FINANCE FINANCE EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT Demystifying Sovereign ESG Ekaterina M. Gratcheva, Teal Emery, and Dieter Wang © 2020 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved. This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. 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Examples of components can include, but are not limited to, tables, figures, or images. All queries on rights and licenses should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; e-mail: pubrights@worldbank.org. This paper forms part of a series of publications under the Practice (GP) in collaboration with World Bank Treasury (TRE), Global Program on Sustainability (GPS). The series is a Development Economics Vice Presidency (DEC), and other knowledge product of GPS Pillar 3 with the objective to promote GPs. Focusing on ESG issues in sovereign investing, the series the use of high-quality data and analysis of sustainability to disseminates practical, evidence-based recommendations for better inform decisions made by governments, the private market participants, including institutional investors, sovereign sector, and financial institutions. GPS Pillar 3 is led by the World issuers, credit rating agencies, and ESG data and service Bank’s Finance, Competitiveness and Innovation (FCI) Global providers, among others. >>> “A New Dawn: Rethinking Sovereign ESG” “Demystifying Sovereign ESG” focuses proposes improvements to the sovereign on comparing the sovereign ESG ESG framework and builds on findings methodologies of leading sovereign and recommendations discussed in other ESG providers and describes structural papers in the series. challenges posed by the current sovereign ESG framework. “Riding the Wave: Navigating the ESG “Paving the Path: Lessons from Chile’s Landscape for Sovereign Debt Managers” Experiences as a Sovereign Issuer for provides a thorough discussion of Sustainable Finance Action” provides sovereign ESG from a debt management a concentrated study of Chile’s ESG- office perspective. focused issuances to date and relevant lessons. “Spatial Finance: Challenges and “Credit Worthy: ESG Considerations in Opportunities in a Changing World” Sovereign Credit Ratings” demystifies (produced in partnership with the World the role of ESG factors in country Wildlife Fund) discusses challenges with credit ratings and highlights potential the E data, including at the sovereign ESG impact on the creditworthiness of level, and explores the use of satellite data countries with the application of the World to address the quality and availability of E Bank’s wealth and stranded asset data. data. “1% Growth in Natural Capital: Why It The chapter “Natural Allies: Wealth Matters for Sovereign Bonds” quantifies and Sovereign ESG” from the book the materiality of natural capital and its The Changing Wealth of Nations 2021: impact on sovereign bonds by adjusting Managing Assets for the Future focuses for ingrained income bias. on challenges in ESG data and discusses solutions with the application of the World Bank wealth data. “Natural Capital and Sovereign Bonds” introduces the concept of ingrained income bias and presents evidence that sovereign bond yields reflect a country’s various types of natural capital. DEMYSTIFYING SOVEREIGN ESG >>> 1 >>> Contents Abbreviations 5 Acknowledgments 7 Executive Summary 9 1. Introduction: Rise of Sovereign ESG Integration 15 2. State of Sovereign ESG: Empirical Analysis of Sovereign ESG Scores 23 Key questions: 1. Is there agreement among sovereign ESG data providers on how to measure a sovereign’s ESG performance? Does this measurement differ depending on the pillar? 2. How does this measurement compare with the well-documented divergence among ESG data providers for corporate ESG scores? 23 3. Relationship of Sovereign ESG Scores to a Country’s National Income 31 Key questions: 1. What is the relationship between sovereign ESG scores and a country’s level of development, or national income? 2. What are the important policy implications of that relationship? 3. How does adjustment of the sovereign ESG methodology for income affect sovereign ESG scores? 31 4. Comparing Sovereign ESG Score Methodologies 41 Key questions: 1. What are the key tenets of the sovereign ESG score methodologies of ESG data providers? 2. What are their unique aspects, and how do these differences contribute to differences in respective sovereign ESG scores? 41 5. Conclusion 47 Appendix A Research Methodology 49 Appendix B Principal Component Analysis of Sovereign ESG Scores 51 Appendix C Supplementary Data Description 55 Appendix D Key Tenets of ESG Providers’ Methodologies 57 References 63 DEMYSTIFYING SOVEREIGN ESG >>> 3 Figures Figure ES.1 Correlations of ESG-related indexes with a country’s national income (GNI per capita) 11 Figure ES.2 “Wheel of Fortune”: Network analysis of ESG providers’ methodologies 13 Figure 1.1 Sustainable development investing spectrum 16 Figure 1.2 The structure of sovereign ESG financial ecosphere 18 Figure 2.1 Composition of sovereign ESG scores by pillars for major ESG providers 26 Figure B3.1 GDP adjustment for the ND-GAIN Country Index 34 Figure 3.1 Correlations of ESG-related indexes with a country’s national income (GNI per capita) 33 Figure 3.2 Scatter matrix for sovereign ESG scores 36 Figure 3.3 Scatter matrix for sovereign ESG scores, linearly adjusted for national income 37 Figure 3.4 Sovereign ESG scores and sovereign ESG income-adjusted scores for ESG providers collectively 38 Figure 3.5 Sovereign ESG scores and sovereign ESG income-adjusted scores for individual ESG providers 39 Figure 3.6 Environmental Kuznets curve 39 Figure 4.1 Clusters of ESG providers for sovereign ESG and individual pillars 42 Figure 4.2 Word clouds for ESG providers’ sovereign ESG methodologies 43 Figure 4.3 “Wheel of Fortune”: Network analysis of ESG providers’ methodologies 45 Figure B.1 Sovereign ESG scores’ variance explained by principal components 52 Figure B.2 Principal component 1 for ESG scores, individual pillars, a country’s national income 53 Figure B.3 Correlation of sovereign ESG principal component 1 with sustainability variables 54 Figure C.1 Total Wealth Composition. 56 Figure D.1 FTSE Russell/Beyond Ratings Sovereign Risk Monitor Framework 58 Figure D.2 ISS ESG Country Ratings Framework 58 Figure D.3 MSCI ESG Government Ratings Framework 59 Figure D.4 Robeco Country Sustainability Ranking Framework 60 Figure D.5 Sustainalytics Country Risk Ratings Framework 61 Tables Table 2.1 Summary of ESG providers included in analysis 25 Table 2.2 Correlation of aggregate sovereign ESG scores for major ESG providers 27 Table 2.3 Illustrative comparison of ESG scores for companies versus sovereigns 27 Table 2.4 Correlation of sovereign E pillar scores for major ESG providers 27 Table 2.5 Correlation of sovereign S pillar scores for major ESG providers 28 Table 2.6 Correlation of sovereign G pillar scores for major ESG providers 28 Table 3.1 Correlations of aggregate ESG and individual pillar scores with a country’s national income (GNI per capita) for ESG providers. 32 Boxes Box 1.1 Select findings from market survey on ESG investing in the EM sovereign asset class by J.P. Morgan 17 Box 1.2 NGFS’s 2021 Sustainable Finance Market Dynamics Report: Role of ESG data providers 21 Box 1.3 Divergence of ESG scores for firms: Key drivers 24 Box 3.1 Existing methodologies for sovereign ESG income adjustment 34 Box D.1 V.E Sovereign Sustainability Ratings Framework 62 4 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Abbreviations CRA Credit Rating Agency E Environmental EKC Environmental Kuznets Curve EM Emerging Market EPI Environmental Performance Index (Yale) ESG Environmental, Social, and Governance G Governance GDP Gross Domestic Product GNI Gross National Income GPS Global Program on Sustainability IIB Ingrained Income Bias JESG J.P. Morgan ESG ND-GAIN Notre Dame Global Adaptation Initiative (Country Index) NGFS Network for Greening the Financial System NLP Natural Language Processing OECD Organisation for Economic Co-operation and Development PCA Principal Component Analysis RRI RepRisk Country ESG Risk Index S Social SDG Sustainable Development Goals UN United Nations WAVES Wealth Accounting and the Valuation of Ecosystem Services WBG World Bank Group WGBI World Government Bond Index WGI Worldwide Governance Indicators DEMYSTIFYING SOVEREIGN ESG >>> 5 6 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Acknowledgements This publication was prepared by a team consisting of Ekaterina M. Gratcheva, Teal Emery, and Dieter Wang, with substantive input from Bryan Gurhy, under the supervision of Anderson Silva, all from the Finance, Competitiveness and Innovation (FCI) Global Practice of the World Bank Group (WBG).1 The authors would like to thank those who provided the comments received during the formal peer review process, including Girum Dagnachew Abate (Economist, CROCR), Kassia Antoine (Economist, CROCR), Michael Brown (Economist, CROCR), Marc Schrijver (Senior Financial Sector Specialist, EAEF2), Heike Reichelt (Head Financial Officer/Head of Investor Relations and New Program Development, TRECI), Fiona Stewart (Lead Financial Sector Specialist, EFNLT, FCI), Aart C. Kraay (Deputy Chief Economist and Director of Development Policy, DECVP), James Cust (Economist, AFECE), Raffaello Cervigni (Lead Environmental Economist, SENGL, and Task Team Leader for the Global Program on Sustainability), Eric Bouyé (Manager, TREPK), Rodrigo Cabral (Senior Financial Officer, EMFMD), James Seward (Senior Financial Officer, TRECI), Nepomuk Dunz (Junior Professional Officer, EFNLT), and Samantha Power (Consultant, EFNLT). External comments and feedback were also received from FTSE Russell/Beyond Ratings, ISS, MSCI, RepRisk, Robeco (formerly RobecoSAM), Sustainalytics, and V.E; Robert Patalano, OECD; Harun Đogo, Morgan Stanley; Liliana Jerónimo, Central Bank of Portugal; Diane Menville, Scope, Credit Rating Agency; Rodolphe Bocquet, Qontigo, founder of Beyond Ratings; Jonathan Amacker, Imperial College; and Yvette Babb, William Blair and Co. Also, a special thank you to Jean Pesme, Global Director of the Finance, Competitiveness and Innovation Global Practice of the World Bank Group. The authors would also like to thank the J.P. Morgan team for the collaboration. Special thanks to Luis Oganes, Lydia Harvey, Jarrad K. Linzie, Katherine Marney, Jessica Murray and Rupert Rink. The publication has been funded by the Global Program on Sustainability. The views expressed herein are solely the authors’ and should not be attributed to the WBG. The report was edited by Mary-Ann Moalli and Marcy Gessel, Publications Professionals LLC. Florencia Micheltorena led the creative design and formatting of the publication. We thank them all. 1 E-mail contacts: Ekaterina M. Gratcheva (egratcheva@worldbank.org), Teal Emery (lemery@worldbank.org), and Dieter Wang (dwang5@worldbank.org). DEMYSTIFYING SOVEREIGN ESG >>> 7 8 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Executive Summary The evolution of sustainable finance to mainstream finance has been motivated by a growing demand for the financial sector to play a greater role in the transformation of the current economic model into a more sustainable one (Boitreaud et al. 2020). The introduction of the United Nation’s (UN) Sustainable Development Goals (SDG) and the Paris Agreement on climate change in 2015 have helped galvanize a societal shift to ensure a sustainable future and to fight climate change in particular. As a result, the pace of environmental, social, and governance (ESG) integration,1 which has become the most prevalent form of sustainable finance, has accelerated in recent years. Market participants continue to grapple with adapting the ESG framework to the sovereign context, despite significant progress of ESG integration in the corporate bond and equity asset class. This challenge is due to the multifaceted nature of ESG-related issues facing governments in relation to corporate entities, as well as a more complex transmission mechanism of the sovereign debt asset class to sustainable outcomes in the real economy. Sovereign ESG is distinct from corporate ESG on both data and methodology levels. Because sovereign ESG is an underresearched area of ESG investing, the current vacuum has been filled with heuristics and extrapolations from the more developed area of corporate ESG. Specifically, the financial industry’s emerging consensus for what needs to be addressed for corporate ESG in terms of quality of input data and methodologies has been applied to all asset classes. Our empirical analysis, however, reveals that issues with sovereign ESG are unique and call for different actions by stakeholders to ensure that the operationalization of ESG investing evolves in line with the fast-changing sustainable finance landscape and political commitments. This paper demystifies sovereign ESG as a distinct segment of the ESG sector by assessing the major sovereign ESG providers that have laid the foundation for the operationalization of ESG investing in sovereign fixed income markets. This is the first publication that provides an empirically based assessment of sovereign ESG as a sector, the way leading sovereign ESG providers compare and contrast with each other, and the way their respective sovereign ESG product contributes to the industry’s increasing demand for being able to measure sustainability within different investments. To illuminate distinct features of sovereign ESG, we analyze sovereign ESG methodologies and their outputs—sovereign ESG scores—from the leading sovereign ESG providers, including FTSE Russell/Beyond Ratings, ISS, MSCI, RepRisk,2 Robeco (previously known as RobecoSAM), Sustainalytics, and V.E (previously known as Vigeo Eiris). 1 Environmental, social, and governance integration is the practice of incorporating ESG-related information into investment decisions to help enhance risk-adjusted returns, regardless of whether a strategy has a sustainable mandate. 