A Reputational and Labor Factors. Arun Balasubramaniam

Socially Responsible Investing: A comparative analysis of environmental, social, governance, ARC IVE:S reputational and labor factors.

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

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 Master of Science in Engineering and Management

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 corporate social performance (CSP) in its investment d s . I has giesgnfct 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 Pro- gram

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 part- time 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 Introduction 13

1.1 M otivation ...... 13

1.2 Approach and Thesis organization ...... 14

2 Definitions and Literature Review 17 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

2.4 H ypotheses ...... 26

3 Data Description 27

3.1 D ata Sources ...... 27

3.2 Data Description ...... 28 3.3 Factor Classifications ...... 36

3.4 Data Limitations ...... 38

4 Comparative Data Analysis : Factor Correlations 39

4.1 Summary Data ...... 39

4.2 Factor Correlations ...... 43

4.2.1 Hypothesis IV - Labor and Environment ...... 46

5 Comparative Data Analysis : Factor Analysis 49

5.1 Exploratory Factor Analysis (EFA) ...... 50

7 5.2 Principal Component Analysis (PCA) ...... 52

6 Comparative Data Analysis : Regression 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 A.2 System Components ...... 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

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 ...... 62 6.2 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 B.3 Innovest Summary Statistics: Germany ...... 76 B.4 Innovest Summary Statistis : France ...... 77 B.5 Innovest Summary Statistics: Great Britain ...... 77 B.6 Asset4 Summary Statistics : United States ...... 78 B.7 Asset4 Summary Statistics : Japan ...... 78

11 B.8 Asset4 Summary Statistics : Germany ...... 79

B.9 Asset4 Summary Statistics : France ...... 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 Re- sults [2002-2009] with Cap > USD 250M ...... 82 B. 16 Asset4 Average Country Level Fixed Effects Pooled Regression Results [2002-2009] with Cap > USD 250M ...... 82

12 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. It considers both the investor's finCacial needs andu investent imac on societ5uy. 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) 1995 1997 1999 2001 2003 2005 2007 2010

Slr mAdocacv S5473 I S736 922 897 448 $703 739 4 8 14 25 SaN/A $84) $5 $592) $441 $117) $151 $69 $1r,185 12r159 $,323 $2164 $290 $2W11 6 SOUME: SocialInvestment Forum Foundation NOTE:Overlapping assets involved in somecombination of ESGincorporation, filing shareholderresolutions or communityinvesting are subtracted to avoidpotential effects of doublecounting. Separate tracking of the overlappingstrategies only beganin 1997, so thereis no datum for 1995. Priorto 2010, assetssubject to ESGincorporation were limitedto sociallyand environmentallyscreened 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 in- corporate 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 publicly- held 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 mislead- ing 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 glob- alization 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 por-

20 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 incon- sistencies 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 Cor- porate Financial Performance (CFP)

Like SRI, the literature on CSR/CSP presents its potentials and the reality of partially- realized 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) environ- ments [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 multi- faceted 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]. Gom- pers (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, se- nior 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 reten- tion 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 im- provements 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 develop-

ment 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 proportion- ate 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 long- term revenue growth. It reflects a company's capacity to grow, while maintaining a loyal client base through satisfaction programs and avoiding anti- competitive 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 orga- nizations 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 tech- nologies 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 providing 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 com-

pany'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 man-

agement commitment and effectiveness towards maintaining the company's reputation 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 Report- ing, Environmental Management Systems, Environmental Strategy, Environ- mental 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 Commu-

36 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, His- toric Liabilities, Operating Risk, Human Rights, Child and Forced Labor, Prod- uct 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, Corpo- rate Strategy, Performance

5. Reputational Factors: Client Loyalty, Shareholder Loyalty, Performance, Prod- uct 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-to- price 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 Var Obs. Mean Std Dev Median Trimmed Mad Min Max Range Skew Kurtosis SE stock-return 1 8471.00 -0.02 0.10 -0.02 -0.02 0.09 -0.71 0.67 1.39 -0.14 3.28 0.00 beta 2 8463.00 1.05 0.54 1.00 1.01 0.50 -0.85 3.85 4.69 0.77 1.34 0.01 bp 3 7931.00 0.60 0.53 0.47 0.52 0.31 -1.71 5.00 6.71 3.37 19.84 0.01 auditinteg 4 7931.00 2.75 2.80 2.09 2.33 1.35 -16.00 16.00 32.00 1.43 11.71 0.03 certification 5 7852.00 3.55 2.57 4.00 3.41 2.97 0.00 10.00 10.00 0.27 -0.62 0.03 corporategovernance 6 8208.00 4.66 2.35 5.00 4.72 1.48 0.00 10.00 10.00 -0.21 -0.40 0.03 customerstakeholderpartnerships 7 8005.00 5.28 2.07 5.00 5.31 1.48 0.00 10.00 10.00 -0.06 -0.14 0.02 employeemotivationanddevelopment 8 8033.00 5.86 1.97 6.00 5.93 1.48 0.00 10.00 10.00 -0.31 -0.01 0.02 environmentalaccountingreporting 9 8262.00 4.54 2.71 5.00 4.54 2.97 0.00 10.00 10.00 0.00 -0.85 0.03 environmentalmanagementsystems 10 8301.00 4.92 2.56 5.00 4.99 2.97 0.00 10.00 10.00 -0.19 -0.72 0.03 environmentalopportunity 11 7970.00 5.25 1.95 5.00 5.40 1.48 0.00 10.00 10.00 -0.76 1.10 0.02 environmentalstrategy 12 8469.00 5.41 2.25 6.00 5.50 2.97 0.00 18.00 18.00 -0.31 -0.20 0.02 envtraininganddevelopment 13 8031.00 4.84 2.59 5.00 4.91 2.97 0.00 10.00 10.00 -0.24 -0.48 0.03 healthandsafety 14 8016.00 5.31 2.08 5.00 5.36 1.48 0.00 10.00 10.00 -0.16 -0.19 0.02 historicliabilities 15 7995.00 5.20 2.29 5.00 5.27 1.48 0.00 10.00 10.00 -0.22 0.21 0.03 humanrightschildandforcedlabor 16 7320.00 5.10 2.17 5.00 5.21 1.48 0.00 10.00 10.00 -0.39 0.36 0.03 industryspecificrisk 17 7639.00 4.63 2.51 5.00 4.70 1.48 0.00 10.00 10.00 -0.33 -0.37 0.03 laborrelations 18 7977.00 5.38 1.83 5.00 5.40 1.48 0.00 10.00 10.00 -0.16 0.41 0.02 leadingsustainabilityriskindicators 19 8431.00 5.22 1.92 5.00 5.26 1.48 0.00 10.00 10.00 -0.22 -0.05 0.02 localcommunities 20 8021.00 5.63 2.09 6.00 5.68 1.48 0.00 10.00 10.00 -0.24 -0.07 0.02 operatingrisk 21 8071.00 5.11 2.36 5.00 5.25 1.48 0.00 10.00 10.00 -0.43 0.20 0.03 opportunity 22 8448.00 5.04 2.20 5.00 5.07 1.48 0.00 10.00 10.00 -0.16 -0.44 0.02 performance 23 8232.00 4.81 2.61 5.00 4.87 2.97 0.00 10.00 10.00 -0.17 -0.61 0.03 productsafety 24 7891.00 5.28 2.10 5.00 5.40 1.48 0.00 10.00 10.00 -0.43 0.29 0.02 productsmaterials 25 8258.00 4.42 2.55 5.00 4.42 2.97 0.00 10.00 10.00 0.01 -0.59 0.03 strategicgovernance 26 7011.00 5.46 1.82 5.00 5.47 1.48 0.00 10.00 10.00 -0.05 -0.31 0.02 supplychain 27 8037.00 5.13 2.26 5.00 5.15 1.48 0.00 10.00 10.00 -0.07 -0.27 0.03 logsize 28 8471.00 8.89 1.19 8.80 8.85 1.14 5.56 13.17 7.62 0.29 0.11 0.01

