Sovereign debt and credit rating bias 9781137391506, 1137391502

Table of contents :
Front Matter ....Pages i-x
Credit Rating Agencies as Gatekeepers (David F. Tennant, Marlon R. Tracey)....Pages 1-14
Establishing the Determinants of Sovereign Debt Ratings: Is There Really Room for Bias? (David F. Tennant, Marlon R. Tracey)....Pages 15-35
Resilience in Spite of Controversy: Conditions for Bias in the Credit Rating Industry (David F. Tennant, Marlon R. Tracey)....Pages 36-60
Trends in Sovereign Debt Ratings: Are There any Preliminary Signs of Bias? (David F. Tennant, Marlon R. Tracey)....Pages 61-75
Introducing Greater Rigor— Methodological Approach (David F. Tennant, Marlon R. Tracey)....Pages 76-86
Are Poorer Countries Disadvantaged by the CRAs? Empirically Establishing a Bias (David F. Tennant, Marlon R. Tracey)....Pages 87-102
Now That We Have Found Bias, What Are We Going to Do with It? (David F. Tennant, Marlon R. Tracey)....Pages 103-114
Back Matter ....Pages 115-125

Citation preview

Sovereign Debt and Credit Rating Bias

DOI: 10.1057/9781137391506.0001

Also by David F. Tennant DEBT AND DEVELOPMENT IN SMALL ISLAND DEVELOPING STATES coedited with Damien King, 2014)

DOI: 10.1057/9781137391506.0001

Sovereign Debt and Credit Rating Bias David F. Tennant Professor of Development Finance and Associate Dean, University of the West Indies, Mona, Jamaica and

Marlon R. Tracey Doctoral Student, Binghamton University, State University of New York, USA

DOI: 10.1057/9781137391506.0001

sovereign debt and credit rating bias Copyright © David F. Tennant & Marlon R. Tracey, 2016. Softcover reprint of the hardcover 1st edition 2016 978–1–137–39710–2 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No portion of this publication may be reproduced, copied or transmitted save with written permission. In accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, Saffron House, 6-10 Kirby Street, London EC1N 8TS. Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages. First published 2016 by PALGRAVE MACMILLAN The authors have asserted their rights to be identified as the authors of this work in accordance with the Copyright, Designs and Patents Act 1988. Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS. Palgrave Macmillan in the US is a division of Nature America, Inc., One New York Plaza, Suite 4500 New York, NY 10004–1562. Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world. ISBN 978–1–349–67954–6 E-PDF ISBN: 978–1–137–39150–6 DOI 10.1057/9781137391506 Distribution in the UK, Europe and the rest of the world is by Palgrave Macmillan®, a division of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS. Library of Congress Cataloging-in-Publication Data is available from the Library of Congress A catalog record for this book is available from the Library of Congress A catalogue record for the book is available from the British Library

To Sandria, Sean and Elia who light up my world, and my Heavenly Father who lights my path David To Tannia, for her sustained love and support, and to my Father above, for His unrelenting blessings Marlon

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Contents List of Figures

vii

List of Tables

ix

Acknowledgments

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1 Credit Rating Agencies as Gatekeepers

1

2 Establishing the Determinants of Sovereign Debt Ratings: Is There Really Room for Bias?

15

3 Resilience in Spite of Controversy: Conditions for Bias in the Credit Rating Industry 36

vi

4 Trends in Sovereign Debt Ratings: Are There any Preliminary Signs of Bias?

61

5 Introducing Greater Rigor—Methodological Approach

76

6 Are Poorer Countries Disadvantaged by the CRAs? Empirically Establishing a Bias

87

7 Now That We Have Found Bias, What Are We Going to Do with It?

103

References

115

Index

123

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List of Figures 4.1 Distribution of rating changes by levels of development 4.2 Distribution of rating changes by developing regions 4.3 Distribution of rating changes by development level by year for Moody’s 4.4 Distribution of rating changes by development level by year for Fitch 4.5 Distribution of rating changes by development level by year for S&P

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67 68 68 68 69

vii

List of Tables 2.1 Comparison of indicators used by the big-three CRAs 25 2.2 Key empirical studies on the determinants of sovereign ratings 28 4.1 Distribution of rating categories for each rating 63 4.2 Regional distribution of sovereign ratings 64 4.3 Distribution of sovereign ratings by development level 65 4.4 Frequency distribution of rating changes 66 4.5 Comparison of odds of rating change across different country groupings 70 4.6 Selected economic fundamentals by development level and developing regions 72 A4.1 List of sample sovereigns by development level, developing region and rating agency 74 A5.1 Description of variables and their sources 85 A5.2 Simple descriptive statistics 86 6.1 Estimation results – thresholds varying by development level 98 6.2 Estimation results – thresholds varying by developing regions 100

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Acknowledgments We offer our heartfelt appreciation to everyone who assisted directly or indirectly in the completion of this book. Our research assistants, Francien Bailey, Sherna Morris, Kaymara Barrett and Altricia Dawson, for their tireless effort at the start of this project. Our project manager, Altricia Dawson, who saw us through to the end of the project. Our wives, Sandria and Tannia, who consistently offered their patient support and advice. The University of the West Indies, our academic home. Our publisher, Palgrave Macmillan.

