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Reflection on the Articles
Article One
The article is named "Exchange," and it is written by Jeffrey
Pfeffer from Stanford University. The article talks about the
changes that young employees experience and how fast they
adjust to those changes compared to older employees. These
changes have brought about organization theories that explain
this reality. The article states that these days, the old ideas
about something are not considered as they were back in the
day, and they are considered irrelevant. The world has changed
in many different ways that everything that used to be done for
one to gain power and influence in the past has been abandoned
since new strategies have come up, and people in the current
world are using them. Jeffery asks some questions regarding the
organization theory. These questions are asked to try and find
out if the change in people and work can affect the opinions of
power and influence in the current world.
The article also explains why younger employees adjust to

change quickly. Jeffrey states that the younger employees are
brought up in less competitive environments, and influences are
many compared to the older employees. On the other hand,
although the world has changed along with the people and
strategies they use to gain power and influence, some
organizational dynamics are still relevant, and they are never
changing from the past. These dynamics cannot always be seen
as desirable by society, but they help to understand human
behavior and how they change in different organizations. The
dynamics that interested me include hierarchy whereby the
control that comes with it encourages creativity and employee
engagement in the organization. Even though the invention of
social networks and technology has overtaken hierarchy, it is
still there, and it can never end. Another dynamic is the self-
enhancement motive whereby an individual has the freedom to
motivate themselves by thinking positively and seeing ourselves
as capable beings in the organization. It also includes never
having the urge to give up even if the going gets tough.
Individuals with self-enhancement motives always end up on
top since they motivate themselves, and they do the things they
have set the goals for, hence, working hard to reach the top and
stays there. Others even drive others to believe in themselves
and work hard to achieve their goals. The behavior never
changes since every individual working for an organization,
whether young or old.
Another dynamic behavior is us versus them and the importance
of similarity whereby an individual tries to mimic the practices
of the people who are on top by reading their books or listening
to what they did before they made it to where they are now,
among others. The mimicking shows that these individuals want
to be like the mentors or role models, do what they did to gain
the power they have now. Hence, whether the world is changing
or not, trying to do similar things or have identical behaviors
like one's role model will never change, and it is there to stay.
In conclusion, there is always going to be something new every
day in this world since it is always changing, and no one can

predict what happens in the future. However, some behaviors
will never change, no matter how the world changes because
they are constant, and they are inside all individuals.
Work Cited
Pfeffer, Jeffrey. "You're still the same: Why theories of power
hold over time and across contexts." Academy of Management
Perspectives 27.4 (2013): 269-280.
Article Two
The article is about Organizational Ambidexterity: Balancing
Exploitation and Exploration for Sustained Performance, written
by Sebastian Raisch, Julian Birkinshaw, Gilbert Probst, and
Michael L. Tushman, 2009. The article states that, for an
organization to gain success, it has to depend on its ability to
take advantage of its available capabilities and also taking
advantage of essential new capabilities on its display. These
organizations are referred to as ambidextrous organizations. The
ambidexterity concept has increased over the years, from less
than ten percent to more than eighty percent in the current
world.
However, four tensions have to been addressed because,
according to research, the ambidexterity concept can somehow
be vague, ambiguous, and unexplored. The first tension is
differentiation vs. integration whereby differentiation includes
the partition of exploitative and explorative exercises into
unmistakable organizational units, while integration involves
the mechanisms that empower associations to address
exploitative and explorative activities inside the equivalent
hierarchical unit. Until this point, the two methodologies have
regularly been positioned as totally unrelated arrangements; in
any case, scholars have indicated different weaknesses inborn in
both. Further progress around there may rely upon a wager for
comprehension of the strains and complementarities between the
two approaches. The second tension is individual or

organizational tension, whereby the question is asked if
ambidexterity can be seen in either. The ability to use both
hands looks into generally portrays hierarchical components to
empower ambidexterity, for example, formal structures or
horizontal coordination components. Alternately, a few
investigations show that the ability to use both hands is
established in a person's capacity to investigate and misuse.
Hierarchical components might be required to empower the
ability to use both sides at the individual level, what's more,
able to use both hands people might be fundamental to the
helpfulness of hierarchical systems. There is, in this way, a
requirement for hypotheses that catch the ability to use both
hands crosswise over multiple levels of investigation.
Thirdly, static versus dynamic perspectives are discussed on
whether they relate to ambidexterity. Albeit some examination
proposes that consecutive consideration ought to be paid to
abuse and investigation, most of organizational ability to use
both perspectives in research displays a scope of arrangements
that empowers associations to at the same time seek after the
two exercises. These investigations take a static view on
authoritative conduct: Organizations become able to use both
perspectives by embracing specific designs. Given the
dynamism of business sectors and associations, it is essential to
create speculations that consolidate static components with a
progressively dominant view of the ability to use both
perspectives. Lastly, the article states whether ambidexterity
can be external or internal. Research on authoritative ability to
use both hands has concentrated on how organizations address
misuse and investigation inside. Related research on the
development and learning forms stresses the significance of the
external securing of new learning for study. Concentrates on
unique capabilities portray interrelations among inner and outer
information forms that assume a significant job in corporate re-
establishment. There is a need to investigate the interchange
among internal and external procedures in the creation, what's
more, safeguarding of authoritative ability to use both

perspectives.
In conclusion, the article provides the essential strategies and
procedures of how some ambidextrous organizations succeed in
the end, even though there are some challenges and the tensions
that they go through in the long run.
Work Cited
Raisch, Sebastian, et al. "Organizational ambidexterity:
Balancing exploitation and exploration for sustained
performance." Organization science 20.4 (2009): 685-695.
Article Three
The article’s title is “What monetary rewards can and cannot
do: How to show employees the money," and it is written by
Herman Aguinis, Harry Joo, and Ryan K. Gottfredson. The
article talks about how employees work when they are rewarded
with money and what the monetary reward cannot do to change
the way employees work. When employees are offered financial
rewards by their employers, it motivates them to work hard even
more to gain the award and to live the life they have always
wanted. It’s because the money reward saves the employees a
lot of costs that they incur with their average salaries and
allowances. Hence they feel if they earn the monetary award
given to them by their employers, they will live more
comfortably. Therefore, the employees will become more
motivated and willing to do anything to earn that monetary
reward provided by their employers. The monetary reward has a
compelling impact on the employees, and they work hard to
receive it. It is the highest form of motivation an employee can
get from his/her employer. The employees who are paid higher
are the most sought after individuals since they bring about
exceptional work performance, therefore, leading to the success
of the organization. The article explains that monetary reward
can be the highest form of motivation because it meets various
needs for the employees. Such requirements include food,

shelter (rent), and can also afford luxury needs, such as going
for a vacation or purchasing a car of the current model. It also
enables the employee to fit in particular groups of people at
work, such as the top performers, among others.
On the other hand, monetary rewards cannot do certain things
for the employees. The article explains that monetary rewards
cannot improve an employee's behavior or way of working in
the organization. The employee stays the same, and it is only
his/her work that gets him/her the monetary reward. Being able
to work hard and smart for an organization will depend on the
employees even though the monetary reward leads to the
increase of the self-motivation of the employee. Also, monetary
rewards cannot lead to job satisfaction since there are some
decisions that the employers make that the employees do not
agree with, but they have to go with it since it has already been
decided. Hence, monetary rewards will not affect the decisions
made and the duties that the employees have to perform related
to the decision.
In conclusion, employers should consider the discussions above
and see what monetary does and does not do for the employees.
There are also some recommendations that employers need to
look at when rewarding employees with money or any other
reward. First, they should look at the measure of performance
done by the employees, reward the best-performed employees
according to the work that is done, reward the employees in an
appropriate time whereby right after they have shown excellent
performance and behavior, and be fair when choosing the
employees eligible for the monetary reward. Finally, they
should both rewards, which are monetary and nonmonetary.
When they follow these recommendations, the employees can
try their best to perform well for them to taste these rewards,
whether financial or nonmonetary, as long as they are rewards.
Therefore, it will lead to the motivation of the employees to do
better and the success of the organization.
Work Cited.

Aguinis, Herman, Harry Joo, and Ryan K. Gottfredson. "What
monetary rewards can and cannot do: How to show employees
the money." Business Horizons 56.2 (2013): 241-249.
– Working Remotely
Citations:
1. Chiru, C. (2017). Teleworking: Evolution and Trends in USA,
EU and Romania. Economics, Management, and Financial
Markets, 12(2), 222–229.
2. Felstead, A., & Henseke, G. (2017). Assessing the growth of
remote working and its consequences for work effort, well-
being, and work-life balance. New Technology, Work and
Employment, 32(3), 195–212.
3. Noonan, M. C., & Glass, J. L. (2012). The hard truth about
telecommuting. Monthly Labor Review, 38–45.
Discussion Questions:
1. Have you experienced telecommuting, or the option to
telecommute? If so, what was your experience with it, would
you do it again, would you prefer to work in the office all of the
time?
2. What are some positive effects you might experience from
telecommuting as an employee or a manager? What are some
negative effects or concerns employees or managers might have
about telecommuting?
3. How might telecommuting affect ‘normal’ working hours?
4. What industries do you think telecommuting would work best
for and why? Which ones should avoid telecommuting and why?
5. How might telecommuting affect how employees need to be
motivated?

Other Reflections:
- Multi-Tasking
Citations:
Rogers, R., & Monsell, S. (1995, June). Costs of a Predictable
Switch Between Simple Cognitive Tasks. Journal of
Experimental Psychology General, 207-231.
Sanderson, K. R., Bruk-Lee, V., Viswesvaran, C., Gutierrez, S.,
& Kantrowitz, T. (2013). Multitasking: Do preference and
ability interact to predict performance at work? Journal of
Occupational and Organizational Psychology, 86, 556-563.
Shao, D. H., & Shao, L. P. (2012, November 1). The Effects of
Multitasking on Individual's Task Performance. International
Journal of Business Strategy, 12(1), 75-80.
1. If you had to make a limit, how many times can you look at
your cellphone before it starts distracting from performance in a
day? Do you look at your phone while at work too much
according to your limit?
2. Can you scroll Instagram and simultaneously learn class
material? Or are they more separate tasks that you switch your
attention back and forth from?
3. What kinds of tasks do you multitask throughout your day?
Should you separate the tasks to be more effective or is
multitasking a job requirement?
4. Do you feel more productive while multi-tasking or
completing each daily task independently?
5. Why do companies require multitasking when there are key
signals of worsened performance and accuracy?