Table 4.1: Innovest Data Descriptives All World - Min Cap USD 250 MM var n mean sd median trimmed mad min max range skew kurtosis se beta 1 8773.00 1.05 0.55 1.00 1.01 0.50 -0.85 3.85 4.69 0.79 1.45 0.01 bp 2 8203.00 0.60 0.54 0.47 0.52 0.31 -1.71 5.00 6.71 3.39 19.82 0.01 boardfunctions 3 8782.00 0.52 0.32 0.63 0.53 0.34 0.02 0.93 0.91 -0.36 -1.52 0.00 boardstructure 4 8782.00 0.51 0.31 0.56 0.52 0.41 0.01 0.96 0.95 -0.23 -1.42 0.00 compensationpolicy 5 8782.00 0.50 0.30 0.58 0.51 0.36 0.01 0.96 0.95 -0.35 -1.33 0.00 visionandstrategy 6 8782.00 0.53 0.32 0.48 0.52 0.44 0.10 0.99 0.89 0.13 -1.64 0.00 shareholderrights 7 8782.00 0.52 0.31 0.52 0.52 0.43 0.01 0.98 0.97 -0.03 -1.38 0.00 marginsperformance 8 8782.00 0.51 0.30 0.50 0.51 0.41 0.04 0.99 0.95 0.05 -1.32 0.00 shareholderloyalty 9 8782.00 0.53 0.30 0.53 0.54 0.41 0.02 0.99 0.98 -0.10 -1.31 0.00 clientloyalty 10 8782.00 0.55 0.29 0.54 0.55 0.38 0.01 0.98 0.97 -0.10 -1.23 0.00 emissionreduction 11 8782.00 0.55 0.32 0.57 0.55 0.50 0.07 0.98 0.91 -0.08 -1.65 0.00 productinnovation 12 8782.00 0.52 0.31 0.43 0.51 0.39 0.09 1.00 0.91 0.24 -1.62 0.00 resourcereduction 13 8782.00 0.55 0.32 0.61 0.56 0.45 0.08 0.97 0.89 -0.17 -1.61 0.00 productresponsibility 14 8782.00 0.53 0.30 0.52 0.53 0.44 0.03 0.99 0.96 0.01 -1.42 0.00 community 15 8782.00 0.55 0.30 0.59 0.56 0.41 0.03 0.97 0.95 -0.24 -1.35 0.00 humanrights 16 8782.00 0.51 0.30 0.33 0.49 0.20 0.02 1.00 0.98 0.44 -1.48 0.00 workdiversityopportunity 17 8782.00 0.54 0.31 0.54 0.54 0.49 0.05 0.99 0.94 0.01 -1.56 0.00 employmentquality 18 8782.00 0.52 0.31 0.53 0.52 0.44 0.03 0.99 0.96 -0.05 -1.40 0.00 healthandsafety 19 8782.00 0.51 0.31 0.46 0.50 0.41 0.02 0.99 0.97 0.23 -1.40 0.00 traininganddevelopment 20 8782.00 0.54 0.31 0.60 0.55 0.42 0.05 0.97 0.93 -0.19 -1.51 0.00 logsize 22 8782.00 8.89 1.20 8.81 8.85 1.15 5.56 13.17 7.62 0.30 0.10 0.01

Table 4.2: Asset4 Data Descriptives All World - Min Cap USD 250 MM 4.2 Factor Correlations

Correlation between all Innovest factors and logsize ( indicator of firm size as a func-

tion 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 individual factors. Almost all factors have low positive correlations with each other. This pattern can also be seen in correlation matrix 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

correlated with each other. Vision and strategy governance factor was positively correlated with environmentalfactors and negatively correlated with other governance fac-

tors. 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.

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45 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 Asset4 Factor World USA Japan Germany France Britain customerstakeholderpartnerships shareholderrights -0.011 0.075 0.028 0.055 -0.032 0.113 productsafety productresponsibility 0.127 0.086 0.184 0.097 -0.058 0.030 customerstakeholderpartnerships clientloyalty 0.162 0.108 0.168 0.131 0.053 0.119 humanrightschildandforcedlabor humanrights 0.209 0.170 0.186 0.216 0.146 0.262 employeemotivationanddevelopment employmentquality 0.313 0.223 0.303 0.191 0.148 0.343 employeemotivationanddevelopment workdiversityopportunity 0.340 0.334 0.342 0.204 0.263 0.337 employeemotivationanddevelopment traininganddevelopment 0.342 0.273 0.355 0.232 0.276 0.256 localcommunities community 0.346 0.377 0.326 0.274 0.329 0.452 healthandsafety healthandsafety 0.355 0.273 0.400 0.292 0.204 0.370 productsmaterials productinnovation 0.411 0.360 0.388 0.223 0.303 0.341 strategicgovernance visionandstrategy 0.433 0.342 0.439 0.295 0.241 0.395 environmentalmanagementsystems resourcereduction 0.548 0.427 0.570 0.614 0.436 0.518 environmentalmanagementsystems emissionreduction 0.549 0.412 0.574 0.584 0.483 0.574