DOI: 10.1057/9781137391506.0004

ix

1

Credit Rating Agencies as Gatekeepers Abstract: This chapter begins by introducing the theme of the book and providing a brief outline of subsequent chapters. It then contextualizes the discussion by presenting the varied but conjoined interests of the key players in the sovereign debt market—the national governments which seek to borrow on the international capital market, and the investors which seek to hedge their risks as they supply the requisite funds. It shows how the intersection of these interests has established the big-three CRAs as the gatekeepers of the international capital markets, indicates how these agencies may be flawed as they fulfill this role, and highlights the implications of this for the affected countries. Keywords: Credit Rating Agencies; Developing Countries; Gatekeepers; International Capital Markets; Sovereign Debt Tennant, David F., and Marlon R. Tracey. Sovereign Debt and Credit Rating Bias. New York: Palgrave Macmillan, 2016. doi: 10.1057/9781137391506.0005.

DOI: 10.1057/9781137391506.0005

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In the past three decades, credit rating agencies (CRAs) have had significant influence on the terms on which developing countries can tap international capital markets. This is because of the stagnation of concessional financial assistance and significant increases in private capital flows. Reisen (2002) asserts that, unlike in the 1960s, 1970s and 1980s, the most important visit that a developing country now expects is not from major aid agencies or from the International Monetary Fund (IMF), but rather from one of the big-three credit rating agencies (Moody’s, Standard and Poor’s [S&P] and Fitch). This is because in developing countries, problems of limited information and lack of transparency are particularly severe, forcing investors to primarily rely on the “expert” opinions encapsulated in the ratings when making their investment decisions (Ferri 2004). This is exacerbated by the fact that many institutional investors in developed countries are only permitted to hold securities with ratings above a certain threshold (Cantor and Packer 1996). When a sovereign debt rating is downgraded, particularly if it crosses the threshold from investment grade to speculative grade, there are costly implications for national governments. It is in this context that this book investigates rating agencies’ assignments of sovereign debt upgrades and downgrades to ascertain whether any biases can be shown. Subsequent chapters examine the pervasive allegation that there is a systemic difference in rigor and accuracy of ratings ascribed to certain categories of countries. This allegation is serious, as, if true, it could unduly disadvantage both those countries and the companies operating within them as they seek access to international capital markets. In the next two chapters, we contextualize the discussion by examining how the CRAs determine their rating assignments and analyzing the demand-side and supply-side conditions in the credit ratings industry. This allows us to highlight the conditions that present opportunities for biases to be introduced in the CRA industry. We, however, can only show such biases in sovereign debt ratings when we control for the key theorized determinants of such ratings, and then examine whether CRAs still exhibit a tendency to downgrade or not upgrade a particular category of country. This analysis and the methodology used are presented in chapters 4 to 6, wherein we explore preliminarily and econometrically whether biases are evident on the basis of a country’s level of development, or are apparent for certain regional groupings of countries. The implications of our findings are discussed in the final chapter. DOI: 10.1057/9781137391506.0005

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The rest of this introductory chapter presents the varied but conjoined interests of the key players in the sovereign debt market—the national governments which seek to borrow on the international capital market, and the investors which seek to hedge their risks as they supply the requisite funds. We show how the intersection of these interests have established the big-three CRAs as the gatekeepers of the international capital markets, indicate how these agencies may be flawed as they fulfill this role, and highlight the implications of this for the affected countries.

Sovereign indebtedness and the role of credit rating agencies All countries have needed to borrow money at some point in time. Borrowing makes more resources available to finance the technology gains and capital deepening that precipitate economic progress. Sovereign debt is, however, different from private debt in that there is no clearly defined procedure for enforcing sovereign debt contracts, with the legal recourse available to creditors having limited applicability and uncertain effectiveness (Panizza et al. 2010). This is of concern, particularly as there is much empirical evidence indicating that, even though low to moderate levels of sovereign debt can be beneficial to economic growth, high levels of debt tend to be deleterious to such outcomes.1 Debt at such levels can be unsustainable, heightening the likelihood of sovereign debt default (Tennant 2014a). There has been a significant increase in sovereign indebtedness in the past three decades (Cecchetti et al. 2011; Panizza et al. 2010). Increased borrowing and lending were incentivized with the popularization of financial liberalization after the late 1970s. Financial liberalization precipitated the removal of restrictions on financial market activity and lending in many countries, and intensified financial innovation stemming from improved financial theory and information technology. This, when coupled with a relatively stable global macroeconomic climate between the mid-1980s and the start of the 2007–2009 global crisis, created the conditions that fostered heightened demand for sovereign debt and the supply of the requisite funds (Cecchetti et al. 2011). The instability associated with the global crisis did not reduce sovereign debt levels. Direct bail-out costs in some countries, the usage of stimulus packages by many governments to deal with the recession, and the DOI: 10.1057/9781137391506.0005

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substantial declines in government revenues that affected most economies, led to increased debt levels (Tennant 2014a). Reinhart and Rogoff (2010) note that between 2007 and 2009, average debt levels in countries that did not experience systemic financial crises increased in real terms by approximately 20, and by approximately 75 in countries that did. With many countries having much higher levels of sovereign debt, borrowers, particularly the national governments of developing countries, now face an intensified debt-development dilemma. Tennant (2014b, 1) notes that: To protect the world’s poor and vulnerable from the continuing impacts of the Triple F crises (food–fuel–financial) and impending effects of a likely climate change crisis, developing countries need resources. Herein lies the often unacknowledged Fifth Crisis facing many poor countries—the burden of increasingly unsustainable debt, the servicing of which precludes growthinducing and poverty-reducing government expenditures.