2 Its product (RepRisk Country ESG Risk Index) does not have official environmental, social, and governance pillars, but we processed it to create proxies based on ESG percentages for the purpose of comparability. DEMYSTIFYING SOVEREIGN ESG >>> 9 Our analysis reveals that contrary to the divergent (five years on average), lack of consensus on the definition of corporate ESG scores—dubbed as “aggregate confusion” a “good” environmental performance, longer time horizon for (Berg, Kölbel, and Rigobon 2019)—there is convergence environmental risks to materialize, and the nonlinear nature of among sovereign ESG scores across ESG providers. environmental risks, among other factors. Sovereign ESG scores exhibit different patterns from corporate ESG scores at the aggregate and individual E, S, and G pillar Our findings suggest that ingrained income bias is a levels. Most notably, the E pillar is the most challenging of the plausible explanation for the convergence in sovereign three pillars for sovereigns because sovereign E scores vary ESG scores. We find that about 90 percent of a country’s widely among the providers. This variance contrasts with sovereign ESG score is explained by the country’s level of the highest level of convergence for E scores for corporate development and that a country’s national income permeates entities as compared to other corporate ESG pillars. Although all sustainability-linked measures used by the market. Failure our methods shed limited additional insight into the cause of to account for this bias in investment decisions would lead divergence for the E pillar at the sovereign level, our inquiry to misaligned incentives for investors and could potentially provides plausible explanations for this divergence among divert flows to wealthier countries at the expense of lower- ESG providers, including sovereign environmental data lags income countries in need of finance for development. This 10 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT consideration is critical because sovereign ESG scores by As investors pursue different objectives in integrating their nature should measure aspects in addition to income sovereign ESG into their investment decisions—from which other metrics, such as credit ratings, already reflect. We risk management to measurable sustainability impact—a also find that sustainability-related data and indexes, such as robust taxonomy of sovereign ESG methodologies that the SDG (Sustainable Development Goal) Index, EPI (Yale clearly define unique features of individual ESG providers Environmental Performance Index), and ND-GAIN (Notre would provide clarity to the market for better alignment of Dame Global Adaptation Initiative) Country Index, are affected their investment strategies with the right sovereign ESG by the same structural issue: they are mostly explained by tools. Although the current level of disclosure by ESG providers the country’s level of development, or its income. Figure ES.1 presents challenges to produce a well-defined taxonomy, the presents individual ESG providers and sustainability-linked results of our analysis provide a helpful first step as presented indices’ correlation with national income. in Figure ES.2 > > > F I G U R E E S . 1 Corr l tions of ESG-r l t d indic tors with n tion l incom (GNI p r c pit ) . Incom nd sov r i n ESG scor s of six provid rs Th E, S, nd G individu l scor s nd combin d ESG scor s r corr l t d with GNI p r c pit to v r in d r s, d p ndin on th ESG provid r. Th S scor s r most corr l t d with littl v ri tion cross provid rs, whil th E scor s r l st corr l t d with l r discr p nci s mon provid rs. A r t E Scor 58.5% l M BR Su s sk s ris I tic in SA SC Ri ll/ Ei (m n) Ro R p M st co o ss Ru Vi b SE SA s FT s o in M tic sc t ris b dR l isk Ei in Ro on pR o I st SC A r t G Scor Su Vi B R M 69.8% (m n) s Su nd R M s in in tic o SA t ris l o isk Ei B sc o pR I SC st b Ro A r t ESG Scor Vi M R 83.2% (m n) s M nd AM tin s tic o S ris SC R o l isk sc Ei in pR o Su I b st Ro A r t S Scor Vi B R 85.2% (m n) Note: ESG = environmental, social, and governance; GNI = gross national income; BR = Beyond Ratings. b. Incom nd oth r indic s Th SDG Ind x, EPI, nd ND-GAIN Ind x r simil rl nd stron l corr l t d with GNI p r c pit . 84.7% SDG Ind x EPI 86.9% 91.0% ND-GAIN Countr Ind x Note: EPI = Environmental Performance Index (Yale); ND-GAIN = Notre Dame Global Adaptation Initiative; SDG = Sustainable Development Goals. Source: World Bank staff analysis. DEMYSTIFYING SOVEREIGN ESG >>> 11 12 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E E S . 2 “Wh l of Fortun ”: N twork n l sis of ESG provid rs’ m thodolo i s This n twork positions v rious ESG provid rs b s d on how oft n th ir t chnic l nd m rk tin docum nts cont in th t rms not d on th p riph r . Th thick r th chords, th mor oft n word is m ntion d. Whil r co ni in th limit tions of this d piction, w c n distin uish ISS, V.E, nd R pRisk s mor sp ci li d provid rs from th mor b l nc d provid rs MSCI, FTSE Russ ll/B ond R tin s, Rob co, nd Sust in l tics. environment ability inequa corr sustain labor u s ptio lity inc s t righ icie om de n im pol le ve e ofi pa lop go pr ct rt ve m s po rn rie en an nt re t ce u en co erg ia ter app y ISS cri anc e roa orm ch f per asse ssm e ent stat data V.E source Sust in bilit /SDG-r l t d Robeco weight group M thodolo ic l MSCI trend FTSE Russell/BR exposure D scriptiv Sustainalytics value l mode Fin nci l rati ber ngs num deb rk RepRisk t e wo ris fram or e k sc cr ch ed r it ea es inv r r to es ica co on tm mp ind cap ati ogy en busin m an managem orm index analysis t ital syste dol ies inf tho ess me ent Source: World Bank staff. Note: ESG = environmental, social, and governance; SDG = Sustainable Development Goals; BR = Beyond Ratings. Investors that engage in sovereign ESG outlays rely data, (4) incorporation of forward-looking scenarios and (5) extensively on ESG providers for data, methodology, unbiased from a country’s level of income (Gratcheva et al. or advice. Sovereign ESG scores are becoming part of the 2021). structural foundations for the investment industry, so it is critical that their methodologies are clear and transparent. This approach calls for more transparency in both The different approaches to measuring countries’ ESG sovereign ESG methodology and its data sources. While performance may appeal to different investment objectives, as this is true for all pillars, it is especially important for the E long as these differences are in fact representing measurable pillar. Given significant challenges in the quality and availability methodological differences. In contrast, the current sovereign of E data comparable across countries, new solutions and ESG scores converge due to the strong income component. approaches to measuring ESG are needed to provide a stronger data foundation as a critical input into sovereign ESG Current sovereign ESG scores are affected by structural investment decisions. Geospatial and wealth solutions are issues, such as the ingrained income bias and the lack promising and can address challenges of sovereign ESG that of clarity around the environmental pillar. Sovereign did not befall corporate ESG. At the same time, these novel ESG approaches need to evolve in line with growing data sources require technical expertise that is not always demand for better attribution to sustainability outcomes. A available. More research is needed to ensure that a new more transparent framework needs to include (1) clarity on generation of ESG scores is developed using these lessons investment objective (2) transparent methods, (3) improved and that these new ESG scores foster sustainability. DEMYSTIFYING SOVEREIGN ESG >>> 13 1. 14 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Introduction: Rise of Sovereign ESG Integration The shift to sustainable finance has been motivated by a growing demand for the financial sector to play a greater role in the transformation of the current economic model into a more sustainable one (Boitreaud et al. 2020). The introduction of the United Nation’s (UN) Sustainable Development Goals (SDG) and the Paris Agreement on climate change in 2015 have helped galvanize the societal shift to ensure a sustainable future and to fight climate change in particular. The pace of environmental, social, and governance integration,3 which has become the most prevalent form of sustainable finance, has accelerated over recent years. (This is illustrated by Figure 1.1 and is reflected in the results of a recent J.P. Morgan survey presented in Box 1.1.) The International Monetary Fund; Network for Greening the Financial System (NGFS);4 Organisation for Economic Co-operation and Development (OECD); the World Bank Group; and numerous policy, academic, and financial institutions have been documenting extensively how these changes affect the evolving financial sector ecosystem and investment decision-making processes across different asset classes (Boffo and Patalano 2020; CFA Institute 2020; NGFS 2021). The ESG ratings industry has emerged and grown considerably over the past decade in response to the market demand for sustainability-related data, evolving from specialized companies providing ESG-specific products to influencers of public debate on sustainable finance. ESG data providers have been offering a growing set of ESG-related data and scores, starting coverage initially with corporate entities before expanding in recent years into the sovereign space. This growth has been accompanied by a significant body of research on the ESG rating industry’s evolution, ESG methodologies, and the issue of whether integration contributes to improved sustainability (Escrig-Olmedo et al. 2019; Wong and Petroy 2020). Most research has focused, however, on ESG for corporate entities, which we will denote as “corporate ESG” in this paper. Box 1.2 presents the key points and relevant statistics on the current ESG data landscape from the latest report by NGFS (2021), and Box 1.3 also gives an overview of key findings related to corporate ESG. 3 Environmental, social, and governance integration is the practice of incorporating ESG-related information into investment decisions to help enhance risk-adjusted returns, regardless of whether a strategy has a sustainable mandate. 4 The Network for Greening the Financial System (NGFS) is a group of Central Banks and Supervisors to shre best practices and contribute to the development of the environmental and climate risk management in the financial industry and to mobilize mainstream finance to support transition toward a sustainable economy. NGFS produces regular reports on the progress to date on “greening” of the financial sector. DEMYSTIFYING SOVEREIGN ESG >>> 15 > > > F I G U R E 1 . 1 Sust in bl d v lopm nt inv stin sp ctrum Sust in bl D v lopm nt Inv stin ESG inv stm nt str t i s Imp ct inv stin Tr dition l inv stin ESG Positiv or N tiv int r tion & B st-Cl ss Sust in bilit th m d M rk t r t Conc ssion l Scr nin n m nt scr nin Inv st to Exclud Int r t S l ctin Inv st in Inv st with th int ntion to K m ximi ctiviti s or ESG f ctors b st th m s or n r t positiv , f tur s fin nci l industri s into p rformin ss ts m sur bl soci l nd r turns with cl rl inv stm nt comp ni s construct d nvironm nt imp ct r rdl ss of d fin d d cisions to cross round th lon sid fin nci l r turn ESG f ctors n tiv b tt r industri s in SDCs ( . ., imp cts m n risk t rms of w t r nd from n nd possibil sust in bilit nd r) inv stm nt nh nc p rform nc portfolio fin nci l ( . . rms) r turns R turn Fin nci l Soci l r turn & Soci l r turn & xp ct tion m rk t onl Fin nci l m rk t r t focus d m rk t sub-m rk t fin nci l r turn fin nci l r turn do no h rm Inv stm nt lik l to cr t positiv sust in bl d v lopm nt outcom s Imp ct Source: GISD Alliance 2020. Note: ESG = Environmental, social, and governance; SDGs = Sustainable Development Goals. Whereas credit ratings have been used by the market for a stand-alone sovereign ESG score product to complement its some time, ESG scores5 have emerged relatively recently existing sovereign credit ratings (Moody’s 2020). and are distinct from credit ratings, whose accuracy can be measured quantitatively against observed default Because there is no explicit definition of what sovereign rates. Credit rating agencies (CRAs) are a part of an issuer- ESG scores measure, a recent survey by J.P. Morgan driven market that has evolved over more than 100 years. on sovereign ESG offers insights into how the market Their primary focus is on assessing an issuer’s ability to perceives ESG scores (Oganes et al. 2021). The survey repay its debt obligation. In contrast, ESG providers—driven showed that 64 percent of respondents are interested to largely by investors—are not regulated and have considerable see quantification of material credit risks in sovereign ESG discretion in how to produce ESG products, though they have scores, 25 percent are interested in sovereign ESG measuring started to attract increasing scrutiny from regulators (Maijoor a country’s sustainability effort, and 9 percent are interested 2021). Although CRAs have been asserting that ESG-related in quantifying a country’s sustainability profile. Further, 78 risks have always been implicitly included in their credit rating percent believe that CRAs will have a larger role in emerging methodologies, in 2019 they started introducing explicit ESG market (EM) sovereign ESG ratings compared to ESG enhancements for their sovereign credit ratings. In 2019, providers, while 22 percent disagree. Also, 78 percent believe Fitch launched ESG Relevance Scores and began producing that improving sovereign ESG fundamentals will lead to lower research on how ESG factors affect individual credit rating sovereign credit risk, 16 percent are neutral, and 6 percent decisions (Fitch Ratings 2019). In early 2021, Moody’s released disagree somewhat. 5 ESG scores are also called ESG ratings. We use the term ESG scores to distinguish them from credit ratings produced by credit rating agencies. 16 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> B O X 1 . 1 Select findings from the market survey on ESG investing in the emerging market sovereign asset class by J.P. Morgan ESG investing: interest and implementation. Most respondents agree that client demand/fiduciary duty/mission statement are primary drivers. Sustainable Development Goals (SDGs)/Paris Agreement goals are not the primary drivers, and there is wide disagreement on the degree of their relevancy. In general, asset managers are either fully committed or not committed at all to environmental, social, and governance investing—a finding based on the amount of assets under management for emerging market (EM) ESG strategies. The vast majority of clients interested in sovereign ESG strategies are in Europe, 6 percent are in the United States, and 4 percent are in Asia-Pacific. In EM sovereign ESG strategies, 94 percent are pursuing ESG integration, 76 percent are pursuing exclusionary screening, and 50 percent are interested in engagement/stewardship (although half of respondents reported that they do not perform engagement with debt management offices enough and want to improve). In addition, 25 percent do sustainability-themed investing in specific themes or assets (such as clean energy, green technology, or sustainable agriculture). The response “1.5 or 2°C alignment and/or transition risk assessment” was the least relevant for EM sovereign debt investment strategy (selected by 16 percent of respondents). Responses showed that 70 percent of interested clients do not manage against ESG benchmarks while 30 percent do so. One-half of respondents expressed that the key issues with ESG benchmarks for them are ESG methodology and/or lack of industry standards, while data transparency is the key concern of 24 percent of respondents. Income bias. The most dominant primary concern about sovereign ESG investing is the income bias, expressed by 24 percent of respondents, followed by coverage (21 percent) and timeliness (20 percent). Concerns such as transparency, regional inconsistencies, methodology, and some others are far less prevalent as a primary concern. View on sustainability. Ranging from strongly to somewhat, 74 percent of respondents agree that sovereign ESG should support sovereign issuers that have the greatest sustainable development to accomplish rather than the best ESG scores, while 13 percent are neutral and only 11 percent somewhat disagree. Furthermore, 65 percent are interested in the reflection in sovereign ESG scores of a recent success or setback in sustainable developments rather than a long-term trend such as national income; 15 percent are neutral and 17 percent disagree. Asset managers do focus on sustainability. For 30 percent of respondents, it is integral to their sovereign ESG framework, 60 percent have a separate but complementary SDG framework, and 10 percent note that sustainability is separate from the ESG framework. Only 16 percent of respondents say their greatest concern is that sovereign ESG lacks intended real world impact, while 42 percent are primarily concerned with the lack of ESG standardization and 24 percent primarily with greenwashing. Relationship with credit rating agencies (CRAs)/credit risks. Only 25 percent of respondents want sovereign ESG to capture the quantification of a country’s sustainability effort and 9 percent want quantification of a country’s sustainability profile, while 64 percent want to see quantification of material credit risks in sovereign ESG scores. Further, 78 percent believe that CRAs will have a larger role in EM sovereign ESG ratings compared to ESG providers, while 22 percent disagree. Also, 78 percent believe that improving sovereign ESG fundamentals will lead to lower sovereign credit risk, 16 percent are neutral, and 6 percent disagree somewhat. Role of ESG providers. Only 17 percent of respondents produce proprietary sovereign ESG framework in house, while 71 percent have a combination of ESG providers and in-house producers, and 10 percent use EGS provider(s) exclusively. Further, 10 percent do not license any ESG providers, 24 percent license one provider, 30 percent license two providers, and 36 percent license three or more providers. Sustainalytics and MSCI are used by 33 percent of respondents, and no other provider in our sample came close to that number ESG pillars. Although 50 percent of respondents consider G as the most important pillar, only 2 percent consider E or S as the most important. Further, 35 percent consider pillars based on their materiality, and 11 percent consider them equally. Owing to data challenges, 70 percent of respondents underrepresent E pillar, 26 percent S pillar, and only 4 percent G pillar. Source: Oganes et al. 2021. >>> DEMYSTIFYING SOVEREIGN ESG >>> 17 > > > F I G U R E 1 . 