Socially Responsible Investing: A comparative
analysis of environmental, social, governance,
reputational and labor factors.
ARC IVE:S
by
Arun Balasubramaniam
Submitted to the Engineering Systems Division
in partial fulfillment of the requirements for the degree of
Master of Science in Engineering and Management
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2011
© Massachusetts Institute of Technology 2011. All rights reserved.
A uthor ................................
..
n
.......................
ring Systems Division
June 08, 2011
...
Certified by........................
-........
, .......
Nicholas A Ashford
Professor of Technology and Policy and Director of Technology and
Law Program
Thesis Supervisor
Accepted by ........................
........
Par 1 Hale
Director, System Design and Management Program
Socially Responsible Investing : A comparative analysis of
environmental, social, governance, reputational and labor
factors.
by
Arun Balasubramaniam
Submitted to the Engineering Systems Division
on June 08, 2011, in partial fulfillment of the
requirements for the degree of
Science in Engineering and Management
of
Master
Abstract
Socially Responsible Investing (SRI) aims to deliver competitive investment returns
while fostering social good. It aims achieves its objective by including a firm's corpoI has giesgnfct
.
rate social performance (CSP) in its investment d s
momentum over the past few years and is poised to assume a mainstream role in
the asset management business. However, the scholarship on the effect of corporate
social performance on a firm's corporate financial performance (CFP) is ambiguous.
CSP is a complex entity made of multi-dimensional sub-components. This thesis attempts to breakdown the multi-dimensional CSP into its core constituent dimensions
and to examine their inter-relationships and relationship with CFP, using statistical analysis. Two different vendor data sets were used as samples to understand if
proprietary transformations made by vendors affect results. Analysis reveals that
differences in factor payoff horizons, difficulties in transforming environmental, social and governance data into composite CSP ratings and the proprietary nature of
such transformation could be some of the contributing factors to the ambiguity in
establishing the nature of CSP-CFP relationship.
Thesis Supervisor: Nicholas A Ashford
Title: Professor of Technology and Policy and Director of Technology and Law Program
3
Acknowledgments
It is my pleasure to take this opportunity to convey my gratitude to all the people
who have contributed to this thesis in many different ways. This work would not
have been possible without the knowledge and guidance of my advisor, Dr. Nicholas
Ashford. In addition to providing insightful comments, and beneficial data pointers,
his immediate responses even on weekends, helped me make steady progress. I also
greatly appreciate his flexibility with schedules and willingness to work with my parttime schedule. I express sincere appreciation and thanks to Dr.Jeffery Wurgler, NYU
Stern school of business for his periodic reviews. Through his finance acumen and
experience, he was able to provide insights which accelerated research efforts and
helped anticipate pragmatic limitations in financial data. I am deeply indebted to
Bryan Carter, James Dufort, John Chisholm and Geoff Kemmish at Acadian Asset
Management LLC, Boston for their thoughtful input. The support and guidance from
Acadian was invaluable and helped me get up to speed on basic econometric analysis.
I
am pleased to acknowledge the flexibility pioviueU uy Dr. Patrick Hale and the
SDM program without which I could have not gathered enough background for this
work. Finally, I express my special thanks and appreciation to my wife, Soumya for
her endless patience, encouragement and support that enabled me to complete this
work and to my parents for their belief in me.
5
6
Contents
1
2
1.1
M otivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
1.2
Approach and Thesis organization . . . . . . . . . . . . . . . . . . . .
14
4
2.1
Socially Responsible Investing (SRI)
. . . . . . . . . . . . . . . . . .
17
2.2
Corporate Social Responsibility (CSR) . . . . . . . . . . . . . . . . .
21
2.3
Corporate Social Performance (CSP) and Cornorate Financial Perfor-
m ance (CFP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
H ypotheses
27
Data Description
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
28
3.3
Factor Classifications . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
3.4
Data Limitations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
38
3.1
Data Sources
3.2
Data Description
Comparative Data Analysis : Factor Correlations
39
4.1
Summary Data
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
4.2
Factor Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43
. . . . . . . . . . . .
46
4.2.1
5
17
Definitions and Literature Review
2.4
3
13
Introduction
Hypothesis IV - Labor and Environment
Comparative Data Analysis : Factor Analysis
5.1
Exploratory Factor Analysis (EFA)
7
. . . . . . . . . . . . . . . . . . .
49
50
5.2
Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . .
6 Comparative Data Analysis : Regression
52
57
6.1
Model Development . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
6.2
Model Limitations
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
59
6.3
Treatment of returns over different time frames
. . . . . . . . . . . .
60
6.4
Regression results . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
7 Summary
65
A Technical Architecture
69
A .1 O verview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
69
. . . . . . . . . . . . . . . . . . . . . . . . . . .
70
A.2.1
Database Tables: Data from Financial Sources . . . . . . . . .
70
A.2.2
Database Tables: Generated data . . . . . . . . . . . . . . . .
71
A.2.3
Stored Procedures
. . . . . . . . . . . . . . . . . . . . . . . .
71
A .2.4 Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
72
A.2 System Components
B Tables
75
C Figures
83
8
List of Figures
2-1
SRI growth in the US.
. . . . . . . . . . . .
18
4-1
Innovest Data Correlations : All World . . .
44
4-2
Asset4 Data Correlations : All World . . . .
45
5-1
Innovest Data . . . . . . . . . . . . . . . . .
50
5-2
Asset4 Data . . . . . . . . . . . . . . . . . .
51
A-1
System Overview
. . . . . . . . . . . . . . .
69
C-1 Innovest Factor Corrleations : United States
. .
84
C-2 Innovest Factor Corrleations : Japan . . . .
. .
84
C-3 Innovest Factor Corrleations : Germany
. .
. .
85
C-4 Innovest Factor Corrleations : France . . . .
. .
85
C-5 Innovest Factor Corrleations : Great Britain
. .
86
C-6 Asset4 Factor Corrleations
United States .
. .
86
C-7 Asset4 Factor Corrleations
Japan . . . . .
. .
87
C-8 Asset4 Factor Corrleations
Germany
. . .
. .
87
C-9 Asset4 Factor Corrleations
France . . . . .
. .
88
C-10 Asset4 Factor Corrleations
Great Britain .
. .
88
9
10
List of Tables
4.1
Innovest Data Descriptives : All World - Min Cap USD 250 MM . . .
41
4.2
Asset4 Data Descriptives: All World - Min Cap USD 250 MM . . . .
42
4.3
Cross Vendor Data Correlations For Similar Factors . . . . . . . . . .
46
4.4
Innovest Data: Labor and Environmental Factors - All World
. . . .
47
4.5
Asset4 Data: Labor and Environmental Factors - All World
. . . . .
48
5.1
EFA Model Goodness Of Fit . . . . . . . . . . . . . . . . . . . . . . .
51
5.2
Innovest Data - PCA Summary . . . . . . . . . . . . . . . . . . . . .
54
5.3
Innovest Data - Component Loadings . . . . . . . . . . . . . . . . . .
54
5.4
Asset4 Data - PCA Summary . . . . . . . . . . . . . . . . . . . . . .
55
5.5
Asset4 Data - Component Loadings . . . . . . . . . . . . . . . . . . .
55
6.1
Innovest Individual Factor Pooled Regression Results [2002-2009] with
Cap > USD 250M
6.2
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
62
Asset4 Individual Factor Pooled Regression Results [2002-2009] with
Cap > USD 250M
. . . . . . . . . . . . . . . . . .
63
B. 1 Innovest Summary Statistics: United States . . . . . . . . . . . . . .
75
B.2 Innovest Summary Statistics: Japan . . . . . . . . . . . . . . . . . .
76
. . . . . . . . . . . . . . . .
76
. . . . . . . . . . . . . . . . . .
77
. . . . . . . . . . . . .
77
. . . . . . . . . . . . . .
78
B.7 Asset4 Summary Statistics : Japan . . . . . . . . . . . . . . . . . . .
78
B.3 Innovest Summary Statistics: Germany
B.4 Innovest Summary Statistis : France
B.5 Innovest Summary Statistics: Great Britain
B.6 Asset4 Summary Statistics : United States
11
B.8 Asset4 Summary Statistics : Germany
B.9
Asset4 Summary Statistics : France
. . . . . . . . . . . . . . . . .
79
. . . . . . . . . . . . . . . . . .
79
B.10 Asset4 Summary Statistics : Great Britain
. . . . . . . . . . . . . .
79
B.11 Innovest Factor Correlations : All World . . . . . . . . . . . . . . . .
80
B.12 Innovest Factor Correlations Legend
. . . . . . . . . . . . . . . . . . .
80
B.13 Asset4 Factor Correlations : All World . . . . . . . . . . . . . . . . .
81
B. 14 Asset4 Factor Correlations Legend
81
. . . . . . . . . . . . . . . . . . . .
B.15 Innovest Average Country Level Fixed Effects Pooled Regression Results [2002-2009] with Cap > USD 250M . . . . . . . . . . . . . . . .
82
B. 16 Asset4 Average Country Level Fixed Effects Pooled Regression Results
[2002-2009] with Cap > USD 250M . . . . . . . . . . . . . . . . . . .
12
82
Chapter 1
Introduction
1.1
Motivation
In recent years, values-based investing has emerged as a serious alternative to mainstream offerings in the asset management business. Socially Responsible Investing
(SRI), is often used as an umbrella term that incorporates goals with respect to ethical, environmental, social and governance concerns in addition to financial returns
in the investment process. Corporate social performance/responsibility (CSP/CSR)
is the basis for SRI. It is easy to see that CSP and SRI are entwined, each one a
benefactor and a beneficiary of the other. A large body of work has explored SRI
and CSP for its links to corporate financial performance (CFP) but the scholarship
on the effect of CSP on a firm's CFP is ambiguous.
CSR and SRI are complex entities and made of multi-dimensional constituents.This
makes it difficult to uncover the complex interdependencies that exist between a firm
and society, which SRI seeks to explain in a market context. For example,the interdependencies (collective and individual) for each firm fall into: (Porter and Kramer,
2006) [35]
1. Generic interdependencies,
2. Value chain impacts/interdependencies, and
3. Social issues closely connected with the firm's competitive context.
13
Furthermore, these dependencies can run both ways with the society affecting the firm
and vice-versa. One reason why very few significant relationships between CFP and
CSP is revealed by empirical literature might be due to the inappropriate aggregation
of factors relevant to in different dimensions (Environmental, Social, Reputational
etc) that can interact with each other to produce confounded results (Scholtens and
Zhou,08). [38]. This thesis attempts to study these constituent CSP factors to elicit
additional insight on possible reasons for ambiguity using multi-vendor data.
1.2
Approach and Thesis organization
What follows in this thesis includes literature research and statistical analysis of
commercial CSP data from mulitple vendors. In particular, data will be analyzed
to look for relationships between CSP constituents, realtionship with CFP and any
underlying structure.
As noted in the previous section, the literature on CSP/CSR and analysis of
its impact on CFP is considerably vast. Literature research reveals details of several
studies done in this area that contribute to the development of a deeper understanding
of the problem, the nature of the data analyzed and analysis techniques that have been
used. Insights gained in this phase will guide statistical analysis. Data appropriate
for analysis are identified and used to build a system to conduct empirical analysis.
The author utilizes data that is available through his employers.
Finally, the
author uses the results of the analysis to draw conclusions on the thesis hypothesis.
" In Chapter 2, a brief background on current state of SRI and CSR and details
of the literature research is presented.
" In Chapter 3, Data elements used in empirical analysis are detailed.
" In Chapter 4, Comparative correlation analysis is conducted on the data and
its results are discussed.
" In Chapter 5, Comparative data structural analysis is conducted and its results
are presented.
14
* In Chapter 6, Comparative regression analysis is conducted and its results are
discussed.
" In Chapter 7, Thesis summary and implications for policy are presented.
" Thesis appendix includes technology architecture and additional results.
15
16
Chapter 2
Definitions and Literature Review
2.1
Socially Responsible Investing (SRI)
According to SRI forum, SRI recognizes that corporate responsibility and societal
concerns are valid parts of investment decisions.
finCacial needs andu investent
imac
It considers both the investor's
on societ5u
y. Its in1vestors often encourage
corporations to improve their practices on environmental, social, and governance
issues(SRI Forum, 2010)[12].
SRI has existed in some form for a very long time. In the US, Quakers have never
invested in war or slavery. The Methodists have been managing money in the U.S.
using "social screens" for over two hundred years. The modern roots of SRI can be
traced back from the 1960s, in the voicing of concerns about social causes such as
civil rights, labor issues and equality of women. SRI has since matured with time
to include measures on corporate governance and investment consequences on the
environment (Scheuth, 2003) [39].
SRI is typically implemented in three ways [12, 39]:
* Investment screens involve the use of positive and negative screens. The use of
investment screens is the oldest and most common way of implementing SRI.
A majority of the practitioners employ SRI screens. Several commercial data
providers (KLD, Innovest, Asset4 etc.) collect data and classify firms across a
17
Fig. B: Socially Respnsible Investing in the United States 1995-2010
(in Billions)
Slr
mAdocacv
1995
1997
1999
2001
5473
S
I S736
922
897
8
4
SaN/A
$84)
$69 $1r,185
$5
12r159
$592)
$,323
2003
448
14
$441
$2164
2005
$703
$117)
$290
2007
2010
739
25
$151
$2W11
6
SOUME: SocialInvestment
Forum
Foundation
NOTE:Overlapping
assets
involved
in somecombination
of ESGincorporation,
filing shareholder
resolutions
orcommunity
investing
aresubtracted
to
avoidpotentialeffectsof doublecounting.
Separate
tracking
of theoverlapping
strategies
only beganin 1997, sothereisnodatumfor1995.
Priorto 2010, assets
subject
to ESGincorporation
were limitedto socially
and environmentally
screened
assets.
Figure 2-1: SRI growth in the US
variety of social criteria, which are used by asset managers to make investment
decisions.
" Shareholder advocacy involves buying into corporations to influence their actions 'from the inside'. This involves initiating dialogue with companies on
specific issues, filing shareholder resolutions, and voting on key issues.
" Community investing is used to serve communities that are ignored by traditional financial services corporations. This involves providing access to credit,
basic banking and other financial services to people and places that would not
normally qualify for traditional services. Microfinance is a proven community
investing technique that has brought benefits to the very poor in many parts of
the world.
Several plausible reasons have been attributed to the success of SRI in the literature. These include:
1. SRI research reveals a link between existing mass social trends and the financial
performance of corporations (Camejo, 2002)[6]
2. In certain cases, SRI is able to account better for some intangibles that are
not priced by the market. Screens based on employees' satisfaction ratings
provided by Fortune magazine was shown to outperform the market on this
criteria (Edmans, 2010)[7]
18
3. SRI might be able to compensate for some additional risk factors or a temporary
mis-pricing in the market (Kempf, 2007) [25].
The proponents of SRI make a strong case for "investing with a conscience" and
typically point both the spectacular growth ( Figure 2-1 ) of SRI over the last decade
due to demands from consumers and to studies that show that SRI has outperformed
conventional investment strategies. SRI adds a "feel-good" factor to investing and
aims to bring about societal changes that are beneficial. SRI has the potential to:
1. Use market mechanisms to reward good corporate citizens and punish bad ones.
In the long run, SRI aims to change investment and corporate cultures to incorporate broad societal factors beyond profits alone.
2. Bring about change from within a firm by engaging in shareholder activism.
3. Serve communities and people ignored by traditional financial services.
4. Serve as a watch-dog on corporate governance practices on issues that are beyond existing regulation.
The benefits of SRI are easy to see, and so is its marketing potential, a fact not
lost on its proponents. However, several criticisms have been leveled against SRI.
These include:
1. Firm's are not mandated to report on SRI criteria ( Goldman Sach's published
its 2007 CSR report in 2009). Firm's can pick and choose how and what they
report. The methodology used by rating agencies to rate firms is not open to
scrutiny.