Other Reflections:
– Corporate Social Responsibility
Citations:
Jizi, M., Nehme, R., & Salama, A. (2016). Do Social
Responsibility Disclosures Show Improvements on Stock
Price? Journal of Developing Areas, 50(2), 77-95.
Rehman, A., Baloch, Q. B., & Sethi, S. (2015). Understanding
the Relationship Between Firm's Corporate Social
Responsibility and Financial Performance: Empirical
Analysis. Abasyn University Journal of Social Sciences, 8(1),
98-107.
von Arx, U., & Ziegler, A. (2013, June 11). The Effect of
Corporate Social Responsibility on
Stock Performance: New Evidence for the USA and
Europe. Quantitative Finance, 14(6),
977-991.
Zeb, S., & Yasmin, R. (2016). An Empirical Study into the
Mediating Role of Job Satisfaction on the Linkage Between
Corporate Social Responsibility and Organizational
Performance. Abasyn University Journal of Social
Sciences, 9(2), 454-478.
1. Do you care if a company has CSR policies in place? Does
that make you more likely to purchase products or services from
a certain business? Why or why not?
2. There are large movements for businesses to incorporate

corporate social responsibility into their business. What are
possible barriers to incorporating corporate social
responsibility?
3. Is corporate social responsibility simply doing a little more
than following the bare minimum laws regarding the
environment, how waste/emissions are handled, or donating to a
good cause? What are some other components of corporate
social responsibility?
4. In what instances could corporate social responsibility have
negative effects on a firm’s reputation and possibly its
performance?
5. How has the internet of things had an impact of corporate
social responsibility? How could that impact a company’s
performance?
Other Reflections:
The effect of corporate social responsibility on
stock performance: new evidence for the USA and
Europe
URS VON ARXyz and ANDREAS ZIEGLER*yx
yCenter for Corporate Responsibility and Sustainability,
University of Zurich, Zähringerstr. 24, 8001 Zurich,
Switzerland
zCenter of Economic Research, Swiss Federal Institute of
Technology (ETH) Zurich, Zürichbergstr. 18, 8032 Zurich,
Switzerland
xDepartment of Economics, University of Kassel, Nora-Platiel-

Str. 5, 34109 Kassel, Germany
(Received 22 June 2010; in final form 11 June 2013)
This paper provides new empirical evidence for the effect of
corporate social responsibility on
corporate financial performance. In contrast to former studies,
we examine two different regions,
namely the USA and Europe, and disentangle firm and sector
specific impacts. Our econometric
analysis shows that environmental and social activities of a firm
compared with other firms
within the industry are valued by financial markets in both
regions. However, the respective posi-
tive effects on average monthly stock returns between 2003 and
2006 are more robust in the
USA and, in addition, non-linear. Our analysis furthermore
points to biased parameter estimates
if incorrectly specified econometric models are applied: the
seemingly significantly negative effect
of environmental and social performance of the industry to
which a firm belongs strongly
declines and mostly becomes insignificant if the explanation of
stock performance is based on
the Fama–French three-factor or the Carhart four-factor models
instead of the simple Capital
Asset Pricing Model.
Keywords: Corporate social responsibility; Financial
performance; Asset pricing models
JEL Classification: G12, M14, Q01, Q56
1. Introduction
This paper empirically studies economic effects of

‘corporate social performance’ or ‘corporate social responsi-
bility’ (CSR). These terms are typically used synonymously
and comprise both corporate social as well as corporate
environmental activities (e.g. Waddock and Graves 1997,
Orlitzky 2001, Orlitzky et al. 2003). According to the popu-
lar definition of McWilliams and Siegel (2001), CSR is
described as the ‘actions that appear to further some social
good, beyond the interest of the firm and what is required
by law’. Another definition of CSR refers to ‘actions which
reduce the extent of externalized costs’ (besides the avoid-
ance of distributional conflicts, Heal 2005). In line with
some former studies, we consider the financial impacts of
CSR, i.e. the effects of CSR on stock performance which is
used as an indicator for corporate financial performance.
Our main contribution to the corresponding literature is
twofold: first of all, we examine this relationship in two dif-
ferent regions, namely the USA and Europe. In this respect,
we are particularly able to incorporate the same CSR indi-
cators for both regions. This allows a comparative analysis
for these two world-wide leading stock markets. Therefore,
we can analyse whether potential CSR impacts are
interregional or whether regional differences arise. Sec-
ondly, we apply different asset pricing models for the expla-
nation of stock performance, i.e. the three-factor model
according to Fama and French (1993) and particularly the
four-factor model according to Carhart (1997) beside the
simple Capital Asset Pricing Model (CAPM). While the
corresponding factors for these models are publicly avail-
able for the USA and some other specific stock markets,
they have to be calculated for the entire European stock
market. This is obviously the reason why such multifactor
models have not often been applied for this region yet.
Knowledge about the effect of corporate environmental
performance as one component of CSR on

corporate*Corresponding author. Email: [email protected]
� 2013 Taylor & Francis
Quantitative Finance, 2014
Vol. 14, No. 6, 977–991,
http://dx.doi.org/10.1080/14697688.2013.815796
financial performance contributes to the debate about
whether managers systematically miss profit opportunities if
they decide against the protection of the natural environ-
ment (e.g. King and Lenox 2002). This debate has been
going on for a while in the corresponding literature (e.g.
Hart and Ahuja 1996, King and Lenox 2001, Guenster
et al. 2011). Furthermore, an understanding of this relation-
ship is also interesting for environmental policy: if a posi-
tive effect of corporate environmental performance really
exists, it can be argued that traditional mandatory command
and control regulations as well as market-based instru-
ments—such as green taxes—should be relaxed. Instead,
these regulations could be supplemented or even substituted
by information-based instruments, namely by improving the
flow of information with respect to this effect (e.g. Telle
2006). Just like other non-mandatory approaches in
environmental policy—such as voluntary green management
measures—these regulations can be thought to be more
cost-efficient because they leave firms the flexibility to
choose the cheapest pollution abatement strategy and reduce
governments’ enforcement costs (e.g. Alberini and Segerson
2002). All these conclusions do not only apply for the
effect of corporate environmental, but also corporate social
activities and thus CSR on corporate financial performance.
Due to the inconclusiveness of theory, the effect of CSR

on corporate financial performance in general and on stock
performance in particular is ultimately an empirical ques-
tion. Against this background, we provide new empirical
evidence for this issue. As an indicator for CSR, we use
environmental and social activities of a firm compared with
other firms in the same industry. In line with only few
former studies (e.g. King and Lenox 2001, Ziegler et al.
2007a), we additionally consider sector-specific influences
by incorporating environmental and social performance of
the industry to which a firm belongs. As an indicator for
stock performance, we consider the average monthly stock
returns between 2003 and 2006. Due to this specific depen-
dent variable, our final cross-sectional regressions have to
be based on the estimation of asset pricing models since
financial economics suggests the use of corresponding fac-
tors to explain average stock returns.
The remainder of the paper is structured as follows:
Section 2 briefly provides some theoretical background. In
the third section, we review the empirical literature regard-
ing the relationship between CSR and corporate financial
performance. Section 4 discusses our different two-stage
econometric approaches. In the fifth section, the used data
and variables are described. Section 6 reports the empirical
results and the final section discusses our results and
concludes.
2. Theoretical background
Overall, current theory concerning the effects of CSR on
corporate financial performance is ambiguous (e.g.
Waddock and Graves 1997, Guenster et al. 2011). Argu-
ments for a negative influence can be based on neoclassical
microeconomics. According to this, it is mainly emphasized
that the operating costs of corporate environmental (e.g.

Telle 2006) or social activities outweigh their financial
benefits due to cost reductions. Therefore, CSR can lead to
reduced profits, decreased firm values or competitive disad-
vantage besides lower shareholder returns. This neoclassical
argumentation is supported by corporate governance theory
(e.g. Shleifer and Vishny 1997, Tirole 2006). According to
a rather narrow definition, corporate governance comprises
all measures which assure that investors get an adequate
return for their investments. According to this, it is argued
that, for example, the consideration of goals of other
groups—such as the general public—as motivation for
corporate environmental and social activities unnecessarily
enlarges the latitude of management which is misused for
maximizing the utility of managers. Therefore, investors
have to reckon with lower returns on their investments if
the respective corporations deviate from the optimal path
due to excessive environmental or social activities (e.g.
Heinkel et al. 2001, Beltratti 2005).
However, positive effects of CSR on corporate financial
performance can also be based on neoclassical microeco-
nomics by emphasizing the role of CSR in reducing the
extent of externalized costs. While, for example, Friedman
(1970) assumes that the government defines property rights
such that no external effects exist, Heal (2005) argues that
the government does not fully resolve all problems with
external effects and that the competitive markets are not
efficient. Therefore, CSR can substitute missing markets if
external effects arise from them and can reduce conflicts
between firms and stakeholder groups such as the govern-
ment, non-governmental organizations, employees or cli-
ents. As a consequence, it can be argued that the reduction
of these conflicts increases corporate profits or corporate
financial performance at least in the long run which also
makes firms with a high intensity of CSR more attractive to
investors.