2 Th structur of th sov r i n ESG fin nci l cosph r Fin nci l Int rm di tion Sovereign End investors issuers Credit rating ESG rating ESG index Asset Institutional agencies providers providers managers investors All issuers that Firms that rate Firms that Firms that Firms that - Owners receive an ESG issuers provide raw construct construct and duciary respon- who bear ESG rating ESG data and ESG indices market ESG sibilitites to the ultimate composite funds, ETFs, manage reward and ESG scores etc. assets risks Rules & requirements Ethical standard setters (such as regulators, supervisors) (such as WBG, UN, OECD, CBI, ICMA) Source: Boitreaud and others 2020. Note: CBI = Climate Bonds Initiative; ESG = environmental, social, and governance; ETF = exchange-traded fund; ICMA = International Capital Market Association; OECD = Organisation for Economic Co-operation and Development; UN = United Nations; WBG = World Bank Group. Despite significant progress in ESG investing in the fixed income have become mainstream (CFA Institute 2020). corporate bond and equity space, market participants Although investment managers had undertaken some form of continue to grapple with adapting this framework to the nonfinancial screening for sovereign bonds to various degrees, sovereign context. For example, investor engagement on since 2017 an increasing number of asset managers have ESG-related issues with companies has gone mainstream, started publishing white papers on their approaches to ESG while engagement with sovereigns has been more difficult and, investing in sovereign bonds, most major investment managers at times, politically sensitive. This sentiment, however, has having done so by 2020 (Boitreaud et al. 2020). Further, over been changing in the industry with the COVID-19 pandemic the past three years two index providers introduced ESG indices bringing a strong reminder of the importance of sovereign for sovereign bonds, which was a highly anticipated addition to ESG performance in shaping sustainable development globally a wide variety of ESG benchmarks in the equity space.7 and nationally. Many investors are increasingly realizing that sovereigns play a fundamental role in setting national policies— The scale and breadth of ESG-related issues that arise for including public health and environmental and sustainable governments versus corporate entities, as well as more infrastructure investment—that drive a country’s development complex transmission mechanisms for these issues to the and its response to crises, as well as in shaping international real economy, are among key challenges in advancing ESG agreements, such as the Paris Agreement and Sustainable integration for sovereign bonds compared to other asset Development Goals (SDGs). classes. Sovereign debt is a unique asset class. The sovereign issuer is fundamentally different from a corporate issuer because The sovereign ESG landscape has started to change with of its differing roles, scale, and incentive structures. As a result, notable developments across the industry over the past the external validity of empirical findings and mechanisms couple years (Figure 1.2). Sovereign bonds are the largest related to corporate ESG should not be assumed to apply to asset class,6 and expectations for ESG integration for sovereign sovereign ESG. 6 In 2019, the total outstanding value of global bond markets amounted to US$106 trillion, exceeding global stock market capitalization of US$95 trillion and US$21 trillion of bonds issued, compared to US$541 billion in new equity (SIFMA 2019). 7 In 2018, J.P. Morgan introduced a new class of ESG-tilted benchmark indexes for emerging market sovereign bonds, and in 2019, FTSE Russell introduced a climate-adjusted developed market sovereign bonds index (Boitreaud et al. 2020). 18 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT Because sovereign ESG is an underresearched area of • Sovereign ESG methodologies: What are the key tenets of ESG investing, the current vacuum has been filled with ESG data providers’ sovereign ESG score methodologies? heuristics and extrapolations from the more developed area What are their unique aspects, and how do these differences of corporate ESG. Through active dialogue with the industry manifest in respective sovereign ESG score products? participants and sustainable finance policy makers, outside of the narrow set of sovereign ESG specialists, we observed the With answers to these questions, we offer our perspective tendency to apply findings from corporate ESG to the sovereign on the current state of sovereign ESG by assessing the context. As an example, following several prominent studies major sovereign ESG providers that have provided the about divergence of ESG scores (Berg, Kölbel, and Rigobon foundation for the operationalization of ESG investing in 2019; Boffo and Patalano 2020), these conclusions have often sovereign fixed income markets. As an integral part of our been used for decision and policy development across the assessment, we engaged with various market participants to entire ESG field. Furthermore, most studies and industry papers share our preliminary results in order to seek their feedback on ESG data issues focus exclusively on corporate ESG, based on their specific practices and experiences. Our and ESG methodological and data challenges are analyzed discussions with these institutions revealed that many were exclusively from that prism. In this paper, however, we show aware of existing limitations of the sovereign ESG approaches that the data and methodological challenges for sovereign ESG and have been exploring ways to address them, as we discuss are substantively different and should not be conflated. in detail in this paper. These institutions welcomed efforts to raise these issues within the industry and provide solutions for To demystify sovereign ESG, we analyze methodologies sovereign ESG’s evolution to a more transparent framework and scores from the leading ESG providers, including FTSE that is better aligned with investors’ growing interest in a more Russell/Beyond Ratings, ISS, MSCI, RepRisk,8 Robeco purposeful investing in sovereign bonds. The companion (previously known as RobecoSAM), Sustainalytics, and V.E paper (Gratcheva et al. 2021) discusses key issues that need (previously known as Vigeo Eiris). Each data provider shared to be addressed for sovereign ESG to be better aligned with its sovereign ESG scores and internal methodology/analytical investors’ sustainability objectives and presents our perspective papers with us to help develop and advance the understanding on potential improvements going forward. of sovereign ESG issues in the industry. We have analyzed the methodologies using a variety of techniques to demystify The rest of this paper is organized as follows. Section 2 presents current approaches to sovereign ESG. These providers have our empirical results of sovereign ESG scores across ESG been open throughout the process to share their data and providers and contrasts our findings with comparable studies insights into sovereign ESG issues from their perspectives and of corporate ESG scores. Section 3 provides the main rationale to answer questions in structured questionnaires and follow- for convergence of sovereign ESG scores—a high level of up discussions. Appendix A provides details on the analytical relationship with a country’s national income—and its potential approaches we use for our study. policy implications. Section 4 presents the comparative summary of sovereign ESG methodologies, and section 5 concludes. We present results of our sovereign ESG study across individual providers and for the sovereign ESG segment as a whole to shed light on the following questions: • State of sovereign ESG scores: Is there an agreement among sovereign ESG providers on how to measure sovereign ESG, as well as sovereign E, S, and G pillars? How does this approach compare with the well-documented divergence among ESG data providers for corporate ESG scores? • Relationship of sovereign ESG scores to country income: What is the relationship between sovereign ESG scores and a country’s level of development, or national income? What are the important policy implications of that relationship? How does adjusting the sovereign ESG methodology for national income affect sovereign ESG scores? 8 Their product (RepRisk Country ESG Risk Index) does not have official E, S, and G pillars, but we processed it to create proxies based on ESG percentages for the purpose of comparability. DEMYSTIFYING SOVEREIGN ESG >>> 19 20 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> B O X 1 . 2 NGFS’s 2021 Sustainable Finance Market Dynamics Report: Role of ESG data providerss The recent report by the Network for Greening the Financial System (NGFS) on the state of the greening of the financial section distills key findings across a wide array of market participants and acknowledges the issues arising from the heterogeneity of green and sustainable finance definitions and the wide variety of approaches taken by many investors and financial institutions around the concepts of sustainability, or environmental, social, and governance. The NGFS report focuses in particular on the impact of ESG-related data in the transformation of the financial industry. The NGFS (2021) report describes the large private market for ESG data that has emerged to support investors and financial institutions. With the help of traditional and new technologies, ESG data providers offer a growing set of ESG data and scores for firms and, since relatively recently, sovereigns. Using information from the NGFS (2021) report accounts for some 150 ESG data providers, although the market has been consolidating and is currently dominated by a handful of players. Their products include climate data, analytics, advisory services, corporate and country ESG research and scores, alternative data on controversies, ESG portfolio monitoring, second opinions on compliance with bond principles, third-party assurance, certification and verification, and proxy-voting advisory services. The report also presents estimates by Foubert (2020) that annual spending on ESG data has grown by double digits since 2016 and is expected to reach US$1 billion in 2021. It also notes that the recently launched World Bank Sovereign ESG Data Portal makes quality sovereign ESG data for 139 countries across 67 ESG metrics publicly available. The NGFS (2021) report details the fast-moving landscape for ESG data for corporates. It is based on Escrig-Olmedo et al. (2019) and Wong and Petroy (2020) and notes that major ESG ratings providers cover around 4,000 to 22,000 firms, and as of 2018 more than 600 ESG ratings data products were offered in the market. It also presents recent findings by Boffo and Patalano (2020) on corporate ESG scores’ dispersion and low correlation that result from differences in methodologies among providers and lack of consistent, comparable, and reliable data—particularly in weights for E, S, and G factors within the total ESG scores for firms. The report also highlights the findings by Boitreaud et al. (2020) that, in contrast to ESG scores for corporates, sovereign ESG scores are highly correlated among major providers, with the exception of the E pillar. The NGFS (2021) report concludes that ESG scores lack transparency and face methodological challenges. One of the report’s recommendations (Takaway 3) states that “there is a need for credit as well as ESG rating providers to enhance transparency surrounding their methodologies, disclosing the criteria they use to assess the materiality of climate and sustainability factors, the manner in which these are measured and incorporated into ratings, and the weights they assign to them.” >>> DEMYSTIFYING SOVEREIGN ESG >>> 21 2. 22 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> State of Sovereign ESG: Empirical Analysis of Sovereign ESG Scores >>> Key questions 1. Is there agreement among sovereign ESG data providers on how to measure a sovereign’s ESG performance? 2. Does this measurement differ depending on the pillar? 3. How does this measurement compare with the well-documented divergence among ESG data providers for corporate ESG scores? >>> Our analysis reveals convergence of sovereign ESG scores among all providers. Within individual pillars, we find consistency among S and G pillar scores but a wide deviation among sovereign E pillar scores. The fact that sovereign ESG scores do not conform to the well- documented divergence of corporate ESG scores discussed in Box 1.3 was first introduced by Bouyé and Menville (2021). Their inquiry focused on, among other issues, the aggregate sovereign ESG level and found a high level of convergence for sovereign ESG scores with correlation ranging from 72 percent to 95 percent among the four sovereign ESG providers examined (Bouyé and Menville 2021). Motivated to understand the underlying causes of this convergence and the respective contributions of individual E, S, and G pillars, we pursued a more granular analysis. We also expanded the list of the participating ESG providers from four to seven to include ISS, Robeco, and V.E in addition to FTSE/Beyond Ratings, MSCI, RepRisk, and Sustainalytics, which were included in the original study. Table 2.1 presents the summary of the ESG providers in our study, Table 2.1 presents weighs for each ESG pillar, and Appendix D presents the key tenets of their respective ESG methodologies. DEMYSTIFYING SOVEREIGN ESG >>> 23 >>> B O X 1 . 