Such poor countries often end up in a vicious cycle wherein they are forced to borrow to repay debt. The critical questions that they face is how to get the best terms for future debt, and whether the cost of restructuring existing debt exceeds the benefit of the improved terms that they can negotiate. Not even the rich countries of the world have been able to avoid the difficult questions that come with significantly increased levels of sovereign debt. Cecchetti et al. (2011, 1) note that: The ratio of debt to GDP in advanced economies has risen relentlessly from 167 in 1980 to 314 today, or by an average of more than 5 percentage points of GDP per year over the last three decades. Given current policies and demographics, it is difficult to see this trend reversing any time soon. Should we be worried? What are the real consequences of such rapid increase in debt levels? When does its adverse impact bite?

These questions are of concern not only to the borrowers, but naturally to the creditors as well. Investors face the complex task of assessing the creditworthiness of sovereign debt issues in an environment characterized by increased uncertainty in the post-global crisis era. Even without the heightened uncertainty, the complexity of assessing the creditworthiness of sovereign debt should not be underestimated. This is because a country’s ability and willingness to repay debt is affected not only by its economic, social and political dynamics, but also by increasingly commonplace internal and external shocks. DOI: 10.1057/9781137391506.0005

Credit Rating Agencies as Gatekeepers

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How do creditors make their decisions as to a country’s creditworthiness? Increasingly they have come to rely on the assessments provided by credit rating agencies (CRAs). CRAs garner information on borrowers from a variety of sources and assess the default risk of the financial products being offered. The default risk is computed and condensed into a single relative measure—a credit rating in the form of a letter grade (Kruck 2011). This rating is a judgment on the “future ability and willingness of an issuer to make timely payments of principal and interest on a security over the life of the instrument.”2 This judgment forms the basis of the important but often uneasy relationship between countries which rely on funds from international capital markets, and the CRAs which influence the countries’ ability to access such funds.

Gatekeepers to the international capital markets The CRAs’ outputs are used to guide the investment choices of government agencies and key institutional investors in the capital markets (pension funds, investment banks and other financial institutions). At the start of the millennium, the two major CRAs (Moody’s and S&P) passed judgment on approximately US$30 trillion worth of securities each year. According to their own estimates, at that time Moody’s had 4,000 clients for its publications and approximately 30,000 regular readers of its print output, and both Moody’s and S&P regularly issued press statements about credit conditions (Sinclair 2005). Output produced for dissemination through the Internet was just then gaining popularity. By the end of the 2007–2009 global crisis, however, CRAs have become known to even the casual newspaper reader. This is not only because of the plethora of articles that have blamed the CRAs for several failures leading up to and exacerbating the crisis, but also because of the demonstration of the critical role that the CRAs play in global investment decisions (Kruck 2011). Canuto et al. (2012, 5) have noted that “in spite of the criticisms and shortcomings of sovereign risk assessment, the importance of ratings has tended to increase . . . Ratings increasingly influence decisions in both developed and developing countries.” As of 2011, Moody’s, S&P and Fitch issued a staggering 2,219,437 government, municipal and sovereign debt ratings. This represents 99.4 of the total number of such ratings issued by Nationally Recognized Statistical Rating Organizations (NRSROs) globally.3 The credit rating market, particularly that for sovereign debt, DOI: 10.1057/9781137391506.0005

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is thus heavily dominated by the big-three CRAs. These CRAs operate globally, having subsidiaries in numerous countries. Their ratings are typically requested and paid for by issuers, because investors rely heavily on them. According to Alcubilla and del Pozo (2012, 12), Issuers seek ratings because they help them to place their securities in the markets at a lower cost. Ratings are disclosed in promotional materials distributed in connection with the issue of a security as a way to ease the selling process. Ratings are also used to help price securities that are traded in the secondary markets. Several studies confirm that liquidity is enhanced and pricing is more favourable when a security has at least one rating.

It is virtually impossible to raise reasonably priced funds in the international capital markets without being rated by at least one of the big-three CRAs. Sinclair (2005, 2) thus notes that these agencies control access to capital markets. They are the gatekeepers to these markets, and, as such, wield enormous power. Ratings by the big-three CRAs “affect the interest rate or cost of borrowing for businesses, municipalities, national governments, and, ultimately, individual citizens and consumers” (Sinclair 2005, 4). As gatekeepers to the international capital markets— often the only viable alternative to the IMF that national governments have for financing their budget deficits—the big-three CRAs possess, via rating downgrades (or the threat thereof), the capacity to forcibly direct borrowing governments that are eager to obtain scarce funds. These agencies “put a price on the policy choices of governments . . . seeking funds” (Sinclair 2005, 10). Numerous governments have further legitimized the big-three CRAs’ role as gatekeepers by enshrining their ratings in the legislation governing investors. The United States is noted as having the most widespread usage of credit ratings in regulations that establish capital requirements in the banking and securities sectors. The legislation further restricts the ratings used for these purposes almost exclusively to those issued by CRAs designated as NRSROs, which we have already established as being heavily dominated by the big-three. Such practices are, however, not limited to the US government. In 2009, a Joint Forum investigating the use of credit ratings in 12 advanced economies, found that such ratings are also heavily used in their legislation.4 Kruck (2011, xv) thus argues that state regulatory authorities have empowered CRAs by making increasing use of private credit ratings in public financial regulation. This is a practice which he asserts should call into question the power, reliability and accountability of CRAs. DOI: 10.1057/9781137391506.0005