This stakeholder argument is strengthened in the strategic
management literature (e.g. Waddock and Graves 1997,
Barnett and Salomon 2006, Curran and Moran 2007).
Stakeholder theory suggests that management has to satisfy
several groups who have some interest or ‘stake’ in a firm
and can influence its outcome (e.g. McWilliams et al.
2006). Regarding corporate financial performance, it can,
therefore, be worthwhile to engage in CSR because other-
wise these stakeholders could withdraw the support for the
firm. For example, a minimum of corporate environmental
activities and the avoidance of child labour in the full
value-added chain of the products can reduce risk due to,
for example, aggressive campaigns of non-governmental
organizations. These arguments from stakeholder theory can
be embedded in the resource-based view of the firm (e.g.
Barney 1991) which suggests that competitive advantage
evolves from internal capabilities which are valuable, rare
and difficult to imitate or substitute (e.g. Russo and Fouts
1997, Klassen and Whybark 1999, King and Lenox 2001,
McWilliams et al. 2006). In this respect, stakeholder man-
agement can be considered an important organizational
capability or resource.
This discussion of possible positive and negative effects
of CSR also implies that they can be different across sev-
eral countries if some aspects play a more important role in
978 U. von Arx and A. Ziegler
specific regions. It can, for example, be argued that the
stakeholder theory argument is of high relevance for Europe
since the higher awareness of climate change and stringency
of climate policy in this region (at least in the observation

period until 2006) can lead to a high institutional pressure
(e.g. by non-governmental organizations) that demands cor-
porate responses to climate change (e.g. by reducing corpo-
rate greenhouse gas emissions, e.g. Ziegler et al. 2011).
This would lead to a more positive effect of such corporate
responses as component of CSR in Europe compared with
the USA. On the other hand, however, it can also be argued
that the USA has a much longer tradition of ethical compo-
nents of CSR and particularly socially responsible investing
(SRI) than Europe. For example, (ethically and religiously
motivated) SRI in the USA has already its roots in the eigh-
teenth century. This long tradition would rather support a
more positive effect of CSR on financial performance in the
USA.
Based on this discussion of positive and negative effects
of CSR, it can also be argued that there are many converse
corporate environmental and social factors such that no sig-
nificant effect exists (e.g. Waddock and Graves 1997,
Elsayed and Paton 2005). As a consequence, the effect of
CSR on corporate financial performance is ultimately an
empirical question. Regarding the measurement of corporate
financial performance, the use of forward-looking average
stock returns is theoretically attractive in this respect.
According to the efficient market hypothesis, stock prices
reflect all publicly available information about the future
financial performance of firms. In compliance with the
well-known dividend discount model, a stock price, there-
fore, equals the discounted expected future stream of divi-
dends paid to the shareholders. Our approach, therefore, has
the advantage that the focus is not on the past realized but
on the future expected financial performance (e.g. Miller
and Modigliani 1961).
3. Empirical literature review

The relationship between CSR and corporate financial per-
formance can be empirically analysed with three methodo-
logical approaches, namely portfolio analyses, event studies
and longer term (micro-) econometric approaches. Portfolio
analyses in this field typically compare the risk-adjusted
stock returns of portfolios that consist of corporations with
a higher environmental or social performance and portfolios
that consist of stock corporations with a lower environmen-
tal or social performance. Recent studies are mostly based
on the estimation of alphas within multifactor models such
as the Carhart four-factor model (e.g. Derwall et al. 2005,
Bauer et al. 2005, 2007, Kempf and Osthoff 2007, Ziegler
et al. 2011). However, the identification of isolated causal
effects of CSR on corporate financial performance needs
more sophisticated approaches. In this respect, event studies
examine the mean stock returns for corporations experienc-
ing a specific event (i.e. new information) and, therefore,
aim to measure the effect on the value of a corporation
(e.g. MacKinlay 1997, Kothari and Warner 2006). In the
meantime, a growing number of CSR-related event studies
is available (e.g. Hamilton 1995, Posnikoff 1997, Dasgupta
et al. 2001, Gupta and Goldar 2005, Curran and Moran
2007). However, one main weakness of event studies is that
the application of them in general depends on unexpected
events.
But, CSR rather refers to long-term corporate activities
and thus cannot often only be characterized by unex-
pected positive or negative events. As a consequence,
longer term econometric approaches have received
increasing attention for a while. These studies apply—in
the same way as portfolio analyses and event studies—
very different indicators for CSR. They additionally use
different indicators for corporate financial performance.
Due to the theoretical attractiveness as discussed above,

some of these studies (e.g. Filbeck and Gorman 2004,
Ziegler et al. 2007a) also use stock returns as they are a
constitutive element in portfolio analyses and event stud-
ies. In contrast, most other studies (e.g. Hart and Ahuja
1996, Russo and Fouts 1997, Waddock and Graves 1997,
McWilliams and Siegel 2000, King and Lenox 2001,
2002, Elsayed and Paton 2005, Telle 2006, Becchetti
et al. 2008, Guenster et al. 2011, Ziegler 2012) also
apply accounting data-based indicators for corporate finan-
cial performance such as Tobin’s Q, return on assets,
return on sales or return on equity.
Using a broad measurement of CSR, we consider both
corporate environmental and social activities. In contrast,
many former studies neglect the social dimension of CSR
by using one-dimensional indicators such as emissions of
pollutants (e.g. Hart and Ahuja 1996, King and Lenox
2001, 2002, Telle 2006). However, such emission data
seem to be a weak indicator for CSR in general because
they only give information about a single constituent of
corporate environmental performance. Other econometric
analyses use more general CSR indicators which, how-
ever, only refer to the environmental dimension (e.g. Rus-
so and Fouts 1997, Filbeck and Gorman 2004, Elsayed
and Paton 2005, Guenster et al. 2011). Studies which
also incorporate CSR dimensions such as Waddock and
Graves (1997), McWilliams and Siegel (2000), Ziegler
et al. (2007a) and Becchetti et al. (2008) are exceptions
in this respect. Furthermore, our study disentangles—such
as King and Lenox (2001) and Ziegler et al. (2007a)—
firm- and sector-specific influences and, therefore, addi-
tionally analyses industry environmental and social
performance.
4. Econometric approach

Our study applies cross-sectional regressions of average
stock returns over time on CSR. To explain such stock
performance, we include some control variables which are
received by time-series regressions of asset pricing models.
The estimated corporate beta parameters from this first
stage are then—in addition to the mainly interesting
CSR variables—incorporated in the final cross-sectional
regressions.
The effect of corporate social responsibility 979
4.1. Time-series regressions of asset pricing models
So far, the main asset pricing model for estimating stock
returns is the one-factor model based on the market model
(e.g. Sharpe 1963) and the CAPM (e.g. Lintner 1965, Fama
and French 2004, Perold 2004). This model can be formu-
lated for a corporation or stock i in month t
(i ¼ 1; . . . ; N; t ¼ 1; . . . ; T) as follows:
reit ¼ aCAPMi þ bCAPMi remt þ eit (1)
The excess returns are reit ¼ rit � rft and remt ¼ rmt � rft.
In this approach, rit and rmt are the (continuous) stock
returns for corporation i and the market at the end of month
t, rft is the risk-free interest rate at the beginning of month
t, and ɛit is the disturbance term with expectation zero and
(unknown) variance r2e . Finally, a
CAPM
i and b
CAPM
i are fur-

ther unknown parameters which are estimated by ordinary
least squares (OLS). The idea is that the estimated market-
beta parameters b̂
CAPM
i capture the non-diversifiable risk of
each stock which can be used in the final cross-sectional
regressions to explain average stock returns over time.
Based on the ‘anomalies’ discussion which questions the
validity of the CAPM (e.g. Banz 1981, DeBondt and Thaler
1985, Fama and French 1992), Fama and French (1993)
developed a three-factor model which includes—besides the
excess returns rmt�rft of the market—two additional factors
with respect to size and value to explain the excess returns
rit�rft:
reit ¼ aFFi þ bFFi1 remt þ bFFi2 SMBt þ bFFi3 HMLt þ eit (2)
In this model, SMBt is (at the end of month t) the differ-
ence between the returns for portfolios comprising stocks of
‘small’ corporations and portfolios comprising stocks of
‘big’ corporations. HMLt is (at the end of month t) the dif-
ference between the returns for portfolios comprising stocks
of corporations with a ‘high’ book-to-market equity and
portfolios comprising stocks of corporations with a ‘low’
book-to-market equity. The main unknown parameters are
now aFFi ; b
FF
i1 ; b
FF
i2 ; and b

FF
i3 . Many studies show that this
three-factor model has more explanatory power than the
one-factor model based on the CAPM, for example, Fama
and French (1993, 1996) for the USA, Berkowitz and Qiu
(2001) for the Canadian, Hussain et al. (2002) for the
British and Ziegler et al. (2007b) for the German stock
market.
Almost at the same time, however, a broad discus-
sion about another factor, namely the momentum factor,
began (e.g. Jagadeesh and Titman 1993, Rouwenhorst
1998, Jagadeesh and Titman 2001). As a consequence,
the following four-factor model of Carhart (1997) which
additionally includes this momentum factor—besides the
three Fama-French factors—is in the meantime, due to
the highest explanatory power, the most common asset
pricing model for applications in financial economics (e.
g. L’Her et al. 2004, Bollen and Busse 2005) and par-
ticularly, as discussed above, for portfolio analyses on
the relationship between CSR and stock performance:
reit ¼ aCARi þ bCARi1 remt þ bCARi2 SMBt þ bCARi3 HMLt
þ bCARi4 MOMt þ eit (3)
In this model, MOMt is (at the end of month t) the differ-
ence between the returns for portfolios comprising stocks of
‘winners’ in the past and portfolios comprising stocks of
‘losers’ in the past. The main unknown parameters are now
aCARi ; b
CAR
i1 ; b

