms by doing good (Sen
& Bhattacharya, 2001)?
The question raised above will be sought answered through the use of data on inclusions and exclusions
from the FTSE4Good Global Index. Two different methodologies will be used for testing this data. These
methodologies are the event study methodology and the difference in difference methodology. The event
study methodology has already been used extensively in research on the relationship between CSR and
CFP, and has already previously been used by Clacher and Hagendorff (2012) to test inclusions from the
FTSE4Good Global Index. This thesis will build on this study by using a larger sample of inclusions, while also
considering the effects of exclusions from the index. The use of the difference in difference methodology in
connection to the relationship between CSR and CFP is, unlike the event study methodology, quite novel.
The use of this methodology will therefore contribute to the current body of empirical research on this
relationship by offering a new methodological approach.
The objective of this thesis is more formally described in the problem statement below.
1.1. Problem statement While it has previously been argued that CSR is a misappropriation of shareholder wealth (Friedman, 1970),
the last decades have shown a sharp increase in the amounts of capital invested in so-called SRI funds,
where possible investments are screened based on their activities in terms of both the impact of their core
business, as well as their CSR undertakings (US SIF, 2014; Vigeo, 2014).
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The purpose of this thesis will be to investigate whether economically founded considerations can explain
this development. More specifically this thesis will attempt to answer the following research question: “Do
companies’ CSR initiatives create or diminish shareholder value?”
1.2. Delimitation Suggestions have been made that a singular focus on shareholder value is not sufficiently comprehensive
when considering the impacts of CSR initiatives as it will fail to account for the positive effects on other
stakeholders such as customers, employees and the society in general (McWilliams, et al., 1999). This is a
common argument of normative CSR, which asserts that all stakeholders should be attended to because
their interests are of intrinsic value, and not due to their potential to further the financial interests of
shareholders (Boesso, et al., 2013).
From a cost benefit perspective this critique seems justified if one wishes to analyze the impact of CSR from
an overall societal perspective. This thesis will, however, purely focus on the economic impacts of CSR on
the individual firm level in a more traditional economic sense, and so it will not consider the effects of CSR
on these other stakeholders, but instead focus solely on the effect of CSR on shareholder value.
2. Methodology and structure
This thesis can generally be said to consist of two parts; a descriptive section which establishes the theoret-
ical foundation of the thesis, and an empirical section testing the hypotheses established in the descriptive
section.
Chapter 1, Introduction, has already set the stage by describing the topic of interest as well as the problem
statement, which this thesis will attempt to clarify. This chapter, chapter 2, is dedicated to a review of the
methodological approach of this thesis, while it will also describe the structure of the thesis.
Chapter 3, Theory, will elaborate the theoretical framework of this theses. This chapter contains the de-
scriptive section of this thesis and will describe such topics as CSR and SRI investments. The chapter is
based on a literature review of relevant reports and journal articles. The relevant literature has been col-
lected through an iterative process. An initial search was completed through which both theoretical and
empirical literature was uncovered. Based on the results of this search more relevant literature was uncov-
ered, and lastly the theoretical and empirical literature was complimented by relevant methodological lit-
erature to aid the empirical work of this thesis. This approach is recommended by Uwe Flick (2009).
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Chapter 4 presents the empirical work of this thesis. Data on inclusions and exclusions from the FTSE4Good
Global Index is used to conduct both an event study as well as a difference in difference study on the im-
pact of CSR on financial performance. The methodology regarding both the event study and the difference
in difference study is described in more detail in chapter 4.
The findings of chapter 4 are summarized and evaluated in chapter 5 and chapter 6. Chapter 5 contains a
discussion of the results in which the results of chapter 4 are compared to the findings of the literature
review in chapter 3. Chapter 6 the conclusion to the work of this thesis, while it also discusses possible di-
rections for future research.
The structure of the assignment is visualized in the figure below. A couple of indicative key words regard-
ing the content of each chapter is also depicted in the figure.
Figure 1: Structure of this thesis
•Introduction
•Problem statement 1. Introduction
•Methodology
•Structure
2. Methodology and structure
•CSR
•Socially Responsible Investing and SRI indexes
•Formulation of working hypotheses 3. Theory
•Data description
•Event study
•Difference in difference study 4. Data analysis
•Discussion of results
•CSR as strategy 5. Discussion
•Conclusion
•Suggestions for further work 6. Conclusion
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3. Theory
This chapter presents the theoretical frame for this thesis and is essentially consisting of two parts:
The majority of the chapter is dedicated to an uncovering of the concept of CSR, possible motiva-
tions for companies to engage in such activities and, finally, the empirical evidence of a relationship
between CSR and financial performance.
The second section of this chapter is concerned with the topic of socially responsible investing and
SRI indexes. This section uncovers the current extent of this type of investments as well as the prac-
tices currently used to guide such investments. Finally, this section will also provide a review of the
FTSE4Good Global Index, the index used for the empirical work of chapter 0.
3.1. CSR The emphasis on CSR has never been more widespread than it is today. A survey conducted by KPMG
(2008) estimated, that approximately three-quarters of the global fortune 250 companies had defined cor-
porate sustainability strategies in place by 2008 - a number which is likely to have only increased since
then. The investment community is also highly influenced by the emergence of CSR, and the amount of
assets under management in SRI funds exceeded more than one of every six dollars under management in
the US by the beginning of 2014 (US SIF, 2014). CSR is in fact so widespread these days, that the question
no longer seems to be “whether” companies should commit resources to corporate sustainability but ra-
ther “how” (Robinson, et al., 2011).
However, while CSR is becoming more and more important for companies, there is still much confusion as
to the exact meaning of the term (Dahlsrud, 2006). This confusion is well depicted by the very different
understandings of the term across the Atlantic. In Europe CSR often focuses on proactive policies regarding
the environment and human resources, while CSR in the US is mainly concerned with control of negative
issues such as tobacco, alcohol and gambling and more often concerned with social activities aimed to-
wards the local community (Lopez, et al., 2007).
This section is dedicated to shedding light on the term CSR and related concepts such as the Triple Bottom
Line (Elkington, 1997) and Stakeholder Theory (Freeman, 1984), while also looking into the theoretical mo-
tivations for companies to engage in CSR as well as the emergence of SRI. This chapter will then be con-
cluded by a review of the existing empirical evidence of a relationship between CSR and CFP, which is also
the focal point of this thesis.
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3.1.1. Defining CSR
There are about as many definitions of CSR as there are papers concerning the subject. This section seeks
to provide an overview of these definitions, their differences and, finally, to establish the definition which
this thesis will rely upon.
According to Curran and Moran (2007), the meaning of the term CSR is that:
“Companies are responsible for their social and environmental impacts and should seek to manage and
monitor those impacts accordingly”
- Curran and Moran, 2007, p. 529
This view is supported by Van de Velde et al. (2005), which describes socially responsible companies as
companies, who are putting the social and environmental interests of other stakeholders on par with the
economic interests of shareholders. This focus on social issues and the inclusion of multiple stakeholders in
the decision process is also acknowledged by Ioannou and Serafeim (2014) as traits of CSR.
A more methodological approach to defining CSR is offered by Dahlsrud (2006), who has conducted a litera-
ture review on a large number of definitions of CSR. Dahlsrud (2006) dates the first formal definition of CSR
back to Bowen (1953), however, many definitions has followed since this first definition, and Dahlsrud
analyses a total of 37 different definitions in his article. The analysis of these many definitions identifies
five different dimensions, which figure consistently throughout the definitions, and which together are said
to constitute the concept of CSR. These five dimensions are as follows:
The environmental dimension
The social dimension
The economic dimension
The stakeholder dimension
The voluntariness dimension
The three next sections will be devoted to a review of three different theories and frameworks all related
to the concept of CSR and the 5 dimensions identified by Dahlsrud (2006). These three theories and frame-
works are the Stakeholder Theory (Freeman, 1984), The Pyramid of CSR (Carroll, 1979; Carroll, 1991) and
the Triple Bottom Line framework (Elkington, 1997). These three theories and frameworks have been cho-
sen because they are all central to the understanding of CSR (Carroll & Buchholz, 2014).
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3.1.1.1. Stakeholder Theory
Stakeholder Theory first rose to prominence when R. Edward Freeman published his book “Strategic
Management: A Stakeholder Approach” in 1984. The main argument of Freeman’s Stakeholder Theory is
that traditional management theories, focused on efficiency and effectiveness in bringing products and
services to market, are outdated because they do not take the environment into account – all the external
stakeholders that have an influence on the economic success of the corporation (Freeman, 1984).
According to Freeman the initial paradigm under which businesses operated was focused around trans-
forming inputs into outputs. These first businesses were small family-owned business where management
and ownership were largely overlapping. This paradigm of the business is coined as The Production View of
the Firm (Freeman, 1984). As a result of the industrial revolution businesses grew, the number of employ-
ees increased and management and ownership grew more separated. This new environment with more
internal stakeholders in the form of multiple owners and several employees, forced managers to replace
the existing paradigm with a new understanding of the world in order to succeed. This new paradigm cen-
tered around efficiency to keep the small number of stakeholders satisfied, and Freeman (1984) named this
new paradigm The Managerial View of the Firm.
The argument of Freeman is, that new changes have happened to the business environment, which has
deemed the managerial view of the firm just as obsolete as the production view of the firm. These changes
have affected the traditional internal stakeholders. Both employees and especially customers are increasing
their expectations towards the conduct of not only the company but also of its suppliers, while also owners
have begun to show interest in other aspects of the business than its earnings. The fundamental change,
however, comes from the emergence of a whole new set of stakeholders, the external stakeholders. This
group of stakeholders includes governments, competitors, NGOs, the media and any other individual or
constituency “that contributes, either voluntarily or involuntarily, to its wealth-creating capacity and activi-
ties, and are therefore its potential beneficiaries and/or risk bearers." (Post, et al., 2002, p. 19). Companies
neglecting to acknowledge this new reality, will be irresponsive to these new pressures and are therefore
unlikely to succeed in the market place (Freeman, 1984).
As an alternative to the failing managerial view of the firm, Freeman instead proposes the stakeholder ap-
proach as the new emerging paradigm. Stakeholder Theory and the stakeholder framework offers an ap-
proach and a philosophy for dealing with all these new stakeholders by channeling company resources and
time towards stakeholder dialogue and the formulation of targeted stakeholder strategies. According to
Jones (1995), embracing this new framework is not only beneficial to corporate financial performance, the
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satisfaction of multiple different groups of stakeholders is instrumental for the financial performance of the
company.
The Stakeholder Theory has, however, also received criticism. According to Jensen (2002), Stakeholder
Theory implies that managers should: “make decisions so as to take account of the interests of all the
stakeholders in a firm” (Jensen, 2002, p. 236). However rational this might seem, it is inferior to traditional
value maximization because it does not define one single metric to optimize, which means it is unsuitable
as a guide for rational behaviour (Jensen, 2002). According to Jensen the two concepts are, however, not
entirely at odds, as he acknowledges that the value of the firm is highly dependent on several
constituencies such as the employees, regulators, suppliers and not least customers, and that long term
value-maximization cannot be achieved if any of these important stakeholders are mistreated. This criticism
is therefore not so much directed at the normative content of the stakeholder theory, but more the lack of
a metric by which to measure its effectiveness (Jensen, 2002).
In regards to the concept of CSR and the five dimensions identified by Dahlsrud (2006), it is obvious that the
Stakeholder Theory has had a clear influence on the stakeholder dimension of Dahlsruds framework.
3.1.1.2. Carroll’s Pyramid of CSR
One of the most influential frameworks for the definition of CSR is presented in Carroll’s Pyramid of CSR.
The concept of the four-part pyramid of CSR was first publicized in Carroll’s article “The Pyramid of Corpo-
rate Social Responsibility: Toward the Moral Management of Organizational Stakeholders” (1991), howev-
er, the four elements of the pyramid were already mentioned in an article dating back more than a decade
from this time (Carroll, 1979).
In his initial article, Carroll (1979) bases his definition of CSR on a review of existing views on CSR of that
time, and articulates a definition of CSR constituted of four domains: the economic responsibilities, the
legal responsibilities, the ethical responsibilities and the discretionary responsibilities.
These four domains also appear in the 1991 article, although the latter, the discretionary responsibilities, is
renamed as the philanthropic responsibilities. It is from these four domains that the Pyramid of CSR is con-
structed. A visual representation of Carroll’s pyramid is shown below in Figure 2.
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Figure 2: The Pyramid of CSR
Source: Carroll, 1991, p. 42
Economic responsibilities:
The foundation of Carroll’s model is constituted by the economic responsibilities of the firm. The main ob-
jective of businesses is to supply the goods and services demanded by society, and their ability to remain
profitable indicates their ability to satisfy this basic responsibility. Profitability is also a requirement for
staying in business in the long run, and therefore also a precursor for providing job opportunities for work-
ers, as well as a precondition for contributing to society through any of the three other domains in Carroll’s
model (Carroll, 1991). The recognition of the economic responsibilities as the basic social responsibility of
businesses is furthermore an acknowledgement of the view presented by Friedman (1970), namely that
companies have a social responsibility towards their shareholders.