2. A study(Hawken, 2004) on how SRI was summarized reveals several serious issues in SRI practice, such as the facts that the cumulative investment portfolio
of the combined SRI mutual funds is virtually no different than the combined
portfolio of conventional mutual funds, and the screening methodologies and
exceptions employed by most SRI mutual funds allow practically any publiclyheld corporation to be considered as an SRI portfolio company. Fund names
19
and literature can be deceptive, not reflecting the actual investment strategy
of the managers, and SRI fund advertising caters to peoples desires to improve
the world by avoiding bad actors in the corporate world, but it can be misleading and oftentimes has little correlation to portfolio holdings. There is lack of
transparency and accountability in screening and portfolio selection, the ability
for investors to do market basket comparisons of different funds is difficult if
not impossible, a strong bias towards companies that aggressively pursue globalization of brands, products and regulations and the language used to describe
SRI mutual funds, including the term SRI itself, is vague and indiscriminate
and leads to misperception and distortion of investor goals. Finally, few SRI
mutual funds engage in shareholder advocacy or sponsor activist shareholder
resolutions (Hawken, 2004). [18]
3. Much of SRI research (including this thesis) relies on vendor data. Concerns
about the nature and quality of vendor data and the fact that no social research
organization or socially responsible mutual fund has yet presented a coherent
case for why its criteria are ethical or socially responsible or better at effecting
social change. (Entine, 2003) [8] Entine also raises several additional concerns
about the methodology of CSP research: using arbitrary standards, ignoring
aspects of corporate activity not easily measurable and having numerical ratings that create an illusion of objectivity. Sharfman [40] notes low correlations
between similar data provided by KLD and Fortune, while conceding that KLD
does measure "some-aspects" of CSP.
4. SRI can polarize complex geopolitical issues by make painting them as 'black
and white' issues and in the process make things worse. As a case in point
is the Sudan divestment boycott, that Soederberg (2007) where notes that the
marketisation of social issues has occurred three interrelated ways. First, the
market is represented as profit-seeking, apolitical, and autonomous. Second, the
dominance of moral discourse simplifies the conflict to such a degree that the
political and historical complexity of the country is denied, resulting in the por20
trayal of the conflict as existing in a one-dimensional space in which the tensions
between Africans and Arabs that can be easily and painlessly resolved through
the application of economic sanctions. Lastly, SRI redirects investors and the
general publics concern with corporate complicity in abuses against humanity
to a more sanitized language of risk analysis and concerns for the bottom line,
where social issues, such as human rights, are treated as an afterthought. [42].
Despite these criticisms from both practitioners and academia, SRI is growing rapidly
and has already become an important constituent of the global asset management
business.
2.2
Corporate Social Responsibility (CSR)
The concept of corporate social responsibility is based on the perception that firms
should no longer base their actions on the needs of their shareholders alone, but rather
have obligations towards the society in which they operate in general (UNCTAD,
2001) [46]. CSR is adopted by companies on a voluntary basis. This also implies that
the business case for particular actions differs according to various factors including
the companys visibility, location, size and ownership structure and the sector and
market segments in which it operates (Fox, 2004) [13]
A broader characterization of CSR as noted by Blowfield and Frynas (2005)[4],is
an umbrella term for a variety of theories and practices all of which recognize the
following:
" (a) that companies have a responsibility for their impact on society and the
natural environment, sometimes beyond legal compliance and the liability of
individuals;
" (b) that companies have a responsibility for the behavior of others with whom
they do business (e.g., within supply chains); and that
" (c) business needs to manage its relationship with wider society, whether for
reasons of commercial viability, or to add value to society .
21
The practice of CSR is varied among firms and can be largely grouped into:
1. Philanthropy with emphasis on charity, sponsorships, employee voluntarism etc.
2. CSR Integration into business practices with emphasis on conducting existing
business operations more responsibly.
3. CSR Innovation with emphasis on developing new business models for solving
social and environmental problems.
(Halme and Lurila, 2009) [17].
In adopting CSR, companies are allegedly in a better position to attract and retain
committed employees and loyal customers, avoid consumer boycotts, to obtain capital
at lower cost, target efficiencies (reduction in energy use), get access to new markets (
investing in communities, private-public partnerships) and improve their reputation.
As part of CSR, a firm's increased involvement of stakeholders can increase its innovation ( Kong et al 2002;Von Hippel, 1989) [27, 48]. Finally, CSR presumably builds
stakeholder trust and reduces long-term risks associated with unsustainable practices.
Vogel (2005) lists several CSR benefits: CSR has produced important changes in
corporate practices over the last two decades in the reduction of child labor and sweat
shop conditions and produced better health and safety conditions for many factories
in the developing world that supply the West. CSR has helped primary producers
and small farmers in developing countries ( especially coffee growers) getting a fair
price for their products. CSR has reigned in the logging of old growth and endangered
forests in the developed world. CSR has led to programs that have helped reduce
greenhouse gas emission or their rate of growth and to the reduction of adverse social and environmental impacts of natural resource development in some developing
countries.[47]. CSR is an important part of corporate strategy in sectors where inconsistencies arise between corporate profits and social goals, or where discord can arise
over fairness issues. A CSR program can make executives aware of these conflicts
and commit them to taking the social interest more seriously. It can also be critical
to maintaining or improving staff morale, to the stock markets assessment of a companys risk and to negotiations with regulators. The payoff to anticipating sources of
22
conflict can be very high indeed it can be a matter of survival, as societies penalize
companies perceived to be in conflict with underlying values (Heal, 2005)[19]. How
well companies adopt and deliver on their CSR is reflected in their corporate social
performance (CSP), but CSP and CSR tends to be used interchangeably in literature.
If CSR can be an all round win-win for both the firm and society, why then do firms
engage in unsustainable practices? Reinhardt and Stavins suggest that this might be
due to government policies and regulations (or lack of) that incentivise unsustainable
practices and due to principal-agent problems that may lead managers to focus on
short term gains (Reinhardt and Stavins,2010)[37]. The definition of corporate social
responsibility has thus far been mostly in terms of environmental and labor relations,
sometimes also in terms of global stakeholder relations, but the problem of defining
social responsibility exists, even in this limited definition (some companies claim a
project that is devastating for the environment can be socially responsible because
it creates jobs) (de Keuleneer, 2006)[26].
de Keuleneer also notes the difficulties
is measuring the triple bottom line : It is easy to measure financial performance
while it is difficult to measure social performance, this despite efforts to create global
standards such as GRI (the UN Global Reporting Initiative). Moneva (Movena et
al, 2006) provide a detailed overview of such shortcomings[24].
Pogutz (Pogutz,
2008) notes that using a much more strong sustainability perspective, companies that
are CSR-oriented when considered separately, are not necessarily sustainable when
considered all together and highlights the example of automobile industry, where any
efficiency gains made by the company are offset by aggressive development of new
emerging markets [34]. Another issue with CSR is that many of the world's largest
corporations and business associations lobby hard for reforms in labor and financial
markets that can result in weakening of institutions and regulatory infrastructure
that provide social protection while actively promoting CSR on an individual basis
(Farnsworth 2005) [10]. From a developmental context, Fox (2004) [13] raises concerns
noting claims like those by Vogel are skewed by being patchy and unsystematic,
dominated by actors in the global north and its focus on large business. SRI/CSR also
tends to be affected by the economic cycles, with many practices requiring additional
23
scrutiny and justification during economic downturns.
2.3
Corporate Social Performance (CSP) and Corporate Financial Performance (CFP)
Like SRI, the literature on CSR/CSP presents its potentials and the reality of partiallyrealized benefits due to the complexity of scope, situational and implementation difficulties.
Baron (Baron, 2000) notes that a business organization's performance is affected
by its strategies and operations in both market (CFP) and non-market (CSP) environments [3]. Many studies have been conducted to establish this relationship between
CFP and CSP over the last thirty years with some reporting a positive relationship (Johnson and Greening, 99; Waddock and Graves, 97) [22, 49], some a negative
relationship (Brooks, 2006) [5] and some conclusions with no statistical significance
(Ullmann, 1985) [45]. Two meta-studies (Orlitzky, 2003; Wu, 2006), however, suggest
that there might be positive correlations between CSP and CFP [30, 50].
Several studies have looked at specific aspects of CSR and have reported positive
relationships. Stanwick (Stanwick, 1998) acknowledge that CSP is a complex multifaceted construct. Using pollution emissions as a proxy for environmental performance, show positive relationship and mutual dependencies between environmental,
financial and social performance [43]. Hillman and Keim (2001) [20] report evidence
of a clearer link between the stakeholder management influence component of overall social ratings and financial performance than general social issue participation.
Galema et al. (2008) show that aggregate scores for several CSP rating criteria (community, diversity, environment, product and governance) are not significant in firm
performance, although employee ratings are significant. Edmans notes that firms
with higher levels of employee satisfaction perform better (Edmans, 2010)[7]. Gompers (Gompers et al, 2003) [33] finds a positive relation between firm-level corporate
governance based on vendor data and firm value.
24
However, each of these aspects of CSP are composite entities themselves, and
could have very different payoff structures over time. Also, the interaction between
common factors used in linear models of the market and CSP are complex. Kurtz
(Kurtz, 1997) notes several studies where interaction between price based factors and
CSP have been studied. [28] Wu (Wu, 2006) notes the firm size has no impact on
CFP or CSP [50] while UdayShankar (UdayaShankar, 2007) suggests that firm size
has a U-shaped relationship. [44]. The study by Galema [36] notes that SRI ratings
for diversity, environmental and product have a significant negative effect on book to
market ratios, hence impacting stock return.
In his meta-analysis, Orlitzky [30] finds that CSP is both a cause of and a result
of CFP and that the effect of CSP on CFP is moderated by reputation, with social
performance adding value to the firm with environmental performance detracting
from firm value. Fombrun ( Fombrun and Shanley 1990) established that investing
in CSR attributes and activities is an important factor in product differentiation and
reputation building. [11] However, in Orlitzky and Benjamin (Orlitzky, 2001) [32],
the authors conclude that the relationship between CSP and risk appears to be one of
reciprocal causality, because prior CSP is negatively related to subsequent financial
risk, and prior financial risk is negatively related to subsequent CSP. Also, sound
environmental practices by a firm is seen as a mitigating factor in firm's financial risk
(Bansal, 2004) [2]
The above examples in literature serve to highlight the difficulties associated with
trying to establish a link between CSP and CFP and the inadequacy of a single rating
to describe firm CSP. This thesis will evaluate several factors at the lowest granularity
( highest disaggregation) reported by vendors to add to the body of knowledge on
the CSP-CFP relationship. Another interesting question that comes to mind is that
when a firm has a high CSP rating in one dimension factor, does this reflect a core
corporate culture? A case in point that is of interest to the author is the idea that
firms that are have an excellent labor and workplace relationship record will be also
be good to the environment. While most of the literature analyzed studied short
term stock returns and its link to CSP primarily using KLD data, this study will
25
attempt to tease out relationships over different pay-off horizons of individual CSP
factors using Innovest and Asset4 as data sources.
2.4
Hypotheses
In light of the above discussion, the implications are that individual factors that make
up the complex CSP rating of a firm have a direct relationship (positive or negative)
with firm CFP and it can be hypothesized that:
" CSP factors have effects on pay-off over varying time periods, making
aggregation into a single composite score difficult for econometric
analysis. (I).
" Proprietary scoring techniques used in the quantification of qualitative data might cause additional obfuscation of results. (II).
In addition, other allegations worth investigating are:
" Reputation factors contribute to CFP, while environmental factors
per se may detract from CFP (III).
" Firms that treat their labor well are good environmental performers
(IV).
26
Chapter 3
Data Description
This chapter provides details on the various data elements that make up the bulk of
the data used in analysis.
3.1
Data Sources
Data analysis requires data over many years for financial returns, market risk, and
social performance of firms. Acadian Asset Management ( where the author is employed) were generous to open their proprietary data on firm returns and other market
data for this research. Through Acadian, the author was able to acquire social performance data from Risk Metrics Innovest and Thomson Reuters Asset4 databases
on firm social responsibility. In all cases, CSR ratings at the end of year for each year
from 2002 to 2009 was used. In identifying the tenuous relationships between CSP
and CFP, firms with a cap lower than USD 250 millions was not used. Data becomes
increasingly unreliable and error prone with small caps. This is consistent with many
studies that only look at S&P500 or other index listed securities in their analysis.
27
3.2
Data Description
Market returns data
Firm monthly market returns were acquired from the Acadian internal database,
computed using proprietary software. However, these returns in turn have over 99.9%
correlation with S&P global returns series and Compustat returns.
Market risk data
The firm beta ( # ) is computed as the number that relates its market returns to that
of the financial market as a whole. In the data analysis, firm beta was estimated from
time-series data provided by Acadian using the method described by Fama-Macbeth.
However, this beta was found to have over 90% correlation with the commercially
available MSCI Barra beta computation.
CSP ratings data from Risk Metrics Innovest
RiskMetrics Innovest provides its Intangible Value Assessment (IVA) combining qualitative sustainability research with fundamental and quantitative research. At the
heart of the IVA analytical model is an assessment of a companys managerial and
financial capacity to manage ESG investment risk successfully and profitably. CSP
ratings provided by Innovest uses over one hundred factors that are grouped and
scored. The ratings methodology can be summarized as using in-depth sector analysis,
firm data collection from sources like company press reports on CSR, annual reports,
news reports, industry specific news sources, data from NGOs and media searches
to produce sector based analysis, and scoring. These results are further honed after
interviews with the company by analysts, resulting in rating adjustments that give
their final scoring that includes industry specific factor weightings (Innovest, 2007).
Firms are broadly assessed on social and environmental criteria with final ratings at
firm level and for five sub-levels of stakeholder, human, governance, risk and environmental. The first three contribute towards social social criteria, risk contributing to
both criteria, and environmental sub-level to the environment criteria.
28
From the Innovest ratings methodology literature, we were able to identify the
various subcomponents of their scoring process and use them to broadly define the
following individual firm-level factors:
1. Audit Integrity: Assesses the existence, adequacy, frequency and impartiality
of firm audits.
2. Certification : Certification by CERES and other external bodies and whether
the firm adopts voluntary EPA programs.
3. Corporate Governance: This rating looks at board structure and diversity, senior environmental officer level and environmental factors in compensation.
4. Customer Stakeholder Partnerships: Rates controversy,protests, claims, litigation and fines relating as well as awards that relate to stakeholder engagement.
The extent of stakeholder engagement activities, use of external stakeholder
input and advisory boards and stakeholder access is also considered.
5. Employee Motivation And Development: This rating looks at employee retention rates, work policies that include job sharing, flexible schedule and location,
and access to management.Training and knowledge dissemination, benefits that
include health care, wellness programs, child care, and the monitoring of employee satisfaction rates.
6. Environmental Accounting and Reporting : The frequency and depth of environmental reporting and firm environmental accounting practices.
7. Environmental Management Systems: The number and qualifications of environmental staff, ISO 14000 or other certified EMS and firm environmental
performance indicators.
8. Environmental Strategy: This rating looks at policies adopted, integration with
core business and consistency across operations and how much environmental
strategy is part of the firm culture.
29
9. Environmental Training and Development : Resources for environmental training and development within the firm.
10. Environmental Opportunity : This rating takes into account the environmental sensitivity of geographic regions and demographic groups served, hows risks
products and services are being phased out, potential for environmental improvements and firm's environmental positioning within sector.
11. Worker Health And Safety: This rating is based on details of health and safety
policy and its audit history and health and safety performance that includes
absentee, injury rates etc.
12. Historic Liabilities: This includes firm contaminated site liabilities and other
historic liabilities.
13. Human Rights, Child and Forced Labor: This rating is based on firm's history
of controversies, protests, claims, litigation and fines and its implementation of
policies relating to human rights, child labor,forced labor and equal opportunities.
14. Industry Specific Factors : This ratings looks at risk factors that are specific to
the industry and how the firm rates in these specific areas.
15. Labor Relations: This rating is based on union policy and issues, claims and
litigations and procedures for whistle-blower protection.
16. Local Communities: This rating assesses firms involvement in its local community through philanthropy, community support programs such as volunteering,
local development. Its policy on using local suppliers and contractors contractors, policies on plant closure policy and its impact as well as disaster planning
with extent of local approval and third Party audits.
17. Operating Risk: This includes toxic spills and releases in the firms history, regulatory compliance scores from methodology developed by NYU, toxic emissions
and hazardous waste from firm operations and other operating risks.
30
18. Performance: Evaluates current environmental businesses and environmental
businesses under development.
19. Product safety: This ratings includes product social and ethical impact, historical boycott of products, product claims and litigation, product certification
and labeling and other safety and quality issues
20. Products/Materials : This rating criteria includes if the firm conducts life cycle
analysis on its products, screens its suppliers for sound environmental practices
and uses eco-labels.
21. Sustainability Risk: This rating takes into account the resource use efficiency/recycling,
energy efficiency,market risks including environmental sensitivities of customers,
other regulatory and legal risk and operational sustainability risks.
22. Strategic Competence: This rating evaluates environmental business development strategy and planning alni urganizational sLructure.