4.2.1 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 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 Enviromental Factor Percentile Correlation healthandsafety environmentalaccountingreporting > 0.90 0.1905 healthandsafety environmentalmanagementsystems > 0.90 0.2617 healthandsafety performance > 0.90 0.1642 healthandsafety environmentalaccountingreporting < 0.10 0.1764 healthandsafety environmentalmanagementsystems < 0.10 0.1640 healthandsafety performance < 0.10 0.1589 employeemotivationanddevelopment environmentalaccountingreporting > 0.90 0.1387 employeemotivationanddevelopment environmentalmanagementsystems > 0.90 0.1227 employeemotivationanddevelopment performance > 0.90 0.0413 employeemotivationanddevelopment environmentalaccountingreporting < 0.10 0.1152 employeemotivationanddevelopment environmentalmanagementsystems < 0.10 0.1104 employeemotivationanddevelopment performance < 0.10 0.1223

47 Table 4.5: Asset4 Data: Labor and Environmental Factors - All World

Labor Factor Enviromental Factor Percentile Correlation healthandsafety emissionreduction > 0.90 0.4615 healthandsafety resourcereduction > 0.90 0.3325 healthandsafety productinnovation > 0.90 0.4009 healthandsafety emissionreduction < 0.10 0.4748 healthandsafety productinnovation < 0.10 0.4087 healthandsafety resourcereduction < 0.10 0.4874 employmentquality emissionreduction > 0.90 0.0899 employmentquality resourcereduction > 0.90 0.0332 employmentquality productinnovation > 0.90 0.1221 employmentquality emissionreduction < 0.10 -0.496 employmentquality productinnovation < 0.10 -0.0312 employmentquality resourcereduction < 0.10 -0.0389

48 Chapter 5

Comparative Data Analysis : Factor Analysis

The SRI community, data vendors and marketing materials use ESG factors to de- compose 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 infor- mational 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) oci A Parallel Analysis (n - 11 ) Optimal Coordinates (n - 2) Ace leranon Facnr (s 7

020

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 deter- mining 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 so- lution 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) A19 ~ A Parallel Analysis (n =9 ) 0n Optimal Coordinates (n .4) q- Acoeleratton Factor (n - I) O00

0 A 0 0

e 0 0 0 N, 0 In 01- 0 0

0 I - 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 nVars X2 stat DOF p value Asset4 9 162.89 36 4.95e-18 Innovest 11 476.68 81 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 principal component 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 load-

ings 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 PCl PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 Standard deviation 3.0829 1.4126 1.2725 1.0334 0.9728 0.9296 0.8990 0.8869 0.8503 0.8193 Proportion of Variance 0.3960 0.0832 0.0675 0.0445 0.0394 0.0360 0.0337 0.0328 0.0301 0.0280 Cumulative Proportion 0.3960 0.4792 0.5466 0.5911 0.6306 0.6666 0.7003 0.7330 0.7631 0.7911

Table 5.2: Innovest Data - PCA Summary

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 auditinteg 0.0102 -0.2263 0.0655 -0.5100 0.6767 -0.3054 0.2914 -0.1178 0.0032 -0.0921 certification -0.1882 0.1404 -0.0133 -0.1984 0.0247 0.1099 -0.0778 -0.2640 0.0969 0.8027 corporategovernance -0.2498 0.1851 0.0355 -0.1942 -0.0383 0.0737 0.0788 0.0140 0.0154 0.0535 customerstakeholderpartnerships -0.2024 -0.2362 -0.1803 0.0695 0.0072 0.0422 -0.1210 -0.2157 0.0403 -0.0687 employeeniotivationanddevelopinent -0.2002 -0.2525 -0.3088 0.0821 0.0589 -0.0660 -0.0141 0.1296 -0.0665 0.1713 environinentalaccountingreporting -0.2664 0.1628 0.0267 -0.1541 -0.1528 -0.0246 0.1409 0.0704 0.0195 -0.0593 environmentalmanagenentsysteins -0.2712 0.1742 0.0563 -0.1230 -0.1114 -0.0151 0.1328 0.0715 0.0149 -0.0883 environmentalopportunity -0.1659 -0.0348 0.3154 0.2523 0.4420 0.1765 -0.1627 0.1071 0.0714 0.0760 environmentalstrategy -0.2826 0.1365 0.0312 -0.0896 -0.0473 0.0013 0.0820 0.0167 -0.0057 -0.1269 envtraininganddevelopmrent -0.2457 0.1912 0.0702 -0.1617 -0.0352 0.0451 0.0997 0.0669 -0.0464 0.0134 healthandsafety -0.2080 -0.1957 -0.2182 0.0754 -0.0659 -0.0700 0.1733 0.0942 -0.0559 -0.0496 historicliabilities -0.0741 -0.3054 0.4054 0.1327 -0.1641 0.0138 0.0933 -0.2641 -0.6364 0.1127 humanrightschildandforcedlabor -0.1153 -0.2908 0.1802 -0.0481 0.0990 0.6650 0.0216 0.3659 0.2484 -0.0392 industryspecificrisk -0.1201 -0.0328 0.2496 0.5392 0.0595 -0.1769 0.4622 -0.2357 0.4370 0.1041 laborrelations -0.1481 -0.3099 -0.1876 0.1018 -0.0616 -0.2794 0.1537 0.5253 -0.1311 0.3215 leadingsustainabilityriskindicators -0.2272 0.0735 0.1253 0.0878 -0.1254 -0.0889 0.2130 -0.0315 0.0061 -0.2638 localconimunities -0.1827 -0.1781 -0.2864 0.0570 0.0903 0.2286 -0.1059 -0.4771 -0.0484 -0.0978 operatingrisk -0.1119 -0.2711 0.4800 -0.2359 -0.1681 -0.0529 -0.1701 0.0345 -0.0860 -0.0387 opportunity -0.2513 0.2263 0.0257 0.1615 0.2467 -0.1384 -0.2979 0.1342 -0.1671 -0.0959 performance -0.2294 0.2371 0.0306 0.2519 0.2784 -0.1553 -0.3330 0.0699 -0.1834 -0.0177 productsafety -0.1342 -0.2718 0.1497 -0.1219 -0.2314 -0.3970 -0.4880 -0.0634 0.4750 -0.0381 productsmaterials -0.2574 0.1394 0.0166 -0.1285 -0.0947 0.0175 0.0745 -0.0541 0.0273 0.0449 strategicgovernance -0.2530 -0.1536 -0.1641 -0.0037 -0.0199 -0.0066 0.0164 -0.0423 0.0232 -0.1468 supplychain -0.2211 -0.1175 -0.1896 -0.0181 0.0001 0.1679 -0.0420 -0.1458 -0.0154 -0.1753