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Are credit rating agencies flawed gatekeepers? Markets and governments take account of CRAs because they are thought to be an authoritative source of judgments, providing clear, internationally harmonized indicators of the risk of default (Sinclair 2005; Alcubilla and del Pozo 2012). Their reputation as such has allowed them to serve as gatekeepers to the international capital markets. This reputation has, however, taken a severe beating in recent times, particularly after the Asian crisis of the mid-to-late 1990s and the 2007–2009 global crisis. In the aftermath of both crises, dissatisfaction with the role played by the CRAs precipitated deeper examination of the variables and models used in the calculation of debt ratings. As a consequence, numerous studies have now confirmed widely held notions that the big-three CRAs made mistakes leading up to both crises. Kruck (2011, xv) notes that “innumerable articles have appeared blaming credit rating agencies for failing to properly evaluate the default risks of complex structured finance products . . . and for destabilizing whole countries through downgrades of sovereign ratings.” Mattarocci (2013) thus concludes that the current rating market is limited in many respects, with CRAs having markedly different capabilities to properly evaluate market signals. Why were mistakes made by the rating agencies, particularly the bigthree, which should have amassed considerable expertise and experience in their several years of oligopolistic operation? The explanatory factors identified in recent empirical studies have included overly optimistic assumptions, underspecified models and sluggishness in rating adjustments (Jeon and Lovo 2013; Griffin and Tang 2011; Bar-Issac and Shapiro 2011). However, fundamentally, questions are arising as to whether these are issues of skewed motives or a flawed process.

Skewed motives? CRAs earn the bulk of their revenue from fees charged to bond issuers. This leads to concerns about conflict of interest, as issuers whom the CRAs should be independently and objectively rating, are the ones who fund their operations. Critics of the CRAs thus question their independence relating to the main market players, which are typically the clients that request the most services from the agency (Mattarocci 2013). In the sovereign debt market such issuers would tend to be the larger, richer, advanced and emerging economies. This opens the question

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as to whether conflicts of interest arising from the issuer-pays model disadvantages poorer, smaller economies. Sinclair (2005, 29) argues that it does not, particularly for the largest CRAs (S&P and Moody’s), as “both these firms have fee incomes of several hundred million dollars a year, making it difficult for even the largest issuer to manipulate them through their revenues.” An even more important argument is the fact that “any hint of corruption in ratings would diminish the reputation of the major agencies—and reputation is the very basis of the rating franchise” (Sinclair 2005, 29). Studies of the role of the CRAs in the sub-prime crisis have, however, provided contrary evidence. Griffin and Tang (2011) show that the bigthree CRAs may have been overly aggressive in their assumptions in order to gain market share. Their findings indicate that CRAs tended to make more favorable assumptions for the issuers within collateralized debt obligations (CDOs) that brought in big business. This was precipitated by the conflict of interest at the heart of the issuer-pays model adopted by the big-three (Tennant et al. 2015). He et al. (2011) similarly found that the big-three CRAs were more optimistic for securities sold by large issuers during boom years. They thus concluded that ratings mistakes were systematically correlated with issuer size and market conditions. This debate is relevant to the issues explored in this book, as misplaced motives could be the source of bias against certain categories of countries in sovereign debt ratings. As such, the controversy surrounding the issuer-pays model is revisited in more depth in Chapter 3.

Flawed processes? It is possible for biases to be introduced not only through skewed motives, but also via processes that are fundamentally and systemically flawed. Relatively simple processes are used by the big-three CRAs in assigning and modifying traditional ratings.5 The issuer typically makes a request for a rating, a contract is signed and the CRA sets up a team of analysts. All relevant information about the issuer is then collated from public sources, information generated by the CRA and third party sources. Issuers also normally provide the CRA with non-public information typically regarding forecasts and strategic orientations, and a meeting with management/governments is held to, inter alia, discuss these plans. The information garnered is then analyzed to assess the creditworthiness of the issuer or security. The process typically includes a quantitative analysis as well as an assessment of qualitative parameters.6 Ferri et al. DOI: 10.1057/9781137391506.0005

Credit Rating Agencies as Gatekeepers

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(1999, 346) elucidate by noting that “the actual ratings issued by credit rating agencies can be expressed as a function of two determinant parts: ratings generated from quantitative models that reflect the sovereign country’s economic fundamentals and ratings generated from ad hoc country information that reflect agencies’ qualitative judgements.” A draft internal report is then prepared with a rating recommendation. This report is discussed by a rating committee and a final rating decision is made. The committee is typically comprised of a senior officer of the CRA who acts as a moderator, the lead analyst who prepared the draft report, and other analysts with experience in the sector or instrument being rated. The decision of the committee is then communicated to the issuer along with the report and draft press release, and a small window of opportunity is given for factual verification. Ratings are then posted. The CRAs maintain surveillance of their ratings on an on-going basis, with the same process being followed for any rating changes (Alcubilla and del Pozo 2012). This process presents opportunities for biases to be introduced. Note that the entire process is extremely information intensive and sensitive. The veracity of the analysis is based on the relative completeness and accuracy of the information which the CRAs are able to garner. In fact, this is one of the raisons d’être of the CRAs—to help mitigate the fundamental information asymmetry in capital markets between investors and entities seeking external financing.7 The CRAs have however acknowledged that, in some critical instances, a dearth of quality information has adversely affected the rating process. For example, S&P highlighted a lack of transparency as being at the heart of the Asian crisis, implying that rating agencies were “victims of the data” (Sinclair 2005, 165). The CRAs further insist that “they do not and cannot perform an audit of the information they use or receive from the issuers . . . They do not verify or independently validate the information” (Alcubilla and del Pozo 2012, 19). Even the enactment of the Credit Rating Agency Reform Act of 2006 did not correct this problem, as it does not obligate the CRAs to conduct any due diligence. Lynch (2010, 249) thus notes that CRAs “are not obligated by any law or regulation to audit the integrity or accuracy of the information given for their analysis, and, consequently, the quality of their analysis, is, in part, a function of the quality of the information they are provided by the issuing firms.” So what do the CRAs do when they recognize that the information received from the issuer is inadequate or questionable? Deven Sharma, DOI: 10.1057/9781137391506.0005