23. Supply Chain: Rates supplier screening policy for CSR performance, ethnicity,
gender, size,. Also includes requirements code of conduct from suppliers, supplier training and development programs, supplier social audits and third party
review.
24. Strategic Governance: This ratings looks at strategic capability/direction, shareholder activism response, reporting, disclosure and transparency, social/ethical
standards, codes signatory global Compact, OECD, child labor, UN declaration
on human rights, SA 8000, ILO, etc, investment policy and screening, charitable
giving policy and performance and bribery policy and enforcement.
CSP ratings data from Thomson Reuters Asset4
Thomson Reuters ASSET4 is an equity research firm which is broadly categorized
as an SRI (Socially Responsible Investment) research provider. The company is a
31
provider of specialty integrated financial and extra-financial company data. It examines companies on the basis of their economic, environmental, social and corporate
governance practices. It currently covers about 3000 corporations which includes the
S&P 500, MSCI Europe, FTSE 350 and the MSCI World Index, and claims to use
over 750 data points and over 280 key performance indicators to create 18 integrated
and structured categories. These represent either economic, environmental, social or
corporate governance which are then combined to produce overall firm score. Asset4
produces transformations that enable quantitative analysis of qualitative data, where
scores can be used in stock selection.
From the Asset4 ratings methodology, data and marketing literature [1], we were
able to identify the various subcomponents of their scoring process and use them to
asses the following individual firm level factors:
1. Corporate board structure: The board of directors/board structure category
measures a company's management commitment and effectiveness towards following best practice corporate governance principles related to a well balanced
membership of the board. It reflects a company's capacity to ensure a critical exchange of ideas and an independent decision-making process through an
experienced, diverse and independent board.
2. Compensation: The board of directors/compensation policy category measures
a company's management commitment and effectiveness towards following best
practice corporate governance principles related to competitive and proportionate management compensation. It reflects a company's capacity to attract and
retain executives and board members with the necessary skills by linking their
compensation to individual or company-wide financial or extra-financial targets.
3. Board Policies: The board of directors/board functions category measures a
company's management commitment and effectiveness towards following best
practice corporate governance principles related to board activities and functions. It reflects a company's capacity to have an effective board by setting up
the essential board committees with allocated tasks and responsibilities.
32
4. Shareholder Rights: The shareholders/shareholder rights category measures a
company's management commitment and effectiveness towards following best
practice corporate governance principles related to a shareholder policy and
equal treatment of shareholders. It reflects a company's capacity to be attractive
to minority shareholders by ensuring them equal rights and privileges and by
limiting the use of anti-takeover devices.
5. Corporate Strategy: The integration/vision and strategy category measures
a company's management commitment and effectiveness towards the creation
of an overarching vision and strategy integrating financial and extra-financial
aspects. It reflects a company's capacity to convincingly show and communicate
that it integrates the economic (financial), social and environmental dimensions
into its day-to-day decision-making processes.
6. Client Loyalty: The revenue/client loyalty category measures a company's management commitment and effectiveness towards generating sustainable and longterm revenue growth. It reflects a company's capacity to grow, while maintaining a loyal client base through satisfaction programs and avoiding anticompetitive behaviors and price fixing.
7. Performance: The margins/performance measures a company's management
commitment and effectiveness towards maintaining a stable cost base. It reflects
a company's capacity to improve its margins by increasing its performance (production process innovations) or by maintaining a loyal and productive employee
and supplier base.
8. Shareholder Loyalty: The profitability/shareholders loyalty category measures
a company's management commitment and effectiveness towards generating a
high return on investments. It reflects a company's capacity to maintain a
loyal shareholder base by generating sustainable returns through a focused and
transparent long-term communications strategy with its shareholders.
9. Resource Reduction: The resource reduction category measures a company's
33
management commitment and effectiveness towards achieving an efficient use
of natural resources in the production process. It reflects a company's capacity
to reduce the use of materials, energy or water, and to find more eco-efficient
solutions by improving supply chain management.
10. Emission Reduction: The emission reduction category measures a company's
management commitment and effectiveness towards reducing environmental
emission in the production and operational processes. It reflects a company's
capacity to reduce air emissions (greenhouse gases, F-gases, ozone-depleting
substances, NOx and SOx, etc.), waste, hazardous waste, water discharges,
spills or its impacts on biodiversity and to partner with environmental organizations to reduce the environmental impact of the company in the local or
broader community.
11. Product Innovation: The product innovation category measures a company's
management commitment and effectiveness towards supporting the research
and development of eco-efficient products or services. It reflects a company's
capacity to reduce the environmental costs and burdens for its customers, and
thereby creating new market opportunities through new environmental technologies and processes or eco-designed, dematerialized products with extended
durability.
12. Workforce Employment Quality: The workforce employment quality category
measures a company's management commitment and effectiveness towards providing high-quality employment benefits and job conditions. It reflects a company's capacity to increase its workforce loyalty and productivity by distributing
rewarding and fair employment benefits, and by focusing on long-term employment growth and stability by promoting from within, avoiding lay-offs and
maintaining relations with trade unions.
13. Health and Safety: The workforce health and safety category measures a company's management commitment and effectiveness towards providing a healthy
34
and safe workplace. It reflects a company's capacity to increase its workforce
loyalty and productivity by integrating into its day-to-day operations a concern
for the physical and mental health, well-being and stress level of all employees.
14. Training and Development: The workforce training and development category
measures a company's management commitment and effectiveness towards pro-
viding training and development (education) for its workforce.
It reflects a
company's capacity to increase its intellectual capital, workforce loyalty and
productivity by developing the work force's skills, competences, employability
and careers in an entrepreneurial environment.
15. Diversity and Opportunity:
The workforce diversity and opportunity cate-
gory measures a company's management commitment and effectiveness towards
maintaining diversity and equal opportunities in its workforce. It reflects a company's capacity to increase its workforce loyalty and productivity by promoting
an effective life-work balance, a family friendly environment and equal opportunities regardless of gender, age, ethnicity, religion or sexual orientation.
16. Human Rights: The human rights category measures a company's management
commitment and effectiveness towards respecting the fundamental human rights
conventions.
It reflects a company's capacity to maintain its license to oper-
ate by guaranteeing the freedom of association and excluding child, forced or
compulsory labor.
17. Community Involvement: The community category measures a company's management commitment and effectiveness towards maintaining the company's rep-
utation within the general community (local, national and global). It reflects a
company's capacity to maintain its license to operate by being a good citizen
(donations of cash, goods or staff time, etc.), protecting public health (avoidance
of industrial accidents, etc.) and respecting business ethics (avoiding bribery
and corruption, etc.).
18. Product Responsibility: The product responsibility category measures a com-
35
pany's management commitment and effectiveness towards creating value-added
products and services upholding the customer's security. It reflects a company's
capacity to maintain its license to operate by producing quality goods and services integrating the customer's health and safety, and preserving its integrity
and privacy also through accurate product information and labeling.
Other market data
All other market data such as industry classifications, country code details and other
firm fundamental data used in the thesis was provided by Acadian. Data used was a
combination of both vendor supplied as well as generated using proprietary processes
by Acadian and code written for this thesis.
In general, ESG factors have low firm level variance when compared with other
market factors such as beta, size and bp or excess returns. All analysis is made with
end of year firm ratings for the seven year period from 2002 to 2009.
3.3
Factor Classifications
In this section, factors are qualitatively classified as one of Environmental, Social,
Labor or Governance related. Some factors can also have a strong impact on a firm's
reputation and are thus classified as reputational as well. The basis for classifications
comes from vendor's own classification systems and documentation.
Innovest Factors
1. Environmental Factors: Certification, Environmental Accounting and Reporting, Environmental Management Systems, Environmental Strategy, Environmental Training and Development, Environmental Opportunity, Sustainability
Risk, Product Materials, Performance
2. Social Factors: Customer Stakeholder Partnerships, Human Rights, Child and
Forced Labor, Operating Risk, Product Safety, Supply Chain, Local Commu36
nities.
3. Labor Factors: Employee Motivation And Development,Health And Safety and
Labor relations.
4. Governance Factors: Audit Integrity, Corporate Governance, Industry Specific
Risk Factors, Strategic Governance, Strategic Competence
5. Reputational Factors: Certification, Customer Stakeholder Partnerships, Historic Liabilities, Operating Risk, Human Rights, Child and Forced Labor, Product Safety, Supply Chain, Local Communities
Asset4 Factors
1. Environmental Factors: Resource Reduction, Emission Reduction and Product
Innovation.
2. Social Factors: Community, Shareholder rights, Shareholder Loyalty, Product
Responsibility.
3. Labor Factors: Health and Safety, Training and Development, Work Diversity
and Opportunity. Human Rights.
4. Governance Factors: Board Structure, Compensation, Board Policies, Corporate Strategy, Performance
5. Reputational Factors: Client Loyalty, Shareholder Loyalty, Performance, Product Responsibility, Human Rights
In examining vendor classifications, it would seem that Asset4's individual factors
have less domain overlap when compared to Innovest. Of Asset4's factors, a priori,
we feel that emission reduction, product innovation, work force employment quality,
health and safety, training and development, human rights, community involvement
and product responsibility are most likely to be informative in explaining CFP. See
details in Section 6.4 for results.
37
3.4
Data Limitations
While some of issues with the SRI were touched upon earlier, it is important to
highlight the nature of SRI vendor data and some of its limitations in the current
context. It is clear from the above list that several reporting requirements for SRI data
can only met by bigger (and already financially performing) firms. Also, the amount of
resources firms can devote towards some of the assessment criteria will mean that the
relationships unearthed by data in some cases might stress on CSP's dependence on
CFP rather that the other way round. There are no uniforms standards or legislation
for reporting of CSP and in many cases it is done on an voluntary basis. This also
means that it is possible for firms to game the system by reporting data that casts it's
performance in a favorable light. The methods used in generating aggregate scores
from individual factors is not transparent. For example, the case of Innovest, many
of the factors that are aggregated into its environment rating have high correlation,
make this relatively easy. However, the factors that make up governance and strategy
are not well correlated and it is not clear how these are aggregated to provide the final
score. Also, relationship between factors and their market pay-offs will have regional
and temporal differences.
Many aspects of SRI data are not easily measured and
the requirements that stress quantification and financialization of SRI data means
that vendors use a proprietary method to come up with firm ratings and users of the
data do not have any insight into how the ratings are developed other than high level
marketing literature. The assumptions used and the amount of judgment employed
by analysts during transformation, while opaque, provides a false sense of security
that we are dealing with quantitative data. As with any data assimilation exercise,
there are errors in both the collection and transformation stages of the process. Users
of the data do not have good insight into these errors. Entine (2003) [8] and Sharfman
(1996) [40] provides details on another vendor KLD in this aspect. Most studies (
including this one) take these limitations for granted. A prospect for future research in
this area is to determine policy details to be implemented by governments to produce
accurate SRI reporting outcomes.
38
Chapter 4
Comparative Data Analysis :
Factor Correlations
In this chapter, correlation analysis considers factor relationships across world data
as well as the five biggest OECD economies separately.
Comparative analysis is
conducted within each vendor and between vendors. The factors that make un CSP
and firm CSP ratings do not change frequently. Their month over month turn-over
is very low. Innovest provides data on a monthly basis. To keep the comparison with
Asset4's yearly data consistent, only year-end ratings data from Innovest was used in
the analysis process.
4.1
Summary Data
Summary statistics on Innovest data for composite world data is provide in Table:4. 1.
Innovest factor data have discrete ratings between 0 and 10. Similar statistics on
Asset4 data for all world is provide in Table:4.2. Asset4 factor data have continuous
ratings between 0 and 1.0. Summary data from either vendor does not indicate any
data distortions.
In addition to vendor data, summary information about stock returns, book-toprice and size are also provided. The were extracted or computed from data obtained
from Acadian's database.
"logsize" factor is the result of applying the logarithm
39
function to firm size expressed in millions of USD. This additional data has been
filtered to correspond to the each vendor's set of covered firms.
A point worthy of note is that the US represents about 37.5% of all data in sample
for both vendors and this could cause trends in US data could significantly affect all
world results. As a result, this thesis presents analysis results of the five biggest
OECD economies in addition to all world, where applicable.
A comparison of world data between the two vendors does not show significant
differences, except in the case of governance factors. For governance, Asset4 rates
US ahead of the rest. In addition, summary of vendor for each of the five largest
OECD economies are presented in Appendix B. Data is in line with expectations that
continental Europe performs better in almost every CSP indicator when compared
to Japan, Britain or the US. Median ratings from both vendors seem to indicate that
France, Germany, Britain, US and Japan would be in descending order of overall CSP
at country level.
40
Description
stock-return
beta
bp
auditinteg
certification
corporategovernance
customerstakeholderpartnerships
employeemotivationanddevelopment
environmentalaccountingreporting
environmentalmanagementsystems
environmentalopportunity
environmentalstrategy
envtraininganddevelopment
healthandsafety
historicliabilities
humanrightschildandforcedlabor
industryspecificrisk
laborrelations
leadingsustainabilityriskindicators
localcommunities
operatingrisk
opportunity
performance
productsafety
productsmaterials
strategicgovernance
supplychain
logsize
Var
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Obs.
8471.00
8463.00
7931.00
7931.00
7852.00
8208.00
8005.00
8033.00
8262.00
8301.00
7970.00
8469.00
8031.00
8016.00
7995.00
7320.00
7639.00
7977.00
8431.00
8021.00
8071.00
8448.00
8232.00
7891.00
8258.00
7011.00
8037.00
8471.00
Mean
-0.02
1.05
0.60
2.75
3.55
4.66
5.28
5.86
4.54
4.92
5.25
5.41
4.84
5.31
5.20
5.10
4.63
5.38
5.22
5.63
5.11
5.04
4.81
5.28
4.42
5.46
5.13
8.89
Std Dev
0.10
0.54
0.53
2.80
2.57
2.35
2.07
1.97
2.71
2.56
1.95
2.25
2.59
2.08
2.29
2.17
2.51
1.83
1.92
2.09
2.36
2.20
2.61
2.10
2.55
1.82
2.26
1.19
Table 4.1: Innovest Data Descriptives
Median
-0.02
1.00
0.47
2.09
4.00
5.00
5.00
6.00
5.00
5.00
5.00
6.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
6.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
8.80
Trimmed
-0.02
1.01
0.52
2.33
3.41
4.72
5.31
5.93
4.54
4.99
5.40
5.50
4.91
5.36
5.27
5.21
4.70
5.40
5.26
5.68
5.25
5.07
4.87
5.40
4.42
5.47
5.15
8.85
Mad
0.09
0.50
0.31
1.35
2.97
1.48
1.48
1.48
2.97
2.97
1.48
2.97
2.97
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
2.97
1.48
2.97
1.48
1.48
1.14
Min
-0.71
-0.85
-1.71
-16.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
5.56
Max
0.67
3.85
5.00
16.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
18.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
13.17
All World - Min Cap USD 250 MM
Range
1.39
4.69
6.71
32.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
18.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
7.62
Skew
-0.14
0.77
3.37
1.43
0.27
-0.21
-0.06
-0.31
0.00
-0.19
-0.76
-0.31
-0.24
-0.16
-0.22
-0.39
-0.33
-0.16
-0.22
-0.24
-0.43
-0.16
-0.17
-0.43
0.01
-0.05
-0.07
0.29
Kurtosis
3.28
1.34
19.84
11.71
-0.62
-0.40
-0.14
-0.01
-0.85
-0.72
1.10
-0.20
-0.48
-0.19
0.21
0.36
-0.37
0.41
-0.05
-0.07
0.20
-0.44
-0.61
0.29
-0.59
-0.31
-0.27
0.11
SE
0.00
0.01
0.01
0.03
0.03
0.03
0.02
0.02
0.03
0.03
0.02
0.02
0.03
0.02
0.03
0.03
0.03
0.02
0.02
0.02
0.03
0.02
0.03
0.02
0.03
0.02
0.03
0.01
beta
bp
boardfunctions
boardstructure
compensationpolicy
visionandstrategy
shareholderrights
marginsperformance
shareholderloyalty
clientloyalty
emissionreduction
productinnovation
resourcereduction
productresponsibility
community
humanrights
workdiversityopportunity
employmentquality
healthandsafety
traininganddevelopment
logsize
var
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
22
n
8773.00
8203.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
8782.00
mean
1.05
0.60
0.52
0.51
0.50
0.53
0.52
0.51
0.53
0.55
0.55
0.52
0.55
0.53
0.55
0.51
0.54
0.52
0.51
0.54
8.89
sd
0.55
0.54
0.32
0.31
0.30
0.32
0.31
0.30
0.30
0.29
0.32
0.31
0.32
0.30
0.30
0.30
0.31
0.31
0.31
0.31
1.20
median
1.00
0.47
0.63
0.56
0.58
0.48
0.52
0.50
0.53
0.54
0.57
0.43
0.61
0.52
0.59
0.33
0.54
0.53
0.46
0.60
8.81
Table 4.2: Asset4 Data Descriptives
trimmed
1.01
0.52
0.53
0.52
0.51
0.52
0.52
0.51
0.54
0.55
0.55
0.51
0.56
0.53
0.56
0.49
0.54
0.52
0.50
0.55
8.85
mad
0.50
0.31
0.34
0.41
0.36
0.44
0.43
0.41
0.41
0.38
0.50
0.39
0.45
0.44
0.41
0.20
0.49
0.44
0.41
0.42
1.15
min
-0.85
-1.71
0.02
0.01
0.01
0.10
0.01
0.04
0.02
0.01
0.07
0.09
0.08
0.03
0.03
0.02
0.05
0.03
0.02
0.05
5.56
max
3.85
5.00
0.93
0.96
0.96
0.99
0.98
0.99
0.99
0.98
0.98
1.00
0.97
0.99
0.97
1.00
0.99
0.99
0.99
0.97
13.17
range
4.69
6.71
0.91
0.95
0.95
0.89
0.97
0.95
0.98
0.97
0.91
0.91
0.89
0.96
0.95
0.98
0.94
0.96
0.97
0.93
7.62
All World - Min Cap USD 250 MM
skew
0.79
3.39
-0.36
-0.23
-0.35
0.13
-0.03
0.05
-0.10
-0.10
-0.08
0.24
-0.17
0.01
-0.24
0.44
0.01
-0.05
0.23
-0.19
0.30
kurtosis
1.45
19.82
-1.52
-1.42
-1.33
-1.64
-1.38
-1.32
-1.31
-1.23
-1.65
-1.62
-1.61
-1.42
-1.35
-1.48
-1.56
-1.40
-1.40
-1.51
0.10
se
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
4.2
Factor Correlations
Correlation between all Innovest factors and logsize ( indicator of firm size as a function of market capitalization) for world data in presented in Figure 4-1, does not
show strong correlation among factors. Amongst composite world as well as all the
individual big five economies, there are no strong negative correlations between social
factors. In general, environmental factors show higher correlation between themselves
and with corporate governance and product materials individualfactors. Almost all
factors have low positive correlations with each other. This pattern can also be seen
in correlationmatrix of the big five as well, with small negative correlations between
risk factors and other ESG factors in France and Germany. France, which has the
highest median scores in terms of summary statistics also has the least correlation
among factors in the countries studied.