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Legal responsibilities:
The next domain of Carroll’s model consists of the legal responsibilities of the business. Carroll’s argument
for the legal responsibilities of businesses is grounded in the concept of a so-called “social contract” exist-
ing between the greater society and the business (Carroll, 1979). The “social contract” sets out the rules to
which the company must adhere in exchange for the right to undertake its productive role in the economic
system. These laws are for example anti-competitive laws, anti-bribery laws and laws regarding the issu-
ance of truthful financial statements, and while it might seem a truism that companies adhere to these
rules, several examples can be given of companies failing to satisfy these requirements.
Ethical responsibilities:
The ethical responsibilities of the company describes those norms to which society expects the company to
adhere, although no legislative restrains have been put in place. The complexity of the modern world, es-
pecially for large multinational corporations, entails that the difference between this domain and the legal
domain is often blurred. Carroll acknowledges this, and he also refers to the legal responsibilities as “codi-
fied ethics” (Carroll, 1991), just as one might refer to the ethical responsibilities as “un-codified legislation”.
Critics of CSR might argue, that companies choosing to conform to these ethical expectations are in fact
voluntarily limiting their available actions to a larger extent that what is required by law. Often ethical con-
siderations could, however, be seen as a forerunner of future legislation (Carroll, 1991), which has been the
case both in terms of environmental legislation and not least the abolishment of child labor in most parts of
the world. This dynamic relationship between the different domains of Carroll’s model, is one of the rea-
sons why CSR has been heralded as “enlightened self-interest” (Kramer & Porter, 2006), because socially
responsible companies can preempt new legislation and get a head start on competitors, which in turn
could grant them a competitive advantage.
Philanthropic responsibilities:
The last domain of Carroll’s model is the philanthropic responsibilities of the firm. These responsibilities
include such actions as contributions to charitable causes, and it might be contested whether this domain is
actually a responsibility, since companies are not perceived as unethical if they do not adhere to these “re-
sponsibilities” (Carroll, 1979). For many individuals it is, however, this last domain which, together with the
ethical responsibilities, have become synonymous with CSR. The perception of CSR as encompassing only
the latter two domains of Carroll’s model has also been acknowledge by Carroll himself (Carroll & Shabana,
2010). While the economic and legal responsibilities have become required to the point where they are
taken for granted, the ethical responsibilities are expected, and the philanthropic responsibilities are mere-
ly desired by society (Carroll & Shabana, 2010).
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In relation to Dahlsrud’s (2006) framework, the Pyramid of CSR has especially contributed by acknowledg-
ing the economic responsibilities of businesses as part of the social responsibilities of these.
3.1.1.3. Triple Bottom Line
The influences of the Triple Bottom Line are seen more clearly in Dahlsrud’s five dimensions, than what is
the case for the other two theories treated in this chapter, and these links will also become apparent from
the description of the Triple Bottom Line framework as presented below.
The Triple Bottom Line framework originates from John Elkington’s book “Cannibals with Forks - the Triple
Bottom Line of 21st Century Business” published in 1997, however, the Triple Bottom Line is based on the
three P’s concept articulated by Elkington already in 1994 (The Economist, 2009).
The 3 P’s stand for People, Planet and Profit. Elkington (1997) argues that the social responsibilities of busi-
nesses are towards these three constituents. The people dimension concerns both the employment prac-
tices of the business as well as impacts the firms' practices have on the community in which the company
operates. In relation to Dahlsrud’s framework, this dimension is closely related to what Dahlsrud coins “The
Social Dimension”. The planet relates to the company’s environmental performance; its use of scarce re-
sources as well as other externalities the company might be inflicting on the planet. This dimension is of
course closely related to Dahlsrud’s environmental dimension. The last dimension of Elkington’s framework
is the profit dimension. This dimension is also present in Carroll’s Pyramid of CSR, signifying that both of
these frameworks acknowledges profitability as a social responsibility of businesses.
The primary claim of Elkington’s Triple Bottom Line framework is that while profitability is certainly a re-
sponsibility of any business, modern businesses pay undue focus to this latter dimension, the profit dimen-
sion, on expense of the other two dimensions. This narrow focus provides managers with an incentive to
sacrifice environmental and social performance in order to boost the financial bottom line in the short run.
This type of thinking is, however, detrimental to shareholder wealth in the long term.
3.1.1.4. This thesis’ understanding of CSR
As the reviews presented above show, CSR is a broad and complex term with several dimensions. The
above descriptions of the theories and frameworks of Dahlsrud (2006), Freeman (1984), Carroll (1979;
1991) and Elkington (1997), do however also highlight, that many commonalities exists between these dif-
ferent concepts, and it should be duly noted, that many of the concepts introduced in this chapter have
developed concurrently. The obvious overlaps between the different concepts bears witness to this fact,
and the different concepts should rather be seen as complimentary than being seen as being at odds with
each other.
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Several conclusions regarding the nature of CSR stands out from the above literature review. Some of the
most prominent of these conclusions are:
CSR is related to the interaction between the company and society at large. When the interests of
all different stakeholders is being included in corporate decision making, then the business is
showing social responsibility (Freeman, 1984).
While the fundamental responsibility of businesses is to remain profitable by producing goods
demanded by society (Carroll, 1979; Carroll, 1991), CSR encompasses more than the financial per-
spective. CSR is also related to both the social bottom line of the firm, the people dimension, as
well as the environmental bottom line, the planet dimension (Elkington, 1997). A truly responsible
company should target positive returns on each of these three dimensions, and at the very least
try to manage its impacts in all three aspects.
Lastly, for a company to be considered socially responsible, it should be both profitable while also
adhering to the rules set out by legislation (Carroll, 1979; Carroll, 1991). CSR is, however, more
than just delivering on these two parameters. If a company wishes to be considered truly respon-
sible, it needs to go beyond these basic requirements and show ethical conduct on a voluntary ba-
sis, by doing what is right and not just what is obligatory (Dahlsrud, 2006).
The definition of CSR for the purpose of this thesis is generally in accordance with the points presented
above. The importance of the voluntariness dimension should, however, be stressed. While it will not be
contested that the responsibilities contained within the economic and legal domains of the Pyramid of CSR
are prerequisites for being considered a responsible company, most companies do adhere to these princi-
ples. The voluntariness dimension entails that firms should show commitment beyond the regulatory re-
quirements, and for companies to truly be considered responsible, and eligible for SRI indexes such as the
FTSE4Good, they need to show such extended commitment. This emphasis on the voluntariness dimension
is also in agreement with the definitions of CSR as presented by McWilliams and Siegel (2001) and
McWilliams et al. (2006). In these papers CSR is defined as “..situations where the firm goes beyond compli-
ance and engages in actions that appear to further some social good, beyond the interests of the firm and
that which is required by law” (McWilliams, et al., 2006, p. 1). No one has, meanwhile put it better than
Davis (1973). According to him: “social responsibility begins where the law ends. A firm is not being socially
responsible if it merely complies with the minimum requirements of the law, because this is what any good
citizen would do. A profit maximizing firm under the rules of classical economics would do as much. Social
responsibility goes one step further. It is a firm's acceptance of a social obligation beyond the requirements
of the law.” (Davis, 1973, p. 313).
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More generally, this thesis’ understanding of CSR is congruent with The Commission of the European
Communities, which defines CSR as: “A concept whereby companies integrate social and environmental
concerns in their business operations and in their interaction with their stakeholders on a voluntary basis”
(Commission of the European Communities, 2001).
A final dimension of CSR which is of the utmost importance in relation to this thesis, and which therefore
needs to be discussed, concerns the possibility of creating shareholder value through CSR. In the view of
Milton Friedman (1970) CSR is defined as situations where management diverts corporate resources to-
wards objectives at their own discretion, at the expense of the shareholders. As a result of this, Friedman
does not view CSR initiatives which creates shareholder value, what is known as strategic CSR (Baron,
2001), as actual CSR. Such activities causing pareto improvements are instead viewed by Friedman as part
of the normal business activities, which ultimately makes his statement, that CSR will always destroy share-
holder value, a self-fulfilling prophecy.
Unlike Friedman (1970) this thesis does not agree with this contention. This type of zero sum-thinking ex-
cludes any sort of business case for CSR. This thesis is instead in agreement with the view presented by
McWilliams and Siegel (2001), in which they encourage that CSR initiatives are measured through cost-
benefit analysis, which will secure a maximization of shareholder value and help avoid initiatives which
destroy shareholder value. Only in these cases where both society and the company are both better off
does CSR become truly responsible, and it is only in these cases that CSR does not come to odds with the
fundamental economic responsibilities of businesses as formulated by Carroll (1979).
Figure 3: The Sustainable Value Matrix
Source: Laszlo (2003, p. 151)
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A good depiction of this sort of outcomes is presented in Figure 3 above, which is taken from the book “The
Sustainable Company: How to Create Lasting Value through Social and Environmental Performance” (Laszlo,
2003). In terms of this matrix the really interesting initiatives are found in the upper right corner where
both shareholder and stakeholder value increases.
3.1.2. Motivations for engaging in CSR activities
While the previous section set out to clarify a definition of CSR, the purpose of this section is to shed light
on what motivations the academic literature has uncovered to justify CSR. Many arguments have been
presented throughout the last few decades, and some of these are built on the opinion, that companies
ought to divert resources towards the social issues of society simply because it is the moral thing to do. This
argument is known as the “moral obligation” (Kramer & Porter, 2006). Since this thesis tries to establish the
relationship between CSR and CFP, this section will not be focused on this sort of reasoning, but will instead
exclusively consider arguments, which are grounded in traditional economic thinking. That is: only argu-
ments for CSR, which are based on financial grounds will be entertained in this chapter.
The motivations for companies to devote resources to CSR are many, and many of these have their roots in
other theories, such as the generic strategies of Porter (1980) and the Resource Based View of the Firm
(Wernerfelt, 1984). In order to give a structure to the presentation of these many different motivations,
they will be arranged according to whether they are internally or externally focused. Internally focused
motivations concentrate on benefits accruing to internal stakeholders of the firm, and are often closely
related to the Resource Based View of the Firm (Wernerfelt, 1984). Externally focused motivations consider
how companies can leverage CSR in relation to external stakeholders such as consumers. These motivations
are often closely associated with which the generic strategies of Porter (1980). The distinction between
these two types of motivations is also made in Orlitzky et al. (2003).
3.1.2.1. Externally focused motivations
The most prominent externally oriented argument in favor of CSR is focused on the resulting reputational
effects (Fombrun & Shanley, 1990). In their paper from 1990, Fombrun and Shanley study the determinants
of the reputations of 292 of the largest U.S companies, and finds a significant positive relationship between
corporate contributions to charities and charitable funds and corporate reputation. Such reputational ef-
fects of CSR are argued to enable companies to charge premium prices for their products (Milgrom &
Roberts, 1986), while it could also have the potential to increase the brand loyalty of consumers
(Bhattacharya & Sen, 2004). In this way the reputational effects of CSR are closely related to the generic
strategy of differentiation (Porter, 1980). The reputational effects are, however, also shown to have the
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ability to affect several other types of stakeholders besides customers. Reputations can for example enable
companies to attract the most talented members of the workforce (Stigler, 1962), as well as enhance the
ability to gain external funding from capital markets (Beatty & Ritter, 1986; Cheng et al., 2014). Of great
importance to this thesis, it has also been shown that the reputational effects of CSR has the potential to
increase the attractiveness of the firm towards the investor base (Fombrun & Shanley, 1990). One mediator
of this link between CSR and the investor community are so-called SRI indexes such as the FTSE4Good Index
or the Dow Jones Sustainability Index (Henceforth referred to as DJSI). By being included into such indexes
companies can reach a broader investor base, since several SRI funds are not conducting their own screen-
ings, but are instead relying upon the screening process of these aforementioned SRI indexes to guide their
investment decisions (Robinson, et al., 2011).
Closely related to the reputational argument for CSR is the license-to-operate argument. This motivation is
directed more towards companies in industries with strenuous stakeholder relationships (Kramer & Porter,
2006). In such industries CSR is not so much leveraged as a source of differentiation, but instead a means to
gain acceptance of other harmful activities (Carroll & Shabana, 2010).
A final external motivation for firms to adopt CSR practices relates to stakeholder management. This moti-
vation is based on the idea that self-discipline and active dialogue can alleviate possible future threats to
the viability of the company. Examples of such threats may by the passing of restricting legislation (Carroll
& Shabana, 2010), or costly consumer boycotts (Moskowitz, 1972). Jones (1995) developed a model, which
concluded that companies conducting business with stakeholders based on trust and strong ethics,
achieved a competitive advantage through lasting relationships with these stakeholders.