Table 5.3: Innovest Data - Component Loadings

54 PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 Standard deviation 2.5830 1.8001 0.9763 0.9125 0.8761 0.8032 0.7785 0.7610 0.7480 0.7120 Proportion of Variance 0.3706 0.1800 0.0529 0.0463 0.0426 0.0358 0.0337 0.0322 0.0311 0.0282 Cumulative Proportion 0.3706 0.5507 0.6036 0.6499 0.6925 0.7284 0.7620 0.7942 0.8253 0.8535

Table 5.4: Asset4 Data - PCA Summary

Pci PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 boardfunctions 0.0071 -0.4996 0.1740 -0.0035 0.0899 -0.0149 0.0062 -0.1024 0.0203 -0.0201 boardstructure -0.0026 -0.4710 0.1912 -0.0153 0.0953 -0.0129 0.0331 -0.1709 0.0586 -0.0725 compensationpolicy 0.0048 -0.4481 0.0278 -0.1752 0.1815 0.0418 -0.0414 -0.2328 0.3844 0.0041 visionandstrategy 0.3216 0.0800 0.1653 -0.1312 0.0191 -0.0696 -0.0439 0.0309 0.0159 -0.0856 shareholderrights 0.0226 -0.3944 0.1916 0.2918 0.0112 -0.0401 0.3816 0.4345 -0.4572 -0.0329 marginsperformance 0.2312 -0.0465 -0.4121 -0.3335 -0.1857 -0.2769 0.5104 -0.2073 -0.1202 -0.4569 shareholderloyalty 0.0905 -0.2942 -0.2039 -0.0690 -0.8338 0.1344 -0.1905 0.1149 0.0163 0.2742 clientloyalty 0.2166 -0.0556 -0.3015 0.5905 0.0513 -0.5027 0.0218 0.0607 0.4311 0.1160 emissionreduction 0.3222 0.1121 0.3040 -0.0538 -0.0701 -0.0892 0.0849 -0.1000 -0.0067 0.1552 productinnovation 0.2733 0.1253 0.2923 0.0616 -0.1108 -0.0232 0.3452 -0.1527 0.0166 0.3921 resourcereduction 0.3233 0.1032 0.2830 -0.0513 -0.0301 -0.0579 0.0918 -0.0950 0.0307 0.1380 productresponsibility 0.2301 0.0309 -0.1334 0.5031 -0.0394 0.6913 0.1488 -0.3104 0.0399 -0.2420 coImuity 0.2678 -0.0830 -0.0617 0.1719 -0.0067 -0.2775 -0.5049 -0.2482 -0.3678 -0.1311 humianrights 0.2771 0.0337 0.0497 -0.0487 0.0185 0.1129 -0.0345 0.6418 0.3108 -0.3328 workdiversityopportunity 0.2838 -0.0633 -0.1181 0.0202 0.2081 0.0705 -0.2593 0.1323 -0.3710 0.0550 enploymentquality 0.2335 -0.1429 -0.4009 -0.2496 0.3097 0.2031 0.0762 0.1557 0.0667 0.4519 healthandsafety 0.2808 -0.0401 0.2598 -0.1404 -0.1013 0.0697 -0.2533 0.0310 0.2067 -0.2985 traininganddevelopment 0.2982 0.0055 -0.2046 -0.1588 0.2181 0.1162 -0.0459 -0.0397 -0.1379 0.0936 Table 5.5: Asset4 Data - Component Loadings

55 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 (6.1)

57 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 + 'y3BTMit + yntpnit + fit (6.2)

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

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.. error- in-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 (n) = (1 + Rt)(1 + Rt_1 )(1 + Rt-2)...(1 + Rtn+i) - 1 (6.4)

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 corre- sponds 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 related factors 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 environmental fac-

60 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 inno-

vation, 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 1 Month 3 Months 6 Months 12 Months 36 Months t Stat. t Stat. t Stat. t Stat. t Stat. auditinteg -0.899 -0.191 -1.892 -1.001 -1.857 certification 0.1 2.399 0.729 0.760 0.117 corporategovernance -1.717 2.050 3.827 3.133 3.831 customerstakeholderpartnerships -2.434 0.083 -0.221 1.249 0.946 employeemotivationanddevelopment -2.147 -0.705 -0.855 0.973 0.580 environmentalaccountingreporting -1.407 1.587 2.822 2.634 2.904 environmentalmanagementsystems -0.372 1.755 2.468 2.144 3.999 environmentalopportunity 0.093 -0.151 0.476 0.990 -0.223 environmentalstrategy 0.158 2.663 3.189 2.512 3.505 envtraininganddevelopment -0.717 2.564 3.908 3.514 3.859 healthandsafety -1.985 -0.577 0.160 0.021 1.349 historicliabilities -1.081 -0.285 -0.702 0.299 -1.661 humanrightschildandforcedlabor -0.63 1.229 1.127 0.623 -3.548 industryspecificrisk -1.644 0.508 2.181 0.316 0.903 laborrelations -1.342 0.229 -0.641 1.318 -0.048 leadingsustainabilityriskindicators -0.337 -0.034 -0.175 0.873 0.875 localcommunities -0.933 -0.654 -0.829 -0.059 0.812 operatingrisk -0.473 2.444 2.561 4.204 4.853 opportunity -0.449 1.059 3.586 1.355 3.559 performance -0.751 0.511 3.411 0.713 3.336 productsafety -1.338 1.408 0.104 1.107 -1.055 productsmaterials -0.406 1.391 0.189 0.025 1.186 strategicgovernance -2.208 0.376 0.466 0.784 0.957 supplychain -0.397 0.943 -0.341 0.189 1.686