In Asset4's composite world data, presented in Figure 4-2, not all ESG factors
were positively correlated.
Governance factors (except vision and strategy) and en-
vironmental fact or were positively correlated amongst themselves but were negatively
correlatedwith each other. Vision and strategy governance factor was positively correlated with environmentalfactors and negatively correlated with other governance factors. Interrelated environmental factors were the most strongly positively correlated
factors. Environmental and social factors had moderate positive correlations. On the
whole, there were very few strong correlations between factors much like Innovest, but
the relationship between governance and other factors were different between the two
vendors. In the US and UK, however, governance factors (except vision and strategy)
had almost no correlation with other factors.In the US, firm size had a small negative
correlation across the board with other social factors and Japanese firms had higher
correlations between labor,social and environmental factors. Tabular presentation of
the all world correlation data is available in Appendix B, Table B. 11 for Innovest and
Table B. 13 for Asset4. Correlation matrices for the big five economies are presented
for both vendors in Appendix C.
43
Amm"m
catIkAban
dxam-
II
I
K*JvAbmm
I
IIIII
I
0
=rAw.._dA!wmqwrm*mvjwkmnw
r
-vAr-vrwr*WmbidmW
9
II
qmdftvmiap-t
hmd*mrdmmPvtV
U
rw.
II I
11
ftc! ddis mft culdw
kdzrTvidkzu
0 a
L
w
e
bcAkxrrwnunbm
U
ked-
I
cpwutkvt*
cppzbjriLy
pwfb-
U U
S
U U
0i
pmd-bufty
p-Ixturmtortda
U U a F,
U U U U
a9
-1
-0.8 -0.6 -0.4 -0.2
1.-
L
0
0.2
im
i
0.4
Figure 4-1: Innovest Data Correlations : All World
44
III
U -I
U
Q
L.A
Legend:
I
U
m
U
r I
N1Tj[3131]ir '_j'11rL
0.6
0.8
1
A
~II
I
boarftuchre
dertwoytty
cnnvmLY
in
mn
In
&xwx ea obilty
mmB
MIN
IlmTdeff1 Igm
I
trairugif devdc=%jwT1cwvt
.!WcnvK
Wxate
k*Vefm2ycppazfy
Legend
-1
-0.8 -0.6
-0.4
-0.2
0
0.2
Figure 4-2: Asset4 Data Correlations : All World
45
0.4
0.6
0.8
1
Cross vendor Comparisons
As part of data analysis, cross vendor correlation of similarly defined factor data
was studied. In almost every case, there was significant correlation between vendors.
The highest correlation were observed for environment factors. This might be due
to environmental data being easier to measure and quantify (e.g., carbon footprint,
emissions etc.) than the qualitative data used for other CSP factors. Reasons for
low observed correlations could be differences in sub-constituents that make up these
individual factors and the actual quantification process used. CSP data from Japan
and France have higher and lower correlation between vendor data when compared
to composite world data. For US, UK and Germany results are mixed with factor
correlations being higher and lower than composite world levels. Table:4.3 details
cross vendor factor comparisons for world and the big five OECD economies.
Table 4.3: Cross Vendor Data Correlations For Similar Factors
Innovest Factor
customerstakeholderpartnerships
productsafety
customerstakeholderpartnerships
humanrightschildandforcedlabor
employeemotivationanddevelopment
employeemotivationanddevelopment
employeemotivationanddevelopment
localcommunities
healthandsafety
productsmaterials
strategicgovernance
environmentalmanagementsystems
environmentalmanagementsystems
4.2.1
Asset4 Factor
shareholderrights
productresponsibility
clientloyalty
humanrights
employmentquality
workdiversityopportunity
traininganddevelopment
community
healthandsafety
productinnovation
visionandstrategy
resourcereduction
emissionreduction
World
-0.011
0.127
0.162
0.209
0.313
0.340
0.342
0.346
0.355
0.411
0.433
0.548
0.549
USA
0.075
0.086
0.108
0.170
0.223
0.334
0.273
0.377
0.273
0.360
0.342
0.427
0.412
Japan
0.028
0.184
0.168
0.186
0.303
0.342
0.355
0.326
0.400
0.388
0.439
0.570
0.574
Germany
0.055
0.097
0.131
0.216
0.191
0.204
0.232
0.274
0.292
0.223
0.295
0.614
0.584
France
-0.032
-0.058
0.053
0.146
0.148
0.263
0.276
0.329
0.204
0.303
0.241
0.436
0.483
Hypothesis IV - Labor and Environment
Additional analysis was done to check the hypothesis that firms that treat labor well
are also good environmental citizens. In order to conduct this analysis, a total of four
tests were conducted on each vendor. In the first set of tests, health and safety was
used as a proxy for worker treatment and the top and bottom 10 percentile for firms
for each year were identified and correlated with their environmental ratings for the
46
Britain
0.113
0.030
0.119
0.262
0.343
0.337
0.256
0.452
0.370
0.341
0.395
0.518
0.574
corresponding year. In the second set of tests employee motivation and development/
employee diversity and opportunity was used as a proxy for worker treatment and
the tests were performed again.
The relationship between labor and environmental was weak across the board for
both vendors for composite world data. Using Innovest data, a clear relationshipcould
not be inferred from the data. However, with Asset4's data, a very weak positive relationship between employmentquality factor of labor and the environment was revealed.
Tables 4.4 for Innovest and 4.5 for Asset4 document test results.
In looking at the big five economies in isolation, only the US had enough data
points and the result of the analysis for the US was consistent with composite world
data.
Table 4.4: Innovest Data: Labor and Environmental Factors - All World
Labor Factor
healthandsafety
healthandsafety
healthandsafety
healthandsafety
healthandsafety
healthandsafety
Enviromental Factor
environmentalaccountingreporting
environmentalmanagementsystems
performance
environmentalaccountingreporting
environmentalmanagementsystems
performance
Percentile
> 0.90
> 0.90
> 0.90
< 0.10
< 0.10
< 0.10
Correlation
0.1905
0.2617
0.1642
0.1764
0.1640
0.1589
employeemotivationanddevelopment
employeemotivationanddevelopment
employeemotivationanddevelopment
employeemotivationanddevelopment
employeemotivationanddevelopment
employeemotivationanddevelopment
environmentalaccountingreporting
environmentalmanagementsystems
performance
environmentalaccountingreporting
environmentalmanagementsystems
performance
>
>
>
<
<
<
0.1387
0.1227
0.0413
0.1152
0.1104
0.1223
47
0.90
0.90
0.90
0.10
0.10
0.10
Table 4.5: Asset4 Data: Labor and Environmental Factors - All World
Labor Factor
healthandsafety
healthandsafety
healthandsafety
healthandsafety
healthandsafety
healthandsafety
employmentquality
employmentquality
employmentquality
employmentquality
employmentquality
employmentquality
Enviromental Factor
emissionreduction
resourcereduction
productinnovation
emissionreduction
productinnovation
resourcereduction
emissionreduction
resourcereduction
productinnovation
emissionreduction
productinnovation
resourcereduction
48
Percentile
> 0.90
> 0.90
> 0.90
< 0.10
< 0.10
< 0.10
> 0.90
> 0.90
> 0.90
< 0.10
< 0.10
< 0.10
Correlation
0.4615
0.3325
0.4009
0.4748
0.4087
0.4874
0.0899
0.0332
0.1221
-0.496
-0.0312
-0.0389
Chapter 5
Comparative Data Analysis :
Factor Analysis
The SRI community, data vendors and marketing materials use ESG factors to decompose CSP. ESG factors themselves are aggregate scores of individual ( factor
with the lowest level of disaggregation reported as by data vendors
)
factors such
as those described in Chapter 4. While such a classification is intuitive and aids in
marketing, this section examines if underlying data exhibits characteristics that make
this classification appropriate for quantitative analysis. The basis for classification
of quantitative individual factor data and subsequent aggregation to Environmental,
Social and Governance factor among vendors could be due to:
1. Environment, Social and Governance factors cannot be directly measured but
only inferred using the multidimensional individual factors that are measurable.
To explore this line of reasoning, we can use Exploratory Factor Analysis (EFA).
2. Another line of reasoning could be that the individual factors that are measured
can be reduced along E,S, and G related dimensions without losing much informational content. Principal component analysis (PCA) can help identify if the
underlying data exhibits such a behavior.
49
Non Graphical Solutions to Scree Test
0
0 Elgenvalues (>mean = 12)
A Parallel Analysis (n - 11 )
oci
Optimal Coordinates (n - 2)
Ace leranon Facnr (s 7
020
0,
C
0
5i
,
0
0
0
0
0
0I
5
10
15
20
Components
Figure 5-1: Innovest Data
5.1
Exploratory Factor Analysis (EFA)
Factor analysis using exploratory factor analysis techniques involves the identification
of common underlying factors that can be inferred from measurable data. A common
technique from EFA involves identification of optimal number of factors and determining factor loadings from measurable factors. If the measured data indicates strong
presence of the E,S and G factors, the loadings of measured individual vendor factors
will align themselves strongly along these aggregate factors. Four non-graphical solution methods for Scree test were used to determine the optimal number of factors
following Raiche et al (2006) [14]. The reader is referred to the paper for details on
these tests. Figure 5-1 for Innovest data and Figure 5-2 for Asset4 data plot the
results from the four different techniques to determine optimal number of factors.
If there exists underlying unmeasurable distinct composite E, S and G factors, they
should be identifiable through EFA. However, in case of either vendor, three distinct underlying factors do not seem adequate to explain the variability in measured
factors. For Innovest, 10 factors and for Asset4 9 factors were chosen as basis.
50
Mon Graphical Solutions to Scree Test
0
0o Elgenvatues
(>mean- 10)
A Parallel Analysis (n =9 )
~
A19
Optimal Coordinates (n .4)
Acoeleratton Factor (n - I)
0n
qO00
0
A
0
0
0
e
0
0
N,
0
In
010
0
I
0
5
-
10
15
Components
Figure 5-2: Asset4 Data
The next step of exploratory factor analysis involves rotation of these individual
factors for maximum likelihood to maximize and minimize loadings on the chosen
number of new factors to obtain the best structure based on the new factor set. Even
if three distinct E,S,G factors did not exist, rotational loadings could reveal other significant sub-grouped structure within data. The reader is referred to Gorsuch (1983)
[15] for details on such rotations and interpretation of resulting loadings. There are
several mathematical techniques of rotation such as varimax ( produces uncorrelated
orthogonal factors) or promax (correlated factors). If we make no assumptions about
the nature of relationship between E, S and G factors (e.g., in that they are orthogonal), a promax rotation is appropriate for analysis.
Table 5.1: EFA Model Goodness Of Fit
Vendor
Asset4
Innovest
nVars
9
11
X2 stat DOF
162.89
476.68
36
81
p value
4.95e-18
2.28e-57
When data was fitted using a promax rotation for both Innovest and Asset4 data
51
with 10 and 9 factors and the loadings did not have an identifiable structure. p-values
associated with the good model fit based on the suggested number of variables (i.e, 9
factors are sufficient for Asset4 data) was too low, as shown in Table 5.1.
5.2
Principal Component Analysis (PCA)
Principal Component Analysis can be used to reveal hidden structures in the data
set. It is a popular dimensionality reduction technique but it can also be used to
identify dynamics of the system. The main idea being that under the assumption
of linearity and normality, the largest variances in the system contribute to most
of its dynamics.
PCA attempts to recast the existing variables into a set of new
variables along orthogonal principal components.
These principal components are
arranged in the order in which they account for system variance. By choosing the
number of components ( and leaving out the least important ones), dimensionality
of data set under consideration can be reduced with minimal loss of information
(Mankin, 2010)[31]. Jolliffe [23] provides an in-depth study of PCA, its techniques
and limitations.
In the current context, PCA can be used to understand the underlying data structures. Given the multiple dimensionality of the individual factors, it can be used to
analyze if they can be dimensionally reduced into categories that vendors associate
them with. If governance, imprisonment, social factors are distinctly quantifiable and
largely independent components of CSP, they might be more or less orthogonal to
each other and present themselves as independent principal components.
The summary and results on PCA are detailed in tables 5.2 and 5.3 for Innovest
and in tables 5.4 and 5.5 for Asset4. In Innovest's data, the first principalcomponent
accounts for almost 40% of the variance. However, the loadings on this component
contain individual factors from governance, social and environmental domains. All
other components account for less than 10% of the variance and do not reveal a clear
underlying structure. Asset4s data the first two components account for about 37%
and 18% of the variance. However, the first principal component has high loadings
52
from only social and environmental factors and the second component has high loadings on governance. In Assets data, the delineation in structure between governance
and other social factors is more pronounced. Other components do not offer any additional insight.