3 Divergence of ESG scores for firms: Key drivers A number of studies have reached the common conclusion that the comparability of environmental, social, and governance scores for firms is low. These studies use different methods to explain the reasons behind divergence of ESG scores for firms. We present key findings from two academic studies that have been widely referred to in the industry. Aggregate Confusion (Berg, Kölbel, and Rigobon 2019). In their 2019 study, the authors from the Massachusetts Institute of Technology evaluate the contribution to divergence of ESG scores for firms by (a) a scope of attributes—that is, elements that make up the concept of ESG performance; (b) indicators that represent numerical measures of these attributes; and (c) an aggregation rule, or weights for these indicators, to derive the ESG score for a firm. They evaluate corporate ESG scores from five different ESG providers—Sustainalytics, Robeco, V.E, KLD, and ASSET4—and find that average correlations among their ratings are 61 percent, ranging from 42 percent to 73 percent. The average correlation of the environmental ratings is 65 percent, with social and governance ratings having the lowest correlations with an average of 49 percent and 38 percent, respectively. They then quantify different drivers of divergence between ESG scores and show that, on average, differences in (a) scope explain 44 percent of divergence, (b) measurement explain 53 percent, and (c) weights explain 3 percent. Hence, they conclude, raters disagree both on the definition of ESG and on the way the various aspects of ESG are measured. They also find that ESG ratings can be replicated with a dramatically reduced set of indicators and may point to potential redundancies. Exploring Social Origins in the Construction of ESG Measures (Eccles and Stroehle 2018). This study’s premise is that there are underlying reasons for technical differences among ESG providers and that differences in ESG scores for firms have to be seen as a function of diverse origins rather than simply of diverse measurement. This study considers contextual factors, such as diverse local environment and motives for creating these scores with the premise that external environment and internal organizational processes affect how a company responds to market demand. The authors conclude that concepts used for the creation of ESG data are socially constructed and, as a result, there is no objective right or wrong when measuring ESG performance for firms. The study also proposes a taxonomy for explaining various approaches for ESG scores for firms: those with a value-driven approach and those with a values-driven approach. The former approach’s objective is to inform the world (for example, about investment decisions) and the latter approach’s objective is to transform the world. This taxonomy offers a way to differentiate ESG providers by how they define sustainability (long-term financial performance versus a strategy to socially reform business) and materiality (materiality based on financial returns versus materiality as externality, based on benefits for the society as a whole). The authors view these differences in defining sustainability and materiality as driving the technical aspects of providers’ ESG methodologies—that is, what ESG indicators are being used to measure ESG and how these indicators are weighted and interpreted. >>> 24 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> T A B L E 2 . 1 Summary of ESG providers included in analysis Company Product Brief Description Number of countries and regions FTSE Russell/ Sovereign Risk The Sovereign Risk Monitor focuses on ESG factors that can be 146 Beyond Ratings Monitor quantifiably linked with sovereign credit risk. ISS ISS ESG Country ISS ESG Country Ratings assess the extent to which a sovereign 121 Rating issuer is positioned to successfully manage salient risks related to ESG themes such as climate change, biodiversity loss, human and labor rights violations, and as well as political and social instability. MSCI MSCI ESG ESG ratings reflect a country’s exposure to ESG risks weighed 198 Government against their management of those risks. Ratings RepRisk RepRisk Country The RepRisk Country ESG Risk Index (Country RRI) is not an 225 ESG Risk Index ESG rating. Instead, it quantifies business conduct risk exposure related to ESG Issues in a country. RepRisk screens 100,000+ media and stakeholder sources on a daily basis in 20 languages using artificial intelligence and machine learning technologies combined with human intelligence to provide data updated daily. The RRI is included in this study despite a difference in concept because it is an input to the J.P. Morgan JESG sovereign debt indexes. Robeco Country The country ESG score should provide the investor with an 150 Sustainability indication about whether a sovereign will be able and willing to Ranking honor its debt obligations. It provides an ESG assessment that can complement traditional sovereign risk analysis and provide investors’ another perspective on a country's long-term investment outlook. Sustainalytics, Country Risk The Country Risk Ratings measure a country’s ability to manage 172 a Morningstar Ratings its wealth (i.e. Natural and Produced Capital, Human Capital, Company Institutional Capital) through an ESG-lens. The score aggregates the country’s ESG Performance Score, Trends and Events. V.E Sovereign V.E places greater emphasis on sustainable development 180 Sustainability materiality. E, S, and G are equally weighted. ESG score Ratings balances two types of indicators: Commitment Indicators reflect a country’s level of commitment to the goals and principles that are outlined by major international agreements such as conventions and treaties. Results Indicators measure the effectiveness of a country’s sustainable development actions. Note: ESG = environmental, social, and governance; JESG = J.P. Morgan ESG. DEMYSTIFYING SOVEREIGN ESG >>> 25 > > > FIGURE 2.1 Composition of sov r i n ESG scor s b pill rs for m jor ESG provid rs On v r , E, S, nd G pill rs r pproxim t l qu ll w i ht d with sm ll mph sis on G. But th w i htin sch m s v r consid r bl mon ESG provid rs. Av r 29% 28% 43% V.E 33% 33% 33% Sust in l tics 15% 35% 50% Rob co 20% 30% 50% MSCI 25% 25% 50% ISS 50% 15% 35% FTSE Russ ll/ 30% 30% 40% B ond R tin s 0 10 20 30 40 50 60 70 80 90 100 Envirom nt l Soci l Gov rn nc Source: World Bank staff. Note: ESG = environmental, social, and governance. RepRisk is excluded because of its different methodology. The underlying data for sovereign ESG scores largely with correlation ranging from 69 percent to 98 percent across come from publicly available data sources provided individual providers as Table 2.2 presents. For illustrative by multilaterals, such as the World Bank, and large purposes, Table 2.3 also compares average correlations nongovernmental organizations, comprising up to 70 across individual pillars for our sovereign ESG analysis and the percent of data used by ESG providers (Herzog et al. findings by Berg, Kölbel, and Rigobon (2019) for corporate ESG 2020). Data providers have responded to the market demand scores, though not for the same set of ESG providers. It is quite for scoring that covers the entire universe of sovereign fixed notable that the E pillar has the highest correlation for corporate income issuers or potential issuers. Only a limited universe ESG scores, whereas it constitutes the lowest correlation for of underlying sovereign-level data sets provides sufficiently sovereign ESG scores. Similarly, while S and G pillars are broad country coverage over a time series. An analysis of the relatively low for corporate ESG scores, they are comparatively data in the World Bank’s Sovereign ESG Data Portal found high for sovereign ESG scores, as presented in Table 2.3. that data have significant lags and gaps (Herzog et al. 2020). Social and governance pillar data had a three-year median lag, Our results further highlight that there is little agreement while environmental pillar data had a five-year median lag.9 In on how to measure the sovereign E pillar among ESG sum, the key value proposition for sovereign ESG providers providers. In contrast to the relatively high level of correlation is in constructing a coherent methodology for aggregating for aggregate ESG scores, there is a markedly lower level of data and dealing with the challenges of gaps and lags in the correlation among E pillar scores. The E pillar has an average underlying data. correlation of 42 percent with aggregate ESG scores and ranges from -14 percent to 88 percent as presented in Table 2.4. The Our study also reveals that, on average, sovereign ESG lack of consensus on the E pillar highlights the difficulties that scores of the seven providers are highly correlated with investors and policy makers alike have in deciphering how the each other. Average correlation among providers is 85 percent different environmental issues contribute to sustainability. 9 The analysis was conducted in June 2020, and the years of lag are defined as the current year minus the last year of available data. Many key data sources, such as the World Bank’s Worldwide Governance Indicators, are updated in the second half of the year. 26 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> T A B L E 2 . 2 Correlation of aggregate sovereign ESG scores for major ESG data providers Provider ISS FTSE Russell/BR MSCI RepRisk Robeco Sustainalytics V.E ISS 0.80 0.77 0.80 0.85 0.78 0.87 FTSE Russell/BR 0.80 0.94 0.91 0.98 0.97 0.76 MSCI 0.77 0.94 0.86 0.95 0.93 0.75 RepRisk 0.80 0.91 0.86 0.91 0.86 0.69 Robeco 0.85 0.98 0.95 0.91 0.96 0.77 Sustainalytics 0.78 0.97 0.93 0.86 0.96 0.72 V.E 0.87 0.76 0.75 0.69 0.77 0.72 Source: World Bank staff. Note: BR = Beyond Ratings; ESG = environmental, social, and governance. >>> T A B L E 2 . 3 Illustrative comparison of ESG scores for companies versus sovereigns (percentage) The first column contains correlation figures from Berg, Kölbel, and Rigobon (2020) for corporate ESG scores. The second column presents the average correlations among seven sovereign ESG providers in our study: ISS, FTSE Russell/Beyond Ratings. MSCI, RepRisk, Robeco, Sustainalytics, and V.E. Provider Coporate ESG Sovereign ESG ESG 61% 85% Environmental 65% 42% Social 49% 85% Governance 38% 71% Source: World Bank staff. Note: ESG = environmental, social, and governance. >>> T A B L E 2 . 4 Correlation of sovereign Environmental scores for major ESG providers Provider ISS FTSE Russell/BR MSCI RepRisk Robeco Sustainalytics V.E ISS 0.18 0.05 0.23 0.41 0.32 0.63 FTSE Russell/BR 0.18 0.38 0.49 0.68 0.63 0.34 MSCI 0.05 0.38 -0.14 0.29 0.32 0.36 RepRisk 0.23 0.49 -0.14 0.74 0.72 0.29 Robeco 0.41 0.68 0.29 0.74 0.88 0.51 Sustainalytics 0.32 0.63 0.32 0.72 0.88 0.43 V.E 0.63 0.34 0.36 0.29 0.51 0.43 Source: World Bank staff. Note: BR = Beyond Ratings; ESG = environmental, social, and governance. More generally, our analysis also shows that the E pillar governance has the highest impact, followed by social factors, had a relatively low contribution to aggregate ESG scores. while finding that environmental performance appears to have This finding is consistent with multiple academic studies had no impact. Margaretic and Pouget (2018) find a similar lack that have had trouble empirically documenting the financial of evidence for the financial materiality of environmental factors materiality of environmental factors on sovereign debt markets. when looking at emerging market hard currency bonds. Kling Capelle-Blancard et al. (2016) examine ESG performance et al. (2018) do find evidence that climate-vulnerable countries and sovereign spreads in OECD countries. They find that pay a risk premium for debt, after controlling for relevant DEMYSTIFYING SOVEREIGN ESG >>> 27 >>> T A B L E 2 . 5 Correlation of sovereign S pillar scores for major ESG providers Provider ISS FTSE Russell/BR MSCI RepRisk Robeco Sustainalytics V.E ISS 0.88 0.90 0.70 0.92 0.88 0.87 FTSE Russell/BR 0.88 0.95 0.73 0.89 0.91 0.88 MSCI 0.90 0.95 0.77 0.91 0.94 0.89 RepRisk 0.70 0.73 0.77 0.76 0.76 0.72 Robeco 0.92 0.89 0.91 0.76 0.88 0.86 Sustainalytics 0.88 0.91 0.94 0.76 0.88 0.86 V.E 0.87 0.88 0.89 0.72 0.86 0.86 Source: World Bank staff. Note: BR = Beyond Ratings; ESG = environmental, social, and governance. >>> T A B L E 2 . 6 Correlation of sovereign G pillar scores for major ESG providers Provider ISS FTSE Russell/BR MSCI RepRisk Robeco Sustainalytics V.E ISS 0.90 0.86 0.42 0.91 0.88 0.70 FTSE Russell/BR 0.90 0.94 0.52 0.99 0.95 0.55 MSCI 0.86 0.94 0.49 0.94 0.90 0.53 RepRisk 0.42 0.52 0.49 0.53 0.48 0.30 Robeco 0.91 0.99 0.94 0.53 0.96 0.55 Sustainalytics 0.88 0.95 0.9 0.48 0.96 0.52 V.E 0.70 0.55 0.53 0.3 0.55 0.52 Source: World Bank staff. Note: BR = Beyond Ratings; ESG = environmental, social, and governance. macroeconomic variables. Notably, the academic literature on financial materiality with that of environmental materiality, which the financial materiality of environmental factors on sovereign may not always be aligned. In addition, many environmental debt is nascent, and studies tend to use different data, making risks materialize over a long time frame (with the notable them difficult to compare. Furthermore, as we show later in this exception of accelerating climate change risks)—that is, they paper, studies such as these use data sources that are likely to become financially material over time periods longer than be affected by ingrained income bias, predominantly reflecting most investors’ investment horizons (Carney 2015). Finally, countries’ level of development, or national income, rather than as the Bank for International Settlements recently highlighted underlying materiality of ESG-related factors. in The Green Swan (Bolton et al. 2020), environmental risks are nonlinear and are likely to be worse in the future than in Although our exploratory methods shed limited additional the past. Dealing with forward-looking risks requires models insight into the cause of divergence for the E pillar, our and assumptions that will increase the potential for divergent inquiry provides insights into plausible explanations: (a) outcomes even when using similar underlying data. environmental data lags, (b) nonalignment of financial and environmental materiality, and (c) the longer time horizon and In contrast to corporate ESG scores, sovereign ESG nonlinear nature of environmental risks. Gaps and lags for scores have a relatively high correlation for S and sovereign-level environmental data are particularly severe; the G pillars, 85 percent and 71 percent, on average, most comprehensive sovereign ESG data indicate they are respectively. Tables 2.5 and 2.6 present correlations across about five years (WWF and World Bank 2020). Further, the E individual providers. For the S pillar, RepRisk stands out pillar in particular appears to try to balance the measuring of with an average correlation of 74 percent with the other six 28 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT providers (Figure 2.5). Excluding RepRisk,10 sovereign S income. To better understand the nature of sovereign ESG pillar correlation increases to 89 percent. Similarly, for the scores, we use principal component analysis (PCA), a useful sovereign G pillar, RepRisk and V.E stand out for having a technique for finding structure in data sets of highly correlated lower correlation with other providers with 46 percent and variables. We find that for aggregate ESG scores, the first 53 percent, respectively. For the other five providers, the principal component explains nearly 90 percent of the variance average correlation is 92 percent (Figure 2.6). One-half of of the data set, which confirms the convergence across ESG the J.P. Morgan survey’s respondents consider G pillar as providers on sovereign ESG scores. This finding has two the most important for sovereign ESG, while only 2 percent intuitive interpretations. First, most of the information of the consider E or S pillar as the most important (Oganes et al. data set is related to one component. Second, we can use 2021). Further, 35 percent of respondents consider pillars on the first principal component as a representation of aggregate the basis of their materiality, and 11 percent consider them sovereign ESG scores for the ESG industry as a whole. equally. Owing to data challenges, 70 percent of respondents The other notable finding, in line with what we saw in the underrepresent E pillar, 26 percent S pillar, and only 4 correlation analysis, is that for the E scores, the subsequent percent G pillar. Finally, 70 percent of respondents use the principal components explain a more sizable amount of the World Bank’s Worldwide Governance Indicators11 for G pillar. variance. Details of PCA are included in Appendix B. In the next section, we explore the significant impact of national Sovereign ESG Scores are dominated by one specific income on sovereign ESG scores and determine whether this variable—a country’s level of development, or national impact can be mitigated. 