CAR
i2 ; b
CAR
i3 ; and b
CAR
i4 .
4.2. Final cross-sectional regressions
The final cross-sectional regressions with the average
monthly stock returns �ri between 2003 and 2006 for corpo-
ration i as dependent variables incorporate—besides the
mainly interesting CSR variables (including environmental
and social performance of the industry to which a firm
belongs), summarized in the (column) vector CSRi—the
estimated beta parameters from the time-series regressions
of the several asset pricing models in the first stage as
explanatory variables. In other words, these regressions
either comprise b̂
CAPM
i or b̂
FF
i1 , b̂
FF
i2 and b̂
FF
i3 or b̂
CAR

i1 , b̂
CAR
i2 ,
b̂
CAR
i3 and b̂
CAR
i4 such that the following three estimation
equations arise (ζi are the respective disturbance terms):
�ri ¼ c þ d0CSRi þ kb̂
CAPM
i þ fi (4)
�ri ¼ c þ d0CSRi þ k1b̂
FF
i1 þ k2b̂
FF
i2 þ k3b̂
FF
i3 þ fi (5)
�ri ¼ c þ d0CSRi þ k1b̂
CAR
i1 þ k2b̂
CAR

i2 þ k3b̂
CAR
i3 þ k4b̂
CAR
i4 þ fi
(6)
The (heteroskedasticity robust OLS) estimation of the
parameters (or parameter vectors) leads to ĉ, d̂ and k̂ in
the first approach based on the CAPM, to ĉ, d̂ , k̂ 1, k̂ 2
and k̂ 3 in the second approach based on the Fama–French
three-factor model and to ĉ, d̂ , k̂ 1, k̂ 2, k̂ 3 and k̂ 4 in the
third approach based on the Carhart four-factor model. As
the estimated beta parameters can be theoretically consid-
ered as risk factors, the corresponding estimates k̂ , k̂ 1, k̂ 2,
k̂ 3 and k̂ 4 in the final cross-sectional regressions are
expected to be positive.
In this respect, it should be noted that the cross-sectional
regressions for the European stock market additionally
include nine country dummies as further explanatory vari-
ables to control for possible regional differences regarding
the average stock returns over time. The corresponding vari-
ables Fini, Frai, Geri, Itai, Neti, Spai, Swe, Swii and UKi
take the value one if corporation i stems from Finland,
France, Germany, Italy, the Netherlands, Spain, Sweden,
Switzerland and the UK, respectively. The final cross-sec-
tional regressions additionally comprise corporations from
Austria, Belgium, Denmark, Greece, Ireland, Norway and
Portugal. Due to the small number of firms from these

countries, the corresponding dummy variables are not
included in the regressions, but serve as a summarized
omitted reference category for the other country dummies.
All calculations for this paper were performed with the soft-
ware package STATA.
5. Data and variables
5.1. CSR data and variables
Concerning the CSR variables, we use data from the Swiss
bank Sarasin & Cie in Basle. Reliably beginning in 2001/
2002, this bank has assessed environmental and social
activities for 317 corporations in the USA and 720 Euro-
pean corporations quoted on different stock exchanges over
time. While most of these corporations are large, some of
them have a very low market capitalization. The latter firms
were only assessed due to their sustainability profile (from
the perspective of Sarasin). However, the problem is that
such diverse firms which belong to very different sectors
cannot be reliably compared regarding the effect of CSR on
stock performance. Therefore, we only examine those
assessed firms which were member of the Morgan Stanley
Capital International (MSCI) Europe Index or the MSCI US
Index at least once over the period between 1996 and 2006.
This period was chosen because we had only access to
financial data for these 11 years as discussed below. As a
consequence, the number of corporations reduces to 212 in
the USA and 419 in Europe.
Indeed, the corresponding necessary financial data and

the exclusively used assessments for 2002 are only avail-
able for N = 173 USA and N = 268 European corporations
which are finally considered in our empirical analysis. In
this respect, it should be noted that only those corpora-
tions are examined whose financial data are available for
all 132 months over the entire period between 1996 and
2006. The reason for this is that the number T of
observations should be large for the time-series regres-
sions of asset pricing models. In other words, if we had
additionally incorporated corporations whose financial data
are only available for a lower number of months, the
corresponding estimations of the beta parameters would
be less reliable. Furthermore, we incorporate lagged
explanatory variables by using the 2002 assessments and
the average monthly stock returns between 2003 and
2006 as dependent variables in the final cross-sectional
regressions.
In its assessments, Sarasin combines environmental and
social risk indicators in a two-dimensional rating and,
therefore, considers both activities of a firm compared with
other firms in the same industry as well as environmental
and social performance of the industry to which a firm
belongs. These two ratings are ultimately used to deter-
mine whether a corporation is suitable for Sarasin’s sus-
tainable investment funds and portfolios or not. The first
rating indicates how successfully firms manage the
industry-specific risks. Concerning the environmental
dimension of this rating, all measures of a corporation to
reduce environmental risks in the full value-added chain of
the products (pre-production, production, use of products
or services) are assessed. Furthermore, environmental
strategies and management systems are considered. Specifi-
cally, Sarasin uses sub-criteria which are proposed by the
World Business Council for Sustainable Development.

These environmental sub-criteria are energy intensity,
use of renewable energies, material intensity, toxicity,
revalorisation, durability and service intensity.
Concerning the social dimension of the first rating, it is
assessed how well a firm manages its internal and
external conflict potential, i.e. requirements of different
stakeholder groups. The following groups are considered
as stakeholders: employees, suppliers, investors, the gen-
eral public as well as—regarding the market—customers
and competitors. Key elements for the assessment are the
social strategy and social management systems of firms.
As social sub-criteria, Sarasin considers health risks, par-
ticipation, wealth creation and distribution, and knowledge
creation regarding their effects on stakeholder groups.
These single social and environmental sub-criteria—which
are assessed on a five-stage scale, respectively—are then
aggregated to the first broad rating. It should be noted
that the relevance of the several environmental sub-criteria
differs between sectors with respect to the value-added
chain of the products, whereas the relevance of the sev-
eral social sub-criteria differs between sectors with respect
to the importance of the individual stakeholder groups. As
a consequence, the final aggregation is based on different
weightings.
The second industry-specific rating refers to the assess-
ment of the environmental and social impacts and risks
which are particular for this sector. In this assessment, not
only the direct effects of producing the products and
services, but also indirect influences along the product
chain as well as lifecycle considerations are included.
Regarding the environmental dimension, two main sub-
criteria are considered, namely resource consumption and
emissions. According to these criteria, for example, pri-
mary industries such as chemicals, energy, energy suppli-

ers, metal production, mining, paper and cement all belong
to industries with higher environmental risks with respect
to the substantial direct impact of those industries on the
environment.
Regarding the social dimension, Sarasin distinguishes
between internal conflict potential (e.g. downsizing or inad-
equate working conditions) and external conflict potential
which comprises, for example, health risks caused by prod-
ucts and productions methods, concentration of economic
power, corruption and ethical conflicts. While each of these
single environmental and social industry-specific sub-criteria
is again independently assessed on a five-stage scale, the
several sub-criteria are finally—based on different weigh-
tings—aggregated to the second broad rating. Both aggre-
gated broad ratings are also based on a five-stage scale and,
therefore—in the same way as the single sub-criteria—
coded with the integers from one to five. In this respect, the
number one designates the worst and the number five the
best assessment.
In the following, Corpi symbolizes the corresponding
ordinal variable for the environmental and social activi-
ties of a firm i compared with other firms in the same
industry and Indui symbolizes the ordinal variable for
environmental and social performance of the industry to
which a firm i belongs. Since it is not certain that these
ratings are equidistant in each case, i.e. that the distance
between two numbers is always identical, we also examine
dummy variables based on these ordinal variables. How-
ever, preliminary investigations showed that the incorpora-

tion of overall eight single dummies lead to ambiguous
estimation results, obviously because the effects of these
variables are not linear. Therefore, we analyse two alterna-
tive aggregated dummy variables for both ratings in more
detail. The dummies Corp54i or Indu54i take the value one
if Corpi or Indui take the values five or four, respectively.
Furthermore, the dummies Corp543i or Indu543i take the
value one if Corpi or Indui take the values five, four or
three, respectively. As a consequence, the vectors CSRi in
the final cross-sectional regressions always comprise
exactly one pair of the variables Corpi and Indui, Corp54i
and Indu54i or Corp543i and Indu543i. Table 1 reports the
corresponding frequencies for the distribution of the values
of Corpi and Indui for the N = 173 USA and the N = 268
European corporations. Here, the relative frequencies (in%)
for the values of Corpi refer to the respective values of
Indui in the columns.
5.2. Financial variables
As aforementioned, we had access to financial data on
total return indexes (which contain both stock prices and
cash flows to the investor), market values and book values
(in US $, respectively) for the period between 1996 and
2006. These data stem from the Thomson Financial Data-
stream database. All monthly stock returns rit (in%) for
both the USA and European corporations in the empirical
analysis were calculated with these total return indexes.
The time-series regressions in the first stage of the econo-
metric analysis additionally require the inclusion of a
risk-free interest rate for the calculation of excess returns.
In this respect, we used the monthly return of one-month
US Treasury Bills. Furthermore, the time-series regressions
additionally require the inclusion of the monthly excess
stock returns remt for the market. For the USA, we directly
used the corresponding data (in%) from the homepage of

Kenneth R. French (http://mba.tuck.dartmouth.edu/pages/
faculty/ken.french/data_ library.html). The calculation of the
monthly returns rmt of a European stock market portfolio
(in%) is based on the total return indexes of the MSCI
Europe (in US $).
In the same way as remt, the factors SMBt, HMLt and
MOMt for the US stock market were directly extracted
from the homepage of Kenneth R. French. In contrast,
these factors were not publicly available for the entire
European stock market and thus had to be constructed.
The bases for this calculation were 917 European corpora-
tions which were member of the MSCI Europe at least
once over the complete period between 1996 and 2006.
Regarding SMBt and HMLt, corporations were ranked each
year on their market capitalization in June and indepen-
dently on their ratio between the published book value
for the previous year and the market value in December
of the previous year. Then, the median of the market
capitalizations as well as the 30 and 70% percentiles of
the book-to-market equity were calculated such that six
portfolios could be constructed from these three values. In
each June over time, the corporations were allocated anew
to one of these six portfolios and stay there from July for
the subsequent 12 months.
The construction of these portfolios only comprises
those corporations with corresponding available data for
June of the respective year and additionally with positive
book values for the previous year. Furthermore, stock
return data and market value data for the subsequent
12 months had to be available. The resulting times-series
of the value-weighted returns of these six stock portfolios
(between July 1997 and June 2006) were the basis for
the final calculations of SMBt which is the (weighted)

difference between the returns of ‘small’ corporations and
‘big’ corporations as well as HMLt which is the (weighted)
difference between the returns for corporations with a
‘high’ book-to-market equity and corporations with a ‘low’
book-to-market equity (according to the procedure of Fama
and French 1993).
Concerning MOMt, corporations were ranked in each
month t�1 on their market capitalization and independently
on their average stock returns between the months t�12
and t�2. Then, the median of the market capitalizations as
well as the 30 and 70% percentiles of the average stock
returns were calculated leading to six portfolios based on
these three values. The firms were allocated anew in each
month t�1 over time to one of these six portfolios. Their
construction only incorporates those corporations with avail-
able market values for this and the subsequent month and
additionally with available stock returns for the subsequent
month t and for each month between t�12 and t�2. The
resulting times-series of the value-weighted returns of four
stock portfolios (between February 1997 and December
2006) with respect to the bottom and top 30% of the past
average returns were the basis of the final calculations of
MOMt which is the (weighted) difference between the
returns of ‘winners’ and ‘losers’ in the past (according to
the procedure described on the homepage of Kenneth R.
French).
Table 2 reports descriptive statistics (mean, standard devi-
ation, minimum and maximum) for the average monthly
stock returns between 2003 and 2006 as well as for the esti-
mated corporate beta parameters from the times-series
regressions of the different asset pricing models. It addition-
ally reports descriptive statistics for two important firm
characteristics, namely the market value (in million US $)
and the book-to-market equity at the end of the observation