3.1.2.2. Internally focused motivations
Besides these abovementioned externally oriented motivations, the academic literature has also uncovered
several internally driven motivations for companies to engage in CSR. A literature on this topic has identi-
fied three general branches of this research: cost reductions (Berman, et al., 1999; Carroll & Shabana,
2010), better risk management (Waddock & Graves, 1997) and finally increased innovativeness and adapt-
ability (Hart, 1995; Orlitzky, et al., 2003).
According to Berman et al. (1999, p. 489) “being proactive on environmental issues can lower the costs of
complying with present and future environmental regulations”, and “enhance firm efficiencies”. This view is
supported by Carroll and Shabana (2010), who finds that CSR can help to build competitive advantage
through a cost leadership strategy. Such cost savings might be achieved through more efficient uses of
inputs, or substitution of some inputs towards less strained resources.
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The risk management argument is closely related both to the argument of better stakeholder management,
as well as the claim of CSR leading to cost reductions. According to Waddock and Graves (1997) CSR activi-
ties may decrease the variability of future cash flows, because the likelihood of adverse effects such as con-
sumer boycotts or legal disputes is reduced, while possible unforeseen costs arising from new legislation or
shortages of scarce inputs are less likely to occur.
The last argument; increased innovativeness and adaptability is grounded in the Resource Based View of
the Firm (Wernerfelt, 1984). According to this argument, investments in CSR activities may lead to the de-
velopment of new resources and capabilities, especially when the firm’s environment is dynamic (Hart,
1995). Increased awareness through stakeholder management may also lead to better adaptability, since
managers will be more adapt at scanning and processing external changes (Orlitzky, et al., 2003)
As it was also the case in terms of the definitions of CSR in chapter 3.1.1, the individual motivations are to
some extent overlapping, which is especially the case for the internally-driven motivations. The categoriza-
tion presented in this chapter does, however, help by providing a structured view on the array of different
motivations discussed in the academic literature.
The next section will look beyond the theoretical arguments for investments into CSR, and will instead be
dedicated to a review of the existing empirical evidence of an actual relationship between CSR and CFP.
3.1.3. The relationship between CSR and CFP
Research into the relationship between CSR and CFP has been undertaken ever since the 1970’s, and for all
these years the discussion relating to this relationship has focused on one central tenet, namely whether
CSR creates or diminishes shareholder value.
The first research into the relationship was carried out by Moskowitz (1972). In his study Moskowitz named
14 companies as good investment opportunities based on the claim that each of these companies excelled
in terms of their social awareness. He then calculated the appreciation of these companies’ stocks during
the first half of 1972, and after finding an average appreciation of 7.28 percent for these companies, which
was larger than the appreciation of any of the major stock indices in the same period, Moskowitz concluded
that CSR was creating value without, however, conducting any form of risk adjustment of his results.
In his study from 1975 Vance provided the first empirical evidence for the opposing view, that socially re-
sponsible firms suffers under a competitive disadvantage in relation to firms that do not attend to the so-
cial needs of society. Basing his study on the rankings of 45 leading companies rated in accordance to their
perceived social responsibility by businessmen and students, Vance found a negative correlation between
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the perceived degree of social responsibility and financial performance. However, just as it was the case
with Moskowitz’s paper (1972), no adjustment was conducted to account for differences in market risk.
Alexander and Buchholz (1978) were the first to conduct an examination of the subject on a risk adjusted
basis. By using the same sample as Vance (1975), and correcting the monthly return for each stock for the 5
year period from 1970 unto 1974 for market risk through the Capital Asset Pricing Model, limited evidence
was found for a relationship between stock price and the social responsibility rankings (Alexander &
Buchholz, 1978). Alexander and Buchholz (1978) did, however, argue themselves that under the assump-
tions of market efficiency, any abnormal gain or loss accruing from social responsibility, would be reflected
in the share price shortly after the information was available to stock markets. If this is indeed the case, the
methodology used would therefore not be able to detect any potential gain or loss.
Since these three studies numerous others have followed. Different methodologies have been used as well
as more advanced statistical approaches, however, no consensus has yet been reached as to the actual
relationship between CSR and financial performance. The proponents of a negative relationship claim that
companies investing in CSR are at a competitive disadvantage since they incur costs that more traditional
companies are not facing (Aupperle, et al., 1985). In opposition to these arguments are the proponents of a
positive relationship between CSR and financial performance. The arguments behind a positive relationship
are many, and several are mentioned in chapter 3.1.2 above.
The research on the relationship between CSR and financial performance since these first early papers can
generally be divided into two groups: research based on the use of event studies and research based on
other statistical approaches such as examinations of accounting numbers or returns on investment portfo-
lios. The body of research seems to be approximately evenly distributed between these two strands.
3.1.3.1. The use of the event study methodology
The most prominently used research methodology for investigating the relationship between CSR and fi-
nancial performance is the event study methodology. This methodology examines short run changes in
stock price following some unanticipated event. Because CSR is such an abstract subject, the identification
of relevant events signaling a heightened focus on CSR are hard to come across. Some of the first event
studies on the subject used divestments from South Africa during the Apartheid as the event indicator.
(Posnikoff, 1997; Teoh et al., 1999; Wright & Ferris, 1997). These studies found mixed results, with Posni-
koff (1997) finding a positive result, Wright & Ferris (1997) finding a negative result and Teoh et al. (1999)
finding no significant relationship. The appropriateness of using divestments from South Africa as the event
indicator might, however, be up for discussion. One of the fundamental requirements for using an event in
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an event study, is that no confounding events are taken place, and the divestment from South Africa is ar-
guably a signal of more than just a company’s commitment to CSR, as it also signals the withdrawal from a
large market. The use of this specific event might therefore lead to a negative bias on the results.
Later event studies have primarily relied on another type of event indicator; inclusions and exclusions from
SRI indexes such as the FTSE4Good Index, the DJSI and the Calvert Social Index. Just as it was the case for
the first event studies, the results of these have been mixed.
Through an examination of inclusions into the DJSI from 2003 until 2007, Robinson et al. (2011) were able
to find evidence of a significantly positive reaction to stock prices. In a similar study of both inclusions and
exclusions from the DJSI from 2002 until 2008 no robust significant results were found for neither inclu-
sions nor exclusions (Wai & Cheung, 2011). Similar mixed results were also found by Doh et al. (2010), who
considered inclusions and exclusions from the Calvert Social Index. The results here indicated no significant
positive reaction following inclusions, while exclusions where found to be followed by significant negative
stock price reactions. Doh et al. (2010) argued that these findings were due to companies publicizing infor-
mation prior to inclusions, which diluted the market response, while not doing the same prior to exclusions.
Of particular interest to this thesis are the two studies by Curran and Moran (2007) and Clacher and Ha-
gendorff (2012), since both of these studies are based on the FTSE4Good Index series. Curran and Moran
(2007) considers both inclusions and exclusions from the FTSE4Good UK Index, finding partially significant
results following inclusions while not finding any significant reaction following exclusions. The Study by
Clacher and Hagendorff (2012) examines effects following inclusions to the FTSE4Good Global Index, which
is the same index used for the empirical work of this thesis. The article by Clacher and Hagendorff does,
however, only use data concerning inclusions from 2001 until 2008, which is less than the range used for
this thesis which uses data from 2001 until 2013. Furthermore the effect of exclusions is not considered.
The results of Clacher and Hagendorff (2012) indicate a significant positive reaction following inclusions,
although the relationship is only detected for the announcement date, and not when longer event windows
are considered.
It can be argued that this second type of event indicators used in latter event studies might also be subject
to a bias, since the event indicator also indicates a listing of the company in question. This possibility has,
however, been examined by Robinson et al. (2011) who found no significant relationship between the ef-
fect of being included into the DJSI and whether the company being included was already part of a major
stock index such as the S&P 500. This serves to show, that the positive relationship is not only a listing ef-
fect (Robinson, et al., 2011).
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3.1.3.2. The use of other methodologies
As already pointed out earlier, the studies on the relationship between CSR and financial performance,
which has not relied on the use of an event study methodology, have generally either relied on the use of
accounting numbers or the performance of investment portfolios.
An example of the latter is presented in an article by Van de Velde et al. (2005). By constructing four differ-
ent portfolios based on corporate social responsibility scores from the CSR rating agency Vigeo, the re-
searchers found that high sustainability-rated portfolios outperformed low sustainability-rated portfolios
on a style-adjusted basis. While positive the results were, however, not statistically significant. Similar re-
sults are found by Consolandi et al. (2009).
One of the first studies based on accounting numbers was done by Bowman and Haire (1975). After ranking
82 firms into three groups ranging from low to high based on the number of lines devoted to CSR in their
annual reports, an inverse u-shaped relationship was uncovered, in which the companies with medium
ratings showed the highest profitability, while the companies with a low rating showed the poorest per-
formance. The study did, however, suffer from several methodological shortcomings, such as the use of
return-on-equity to gauge profitability even though this measure is influenced by the company’s capital
structure.
Other studies relying on the use of accounting numbers include Cochran and Wood (1984), Aupperle et al.
(1985) and Waddock and Graves (1997). Cochran and Wood (1984) found only marginally significant evi-
dence for a positive relationship between corporate social responsibility and the market-to-book value of
equity ratio, while Aupperle et al. (1985) were not able to find any significant evidence of a relationship
between CSR and firm profitability as measured by the risk adjusted return-on-assets. The last-mentioned
article by Waddock and Graves (1997) uses KLD data from 1990 for S&P 500 companies and return on as-
sets from these same companies in 1989 and 1991, and finds support of a reciprocal relationship between
CSR and financial performance; KLD ratings for 1990 showed a significant positive correlation with the re-
turn on assets (Henceforth referred to as ROA) from 1989, while the ROA from 1991 showed a significant
positive relationship with KLD ratings from 1990.
Another approach was taken by Ioannou and Serafeim. Based on the assumption that recommendations
from stock analysts have a significantly positive influence on stock prices, Ioannou and Serafeim (2014)
investigated how stock analysts’ recommendations of companies depends on CSR strengths and concerns
as measured by KLD ratings over time. The results indicated that from 1997 and onwards a significantly
positive relationship emerged.
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Of specific interest towards the second part of the empirical work of this thesis, the difference in difference
study, is the paper of Lopez et al. (2007). Using a sample of 110 companies, 55 companies included in the
DJSI and 55 similar, matched companies which were not included, they were able to find significant reduc-
tions in the profitability of companies included in the DJSI. These changes did, however, not apply to
measures of revenue, and the changes were therefore considered to stem from the increased costs related
to comply with the standards for inclusion into the DJSI. Furthermore, they found that the changes in prof-
itability seemed to disappear over time, which begs the question as to whether the relationship reverses
(Lopez, et al., 2007). These findings highlight the fact, that while the costs of CSR activities are often in-
curred in the short run, the rewards are first received in a more long-term perspective (Eccles & Serafeim,
2013). This idea adds support to the use of an event study methodology, since changes in stock price theo-
retically should reflect changes in all future cash flows, and not just cash flows in the short-term.
The results of the previous studies on the relationship between CSR and financial performance are summa-
rized in table 1.
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Table 1: Studies on the relationship between CSR and financial performance Author(s) Methodology CSR event / action Results
Posnikoff (1997) Event study Divesture from South Africa
Positive stock price reaction following divestment
Wright & Ferris (1997)
Event study Divesture from South Africa
Negative stock price reaction following divestment
Teoh et al. (1999) Event study Divesture from South Africa
Neutral stock price reaction following divestment
Robinson et al. (2011)
Event study Incl./excl. from SRI in-dex
Positive stock price reaction following inclusion
Wai & Cheung (2011)
Event study Incl./excl. from SRI in-dex
Neutral stock price reaction following both incl. and excl.
Clacher & Ha-gendorff (2012)
Event study Incl./excl. from SRI in-dex
Positive stock price reaction following inclusion
Curran & Moran (2007)
Event study Incl./excl. from SRI in-dex
Small positive reaction following incl./ neutral reaction to excl.
Doh et al. (2010) Event study Incl./excl. from SRI in-dex
Neutral reaction following incl./ negative reaction to excl.
Van de Velde et al. (2005)
Regression analysis
Performance of stock portfolio
Positive relationship between returns and CSR
Consolandi et al. (2009)
Regression analysis
Performance of stock portfolio
Neutral relationship between returns and CSR
Bowman & Haire (1975)
Regression analysis
Content analysis of an-nual reports
Inverse U-shaped relationship between CSR and return on equity
Cochran & Wood (1984)
Regression analysis
Content analysis of an-nual reports
Small positive relationship between CSR and market-to-book value of equity.
Aupperle et al. (1985)
Regression analysis
Social orientation of company CEO
Neutral relationship between CSR and risk adjusted return on assets
Waddock & Graves (1997)
Regression analysis
KLD data Positive reciprocal relationship between CSR and return on assets
Ioannou and Serafeim (2014)
Regression analysis
Analysts’ recommenda-tions / KLD data
Positive relationship between CSR and analysts’ recommendations
Lopez et al. (2007)
Difference-in-difference
Incl./excl. from SRI in-dex
Reductions in profitability following in-clusion into the DJSI
As the above table show, there seems to be a small overweight of results indicating a positive relationship
between CSR and CFP. This is supported by the findings of a meta-analysis conducted by Orlitzky et al.