10 As we mentioned earlier, RepRisk quantifies business conduct risk exposure related to ESG issues in a country, and it does not consider that a sovereign ESG rating. 11 World Bank, Worldwide Governance Indicators, https://info.worldbank.org/governance/wgi/. DEMYSTIFYING SOVEREIGN ESG >>> 29 3. 30 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Relationship of Sovereign ESG Scores to a Country’s National Income >>> Key questions 1. What is the relationship between sovereign ESG scores and a country’s level of development, or national income? 2. What are the important policy implications of that relationship? 3. How does adjustment of the sovereign ESG methodology for income affect sovereign ESG scores? >>> Our analysis reveals a high correlation between sovereign ESG scores and a country’s national income: 81 percent for aggregate ESG, 85 percent for S pillar, 70 percent for G pillar, and 51 percent for E pillar. Table 3.1 presents details for individual providers’ correlation with national income for each ESG pillar. At the aggregate sovereign ESG level and on average across different pillars, Sustainalytics has the highest correlation with national income, while V.E has the lowest. In our discussions with providers, some shared that they were aware of this phenomenon and indicated that they have been exploring adjustments in their methodologies. One provider expressed surprise at the level of the correlation with the national income because it said it had developed its methodology explicitly to address the relationship with a country’s level of development. These results suggest that the ingrained income bias is a possible explanation for the sovereign ESG convergence. Several studies have found that countries with high ESG scores tend also to rank high in income and development levels. This finding is not surprising because, among other reasons, high labor participation and access to electricity, political stability, rule of law, and forest depletion do not exist in a vacuum. These indicators are representative of a country’s long-term growth and development. In the case of ESG scores, investors are expecting them to capture some form of sustainability distinct from a country’s national income, creditworthiness, or credit ratings. Thus, sovereign ESG scores currently are affected by excessive representation of the country’s national income, which we term ingrained income bias DEMYSTIFYING SOVEREIGN ESG >>> 31 >>> T A B L E 3 . 1 Correlations of aggregate ESG and individual pillar scores with a country’s national income (GNI per capita) for ESG providers Factor ISS FTSE MSCI RepRisk Robeco Sustainalytics V.E Sovereign Russell/BR ESG ESG 0.68 0.91 0.84 0.78 0.89 0.95 0.60 0.81 E 0.07 0.74 0.10 0.79 0.82 0.83 0.23 0.51 S 0.86 0.88 0.90 0.75 0.85 0.94 0.79 0.85 G 0.77 0.84 0.77 0.37 0.85 0.93 0.39 0.70 Source: World Bank staff. Note: BR = Beyond Ratings; ESG = environmental, social, and governance. (IIB) (Gratcheva, Gurhy, and Wang 2021; Wang 2021), and with a country’s level of income. Appendix C describes the data this bias is ingrained into any type of cross-country analysis series and indexes that we included in our income analysis, that compares development-related indicators.12 Failure to and Figure 3.1 provides the correlation of these variables with account for the IIB leads to two important consequences: national income. • The income bias leads to perverse investment Income has long been recognized as a key factor driving outcomes: tilting investment portfolios towards higher ESG sovereign credit risk (Cantor and Packer 1996), and scores leads to rewarding rich countries for their prosperity. wealthier countries have greater resources to mitigate • The ingrainedness leads to disheartening policy ESG risks. For example, a wealthy country may have more incentives: policy efforts in the short run are unlikely to resources to reinforce critical infrastructure for more resilience affect a country’s income level, which is the result of decades to the rise of extreme weather events related to climate change. or centuries of development. Such investments in risk mitigation may help the country avoid disruptions to economic activity or the fiscal costs of rebuilding, Similar bias with potential for misallocation of capital both of which may be relevant to financial assessments of to wealthier countries at the expense of lower-income sovereign credit risk. countries has also been found in corporate ESG scores affected by a company’s size. Drempetic, Klein, and Zwergel Practitioners have been cognizant about the IIB and have (2020) find that current corporate ESG scores in Thomson proposed ways to adjust for its impact, in particular using Reuters’s ASSET4 database have a firm-size bias, wherein larger linear adjustment as discussed in Box 3.1. While noting that firms are more highly rated after controlling for other relevant this method has emerged as a practical solution in the industry, factors, and measures of sustainability. They explain that the we caution that its application has shortcomings: namely, that key mechanism driving this bias is that larger companies have a while expected to produce income-adjusted outputs (scores, greater capacity to supply ESG data compared to that of smaller index values, and so on), linearly adjusted values still exhibit companies. By implication, this bias means that the scores do not income bias. After decorrelating the scores with respect to measure corporate sustainability performance correctly and will gross national income (GNI) per capita, we observe that the not fulfill the goal to “reorient capital flows towards sustainable first principal component still explains more than 70 percent of investment in order to achieve sustainable and inclusive growth”. the total variation in the sovereign ESG scores (as compared Thus, similar to our finding in relation to ingrained income with 90 percent for the original sovereign ESG scores), and bias in sovereign ESG scores, the size bias in corporate ESG adjusted scores still seem to be in agreement regarding what scores leads to a perverse policy incentive from misdirecting ESG measures, even after eliminating income. Figure 3.2 capital flows intended for greater sustainability impacts. presents a scatter matrix for original sovereign ESG scores of the ESG providers, while Figure 3.3 presents the sovereign We further find that other commonly used measures of ESG scores adjusted by us for national income. Although this sustainable development and resilience also demonstrate a approach is intuitively appealing, our review of approaches similar strong relationship with a country’s level of income, presented in Box 3.1 led us to conclude that they do not that is, they are subject to ingrained income bias. We necessarily lead to the desired outcome. In some cases, they examined the SDG Index, the Yale Environmental Performance also rely on third-party data or subjective inputs or both that, Index (EPI), and the Notre Dame Global Adaptation Initiative as we have shown, may also be subject to biases and are (ND-GAIN) Country Index and found a similar high correlation impossible to reproduce independently. 12 In econometric terms, these types of analyses suffer from endogeneity, or, specifically, omitted variable bias. See Wang (2021) for an in-depth discussion. 32 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E 3 . 1 Corr l tions of ESG-r l t d ind x s with countr ’s n tion l incom (GNI p r c pit ) Th E, S, nd G individu l nd combin d ESG scor s r corr l t d with GNI p r c pit to v r in d r s, d p ndin on th ESG provid r. Th S scor s r most corr l t d with littl v ri tion cross provid rs, whil th E scor s r l st corr l t d with l r discr p nci s mon provid rs. . Incom nd sov r i n ESG scor s of six provid rs A r t E Scor 58.5% l M BR Su b s isk s ris I tic in SA SC R ll/ Ei (m n) Ro R p M st co o ss Ru Vi SA s SE s o in M tic FT sc t ris b dR l isk Ei in Ro on pR o I st SC A r t G Scor Su Vi B R M 69.8% (m n) s Su nd R M s in in tic o SA t ris l o isk Ei B sc o pR I SC st b Ro A r t ESG Scor Vi M R 83.2% (m n) s > > > M nd AM tin s tic o S ris SC R B sco l isk Ei in pR o Su I b st Ro A r t S Scor Vi R 85.2% (m n) Note: E = environmental; G = governance; S = social; GNI = gross national income; BR = Beyond Ratings. b. Incom nd w lth ccountin N tur l c pit l (tot l, lo 10) 14.0% N tur l c pit l (p r c pit ) 31.4% Hum n c pit l (tot l, lo 10) 59.0% Produc d c pit l (tot l, lo 10) 66.7% Hum n c pit l (p r c pit ) 78.3% Produc d c pit l (p r c pit ) 81.1% Note: Natural, produced, and human capital figures are presented in total (transformed with base-10 logarithm) or per capita nu mbers. Natural capital is least correlated with GNI per capita. c. Incom nd oth r ind x s 84.7% SDG Ind x EPI 86.9% 91.0% ND-GAIN Countr Ind x Source: World Bank staff. Note: EPI = Yale Environmental Performance Index; ND-GAIN = Notre Dame Global Adaptation Initiative; SDG = Sustainable Development Goals. The SDG Index, Yale EPI, and ND-GAIN Country Index are similarly and strongly correlated with GNI per capita. DEMYSTIFYING SOVEREIGN ESG >>> 33 >>> BOX 3.1 Existing methodologies for sovereign ESG income adjustment Some academic and market practitioners have dealt with ingrained income bias by adjusting for GDP per capita. The key argument given is that because these indices are highly correlated with national income, countries should be judged on the basis of whether they outperform or underperform for their income level. We present a sample of approaches based on publicly available sources. Notre Dame Global Adaptation Initiative (ND-GAIN) Country Index: The ND-GAIN Country Index measures a country’s vulnerability to climate change along with its readiness to improve resilience. The creators of the index note in the methodology document that despite purposefully excluding GDP per capita or any closely related measures, the final index is highly correlated with a country’s level of income. As a result, they create a version that adjusts the index scores for a country’s GDP per capita. Figure B3.1.1 presents ND-GAIN Country Index and ND-GAIN Country Index GDP-adjusted scores and the impact of the adjustment on countries. The adjusted version still exhibits a much weaker but still discernable income bias. > > > F I G U R E B 3 . 1 GDP djustm nt for th ND-GAIN Countr Ind x P n ls nd b show th ND-GAIN Countr Ind x nd th GDP- djust d v rsion, r sp ctiv l . Both ind x s r plott d inst GNI p r c pit . P n l shows stron incom ff ct. P n l b still xhibits w k incom ff ct ft r th GDP djustm nt. In ddition, th djustm nt l ds to som hi h-incom countri s (from low to hi h: Q t r, Kuw it, Lux mbour , Sin por , Ir l nd, Unit d Ar b Emir t s, nd S udi Ar bi ) to t k on si nific ntl low r v lu s th n th ir incom p rs. . ND-GAIN Countr Ind x b. ND-GAIN Countr Ind x, GDP djust d 10 70 0 60 -10 50 -20 40 -30 30 -40 -2 -1 0 1 -2 -1 0 1 GNI p r c pit GNI p r c pit Low incom Low r middl incom Upp r middl incom Hi h incom Source: World Bank staff. Renaissance Capital: In a 2018 report, Robertson and Lopez (2018) construct and analyze sovereign ESG scores. Noting the strong relationship between environmental, social, and governance scores and national income, they calculate overperformance and underperformance of a country versus its GDP per capita. They comment that many emerging markets have better ESG standards than developed countries did when those countries were at similar income levels. Robertson and Lopez (2018) argue that for investors wishing to improve ESG outcomes, emerging and frontier markets offer the greatest opportunity, despite having lower ESG scores. In contrast, investing in countries that are already wealthy and have high ESG scores promises very little future improvement. They also comment that for unadjusted ESG scores, there is a relationship with a country’s ESG scores. They further conclude that once per capita GDP is removed, the correlation with ESG effectively disappears. >>> 34 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> BOX 3.1 continued Morgan Stanley Investment Management: In a 2019 white paper, Caron and Emery (2019) note the challenge of finding a standardized methodology to benchmark ESG factors among a broad array of issuers across the wide spectrum of their level of development. They argue that standard ESG scores are systematically biased against developing countries and propose an adjustment framework: (a) adjust scores based on GDP per capita; and (b) adjust scores based on a momentum factor, consisting of two parts. The first part is provided by a third party and is based on momentum in the existing data. The second part is a momentum factor based on an MSIM (Morgan Stanley Investment Management) sovereign analyst’s subjective analysis of the ESG developments in a country over the past 12–18 months. They argue that this focus on change at the margin aligns better with investors’ sustainability goals and provides a more interesting opportunity set for investing. HSBC Global Research: In a 2020 report, Kini, Phippen, and White (2020) [[AQ: Add missing entry to references]] analyze emerging market local currency bond portfolios using two sovereign ESG scores, one adjusted for GDP per capita and one not adjusted. They explore a strategy for optimizing impact by treating risk, return, and ESG as three separate dimensions. They perform a portfolio optimization exercise that concludes that both sovereign ESG score–constrained approaches can achieve excess returns close to an unconstrained portfolio optimization. Given this finding, they favor the income-adjusted approach to ESG because it increases the ESG level of the portfolio, without penalizing poor issuers in favor of richer issuers or sacrificing much excess return. >>> DEMYSTIFYING SOVEREIGN ESG >>> 35 > > > F I G U R E 3 . 