53
Standard deviation
Proportion of Variance
Cumulative Proportion
PCl
3.0829
0.3960
0.3960
PC2
1.4126
0.0832
0.4792
PC3
1.2725
0.0675
0.5466
PC4
1.0334
0.0445
0.5911
PC5
0.9728
0.0394
0.6306
PC6
0.9296
0.0360
0.6666
PC7
0.8990
0.0337
0.7003
PC8
0.8869
0.0328
0.7330
PC9
0.8503
0.0301
0.7631
PC10
0.8193
0.0280
0.7911
Table 5.2: Innovest Data - PCA Summary
auditinteg
certification
corporategovernance
customerstakeholderpartnerships
employeeniotivationanddevelopinent
environinentalaccountingreporting
environmentalmanagenentsysteins
environmentalopportunity
environmentalstrategy
envtraininganddevelopmrent
healthandsafety
historicliabilities
humanrightschildandforcedlabor
industryspecificrisk
laborrelations
leadingsustainabilityriskindicators
localconimunities
operatingrisk
opportunity
performance
productsafety
productsmaterials
strategicgovernance
supplychain
PC1
0.0102
-0.1882
-0.2498
-0.2024
-0.2002
-0.2664
-0.2712
-0.1659
-0.2826
-0.2457
-0.2080
-0.0741
-0.1153
-0.1201
-0.1481
-0.2272
-0.1827
-0.1119
-0.2513
-0.2294
-0.1342
-0.2574
-0.2530
-0.2211
PC2
-0.2263
0.1404
0.1851
-0.2362
-0.2525
0.1628
0.1742
-0.0348
0.1365
0.1912
-0.1957
-0.3054
-0.2908
-0.0328
-0.3099
0.0735
-0.1781
-0.2711
0.2263
0.2371
-0.2718
0.1394
-0.1536
-0.1175
PC3
0.0655
-0.0133
0.0355
-0.1803
-0.3088
0.0267
0.0563
0.3154
0.0312
0.0702
-0.2182
0.4054
0.1802
0.2496
-0.1876
0.1253
-0.2864
0.4800
0.0257
0.0306
0.1497
0.0166
-0.1641
-0.1896
PC4
-0.5100
-0.1984
-0.1942
0.0695
0.0821
-0.1541
-0.1230
0.2523
-0.0896
-0.1617
0.0754
0.1327
-0.0481
0.5392
0.1018
0.0878
0.0570
-0.2359
0.1615
0.2519
-0.1219
-0.1285
-0.0037
-0.0181
PC5
0.6767
0.0247
-0.0383
0.0072
0.0589
-0.1528
-0.1114
0.4420
-0.0473
-0.0352
-0.0659
-0.1641
0.0990
0.0595
-0.0616
-0.1254
0.0903
-0.1681
0.2467
0.2784
-0.2314
-0.0947
-0.0199
0.0001
PC6
-0.3054
0.1099
0.0737
0.0422
-0.0660
-0.0246
-0.0151
0.1765
0.0013
0.0451
-0.0700
0.0138
0.6650
-0.1769
-0.2794
-0.0889
0.2286
-0.0529
-0.1384
-0.1553
-0.3970
0.0175
-0.0066
0.1679
Table 5.3: Innovest Data - Component Loadings
54
PC7
0.2914
-0.0778
0.0788
-0.1210
-0.0141
0.1409
0.1328
-0.1627
0.0820
0.0997
0.1733
0.0933
0.0216
0.4622
0.1537
0.2130
-0.1059
-0.1701
-0.2979
-0.3330
-0.4880
0.0745
0.0164
-0.0420
PC8
-0.1178
-0.2640
0.0140
-0.2157
0.1296
0.0704
0.0715
0.1071
0.0167
0.0669
0.0942
-0.2641
0.3659
-0.2357
0.5253
-0.0315
-0.4771
0.0345
0.1342
0.0699
-0.0634
-0.0541
-0.0423
-0.1458
PC9
0.0032
0.0969
0.0154
0.0403
-0.0665
0.0195
0.0149
0.0714
-0.0057
-0.0464
-0.0559
-0.6364
0.2484
0.4370
-0.1311
0.0061
-0.0484
-0.0860
-0.1671
-0.1834
0.4750
0.0273
0.0232
-0.0154
PC10
-0.0921
0.8027
0.0535
-0.0687
0.1713
-0.0593
-0.0883
0.0760
-0.1269
0.0134
-0.0496
0.1127
-0.0392
0.1041
0.3215
-0.2638
-0.0978
-0.0387
-0.0959
-0.0177
-0.0381
0.0449
-0.1468
-0.1753
Standard deviation
Proportion of Variance
Cumulative Proportion
PC1
2.5830
0.3706
0.3706
PC2
1.8001
0.1800
0.5507
PC3
0.9763
0.0529
0.6036
PC4
0.9125
0.0463
0.6499
PC5
0.8761
0.0426
0.6925
PC6
0.8032
0.0358
0.7284
PC7
0.7785
0.0337
0.7620
PC8
0.7610
0.0322
0.7942
PC9
0.7480
0.0311
0.8253
PC10
0.7120
0.0282
0.8535
Table 5.4: Asset4 Data - PCA Summary
boardfunctions
boardstructure
compensationpolicy
visionandstrategy
shareholderrights
marginsperformance
shareholderloyalty
clientloyalty
emissionreduction
productinnovation
resourcereduction
productresponsibility
coImuity
humianrights
workdiversityopportunity
enploymentquality
healthandsafety
traininganddevelopment
Pci
0.0071
-0.0026
0.0048
0.3216
0.0226
0.2312
0.0905
0.2166
0.3222
0.2733
0.3233
0.2301
0.2678
0.2771
0.2838
0.2335
0.2808
0.2982
PC2
-0.4996
-0.4710
-0.4481
0.0800
-0.3944
-0.0465
-0.2942
-0.0556
0.1121
0.1253
0.1032
0.0309
-0.0830
0.0337
-0.0633
-0.1429
-0.0401
0.0055
PC3
0.1740
0.1912
0.0278
0.1653
0.1916
-0.4121
-0.2039
-0.3015
0.3040
0.2923
0.2830
-0.1334
-0.0617
0.0497
-0.1181
-0.4009
0.2598
-0.2046
PC4
-0.0035
-0.0153
-0.1752
-0.1312
0.2918
-0.3335
-0.0690
0.5905
-0.0538
0.0616
-0.0513
0.5031
0.1719
-0.0487
0.0202
-0.2496
-0.1404
-0.1588
PC5
0.0899
0.0953
0.1815
0.0191
0.0112
-0.1857
-0.8338
0.0513
-0.0701
-0.1108
-0.0301
-0.0394
-0.0067
0.0185
0.2081
0.3097
-0.1013
0.2181
PC6
-0.0149
-0.0129
0.0418
-0.0696
-0.0401
-0.2769
0.1344
-0.5027
-0.0892
-0.0232
-0.0579
0.6913
-0.2775
0.1129
0.0705
0.2031
0.0697
0.1162
PC7
0.0062
0.0331
-0.0414
-0.0439
0.3816
0.5104
-0.1905
0.0218
0.0849
0.3452
0.0918
0.1488
-0.5049
-0.0345
-0.2593
0.0762
-0.2533
-0.0459
Table 5.5: Asset4 Data - Component Loadings
55
PC8
-0.1024
-0.1709
-0.2328
0.0309
0.4345
-0.2073
0.1149
0.0607
-0.1000
-0.1527
-0.0950
-0.3104
-0.2482
0.6418
0.1323
0.1557
0.0310
-0.0397
PC9
0.0203
0.0586
0.3844
0.0159
-0.4572
-0.1202
0.0163
0.4311
-0.0067
0.0166
0.0307
0.0399
-0.3678
0.3108
-0.3710
0.0667
0.2067
-0.1379
PC10
-0.0201
-0.0725
0.0041
-0.0856
-0.0329
-0.4569
0.2742
0.1160
0.1552
0.3921
0.1380
-0.2420
-0.1311
-0.3328
0.0550
0.4519
-0.2985
0.0936
56
Chapter 6
Comparative Data Analysis :
Regression
6.1
Model Development
This chapter employs pooled regression with fixed effects to empirically test if any
of the various CSP factors can explain any systematic variation in stock returns (i.e,
CFP). Pooled regression can be carried out on panel ( time-series and cross-sectional)
data when each cross sectional unit has data repeated for over time. Using a panel
fixed effects model, the existence of subgroups within a set of panel constituents that
are incorporated in each cross-sectional unit of time span can be taken into account.
Regression analysis of the stock market is based on CAPM ( capital asset pricing
model) proposed by Sharpe (1964)[41] and Lintner (1965) [29], based on earlier work
on diversification by Markowitz. Using a single factor # (asset specific non diversification risk), CAPM tries to explain the excess returns from a portfolio, in terms
of excess market returns assuming a risk-free rate at which lending and borrowing
takes place. CAPM is a single period model, and to analyze historical behavior in a
time series, additional assumption of iid (independently and identically distributed)
multivariate normal return distribution gives the well known equation [21]:
R~it = ait + Oi Rrt + fit
57
(6.1)
where Rt is the excess return on the ith asset of a portfolio at time t,Rmt is the excess
market return at time t, /3i is the ith asset's historical relative risk estimate at time,
ait = E(Ri) -
#iE(Rm)
and eit is the error term that is normally distributed with
zero mean and constant variance (model assumption) and independent of the market
return. Equation 6.1 is used to estimate stock /3 using a time series regression of the
asset and market excess returns.
The Fama-French (1993)[9] market model modifies CAPM and adds firm level
explanatory risk factors, asset size and book to market value, in addition to asset /.
This basic model was modified for this study to include an individual CSP factor.
Rit+1 =
No
+ 11t +
-y2T1it
(6.2)
+ 'y3 BTMit + yntpnit + fit
where Rit+1 is the expected return from asset i at time period t+1,
it
is the
estimated beta of asset i at time t, yit is the logarithm of firm size of i at time t,
BTMit is the book to market of i at time t and <pnit is the nth individual CSP factor
of asset i at time t. and eit the error term. Here, the coefficients of estimation
from the regression, - n where n = 1,2,3,... ,m can be tested to see if each of these
factors are statistically significant. This model does not take differences between subgroups of data in the panel. Different countries will have different intercepts (group
means) that is specific to the average market risk, firm size, BTM and CSP factor
for each country. and each year represents a different slice of the economic cycle. In
order to account for these fixed effects, we will modify this model to include dummy
variables that measure fixed effects of these groups. In this pooled time-series and
cross-sectional fixed effects model, the resulting equation assumes that every country
and every year has its own specific effect that differs from other countries or years
and can be measured using the specific constant.
Equation 6.2 can be modified to have:
Rit+1 = ao+a2o2+a203+........+a2009+POP+P2±..... +pj+71±it+72ft+73BTMit+yp+oniti
(6.3)
where the new equation has includes of fixed effects from years 2002 to 2009 (a) and
58
countries (p) for the j countries in sample. The mean effect associated with each of the
five OECD economies can also be obtained from the above equation.These country
level fixed effects not specifically attributable to CSP.
The resulting analysis should reveal which CSP individual factors, if any, are
significant.
6.2
Model Limitations
A fundamental criticism of the model is that the risk-return trade-off has not held in
empirical verifications. Also, the risk proxies such as
#,size
or book to market have
been shown to be ineffective in various empirical studies. Other model assumptions,
such as excess returns being normally distributed, availability of risk-free borrowing
and lending and assumptions about error structure and independence ( e.g.. errorin-variables issue) have all revealed flaws and spawned numerous work-arounds.
In trying to explain returns in terms of CSP, the return time horizon becomes
important. Common regression models use monthly or yearly returns in the above
equations. In vendor data, CSP is usually reported ex-post and firm ratings remain
relatively static compared to returns. So, as part of the study, CSPs relationship
with averaged returns was also considered. It should be noted that many of the risk
proxies used in conventional models to explain excess returns have a first or second
order relationship with the underlying asset price. This makes their relationships
with excess returns much easier to tease out than CSP, which if any, will have higher
order relationships. In any case, the explanatory coefficients from CSP in explaining
CFP is expected to be very modest at best.
Despite these limitations, Graham and Harvery [16] report that three-fourths of
CFOs surveyed used some form of CAPM to estimate equity risk premium. The
Fama-French based market model is in extensive use today in both industry and
academia. The model and its variants form the basis for several quantitative asset
management techniques.
59
6.3
Treatment of returns over different time frames
This thesis attempts to analyze how different ESG factors pay off over time. As noted
earlier, ESG factors have a complex relationship with CFP and it is expected that
factors will be effective in explaining excess returns over different time periods. In
order to test this, factor regressions were conducted over multiple time periods, not
just the over one month returns. The rationale behind averaging a time span instead
of trying to explain a single month forward returns is that returns have very high
variance compared to ESG factors. As a result, smoothed version of returns might
provide better model fits over a longer investment horizon. Time periods that we
considered as part of the study were 1, 3, 6, 12 and 36 months. The average return
R for any n periods at time t is computed as a geometric mean using:
Rt
6.4
(n) =
(1 + Rt)(1 + Rt_1 )(1 + Rt-2)...(1 + Rtn+i) - 1
(6.4)
Regression results
The common theme in regression results across both vendors is that the payoffs of
most CSP factors in explaining average excess returns over the next period increases
with increase in period length. This relationship is more pronounced for environmental factors. In examining the results from regression, correlation and principal
component analysis the best case can made be for aggregation of environmental factors. In the case of governance and social scores, aggregation can lead to obfuscation
due differences in payoff structures. In the results presented, a ItStat.| ;> 1.96 corresponds to a p-value that is greater than 0.05 and can be considered significant.
Pooled regression results for Innovest are detailed in Table 6.1 and country specific
fixed effects in Table B.15. Of all innovest factors, environmental and risk relatedfac-
tors seems to be the most efficacious. Other social and governance factors have either
low or mixed payoffs. Regression results for Asset4 detailed in Table 6.2 with country
specific effects in Table B.16. Most Asset4 factors show increasing efficacy as time
spans for averaging returns increase. In Asset's data, social and environmentalfac60
tors are effective in explaining excess returns over time in addition to healthandsafety
and visionandstrategy among labor and governance factors.
Efficacy of individual factors are not always aligned, except in the case of environmental factors for both vendors and community factors ( without labor) for Asset4
and provide support for hypothesis I. Results from regression along when combined
with results from PCA that suggest structural differences between vendor data show
support for hypothesis 1I. However, it is not clear that hypothesis III holds as reputational factors do not seem to be significantly robust when compared to other social
factors.
In section 3.1, it was speculated that Asset4's emission reduction, product innovation, work force employment quality, health and safety, training and development,
human rights, community involvement and product responsibility are most likely to
be informative in explaining CFP. Of these, all factors except product innovation,
product responsibility and employment quality seems to support that intuition. Also,
average t-stats of Asset4 factors were higher than those of innovest when a factor was
efficacious.
The most efficacious factors are highlighted in the result below.
61
Factor
auditinteg
certification
corporategovernance
customerstakeholderpartnerships
employeemotivationanddevelopment
environmentalaccountingreporting
environmentalmanagementsystems
environmentalopportunity
environmentalstrategy
envtraininganddevelopment
healthandsafety
historicliabilities
humanrightschildandforcedlabor
industryspecificrisk
laborrelations
leadingsustainabilityriskindicators
localcommunities
operatingrisk
opportunity
performance
productsafety
productsmaterials
strategicgovernance
supplychain
1 Month
t Stat.
-0.899
0.1
-1.717
-2.434
-2.147
-1.407
-0.372
0.093
0.158
-0.717
-1.985
-1.081
-0.63
-1.644
-1.342
-0.337
-0.933
-0.473
-0.449
-0.751
-1.338
-0.406
-2.208
-0.397
3 Months
t Stat.
-0.191
2.399
2.050
0.083
-0.705
1.587
1.755
-0.151
2.663
2.564
-0.577
-0.285
1.229
0.508
0.229
-0.034
-0.654
2.444
1.059
0.511
1.408
1.391
0.376
0.943
6 Months
t Stat.
-1.892
0.729
3.827
-0.221
-0.855
2.822
2.468
0.476
3.189
3.908
0.160
-0.702
1.127
2.181
-0.641
-0.175
-0.829
2.561
3.586
3.411
0.104
0.189
0.466
-0.341
12 Months
t Stat.
-1.001
0.760
3.133
1.249
0.973
2.634
2.144
0.990
2.512
3.514
0.021
0.299
0.623
0.316
1.318
0.873
-0.059
4.204
1.355
0.713
1.107
0.025
0.784
0.189
36 Months
t Stat.
-1.857
0.117
3.831
0.946
0.580
2.904
3.999
-0.223
3.505
3.859
1.349
-1.661
-3.548
0.903
-0.048
0.875
0.812
4.853
3.559
3.336
-1.055
1.186
0.957
1.686
Table 6.1: Innovest Individual Factor Pooled Regression Results [2002-2009] with Cap > USD 250M
Factor
boardfunctions
boardstructure
compensationpolicy
visionandstrategy
shareholderrights
marginsperformance
shareholderloyalty
clientloyalty
emissionreduction
productinnovation
resourcereduction
productresponsibility
community
humanrights
workdiversityopportunity
employmentquality
healthandsafety
traininganddevelopment
1 Month
t Stat.
0.04
0.41
-1.78
0.78
2.00
1.32
0.37
-0.03
0.66
-1.24
1.50
1.73
1.22
0.47
1.70
0.22
0.68
0.98
3 Months
t Stat.
-'0.11
1.00
-0.72
2.70
0.60
3.58
2.64
-0.03
3.54
1.63
3.77
1.06
1.66
3.58
-0.10
-0.66
4.05
1.83
6 Months
t Stat.