(2003), which also finds some evidence of a positive relationship between the two concepts.
3.2. Socially Responsible Investing and SRI indices As it has already been stated in the introduction, tremendous amounts of funds are today being managed
in so-called SRI funds, with the latest numbers suggesting that assets of close to $7 trillion are under man-
agement in funds using SRI strategies in the US alone (US SIF, 2014), while the number for Europe is roughly
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€127 billion across 957 different funds (Vigeo, 2014). This section will examine the concept of SRI, its histo-
ry and how it is applied in reality.
According to a report issued by Mercer Investment Consulting (2007) Socially Responsible Investing, or just
SRI, is defined as: “an investment process that seeks to achieve social and environmental objectives along-
side financial objectives” (Mercer, 2007, p. 10). This definition is in line with Van de Velde et al. (2005),
which defines socially responsible investment as an investment practice which includes considerations re-
garding social, environmental or ethical conditions in investment decisions through the application of both
negative and positive screens (Van de Velde, et al., 2005). It is evident that these definitions are closely
influenced by the Triple Bottom Line of Elkington (1997).
The inclusion of these types of considerations in investment decisions has been around for more than 40
years. According to Fowler and Hope (2007), the first SRI mutual fund is considered to have been the PAX
World Fund. This fund was first launched in 1971 and incorporated social responsibility considerations by
conducting a negative screen of military-related stocks.
SRI indices are, however, a much more recent phenomenon, with the KLD Domini 400 Social Index
launched as the first SRI index in 1990 (Guerard, 1997). Since the launch of the KLD Domini 400 Social Index
things have, however, developed quickly, and today both global and regional SRI indices are being offered
from not only KLD Analytics but also Calvert Group, Vigeo, Dow Jones and not least FTSE, which administers
the FTSE4Good Global Index used in this thesis (Fowler & Hope, 2007). The driver behind this large increase
in the number of SRI indices is seemingly the increasing number of funds managed by SRI mutual funds,
which again is driven by a demand for such investments (Fowler & Hope, 2007).
The selection process regarding what companies to include in SRI indices can generally be distinguished by
whether a positive or negative screening process is applied. While negative screening processes relies on
exclusion of companies due to an inability to live up to certain minimum requirements, such as not being
active in certain industries, a positive screening process is based on choosing only companies performing
extraordinary on some key parameters. Since a positive screening process is much more difficult and time
consuming to apply, the most basic approach applied in most SRI indices is a negative screen. The use of
negative screening is used as the primary approach for SRI indices administered by Calvert Group and KLD,
while also featuring heavily in the FTSE4Good index series (Fowler & Hope, 2007).
Acknowledging that most small SRI mutual funds are unable to apply positive screening processes due to
financial and time constraints, have although led several SRI indices, who offers licensing to fund managers,
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to incorporate some elements of positive screening in their inclusion criteria in order to attract licensees
(Fowler & Hope, 2007).
3.2.1. The FTSE4Good Index
The FTSE4Good index series was first launched in July 2001, when the constituents of the four tradable
indexes: the FTSE4Good UK 50 Index, FTSE4Good Europe 50 Index, FTSE4Good US 100 Index and the
FTSE4Good Global 100 Index, as well as the constituents for two of the four benchmark indexes: the
FTSE4Good UK Index and the FTSE4Good Europe Index, were announced (FTSE, 2011). This launch was
quickly followed with the launch of further two benchmark indexes, the FTSE4Good Global Index and the
FTSE4Good US Index, in November 2001 (Sustainability Investment News, 2001). Since then one more trad-
able index, the FTSE4Good Japan Index, has been added to the index family (FTSE, 2014d).
Three main objectives guided the initial establishment of the indexes. First, a wish to provide investors with
information regarding the social responsibility practices of companies based on a wide range of objective
benchmarks. Second, to establish benchmarks for SRI mutual funds. And lastly, to promote further CSR
engagements amongst companies. (Clacher & Hagendorff, 2012).
The selection process for the constituents of the different indexes in the FTSE4Good Index Series is based
on a combination of both negative and positive screening approaches. In order to be considered for any of
the indexes, the company in question needs to be included in the relevant constituent universe index. For
the FTSE4Good Global Index this universe index is the FTSE All-World Developed Index (FTSE, 2014d). Be-
sides this requirement a further negative screen is applied, which prohibits the inclusion of companies
manufacturing tobacco, weapon systems or smaller components for controversial weapons such as cluster
munitions, chemical weapons or nuclear weapons (FTSE, 2014d).
Firms, who at this point have not been excluded by the negative screens, are then subjected to a positive
screening process, the FTSE ESG Ratings Model. This model is built around performance on three pillars;
environment, social and governance. These three pillars are further divided into 14 different themes and all
in all the model contains more than 300 individual indicators upon which each company is rated (FTSE,
2014c). The FTSE ESG Ratings model is depicted in Figure 4 below.
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Figure 4: FTSE4Good assessment model
Source: FTSE (2014c, p. 4)
In order to secure a credible and transparent evaluation process all criteria are based purely on publicly
available information, while all ratings are conducted under the oversight of an independent committee
consisting of experts from the investment community, companies, NGOs, unions and academia (FTSE,
2014c). Based on the more than 300 individual indicators a final score is then given to each company on a
scale from 0 to 5. Companies, who are not already part of the FTSE4Good Index, and who receive a rating
of 3.5 or more are added to the index, while already included companies are excluded if they fail to
achieve a score of at least 2.5 for 12 consecutive months (FTSE, 2014c).
Historically all inclusions and exclusions have been taking place semi-annually in March and September,
however, this has recently been changed to June and December (FTSE, 2014b). Until the 8th of September
2014 1,317 inclusions had occurred , while only 405 exclusions had taken place.
The appropriateness of using inclusions and exclusions from the FTSE4Good Global Index as an event
indicator is discussed in section 4.2 below.
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3.3. Formulation of hypotheses Based on the evidence from chapter 3.1.3 it seems that there is some evidence to support a positive rela-
tionship between CSR and CFP, which is supported by the findings of the meta-analysis by Orlitzky et al.
(2003).
Most of the empirical research reviewed for this thesis is generated through the use of an event study
methodology, and the findings from this research also supports the existence of a positive relationship be-
tween CSR and financial performance. While one of these studies finds a negative relationship (Wright &
Ferris, 1997), the remainder of the studies find support of either a neutral relationship (Teoh, et al., 1999;
Doh, et al., 2010; Wai & Cheung, 2011) or a positive relationship (Posnikoff, 1997; Curran & Moran, 2007;
Robinson, et al., 2011; Clacher & Hagendorff, 2012).
The largest part of these event studies rely on inclusions or exclusions from SRI indexes, such as the
FTSE4Good Global Index, as event indicators, and one study actually also uses this index and finds a positive
reaction following inclusions (Clacher & Hagendorff, 2012). Based on this previous research the following
hypotheses are formed:
Hypothesis 1: Inclusion into the FTSE4Good Global Index will lead to a significant increase in
the stock price.
Hypothesis 2: exclusion from the FTSE4Good Global Index will lead to a significant decrease
in the stock price.
The implication of the first two hypotheses is a positive relationship between CSR and financial
performance. Assuming that the first two hypotheses are correct, socially responsible companies should
therefore, ceteris paribus, outperform unresponsible companies:
Hypothesis 3: Companies included in the FTSE4Good Global Index are, ceteris paribus,
outperforming companies, which have not been included in the index.
Hypothesis 3 have already been tested in Lopez et al. (2007). This study found that companies included in
the DJSI are being outperformed by a portfolio of matched companies, which is contradicting hypothesis 3.
All three hypotheses will be tested empirically in chapter 0 below.
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4. Data analysis
This chapter contains the empirical work of this thesis, and will focus on testing the hypotheses presented
in section 3.3 above. As it has been shown in section 3.1.3 quite a lot of studies have already been conduct-
ed on the relationship between CSR and financial performance. One of the most applied methodologies for
quantifying this relationship is the event study methodology, and it is therefore also obvious to employ this
methodology in this thesis, because it allows a direct comparison of the results.
The use of the event study methodology is, however, dependent upon the fulfillment of some quite strin-
gent assumptions, which will be further elaborated upon in section 4.2. If these assumptions do not hold,
the event study methodology cannot be expected to be able to observe the actual impact of CSR on finan-
cial performance.
In order to accommodate this possible short-coming of the event study methodology, the empirical work of
this thesis will also contain an analysis based on a difference in difference methodology. The use of this
methodology to measure the impact of CSR on financial performance is quite new, and a literature search
only uncovered few studies using this methodology to analyze the relationship between CSR and CFP
(Lopez, et al., 2007; Shen & Chang, 2009; Chang & Shen, 2014). The further details of the difference in
difference methodology will be explained in section 4.3.
4.1. Data description As it was described in section 3.1.1, CSR is a rather ambiguous subject, and Cochran and Wood (1984) also
highlights the fundamental issue of assessing the relationship between corporate social responsibility and
financial performance – namely the difficulty of finding a satisfactory proxy for companies’ CSR perfor-
mance.
One possible proxy, which has been used extensively in newer event studies on the subject, is offered by
the SRI indexes as described in chapter 3.2. These indexes rely on the use of negative and positive screens
to identify the most responsible companies, which are then included in the index.
The data foundation of this thesis is based on such an index, the FTSE4Good Global Index, and the universe
index of this SRI index, the FTSE All-World Developed Index (FTSE, 2014d). Constituent lists of these index-
es stemming from the 8th of November 2001, the day of the announcement of the constituents of the
FTSE4Good Global Index, have been provided by FTSE (See appendix 14).
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All relevant data for both the event study, as well as for the difference in difference study, such as stock
prices and accounting figures, have afterwards been extracted through the use of Datastream Worldscope.
The data foundation can be found in appendix 11, 12 and 13, and will also be further elaborated upon in
chapter 4.2 and 4.3 where relevant.
4.2. Event study The event study methodology was presented for the first time in a 1969 journal article from the Interna-
tional Economic Review (Fama, et al., 1969). Since then the methodology has been used to investigate the
impact of numerous different events on stock prices.
From the review of studies on the relationship between CSR and CFP presented in section 3.1.3 it is evident
that the event study has also been used extensively for this subject, especially in recent years. According to
Curran and Moran (2007), the use of the event study methodology relies on three assumptions:
- That markets are efficient,
- That the event conveys new information to the market, which is material, and which has not al-
ready anticipated by the financial markets, and lastly;
- That there is no confounding events that has an effect on the stock price reaction.
This first assumption is closely related to the Efficient Market Hypothesis (Henceforth referred to as EMH),
which states that “successive price changes are independent” (Fama, 1965, p. 89). According to Fama (1965,
p. 90): “..a situation where successive price changes are independent is consistent with the existence of an
"efficient" market for securities, that is, a market where, given the available information, actual prices at
every point in time represent very good estimates of intrinsic values”. Under the assumption that the EMH
holds, at least in its semi-strong form, the announcement of any significant news regarding expectations of
the future cash flows of the company in question, should therefore quickly be reflected in the share price
(MacKinley, 1997).
The second assumption, that the event conveys new information and has not already been anticipated by
the financial markets, is examined by Clacher and Hagendorff (2012). Clacher and Hagendorff (2012) find a
significant increase in the trading volume of stocks being included into the FTSE4Good Global Index on the
day of announcement. This indicates both that the announcement of inclusions into the index contains new
information which is material, and that this information has not prematurely been anticipated by financial
markets.
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The last assumption, as highlighted by Curran and Moran (2007), is that no confounding events, that has an
effect on the stock price, are taking place during the event window. This assumption is verified on a case by
case basis. The method for doing so is described in more detail in section 4.2.1.1.
Besides these three abovementioned assumptions McWilliams et al. (1999) points towards another meth-
odological issue concerning the event study methodology; the determination of an appropriate length of
the event window.
The event window chosen in this paper spans from two days prior to the announcement until two days
after. Other event studies on the relationship between CSR and financial performance have used longer
event windows, with several applying event windows of more than 15 days (Wai & Cheung, 2011;Clacher &
Hagendorff, 2012). The use of a relatively short event window is, however, supported by both McWilliams
et al. (1999) and Bartholdy et al. (2007), with McWilliams et al. (1999, p. 352) stressing the fact that as the
event window is expanded, the probability of confounding events increases raising the amount of noise
relative to information, thereby reducing the power of the applied test statistics.
The event study methodology is further elaborated upon in the remaining parts of chapter 4.2.