2 Sc tt r M trix for Sov r i n ESG Scor s FTSE Russ ll/BR MSCI Rob co R pRisk Sust in l tics V.E FTSE Russ ll/BR MSCI Rob co R pRisk Sust in l tics V.E Incom roup Low Low r Middl Upp r Middl Hi h Source: World Bank staff. Note: ESG = environmental, social, and governance; BR = Beyond Ratings. 36 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E 3 . 3 Sc tt r M trix for Sov r i n ESG Scor s, Lin rl Adjust d for Incom FTSE Russ ll/BR MSCI Rob co R pRisk Sust in l tics V.E FTSE Russ ll/BR MSCI Rob co R pRisk Sust in l tics V.E Incom roup Low Low r Middl Upp r Middl Hi h Source: World Bank staff. Note: ESG = environmental, social, and governance; BR = Beyond Ratings. DEMYSTIFYING SOVEREIGN ESG >>> 37 Our analysis reveals that income-adjusted sovereign ESG and per capita income: environmental pressure increases scores of the ESG providers still exhibit nonlinear income income rises and then it decreases. The environmental bias. A closer look at the orthogonalized ESG scores reveals Kuznets curve (EKC) is a hypothesized relationship between a that although the linear dependency between income category country’s income and its level of environmental degradation. As and ESG scores has been removed, the new scores exhibit a a country begins to industrialize, its activity also begins to take a U-shaped relationship, or nonlinear relationship. Both high- and toll on the environment. The EKC postulates the existence of a low-income countries score high on the orthogonalized ESG turning point, beyond which further economic growth begins to scale, while middle-income countries score the lowest. Figure halt and reverse environmental degradation. Stronger regulation 3.4 presents original (panel a) and income-adjusted sovereign and better technologies are possible reasons for this reversal. ESG scores (panel b) for sovereign ESG providers collectively, The empirical validity of the EKC is, however, widely debated. and Figure 3.5 presents both original and adjusted scores for The reversal is particularly questionable when environmental individual providers. This U-shaped relationship resembles the degradation is irreversible, such as resource exploitation or loss environmental Kuznets curve (Figure 3.6). of biodiversity. Further research is needed to understand the nonlinear impact on sovereign ESG scores and its relation to Grossman and Krueger (1991) postulate an inverted, the EKC. U-shaped relationship between environmental degradation > > > F I G U R E 3 . 4 Sov r i n ESG scor s nd sov r i n ESG incom - djust d scor s for ESG provid rs coll ctiv l P n ls nd b show th b for nd ft r of r r ssion-b s d incom djustm nt, r sp ctiv l . Althou h this incom djustm nt m ch nic ll r mov s th lin r incom bi s, p n l b shows th t nonlin r incom bi s still r m ins. . Ori in l scor xhibit lin r incom bi s b. Incom limin tion r v ls qu dr tic incom bi s ESG Scor Av r GNI p r c pit GNI p r c pit Lin r Nonlin r Low Low r Middl Upp r Middl Hi h incom bi s incom bi s incom incom incom incom Source: World Bank staff. Note: ESG = environmental, social, and governance. 38 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E 3 . 5 Sov r i n ESG scor s b for nd ft r incom djustm nt Incom djustm nt is conduct d h r b ortho on li in ESG scor s with r sp ct to GNI p r c pit . FTSE Russ ll/B ond R tin s b. MSCI c. R pRisk ESG scor s ( −scor ) d. Rob co . Sust in l tics f. V.E Lo GNI p r c pit St nd rd ESG scor Incom djust d ESG scor Source: World Bank staff. Note: ESG = environmental, social, and governance. > > > F I G U R E 3 . 6 Enviornm nt l Ku n ts Curv This st li d fi ur d scrib s th m in ch r ct ristics of th h poth si d nvironm nt l Ku n ts curv . Th inv rt d U-sh p p ks for industri li d conomi s th t h v th l r st n tiv ff ct on th nvironm nt. Middl incom l v ls Environm nt l d r d tion Low incom l v ls Hi h incom l v ls Composition Sc l ff ct nd t chniqu ff ct Pr industri l conom industri l conom Post industri l conom Incom l v ls Source: Sarkodie and Streznov (2018). DEMYSTIFYING SOVEREIGN ESG >>> 39 4. 40 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Comparing Sovereign ESG Score Methodologies >>> Key questions 1. What are the key tenets of the sovereign ESG score methodologies of ESG data providers? 2. What are their unique aspects, and how do these differences contribute to differences in respective sovereign ESG scores? >>> Investors that engage in sovereign ESG investing rely extensively on ESG providers for data, methodology, or advice. Based on the recent J.P. Morgan survey, 71 percent of respondents use a combination of ESG providers and in-house analytics, and 10 percent use one or more ESG providers exclusively. While 10 percent of respondents do not license any ESG providers, 24 percent license one provider, 30 percent license two, and 36 percent license three or more. Sustainalytics and MSCI are used by one-third of respondents, and no other provider in our sample came close. These figures suggest that (a) the sovereign ESG industry is still evolving and (b) the investment community navigates the lack of clarity among the providers’ sovereign ESG approaches by augmenting the third-party sovereign ESG scores with their own proprietary analytics and/or subscriptions to several ESG providers. The objective of our research is to better understand the structure of the sovereign ESG landscape and the way various providers relate to each other. Because our analysis reveals broad similarity of sovereign ESG scores of these ESG providers, we seek to understand specific nuances of their methodologies and their impact on aggregate ESG scores, individual pillar scores, or both. More broadly, our objective is to be able to categorize ESG providers on the basis of their unique characteristics, such as labeling corporate ESG providers as value versus values approaches (Eccles and Stroehle, 2018). Regarding sovereign ESG, this analysis is challenging given the high level of convergence of sovereign ESG scores across providers. Furthermore, analysis of individual methodologies is hampered by significant differences across providers on the type of documents and/or the level of granularity of their individual methodologies disclosed to us. Thus, given the nature of the sovereign ESG data and information, we performed two high-level quantitative analyses: (a) a cluster analysis DEMYSTIFYING SOVEREIGN ESG >>> 41 of sovereign ESG scores and (b) a simple natural language (that is, objectively using a purely analytical approach) without processing (NLP) analysis of providers’ technical and marketing comparing scoring methodologies. The NLP analysis is not documents. As a data-driven approach, clustering analysis aims entirely agnostic about the scoring methodology, because at organizing ESG providers within the least number of groups some input documents contain methodological descriptions. As (that is, clusters) that describe similarities among providers in a result, the NLP output is highly dependent on the types of our sample. This method compares ESG scoring outcomes documents used as inputs and should be interpreted cautiously. > > > F I G U R E 4 . 1 Clust rs of ESG provid rs for sov r i n ESG nd individu l pill rs Clust r n l sis tt mpts to form roups b s d on how simil rl ESG provid rs scor individu l countri s. Provid rs (blu box s) r conn ct d to clust r (color d bubbl s) if th t nd to scor simil rl th 133 countri s n l d. If th r is si nific nt dis r m nt mon provid rs, th n ch provid r forms its own clust r or bubbl (for x mpl , E pill r). . ESG scor b. Environm nt l scor V.E FTSE Russ ll/B ond R tin s Sust in l tics R pRisk R pRisk E FTSE Rob co ESG Russ ll/B ond R tin s MSCI Sust in l tics V.E Rob co MSCI c. Gov rn nc scor d. Soci l scor V.E FTSE R pRisk Russ ll/B ond R tin s Rob co MSCI G FTSE Russ ll/B ond S R tin s R pRisk Sust in l tics MSCI V.E Sust in l tics Rob co Source: World Bank staff. Note: ESG = environmental, social, and governance. 42 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E 4 . 2 Word clouds for ESG providers’ sovereign ESG methodologies Th word clouds r construct d b n l in word fr qu nci s xtr ct d from v rious t chnic l nd m rk tin docum nts. Th n l sis includ s prop r nouns onl . Th l r r th font, th mor fr qu ntl th word occurs. FTSE Russ ll/B ond R tin s MSCI R pRisk Rob co Sust in l tics V.E Source: World Bank staff. The clustering analysis reveals similar groupings for ESG, Robeco is also part of this large cluster, except for S scores. S, and G scores but finds little structure for E scores. Figure RepRisk and V.E, in contrast, form their own groups. When 4.1 shows how the six ESG data providers (blue boxes) are there is a lack of consensus on how to score individual countries, grouped into three to five clusters (colored bubbles), depending ESG providers tend to form separate groups (clusters), as is the on the ESG pillar.13 The clustering analysis relies exclusively case for the E scores. on the ESG scores across 133 countries. ESG providers are attached to the same cluster or bubble when they tend to assign This finding is mainly explained by the ingrained income similar scores to individual countries. For the aggregate ESG, S, bias and common input data used by ESG providers to and G score, we find that MSCI, FTSE Russell/Beyond Ratings, produce sovereign scores. As the correlation tables presented and Sustainalytics tend to assess countries in a similar fashion. in the previous sections have shown, the ingrained income bias 13 ISS was not included in this exercise because it had a smaller number of covered countries in its 2017 data. DEMYSTIFYING SOVEREIGN ESG >>> 43 most strongly influences the G scores and, to a lesser degree, significantly often in the content of at least two providers. the S scores. Therefore, if the IIB permeates G and S scores, it This approach excludes, however, terms like wealth, which is not surprising to find higher degrees of convergence among features prominently in the word cloud of Sustainalytics, whose ESG providers. In addition, we also recognize the role of framework revolves around World Bank wealth data (described common input data sources. Convergence among G scores is in Appendix C). likely due to the reliance on the World Governance Indicators (WGI), which are widely used as inputs, because 70 percent While acknowledging limitations of this analysis, we find of survey respondents noted its use in their sovereign ESG that the network output provides helpful insights about analyses. In contrast, there is much less convergence across E the uniqueness of ESG providers. The network result, which scores. The cluster analysis results paint a fragmented picture; we refer to as a “wheel of fortune”, is presented in Figure almost all ESG data providers form their own clusters. The 4.3. It reveals that MSCI, Robeco, and FTSE Russell/Beyond only exceptions are Robeco and Sustainalytics, which tend to Ratings are closely related providers, as shown by their center resemble each other in their E scoring results. positions in the network of four different categories of our inquiry (financial, methodological, SDG-related, and descriptive). In Using product brochures and technical methodology contrast, ISS, V.E, and RepRisk are uniquely positioned within documents provided to us by the seven ESG providers, the network in relation to those categories. Sustainalytics is we conducted a simple word frequency analysis. Figure 4.2 the only provider that uses the World Bank wealth accounting presents a word cloud—a visual display of word frequencies framework (Lange, Cust, et al., forthcoming; Lange, Wodon, in an ESG provider’s documents. After selecting the set of et al. 2018),14 which differentiates it from other sovereign ESG documents with the most comparable information across data providers. ISS appears more focused on SDG-related providers, we counted all proper nouns that were pertinent materiality. RepRisk quantifies the corporate reputational risk to our analysis and grouped them in Figure 4.3 into financial of operating in a country rather than a measure of a country’s (orange), methodological (purple), SDG-related (red), and ESG score, and its position within the network is oriented descriptive (green) terms. We included only terms that occur toward financial materiality. 14 Wealth accounting data are available at World Bank, Wealth Accounting (database), https://datacatalog.worldbank.org/dataset/wealth-accounting. 44 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E 4 . 3 “Wh l of Fortun ”: N twork n l sis of ESG provid rs’ m thodolo i s This n twork positions v rious ESG provid rs b s d on how oft n th ir t chnic l nd m rk tin docum nts cont in th t rms not d on th p riph r . Th thick r th chords, th mor oft n word is m ntion d. Whil r co ni in th limit tions of this d piction, w c n distin uish ISS, V.E, nd R pRisk s mor sp ci li d provid rs from th mor b l nc d provid rs MSCI, FTSE Russ ll/B ond R tin s, Rob co, nd Sust in l tics. environment ability inequa corr sustain labor u s ptio lity inc s t righ icie om de n im pol le ve e ofi pa lop go pr ct rt ve m es po rn tri en an re n t ce ou en c erg ia ter app y ISS cri anc e roa orm ch f per asse ssm e ent stat data V.E source Sust in bilit /SDG-r l t d Robeco weight group M thodolo ic l MSCI trend FTSE Russell/BR exposure D scriptiv Sustainalytics value m odel Fin nci l er rati b ngs num deb w ork RepRisk t me ris fra o re k sc cr h ed a rc it se inv re r to es ica co on tm mp ind cap ati ogy en busin m an managem orm index analysis t ital syste dol ies inf tho ess me ent Source: World Bank staff. Note: ESG = environmental, social, and governance; SDG = Sustainable Development Goals; BR = Beyond Ratings. A robust taxonomy of sovereign ESG methodologies is a depend strongly on the types of input documents used in the useful first step toward clearly defining unique features of process. Although we select documents as similar in nature individual providers and the network output. The limitation of as possible, a significant degree of heterogeneity remains the network analysis will need to be addressed with a deeper and because there are no standard disclosure requirements for ESG more comprehensive examination to obtain more informative providers. Finally, some of the terms could arguably belong to and conclusive results, because the network analysis results more than one of the four colored categories (such as “value”). DEMYSTIFYING SOVEREIGN ESG >>> 45 5. 