period 2006, respectively. Due to unavailable data and the
exclusion of negative values of the book-to-market equity
in Europe, the number of observations is only N = 170 for
the market value and the book-to-market equity in the USA
as well as only N = 253 for the market value and N = 247
for the book-to-market equity in Europe. Due to some
extreme values, the median instead of the mean is used for
these variables. The table shows, for example, that the mean
average stock return for European corporations (2.34%) is
noticeably higher than the mean for US corporations
(1.28%) in this specific period.
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_lib
rary.html
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_lib
rary.html
T
ab
le
1
.
T
h
is
ta
b
le
re
p

o
rt
s
ab
so
lu
te
fr
eq
u
en
ci
es
an
d
re
la
ti
v
e
fr
eq
u
en
ci
es
(i
n
%
re
g

ar
d
in
g
th
e
co
lu
m
n
s)
fo
r
th
e
v
al
u
es
o
f
th
e
o
rd
in
al
v
ar
ia
b
le

s
C
o
rp
i
an
d
In
d
u
i
in
2
0
0
2
.
C
o
rp
i
re
fe
rs
to
th
e
en
v
ir

o
n
m
en
ta
l
an
d
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ci
al
ac
ti
v
it
ie
s
o
f
fi
rm
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co
m
p
ar
ed
w
it
h
o
th
er

fi
rm
s
in
th
e
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m
e
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d
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st
ry
.
In
d
u
i
sy
m
b
o
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s
th
e
v
ar
ia
b
le

fo
r
en
v
ir
o
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m
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ta
l
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d
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ci
al
p
er
fo
rm
an
ce
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f
th
e
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st
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to
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T
h
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m
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er
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ig
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at
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e

w
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rs
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m
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v
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e
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t
as
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ss
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en
t
fo
r
C
o
rp
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an

d
In
d
u
i,
re
sp
ec
ti
v
el
y.
In
d
u
i
C
o
rp
i
5
4
3
2
1
T
o
ta
l

U
S
A 5
1
(1
2
.5
0
%
)
0
(0
.0
0
%
)
6
(7
.2
3
%
)
1
(2
.1
7
%
)
0
(0
.0

0
%
)
8
(4
.6
2
%
)
4
0
(0
.0
0
%
)
1
(3
.8
5
%
)
1
0
(1
2
.0
5
%
)
4

(8
.7
0
%
)
1
(1
0
.0
0
%
)
1
6
(9
.2
5
%
)
3
4
(5
0
.0
0
%
)
7
(2
6
.9
2

%
)
2
6
(3
1
.3
3
%
)
2
2
(4
7
.8
3
%
)
6
(6
0
.0
0
%
)
6
5
(3
7
.5
7
%

)
2
3
(3
7
.5
0
%
)
11
(4
2
.3
1
%
)
3
7
(4
4
.5
8
%
)
1
6
(3
4
.7
8
%
)

2
(2
0
.0
0
%
)
6
9
(3
9
.8
8
%
)
1
0
(0
.0
0
%
)
7
(2
6
.9
2
%
)
4
(4

.8
2
%
)
3
(6
.5
2
%
)
1
(1
0
.0
0
%
)
1
5
(8
.6
7
%
)
T
o
ta
l
8
(1
0

0
.0
0
%
)
2
6
(1
0
0
.0
0
%
)
8
3
(1
0
0
.0
0
%
)
4
6
(1
0
0
.0
0
%
)

1
0
(1
0
0
.0
0
%
)
1
7
3
(1
0
0
.0
0
%
)
In
d
u
i
C
o
rp
i
5
4
3

2
1
T
o
ta
l
E
u
ro
p
e
5
0
(0
.0
0
%
)
2
(4
.1
7
%
)
6
(5
.5
6
%
)

4
(4
.8
8
%
)
2
(8
.7
0
%
)
1
4
(5
.2
2
%
)
4
2
(2
8
.5
7
%
)
1
0
(2
0
.8

3
%
)
1
8
(1
6
.6
7
%
)
2
3
(2
8
.0
5
%
)
7
(3
0
.4
3
%
)
6
0
(2
2
.3
9

%
)
3
4
(5
7
.1
4
%
)
2
2
(4
5
.8
3
%
)
4
7
(4
3
.5
2
%
)
4
0
(4
8
.7
8

%
)
1
0
(4
3
.4
8
%
)
1
2
3
(4
5
.9
0
%
)
2
1
(1
4
.2
9
%
)
1
0
(2
0
.8

3
%
)
2
9
(2
6
.8
5
%
)
1
4
(1
7
.0
7
%
)
4
(1
7
.3
9
%
)
5
8
(2
1
.6
4

%
)
1
0
(0
.0
0
%
)
4
(8
.3
3
%
)
8
(7
.4
1
%
)
1
(1
.2
2
%
)
0
(0
.0
0

%
)
1
3
(4
.8
5
%
)
T
o
ta
l
7
(1
0
0
.0
0
%
)
4
8
(1
0
0
.0
0
%
)
1

0
8
(1
0
0
.0
0
%
)
8
2
(1
0
0
.0
0
%
)
2
3
(1
0
0
.0
0
%
)
2
6
8
(1
0
0

.0
0
%
)
6. Results
Table 3 reports the mutual correlation coefficients of �ri,
the market value, the book-to-market equity and the CSR
variables (including industry environmental and social per-
formance). In this respect, Spearman’s rank correlation
coefficients instead of Pearson’s correlation coefficients
were applied when the ordinal variables Corpi and Indui or
the market value and book-to-market equity are concerned.
The main results in this table are the positive coefficients
between stock performance and the different corporate
CSR variables as well as the negative coefficients between
the average stock returns and the industry environmental
and social performance variables. Concerning the latter,
they are strongly different from zero at least at the 5% sig-
nificance level for the USA. Furthermore, the correlation
coefficients between the average stock returns and
Corp543i for the USA and between stock performance and
Corp54i for Europe are different from zero at the 5%
significance level.
Table 4 reports the mutual Pearson’s correlation coeffi-
cients of �ri and the estimated corporate beta parameters
from the time-series regressions of the several asset pricing
models. The main results in this table are the positive corre-
lation coefficients between the average stock returns and the

different estimated beta parameters. The only exception is
the negative correlation coefficient between stock
performance and b̂
CAR
i4 for the USA, which is in addition
different from zero at the 5% significance level. In contrast,
the correlation coefficients between the average stock
returns and the other estimated beta parameters in this
Table 2. This table reports several descriptive statistics for the
average monthly stock returns �ri (in%) of firm i from 2003 to
2006 and
the estimated beta parameters from the time-series regressions.
In addition, the market value MVi (in million US $) and the
book-
to-market equity BMEi at the end of 2006 are considered. The
numbers of observations for the USA are N = 170 for MVi and
BMEi as well as N = 173 for the other variables. For Europe,
the numbers are N = 253 for MVi, N = 247 for BMEi and N =
268 for
the other variables. The smaller numbers of observations for
MVi and BMEi are due to unavailable data and the exclusion of
negative values of BMEi in Europe. For the case of MVi and
BMEi, the median instead of the mean is used due to some
extreme
values.
Variable Mean/median Standard deviation Minimum Maximum
USA
�ri 1.28 0.93 �1.96 5.28
MVi 22134.63 67038.37 2400.04 455561.80

BMEi 0.30 0.17 0.06 0.78
b̂
CAPM
i 0.92 0.60 �0.30 3.00
b̂
FF
i1 1.08 0.50 �0.07 3.03
b̂
FF
i2 �0.06 0.41 �1.04 1.40
b̂
FF
i3 0.33 0.68 �1.78 2.37
b̂
CAR
i1 1.04 0.45 �0.09 2.87
b̂
CAR
i2 �0.04 0.42 �1.03 1.49
b̂
CAR
i3 0.31 0.68 �1.74 2.29
b̂
CAR
i4 �0.09 0.24 �1.13 0.48
Europe

�ri 2.34 1.21 �8.84 6.95
MVi 13345.10 37158.86 997.98 219363.90
BMEi 0.36 0.21 0.01 1.17
b̂
CAPM
i 1.02 0.47 0.13 2.79
b̂
FF
i1 1.04 0.48 0.10 2.77
b̂
FF
i2 0.49 0.61 �1.82 2.77
b̂
FF
i3 0.16 0.61 �2.26 1.77
b̂
CAR
i1 1.01 0.44 0.18 2.59
b̂
CAR
i2 0.47 0.60 �1.85 2.75
b̂
CAR
i3 0.15 0.61 �2.26 1.76

b̂
CAR
i4 �0.09 0.25 �1.17 0.51
T
ab
le
3
.
T
h
is
ta
b
le
re
p
o
rt
s
th
e
m
u
tu
al
co
rr

el
at
io
n
co
ef
fi
ci
en
ts
b
et
w
ee
n
th
e
av
er
ag
e
m
o
n
th
ly
st
o
ck
re
tu
rn
s
� r i