Table 6.1: Innovest Individual Factor Pooled Regression Results [2002-2009]with Cap > USD 250M 1 Month 3 Months 6 Months 12 Months 36 Months Factor t Stat. t Stat. t Stat. t Stat. t Stat. boardfunctions 0.04 -'0.11 2.09 0.66 0.67 boardstructure 0.41 1.00 0.53 -0.80 -0.71 compensationpolicy -1.78 -0.72 -1.25 -1.97 0.38 visionandstrategy 0.78 2.70 4.61 3.11 3.09 shareholderrights 2.00 0.60 0.54 -0.15 2.19 marginsperformance 1.32 3.58 2.57 1.32 -0.25 shareholderloyalty 0.37 2.64 2.07 2.40 -0.02 clientloyalty -0.03 -0.03 -0.10 0.95 1.82 emissionreduction 0.66 3.54 6.35 4.46 5.03 productinnovation -1.24 1.63 2.93 1.80 3.99 resourcereduction 1.50 3.77 4.01 3.78 4.94 productresponsibility 1.73 1.06 0.47 0.47 2.11 community 1.22 1.66 2.94 3.22 2.36 humanrights 0.47 3.58 3.17 2.01 5.16 workdiversityopportunity 1.70 -0.10 0.02 1.33 2.39 employmentquality 0.22 -0.66 -0.94 -0.36 -0.75 healthandsafety 0.68 4.05 7.09 4.66 6.07 traininganddevelopment 0.98 1.83 1.88 1.37 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. Envi- ronmental 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 represen- tative 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 Clas- sification 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, time- series 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 esti- mates 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 re-

gression with fixed effects on Innovest Data.

* a4-pooled-data-regress-analysis.r : This script performs pooled regression with fixed effects on Asset4 Data.

73 74 Appendix B

Tables

var n mean sd median trimmed mad mnin max range skew kurtosis se stock..return 1 2918.00 -0.02 0.11 -0.01 -0.01 0.08 -0.71 0.58 1.29 -0.25 4.52 0.00 beta 2 2918.00 1.14 0.66 1.05 1.09 0.62 -0.29 3.85 4.14 0.80 0.60 0.01 bp 3 2640.00 0.50 0.47 0.39 0.42 0.25 -1.21 5.00 6.21 3.67 22.86 0.01 auditinteg 4 2640.00 3.18 3.06 2.53 2.79 1.59 -16.00 16.00 32.00 0.78 10.05 0.06 certification 5 2755.00 3.40 2.53 4.00 3.24 2.97 0.00 10.00 10.00 0.27 -0.58 0.05 corporategovernance 6 2836.00 4.11 2.33 4.00 4.14 2.97 0.00 10.00 10.00 -0.09 -0.58 0.04 custonmerstakeholderpartnerships 7 2755.00 4.88 2.09 5.00 4.87 1.48 0.00 10.00 10.00 0.04 -0.15 0.04 employeeniotivationanddevelopment. 8 2762.00 5.77 2.03 6.00 5.83 1.48 0.00 10.00 10.00 -0.28 -0.23 0.04 environmientalaccountingreporting 9 2860.00 3.51 2.48 3.00 3.39 2.97 0.00 10.00 10.00 0.33 -0.66 0.05 environnentalmanagermentsystemns 10 2880.00 3.99 2.38 4.00 3.95 2.97 0.00 10.00 10.00 0.14 -0.54 0.04 environmentalopportunity 11 2792.00 5.12 1.71 5.00 5.19 1.48 0.00 10.00 10.00 -0.51 1.07 0.03 environmentalstrategy 12 2916.00 4.72 2.27 5.00 4.75 1.48 0.00 10.00 10.00 -0.10 -0.37 0.04 envtraininganddevelopment 13 2817.00 4.24 2.57 5.00 4.22 2.97 0.00 10.00 10.00 -0.07 -0.53 0.05 healthandsafety 14 2755.00 5.00 2.05 5.00 5.04 1.48 0.00 10.00 10.00 -0.18 -0.26 0.04 historicliabilities 15 2790.00 5.20 2.52 5.00 5.20 2.97 0.00 10.00 10.00 -0.03 -0.37 0.05 humanrightschildandforcedlabor 16 2509.00 4.97 2.20 5.00 5.09 1.48 0.00 10.00 10.00 -0.41 0.32 0.04 industryspecificrisk 17 2615.00 4.28 2.44 5.00 4.33 1.48 0.00 10.00 10.00 -0.28 -0.40 0.05 laborrelations 18 2748.00 5.06 1.79 5.00 5.12 1.48 0.00 10.00 10.00 -0.29 0.38 0.03 leadingsustainabilityriskinlicators 19 2907.00 4.75 1.83 5.00 4.78 1.48 0.00 10.00 10.00 -0.15 0.04 0.03 localcommunities 20 2760.00 5.72 2.24 6.00 5.79 2.97 0.00 10.00 10.00 -0.26 -0.31 0.04 operatingrisk 21 2821.00 5.21 2.40 5.00 5.29 1.48 0.00 10.00 10.00 -0.27 0.01 0.05 opportunity 22 2915.00 4.43 2.15 5.00 4.43 1.48 0.00 10.00 10.00 0.02 -0.47 0.04 performance 23 2850.00 4.16 2.52 4.00 4.14 2.97 0.00 10.00 10.00 -0.01 -0.63 0.05 productsafety 24 2743.00 5.09 2.04 5.00 5.15 1.48 0.00 10.00 10.00 -0.26 0.25 0.04 productsmaterials 25 2843.00 3.73 2.46 4.00 3.64 2.97 0.00 10.00 10.00 0.22 -0.45 0.05 strategicgovernance 26 2358.00 5.08 1.74 5.00 5.07 1.48 0.00 10.00 10.00 -0.03 -0.26 0.04 supplychain 27 2762.00 4.98 2.34 5.00 4.99 2.97 0.00 10.00 10.00 -0.01 -0.38 0.04 logsize 28 2918.00 9.16 1.17 9.04 9.10 1.06 5.56 13.17 7.62 0.46 0.41 0.02