4.2.1. Data and data treatment
The data used for the event study consists of return index data1 of all companies ever included or excluded
from the FTSE4Good Global Index. As already mentioned, this data was extracted using Datastream. After
the initial extraction, the sample was cleaned for events which were not suitable for inclusion into the final
sample. Examples of such events are exclusions due to removal from the overall index universe of the
FTSE4Good Global Index, rather than due to an inability to satisfy the requirements for inclusion as de-
scribed in section 3.2.1. After this initial removal, all companies with lacking data regarding stock returns
were also excluded from the sample. As it is evident from Table 2, this leads to the exclusion of a large part
of the sample. The main part of these exclusions were due to Datastream not being able to identify the
SODOL-codes of the companies, while only a small share were due to incomplete data on stock returns.
4.2.1.1. Confounding events
In order to test for the occurrence of any confounding events all inclusions and exclusions, from the first
announcement of the initial constituents of the FTSE4Good Global Index until the latest announcement of
inclusions and exclusions was released on the 8th of September 2014, were reviewed. The review process
1 The return index shows the theoretical return of a common stock, assuming all dividends are re-invested in addition-
al stocks of the same company’ equity at the closing price on the ex-dividend date.
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was conducted using the LexisNexis Group database, which was also employed by Curran and Moran
(2007). This database contains newspaper articles and newswire stories and for each inclusion or exclusion
all news relating to said company was considered for a period of one week prior until one week after the
announcement. The impacts of the sample selection process on the sample size is shown in Table 2 below.
Table 2: Overview of event study data process
Event type All inclusions and ex-
clusions
After removal of un-suitable inclusions and
exclusions
All inclusions and ex-clusions for which Datastream return
indexes where availa-ble
After removal of inclu-sions and exclusions
with concurring events
Inclusions 1,317 1,262 615 594
Exclusions 405 265 247 227
4.2.1.2. Market model estimation
After reviewing the data, the daily return was calculated for each stock remaining in the sample, as well as
the return of the chosen reference index. These returns were calculated in the same manner as was done
by Curran and Moran (2007): 𝑅𝑗𝑡 = 𝐿𝑛(𝑃𝑗𝑡 𝑃𝑗𝑡−1)⁄ , where Rjt denotes the return of stock j at time t and Pjt
denotes the price of stock j at time t.
The calculated daily returns of each stock were then adjusted to attain a measure of the excess return. The
three most widespread methods for estimating the excess returns are the mean adjusted returns, where
the returns are adjusted by their own average return during the estimation period, the market adjusted
returns, where returns are adjusted according to the returns of a general market portfolio, and the market
model adjusted returns, where the returns are adjusted based on an estimation of the alpha and beta of
the stock in relation to a broad market portfolio (Brown & Warner, 1985). This study estimates the excess
returns based on the latter approach, the market model adjusted returns. This approach has generally been
preferred in most newer event studies (Bartholdy et al., 2007; Boehmer et al., 1991; Clacher and
Hagendorff, 2012), and is furthermore shown to be the mean adjusted model superior in cases of event
clustering, which is the case for this study (Brown & Warner, 1985, p. 15).
The natural choice for the reference index to use for the market model estimation would have been the
FTSE All World Developed Index, as this acts as the index universe from which the constituents of the
FTSE4Good Global Index is chosen. This was, however, not possible since Datastream Worldscope con-
tained no return index data for the FTSE All World Developed Index, instead the FTSE World Index was em-
ployed.
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This is not deemed to constitute any real issue, as the characteristics and constituents of the two indexes
are fairly similar in 2014. The factsheets of the two indexes reveal a similar number of constituents, average
and median market capitalization as well as an identical composition of the 10 largest constituents by mar-
ket capitalization (FTSE, 2014a; FTSE, 2014b).
As already mentioned, this thesis employs the market model estimation to estimate excess returns which is
suggested by Brown and Warner (1980, 1985). The market model estimation uses 250 trade days of returns
ending at respectively 9 and 10 days prior to the announcement for inclusions and exclusions, and is based
on the CAPM-model meaning that the returns of the individual stock was regressed upon an intercept, the
alpha, and the market returns: 𝐸(𝑅𝑗𝑡) = 𝛼𝑗 + 𝛽𝑗𝑅𝑚𝑡 + 𝜀𝑗𝑡.
The abnormal returns were then calculated using the model: 𝐴𝑅𝑗𝑡 = 𝑅𝑗𝑡 − 𝐸(𝑅𝑗𝑡)
4.2.2. Descriptive statistics
After calculating the excess returns, both the sample consisting of the exclusions as well as the sample of
inclusions were tested for normality. This was done using a Jarque-Bera test, and the test rejected the as-
sumption of normality for both sample during both the entire estimation and event window as well as dur-
ing only the event window. Plots of the distributions did, however, indicate an approximately normal dis-
tribution although all four plots displayed leptokurtosis with a very high concentration of excess returns
around the mean. The calculations of the Jarque-Bera tests as well as the plots of the distributions can be
found in appendix 1. The means to account for the results of these tests are described in chapter 4.2.3.
In table 3 below an overview is given of the distribution of inclusions and exclusions in the final sample
according to the announcement dates. As the table shows, most of the inclusions of the final sample are
centered around the launch date of the FTSE4Good Global Index.
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Table 3: Overview of inclusions and exclusions in final sample
Inclusions Exclusions
Dates N Percent N Percent
8 November 2001 225 37,88% 0 0,00%
17 September 2002 21 3,54% 4 1,76%
19 March 2003 19 3,20% 2 0,88%
18 September 2003 32 5,39% 14 6,17%
12 March 2004 32 5,39% 23 10,13%
10 September 2004 30 5,05% 7 3,08%
10 March 2005 26 4,38% 18 7,93%
07 September 2005 16 2,69% 16 7,05%
08 March 2006 13 2,19% 14 6,17%
07 March 2007 4 0,67% 12 5,29%
12 September 2007 10 1,68% 26 11,45%
13 March 2008 15 2,53% 14 6,17%
11 September 2008 10 1,68% 10 4,41%
11 March 2009 5 0,84% 15 6,61%
09 September 2009 14 2,36% 14 6,17%
10 March 2010 9 1,52% 5 2,20%
09 September 2010 5 0,84% 5 2,20%
10 March 2011 37 6,23% 4 1,76%
08 September 2011 10 1,68% 4 1,76%
08 March 2012 9 1,52% 11 4,85%
13 September 2012 10 1,68% 6 2,64%
07 March 2013 7 1,18% 3 1,32%
12 September 2013 15 2,53% 0 0,00%
08 September 2014 20 3,37% 0 0,00%
Total 594 100% 227 100%
4.2.3. Test statistics
In order to test the abnormal returns around the event of inclusion or exclusion from the FTSE4Good Global
index, several different test statistics are employed. The use of a multitude of statistics is traditional in the
sphere of event studies, and is also recommended by Bartholdy et al. (2007), because no single test statistic
dominates the other in terms of type 1- or type 2-errors, and because the individual test statistics have
differing strengths.
In this study five different test statistics are used. Three of these statistics are parametric tests, relying on
assumptions regarding the distribution of abnormal returns, while the remaining two are non-parametric
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statistics, which allows for making inferences in cases where it is questionable whether the assumptions of
parametric tests are in satisfied. The five test statistics are:
T1 – The cross-sectional t-statistic(Brown & Warner, 1980; Brown & Warner, 1985).
T2 – The ordinary cross-sectional method (Boehmer, et al., 1991).
T3 – The standardized cross-sectional method (Boehmer, et al., 1991).
T4 – The sign test (Corrado & Zivney, 1992).
T5 – The rank test (Corrado, 1989; Corrado & Zivney, 1992).
In the chapter below each of the five test statistics are elaborated on.
4.2.3.1. Parametric tests
Parametric tests are based on standard t tests of the difference between two means (Bartholdy, et al.,
2007), and are as already mentioned based on assumptions regarding the distribution of the excess returns.
The three parametric test statistics are in essence only two, where the latter is performed twice using a
standardization of the abnormal returns as the only difference between the two statistics.
The first of these statistics is the cross-sectional t-statistic (Brown & Warner, 1980; Brown & Warner, 1985),
which will henceforth be referred to as T1. It is calculated in the following way for any specific day:
𝑇1 = 𝐴��
𝜎��
≈ 𝑡, (𝑇 − 1)
Where 𝐴𝑡 = 1
𝑛𝑡⁄ ∑ 𝐴𝑖𝑡
𝑛𝑡𝑖=1
and
𝜎�� = √1
𝑇∑(𝐴�� − 𝐴 )
2
𝑇
𝑡=1
The statistic for assessing the significance of the Cumulative Abnormal Returns (henceforth referred to as
CAR) is formulated as follows:
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𝑇1𝐶𝐴𝑅 =∑ 𝐴𝑡
1𝑡=−1
√3 𝜎��2
≈ 𝑡, (𝑇 − 1) 2
In relation to the two other parametric statistics, the main difference relates to the estimation of the
standard deviation, σ. For T1 the standard deviation is estimated based on the variance during the estima-
tion period, while T2 and T3 rely on a standard deviation estimated from within the event window. The use
of both types of standard deviation estimation ensures robustness of the results even in the case of event-
induced variance (Boehmer, et al., 1991).
The last two of the parametric statistics, T2 and T3, are both found in Boehmer et al. (1991). T2 is known as
the ordinary cross-sectional method, while T3 is a hybrid version of T2 and Patell’s (1976) standardized re-
sidual method named the standardized cross-sectional method, which applies Patell’s method for standard-
izing the excess returns of T2.
T2 is calculated as:
𝑇2 = 𝐴��
𝜎��
≈ 𝑁(0,1)
where
𝐴𝑡 =
1
𝑛𝑡∑ 𝐴𝑖𝑡
𝑛𝑡𝑖=1
and
𝜎�� = √1𝑁(𝑁 − 1)⁄ ∑ (𝐴𝑖𝑡 − 1
𝑛𝑡⁄ ∑ 𝐴𝑖𝑡
𝑛𝑡
𝑖=1 )2𝑁
𝑖=1
As already mentioned, the difference between T2 and T1 lies in the estimation of the standard deviation.
2 For the three day event window (-1;1) spanning from the day prior to the event until the day following the event
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T3 is very similar to T2 and is calculated as:
𝑇3 =
1
𝑛𝑡
∑ 𝐴𝑖𝑡𝐴𝑠𝑛𝑡
𝑖=1
√1
𝑁(𝑁−1)
∑ (𝐴𝑖𝑡
𝐴𝑠−
1
𝑛𝑡
∑ 𝐴𝑖𝑡𝐴𝑠𝑛𝑡
𝑖=1)
2𝑁
𝑖=1
≈ 𝑁(0,1)
Where the standardized excess returns, AAS, are estimated using:
𝐴𝑖𝑡𝐴𝑠 =
𝐴𝑖𝑡
𝑆𝐴(𝐴𝑖𝑡)
where
𝑆𝐴(𝐴𝑖𝑡) = √1
𝑇𝑖−1∑ 𝐴𝑖𝑡
2𝑇𝑖𝑡=1 (1 +
1
𝑇𝑖+
(𝑅𝑚,0−��𝑚)2
∑ (𝑅𝑚,𝑡−��𝑚)2𝑇𝑖𝑡
)
These tests have previously been employed in similar event studies on membership in SRI-indexes. T1 was
employed by Wai & Cheung (2011) in an analysis of inclusions and exclusions from the DJSI, whereas T3
was employed by Clacher &Hagendorff (2012) on a study of the FTSE4Good World Index.
While the use of the market model estimation of excess returns is shown by Brown and Warner (1985) to
be quite robust in the presence of event clustering, T3 is furthermore shown by Boehmer et al. (1991) to be
specifically suited in such cases.
4.2.3.2. Non-parametric tests
In order to test the robustness of the results stemming from the parametric tests, as well as to accommo-
date the results of the Jarque-Bera tests for normality mentioned in section 4.2.2, non-parametric tests are
also employed.
Two such statistics are used, the sign test (Corrado & Zivney, 1992) and the rank test (Corrado, 1989;
Corrado & Zivney, 1992), henceforth referred to as T4 and T5.
T4, the sign test, is based on the assumption that the probability of observing either a negative or a positive
return is 0.5. The method for conducting T4 is as follows:
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For each excess return calculate the sign, Git, by subtracting the median excess return from the time series
of stock i’s excess returns:
𝐺𝑖𝑡 = 𝑠𝑖𝑔𝑛(𝐴𝑖𝑡 − 𝑚𝑒𝑑𝑖𝑎𝑛(𝐴𝑖))
The values of Git are then transformed into either +1, -1 or 0 depending on whether the value of Git is posi-
tive, negative or equal to 0. The test statistic for T4 is:
𝑇4 = (1 √𝑁⁄ ) ∑ 𝐺𝑖0
𝑁𝑗=1
𝑆(𝐺)
Where
𝑆(𝐺) = √1
262∑ (
1
√𝑁∑ 𝐺𝑖𝑡
𝑁𝑗=1 )22
𝑡=−259
For the sample of inclusions which consists of a total of 262 days of return data3. The test statistic for the
three day CAR is:
𝑇4𝐶𝐴𝑅 = ∑(1 √𝑁⁄ ) ∑ 𝐺𝑖0
𝑁𝑗=1
√3𝑆(𝐺)
1𝑡=−1
The rank test, T5, relies on the ordinal rank of the excess returns during the event window relative to the
excess returns during the entire sample period, in order to test for any significant excess returns during the
event window (Bartholdy, et al., 2007). T5 is performed as follows:
Rank the excess returns of each stock during both estimation and event window letting Kit denote the rank.