(i
n
%
)
o
f
fi
rm
i
fr
o
m
2
0
0
3
to
2
0
0
6
as
w
el
l
as
th
e
m
ar

k
et
v
al
u
e
M
V
i
(i
n
m
il
li
o
n
U
S
$
)
an
d
th
e
b
o
o
k
-t
o
-m
ar
k

et
eq
u
it
y
B
M
E
i
at
th
e
en
d
o
f
2
0
0
6
,
re
sp
ec
ti
v
el
y.
F
u
rt
h
er

m
o
re
,
th
e
o
rd
in
al
v
ar
ia
b
le
s
C
o
rp
i
an
d
In
d
u
i
in
2
0
0
2
ar
e

co
n
si
d
er
ed
w
h
ic
h
re
fe
r
to
th
e
en
v
ir
o
n
m
en
ta
l
an
d
so
ci
al
ac
ti

v
it
ie
s
o
f
fi
rm
i
co
m
p
ar
ed
w
it
h
o
th
er
fi
rm
s
in
th
e
sa
m
e
in
d

u
st
ry
as
w
el
l
as
to
th
e
en
v
ir
o
n
m
en
ta
l
an
d
so
ci
al
p
er
fo
rm
an
ce

o
f
th
e
in
d
u
st
ry
to
w
h
ic
h
fi
rm
i
b
el
o
n
g
s.
T
h
e
n
u
m
b
er
o

n
e
d
es
ig
n
at
es
th
e
w
o
rs
t
an
d
th
e
n
u
m
b
er
fi
v
e
th
e
b
es
t
as
se
ss

m
en
t
fo
r
C
o
rp
i
an
d
In
d
u
i,
re
sp
ec
ti
v
el
y.
In
ad
d
it
io
n
,
th
e
d
u
m

m
y
v
ar
ia
b
le
s
C
o
rp
5
4
i
an
d
C
o
rp
5
4
3
i
o
n
th
e
b
as
is
o
f
C
o
rp

i
as
w
el
l
as
th
e
d
u
m
m
y
v
ar
ia
b
le
s
In
d
u
5
4
i
an
d
In
d
u
5
4
3
i

o
n
th
e
b
as
is
o
f
In
d
u
i
ar
e
ex
am
in
ed
.
If
C
o
rp
i,
In
d
u
i,
M
V
i,
o
r

B
M
E
i
ar
e
co
n
si
d
er
ed
,
S
p
ea
rm
an
’s
ra
n
k
co
rr
el
at
io
n
s
co
ef
fi
ci
en
ts

in
st
ea
d
o
f
P
ea
r-
so
n
’s
co
rr
el
at
io
n
s
co
ef
fi
ci
en
ts
ar
e
u
se
d
.
T
h

e
n
u
m
b
er
s
o
f
o
b
se
rv
at
io
n
s
fo
r
th
e
U
S
A
ar
e
N
=
1
7
0
if
M
V
i

an
d
B
M
E
i
ar
e
co
n
si
d
er
ed
as
w
el
l
as
N
=
1
7
3
if
o
n
ly
th
e
o
th
er

v
ar
ia
b
le
s
ar
e
co
n
si
d
-
er
ed
.
F
o
r
E
u
ro
p
e,
th
e
n
u
m
b
er
s
ar

e
N
=
2
5
3
if
M
V
i
is
co
n
si
d
er
ed
,
N
=
2
4
7
if
B
M
E
i
is
in
cl
u

d
ed
an
d
N
=
2
6
8
if
o
n
ly
th
e
o
th
er
v
ar
ia
b
le
s
ar
e
co
n
si
d
er

ed
.
T
h
e
sm
al
le
r
n
u
m
b
er
s
o
f
o
b
se
rv
at
io
n
s
fo
r
M
V
i
an
d
B
M

E
i
ar
e
d
u
e
to
u
n
av
ai
la
b
le
d
at
a
an
d
th
e
ex
cl
u
si
o
n
o
f
n
eg
at
iv

e
v
al
u
es
o
f
B
M
E
i
in
E
u
ro
p
e.
⁄
(⁄
⁄ ,
⁄⁄
⁄ )
m
ea
n
s
th
at
th
e

co
rr
el
at
io
n
co
ef
fi
ci
en
t
is
d
if
fe
re
n
t
fr
o
m
ze
ro
at
th
e
1
0
%
(5
an

d
1
%
)
si
g
n
ifi
ca
n
ce
le
v
el
,
re
sp
ec
ti
v
el
y.
� r i
M
V
i
B
M
E
i
C
o

rp
i
In
d
u
i
C
o
rp
5
4
i
In
d
u
5
4
i
C
o
rp
5
4
3
i
In
d
u
5
4
3

i
U
S
A � r i
1
.0
0
M
V
i
�0
.0
2
1
.0
0
B
M
E
i
�0
.0
0
�0
.0
4
1

.0
0
C
o
rp
i
0
.1
2
0
.0
6
�0
.1
6
⁄⁄
1
.0
0
In
d
u
i
�0
.1
7
⁄⁄
�0

.0
2
0
.0
1
�0
.1
4
⁄
1
.0
0
C
o
rp
5
4
i
0
.0
5
0
.0
6
�0
.0
8
0

.6
4
⁄⁄
⁄
�0
.0
1
1
.0
0
In
d
u
5
4
i
�0
.2
0
⁄⁄
⁄
�0
.0
0
0
.0
9

�0
.1
8
⁄⁄
0
.7
4
⁄⁄
⁄
�0
.1
1
1
.0
0
C
o
rp
5
4
3
i
0
.1
6
⁄⁄
0
.0
6

�0
.1
5
⁄
0
.9
2
⁄⁄
⁄
�0
.1
5
⁄⁄
0
.3
9
⁄⁄
⁄
�0
.1
3
⁄
1
.0
0
In
d

u
5
4
3
i
�0
.1
7
⁄⁄
�0
.0
2
�0
.0
1
�0
.0
8
0
.8
7
⁄⁄
⁄
0
.0
6
0
.3
4

⁄⁄
⁄
�0
.1
3
⁄
1
.0
0
E
u
ro
p
e
� r i
1
.0
0
M
V
i
�0
.1
5
⁄⁄
1
.0
0

B
M
E
i
�0
.1
2
⁄
0
.0
8
1
.0
0
C
o
rp
i
0
.1
2
⁄
0
.0
7
�0
.0
1

1
.0
0
In
d
u
i
�0
.1
1
⁄
�0
.1
5
⁄⁄
�0
.0
5
�0
.1
4
⁄⁄
1
.0
0
C
o
rp

5
4
i
0
.1
6
⁄⁄
0
.0
4
�0
.0
4
0
.8
2
⁄⁄
⁄
�0
.1
0
⁄
1
.0
0
In
d
u

5
4
i
�0
.0
7
�0
.1
0
�0
.0
1
�0
.0
3
0
.7
4
⁄⁄
⁄
�0
.0
2
1
.0
0
C

o
rp
5
4
3
i
0
.0
2
0
.0
8
0
.0
0
0
.8
1
⁄⁄
⁄
�0
.1
1
⁄
0
.3
7
⁄⁄

⁄
�0
.0
1
1
.0
0
In
d
u
5
4
3
i
�0
.1
1
⁄
�0
.1
0
�0
.0
5
�0
.1
7
⁄⁄

⁄
0
.8
9
⁄⁄
⁄
�0
.1
2
⁄
0
.4
1
⁄⁄
⁄
�0
.1
5
⁄⁄
1
.0
0
country are clearly positive and different from zero at the
1% significance level. Regarding Europe, the respective cor-
relation coefficients are without exception positive and only

insignificantly different from zero for b̂
CAPM
i and b̂
FF
i1 .
However, it should be noted that the results in tables 3
and 4 only indicate univariate relationships. Therefore,
tables 5 and 6 report the estimation results of our econo-
metric analysis. The corresponding econometric models
incorporate—besides the mainly interesting CSR variables
(including industry environmental and social perfor-
mance)—the estimated beta parameters from the time-series
regressions of the several asset pricing models as financial
control variables to explain the average monthly stock
returns between 2003 and 2006. While table 5 refers to the
USA, table 6 reports the estimation results for Europe. In
both cases, the cross-sectional regressions according to
equations (4), (4)′ and (4)″ are based on the CAPM, accord-
ing to equations (5), (5)′ and (5)″ are based on the Fama-
French three-factor model and according to equations (6),
(6)′ and (6)″ are based on the Carhart four-factor model.
Furthermore, the respective final regressions according to
the first three equations (4), (5) and (6) incorporate the
ordinal variables Corpi and Indui, while the remaining
regressions either include the dummies Corp54i and Indu54i
or the dummies Corp543i and Indu543i.
According to table 5, the ordinal variable Corpi has a
weakly positive effect (at the 10% significance level) when
the estimated corporate beta parameters from the multifactor
models are included as control variables. This impact is

more significantly positive for the dummy variable
Corp543i. In contrast, the latter effect has a higher signifi-
cance level on the basis of the CAPM and the parameter of
Corpi is not even significantly different from zero in this
case. However, it appears that the estimation results based
on the CAPM are less reliable because the estimated beta
parameters b̂
FF
i2 and b̂
CAR
i2 from the multifactor models—
besides b̂
CAPM
i , b̂
FF
i1 or b̂
CAR
i1 —have a high explanatory
power. Therefore, corporate environmental and social activi-
ties obviously matter for the average monthly stock returns
between 2003 and 2006 in the USA even when the effect is
insignificant for the dummy Corp54i. This latter result
points to possible non-linear effects with respect to the
intensity of these measures.
Table 4. This table reports the mutual Pearson’s correlation
coefficients between the average monthly stock returns �ri
(in%) of firm i

from 2003 to 2006 and the estimated beta parameters from the
time-series regressions. The numbers of observations are N =
173
for the USA and N = 268 for Europe. ⁄ (⁄⁄, ⁄⁄⁄) means that the
correlation coefficient is different from zero at the 10% (5 and
1%) significance level, respectively.
�ri b̂
CAPM
i b̂
FF
i k̂
FF
i1 k̂
FF
i2 b̂
CAR
i k̂
CAR
i1 k̂
CAR
i2 k̂
CAR
i3
USA
�ri 1.00

b̂
CAPM
i 0.27
⁄⁄⁄ 1.00
b̂
FF
i1 0.38
⁄⁄⁄ 0.86⁄⁄⁄ 1.00
b̂
FF
i2 0.47
⁄⁄⁄ 0.32⁄⁄⁄ 0.38⁄⁄⁄ 1.00
b̂
FF
i3 0.21
⁄⁄⁄ �0.44⁄⁄⁄ 0.07 0.28⁄⁄⁄ 1.00
b̂
CAR
i1 0.38
⁄⁄⁄ 0.85⁄⁄⁄ 0.98⁄⁄⁄ 0.38⁄⁄⁄ 0.06 1.00
b̂
CAR
i2 0.47
⁄⁄⁄ 0.37⁄⁄⁄ 0.44⁄⁄⁄ 0.99⁄⁄⁄ 0.28⁄⁄⁄ 0.41⁄⁄⁄ 1.00