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President of S&P, in a statement made during the hearing on reforming CRAs before the US House of Representatives’ Subcommittee on Capital Markets, Insurance and Government-Sponsored Enterprises (2009, 42), noted that the CRAs “do depend on the issuers and the arrangers to give us good quality data. But we also do adjust our criteria to sort of reflect the data that we are getting in our decision making.” This leads to important questions—if the information being provided is limited, what is the nature and magnitude of the criteria adjustments mentioned by Sharma? How likely is it that these adjustments, by themselves, can precipitate the downgrading of a bond? Can this be an avenue through which biases are introduced? As discussed in chapters 3 and 7, there are extant theoretical and empirical models which indicate that the higher cost of acquiring information in poorer countries have increased the likelihood of CRAs underinvesting in information acquisition for such countries, presenting an opportunity for biases against them. The opportunity for such biases exists because, in a model where ratings are determined jointly by quantitative information-intensive models and agencies’ qualitative judgments, qualitative judgments will be emphasized where information inadequacies impinge against the effectiveness of quantitative analyses. Sinclair (2005, 149) thus notes that “rating agencies are not objective, in a scientific sense. The rating process incorporates information-gathering and judgement-determination elements, which are socially and historically conditioned. Contrary to claims to objectivity, what rating agencies produce—a rating judgement—is inherently subjective: it incorporates some values and excludes others.” What, however, remains to be seen is whether the process for sovereign debt ratings is systematically flawed to the extent that it introduces biases that disadvantage certain categories of countries. This issue is critical, because as seen in the next section, adverse sovereign debt rating changes can have monumental impacts on the economic fortunes of countries and the firms operating therein.

The impact of sovereign debt rating changes Friedman (1999, 2) argued that, “the United States can destroy you by dropping bombs, and [rating agencies] can destroy you by downgrading your bonds.” Although alarming, these statements cannot be classified as extremist when one considers the multi-tiered nature of the impact DOI: 10.1057/9781137391506.0005

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of sovereign debt ratings. National governments seek to have their debt rated, as favorable ratings increase access to relatively low-cost funds by the government and by borrowers of the same nationality (Reisen and Maltzan 1998). Kim and Wu (2011) thus show that all forms of capital inflows to emerging markets were significantly increased as their foreign currency long-term sovereign ratings improved. However, the converse is also true, as sovereign credit rating downgrades were shown to reduce international bank flows from developed markets to emerging economies. This is exacerbated by the fact that downgrades are often issued during periods of financial crisis when adverse market conditions heighten the scarcity of alternative sources of finance (Almeida et al. 2014). Rating downgrades of sovereign debt adversely affect the potential pool of investors, have a drastic impact on bond prices, and increase interest costs (Kaminsky and Schmukler 2002). Reinhart (2002, 2) thus concludes that “it is hardly surprising that the countries with the lowest ratings are those that are unable to borrow from international capital markets and are dependent on official loans from multilateral institutions or governments.” Sovereign debt downgrades are also a potentially potent source of negative externality for the private sector in emerging markets (Borensztein et al. 2007). Almeida et al. (2014) found that firms operating in countries that experienced a sovereign downgrade are significantly more likely to also have their ratings downgraded. Firms react to such ratings downgrades by significantly reducing debt issuances and leverage (Kisgen 2009), resulting in a significant decrease in firms’ investment in the year of the sovereign downgrade.8 In particular, unanticipated and abrupt downgrades can lead to large market losses, fire sales and liquidity shortages. These problems may be exacerbated by the pro-cyclicality of rating downgrades, as Alcubilla and del Pozo (2012) note that a first round of downgrades can lead to further downgrades. In a similar vein, sovereign debt rating downgrades do not only influence corporations within a particular country, but have also been shown to have statistically and economically significant spillover effects across countries and financial markets in the developing world.9 Kaminsky and Schmukler (2002) found that rating changes of bonds of one emerging market triggered changes in both yield spreads and stock returns of other emerging economies, with these spillover effects being stronger at the regional level. They further found that “rating upgrades take place following market rallies, while downgrades occur after market DOI: 10.1057/9781137391506.0005

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downturns,” and thus also highlighted the possibility that rating agencies contribute to financial excesses in developing countries. Arezki et al. (2011) came to a similar conclusion having studied the recent European debt crisis, and argued that rating agencies’ announcements could spur financial instability. This occurs when sovereign ratings lag behind financial markets rather than leading them. Reisen and Maltzan (1999) explain by noting that improved ratings “reinforce euphoric expectations and stimulate excessive capital inflows during the boom; (but) during the bust, downgrading might add to panic among investors, driving money out of the country and sovereign yield spreads up.” This was emphatically illustrated in the South East Asian crisis of the mid to late 1990s, as “the downgrading of Asian sovereign ratings reinforced the region’s crisis in many ways: commercial banks could no longer issue international letters of credit for local exporters and importers; institutional investors had to offload Asian assets as they were required to maintain portfolios only in investment-grade securities; and foreign creditors were entitled to call in loans upon the downgrades.”10 Sovereign debt rating downgrades therefore not only impact the terms by which the government and private sector in developing countries access the international capital market, but the announcement of such downgrades can also trigger or worsen financial market instability in regional blocks of such countries. Such effects are exacerbated when the downgrades are unexpected and/or excessive. The aforementioned harsh effects of the Asian crisis of 1997–1998 are pertinent here, as Ferri et al. (2001) highlight numerous papers which claim that during this crisis, rating agencies excessively downgraded the South East Asian countries’ sovereign ratings relative to their underlying fundamentals.11 Alcubilla and del Pozo (2012, 42) also highlight the 2007–2009 global crisis as an example of a sovereign debt crisis where “downgrades have led to a mechanistic reaction by many market participants that sold at the same time, increasing market volatility and causing a self-sustaining downward spiral of the price of the debt instruments that can have serious negative effects for financial stability.” The mechanistic reaction referred to is often a consequence of rating triggers that have been popularly used in contracts to give lenders the right to terminate credit availability, accelerate credit obligations, or have the borrower post collateral in the event of specified rating actions. Such actions typically involve the security’s ratings falling below a certain level. These triggers hardwire buy or sell decisions to ratings, and precipitate negative cliff effects in prices DOI: 10.1057/9781137391506.0005