Table B.1: Innovest Summary Statistics United States

75 var n mean sd median trimmed mad min max range skew kurtosis se stock.return 1 1636.00 -0.01 0.09 -0.00 -0.01 0.08 -0.28 0.31 0.59 0.03 0.70 0.00 beta 2 1636.00 1.02 0.45 1.03 1.03 0.46 -0.18 2.33 2.51 -0.08 -0.33 0.01 bp 3 1600.00 0.76 0.51 0.65 0.70 0.35 -1.02 5.00 6.02 2.61 14.27 0.01 auditinteg 4 1600.00 1.81 1.51 1.52 1.63 0.82 -9.16 16.00 25.16 2.98 30.09 0.04 certification 5 1527.00 3.38 2.38 3.00 3.26 2.97 0.00 10.00 10.00 0.32 -0.47 0.06 corporategovernance 6 1584.00 5.43 2.12 6.00 5.59 1.48 0.00 10.00 10.00 -0.62 0.53 0.05 customerstakeholderpartnerships 7 1530.00 5.15 1.85 5.00 5.17 1.48 0.00 10.00 10.00 -0.11 0.03 0.05 employeemotivationanddevelopment 8 1528.00 5.37 1.80 5.00 5.40 1.48 0.00 10.00 10.00 -0.15 0.30 0.05 environmentalaccountingreporting 9 1588.00 5.94 2.75 6.00 6.14 2.97 0.00 10.00 10.00 -0.53 -0.52 0.07 environmentalmanagementsystems 10 1603.00 5.98 2.42 6.00 6.19 2.97 0.00 10.00 10.00 -0.68 -0.01 0.06 environmentalopportunity 11 1549.00 5.54 1.98 6.00 5.74 1.48 0.00 10.00 10.00 -1.03 1.90 0.05 environmentalstrategy 12 1636.00 6.11 2.00 6.00 6.21 1.48 0.00 18.00 18.00 -0.37 0.89 0.05 envtraininganddevelopment 13 1530.00 5.66 2.44 6.00 5.82 1.48 0.00 10.00 10.00 -0.50 0.04 0.06 healthandsafety 14 1519.00 5.10 2.05 5.00 5.10 1.48 0.00 10.00 10.00 -0.05 -0.24 0.05 historicliabilities 15 1537.00 5.30 1.98 5.00 5.36 1.48 0.00 10.00 10.00 -0.28 0.92 0.05 humanrightschildandforcedlabor 16 1428.00 5.01 2.00 5.00 5.10 1.48 0.00 10.00 10.00 -0.42 0.50 0.05 industryspecificrisk 17 1529.00 4.96 2.44 5.00 5.07 1.48 0.00 10.00 10.00 -0.39 -0.15 0.06 laborrelations 18 1512.00 5.49 1.65 5.00 5.47 1.48 0.00 10.00 10.00 0.04 1.20 0.04 Ieadingsustainabilityriskindicators 19 1632.00 5.78 1.84 6.00 5.88 1.48 0.00 10.00 10.00 -0.42 0.06 0.05 localcommunities 20 1527.00 5.30 1.96 5.00 5.31 1.48 0.00 10.00 10.00 -0.07 0.10 0.05 operatingrisk 21 1583.00 5.41 2.16 5.00 5.53 1.48 0.00 10.00 10.00 -0.51 0.74 0.05 opportunity 22 1629.00 5.84 2.06 6.00 5.92 1.48 0.00 10.00 10.00 -0.31 0.06 0.05 performance 23 1586.00 5.70 2.50 6.00 5.81 2.97 0.00 10.00 10.00 -0.31 -0.20 0.06 productsafety 24 1502.00 5.32 2.19 5.00 5.45 1.48 0.00 10.00 10.00 -0.45 0.08 0.06 productsmaterials 25 1599.00 5.27 2.57 5.00 5.37 2.97 0.00 10.00 10.00 -0.27 -0.53 0.06 strategicgovernance 26 1385.00 5.28 1.72 5.00 5.26 1.48 1.00 10.00 9.00 0.09 -0.13 0.05 supplychain 27 1531.00 5.17 2.09 5.00 5.20 1.48 0.00 10.00 10.00 -0.06 -0.12 0.05 logsize 28 1636.00 8.67 0.97 8.58 8.62 0.96 5.82 12.39 6.57 0.53 0.27 0.02 Table B.2: Innovest Summary Statistics : Japan

var n mean sd median trimmed mad min max range skew kurtosis se stock.return 1 284.00 -0.04 0.14 -0.03 -0.03 0.11 -0.61 0.42 1.03 -0.78 2.57 0.01 beta 2 284.00 0.98 0.42 0.95 0.96 0.47 0.20 2.10 1.89 0.33 -0.59 0.03 bp 3 269.00 0.65 0.53 0.53 0.56 0.32 0.06 4.02 3.95 2.91 12.18 0.03 auditinteg 4 269.00 2.53 2.14 1.89 2.14 1.10 0.25 16.00 15.75 2.63 9.60 0.13 certification 5 266.00 4.21 2.53 4.00 4.14 2.97 0.00 10.00 10.00 0.23 -0.39 0.16 corporategovernance 6 280.00 5.55 2.30 6.00 5.69 1.48 0.00 10.00 10.00 -0.49 0.15 0.14 customerstakeholderpartnerships 7 267.00 6.21 1.82 6.00 6.19 1.48 2.00 10.00 8.00 0.06 -0.61 0.11 employeemotivationanddevelopment 8 267.00 6.67 1.79 7.00 6.74 1.48 0.00 10.00 10.00 -0.58 1.21 0.11 environmentalaccountingreporting 9 278.00 5.38 2.53 5.50 5.52 3.71 0.00 10.00 10.00 -0.29 -0.89 0.15 environmentalmanagementsystems 10 277.00 6.20 2.35 7.00 6.41 2.97 0.00 10.00 10.00 -0.73 -0.14 0.14 environmentalopportunity 11 269.00 5.44 2.17 6.00 5.69 1.48 0.00 10.00 10.00 -1.05 1.24 0.13 environmentalstrategy 12 284.00 6.48 1.89 6.00 6.60 1.48 1.00 10.00 9.00 -0.43 -0.28 0.11 envtraininganddevelopment 13 273.00 5.85 2.49 6.00 6.02 2.97 0.00 10.00 10.00 -0.44 -0.29 0.15 healthandsafety 14 267.00 5.68 2.10 6.00 5.71 1.48 0.00 10.00 10.00 -0.22 0.14 0.13 historicliabilities 15 263.00 4.86 2.45 5.00 4.95 1.48 0.00 10.00 10.00 -0.37 0.00 0.15 humanrightschildandforcedlabor 16 248.00 5.19 2.10 5.00 5.38 1.48 0.00 10.00 10.00 -0.78 0.81 0.13 industryspecificrisk 17 266.00 4.94 2.42 5.00 5.10 1.48 0.00 10.00 10.00 -0.47 -0.21 0.15 laborrelations 18 267.00 5.95 1.86 6.00 6.06 1.48 0.00 10.00 10.00 -0.58 0.58 0.11 leadingsustainabilityriskindicators 19 280.00 5.69 1.77 6.00 5.75 1.48 0.00 10.00 10.00 -0.40 0.50 0.11 localcommunities 20 266.00 5.86 1.81 6.00 5.90 1.48 0.00 10.00 10.00 -0.15 0.31 0.11 operatingrisk 21 263.00 5.00 2.57 5.00 5.10 1.48 0.00 10.00 10.00 -0.45 -0.00 0.16 opportunity 22 282.00 6.21 2.03 6.00 6.37 1.48 1.00 10.00 9.00 -0.56 -0.15 0.12 performance 23 281.00 6.22 2.46 7.00 6.42 2.97 0.00 10.00 10.00 -0.63 -0.02 0.15 productsafety 24 259.00 5.69 1.73 6.00 5.78 1.48 0.00 10.00 10.00 -0.60 1.43 0.11 productsmnaterials 25 281.00 5.70 2.36 6.00 5.79 2.97 0.00 10.00 10.00 -0.32 -0.44 0.14 strategicgovernance 26 220.00 6.18 1.60 6.00 6.23 1.48 2.00 10.00 8.00 -0.26 -0.19 0.11 supplychain 27 267.00 5.64 2.13 6.00 5.72 2.97 0.00 10.00 10.00 -0.31 -0.20 0.13 logsize 28 284.00 9.28 1.28 9.21 9.29 1.41 5.92 11.91 5.99 -0.02 -0.78 0.08 Table B.3: Innovest Summary Statistics : Germany