46 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Conclusion Sovereign ESG has evolved organically from ESG developments in other asset classes, such as equities and corporate bonds, and uses intuitive analytical approaches, such as linear income adjustment. Through our empirical analysis, we find that these developments have led to the state of sovereign ESG that does not necessarily result in intended or desired outcomes for investors interested in more transparent and measurable sustainable investment opportunities. Furthermore, the current sovereign ESG approach is affected by perverse incentives when higher-income countries are rewarded at the expense of lower-income countries, which have larger SDG funding gaps and offer the most potential for making ESG-related improvements. Our analysis reveals that sovereign ESG is a distinct segment within the ESG landscape and that sovereign ESG challenges are distinct from those of corporate ESG. First, the ingrained income bias permeates the sovereign ESG segment overall, as well as sovereign G and S pillars. Second, the sovereign E pillar is the most challenging, which is consistent with the challenging and opaque data landscape of underlying environmental variables. The dominating effect of the ingrained income bias deprives G and S scores of their pillar-specific information, while divergence among E scores casts doubt on the pillar as it has been treated. Currently, practical solutions adopted by select practitioners to adjust income rely on subjective inputs, assumptions, and nontransparent adjustment to existing data gaps and lags. This approach renders the resulting scores difficult to interpret and compare, presenting significant challenges to investors interested in measurable sovereign ESG outcomes. Sovereign ESG scores are becoming part of the structural foundations for the investment industry, and thus it is critical that their methodologies are clear and transparent. The different approaches to measuring countries’ ESG performance may appeal to various investment objectives, as long as these differences represent measurable methodological differences. The challenge with the current sovereign ESG framework is the ESG score convergence because of the structural reasons: the ingrained income bias and the lack of clarity around the E pillar. Evolving sovereign ESG framework needs to acknowledge these shortcomings, and the methods used to achieve a more transparent framework need to account for (a) explicit investment objectives, (b) transparent methodology and data, (c) explicit forward-looking ESG factors, and (d) adjustment of income bias (Gratcheva et al. 2021). DEMYSTIFYING SOVEREIGN ESG >>> 47 This approach calls for more transparency in both specific challenges of sovereign ESG, which did not befall sovereign ESG methodology and data sources. While corporate ESG. At the same time, these novel data sources this is true for all pillars, it is especially important for the E require technical expertise that is not always available. More pillar. New solutions and approaches to measure ESG are research is needed to ensure that a new generation of ESG needed and must be grounded in a stronger data foundation. scores are based on these lessons and lead to ESG scores Geospatial solutions are promising and could potentially meet that foster sustainability. 48 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Appendix A Research Methodology Treatment and standardization of data We standardized the direction of the scores for ease of interpretability. For most data providers, higher scores indicate better environmental, social, and governance performance. For those providers whose lower scores indicate better ESG performance, we changed the direction of the scores by subtracting the score from the maximum score. This approach eases interpretability by ensuring that high scores always indicate better ESG performance for all data providers. Most of the data providers have annual data. We used the 2017 scores because most ESG data providers could more easily provide data pro bono for the study if the data were sufficiently lagged from their current offering. For the purpose of the study, it is important that data have a common base year. Some data providers have subannual data. We aggregated subannual data to annual data using its simple average. For the ESG pillars, we worked with data providers to determine the most appropriate scores to use, because each data provider has a different data taxonomy. RepRisk quantifies corporate reputational risk of operating in a country, and it does not consider that factor an ESG score. Despite the differences, that factor is included because it is an input into J.P. Morgan’s ESG tilted sovereign debt indexes and it maintains consistency with analysis (Bouyé and Menville 2021). RepRisk does not have official E, S, and G pillars either, but we processed it to create proxies for the sake of comparability. Finally, using z-scores, we centered and scaled the data using a group of 133 countries that had scores available from all data providers. Countries covered by only a subset of providers include many countries that are outliers in some respect, such as small island nations, active conflict zones, or low-income countries. This analysis also made use of other data sets. The country coverage for those data sets, such as the World Bank’s wealth accounting data, resulted in an overlapping group of 123 countries. For income data, we used the natural log value of per capita income. Using a log transformation better reflects the nonlinear nature of income. There is a substantial difference between a country increasing from US$5,000 to US$10,000 per capita versus increasing from US$45,000 to US$50,000. When we compared changes in national income, we used the changes in log per capita income. Z-scores were then calculated from these log per capita values. DEMYSTIFYING SOVEREIGN ESG >>> 49 Methods ratings about the score of a country. The first component aimed at summarizing as much information as possible across all Correlation. The initial analysis aimed to explore to what ratings. The second component summarized the information extent sovereign ESG providers agree with each other in their that was left unexplained by the first component, and so on. aggregate ratings and in their assessments of the E, S, and The components were constructed in such a way that they G pillars. The most straightforward process is to calculate the were uncorrelated to each other. If ESG ratings are largely in correlation of scores among data providers. These correlations agreement, we would expect the first component to explain most among providers are then aggregated using simple averages of the variance. If ESG ratings are in complete disagreement, the to show the level of pairwise agreement about the aggregate individual components would have similar shares of explained scores and the pillar scores. However, the pairwise nature of variance. correlations does not imply overall agreement. The principal component analysis (PCA) approach is better suited to measure Cluster analysis. Cluster analysis is a type of unsupervised this correlation. machine learning method that aims at finding as few clusters or groups as necessary to describe the data. The K-means Principal component analysis. Next, we used PCA as a approach separates the data into K disjoint clusters that dimensionality reduction tool. On a practical level, PCA allowed minimize the inertia criterion. We chose the number of clusters us to quantify how much of the overall variance of a data set using common heuristics. The colors of the clusters are is explained by a single principal component. In our case, the arbitrary and should not be compared among E, S, G, and variance stems from the level of disagreement among ESG ESG scores. 50 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Appendix B Principal Component Analysis of Sovereign ESG Scores Principal Component Analysis (PCA) is a technique for dimensionality reduction that can be useful for finding structure in data sets of highly correlated variables. For each variable examined— aggregate environmental, social, and governance, E, S, and G scores—we used the six data providers.15 We then examined the principal components against other sustainability-related variables. These variables include elements of the World Bank’s wealth accounting framework, which attempts to provide a more holistic view of national wealth that includes natural capital and human capital. We also included some commonly used environmental and sustainability indices. A full description of selected data and indices is included in Appendix C. Finally, we included a traditional financial measure of national income—that is, GNI per capita. This exercise is meant to be an initial exploration of key relationships in terms of both additional variables and analytical techniques. The correlation analysis in figures 3.2 and 3.3 included 133 countries that have ESG scores from all six data providers.16 Ten countries from this sample did not have World Bank wealth accounting data, so the sample size for the PCA analysis is 123 countries. All the ESG data and the supplementary variables were centered and scaled into z-scores on the basis of this 123-country sample. We calculated the variance explained by each principal component for each of the data pillars presented in Figure B.1. Because there are six data providers, there are six principal components. We found that for aggregate ESG scores, the first principal component explains nearly 90 percent of the variance of the data set. The second principal component explains only 6 percent of the variance of the data set. Intuitively, these findings mean two things. First, most of the information of the data set is related to one component. Second, we can confidently use the first principal component to describe aggregate ESG scores in general, particularly if we also keep the bivariate correlation plots in mind. The other notable finding, in line with what we saw in the correlation analysis, is that for the E scores, the subsequent principal components explain a more sizable amount of the variance. 15 FTSE Russell/Beyond Ratings, MSCI, RepRisk, Robeco, Sustainalytics, and V.E are included in PCA, while ISS was excluded because its sovereign scores cover fewer countries for the 2017 data. 16 ISS was not included in the PCA analysis because it has only 55 overlapping countries for the 2017 data used. DEMYSTIFYING SOVEREIGN ESG >>> 51 The first principal components of aggregate ESG scores and ND-GAIN (Notre Dame Global Adaptation Initiative) Country all the pillars are highly correlated with GNI per capita, ranging Index, suggesting that they are measuring a similar concept from) 83 percent to 91 percent. The aggregate ESG scores of development compared to a simpler financial measure like have an average correlation of 89 percent, as presented in GNI per capita, as presented in Figure B.3. Figure B.2. Interpretation of subsequent principal components. Most notable is that many of the other measures of Although the interpretation of the first principal component is sustainability or augmented measures of income examined clear, we are able to find no policy-relevant findings for the display the same high correlation with GNI per capita. This subsequent principal components for each pillar using PCA finding suggests that a better interpretation is to say that analysis only. We focus most closely on the E pillar, because all these variables measure an unobserved variable of the subsequent principal components contain a greater development. The first principal component of ESG scores amount of information. has the highest level of correlation, 95 percent, with the > > > F I G U R E B . 1 Sov r i n ESG scor s’ v ri nc xpl in d b princip l compon nts .A r t ESG scor b. A r t S scor PC1 0.89 0.87 PC2 0.06 0.06 PC3 0.03 0.03 PC4 0.01 0.02 PC5 0 0.01 PC6 0 0.01 c. A r t G scor d. A r t E scor PC1 0.75 0.58 PC2 0.12 0.21 PC3 0.1 0.11 PC4 0.02 0.06 PC5 0.01 0.03 PC6 0 0.02 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Source: World Bank staff. Note: PC = principal component. 52 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E B . 2 First princip l compon nt 1 for ESG scor s, individu l pill rs, countr ’s n tion l incom A r t A r t A r t A r t GNI ESG scor E scor S scor G scor p r c pit A r ESG scor Corr: 0.921*** Corr: 0.960*** Corr: 0.978*** Corr: 0.888*** A t E scor Corr: 0.856*** Corr: 0.863*** Corr: 0.832*** r A t S scor Corr: 0.900*** Corr: 0.913*** r A t G scor Corr: 0.836*** r t p r c pit GNI Source: World Bank staff. Note: Corr = correlation; ESG = environmental, social, and governance. Three stars (***) indicate that the correlation is significant on a 1% level. DEMYSTIFYING SOVEREIGN ESG >>> 53 > > > F I G U R E B . 3 Corr l tion of sov r i n ESG princip l compon nt 1 with sust in bilit v ri bl s A r t CO2 GNI p r Tot l ND-GAIN Y l SDG ESG scor c pit missions w lth countr ind x EPI ind x PC1 p r c pit A r ESG scor Corr: Corr: Corr: Corr: Corr: Corr: PC1 0.888*** 0.703*** 0.863*** 0.945*** 0.874*** 0.876*** t p r c pit Corr: Corr: Corr: Corr: Corr: GNI 0.877*** 0.966*** 0.910*** 0.869*** 0.847*** p r c pit Corr: Corr: Corr: Corr: missions CO2 0.837*** 0.800*** 0.702*** 0.794*** Corr: Corr: Corr: w lth Tot l 0.881*** 0.837*** 0.787*** ND-GAIN countr Corr: Corr: ind x 0.864*** 0.903*** Corr: Y l EPI 0.846*** ind x SDG Source: World Bank staff. Note: CO2 = carbon dioxide; EPI = Environmental Performance Index; ESG = environmental, social, and governance; ND-GAIN = Notre Dame Global Adaptation Initiative; PC = principal component; SDG = Sustainable Development Goal. Three stars (***) indicate that the correlation is significant on a 1% level. 54 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Appendix C Supplementary Data Description Traditional financial measure of income. We use gross national income (GNI) per capita, from the World Bank. This is the measure of national income that the World Bank uses to determine income groupings and operational lending policy. World Bank augmented income data. Given that we know that sovereign environmental, social, and governance scores have a strong relationship with a country’s level of income, we wanted to test whether perhaps they more closely resemble an augmented measure of income versus a more traditional financial measure of income. The World Bank’s Wealth Accounting and the Valuation of Ecosystem Services (WAVES) partnership has created a national wealth accounting framework that works to incorporate the value of natural capital and human capital alongside traditional financial measures of national income in order to create a more holistic accounting of national wealth (Figure C.1). The purpose is to move beyond traditional GDP and capture the interaction among economic activity, the environment, and the human capital of a country’s citizens. Natural capital. Natural capital includes the value of natural resources and ecosystem services. Human capital. Human capital is the present value of future earnings for the labor force. Produced capital. Produced capital is the value of machinery, equipment, and structures, as well as urban land. Total wealth. Total wealth captures the sum of the produced capital, natural capital, human capital, and net foreign assets. Sustainable Development Goals. In the European Union Action Plan and elsewhere, sustainable finance is seen as a tool to help achieve sustainability outcomes, such as the Sustainable Development Goals (SDGs). Investors and data providers note a push to map ESG risks to specific SDGs. As such, we have decided to see the relationship between sovereign ESG scores and the SDG Index published by the Sustainable Development Solutions Network and Bertelsmann Stiftung. DEMYSTIFYING SOVEREIGN ESG >>> 55 > > > FIGURE C.1 Tot l w lth composition Lon =t rm prosp rit nd w ll-b in N tion l incom /GDP Tot l w lth N t for i n Produc d c pit l N tur l c pit l Hum n c pit l ss ts M chin r Urb n En r Prot ct d M l /f m l Tot l ss ts quipm nt A ricultur l For st nd mplo d/ tot l l nd nd l nd r s structur s min r ls s lf- mplo d li biliti s Source: Lange (2018), World Bank Wealth Accounts. SDG Index. This index is the global index score quantifying a • ND-GAIN Vulnerability Index. This subindex summarizes a country’s level of progress toward achieving the 17 sustainable country’s vulnerability to climate change. development goals. • ND-GAIN Readiness Index. This subindex summarizes a There are also scores quantifying progress on each of the 17 country’s readiness to address the challenges of climate goals, as well as underlying data. Future analysis may examine change. this index with greater granularity. Environmental Performance Index (EPI). The EPI is produced Environmental Indices. Given the focus of this report on in collaboration by Yale University and Columbia University. the aggregate ESG scores and on the environmental scores, It uses 32 performance indicators across 11 issue categories we wanted to look at the relationship with two often-cited to quantify national progress on environmental health and environmental indices, and their high-level subindices. Both ecosystem vitality, and it can be used to gauge a country’s measure some concept of environmental risk and sustainability, progress toward established environmental policy targets. and we wanted to see the relationship between these indices and the environmental scores. Future analysis may examine • EPI: This index is the overarching score measuring these indices with greater granularity. environmental performance. Notre Dame Global Adaptation Initiative (ND-GAIN). The • EPI Environmental Health Index. This subindex measures initiative promotes adaptation by identifying the places most environmental health. vulnerable to the changing climate and identifying real-world solutions that can prevent these changes from becoming • EPI Ecosystem Vitality Index. This subindex measures disasters. To help, ND-GAIN has developed the following ecosystem vitality. indices. • ND-GAIN Country Index. This index is the overarching score summarizing a country’s vulnerability to climate change and its readiness to address such change. 