𝐾𝑖𝑡 = 𝑟𝑎𝑛𝑘(𝐴𝑖𝑡)
Each rank is then standardized using the formula:
𝑈𝑖𝑡 = 𝐾𝑖𝑡
(1+𝑇𝑖)
Where Ti is the number of excess returns during the entire period. The test statistic for T5 is:
𝑇5 = (1 √𝑁⁄ ) ∑ (𝑈𝑖𝑡−(1 2⁄ ))𝑁
𝑗=1
𝑆(𝐾)
3 For the sample of exclusions the time series consists of 263 returns rather than 262.
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Where
𝑆(𝐾) = √1
262∑ (
1
√𝑁∑ (𝑈𝑖𝑡 − 1 2⁄𝑁
𝑖=1 ))22𝑡=−259
For the sample of inclusions which consists of a total of 262 days of return data 4. The test statistic for the
three day CAR is:
𝑇5𝐶𝐴𝑅 = ∑(1 √𝑁⁄ ) ∑ (𝑈𝑗𝑡−(1 2⁄ ))𝑁
𝑗=1
√3𝑆(𝐾)1𝑡=−1
4.2.4. Results
The results of the five different test statistics are provided in Table 4 below. Individual test statistics have
been calculated for each day during the window (-1;1), as well as for the CAR(-1;1) and CAR(-2;2).
Table 4: Abnormal return and test statistics of incl. and excl. from the FTSE4Good Global Index
Inclusions
Day/test AR T1 T2 T3 T4 T5
0 0.00 0.23 0.42 0.04 -0.66 -0.50
-1 0.00 -0.16 -0.33 -0.02 -0.23 -0.07
1 0.00 0.85 1.48 0.32 0.38 0.16
(-1;1) 0.00 0.53
-0.30 -0.24
(-2;2) 0.00 -0.62 -1.01 -1.25
Exclusions
Day/test AR T1 T2 T3 T4 T5
0 0.00 -0.82 -1.04 -1.36 -0.97 -1.11
-1 0.00 -0.66 -0.88 -0.80 -0.86 -0.98
1 0.00 0.15 0.19 -0.26 0.29 0.21
(-1;1) 0.00 -0.77
-0.89 -1.09
(-2;2) 0.00 -0.66 -0.59 -0.70 ***, **, * refers to statistical significance of 1 %, 5 % and 10 % respectively.
As Table 4 above shows, very little evidence is found indicating significant results following neither inclu-
sions nor exclusions. The signs of the parametric tests, T1 to T3, for both inclusions and exclusions seem to
be in agreement with hypothesis 1 and 2 on the announcement date, although none of the statistics are
significant even at the 10 % significance level. The two remaining test statistics, the non-parametric T4 and
T5, find negative results on the announcement date for both inclusions and exclusions, however, none of
these results are statistically significant.
4 For the sample of exclusions the time series consists of 263 returns rather than 262.
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For the day prior to the announcement date all five statistics find negative results for both inclusions and
exclusions, while nine out of ten test statistics are positive for the day following the announcement. While
this results certainly seem to constitute a pattern, none of the results are even remotely statistically signifi-
cant.
The test statistics for the cumulative abnormal returns for the period spanning one day prior to the an-
nouncement date until one day after, and for the period spanning two days prior to the announcement
date until two days after, are negative for almost all statistics for both inclusions and exclusions, although
neither these results are statistically significant.
In general it must be concluded that the results of the event study find little support of neither hypothesis 1
nor hypothesis 2. Instead, the results seem to emulate those of Wai and Cheung (2011) who find a neutral
reaction following both inclusions and exclusions from the DJSI, and the results of Doh et al. (2010) who
find a neutral reaction following inclusion to the Calvert Social Index and a weak negative reaction following
exclusions.
4.3. Difference in difference study The difference in difference study is conducted to test the robustness of the event study. While the event
study looks at the immediate response from the stock market following the announcement of inclusion or
exclusion from the FTSE4Good Global Index, the difference in difference methodology investigates the de-
velopment in accounting numbers for the companies included in the index. Besides serving as a robustness
check, the use of a difference in difference methodology also contributes to this thesis by applying a rather
novel approach to investigating the relationship between CSR and CFP.
The purpose of the difference in difference methodology is to uncover the effect of a treatment on the
treated by comparing a group of treated individuals or companies with a matched group of non-treated
individuals or companies, which have similar characteristics as the treated group (Keilbach, 2005). The
methodology has its roots in the medical and biological fields, but has recently been used widely in eco-
nomics and finance, where it has been applied to many different topics (Shen & Chang, 2009). The usage of
the difference in difference methodology in respect to the relationship between CSR and CFP is, however,
rather limited, and a database search yielded only three prior journal articles on the subject, Shen and
Chang (2009), Chang and Shen (2014) and Lopez et al. (2007). The first two of these studies both find a
positive relationship between the two concepts, albeit the generalizability of these studies is limited due to
the fact that they are both focused exclusively on Taiwanese companies. The latter study has already been
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mentioned in chapter 3.1.3 and finds a negative performance of companies included in the DJSI compared
to a group of matched companies.
In the following sections a more in-depth description of the difference in difference methodology will be
given, as well as a description of the results achieved through the application of this methodology to the
data on the FTSE4Good Global Index.
4.3.1. Theory of the difference in difference methodology
As already mentioned, the purpose of the difference in difference methodology is to uncover the effect of a
treatment on the treated (Keilbach, 2005). In the case of this thesis the treatment considered, is inclusion
into the FTSE4Good Global Index.
The actual objective of the difference in difference methodology, to uncover the treatment effect, can be
described as:
𝐸(𝑌𝑖,1|𝐷 = 1) − 𝐸(𝑌𝑖,0|𝐷 = 1) = ∆|𝐷=1
Where D is a dummy equaling 1 if the company is subject of treatment. In the above formula 𝐸(𝑌𝑖,1|𝐷 = 1)
denotes the performance of company i following the treatment, while 𝐸(𝑌𝑖,0|𝐷 = 1) denotes the perfor-
mance of the same treated company i, just without being subject to the treatment in question (Shen &
Chang, 2009). This equation can, of course, not be estimated in reality since the second term 𝐸(𝑌𝑖,0|𝐷 = 1)
is unobservable; once the company has been subjected to the treatment, the performance of the company
without the treatment will never be realized. Because the second term cannot be observed in reality, this
term is also known as the counterfactual, and the paradox of the equation is known as the fundamental
evaluation problem (Keilbach, 2005; Caliendo & Kopeinig, 2008).
The straightforward solution to the fundamental evaluation problem is to substitute 𝐸(𝑌𝑖,0|𝐷 = 1), the
counterfactual, with 𝐸(𝑌0|𝐷 = 0), where the latter term denotes the performance of an untreated compa-
ny. The validity of this substitution is, however, dependent upon the conditional independence assumption
(Keilbach, 2005; Shen & Chang, 2009) which states that conditional on a vector of observable variables, the
treated company, Y1, and the untreated company, Y0, are drawn from the same population.
If the conditional independence assumption holds, and companies are in fact randomly chosen to be part of
the treatment group, then the only difference between the performance of the treated and the untreated
companies should belong to the treatment effect, and 𝐸(𝑌0|𝐷 = 0) should be an unbiased estimator of
𝐸(𝑌𝑖,0|𝐷 = 1) (Shen & Chang, 2009). In economics, most treatments are, however, not randomly applied
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to different subjects. In fact, in most cases subjects are either chosen for treatment, or chooses themselves
to undergo the treatment, due to differences in the vector of observable characteristics between these
subjects and the group of non-treated. The group of the treated subjects is therefore not randomly chosen,
and the treatment effect cannot be estimated through a simple comparison between treated and non-
treated subjects. This effect is known as the sample selection bias (Keilbach, 2005), and this is evidently also
of concern in relation to this thesis, as inclusions and exclusions to and from the FTSE4Good Global Index
are based on a selection process as described in section 3.2.1.
The likely presence of a sample selection bias necessitates the use of a matching procedure, in order to
ensure that the vector of observable characteristics of the non-treated companies is similar to that of the
treated companies. The matching procedure chosen in this thesis is based on propensity score matching, a
procedure which has also been used in similar studies (Chang & Shen, 2014; Shen & Chang, 2009). The
propensity score matching procedure, as well as the results of its application, are described in more detail
in section 4.3.3.
4.3.2. Data and data treatment
The initial step in applying the difference in difference methodology is the identification of the group of
treated companies, as well as a control group of non-treated companies (Keilbach, 2005). In the case of this
thesis, the group of treated companies is constituted of all companies, which have consistently been a part
of the FTSE4Good Global index since the time of the first announcement, and for which all relevant finan-
cial data was available. The group of potential surrogates for the counterfactual state is made up of com-
panies, which were part of the FTSE All World Developed Index at the announcement of the initial constitu-
ents of the FTSE4Good World Index in 2001, but who were not initially or subsequently included in this
index. Again, it is of course a requirement that data concerning the relevant financial variables is available5.
The data used for the study stems from two constituent lists of respectively the FTSE All World Developed
Index and the FTSE4Good Global Index. Both of these lists are from the 8th of November 2001; the day of
the announcement of the FTSE4Good Global Index. The sample consists of a total of 2273 companies of
which 525 are included in both indexes.
An exclusion of all companies initially included in the FTSE4Good Global Index but later excluded and also
all companies, which were initially not included in the FTSE4Good Global Index, but who were included at a
later point in time, reduced the total number of observations to 1771. Of these observations 343 was initial
5 The selection process is shown in appendix 13 and described in appendix 8.
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constituents of the FTSE4Good Global Index, which have not been excluded at any later point up until the
latest announcement on the 8th September 2014.
Using the SEDOL of these companies, several accounting variables concerning the financial year 2000, to be
used for the matching process, were gathered using Datastream. The lack of data availability in Datastream
leads to the largest number of exclusions, and after this step only 410 companies remain in the sample.
Finally, companies, for which the full data series from 2000 to 2013 concerning the operating income, reve-
nue and operating profit margin were not available, are excluded from the sample. This yields a final num-
ber of 296 observations, of which 59 have been including in the FTSE4Good Global Index throughout its
existence. The selection process is depicted in the table below.
Table 5: Depiction of selection process
Company type Initial FTSE AWD con-stituent list (08.11.01)
Exclusion due to later excl./incl. (treat-ed/non-treated)
All variables available for FY 2000
Full sample for operat-ing income, revenue and debt/assets ratio and FTSE4Good con-stituents in industry
classification
Treated 525 343 86 59
Non-treated 1,748 1,428 324 237
Total 2,273 1,771 410 296
4.3.3. Matching process
The difference in difference methodology attempts to quantify the effect of a given treatment, by estab-
lishing the performance of the treated subjects without this treatment (Christensen, 2012). Since this per-
formance cannot be observed, this state of the subject is known as the counterfactual, and an estimator of
this state must instead be found. The conditional independence assumption states that if a non-treated
company can be identified which, conditional on a vector of variables, is similar to the treated company,
then this can substitute for the counterfactual state of the treated company (Keilbach, 2005).
Identifying a suitable substitute for the counterfactual state based on a long list of variables is, however,
difficult due to the multidimensionality of these variables. In order to make the approach feasible, one can
instead make use of a so-called balancing score, which is a one-dimensional function of the observed varia-
bles. One such balancing score is the propensity score, which estimates the probability of receiving the
treatment given the observed variables (Caliendo & Kopeinig, 2008). The establishment of the set of coun-
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terfactual companies for this thesis is based on the use of this propensity score matching. This method is
also recommended by the Danish Ministry of Science, Innovation and Higher Education (Christensen, 2012).
The actual application of the propensity score matching is a two-step process, where the first step calcu-
lates the propensity score, while the second step establishes matches based on the outcomes of the first
step. The first step is presented in section 4.3.3.1 and step two is presented in section 4.3.3.2. The success-
fulness of the matching procedure is evaluated in section 4.3.3.3.
4.3.3.1. Propensity score matching
When propensity score matching is used in relation to a binary treatment, such as is the case for inclusion
into the FTSE4Good Global Index, little normative advice is given regarding which functional form to apply
for the model (Caliendo & Kopeinig, 2008). The use of a discrete choice model, such as a logit or probit
model, is preferred over the use of a linear probability model, primarily due to the shortcomings of the
linear probability model, such as the disadvantage that the fitted probabilities can be outside of the [0, 1]
probability bounds (Wooldridge, 2009).