76 var n mean sd median trimmed nad min max range skew kurtosis se stock-return 1 391.00 -0.04 0.10 -0.03 -0.04 0.10 -0.31 0.32 0.64 -0.20 0.10 0.00 beta 2 391.00 1.10 0.47 1.07 1.09 0.43 0.06 2.54 2.48 0.26 0.06 0.02 bp 3 378.00 0.64 0.66 0.51 0.55 0.32 -1.71 5.00 6.71 3.24 18.63 0.03 auditinteg 4 378.00 2.43 2.23 1.88 2.10 1.07 -4.06 16.00 20.06 2.95 14.37 0.11 certifncation 5 360.00 3.80 2.74 4.00 3.66 2.97 0.00 10.00 10.00 0.17 -0.78 0.14 corporategovernance 6 388.00 5.07 1.98 5.00 5.11 1.48 0.00 10.00 10.00 -0.22 0.53 0.10 customerstakeholderpartnerships 7 375.00 5.85 2.10 6.00 5.96 1.48 1.00 10.00 9.00 -0.47 -0.02 0.11 enployeemotivati)nanddevelopment 8 374.00 7.18 1.81 7.00 7.27 1.48 0.00 10.00 10.00 -0.87 1.95 0.09 environmentalaccountingreporting 9 381.00 5.47 2.23 6.00 5.63 1.48 0.00 10.00 10.00 -0.48 -0.04 0.11 environmentalmanagementsystems 10 380.00 5.97 2.06 6.00 6.11 1.48 0.00 10.00 10.00 -0.68 0.37 0.11 environmentalopportunity 11 369.00 5.89 1.96 6.00 6.17 1.48 0.00 10.00 10.00 -1.37 2.27 0.10 environmentalstrategy 12 391.00 6.36 1.82 7.00 6.50 1.48 0.00 10.00 10.00 -0.94 1.46 0.09 envtraininganddevelopment 13 373.00 5.79 2.12 6.00 5.89 1.48 0.00 10.00 10.00 -0.47 0.38 0.11 healthandsafety 14 375.00 6.17 1.96 6.00 6.21 1.48 0.00 10.00 10.00 -0.26 -0.06 0.10 historicliabilities 15 366.00 5.30 2.51 5.00 5.43 2.97 0.00 10.00 10.00 -0.34 -0.01 0.13 humanrightschildandforcedlabor 16 344.00 5.83 2.04 6.00 5.90 1.48 0.00 10.00 10.00 -0.37 0.37 0.11 industryspecificrisk 17 338.00 4.87 2.91 5.00 4.96 2.97 0.00 10.00 10.00 -0.49 -0.79 0.16 laborrelations 18 374.00 6.24 2.12 6.00 6.33 2.97 0.00 10.00 10.00 -0.42 -0.12 0.11 leadingsustainabilityriskindicators 19 391.00 5.89 1.78 6.00 6.05 1.48 0.00 10.00 10.00 -0.93 1.41 0.09 localcommunities 20 373.00 6.01 2.19 6.00 6.01 1.48 0.00 10.00 10.00 -0.12 -0.33 0.11 operatingrisk 21 375.00 5.48 2.27 6.00 5.69 1.48 0.00 10.00 10.00 -0.82 0.82 0.12 opportunity 22 391.00 5.85 1.88 6.00 5.96 1.48 0.00 10.00 10.00 -0.45 -0.07 0.10 performance 23 380.00 5.45 2.55 6.00 5.64 2.97 0.00 10.00 10.00 -0.55 -0.38 0.13 productsafety 24 370.00 6.16 2.06 6.00 6.30 1.48 0.00 10.00 10.00 -0.77 0.85 0.11 productsmaterials 25 382.00 5.38 2.26 5.00 5.47 2.97 0.00 10.00 10.00 -0.32 -0.17 0.12 strategicgovernance 26 327.00 6.34 1.47 6.00 6.41 1.48 2.00 10.00 8.00 -0.28 0.22 0.08 supplychain 27 375.00 6.01 2.12 6.00 6.00 2.97 1.00 10.00 9.00 0.06 -0.73 0.11 logsize 28 391.00 9.42 1.12 9.31 9.39 1.13 5.86 12.29 6.43 0.10 -0.06 0.06