2.09
0.53
-1.25
4.61
0.54
2.57
2.07
-0.10
6.35
2.93
4.01
0.47
2.94
3.17
0.02
-0.94
7.09
1.88
12 Months
t Stat.
0.66
-0.80
-1.97
3.11
-0.15
1.32
2.40
0.95
4.46
1.80
3.78
0.47
3.22
2.01
1.33
-0.36
4.66
1.37
36 Months
t Stat.
0.67
-0.71
0.38
3.09
2.19
-0.25
-0.02
1.82
5.03
3.99
4.94
2.11
2.36
5.16
2.39
-0.75
6.07
3.23
Table 6.2: Asset4 Individual Factor Pooled Regression Results [2002-2009] with Cap > USD 250M
64
Chapter 7
Summary
This thesis has made an attempt to comparatively analyze vendor environmental, social (which subsumes labor), and governance factors ( social and governance factors
overlap with reputational) to examine their structure and understand how they contribute to corporate social performance. The relationship between CSP and CFP is
also examined under this context, one CSP factor at a time. Data from two vendors,
Innovest and Asset4, were analyzed using correlation, factor and regression analysis.
In correlation analysis, relationship between the various ESG factors at the lowest
vendor provided granularity revealed low correlation among factors in general. Environmental factors did reveal stronger correlation than others. This pattern was also
true for tests between vendor data, where common factors existed. In factor analysis, both exploratory factor analysis and principal component analysis was used. In
exploratory factor analysis, the existence of underlying factors that could be representative of E,S and G dimensions was explored but results did not reveal their existence.
In principal component analysis, the component loadings were computed. Loadings
did not reveal strong dimensionality along E,S and G classifications. In addition,
differences between component loadings indicated that relative relationship between
governance factors and other factors was different for the two vendors. In regression
analysis, a pooled panel regression with fixed effects to account for country level and
yearly differences. The results revealed a slight positive relationship between vendor
factors and average excess returns when the period of averaging was extended be-
65
yond three months. In the short term, the relationship was negative or weak. Again,
environmental factors seemed to have the most consistent result, with factor efficacy
increasing with increase in averaging time duration.
The expectations/hypotheses explored in this thesis revealed the following:
" ESG factors payoff over varying time periods, making aggregation into a single
composite score difficult for econometric analysis. Analysis of correlation, factor
structure and regression results seem to indicate that it is not easy to provide
a composite score as well as scores within each of the three major classification
groups without subjective treatments. However, this environmental factors are
the least affected by this phenomena as they seem more closely related to one
another. (I)'
" Proprietaryscoring techniques used in the quantification of qualitative data can
cause additional difficulties in interpretation. Cross vendor analysis of correlation, regression analysis and factor structure do indicate differences in scoring.
By and large, general patterns revealed by both vendors seemed to be consistent.
In tandem with the issues in (I), this does make it more difficult to understand
which factors have been overweighted in the composite score. (II)
" Reputation factors contribute to CFP, while environmental factors may detract
from CFP. Analysis of both vendor data over varying time periods does not
seem to support this hypothesis.(III)
" Firms that treat their labor well are good environmental performers. Analysis
of both vendor data does not provide strong support for this hypothesis for
health and safety, but one vendor data indicated a weak positive relationship
with employment quality while the other did not.(IV)
Minimal regulation of CSR reporting and data requirements will go a long way
towards making the data more transparent for consumers and easier to assimilate and
transform for vendors. Today, CSP reporting in voluntary. If companies with market
capitalization over a certain size are required to publish CSR data using an industry
66
specific template, this can be of tremendous help to SRI. The templates can be based
on several emerging standards such as the UN's GRI, CERES, ISO 26000 etc. If
vendors are required to provide details of raw data used in their processing ( akin
to much of the fundamental financial data that vendors provide), but are allowed to
provide value added analytics and ratings, errors can be identified more easily. This
also will provide a degree of transparency that will aid further research in this area.
This thesis has just scratched the surface in terms of looking at the characteristics
of vendor supplied ESG data and social performance ratings. This area of study has
not gotten as much attention as econometric analysis of CSP, mainly due to most of
academia relying on vendor ratings despite being aware of its limitations. This thesis
did not attempt to identify any transformations of ESG raw values to scores, which
remains a ripe area for research. A set of non-proprietary well known algorithms to
transform ESG data is another research area that will aid both vendors in creating
better data and consumers in validating vendor data and making sure vendor actions
are consistent with their investment objectives and values.
67
68
Appendix A
Technical Architecture
This chapter provides an in-depth view of the components and modules developed
for analysis.
A.1
Overview
PSql Stored Procedures
Innovest Data
Asset4
Postgres DB
R Data
Analysis
Scripts
Other firm level fundamental
data.
Figure A-1: System Overview
Data processing system uses a purpose built database on a relational database
management system (RDBMS) to store all source and intermediate data required for
69
analysis. The open source Postgres RDBMS software was used to this build system.
The system comprises of:
1. Data import scripts that process various extracts from Acadian, Innovest and
Asset4 data to load them into database tables.
2. Postgres stored procedures that generate additional data for analysis.
3. R scripts to perform statistical analysis on the stored data structures.
A.2
System Components
A.2.1
Database Tables: Data from Financial Sources
The section documents the database tables that are used to store financial data and
the important columns in those tables.
" a4-company-ratings: This table holds Asset4 company ratings data. Key
table columns include Asset4 internal firm identifier, ESG aggregate ratings and
all 18 columns that contain normalized ratings of the individual Asset4 factors
described in Chapter 4.
"
a4-company-refinfo: This table holds Asset4 company reference data. It
maps Asset4 internal identifier to firm ISIN ( a financial data standard for
identifying companies uniquely), country, GICS ( S&P's Global Industry Classification System) code.
"
company-gicindustry: This table holds firm level GICS classification data
mapped to company identifier.The key columns are company identifier ( Cusip
for North American stocks and Sedol of rest of the world, which are both standards for firm identification) and GICS code.
" company-pbvals: This table holds firm level price-to-book (book-to-market)
values. The main columns are identifier and pb values.
70
"
company-data: This table holds other additional firm data such as country,
size.
" monthly-returns: This table holds historical firm monthly returns from 1999
to 2010. The main columns in this table are identifier and monthly USD returns.
Returns as expressed as percentage gain or loss over the previous month.
" innovest-ratings: This table holds innovest ratings data. The tables holds
monthly individual factor and aggregated IVA ratings provided by innovest.
Important fields include Cusip, Sedol, country, industry, firm ratings, corporate,
social and environment aggregate ratings and all the individual innovest factors
described in Chapter 4.
A.2.2
Database Tables: Generated data
The section documents the database tables that are used to store computed or consolidated financial data and the important columns in those tables.
company-beta: This table holds firm level computed beta using Fama-
*
Macbeth.
" firm-data : This table holds consolidated firm level data that includes mapped
identifiers, ISIN, beta, pb, size, country and GICS values.
"
firm-returns-data : This table holds consolidated firm level return data,
with average returns computed over various time periods.
A.2.3
Stored Procedures
A stored procedure is software that is written in a RDBMS specific language that is
stored in the database and can be executed ad-hoc to manipulate data. PlPgSql is
the language that Postgres SQL makes available for creating stored procedures.
o populate-firm-data.sql: This stored procedure creates a denormalized consolidated table with all firm level data that can be exported to R scripts.
71
* populate-firm-returns-data.sql: This stored procedure computes firm market returns over different time periods for use in data analysis by R scripts.
A.2.4
Processing
A set of R scripts were custom built to analyze data. R is a language and environment
for statistical computing and graphics. It is an open source project. R provides a wide
variety of statistical (linear and nonlinear modeling, classical statistical tests, timeseries analysis, classification, clustering, ...) and graphical techniques, and is highly
extensible. R is available as Free Software under the terms of the Free Software
Foundation's GNU General Public License in source code form. It compiles and runs
on a wide variety of UNIX platforms and similar systems (including FreeBSD and
Linux), Windows and MacOS.
The set of data processing scripts are:
" innovest-data-export.r : This script reads Innovest ratings data and firm
data for different time periods and exports it in R's internal format for use by
other R scripts.
" a4_data-export.r : This script reads Asset4 ratings data and firm data for
different time periods and exports it in R's internal format for use by other R
scripts.
" gen-firm-betas.r: This script uses firm historical company time series from
1990, along with market data to implement the time series regression that estimates firm beta and persists it in the database.
" innovest factor-correlations.r : This R script computes correlation between
the ratings on various factors supplied by Innovest.
* a4_factor-correlations.r : This R script computes correlation between the
ratings on various factors supplied by Asset4.
* common-factor.correlations.r : This script computes correlation between
Asset4 and Innovest factors that have similar definitions.
72
* innovest-percentile-correlations.r : This script modifies the original factor
correlations script to compute correlations which fall within a specific quantile
for a specified factor for innovest data.
" a4_percentilecorrelations.r : This script perform analysis similar to the
previous script for Asset4 data.
" innovest-efa-analysis.r : This script performs efa analysis on Innovest Data.
* a4_efa-analysis.r : This script performse efa analysis on Innovest Data.
" innovestpca-analysis.r : This script performs pca analysis on Innovest Data.
* a4_pca-analysis.r : This script performs pca analysis on Innovest Data.
" innovest-pooled-data-regress-analysis.r : This script performs pooled regression with fixed effects on Innovest Data.
* a4-pooled-data-regress-analysis.r
with fixed effects on Asset4 Data.
73
: This script performs pooled regression
74
Appendix B
Tables
stock..return
beta
bp
auditinteg
certification
corporategovernance
custonmerstakeholderpartnerships
employeeniotivationanddevelopment.
environmientalaccountingreporting
environnentalmanagermentsystemns
environmentalopportunity
environmentalstrategy
envtraininganddevelopment
healthandsafety
historicliabilities
humanrightschildandforcedlabor
industryspecificrisk
laborrelations
leadingsustainabilityriskinlicators
localcommunities
operatingrisk
opportunity
performance
productsafety
productsmaterials
strategicgovernance
supplychain
logsize
var
n
mean
sd
median
trimmed
mad
mnin
max
range
skew
kurtosis
se
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
2918.00
2918.00
2640.00
2640.00
2755.00
2836.00
2755.00
2762.00
2860.00
2880.00
2792.00
2916.00
2817.00
2755.00
2790.00
2509.00
2615.00
2748.00
2907.00
2760.00
2821.00
2915.00
2850.00
2743.00
2843.00
2358.00
2762.00
2918.00
-0.02
1.14
0.50
3.18
3.40
4.11
4.88
5.77
3.51
3.99
5.12
4.72
4.24
5.00
5.20
4.97
4.28
5.06
4.75
5.72
5.21
4.43
4.16
5.09
3.73
5.08
4.98
9.16
0.11
0.66
0.47
3.06
2.53
2.33
2.09
2.03
2.48
2.38
1.71
2.27
2.57
2.05
2.52
2.20
2.44
1.79
1.83
2.24
2.40
2.15
2.52
2.04
2.46
1.74
2.34
1.17
-0.01
1.05
0.39
2.53
4.00
4.00
5.00
6.00
3.00
4.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
6.00
5.00
5.00
4.00
5.00
4.00
5.00
5.00
9.04
-0.01
1.09
0.42
2.79
3.24
4.14
4.87
5.83
3.39
3.95
5.19
4.75
4.22
5.04
5.20
5.09
4.33
5.12
4.78
5.79
5.29
4.43
4.14
5.15
3.64
5.07
4.99
9.10
0.08
0.62
0.25
1.59
2.97
2.97
1.48
1.48
2.97
2.97
1.48
1.48
2.97
1.48
2.97
1.48
1.48
1.48
1.48
2.97
1.48
1.48
2.97
1.48
2.97
1.48
2.97
1.06
-0.71
-0.29
-1.21
-16.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
5.56