b̂
CAR
i3 0.20
⁄⁄⁄ �0.47⁄⁄⁄ 0.03 0.27⁄⁄⁄ 1.00⁄⁄⁄ 0.03 0.26⁄⁄⁄ 1.00
b̂
CAR
i4 �0.17⁄⁄ �0.43⁄⁄⁄ �0.52⁄⁄⁄ �0.18⁄⁄ �0.05 �0.35⁄⁄⁄ �0.31⁄⁄⁄ 0.02
1.00
Europe
�ri 1.00
b̂
CAPM
i 0.05 1.00
b̂
FF
i1 0.08 1.00
⁄⁄⁄ 1.00
b̂
FF
i2 0.35
⁄⁄⁄ 0.17⁄⁄⁄ 0.22⁄⁄⁄ 1.00
b̂
FF

i3 0.16
⁄⁄ �0.39⁄⁄⁄ �0.37⁄⁄⁄ 0.09 1.00
b̂
CAR
i1 0.12
⁄⁄ 0.98⁄⁄⁄ 0.98⁄⁄⁄ 0.21⁄⁄⁄ �0.38⁄⁄⁄ 1.00
b̂
CAR
i2 0.36
⁄⁄⁄ 0.11⁄ 0.17⁄⁄⁄ 0.99⁄⁄⁄ 0.10 0.17⁄⁄⁄ 1.00
b̂
CAR
i3 0.16
⁄⁄ �0.40⁄⁄⁄ �0.37⁄⁄⁄ 0.09 1.00⁄⁄⁄ �0.38⁄⁄⁄ 0.10 1.00
b̂
CAR
i4 0.13
⁄⁄ �0.56⁄⁄⁄ �0.56⁄⁄⁄ �0.18⁄⁄⁄ 0.11⁄ �0.40⁄⁄⁄ �0.08 0.13⁄⁄ 1.00
T
ab
le

5
.
T
h
is
ta
b
le
re
p
o
rt
s
th
e
O
L
S
p
ar
am
et
er
es
ti
m
at
es
in
th
e

fi
n
al
cr
o
ss
-s
ec
ti
o
n
al
re
g
re
ss
io
n
s
fo
r
th
e
U
S
A
.
T
h
e
d
ep
en
d

en
t
v
ar
ia
b
le
is
th
e
av
er
ag
e
m
o
n
th
ly
st
o
ck
re
tu
rn
� r i
(i
n
%
)
o
f

fi
rm
i
fr
o
m
2
0
0
3
to
2
0
0
6
.T
h
e
m
ai
n
ex
p
la
n
at
o
ry
v
ar
ia
b

le
s
in
(4
),
(5
)
an
d
(6
)
ar
e
th
e
o
rd
in
al
v
ar
ia
b
le
s
C
o
rp
i
an
d
In
d

u
i
in
2
0
0
2
w
h
ic
h
re
fe
r
to
th
e
en
v
ir
o
n
m
en
ta
l
an
d
so
ci
al
ac
ti

v
it
ie
s
o
f
fi
rm
i
co
m
-
p
ar
ed
w
it
h
o
th
er
fi
rm
s
in
th
e
sa
m
e

in
d
u
st
ry
as
w
el
l
as
to
th
e
en
v
ir
o
n
m
en
ta
l
an
d
so
ci
al
p
er
fo
rm
an

ce
o
f
th
e
in
d
u
st
ry
to
w
h
ic
h
fi
rm
i
b
el
o
n
g
s.
T
h
e
n
u
m
b
er

o
n
e
d
es
ig
n
at
es
th
e
w
o
rs
t
an
d
th
e
n
u
m
b
er
fi
v
e
th
e
b
es
t

as
se
ss
m
en
t
fo
r
C
o
rp
i
an
d
In
d
u
i,
re
sp
ec
ti
v
el
y.
T
h
e
m
ai
n
ex
p
la

n
at
o
ry
v
ar
ia
b
le
s
in
(4
)′
,
(5
)′
an
d
(6
)′
ar
e
th
e
d
u
m
m
y
v
ar
ia
b
le

s
C
o
rp
5
4
i
o
n
th
e
b
as
is
o
f
C
o
rp
i
an
d
In
d
u
5
4
i
o
n
th
e
b

as
is
o
f
In
d
u
i.
T
h
e
m
ai
n
ex
p
la
n
at
o
ry
v
ar
ia
b
le
s
in
(4
)″
,
(5
)″

an
d
(6
)″
ar
e
th
e
d
u
m
m
y
v
ar
ia
b
le
s
C
o
rp
5
4
3
i
o
n
th
e
b
as
is

o
f
C
o
rp
i
an
d
In
d
u
5
4
3
i
o
n
th
e
b
as
is
o
f
In
d
u
i.
W
h
il
e
(4

),
(4
)′
an
d
(4
)″
ar
e
b
as
ed
o
n
th
e
C
A
P
M
an
d
th
u
s
o
n
ly
in
co
rp
o

ra
te
b̂
C
A
P
M
i
as
co
n
tr
o
l
v
ar
ia
b
le
,
(5
),
(5
)′
an
d
(5
)″
ar
e
b
as

ed
o
n
th
e
F
am
a–
F
re
n
ch
th
re
e-
fa
ct
o
r
m
o
d
el
in
cl
u
d
in
g
b̂
F
F

i1
,
b̂
F
F
i2
an
d
b̂
F
F
i3
as
co
n
tr
o
l
v
ar
ia
b
le
s
an
d
(6
),
(6
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d
(6
)″
ar
e
b
as
ed
o
n
th
e
C
ar
h
ar
t
fo
u
r-
fa
ct
o
r
m
o
d
el
in
cl
u
d
in

g
b̂
C
A
R
i1
,
b̂
C
A
R
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,
b̂
C
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an
d
b̂
C
A
R
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as
co
n
tr
o
l

v
ar
ia
b
le
s.
T
h
e
n
u
m
b
er
s
o
f
o
b
se
rv
at
io
n
s
ar
e
N
=
1
7
3
,
re

sp
ec
ti
v
el
y.
⁄
(⁄
⁄ ,
⁄⁄
⁄ )
m
ea
n
s
th
at
th
e
ap
p
ro
p
ri
at
e
p
ar
am
et
er

is
d
if
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re
n
t
fr
o
m
ze
ro
o
r—
re
g
ar
d
in
g
th
e
F
-t
es
t—
th
at
al
l

ex
p
la
n
at
o
ry
v
ar
ia
b
le
s
to
g
et
h
er
h
av
e
an
ef
fe
ct
at
th
e
1
0
%

(5
an
d
1
%
)
si
g
n
ifi
ca
n
ce
le
v
el
,
re
sp
ec
ti
v
el
y.
T
h
e
st
an
d
ar
d
d

ev
ia
ti
o
n
s
fo
r
th
e
es
ti
m
at
ed
p
ar
am
et
er
s
ar
e
es
ti
m
at
ed
h
et
er
o
sc

ed
as
ti
ci
ty
ro
b
u
st
.
E
x
p
la
n
at
o
ry
v
ar
ia
b
le
s
(4
)
(5
)
(6
)

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(6
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rp
i
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T
ab
le
6
.
T
h
is
ta
b
le
re
p
o
rt
s
th
e
O
L
S
p
ar
am

et
er
es
ti
m
at
es
in
th
e
fi
n
al
cr
o
ss
-s
ec
ti
o
n
al
re
g
re
ss
io
n
s
fo
r
E

u
ro
p
e.
T
h
e
d
ep
en
d
en
t
v
ar
ia
b
le
is
th
e
av
er
ag
e
m
o
n
th
ly
st
o
ck

re
tu
rn
� r i
(i
n
%
)
o
f
fi
rm
i
fr
o
m
2
0
0
3
to
2
0
0
6
.T
h
e
m
ai
n
ex

p
la
n
at
o
ry
v
ar
ia
b
le
s
in
(4
),
(5
)
an
d
(6
)
ar
e
th
e
o
rd
in
al
v
ar
ia
b

le
s
C
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rp
i
an
d
In
d
u
i
in
2
0
0
2
w
h
ic
h
re
fe
r
to
th
e
en
v
ir
o
n
m

en
ta
l
an
d
so
ci
al
ac
ti
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it
ie
s
o
f
fi
rm
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co
m
p
ar
ed
w
it
h
o
th
er
fi

rm
s
in
th
e
sa
m
e
in
d
u
st
ry
as
w
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as
to
th
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so
ci
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s.
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m
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e
d
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ig
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at
es
th
e
w
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rs
t
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d
th
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n
u
m
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er

fi
v
e
th
e
b
es
t
as
se
ss
m
en
t
fo
r
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an
d
In
d
u
i,
re
sp
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ti
v
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y.