Credit Rating Agencies as Gatekeepers

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and spreads when securities are downgraded. Rating triggers can thus precipitate a liquidity crisis and contribute to firm bankruptcy.12

Sovereign debt and flawed gatekeepers: Indications of a deeper problem This chapter has emphasized the importance of access to low cost sovereign debt to many countries, the critical need that investors have to be able to credibly assess the creditworthiness of such debt, and the key role played by the big-three CRAs as gatekeepers to the international capital market by virtue of the high reliance placed on their rating of sovereign debt. This role allows them to wield enormous power, evidenced by the remarkable adverse effects of sovereign debt downgrades, not only at the macroeconomic level, but also extending downwards to firms, and outwards to countries and firms within regional blocks. The dire consequences of sovereign debt downgrades should imply that due care always be exercised in rating assignments and changes thereto. But the chapter has also pointed to questions surrounding the motives and processes of the big-three CRAs, which suggest that they may be flawed in their ability to objectively fulfill the functions of gatekeepers to the international capital markets. This lends some credence to the allegations of bias that have been leveled against the CRAs and provides the platform from which the remainder of this study has been launched.

Notes     

   

See Tennant (2014a) for a survey of the literature. Moody’s Investor Service, as quoted by Sinclair (2005, 7). Alcubilla and del Pozo (2012). As cited in Sinclair (2005, 16). By traditional ratings, like Alcubilla and del Pozo (2012, 18) we refer to “ratings for most types of instruments (corporates, financial institutions, insurance companies, sovereigns, etc.).” This is distinguished from the processes used for the more complex structure finance instruments. The factors typically considered in these analyses are discussed in Chapter 2. Alcubilla and del Pozo (2012). Almeida et al. (2014). Arezki et al. (2011).

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Sovereign Debt and Credit Rating Bias

 Reisen and Maltzan (1999).  Consistent with this assertion, sovereign ratings exhibited some “upward revision in 1999, as soon as recovery started for East Asian crisis countries” (Ferri et al. 2001).  Alcubilla and del Pozo (2012).

DOI: 10.1057/9781137391506.0005

2

Establishing the Determinants of Sovereign Debt Ratings: Is There Really Room for Bias? Abstract: This chapter examines the determinants of sovereign debt ratings to ascertain whether there is sufficient transparency and consistency to preclude any concerns about the existence of biases. It first presents the long list of variables that the CRAs consider in making their determinations. It then reviews the empirical studies that have sought to econometrically reproduce the ratings assigned by the big-three CRAs, to determine a short list of determinants that have been empirically shown to be of importance to the CRAs’ ratings assignments. The chapter concludes by showing that there is room for bias in sovereign debt rating actions, regarding which determinants are considered and how they are weighted. The recent history of sovereign debt ratings highlights issues with opacity and subjectivity in the rating process, which opens opportunities for biases to be introduced. Keywords: Bias; Credit Rating Agencies; Developing Countries; Gatekeepers; International Capital Markets; Sovereign Debt Tennant, David F., and Marlon R. Tracey. Sovereign Debt and Credit Rating Bias. New York: Palgrave Macmillan, 2016. doi: 10.1057/9781137391506.0006. DOI: 10.1057/9781137391506.0006





Sovereign Debt and Credit Rating Bias

Some hold the view that credit ratings are the output of an impartial scientific process, wherein once the inputs are known the outputs (the ratings) are easily reproducible. They therefore argue that the consistency associated with the process of determining ratings render any biases highly improbable. Others disagree. We neither critique the methodologies used by the credit rating agencies (CRAs) nor provide evidence of bias in this chapter. At this early stage our objective is simply to show that despite the appeals to impartiality and rigor, bias is still possible. This chapter examines the determinants of sovereign debt ratings to ascertain whether there is sufficient transparency and consistency to preclude any concerns about the existence of biases. The next three sections of this chapter delve into the empirical literature to establish the fundamentals. We first seek to ascertain exactly what the CRAs are measuring in their sovereign ratings, and the long list of variables that they consider in making their determinations. We then review the empirical studies that have sought to econometrically reproduce the ratings assigned by the big-three CRAs, to determine a short list of determinants that have been empirically shown to be of importance to the CRAs’ ratings assignments. The final section of the chapter presents our concluding arguments which show that there is indeed room for bias in sovereign debt rating actions, regarding which determinants are considered and how they are weighted. We validate this conclusion by briefly reviewing the recent history of sovereign debt ratings which highlights issues with opacity and subjectivity in the rating process. This opens opportunities for biases to be introduced.