0.58
3.85
5.00
16.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
13.17
1.29
4.14
6.21
32.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
7.62
-0.25
0.80
3.67
0.78
0.27
-0.09
0.04
-0.28
0.33
0.14
-0.51
-0.10
-0.07
-0.18
-0.03
-0.41
-0.28
-0.29
-0.15
-0.26
-0.27
0.02
-0.01
-0.26
0.22
-0.03
-0.01
0.46
4.52
0.60
22.86
10.05
-0.58
-0.58
-0.15
-0.23
-0.66
-0.54
1.07
-0.37
-0.53
-0.26
-0.37
0.32
-0.40
0.38
0.04
-0.31
0.01
-0.47
-0.63
0.25
-0.45
-0.26
-0.38
0.41
0.00
0.01
0.01
0.06
0.05
0.04
0.04
0.04
0.05
0.04
0.03
0.04
0.05
0.04
0.05
0.04
0.05
0.03
0.03
0.04
0.05
0.04
0.05
0.04
0.05
0.04
0.04
0.02
Table B.1: Innovest Summary Statistics
75
United States
stock.return
beta
bp
auditinteg
certification
corporategovernance
customerstakeholderpartnerships
employeemotivationanddevelopment
environmentalaccountingreporting
environmentalmanagementsystems
environmentalopportunity
environmentalstrategy
envtraininganddevelopment
healthandsafety
historicliabilities
humanrightschildandforcedlabor
industryspecificrisk
laborrelations
Ieadingsustainabilityriskindicators
localcommunities
operatingrisk
opportunity
performance
productsafety
productsmaterials
strategicgovernance
supplychain
logsize
var
n
mean
sd
median
trimmed
mad
min
max
range
skew
kurtosis
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
1636.00
1636.00
1600.00
1600.00
1527.00
1584.00
1530.00
1528.00
1588.00
1603.00
1549.00
1636.00
1530.00
1519.00
1537.00
1428.00
1529.00
1512.00
1632.00
1527.00
1583.00
1629.00
1586.00
1502.00
1599.00
1385.00
1531.00
1636.00
-0.01
1.02
0.76
1.81
3.38
5.43
5.15
5.37
5.94
5.98
5.54
6.11
5.66
5.10
5.30
5.01
4.96
5.49
5.78
5.30
5.41
5.84
5.70
5.32
5.27
5.28
5.17
8.67
0.09
0.45
0.51
1.51
2.38
2.12
1.85
1.80
2.75
2.42
1.98
2.00
2.44
2.05
1.98
2.00
2.44
1.65
1.84
1.96
2.16
2.06
2.50
2.19
2.57
1.72
2.09
0.97
-0.00
1.03
0.65
1.52
3.00
6.00
5.00
5.00
6.00
6.00
6.00
6.00
6.00
5.00
5.00
5.00
5.00
5.00
6.00
5.00
5.00
6.00
6.00
5.00
5.00
5.00
5.00
8.58
-0.01
1.03
0.70
1.63
3.26
5.59
5.17
5.40
6.14
6.19
5.74
6.21
5.82
5.10
5.36
5.10
5.07
5.47
5.88
5.31
5.53
5.92
5.81
5.45
5.37
5.26
5.20
8.62
0.08
0.46
0.35
0.82
2.97
1.48
1.48
1.48
2.97
2.97
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
2.97
1.48
2.97
1.48
1.48
0.96
-0.28
-0.18
-1.02
-9.16
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
5.82
0.31
2.33
5.00
16.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
18.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
12.39
0.59
2.51
6.02
25.16
10.00
10.00
10.00
10.00
10.00
10.00
10.00
18.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
9.00
10.00
6.57
0.03
-0.08
2.61
2.98
0.32
-0.62
-0.11
-0.15
-0.53
-0.68
-1.03
-0.37
-0.50
-0.05
-0.28
-0.42
-0.39
0.04
-0.42
-0.07
-0.51
-0.31
-0.31
-0.45
-0.27
0.09
-0.06
0.53
0.70
-0.33
14.27
30.09
-0.47
0.53
0.03
0.30
-0.52
-0.01
1.90
0.89
0.04
-0.24
0.92
0.50
-0.15
1.20
0.06
0.10
0.74
0.06
-0.20
0.08
-0.53
-0.13
-0.12
0.27
se
0.00
0.01
0.01
0.04
0.06
0.05
0.05
0.05
0.07
0.06
0.05
0.05
0.06
0.05
0.05
0.05
0.06
0.04
0.05
0.05
0.05
0.05
0.06
0.06
0.06
0.05
0.05
0.02
Table B.2: Innovest Summary Statistics : Japan
stock.return
beta
bp
auditinteg
certification
corporategovernance
customerstakeholderpartnerships
employeemotivationanddevelopment
environmentalaccountingreporting
environmentalmanagementsystems
environmentalopportunity
environmentalstrategy
envtraininganddevelopment
healthandsafety
historicliabilities
humanrightschildandforcedlabor
industryspecificrisk
laborrelations
leadingsustainabilityriskindicators
localcommunities
operatingrisk
opportunity
performance
productsafety
productsmnaterials
strategicgovernance
supplychain
logsize
var
n
mean
sd
median
trimmed
mad
min
max
range
skew
kurtosis
se
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
284.00
284.00
269.00
269.00
266.00
280.00
267.00
267.00
278.00
277.00
269.00
284.00
273.00
267.00
263.00
248.00
266.00
267.00
280.00
266.00
263.00
282.00
281.00
259.00
281.00
220.00
267.00
284.00
-0.04
0.98
0.65
2.53
4.21
5.55
6.21
6.67
5.38
6.20
5.44
6.48
5.85
5.68
4.86
5.19
4.94
5.95
5.69
5.86
5.00
6.21
6.22
5.69
5.70
6.18
5.64
9.28
0.14
0.42
0.53
2.14
2.53
2.30
1.82
1.79
2.53
2.35
2.17
1.89
2.49
2.10
2.45
2.10
2.42
1.86
1.77
1.81
2.57
2.03
2.46
1.73
2.36
1.60
2.13
1.28
-0.03
0.95
0.53
1.89
4.00
6.00
6.00
7.00
5.50
7.00
6.00
6.00
6.00
6.00
5.00
5.00
5.00
6.00
6.00
6.00
5.00
6.00
7.00
6.00
6.00
6.00
6.00
9.21
-0.03
0.96
0.56
2.14
4.14
5.69
6.19
6.74
5.52
6.41
5.69
6.60
6.02
5.71
4.95
5.38
5.10
6.06
5.75
5.90
5.10
6.37
6.42
5.78
5.79
6.23
5.72
9.29
0.11
0.47
0.32
1.10
2.97
1.48
1.48
1.48
3.71
2.97
1.48
1.48
2.97
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
2.97
1.48
2.97
1.48
2.97
1.41
-0.61
0.20
0.06
0.25
0.00
0.00
2.00
0.00
0.00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
0.00
0.00
2.00
0.00
5.92
0.42
2.10
4.02
16.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
11.91
1.03
1.89
3.95
15.75
10.00
10.00
8.00
10.00
10.00
10.00
10.00
9.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
9.00
10.00
10.00
10.00
8.00
10.00
5.99
-0.78
0.33
2.91
2.63
0.23
-0.49
0.06
-0.58
-0.29
-0.73
-1.05
-0.43
-0.44
-0.22
-0.37
-0.78
-0.47
-0.58
-0.40
-0.15
-0.45
-0.56
-0.63
-0.60
-0.32
-0.26
-0.31
-0.02
2.57
-0.59
12.18
9.60
-0.39
0.15
-0.61
1.21
-0.89
-0.14
1.24
-0.28
-0.29
0.14
0.00
0.81
-0.21
0.58
0.50
0.31
-0.00
-0.15
-0.02
1.43
-0.44
-0.19
-0.20
-0.78
0.01
0.03
0.03
0.13
0.16
0.14
0.11
0.11
0.15
0.14
0.13
0.11
0.15
0.13
0.15
0.13
0.15
0.11
0.11
0.11
0.16
0.12
0.15
0.11
0.14
0.11
0.13
0.08
Table B.3: Innovest Summary Statistics : Germany
76
stock-return
beta
bp
auditinteg
certifncation
corporategovernance
customerstakeholderpartnerships
enployeemotivati)nanddevelopment
environmentalaccountingreporting
environmentalmanagementsystems
environmentalopportunity
environmentalstrategy
envtraininganddevelopment
healthandsafety
historicliabilities
humanrightschildandforcedlabor
industryspecificrisk
laborrelations
leadingsustainabilityriskindicators
localcommunities
operatingrisk
opportunity
performance
productsafety
productsmaterials
strategicgovernance
supplychain
logsize
var
n
mean
sd
median
trimmed
nad
min
max
range
skew
kurtosis
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
391.00
391.00
378.00
378.00
360.00
388.00
375.00
374.00
381.00
380.00
369.00
391.00
373.00
375.00
366.00
344.00
338.00
374.00
391.00
373.00
375.00
391.00
380.00
370.00
382.00
327.00
375.00
391.00
-0.04
1.10
0.64
2.43
3.80
5.07
5.85
7.18
5.47
5.97
5.89
6.36
5.79
6.17
5.30
5.83
4.87
6.24
5.89
6.01
5.48
5.85
5.45
6.16
5.38
6.34
6.01
9.42
0.10
0.47
0.66
2.23
2.74
1.98
2.10
1.81
2.23
2.06
1.96
1.82
2.12
1.96
2.51
2.04
2.91
2.12
1.78
2.19
2.27
1.88
2.55
2.06
2.26
1.47
2.12
1.12
-0.03
1.07
0.51
1.88
4.00
5.00
6.00
7.00
6.00
6.00
6.00
7.00
6.00
6.00
5.00
6.00
5.00
6.00
6.00
6.00
6.00
6.00
6.00
6.00
5.00
6.00
6.00
9.31
-0.04
1.09
0.55
2.10
3.66
5.11
5.96
7.27
5.63
6.11
6.17
6.50
5.89
6.21
5.43
5.90
4.96
6.33
6.05
6.01
5.69
5.96
5.64
6.30
5.47
6.41
6.00
9.39
0.10
0.43
0.32
1.07
2.97
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
2.97
1.48
2.97
2.97
1.48
1.48
1.48
1.48
2.97
1.48
2.97
1.48
2.97
1.13
-0.31
0.06
-1.71
-4.06
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.00
1.00
5.86
0.32
2.54
5.00
16.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
12.29
0.64
2.48
6.71
20.06
10.00
10.00
9.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
8.00
9.00
6.43
-0.20
0.26
3.24
2.95
0.17
-0.22
-0.47
-0.87
-0.48
-0.68
-1.37
-0.94
-0.47
-0.26
-0.34
-0.37
-0.49
-0.42
-0.93
-0.12
-0.82
-0.45
-0.55
-0.77
-0.32
-0.28
0.06
0.10
0.10
0.06
18.63
14.37
-0.78
0.53
-0.02
1.95
-0.04
0.37
2.27
1.46
0.38
-0.06
-0.01
0.37
-0.79
-0.12
1.41
-0.33
0.82
-0.07
-0.38
0.85
-0.17
0.22
-0.73
-0.06
se
0.00
0.02
0.03
0.11
0.14
0.10
0.11
0.09
0.11
0.11
0.10
0.09
0.11
0.10
0.13
0.11
0.16
0.11
0.09
0.11
0.12
0.10
0.13
0.11
0.12
0.08
0.11
0.06
Table B.4: Innovest Summary Statistis : France
stock.return
beta
hp
auditinteg
certification
corporategovernance
customerstakeholderpartnerships
employeemotivationanddevelopment
environmentalaccountingreporting
environmentalmanagementsystems
environmentalopportunity
environmentalstrategy
envtraininganddevelopment
healthandsafety
historicliabilities
humanrigltschildandforcedlabor
industryspecificrisk
laborrelations
lealingsistainabilityriskindicators
localcomimunities
operatingrisk
opportunity
performance
productsafety
productsmnaterials
strategicgovernance
supplychain
logsize
var
n
mean
sd
median
trimmed
moad
rmin
max
range
skew
kurtosis
se
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
368.00
365.00
336.00
336.00
339.00
346.00
356.00
358.00
359.00
355.00
346.00
368.00
348.00
355.00
330.00
319.00
310.00
354.00
364.00
358.00
348.00
368.00
337.00
352.00
356.00
342.00
358.00
368.00
-0.02
1.08
0.50
3.14
3.80
5.34
5.86
6.25
5.15
5.34
5.49
5.99
4.87
6.26
5.40
5.51
4.66
5.77
5.62
6.08
5.38
5.21
5.00
5.74
5.07
6.29
5.63
8.22
0.10
0.45
0.63
5.07
2.82
2.47
2.30
2.10
2.64
2.60
2.22
2.37
2.61
2.44
2.58
2.52
3.14
1.87
2.02
2.18
2.18
2.36
2.67
2.09
2.75
1.93
2.39
1.28
-0.02
1.03
0.35
2.47
4.00
5.00
6.00
7.00
5.00
5.00
6.00
6.00
5.00
7.00
5.00
5.00
5.00
6.00
6.00
6.00
5.00
5.00
5.00
6.00
5.00
6.00
6.00
8.12
-0.02
1.04
0.40
2.98
3.63
5.44
5.98
6.47
5.22
5.46
5.72
6.16
4.94
6.45
5.58
5.69
4.67
5.90
5.69
6.21
5.52
5.35
5.13
5.85
5.15
6.38
5.72
8.15
0.08
0.43
0.29
1.94
2.97
1.48
1.48
1.48
2.97
2.97
1.48
2.97
2.97
2.97
2.97
1.48
2.97
1.48
1.48
1.48
1.48
2.97
2.97
1.48
2.97
1.48
1.48
1.25
-0.35
0.18
-0.39
-16.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.00
0.00
5.56
0.49
2.52
5.00
16.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
12.20
0.83
2.35
5.39
32.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
8.00
10.00
6.64
0.68
0.67
4.18
-0.58
0.27
-0.33
-0.49
-0.96
-0.18
-0.30
-0.81
-0.51
-0.19
-0.65
-0.50
-0.58
-0.27
-0.75
-0.26
-0.61
-0.55
-0.39
-0.33
-0.42
-0.22
-0.32
-0.28
0.50
3.24
0.45
25.29
4.78
-0.79
-0.26
0.23
0.75
-0.62
-0.72
0.44
-0.28
-0.50
0.01
-0.09
0.18
-1.15
1.25
-0.16
0.54
0.61
-0.64
-0.84
0.24
-0.84
-0.61
-0.38
0.09
0.01
0.02
0.03
0.28
0.15
0.13
0.12
0.11
0.14
0.14
0.12
0.12
0.14
0.13
0.14
0.14
0.18
0.10
0.11
0.12
0.12
0.12
0.15
0.11
0.15
0.10
0.13
0.07
Table B.5: Innovest Summary Statistics
77
Great Britain
-4
It
Pi
-4
00000000000000
00000
0 0 -0 0 r-
;
mz
O
o
RRA
tr
0r,
i:
0
w m
I--Mo1ooWwo
o
0
0000
S0
0
0
at00
D
00
1-1
00
4
1
w
---
00
-40a)
cn
o40-
0
T Co
wo
o-'
0(D t
A
0
0 cw 0 00 w 05
C0 1 0
0-0-
o&000000000000
C1
0000000
'0
0'
0'
0'
.
cy
501-00
x
--
C)
- - - - - - 4- - a
00
C M
ou & m
Q'I' CO & -4 om cn w
w0M00
M-N
CL
c
0 0 a. 0
ca IW
w AIMo
w w 4 WMo 0uoo Q
0 0Cn
CM
0000000
CA
~c
e4
U 00000
t'
'0
0 '0
0CD~OOtoo
0l
"IlwAo
C0&
0 - ooooooooooooooom
- w- - -.. - ....
40 w
0 to M & o
w .
'al
0
oooo
- -
0 w CO woo 00
w
Q-101
c w w to w w rAW 0Q w CQN)tj w w w
0003
O
0
1
0011 1 011 00F0
00 0
w w w w W to
NN
0 0 0
So
0
.
0 00
to
0
.
0
0
ul
. .
0
'
9
00
m
0
0c
m
C 0
000
0
0
m toW
N. 0
.
0 0i
.
0000
00
0
00 4
o
M ----o
0000N.0000oo~m
00 0
00 o.
C
w
.C0
0C 01
-
-
oo
o --
o0
oy
oP
00000MC
0 00000
000
AM O
00
C
0
0 c c c c O c 0 to 0 00 P
I.-
N.
0 0 0
. N.
0
00
6
w w m -4 w 0 w cm0
000W MoW0-
tteoeo
.
m w 1o Q
w 00 0w 00
0
-
0oooowooow0oooo oooo
04
0
060000660
o,
0
.
w0
CO0
o
oooooo00ooooooooooooo
0 0
W wlC4w W w
0000000000000000000000
0000400000000000
-"
0-
NO 0
N 0
0
C.
0
C
.,'.
'-1
0
c
m D04
00
' w0
0m M
0
000
-
"
- -00
0.
00
000
000
00o.0o'.o 00m
000
~~~01010
00
-0
-4
00
0'.
w
Z
0
o0
-4
00
0CD 0
OD w'.
C0c0 t; x
00 -
-4
oo o
W'
".
1mIwIn
0'.'
w
1001-
00 ww00000-
'0~0
5, PAbebobokebPo51o,
0 oooooooooooooooooD
n c0
00
c
0
t'.000-00
PP??????????0
-
0 0 00 0
'g
ww
-I
zM 0 -
00
51PPPPB
0
M
CA al0-0w000e
00
0
O4
00000-4C00
PP
00 P p
C pPPPPPPPPPP
00 00 00 00 0
0 00
0'c000
os-oo-o-o--
o
-00
000 M pICW;C
000000
'm- 0'.4
tmaN
C4& 0m&(m.w -4w.,
00
Q-0 i000W001
ocoFFnFuomseam-
o
'-1
cJ'.
0+
ci'.
0+
0+
U)
I-I
S
S
U)
OD
0+
;0D
-4
ci'.
Ci'.
H
-4
o
00
0 '0
.~
6
00
''
300-b
0.
C 00O
-4 -4-
C..'
a
. J
.
P P
. J
.
.
b
4n
~
-
. 0.
.
-4.
.
0
i4:
0.C..'~
0 00 0 0 0 0
4
9 0 '0
0 0 0 0 t 0 0 0 0 04000000-
00
6,
0 - 0
.
. .
''
P
'0
0o-
s
.'..
o0
0
o
.moe0
000
4
0
m
190
?
o
??
4
o
e
s
CIO
C 0 0 -4
C00o
00
._
0
m
_
-
0
mn
_
_
0 0 0
0
_
_
_
m0
0 p
_
'90
0
0 0 0 C'. '
_
'..
0 0 04 an
0 0
-
???????.CC.
0
c
w
m
4
-4
'.
a
ml9
0
1 xO
0 0 0~ 0~ 0 0n p
s
~.
'4
' 0'.0. "1A.
00
P0 0' .
o m
e
oooo
.' C.' '9
0 c 0sa0
0 0 00
GO0 Co
o
0
00P00P0p0P
_
'-'
4 0
0
uo
C
900
o0e0 0
u0
4 .PP-'
on
04 0
_ __ _ .-_ ...-
P P P PF
0
IC
90000
0 0
Q
'0
MPPPPPPPPPPPPPPPPP
m
mos'
0
90000
0.
000000000000000000000no
C o'
0 z 00000n
0
0
-0
0
e
-o
w.
--
e'.
w.' cz
ooooo
oooo
oooo
ooooooooooooooooooooom~'. '0
p.
-
.
c'.'
n
0
000000
00 00
00
00wI;.U 0D
0c
Co 0 o - 0 Q ;o 0
00
1 C'.' 0w -4 4 'pk
0 0 0 P
0
0
00 0
0 0 0 0 0 0
0 00'.
w'' 0
- - -- 4 -- C --0
'
.
0 0 0Z 0 0
c9
0
0+
U)
I--
0+
w.
''
oi'
.
c
C
CD0
..
0
00
00
0
0 A 0 01
~00~~
0 0
0
-0
00
bo
~0
o
-1
'0
0
e
0totot0c0
C mI
-40'
o
-
e
iz'0.9
000.'p,00000O000m
co 0
0 0 -1
-Feom
''~1
W
000
toc0*000
9 C~-.4
-
01
10,19
0
0 .0
-~10
--.
W-W
N)
0
C'.'.
0'.
0
19 kC'
00 A 00
ODM C.'
00
en
0~
.
0'.0O0N A .'.
L'~k-.~
~'0 0 ~0 0 p
1
00
00
00 0
0 0
I 001
W9 M'. IQ
oooooooooooooooooo
.
c0000
0.
me
00
0
31o1oooooooooooooooooooa
m1
0 .
oo
.
b;.
t' moQc 0 4n:
000
0
6 6 ~o6
r-0 0.0 00
'0
OR
-
C
0
L Ia.
n0
ma
0 0
'
&. w~tj
0o
r
VC
-0
0 as m
-
o
0 z~ w -1 m~ (j]
0:0
oQ
<
o
I" w~ w
g
0
n
0
01
o
-"
orI.