This thesis employs a logit model, which estimates the probability of treatment based on a number of vari-
ables. The model can be written as:
𝑃(𝑦 = 1|𝑥) = 𝐺(𝛽0 + 𝛽1𝑥1 + … + 𝛽𝑘𝑥𝑘 = 𝐺(𝛽0 + 𝑥′𝛽)
Where the function, G, strictly takes on values between 0 and 1 for all real values, denoted z. For the logit
model, G is given by the logistic function (Wooldridge, 2009):
𝐺(𝑧) = exp(𝑧)
1+exp(𝑧)
The resulting probabilities of inclusion into the FTSE4Good Global Index, calculated for each of the 296
companies in the final sample with the logit function, are of course highly dependent on the set of variables
chosen as inputs. The variables chosen must ensure that the conditional independence assumption is satis-
fied, thus all variables explaining the selection of the treated should preferably be included in the model.
Omitting any important variables would likely lead to serious biases in the resulting estimates (Caliendo &
Kopeinig, 2008). On the other hand, it is neither recommended to include too many variables in the model.
One should instead only include those variables, which simultaneously affect both the outcome variables as
well as the participation decision, in the model (Caliendo & Kopeinig, 2008). The choice of the model speci-
fication should be guided by previous research, economic theory as well as common knowledge (Caliendo
& Kopeinig, 2008). Furthermore, it is crucial that only variables unaffected by participation should be in-
cluded in the model (Caliendo & Kopeinig, 2008). This is why the logit model presented in this chapter is
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based upon data from 2000, the year prior to the initial announcement of the FTSE4Good Global Index con-
stituents (Caliendo & Kopeinig, 2008).
The chosen variables as well as the outputs of the logit model is presented below:
Table 6: Logit model specification and results
Dependent variable: Inclusion into the FTSE4Good Global Index on the 10th of July, 2001
Variable Coefficient Std. Error
Constant
Revenue (£m)
Operating income (£m)
Operating profit margin
Debt/assets-ratio
Employees
R&D/revenue-ratio
Standard Industrial Classification-code:
Manufacturing (2000-3999)
Transportation, communications…(4000-4999)
Wholesale trade (5000-5199)
Finance, insurance and real estate (6000-6799)
Services (7000-8999)
-2.98
0.02
- 0.08
0.02
- 0.03
0.00
- 0.13
1.79
1.03
2.98
1.62
2.76
1.52
0.02
0.12
0.02
0.01
0.00
0.07
1.47
1.63
1.58
1.55
1.50
**
***
*
*
*
Observations McFadden R-squared Predicted probability
Mean Minimum Maximum Std. Error
296 0.12
0.20 0.00 0.61 0.14
As the table shows, 7 different variables have been chosen for the logit model specification. These are the
revenue, operating income, operating profit margin, debt/assets-ratio, number of employees, the
R&D/revenue-ratio and the industrial classification group of the company. All variables are from year 2000,
to ensure that the values of the variables are independent of the treatment (Caliendo & Kopeinig, 2008).
The variables revenue and number of employees have been included to account for differences in size.
Fombrun and Shanley (1990) argue that size is positively related to the amount of public scrutiny a compa-
ny faces, which again is positively related to the propensity of firms to engage in CSR activities. The variable
revenue has also previously been used in the propensity score matching of Shen and Chang (2009).
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Operating income and operating profit margin are included to account for the profitability of the firms.
Since profitability measures are observed to be mean reverting, it is important to include this in the logit
regression, to ensure a satisfactory matching process (Fama & French, 2000). Operating income was also
included in the propensity score matching of Shen and Chang (2009).
The R&D/revenue-ratio has been included in the logit regression because McWilliams and Siegel (2000)
argues for a positive relationship between CSR and R&D intensity. Since the R&D intensity is also argued by
McWilliams and Siegel (2000) to be positively correlated with financial performance, the R&D intensity
must therefore be accounted for in the matching process. The finding of a significantly negative correlation
in the logit regression is, however, counterintuitive of McWilliams and Siegel’ (2000) proposed link.
The remaining two variables, the debt/assets ratio and the industrial classification group, are included to
account for company risk and possible industry specific effects, respectively. The inclusion of these two
variables is also made by Lopez et al. (Lopez, et al., 2007).
4.3.3.2. Matching procedure
Based on the results of the logit model, the second step of the propensity score matching procedure can be
performed. This step is the actual matching of the treated companies with untreated companies, which
then substitute as counterfactuals for the difference in difference estimation.
Several different matching techniques are proposed in the litterature of which the most oftenly used is the
Nearest-Neighbour Matching criterion (Chang & Shen, 2014; Engel & Keilbach, 2007; Shen & Chang, 2009).
The purpose of the Nearest-Neighbour Matching criterion is to identify the matches where the absolute
difference in the propensity score is minimized between the treated and the non-treated subject. The
principle can formally be formulated as:
𝑚𝑖𝑛𝑖,𝑗[𝐺(𝑧)𝑖 − 𝐺(𝑧)𝑗]
Where G(z)i denotes the probability of being included in the FTSE4Good Global Index for one of the treated
subjects, while G(z)j denotes the probability of inclusion for the non-treated subject with the most similar
propensity score.
The biggest advantage of the nearest-neighbour matching criterion and the propensity score matching
procedure is the simplicity in using these; especially compared to the complex theory of the exact matching
theory (Keilbach, 2005). These advantages are, however, also the biggest disadvantages of the procedures,
since they might end up identifying pairs, which are in fact different on a number of key parameters
eventhough the propensity scores of the two subjects are approximately equal. This might happen when
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some of these differences in the vector of characteristics ends up balancing each other out. A solution to
this issue is provided by the balancing score matching procedure (Engel & Keilbach, 2007), which relies on
the use of the nearest-neighbour matching procedure in association with a set of requirements regarding
specific variables. This procedure should proviide greater accuracy in the matching process, which should of
course be weighted against the reduction in the number of potential matches.
The matching process used in this thesis is based on the balancing score matching procedure as described
above. Within the ± 0.056 propensity score bound, the control company with the most identical digits in the
SIC code was chosen. If no match was found within this bound with at least two identical SIC code digits,
the SIC group, as used for the logit model, was used as restriction. If no match was found within the bound
under either of these two restrictions, the nearest-neighbour approach was used in its pure form. In the
resulting 59 matches, 25 was achieved with 3 or 4 identical SIC code digits, 17 with two identical SIC code
digits, 16 within the same SIC group and only 1 match was based purely on the nearest-neighbour principle.
It should furthermore be noted that the matching process was conducted with replacement, which means
that the same company could be chosen to represent the counterfactual state of more than one member
of the group of treated subjects. The use of replacement increases the quality of the matching procedure
by decreasing the sample selection bias (Caliendo & Kopeinig, 2008). The use of replacement is especially
useful in cases where only few members of the non-treated group are found to have high propensity
scores. In the matching process of this thesis 46 unique, non-treated companies were used in the final
matches. A list of all 59 matches can be found in appendix 4.
A more formal test of the quality of the matching procedure is presented in the chapter below.
4.3.3.3. Evaluation of matching procedure
In order to evaluate the successfulness of the matching procedure an assessment is undertaken. This as-
sessment relies on the use of a simple t test, as it tests for the presence of significant differences between
the means of two groups (Keller, 2009). Differences between the group of treated companies and the initial
group of firms available as the counterfactual state serves as a benchmark for the assessment.
The t test is based on a two-step process (Keller, 2009). In the first step, a F-stat is undertaken first to de-
termine if the variances of the two samples differ: 𝑠12 𝑠2
2⁄ . This statistic is F-distributed with degrees of
freedom v1=n1-1 and v2=n2-2. If the null hypothesis of equal variances is not rejected, the following t-
statistic is employed to test for differences in the means of the two samples:
6 The ± 0.05 propensity score bound corresponds to roughly one third of the standard error of the calculated probabil-
ities of the sample which is 0.14 (See appendix 13)
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𝑡 = ��1−��2
√𝑠𝑝2(
1
𝑛1+
1
𝑛2)
Where 𝑆𝑝2 is the pooled variance of the two samples, which is calculated as:
𝑠𝑝2 =
(𝑛1−1)𝑠12+(𝑛2−1)𝑠2
2
𝑛1+𝑛2−2
The statistic is t-distributed with n1+n2-2 degrees freedom.
If the null hypothesis of equal variance is rejected, the test statistic for differences in the mean is instead
defined as:
𝑡 = ��1−��2
√𝑠1
2
𝑛1+
𝑠22
𝑛2
Which is t-distributed with degrees of freedom calculated as:
𝑣 = (𝑠1
2 𝑛1⁄ +𝑠22 𝑛2⁄ )2
(𝑠12 𝑛1⁄ )2
𝑛1−1+
(𝑠22 𝑛2⁄ )2
𝑛2−1
The results of this test procedure is shown in the table below.
Table 7: mean values of key financial metrics
Portfolio of treated companies
Before matching After matching
Total T-test Control T-test
Revenue (£000's) 11,354 7,872 1.93 * 10,247 0.40 Operating income (£000's) 1,239 803 1.75 * 1,292 -0.12 Operating profit margin (%) 13.76 11.12 1.73 * 14.57 -0.38 Debt/assets-ratio (%) 21.88 28.89 -2.84 *** 21.19 0.25 No. of employees 64,501 42,602 1.91 * 57,737 0.44 R&D/Revenue (%) 0.72 2.12 -3.11 *** 0.74 -0.06
***, **, * refers to statistical significance of 1 %, 5 % and 10 % respectively.
As the results of the table depict, the matching procedure seems to be highly effective in removing any
significant differences between the portfolio of the treated companies and the companies included in the
final control group. Based on these results it seems reasonable to deduce that the control group acts as a
good representation of the counterfactual state.
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4.3.3.4. Choice of performance indicators
Three different metrics are chosen for assessing the treatment effect on CFP. These three metrics are the
revenue of the companies, the operating income and the operating profit margin, which is calculated as the
operating income divided by the revenue.
The relationship between these three metrics is interrelated. In a ceteris paribus setting where costs do not
increase, increased revenue will lead to increases in both operating income and operating profit margin. If
costs are raised by a proportional amount, operating income will increase while the operating profit margin
would remain stable. In the case where costs increase by an amount equal to the increase in revenue, the
operating income will remain stable while the operating profit margin will decrease.
In a neoclassical analysis on the impact of CSR on profitability under perfect market conditions McWilliams
and Siegel (2001) find, that profits will remain unchanged when companies undertake CSR initiatives, be-
cause any excess profits accruing from CSR will lead to imitation from competitors. Therefore, in equilibri-
um, the extra revenue accruing from CSR initiatives will be exactly offset by increased costs. This conclusion
is in accordance with the last proposition, that costs will increase to offset revenue, leading to an increased
revenue, a stable operating income and a decreased operating profit margin. This will furthermore be con-
sistent with the findings of the event study in section 4.2.
4.3.4. Statistical tests
The collected data constitutes a panel data set, as it follows the same cross-section of companies over a
time series dimension (Wooldridge, 2009). Since panel data sets follow the same individuals over time,
unobserved characteristics are likely to persist across time. These unobserved effects, which are likely to be
correlated with other explanatory variables, can be accounted for by allowing for this correlation. In this
specific two-period case, the data is differenced across the two periods, which leads to the unobserved
effects being “differenced away” (Wooldridge, 2009). The differencing of the data means that the data no
longer describes the values of the different performance indicators chosen in chapter 4.3.3.4, but rather
the change in these variables between time 0, year 2000, and any of the subsequent time periods.
Two statistical tests are employed to check for differences between the treated group and the group repre-
senting the counterfactual state. These two tests are a standard t-test and a Wilcoxon signed rank test. The
former of these tests is a parametric test, while the second is a non-parametric test which ensures the ro-
bustness of the results.
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The t-test is identical to the test employed in chapter 4.3.3.3 and relies on the assumption that the data
follows a normal distribution. The other test, the Wilcoxon signed rank test, is used under the following
three circumstances (Keller, 2009):
The objective is to detect any differences in the means of two populations.
The data is interval, but is not normally distributed.
The samples consists of matched pairs.
As it is apparent from this description, this test is very suitable for the data at hand, as it is the non-
parametric counterpart of the abovementioned t-test (Keller, 2009). This implies that this method is robust
in cases where the observations are not normally distributed, and it can therefore help to verify the results
obtained from the t-test.
The statistic is calculated by first subtracting the value of the control company’s performance indicator
from that of the treated company for all of the 59 matches, in order to arrive at the difference between
these two, if the difference is 0 the match is excluded. The absolute values of these differences are then
ranked, and the sum of the ranks is calculated for the positive and the negative ranks respectively. The sum
of the ranks of the positive differences is denoted T+, while the sum of the ranks of the negative differences
is denoted T-. For the calculation of the test statistic one of these sums is arbitrarily chosen and denoted T.
For sample sizes larger than 30 pairs, T is approximately normally distributed with mean and standard devi-
ation calculated in the following manner: (Keller, 2009)
𝐸(𝑇) = 𝑛(𝑛+1)
4
And,
𝜎𝑇 = √𝑛(𝑛+1)(2𝑛+1)
24
The standardized test statistic is then calculated as:
𝑧 = 𝑇−𝐸(𝑇)
𝜎𝑇
The results from these two tests are presented for each of the three performance indicators below. Larger
versions of the figures from below are presented in appendix 5, while the figures are also available in excel
format in appendix 13.