What do sovereign ratings actually measure? Sovereign debt ratings are summary assessments of a government’s capacity and willingness to repay its debts in full and on time (Jaramillo 2010). Simply put, they are measures of the probability of default. Two types of default are included in the definitions used by the big-three CRAs. They are outright default, which is a “failure to pay a material sum of interest or principal on a debt instrument on its due date or within applicable principal or interest grace periods,” and restructuring default, which involves the “rescheduling, exchange or other restructuring of a debt instrument conducted in a manner deemed to be coercive, involuntary and distressed” (Bhatia 2002, 9). While outright defaults are usually DOI: 10.1057/9781137391506.0006

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

easily identified, classifying debt restructuring activities as restructuring defaults require the CRAs to make judgments as to whether a debt restructuring is voluntary or coercive. Because these judgments are made ex ante, they involve nuances such as whether the exercise was voluntary in letter but not in spirit. Such judgments are determined on a case-bycase basis by each agency, and they are made on an increasingly frequent basis, as larger numbers of sovereigns are engaging in debt restructuring exercises.1 The big-three CRAs typically do not include the failure to repay debt owed to other governments and official creditors (including the IMF and World Bank) as default events.2 They also do not include the probability of default by sub-national governments, state-owned enterprises or private enterprises in their considerations. Sovereign debt ratings therefore only refer to the central government’s capacity and willingness to honor its debts with private creditors.3 Note, however, that even within this narrow and well-defined objective function, there are distinctions among the big-three CRAs as to exactly what their sovereign ratings measure. Standard and Poor’s (S&P) measures the risk of default in terms of default probability, meaning that its ratings seek to simply reflect the probability of the occurrence of default. S&P’s ratings do not attempt to speak to the magnitude or severity of default, the length of time during which the government will remain in default, or to the expected amount involved in the recovery of principal. Moody’s ratings are a bit more involved, as they measure expected loss, which is a function of both the probability of default and the expected recovery rate after the default has occurred. Fitch uses a hybrid approach, rating issuers on a default probability basis and instruments on an expected loss basis.4 Despite these definitional differences, the IMF (2010) notes that in practice there is little divergence between the big-three among investment-grade ratings. The jury is still out on whether the divergence between the big-three on speculative ratings is non-negligible and, if so, is attributable to how default risk is defined by each agency.

Long list of theorized determinants Sovereign debt differs from other types of debt in a number of key ways. On the positive side, a sovereign is able to increase taxation or reduce expenditures so as to generate revenue to service debt. There is also DOI: 10.1057/9781137391506.0006



Sovereign Debt and Credit Rating Bias

a high probability of survival even after a default, as countries do not simply cease to exist. On the negative side, creditors have very little recourse when a sovereign stops servicing its debt.5 Sovereign debt risk analysis must therefore involve an assessment of the sovereign’s financial capacity and its willingness to repay. This assessment must recognize that the activities and policy actions of the sovereign both impact on and are influenced by the performance of the economy as a whole.6 Because of this, each of the big-three CRAs has identified a long list of variables that influence its sovereign debt ratings. As would be expected, many of these variables are similar, but the way in which the CRAs categorize and weight these variables differs. We first present the broad categorizations of key rating factors utilized by each of the big-three CRAs, and then briefly compare the variables considered by all three.

Rating factors used by each of the big-three CRAs Moody’s Moody’s first assesses the sovereign’s economic and institutional strength, which contribute equally to their evaluation of the country’s economic resilience. Economic strength is argued to be important in determining a country’s resilience, as its ability to generate revenue and service debt is dependent on the extent to which it can foster economic growth and prosperity. In assessing economic strength, Moody’s focuses on growth potential, diversification, competitiveness, national income and the scale of the economy. It also adjusts for credit booms, as excessive credit growth could unsustainably inflate the indicators used to assess economic strength. Moody’s (2013, 8) notes that “a lack of economic strength has been a decisive factor in past sovereign defaults . . . Large diversified economies are much more resilient to . . . external shocks than smaller non-diversified countries.” The assessment of institutional strength reflects whether the country’s institutions support its capacity and willingness to repay debt, and whether the government has the capacity to implement sound policies that foster economic growth. Moody’s utilizes the World Bank’s Worldwide Governance Indicators to assess the effectiveness of the fundamental institutional framework; inflation performance and volatility as a proxy for assessing policy credibility and effectiveness; and the government’s track record of default as an adjustment factor. It notes that “about 30 of past sovereign defaults have been directly related to DOI: 10.1057/9781137391506.0006

Establishing the Determinants of Sovereign Debt Ratings



institutional and political weaknesses, ranging from political instability to weak budget management and governance problems or to political unwillingness to pay” (Moody’s 2013, 12). The fiscal strength of the sovereign is next included in Moody’s analysis to capture the general health of government finances. Relative debt burdens are assessed by examining the debt to gross domestic product (GDP) and debt to revenue ratios. Interest payments relative to revenue and GDP are used to assess debt affordability. Moody’s also makes adjustments for the structure of government debt. Moody’s (2013, 15) highlights the importance of this assessment of fiscal strength, as “more than a third of sovereign defaults have occurred as a result of persistent external and fiscal imbalances which have, over time, built up an unsustainably high debt burden.” The score of the country’s fiscal strength is combined with the economic resiliency score to determine the financial strength of the government.7 This provides an evaluation of the government’s ability to withstand shocks from a medium-term perspective, and from this, a preliminary, indicative rating range is derived. The country’s susceptibility to a range of event risks is then considered, which, if necessary, will lower the preliminary range that was computed. This seeks to capture the risk of sudden and extreme events that may strain public finances, thus increasing the likelihood of default. The event risks considered by Moody’s include political risk (both domestic and geopolitical); government liquidity risk; banking sector risk and other contingent liabilities; and external vulnerability risk. Moody’s notes that past experience has indicated that shocks such as banking and foreign exchange crises tend to precipitate sovereign defaults.