T
h
e
m
ai
n
ex
p
la
n
at
o
ry
v
ar
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le
s
in
(4
)′
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d
(6
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e
th
e
d

u
m
m
y
v
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le
s
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5
4
i
o
n
th
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as
is
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f
C
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d
In
d
u
5

4
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o
n
th
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is
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In
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h
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m
ai
n
ex
p
la
n
at
o
ry
v
ar
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le
s

in
(4
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,
(5
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d
(6
)″
ar
e
th
e
d
u
m
m
y
v
ar
ia
b
le
s
C
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5
4
3
i
o

n
th
e
b
as
is
o
f
C
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d
In
d
u
5
4
3
i
o
n
th
e
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as
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f
In
d
u

i.
W
h
il
e
(4
),
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)′
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d
(4
)″
ar
e
b
as
ed
o
n
th
e
C
A
P
M
an
d
th
u
s
in

co
rp
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ra
te
b̂
C
A
P
M
i
as
co
n
tr
o
l
v
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,
(5
),
(5
)′
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d
(5
)″
ar

e
b
as
ed
o
n
th
e
F
am
a–
F
re
n
ch
th
re
e-
fa
ct
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m
o
d
el
in
cl
u
d
in
g

b̂
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F
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,
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d
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as
co
n
tr
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l
v
ar
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b
le
s
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d
(6
),

(6
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an
d
(6
)″
ar
e
b
as
ed
o
n
th
e
C
ar
h
ar
t
fo
u
r-
fa
ct
o
r
m
o
d
el
in
cl

u
d
in
g
b̂
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A
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,
b̂
C
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an
d
b̂
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as
co
n

tr
o
l
v
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u
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m
o
re
,
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v
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n
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d
u
m
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s
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e

in
co
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te
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n
-
tr
o
l
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le
s.
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h
e
n
u
m
b
er
s
o
f
o
b
se

rv
at
io
n
s
ar
e
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=
2
6
8
,
re
sp
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ti
v
el
y.
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(⁄
,
⁄⁄
⁄ )
m
ea
n
s
th
at
th
e

ap
p
ro
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ri
at
e
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ar
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er
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d
if
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re
n
t
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o
m
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ro
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re
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d
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g
th
e
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-t
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t—
th
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al
l
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p
la
n
at
o
ry
v
ar
ia
b
le
s
to
g
et
h
er
h
av
e

an
ef
fe
ct
at
th
e
1
0
%
(5
an
d
1
%
)
si
g
n
ifi
ca
n
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le
v
el
,
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h
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d
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d
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ia
ti
o
n
s
fo
r
th
e
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m
at
ed
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ar
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et
er
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ar
e

es
ti
m
at
ed
h
et
er
o
sc
ed
as
ti
ci
ty
ro
b
u
st
.
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x
p
la
n
at
o
ry
v
ar
ia
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le
s
(4
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)
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⁄
Concerning the impact of industry environmental and
social performance for the US stock market, the estimation
results are different. The ordinal variable Indui as well as
the corresponding dummy variables Indu54i and Indu543i
have a strong negative effect on stock performance (at the
1% significance level) when the estimated beta parameters
from the CAPM are incorporated. However, these effects
become much less significant for the case of Indu543i on
the basis of the Fama–French three-factor or the Carhart
four-factor models (the corresponding slightly significant
effect would be in line with some results in Hong and
Kacperczyk 2009, who report in their analysis of sin stocks
in the USA that a lower industry social performance has a
positive effect on stock performance). Furthermore, the
effects become even completely insignificant for the two
other industry environmental and social performance vari-
ables on the basis of the multifactor models. Due to the
high explanatory power of some estimated corporate beta
parameters from the multifactor models as discussed above,
the estimation results based on the CAPM are overall obvi-
ously not reliable, but a typical example for biased parame-
ter estimations due to omitted explanatory variables.
According to table 6, this problem holds true for the
effect of Indu543i and (to a lower extent) of Indui in Eur-
ope. While these variables have a negative influence on
average stock returns (at the 5 or 10% significance level)
based on the CAPM, this effect becomes insignificant on
the basis of both multifactor models. The impacts of
Indu54i are already insignificant, irrespective of the underly-

ing asset pricing models. In the same way, no significant
effects of Corpi and Corp543i arise. In contrast, Corp54i
has a positive effect on stock performance (at the 5% sig-
nificance level) based on the CAPM as well as on both
multifactor models. Therefore, only single positive effects
on average monthly stock returns between 2003 and 2006
appear to be existent in Europe, but no linear effects of an
increasing intensity of corporate environmental and social
activities.
These estimation results are robust in different ways: in
order to account for our two-stage econometric analysis
which includes estimated beta parameters from the first stage
in the final cross-sectional regressions, we have also con-
ducted adjustments of the estimated standard deviations of
the estimated parameters according to Shanken (1992). While
the estimated beta parameters now—as expected—mostly
have no significant effect on stock performance (an exception
is that in Europe b̂
FF
i2 still has a significantly positive impact),
the estimation results for the mainly interesting CSR vari-
ables are not systematically different from the results as dis-
cussed above. In fact, the adjusted estimation results (which
are available on request) even strengthen some previous con-
clusions since Indu543i, for example, in the USA now has a
completely insignificant effect on the basis of the Fama–
French three-factor and the Carhart four-factor models.
Furthermore, it should be noted that corporations were
ranked each year on their market capitalization in June and
on their book-to-market equity in December of the previous
year for the calculation of SMBt and HMLt in Europe as
discussed above. In this respect, we used the book values

which were published in June each year in the Thomson
Financial Datastream database to ensure that the values for
the previous year are actually considered. However, it is
also possible that these values are influenced by new devel-
opments during the current year. Therefore, we additionally
considered the book-to-market equity based on the pub-
lished book and market values in December of the previous
year in a further analysis. Moreover, we also examined the
published book and market values in June from Thomson
Financial Datastream. Indeed, the estimation results based
on these calculations are not systematically different from
the main results as discussed above.
Finally, the estimation results in this paper are in princi-
ple based on the 2002 assessments of CSR (including
industry environmental and social performance). However,
it should be noted that many corporations were assessed for
the first time by Sarasin after 2002. For these firms, we
incorporated the corresponding first assessments in our
empirical analysis. This procedure seems to be justified
because the assessments have an extremely low variability
over time for the respective corporations. We nevertheless
excluded in a further analysis those firms with very recent
assessments, i.e. with first assessments after 2004. However,
we continued to include firms with first assessments in
2003 or 2004. This procedure of extending assessments for
two years is common in empirical analyses of the relation-
ship between CSR and corporate financial performance to
avoid very small samples (e.g. Derwall et al. 2005). Indeed,
the corresponding estimation results are again qualitatively
fully in line with the main results as discussed above.
7. Discussion and conclusions
This paper provides new empirical evidence for the effect

of CSR (which is measured by environmental and social
activities of a firm compared with other firms within the
industry and additionally considers environmental and
social performance of the industry to which a firm belongs)
on average monthly stock returns between 2003 and 2006.
In contrast to former studies, it examines two worldwide
leading stock markets, namely the USA and the European
stock markets, in order to analyse potentially different rela-
tionships between CSR and financial performance. Our
two-stage econometric analysis shows that corporate envi-
ronmental and social activities matter for the explanation of
stock performance in both regions. However, this impact is
obviously not linear for an increasing intensity of these
measures. Compared with Europe, the positive effect fur-
thermore is more robust for the USA. While it can only be
speculated why the positive impact in the USA is slightly
stronger, one explanation could be the longer tradition of
ethical components of CSR and particularly of SRI than in
Europe. In contrast, the industry environmental and social
performance has no robust influence on the average
monthly stock returns between 2003 and 2006 in any
region.
According to these results, the stock markets—and partic-
ularly the US stock market—obviously rewarded invest-
ments in stocks of corporations with a high intensity of
environmental and social activities compared with other
firms within the industry. In other words, investors who
applied a buy-and-hold strategy would have increased their
portfolio value by investing in such stocks. Regarding the
management of a firm, these results imply that such mea-

sures could be increased since they obviously do not lead
to worse financial performance. The results furthermore
support the advocates of information-based regulations by
improving the flow of the respective information. However,
the question is whether the discussed positive effect is
robust for alternative measurements of CSR, for example,
based on assessments from other rating agencies or based
on quantitative and thus more objective indicators such as
emissions, as well as for alternative measurements of corpo-
rate financial performance, for example, on the basis of
accounting data based indicators. Such studies would be
interesting in the future. Another field for further research
would be the econometric analysis of alternative periods to
examine whether the consideration of the period between
2003 and 2006 produces specific estimation results.
Irrespective of such future research, our study supports
the incorporation of more flexible asset pricing models: on
the basis of the simple CAPM, industry environmental and
social performance has a significantly negative impact on
stock performance in the USA. However, the significance
of this effect strongly decreases and mostly disappears if
estimated corporate beta parameters from the Fama–French
three-factor or the Carhart four-factor models are included
as additional control variables. This result (in line with e.g.
McWilliams and Siegel 2000) points to the problem of mis-
leading conclusions regarding the effect of CSR on corpo-
rate financial performance if misspecified econometric
models are applied due to omitted explanatory variables
such that biased parameter estimations occur.
Acknowledgements
We would like to thank a referee for his useful comments,
Eckhard Plinke and the bank Sarasin & Cie in Basle for
providing their assessment data, Kenneth R. French, Ulrich

Oberndorfer, Michael Schröder and participants of several
conferences for stimulating discussions, as well as Eveline
Schwegler for her untiring commitment during data
analysis. Our special thanks go to Peter Schmidt for his
very helpful support in the econometric analysis.
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38 Monthly Labor Review • June 2012
Telecommuting
The hard truth about telecommuting
Telecommuting has not permeated the American workplace, and
where it has become commonly used, it is not helpful in
reducing
work-family conflicts; telecommuting appears, instead, to have
become instrumental in the general expansion of work hours,
facilitating workers’ needs for additional worktime beyond the
standard workweek and/or the ability of employers to increase
or
intensify work demands among their salaried employees
Mary C. Noonan
and
Jennifer L. Glass
Mary C. Noonan is an Associate
Professor at the Department of
Sociology, The University of Iowa;
Jennifer L. Glass is the Barbara
Bush Regents Professor of Liberal
Arts at the Department of Sociol-
ogy and Population Research
Center, University of Texas at
Austin. Email: [email protected]
uiowa.edu or [email protected]
austin.utexas.edu.
Telecommuting, defined here as work tasks regularly performed
at home, has achieved enough
traction in the American workplace to

merit intensive scrutiny, with 24 percent
of employed Americans reporting in recent
surveys that they work at least some hours
at home each week.1 The definitions of
telecommuting are quite diverse. In this ar-
ticle, we define telecommuters as employ-
ees who work regularly, but not exclusively,
at home. In our definition, at-home work
activities do not need to be technologically
mediated nor do telecommuters need a
formal arrangement with their employer to
work at home.
Telecommuting is popular with policy
makers and activists, with proponents
pointing out the multiple ways in which
telecommuting can cut commuting time
and costs,2 reduce energy consumption
and traffic congestion, and contribute to
worklife balance for those with caregiving
responsibilities.3 Changes in the structure
of jobs that enable mothers to more effec-
tively compete in the workplace, such as
telecommuting, may be needed to finally
eliminate the gender gap in earnings and
direct more earned income to children,
both important public policy goals.4
Evidence also reveals that an increasing num-
ber of jobs in the American economy could be
performed at home if employers were willing
to allow employees to do so.5 Often, employees
can perform jobs at home without supervision
in the “high-tech” sector, in the financial sector,
and many in the communication sector that are
technology dependent. The obstacles or barriers

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