Table B.4: Innovest Summary Statistis : France

var n mean sd median trimmed moad rmin max range skew kurtosis se stock.return 1 368.00 -0.02 0.10 -0.02 -0.02 0.08 -0.35 0.49 0.83 0.68 3.24 0.01 beta 2 365.00 1.08 0.45 1.03 1.04 0.43 0.18 2.52 2.35 0.67 0.45 0.02 hp 3 336.00 0.50 0.63 0.35 0.40 0.29 -0.39 5.00 5.39 4.18 25.29 0.03 auditinteg 4 336.00 3.14 5.07 2.47 2.98 1.94 -16.00 16.00 32.00 -0.58 4.78 0.28 certification 5 339.00 3.80 2.82 4.00 3.63 2.97 0.00 10.00 10.00 0.27 -0.79 0.15 corporategovernance 6 346.00 5.34 2.47 5.00 5.44 1.48 0.00 10.00 10.00 -0.33 -0.26 0.13 customerstakeholderpartnerships 7 356.00 5.86 2.30 6.00 5.98 1.48 0.00 10.00 10.00 -0.49 0.23 0.12 employeemotivationanddevelopment 8 358.00 6.25 2.10 7.00 6.47 1.48 0.00 10.00 10.00 -0.96 0.75 0.11 environmentalaccountingreporting 9 359.00 5.15 2.64 5.00 5.22 2.97 0.00 10.00 10.00 -0.18 -0.62 0.14 environmentalmanagementsystems 10 355.00 5.34 2.60 5.00 5.46 2.97 0.00 10.00 10.00 -0.30 -0.72 0.14 environmentalopportunity 11 346.00 5.49 2.22 6.00 5.72 1.48 0.00 10.00 10.00 -0.81 0.44 0.12 environmentalstrategy 12 368.00 5.99 2.37 6.00 6.16 2.97 0.00 10.00 10.00 -0.51 -0.28 0.12 envtraininganddevelopment 13 348.00 4.87 2.61 5.00 4.94 2.97 0.00 10.00 10.00 -0.19 -0.50 0.14 healthandsafety 14 355.00 6.26 2.44 7.00 6.45 2.97 0.00 10.00 10.00 -0.65 0.01 0.13 historicliabilities 15 330.00 5.40 2.58 5.00 5.58 2.97 0.00 10.00 10.00 -0.50 -0.09 0.14 humanrigltschildandforcedlabor 16 319.00 5.51 2.52 5.00 5.69 1.48 0.00 10.00 10.00 -0.58 0.18 0.14 industryspecificrisk 17 310.00 4.66 3.14 5.00 4.67 2.97 0.00 10.00 10.00 -0.27 -1.15 0.18 laborrelations 18 354.00 5.77 1.87 6.00 5.90 1.48 0.00 10.00 10.00 -0.75 1.25 0.10 lealingsistainabilityriskindicators 19 364.00 5.62 2.02 6.00 5.69 1.48 0.00 10.00 10.00 -0.26 -0.16 0.11 localcomimunities 20 358.00 6.08 2.18 6.00 6.21 1.48 0.00 10.00 10.00 -0.61 0.54 0.12 operatingrisk 21 348.00 5.38 2.18 5.00 5.52 1.48 0.00 10.00 10.00 -0.55 0.61 0.12 opportunity 22 368.00 5.21 2.36 5.00 5.35 2.97 0.00 10.00 10.00 -0.39 -0.64 0.12 performance 23 337.00 5.00 2.67 5.00 5.13 2.97 0.00 10.00 10.00 -0.33 -0.84 0.15 productsafety 24 352.00 5.74 2.09 6.00 5.85 1.48 0.00 10.00 10.00 -0.42 0.24 0.11 productsmnaterials 25 356.00 5.07 2.75 5.00 5.15 2.97 0.00 10.00 10.00 -0.22 -0.84 0.15 strategicgovernance 26 342.00 6.29 1.93 6.00 6.38 1.48 2.00 10.00 8.00 -0.32 -0.61 0.10 supplychain 27 358.00 5.63 2.39 6.00 5.72 1.48 0.00 10.00 10.00 -0.28 -0.38 0.13 logsize 28 368.00 8.22 1.28 8.12 8.15 1.25 5.56 12.20 6.64 0.50 0.09 0.07

Table B.5: Innovest Summary Statistics Great Britain

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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 0.03 0.03 0.07 0.45 0.04 1.00 0.24 0.32 0.40 0.34 0.41 0.28 0.38 0.37 0.40 0.43 0.36 0.48 07 0.38 0.33 0.32 0.09 0.30 0.24 1.00 0.16 0.08 0.05 0.07 0.12 0.23 0.13 0.18 0.23 0.20 0.13 08 0.06 0.04 0.05 0.37 0.12 0.32 0.16 1.00 0.37 0.32 0.37 0.37 0.43 0.36 0.40 0.33 0.31 0.38 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 10 -0.13 -0.14 -0.16 0.58 -0.06 0.34 0.05 0.32 0.68 1.00 0.66 0.40 0.39 0.47 0.42 0.28 0.46 0.47 11 -0.11 -0.11 -0.10 0.73 -0.05 0.41 0.07 0.37 0.85 0.66 1.00 0.44 0.50 0.57 0.52 0.37 0.58 0.58 12 -0.05 -0.05 -0.05 0.42 0.03 0.28 0.12 0.37 0.43 0.40 0.44 1.00 0.38 0.38 0.40 0.31 0.37 0.43 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 oo0 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 Table B.14: Asset4 Factor Correlations Legend

01 boardfunctions 02 boardstructure 03 compensationpolicy 04 visionandstrategy 05 shareholderrights 06 marginsperformance 07 shareholderloyalty 08 clientloyalty 09 emissionreduction 10 productinnovation 11 resourcereduction 12 productresponsibility 13 community 14 humanrights 15 workdiversityopportunity 16 employmentquality 17 healthandsafety 18 traininganddevelopment 1 Month 3 Months 6 Months 12 Months 36 Months Country Ave. t Stat. Ave. t Stat. Ave. t Stat. Ave. t Stat. Ave. t Stat. US 0.223 -1.276 -2.18 -0.851 -1.167 JP 0.766 -1.343 -2.686 0.214 -0.738 DE -1.730 -1.385 -0.929 0.331 0.648 FR -1.527 -1.380 -1.379 0.102 0.501 GB 0.5837 -1.408 -3.070 -1.88 -0.869

Table B.15: Innovest Average Country Level Fixed Effects Pooled Regression Results [2002-2009]with Cap USD 250M

1 Month 3 Months 6 Months 12 Months 36 Months Country Ave. t Stat. Ave. t Stat. Ave. t Stat. Ave. t Stat. Ave. t Stat. US 0.204 -1.134 -2.268 -0.832 -1.168 JP 0.803 -1.273 -2.937 0.028 -0.880 00 DE -1.762 -1.611 -0.871 0.510 0.527 IQ FR -1.416 -1.578 -1.675 0.026 -0.265 GB 0.592 -1.392 -3.070 -1.842 -0.752

Table B.16: Asset4 Average Country Level Fixed Effects Pooled Regression Results [2002-2009]with Cap USD 250M Appendix C

Figures

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Figure C-1: Innovest Factor Corrleations : United States

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Figure C-2: Innovest Factor Corrleations : Japan

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Figure C-5: Innovest Factor Corrleations: Great Britain

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I'll IIIIIIiIIIIIIII 0.8 1~FEEI~EEEEEI~EEEIUIm mU UA n o I l O N E U U N 0.6 A -~ -I El U 0.4 - mm M =U = UU 0 L- 0.2 UE U = U i 0 I I -0.2 -0.4

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87 - - -- :s

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