(0
(
0
0
w -4
0
-
000
0000
CR
mwt.NO
e
H
,)
I) Ci3
i4 -o C3
08%
-I.-
O-O
999999999oo9oo9999999
oO-O
-
I
-40 0
00 0'
0000000
000000000000000000
-
-o
9CA
e
99999990000
--
-
oCa~o
-s
iC CZ
k)
0' CC
0
u
00
b.)w 40 O
o
,
-MW0 -0
0
0
OO
0
io
)
a Cco'
eU
0
9.
e
um
0000000090
m
so
0000000
0moC
---
oo
J'
-
-
C-Onu
Q's
OCRCAeho
o 4.4-WO
99999
0O
on-s0CCu-u
000000000000000000-
oo
OOO-00OOOOOOOOO00O0OOO0
00-00O
000mO-0000
wo0C-.
3
999999999999999999
099090P000000000000000000
Om
00000
00000000000000000000000m
a
i..
Wbt.eaWKe
999999999999-999999
co-Com'
9999999
,3
ii C.Z
C
W UaW
PPPP?9-om000--0o00-0000
P99999999999i9999P999999
i,
m00000000000-0-000-000o0-
CD
ta m m
0000W 0
CD oooomo-ooo-ooooooo-oooo
CT
0
W~ 0 M~ K)W~ M wJ to W~ "p M - 4 w
000000
o 0 wa
mo
-4
w
C
&0
c&
Q
-w
I
-0
ooo6o6ooooo
0000o0000000-o0m0O00
NO
m~ w O -w
00000
S
09999999999999999999000
6o o6oooo
-WWW
-
H
Table B.13: Asset4 Factor Correlations : All World
oo0
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
01
1.00
0.77
0.69
-0.08
0.59
0.03
0.38
0.06
-0.12
-0.13
-0.11
-0.05
0.14
-0.04
0.10
0.18
0.09
-0.01
02
0.77
1.00
0.62
-0.10
0.52
0.03
0.33
0.04
-0.13
-0.14
-0.11
-0.05
0.10
-0.05
0.08
0.14
0.07
-0.03
03
0.69
0.62
1.00
-0.08
0.42
0.07
0.32
0.05
-0.13
-0.16
-0.10
-0.05
0.08
-0.03
0.07
0.22
0.08
0.03
04
-0.08
-0.10
-0.08
1.00
-0.05
0.45
0.09
0.37
0.74
0.58
0.73
0.42
0.52
0.60
0.55
0.41
0.60
0.59
05
0.59
0.52
0.42
-0.05
1.00
0.04
0.30
0.12
-0.05
-0.06
-0.05
0.03
0.12
0.02
0.11
0.14
0.07
-0.00
06
07
08
0.03 0.38 0.06
0.03 0.33 0.04
0.07 0.32 0.05
0.45 0.09 0.37
0.04 0.30 0.12
1.00 0.24 0.32
0.24 1.00 0.16
0.32 0.16 1.00
0.40 0.08 0.37
0.34 0.05 0.32
0.41 0.07 0.37
0.28 0.12 0.37
0.38 0.23 0.43
0.37 0.13 0.36
0.40 0.18 0.40
0.43 0.23 0.33
0.36 0.20 0.31
0.48 0.13 0.38
Table B.14: Asset4
09
-0.12
-0.13
-0.13
0.74
-0.05
0.40
0.08
0.37
1.00
0.68
0.85
0.43
0.50
0.54
0.52
0.36
0.60
0.56
Factor
10
11
12
-0.13 -0.11 -0.05
-0.14 -0.11 -0.05
-0.16 -0.10 -0.05
0.58 0.73
0.42
-0.06 -0.05
0.03
0.34
0.41
0.28
0.05
0.07
0.12
0.32
0.37 0.37
0.68
0.85 0.43
1.00
0.66 0.40
0.66
1.00
0.44
0.40
0.44
1.00
0.39
0.50 0.38
0.47
0.57 0.38
0.42 0.52
0.40
0.28 0.37
0.31
0.46 0.58
0.37
0.47 0.58
0.43
Correlations Legend
01
boardfunctions
02
boardstructure
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
compensationpolicy
visionandstrategy
shareholderrights
marginsperformance
shareholderloyalty
clientloyalty
emissionreduction
productinnovation
resourcereduction
productresponsibility
community
humanrights
workdiversityopportunity
employmentquality
healthandsafety
traininganddevelopment
13
0.14
0.10
0.08
0.52
0.12
0.38
0.23
0.43
0.50
0.39
0.50
0.38
1.00
0.41
0.52
0.39
0.47
0.50
14
-0.04
-0.05
-0.03
0.60
0.02
0.37
0.13
0.36
0.54
0.47
0.57
0.38
0.41
1.00
0.49
0.39
0.50
0.50
15
0.10
0.08
0.07
0.55
0.11
0.40
0.18
0.40
0.52
0.42
0.52
0.40
0.52
0.49
1.00
0.47
0.48
0.59
16
0.18
0.14
0.22
0.41
0.14
0.43
0.23
0.33
0.36
0.28
0.37
0.31
0.39
0.39
0.47
1.00
0.37
0.53
17
0.09
0.07
0.08
0.60
0.07
0.36
0.20
0.31
0.60
0.46
0.58
0.37
0.47
0.50
0.48
0.37
1.00
0.49
18
-0.01
-0.03
0.03
0.59
-0.00
0.48
0.13
0.38
0.56
0.47
0.58
0.43
0.50
0.50
0.59
0.53
0.49
1.00
Country
US
JP
DE
FR
GB
1 Month
Ave. t Stat.
3 Months
Ave. t Stat.
0.223
0.766
-1.730
-1.527
0.5837
-1.276
-1.343
-1.385
-1.380
-1.408
6 Months
Ave. t Stat.
-2.18
-2.686
-0.929
-1.379
-3.070
12 Months
Ave. t Stat.
-0.851
0.214
0.331
0.102
-1.88
36 Months
Ave. t Stat.
-1.167
-0.738
0.648
0.501
-0.869
Table B.15: Innovest Average Country Level Fixed Effects Pooled Regression Results [2002-2009] with Cap
1 Month
Ave. t Stat.
3 Months
Ave. t Stat.
6 Months
Ave. t Stat.
12 Months
Ave. t Stat.
US
0.204
-1.134
-2.268
-0.832
36 Months
Ave. t Stat.
-1.168
JP
0.803
-1.273
-2.937
0.028
-0.880
DE
-1.762
-1.611
-0.871
0.510
0.527
FR
GB
-1.416
0.592
-1.578
-1.392
-1.675
-3.070
0.026
-1.842
-0.265
-0.752
Country
00
IQ
Table B.16: Asset4 Average Country Level Fixed Effects Pooled Regression Results [2002-2009] with Cap
USD 250M
USD 250M
Appendix C
Figures
83
-0.8
-0.6
-0.4
'0.2
Iola .A.
I,
.0
*
-0.2
-0.4
u? 00
-0.6
.
I
00
5~o
*
-1
Figure C-1: Innovest Factor Corrleations : United States
1
0.8
............-.
0.6
0.4
U
*
0.2
59
.
44
0
e
.ee
U
S
45
0.2
0.4
Figure C-2: Innovest Factor Corrleations : Japan
84
III
11
I I 11
1
0.8
0.6
0.4
.......
0.2
0
0O VA
leO1 1*
0.2
0.4
-0.6
-0.8
Figure C-3: Innovest Factor Corrleations : Germany
11l IIii ~
i
1111
1
0.8
0.6
-0.4
'0.2
10
-0.2
I I
le
0
-
I
of-
**
LO
44U~ ~I4UW1~J~
-0.4
-0.6
-0.8
-1
Figure C-4: Innovest Factor Corrleations : France
85
0.8
0.6
0.4
0.2
0
,
~
~
t-0.2
*
*
S.
.
N.-
~
*5
~,
6
0.4
6
*
~.
6e
is
*
*
*
*~
#
**
0@
S
is
S
S
S
S
0.6
~
*
i
-0.8
S
Figure C-5: Innovest Factor Corrleations: Great Britain
0.8
0.4
Figure~
C-:Astoatoworetos
86
ntdSae l
-0.6
-1.
I
I
0.8
a gea
g
0.6
0.4
.....
ru.
0.2
-0
-0.2
-0.4
-0.6
-0.8
-1
Figure C-7: Asset4 Factor Corrleations: Japan
I
I
1~FEEI~EEEEEI~EEE
I'll IIIIIIi IIIII I
mU
IUIm
Il
-~
UA
U
ON E
-I El
n o
UN
U
A
0.8
0.6
0.4
-
mm
M =U
E
UU
I
=
UU
=
U
i
I
0 L-
0.2
0
-0.2
-0.4
I
-0.6
-0.8
-1
Figure C-8: Asset4 Factor Corrleations: Germany
87
-
-
Fato
~ME C-9
A0.
Coretin.2Fac
Asset
Figure
F--
--
:s
-0.6
II
S U U -0.2
-0.4
-0.6
-0.8
S-1
Figure C-10: Asset4 Factor Corrleations : Great Britain
88
Bibliography
[1] Asset 4 : Marketing materials and Data Descriptions. Asset 4, 2010.
[2] Pratima Bansal and lain Clelland. Talking trash: Legitimacy, impression management, and unsystematic risk in the context of the natural environment. The
Academy of Management Journal,47(1):93 103, 2004.
[3] D P Baron. Business and its environment. Prentice Hall, 2000.
[4] M Blowfield and J G Frynas. Setting new agendas: Critical perspectives on
corporate social responsibility in the developing world. InternationalAffairs, 81
(3):499 513, 2005.
[5] S Brammer C Brooks and S Pavelin. Corporate social performance and stock
returns: Uk evidence from disaggregated measures. Financial Management,
35(3):97 116, 2006.
[6] Peter Camejo. The SRI Advantage. New Society Publishers, 2002.
[7] Alex Edmans. Does the stock market fully value intangibles? employee satisfaction and equity prices. Journal of FinancialEconomics (JFE), Forthcoming.,
Available at SSRN: http://ssrn.com/abstract=985735, 2010.
[8] Jon Entine. The myth of social investing: A critique of its practice and consequences for corporate social performance research. Organization & Environment,
16(3):352 368, 2003.
[9] E Fama and K French. The cross-section of expected returns. Journalof Finance,
47:426 465, 1993.
[10] Kevin Farnsworth. Promoting business-centred welfare: International and european business perspectives on social policy. Journal of European Social Policy,
15(1):65 80, 2005.
[11] C Fombrun and M Shanley. Whats in a name? reputation building and corporate
strategy. , Academy of Management Journal,33:233 258, 1990.
[12] SRI Forum.
04/01/2011.
Socially responsible investing factsheet, 2010.
89
Retrieved on
[13] Tom Fox. Corporate social responsibility and development: In quest of an
agenda. Development, 47(3):2936, 2004.
[14] Martin Riopel Gilles Raiche and Jean-Guy Blais. nfactors: Non graphical solution to the cattell scree test. in r package version 2.2. InternationalMeeting of
the Psychometric Society, 2006.
[15] Ronald Gorsuch. FactorAnalysis. Psychology Press, 1983.
[16] John R. Graham and Campbell R. Harvey. The equity risk premium amid a
global financial crisis (may 14, 2009), May 2009.
[17] Minna Halme and Juha Laurila. Philanthropy, integration or innovation? ex-
ploring the financial and societa outcomes of different types of corporate responsibility. Journal of Business Ethics, 84:325 339, 2009.
[18] Paul Hawken. How the sri industry has failed to respond to people who want to
invest with conscience and what can be done to change it., October 2004.
[19] Geoffrey Heal. Corporate social responsibility: An economic and financial framework. The Geneva Papers, The InternationalAssociation for the Study of Insurance Economics, 2005.
[20] A Hillman and G Keim. Shareholder value, stakeholder management and social
issues: Whats the bottom line?
Strategic Management Journal, 22:125 139,
2001.
[21] Andrew W. Lo John Y. Campbell and A. Craig MacKinlay. The Econometrics
of Financial Markets. Princeton University Press, 1996.
[22] R. Johnson and D. Greening. The effects of corporate governance and institutional ownership types on corporate social performance. Academy of Management
Journal,42:564 576., 1999.
[23] I T Jolliffe. Principal Component Analysis, Second Edition. Springer, 2002.
[24] Pablo Archelb Jos M. Moneva and Carmen Correac. Gri and the camouflaging
of corporate unsustainability. Accounting Forum, 30 (2):121 137, 2006.
[25] Alexander Kempf and Peer Osthoff. The effect of socially responsible investing
on portfolio performance. European Financial Management, 13, No. 5:908922,
2007.
[26] Eric De Keuleneer. Investing in sustainablility: Delusions and potential benefits
of socially responsible invesing. International Review on Public and Non Profit
Marketing, vol. 3, no 1 (June 2006), pp. 29-4, 3 (1):29-34, 2006.
Moving busi[27] Salzmann 0. Steger U. Kong, N. and A. Ionescu-Somers.
ness/industry towards sustainable consumption: The role of ngos. European
Management Journal,20(2):109127, 2002.
90
[28] Lloyd Kurtz. No effect, or no net effect? studies on socially responsible investing.
Journal of Investing, 6(4):37 49, 1997.
[29] J Lintner. The valuation of risky assets and the selection of risky investments in
stock portfolios and capital budgets. Review ofEconomics and Statistics, 47:13
37, 1965.
[30] F Schmidt M Orlitzky and S Rynes. Corporate social and financial performance:
A meta-analysis. OrganizationStudies, 24(3):403 441, 2003.
[31] Emily Mankin.
Principal component analysis : A how to manual for
r. retrieved from https://parmesan.mc.vanderbilt.edu/turnersd/ggd/2010-11-18pca-tutorial.pdf.
[32] Marc Orlitzky and John D Benjamin. Corporate social performance and firm
risk: A meta-analytic review. Business and Society;, 40, 4:369 396, 2001.
[33] J Ishii P Gompers and A Metrick. Corporate governance and equity prices. The
Quarterly Journal of Economics, 118(1):107 155, 2003.
[34] Stefano Pogutz. Sustainable development, corporate sustainability,. Corporate Accountability and Sustainable Developement, Edited by P Utting and J
Clapp:53, 2008.
[35] Michael E. Porter and Mark R. Kramer. The link between competitive advantage
and corporate social responsibility. Harvard Business Review, December, 2006.
[36] A Platinga R Galema and B Scholtens. The stocks at stake: Return and risk in
socially responsible investment. Journal of Banking and Finance, 32(12):2646
2654, 2008.
[37] Forest L. Reinhardt and Robert N. Stavins. Corporate social responsibility,
business strategy, and the environment. Oxford Review of Economic Policy, 26
(2):164181, 2010.
[38] Zhou Y. Scholtens, B. Stakeholder relations and financial performance. Sustainable Development, 16:213232, 2008.
[39] Steve Schueth. Socially responsible investing in the united states. Journal of
Business Ethics, 43:189 194, 2003.
[40] Mark Sharfman. The construct validity of the kinder, lydenberg & domini social
performance ratings data. Journal of Business Ethics, 15(3):287 296, 1996.
[41] William Sharpe. Capital asset prices: A theory of market equilibrium under
conditions of risk. Journal ofFinance, 19:425 442, 1964.
[42] S. Soederberg. The marketisation of social justice: The case of the sudan divestment campaign. Annual Meeting of the Canadian Political Science Association.
University of British Columbia, Vancouver, BC. 4-6 June, 2008.
91
[43] Peter A Stanwick and Sarah D Stanwick. The relationship between corporate social performance and organizational size... Journalof Business Ethics, 17(2):195
204, 1998.
[44] Krishna Udayasankar. Corporate social responsibility and firm size. Journal of
Business Ethics,, 2007. Available at SSRN: http://ssrn.com/abstract= 1262535.
[45] A A Ullmann. Data in search of a theory: A critical examination of the relationships among social performance, social disclosure, and economic performance of
u.s. firms. Academy of Management Journal, 10:540 557, 1985.
[46] UNCTAD. Social responsibility unctad series on international investment agreements (un doc. unctad/ite/iit/22, 2001). UNCTAD Series on International Investment Agreements, page 3, 2001.
[47] David Vogel. The Market For Virtue. The Brookings Institution, 2005.
[48] E. von Hippel. New product ideas from lead users. Research Technology Management, 32(3):2428, 1989.
[49] S. Waddock and S. Graves. The corporate social performance-financial performance link. Strategic Management Journal,18(4):303--319, 1997.
[50] Meng Ling Wu. The Journal of American Academy of Business, 8 (1):163 171,
2006.
92