56 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> Appendix D Key Tenets of ESG Providers Methodologies FTSE Russell/Beyond Ratings. FTSE Russell bought Beyond Ratings in 2019. Beyond Ratings’ Sovereign Risk Monitor is divided between an economic-financial profile (50 percent), which measures traditional macroeconomic measures of sovereign risk, and a sustainability profile (50 percent), which aims to quantify environmental, social, and governance factors that are material to sovereign credit risk (Figure D.1). In this study, we use the sustainability profile as our aggregate ESG measure and the underlying E, S, and G measures for the pillar scores. In 2020, FTSE Russell launched climate-adjusted versions of its World Government Bond Indices (WGBIs), which cover primarily developed market sovereign bonds. The indices are tilted using climate risk scores from Beyond Ratings. ISS. ISS acquired Oekom Research AG’s ESG Country Ratings in 2018. Its standard of materiality balances financially material ESG risks with assessments of a country’s alignment with internationally accepted norms. It has the highest weight (50 percent) for environmental scores of any of the data providers included in the study. Given the significant data lags in the underlying data sources, ISS augments backward-looking hard data with forward-looking qualitative assessments (Figure D.2). Although ISS has expanded to cover 121 countries and territories, they have only 55 countries overlapping with the common sample for the 2017 data year used for the study. As such, the correlation statistics reported for ISS are calculated separately using z-scores for the 55 overlapping countries, and ISS is not included in the principal component analysis or in the scatter matrices. MSCI. MSCI ESG Government Ratings quantify a government’s ESG risk exposures and its ability to manage risks that are material to the long-term competitiveness and resilience of the country’s economy (Figure D.3). The ratings cover 198 countries and regions. MSCI is one of the largest ESG data providers. DEMYSTIFYING SOVEREIGN ESG >>> 57 > > > F I G U R E D . 1 FTSE Russ ll/B ond R tin s Sov r i n Risk Monitor Fr m work -Economic prosp rit -Mon t r polic Economic p rform nc 40% -Fisc l polic Economic 4 Fisc l fl xibilit 30/25% -Bud t b l nc & fin nci l Th m s -Cr dit qu lit 50% Fin nci l s st m 20/10% -Cr dit p Ext rn l p rform nc 10/25% -Ext rn l b l nc sh t -Exch n -Clim t ph sic l risk -En r polic Environm nt l 30% -Air & W t r 3 -Soci t l Sust in bilit Th m s Soci l 30% -H lth 50% -Empl m nt Gob rn nc 40% -Corruption -Politic l st bilit -R ul tor qu lit Source: FTSE Russell/Beyond Ratings 2020. > > > F I G U R E D . 2 ISS ESG Countr R tin s Fr m work P rform nc D t on Wid R n of ESG Topics Ev lu tion b s d on qu lit tiv nd qu ntit tiv crit ri Environm nt Soci l Gov rn nc • L nd Us • H lth • Politic l S st m • Biodiv rsit • Educ tion nd • Politic l St bilit •W t r Comunic tion • Corruption nd • Clim t Ch n • L bor Ri hts nd Mon L und rin • En r Workin Conditions • S f u rdin of Civil • A ricultur • Soci l Coh sion nd Politic l Ri hts • Industr • And mor • And mor • Tr nsport • Priv t Construction • And mor Source: ISS. Note: ESG = environmental, social, and governance. 58 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E D . 3 MSCI ESG Gov rnm nt R tin s Fr m work > > > Pill r Risk F ctor W i ht Risk Exposur W i ht Risk M n m nt W i ht (%) (%) (%) En r R sourc En r S curit Risk 6% M n m nt 6% N tur l Productiv L nd nd R sourc R sourc 18% W t r R sourc s 6% Cons rv tion 6% Risk Environm nt l W t r R sourc Risk W t r R sourc s 6% M n m nt 6% Vuln r bilit to Environm nt l Environm nt l Environm nt l Ev nts 3% P rform nc 3% Ext rn liti s nd Ext rn liti s Vuln r bilit 7% Risk Environm nt l M n m nt of Ext rn liti s 4% Environm nt l 4% Ext rn liti s B sic Hum n C pit l 5% B sic N ds 5% Hum n C pit l Infr structur 3% Hum n Hi h r Educ tion C pit l 15% T chnolo R din ss 6% Risk Hum n C pit l Soci l P rform nc 3% Risk Knowl d C pit l Knowl d C pit l 4% M n m nt 4% Economic Environm nt 10% Economic Environm nt 10% W lln ss 10% Risk Fin nci l Fin nci l C pit l Gov rn nc 20% Tr d Vuln r bilit 20% Fin nci l M n m nt 20% Risk Gov rn nc Institutions 10% St bilit nd P c 10% Politic l Risk Gov rn nc Judici l nd Risk 30% P n l S st m 10% Corruption Control 10% Gov rn nc Politic l Ri hts nd Eff ctiv n ss 10% Civil Lib rti s 10% Source: MSCI ESG Research 2019. RepRisk. The RepRisk Country ESG Risk Index (Country RRI) and G pillars as official RepRisk metrics. However, for the sake quantifies business conduct and reputational risk exposure of comparability, in consultation with the company, we create for doing business in a given country. RepRisk screens more proxies by multiplying the Country RRI by the percentage of than 100,000 media and stakeholder sources on a daily basis incidents from E, S, and G. Daily values are aggregated to in 20 languages using machine learning combined with human annual data using averages.  intelligence to provide data updated daily. The Country RRI combines data on companies’ ESG risk incidents with the World The Country RRI is measured as the average of a fast-moving Bank’s Worldwide Governance Indicators (WGI). The Country incident index and slow-moving governance data from the RRI is not intended to be a sovereign ESG rating, but it has World Bank.  been included in the study because it is a high-frequency input into J.P. Morgan’s JESG (J.P. Morgan ESG) ESG-tilted RRI = (Incident Index + Scaled WGI)/2  emerging market sovereign debt indices. There are no E, S, DEMYSTIFYING SOVEREIGN ESG >>> 59 Where  Robeco. Robeco’s Country Sustainability Score quantifies how ESG factors can affect the long-term resilience and debt- • the Incident Index is based on the RepRisk ESG Risk servicing capacity of a country (Figure D.4). It is meant as a Platform and is updated daily, and  complement to traditional sovereign risk analysis. Robeco • the Scaled WGI is based on the World Bank’s WGI, which currently ranks 150 countries. Robeco sold its corporate ESG are updated yearly.  ratings business to S&P in 2020, but the country sustainability ratings were not part of that sale and remain with Robeco. > > > F I G U R E D . 4 Rob co Countr Sust in bilit R nkin Fr m work Indic tor L v l Crit ri L v l Dim sion L v l Countr Sust in bilit Scor For ch countr , num rous d t s ri s on v ri t Th indic tors r r t d E ch dim nsion w i ht Th countr scor is of ESG f tur s r coll ct d nd summ ri d in 40 to 15 crit ri , wh r b ch is th sum of th crit ri th w i ht d sum indic tors. E ch indic tor ts pr d fin d w i ht crit rion is lso ssi n d w i ht within th of ll st nd rdi d nd r l tiv scor rn in from 1 to 10. pr d fin d w i ht. r sp ctiv dim nsion. indic tor scor s. •Environm nt l P rform nc nd x •En r Tril mm Ind x •Environm nt l Pr f. (5.0%) •Clim t Risk Ind x •World Risk Ind x •Environm nt l Risk (7.5%) Environm nt l (20%) •ND-GAIN Ind x •Environm nt l Qu lit •Environm nt l St tus (7.5%) •N tur l Environm nt •L bor Forc P rticip tion R t 55-64 •Old-A D p nd nc R tio 2050 •A in (7.5%) •R tir m nt A •Educ tion •Hum n C pit l (5.0%) •H lth •Economic In qu lit •In qu lit (5.0%) •G nd r In qu lit Ind x •GNI Co ffici nt •Soci l Conditions (5.0%) Countr •Wom n, Busin ss & L w Ind x •Childr n´s Ri hts in th Workpl c •Soci l Unr st (7.5%) Soci l (30%) Sust in bilit Scor •B sic Hum n N ds •Economic D clin & Pov rt •Hum n D v lotm nt Ind x •S f t & S curit •Inclusiv n ss World H ppin ss R nkin •Control ofCorruption •Corruption P rc ption Ind x •Corruption (10.0%) •Economic Glob li tion •Go l Innov tion Ind x •Glob li tion & Innov tion (5.0%) •Gov rnm nt Eff ctiv n ss •Rul of L w •Institutions (10.0%) •St t L itim c •Hum n Ri hts •P rson l Fr dom (5.0%) •Fr dom in th World •Politic l Risk R tin •Politic l Risk (10.0%) Gov rn nc (50%) •Voic & Account bilit •Politic l St bilit /No Viol nc •Politic l St bilit (5.0%) •Politic l Risk Ass ssm nt I•nd x of Economic Fr dom •R ul tion & •Ext rn l Int rv ntion Fin. D v lopm nt (5.0%) •Fin nci l D v lopm nt Ind x •R ul tor Qu lit Source: Robeco 2020. Note: ESG = environment, social, and governance; Fin. = financial; Perf. = performance. 60 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT > > > F I G U R E D . 5 Sustainalytics Country Risk Ratings Framework Risk to countr ´s lon t rm prosp rit nd conomic d v lopm nt N tion l W lth M n m nt of W lth N tur l + Produced C pit l Hum n ESG ESG ESG C pit l Perform ce Trend Events Institution l C pit l P rform nc nd Tr nds N tur l + Produc d Hum n C pit l Institution l C pit l C pit l (Gov rn nc ) Ev nts (Soci l) •En r Int nsit •Gov rnm nt •C rbon Int nsit •Acc ss to W t r Eff ctiv n ss Civil Conflict En r •R n w bl n r •Acc ss to S nit tion nd B sic Institution l •R ul tor Qu lit Discrimin tion consumption N ds •Food S curit clim t Str n th •Rul nd L w ch n •En r Imports •Acc ss to El ctricit Dis s Outbr k •Corruption •P rc nt l nd b low 5m •S cond r Educ tion •E s of Doin Busin ss Environm nt l Pollution •N tur l Dis st r Risk L bour Ri hts •Lif Exp nt nc t Birth •Politic l Ri hts N tur l Dis st r •W t r Productivit R sourc •W t r Str ss H lth nd •Ph sici ns p r 1,000 Ri hts nd •Civil Lib rti s us W ll-B in Popul tion Fr doms •Voic nd St t Corruption nd Fr ud •H bit t Prot ction •Air Polution Account bilit St t R pr ssion •G nd r D v lopm nt Tr nsn tion l Conflict •Corruption Equit nd P c nd •Politic l St bilit Opportunit •Un mplo m nt Viol nt Crim Gov rn nc S curit •L v l of p c •Rul of L w •P rc nt of Individu ls usin th Int rn t Source: Sustainalytics 2020. Note: ESG = environmental, social, and governance; m = meter. Sustainalytics. Sustainalytics’ Country Risk Ratings quantify an V.E. Previously known as Vigeo Eiris, V.E was bought by Moody’s ESG-augmented conception of a country’s level of wealth and and rebranded in 2020. V.E’s Sovereign Sustainability Ratings its ability to manage that wealth. The framework is based on the quantify a country’s efforts to achieve globally recognized World Bank’s wealth accounting database. It measures natural sustainability standards (Box D.1). V.E measures this through and produced capital (environment), human capital (social), commitment indicators and results indicators. Commitment and institutional capital (governance). The management of indicators quantify a country’s commitment to the goals and these areas of national wealth are measured using indicators principles of globally recognized conventions, treaties, and that attempt to quantify ESG trends, momentum (direction), and agreements related to sustainability goals. Results indicators ESG-relevant events. They cover 172 countries and territories attempt to quantify the effectiveness of a country’s sustainable currently. Sustainalytics is owned by Morningstar. Sustainalytics’ development actions. V.E’s Sovereign Sustainability Ratings Country Risk Ratings are used as an input into J.P. Morgan’s cover 180 countries and regions. JESG ESG-tilted emerging market sovereign debt indices. DEMYSTIFYING SOVEREIGN ESG >>> 61 >>> BOX D.1 V.E Sovereign Sustainability Ratings Framework V.E’s Sovereign Sustainability Ratings balances two types of indicators: • Commitment Indicators measure a country’s commitment to the goals and principles of globally recognized conventions, treaties, and agreements related to sustainability goals. • Results Indicators measure the effectiveness of a country’s sustainable development actions. The Four Levels of Analysis for V.E’s Sovereign Sustainability Ratings are as follows: • 3 pillars (domains) • 17 themes (subdomains) • 56 criteria • 172 indicators (48 for environmental, 61 for social, and 63 for governance) Source: V.E. 2020. >>> 62 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT >>> References Berg, Florian, Julian F. Kölbel, and Roberto Rigobon. 2019. “Aggregate Confusion: The Divergence of ESG Ratings.” MIT Sloan School Working Paper 5822-19, MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA. 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SIFMA (Securities Industry and Financial Markets Association). 2019. 2019 Capital Markets Fact Book. New York: SIFMA. V.E. 2020. “SSR Methodology: Executive Summary.” V.E., London. Wang, Dieter. 2021. “Natural Capital and Sovereign Bonds.” Policy Research Working Paper 9606, World Bank, Washington, DC. Wong, Christina, and Erika Petroy. 2020. Rate the Raters 2020: Investor Survey and Interview Results, SustainAbility Institute, ERM Company, London. WWF (World Wildlife Fund) and World Bank. 2020. Spatial Finance: Challenges and Opportunities in a Changing World. Washington, DC: World Bank. DEMYSTIFYING SOVEREIGN ESG >>> 65 Other insights into sustainable finance you may be interested in Riding the Wave: Navigating the ESG Landscape for Sovereign Debt Managers. by S. Boitreaud, E. Gratcheva, B. Gurhy, C. Paladines and A. Skarnulis Demystifying Sovereign ESG. by E. Gratcheva, T. Emery and D. Wang A New Dawn - Rethinking Sovereign ESG. by E. Gratcheva, B. Gurhy, T. Emery and D. Wang Credit Worthy: ESG Considerations in Sovereign Credit Ratings. by E. F Gratcheva, B. Gurhy, F. Stewart, A. Skarnulis and D. Wang 1% Growth in Natural Capital: Why it Matters for Sovereign Bonds. by E. Gratcheva, B. Gurhy and D. Wang Natural Allies: Wealth and Sovereign ESG, in: The Changing Wealth of Nations 2021: Managing Assets for the Future. by E. Gratcheva and D. Wang Natural Capital and Sovereign Bonds. by D. Wang Spatial Finance: Challenges and Opportunities in a Changing World by WWF and World Bank. The Global Program on Sustainability (GPS) promotes the use of high-quality data and analysis on natural capital, ecosystem services, and sustainability to better inform decisions made by governments, the private sector, and financial institutions. Find out more on http://worldbank.org/gps 66 >>> EQUITABLE GROWTH, FINANCE & INSTITUTIONS INSIGHT