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A Reputational and Labor Factors. Arun Balasubramaniam

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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 © Massachusetts Institute of Technology 2011. All rights reserved. A uthor ................................ .. ....................... n ring Systems Division June 08, 2011 Certified by........................ ... -........ , ....... Nicholas A Ashford Professor of Technology and Policy and Director of Technology and Law Program Thesis Supervisor Accepted by ........................ ........ Par 1 Hale Director, System Design and Management Program Socially Responsible Investing : A comparative analysis of environmental, social, governance, reputational and labor factors. by Arun Balasubramaniam Submitted to the Engineering Systems Division on June 08, 2011, in partial fulfillment of the requirements for the degree of 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 corpo- rate 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 at- tempts to breakdown the multi-dimensional CSP into its core constituent dimensions and to examine their inter-relationships and relationship with CFP, using statisti- cal 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, so- cial 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 3 Acknowledgments It is my pleasure to take this opportunity to convey my gratitude to all the people who have contributed to this thesis in many different ways. This work would not have been possible without the knowledge and guidance of my advisor, Dr. Nicholas Ashford. In addition to providing insightful comments, and beneficial data pointers, his immediate responses even on weekends, helped me make steady progress. I also greatly appreciate his flexibility with schedules and willingness to work with my 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 main- stream offerings in the asset management business. Socially Responsible Investing (SRI), is often used as an umbrella term that incorporates goals with respect to eth- ical, 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
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