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4.3.4.1. Revenue
Figure 5: Changes in mean revenue for treated companies and control group
***, **, * refers to statistical significance of 1 %, 5 % and 10 % respectively.
The changes in mean revenue from 2000 until 2013 for both the treated group and the control group are
depicted in the figure above. As the figure shows, the revenue of the control group exceeds that of the
treated group in 2001, the launch year of the FTSE4Good Global Index. Since 2002 and onwards the treated
group have, however, overtaken the control group and a visual inspection of the figure seems to support
hypothesis 3; that socially responsible companies will outperform less responsible peers. The results of the
test statistics seem to add further support to the hypothesis with the Wilcoxon signed rank test finding
differences significant at the 5 % level for four of the 13 years, and significance at the 10 % level for a fur-
ther five of the 13 years. Due to large variance of the data the standard t-test is not able to find any signifi-
cant results.
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4.3.4.2. Operating income
Figure 6: Changes in mean operating income for treated companies and control group
***, **, * refers to statistical significance of 1 %, 5 % and 10 % respectively.
The changes in mean operating income seem to emulate the changes observed for the mean revenue. Fol-
lowing a short dip of the treated companies in 2001, compared to the control group, the treated companies
seem to perform much better in all of the subsequent years. The test statistics supports this interpretation.
The Wilcoxon signed rank test finds statistically significant differences at the 10 % significance level or less
for 8 of the 13 years, while the standard t-test finds significant results in 5 of the 13 years. It should be not-
ed than the t-test finds a significant negative result in 2001, which is the only significant negative result
observed for any of the three performance indicators.
4.3.4.3. Operating profit margin
Figure 7: Changes in mean operating profit margin (%) for treated companies and control group
***, **, * refers to statistical significance of 1 %, 5 % and 10 % respectively.
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The changes in the mean operating profit margin are shown in Figure 7. As the operating profit margin
equals operating profit divided by revenue it is natural that the results of this performance indicator is in
line with the two above mentioned. During the entire period both groups have suffered a decrease in their
operating profit margin, although the treated group has incurred a small decrease of 0.7 % while the con-
trol group has decreased its mean operating profit margin by nearly 5 %.
The statistical tests find significant results in several years. The t-test finds a significantly positive result at
the 5 % significance level in 2003 and 2005, while a significantly positive result at the 10 % significance level
is found in another 7 periods. The Wilcoxon signed rank test finds significantly positive differences in four
years; two at the 5 % significance level and two at the 1 % significance level.
4.3.5. Conclusion to difference in difference results
While the event study of chapter 4.2 was unable to find a relationship between CSR and financial perfor-
mance, the results of the difference in difference study presented above seem to provide at least some
evidence of a positive relationship between the two subjects.
The tests above have considered three different accounting measures; revenue, operating income and the
operating profit margin. Significant results were found for all three performance indicators signaling an
outperformance of companies included in the FTSE4Good Global Index compared to a group of matched
control companies. These results support the claim of hypothesis 3; that companies included in the
FTSE4Good Global Index are, ceteris paribus, outperforming companies, which have not been included in
the index.
The results from the difference in difference study are not in line with the analysis of McWilliams et al.
(1999). In their article McWilliams et al. (1999) argue that CSR will have no impact on profits under perfect
market conditions, because any excess profits accruing from CSR will lead to imitation from competitors
leading to an outcome where the excess revenue created from CSR is exactly offset by the associated costs.
While the revenue of the socially responsible companies is found to outperform that of the control group,
as McWilliams et al.(1999) argue it will, the same outperformance can also be observed for the operating
income of the treated companies, which should not be the case if the analysis of McWilliams et al. (1999)
holds. What is more, the analysis of McWilliams et al. (1999) requires a decrease in the operating profit
margin, however, the operating profit margin hardly decreases for the treated companies, while the
operating profit margin decreases significantly more for the control companies.
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The results of section 4.3 should also be compared to those of Lopez et al. (2007). In their study they found
that a group of 55 companies included in the DJSI were outperformed in terms of profitability measures by
a group of 55 similar, matched companies not included in the DJSI, although not in terms of revenue. These
findings indicated increased costs related to the inclusion in the DJSI, although further studies uncovered
that the changes seemed to disappear over time, begging the question as to whether the relationship
would reverse in the longer run, as Eccles and Serafeim (2013) suggest. The data foundation of the analysis
did, however, only span 6 years, which disabled the possibility to investigate this hypothesis.
The results produced by the difference in difference study in this chapter go some way to support the find-
ings of Lopez et al. (2007). For all three key parameters the figures indicate an underperformance from the
treated companies in 2001, the launch year of the FTSE4Good Global Index. There is, although, little statis-
tically significant evidence of this underperformance, and the relationship is found to quickly reverse in
such a manner that significant evidence of an outperformance from the treated group is found for all three
key parameters in 2004. It must therefore be concluded that even though there are similarities between
the results of Lopez et al. (2007) and this thesis, the central conclusion as to whether CSR creates or dimin-
ishes shareholder value is quite different, as this thesis finds significant evidence of a positive result in favor
of hypothesis 3.
The validity of the findings are, however, of course dependent upon the quality of the matching procedure.
Although section 4.3.3.3 indicates a successful matching procedure, the propensity score matching has not
been able to take all firm characteristics into account, thus it is still possible that significant differences in
key parameters exists between the group of the treated and the control group. This concern will be further
exacerbated if the initial constituents of the FTSE4Good Global Index were chosen based also on financial
performance, as is the case for constituents of the DJSI (Fowler & Hope, 2007). The analysis from section
3.2.1 does, however, not indicate that this is the case, thus it is maintained that the results of the differ-
ence in difference study provide evidence in favor of hypothesis 3; that companies included in the
FTSE4Good Global Index are, ceteris paribus, outperforming companies, which have not been included in
the index.
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5. Discussion
The results obtained through the empirical work of chapter 0 does not provide any clear conclusion to the
question as to whether membership of the FTSE4Good Global Index is value creating or not. The event
study finds very little evidence of neither a positive nor a negative correlation between stock prices and
inclusions and exclusions from the FTSE4Good Global Index. The difference in difference study, meanwhile,
finds some evidence of a positive relationship between membership and financial performance.
These ambiguous results fit nicely with the results of the existing empirical literature described in section
3.1.3, which also seems to favor a small positive relationship between CSR and financial performance, how-
ever, without reaching any general consensus.
One possible explanation to these mixed results might be found in the very broad definition of CSR as de-
scribed in section 3.1. CSR is a very complex and multifaceted concept and as a result of this, there is no
one simple right way to approach it (Ioannou & Serafeim, 2014). Companies’ CSR activities can take on a
great many different forms; both in terms of stakeholder focus, people/planet orientation as well as the
means through which to communicate the impact. CSR is in other words in many ways a very strategic con-
cept, which is also the main tenet of supporters of strategic CSR (Baron, 2001; McWilliams et al. 2006).
The implications of this realization is that several variables and situational contingencies mediate the rela-
tionship between CSR and financial performance. This relationship will therefore be neither positive nor
negative in all instances, but will instead be highly dependent on the interaction between the actual formu-
lation of the company’s CSR strategy and a multitude of external factors beyond the direct control of the
company (Carroll & Shabana, 2010). Too often, however, do companies initiate CSR initiatives in the hope
of doing well by doing good, without considering the strategic match of the initiative (Eccles & Serafeim,
2013). When such initiatives fail to address concerns related to their operations and relevant for their
customers, very real tradeoffs between financial and social performance are likely to be incurred (Eccles &
Serafeim, 2013).
According to Kramer and Porter (Kramer & Porter, 2006, p. 80): ”the prevailing approaches to CSR are so
fragmented and so disconnected from business and strategy as to obscure many of the greatest opportuni-
ties for companies to benefit society”. CSR initiatives should not be adopted in a generic manner, but should
rather be integrated with the firm’s general business strategy (Kramer & Porter, 2006). Only in the cases
where the company establishes a convergence between their CSR activities and their economic objectives,
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will CSR have the potential to be a source of competitive advantage and create financial value (Carroll &
Shabana, 2010).
Therefore, as with every other area of strategy, CSR can be a source of value creation if it is well planned,
relevant and properly communicated to the relevant stakeholders. If this is not the case, CSR just might end
up as a waste of shareholders’ money as Friedman (1970) suggests.
6. Conclusion
This thesis set out to answer the following research question: “Do companies’ CSR initiatives create or di-
minish shareholder value?”. This question was sought answered through an empirical analysis of data re-
garding inclusions and exclusions from the FTSE4Good Global Index.
In order to answer this question a literature review of existing theoretical and empirical literature was first
undertaken. Based on this literature review the following three hypotheses were created:
Hypothesis 1: Inclusion into the FTSE4Good Global Index will lead to a significant increase in
the stock price.
Hypothesis 2: exclusion from the FTSE4Good Global Index will lead to a significant decrease
in the stock price.
Hypothesis 3: Companies included in the FTSE4Good Global Index are, ceteris paribus,
outperforming companies, which have not been included in the index.
The results found in chapter 0 finds mixed evidence for the three hypotheses. The event study of section
4.2 finds little evidence in favor of neither hypothesis 1 nor hypothesis 2, indicating a neutral stock price
reaction following both inclusions and exclusions from the FTSE4Good Index. The results of the difference
in difference study from section 4.3 do, however, find support of hypothesis 3 by finding, that a group of
companies included in the FTSE4Good Global Index significantly outperformed a group of matched compa-
nies not included in the index. These results are found in terms of both revenue, operating profit and oper-
ating profit margin. A more in-depth review of the results of the difference in difference study of section
4.3 indicates an underperformance of the companies included in the FTSE4Good Global Index in 2001, the
year of the launch of the index, compared to the group of control companies. This underperformance is,
however, quickly reversed from 2002 and onwards. This finding is in line with the contention that while the
54 of 62
costs of CSR activities are often incurred in the short run, the rewards are first received in a more long-term
perspective (Eccles & Serafeim, 2013).
The ambiguity of the results found in chapter 0 do not allow for any definitive conclusion regarding the
research question, although they do indicate a weakly positive relationship between CSR and CFP. These
results are supported by the conclusions of the already existing body of empirical literature on the subject,
and as the discussion of chapter 0 argues, they might be explained by the very nature of the concept of
CSR. CSR is a multifaceted concept and it can take on a great variety of different forms, which in turn will
have a profound effect on the financial impact of the CSR initiative in question. CSR should therefore by no
means be considered a cure-all for companies, CSR initiatives should instead be sought integrated with the
general strategy of the company in a manner that aligns the financial and social goals of the company
(Kramer & Porter, 2006). In such cases, where the CSR strategy has been carefully considered, CSR is likely
to have the potential to increase shareholder wealth.
6.1. Suggestions for further work The conclusion raises several new questions, such as under what specific conditions CSR is likely to increase
shareholder value. This is the question advocates of an instrumental approach to CSR (Jones, 1995) and
strategic CSR (Van de Ven & Jeurissen, 2005; Kramer & Porter, 2006) have tried to answer for several years.
The findings of these papers indicate that the results of CSR investments are determined by such factors as
industry competition (Van de Ven & Jeurissen, 2005) and the degree of industry cooperation (Jones, 1995).
Such studies can certainly indicate which and how companies are set to gain the most from CSR, but the
realization of CSR as a complex and highly strategic concept, indicates that such general guidelines cannot
uncover all aspects relevant to the outcome of a given CSR initiative. The financial result is instead likely to
be highly dependent on the interaction of a multitude of firm specific situational contingencies. The fore-
most suggestion of this thesis is therefore for academia to stop focusing solely on uncovering the general
relationship between CSR and CFP or the mediating factors, and instead start developing methodologies for
quantifying the actual monetary impact of companies’ CSR investments.
At the moment CSR is measured to a large extent based on the inputs to the process rather than the out-
puts (Salazar, et al., 2012). McWilliams and Siegel (2001) suggests a more away from this praxis towards an
approach of measuring CSR initiatives on a cost-benefit basis. While it is arguably more difficult to measure
the bottom line of CSR initiatives compared to more traditional investments in tangible assets, due to the
long time frame of the rewards (Eccles & Serafeim, 2013), the lack of any methodology for doing so hinders
the avoidance of initiatives which will destroy shareholder value (McWilliams & Siegel, 2001). While most
55 of 62
managers probably like to do good, they most certainly more concerned with the price of their stock. This is
likely also to be the case for most investors. Michael C. Jensen (2002) calls for companies to only attend to
other stakeholders to the point where it optimizes shareholder value. This is what he calls enlightened val-
ue maximization. For such enlightened value maximization to take place, however, methodologies for
quantifying the financial outcomes of CSR are needed. This will therefore be the main suggestion for future
research.
56 of 62
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