S&P S&P’s credit rating framework for sovereigns is broadly similar to that of Moody’s. S&P forms an institutional and governance effectiveness and economic profile for each sovereign, to reflect their view of “the resilience of a country’s economy, the strength and stability of its civil institutions, and the effectiveness of its policymaking.”8 It does this by combining the country’s economic score with its institutional and governance effectiveness score. The economic score is driven by the country’s income levels, growth prospects and economic diversity and volatility. S&P gives each sovereign an initial economic score based on its income level (measured by GDP per capita). It then adjusts this score upwards or downwards DOI: 10.1057/9781137391506.0006

Sovereign Debt and Credit Rating Bias



based on the economy’s growth prospects, and potential concentration or volatility. Like Moody’s, S&P’s assignment of an institutional and governance effectiveness score assesses the extent to which a sovereign’s credit fundamentals are affected by the performance of government institutions and policymakers in the delivery of sustainable public finances, promotion of balanced economic growth, and response to economic or political shocks.9 S&P however, seems to consider more factors than Moody’s in making this assessment, as its institutional and governance effectiveness score is based on the following factors:  

 

The effectiveness, stability and predictability of the sovereign’s policymaking and political institutions; The transparency and accountability of institutions, data and processes, as well as the coverage and reliability of statistical information; The sovereign’s debt payment culture; and External security risks (such as war or threats of war).10

In the computation of this score, the first factor is used as the primary factor, while the second provides additional information and acts as a qualifier. The third and fourth factors are used to make any necessary adjustments. S&P also computes external, fiscal and monetary scores for each sovereign, which it combines to form its flexibility and performance profile. This profile reflects S&P’s view of “the sustainability of a government’s fiscal balance and debt burden, in light of the country’s external position, as well as the government’s fiscal and monetary flexibility.”11 S&P’s external score reflects each sovereign’s ability to obtain the requisite foreign funds to meet its public and private sector obligations to non-residents (Standard and Poor’s Rating Services 2013, 17). Three factors are included when computing the external score:  



The status of a sovereign’s currency in international transactions; The country’s external liquidity, which provides an indication of the economy’s ability to generate the foreign exchange necessary to meet its public and private sector obligations to non-residents; The country’s external position, which shows residents’ assets and liabilities (in both foreign and local currency) relative to the rest of the world.12

DOI: 10.1057/9781137391506.0006

Establishing the Determinants of Sovereign Debt Ratings



S&P’s fiscal score is similar to that of Moody’s, in that it reflects the sustainability of a sovereign’s deficits and debt burden. It considers a country’s “fiscal flexibility, long-term fiscal trends and vulnerabilities, debt structure and funding access, and potential risks arising from contingent liabilities” (S&P 2013, 23). S&P’s monetary score “reflects the extent to which its monetary authority can fulfil its mandate while supporting sustainable economic growth and attenuating major economic or financial shocks.”13 This score includes an analysis of:   

The sovereign’s ability to coordinate monetary policy with fiscal and other economic policies to support sustainable economic growth. The credibility of monetary policy, as measured, among other factors, by inflation trends over an economic cycle. Market-oriented monetary mechanisms’ impact on the real economy, which is largely a function of the depth and diversification of the resident financial system and capital markets.14

S&P notes that the inclusion of the monetary score is important, as a flexible monetary policy can aid in slowing or preventing a deterioration of sovereign creditworthiness in times of stress. This is because monetary policy can be used as a stabilization tool to ease credit conditions when economic growth is below-par, and to tighten credit conditions in a boom. Having formed the flexibility and performance profile from the combination of the external, fiscal and monetary scores, it is then combined with the institutional and governance effectiveness and economic profile to determine an indicative rating level for the sovereign. S&P’s foreign-currency sovereign rating is then determined after factoring in supplemental adjustments.

Fitch Fitch’s sovereign rating analysis incorporates four broad factors: macroeconomic performance, policies and prospects, with a weight of 10.3; structural features of the economy (47.4); public finances (25.4) and external finances (16.9). Variables representing each of these factors are included in a rating model that is estimated using ordinary least squares (OLS) regressions. The weights are determined by the coefficients of the regression model.

DOI: 10.1057/9781137391506.0006



Sovereign Debt and Credit Rating Bias

Fitch (2014, 8) notes that a “track record of macroeconomic stability, underpinned by a credible policy framework, has a material positive influence on sovereign creditworthiness and ratings.” This is because protracted periods of economic instability and fragile policy frameworks increase the economy’s vulnerability to shocks and makes it more likely to face difficulties in meeting debt servicing obligations. Fitch thus assesses macroeconomic volatility using a 10-year rolling standard deviation of the annual percent changes in real GDP, the consumer price index (CPI) and the real effective exchange rate (REER). The agency notes that the legacy of high and volatile inflation is long-lasting and leads to relatively low ratings. Because of the historical link between sovereign defaults and financial crises caused by inappropriate exchange rate policies, Fitch also pays particular attention to sovereigns that have fixed or managed exchange rate regimes. In this respect they emphasize the consistency and sustainability of the macroeconomic policy framework, the robustness of the financial sector, balance-of-payments trends and the level of international reserves. Fitch also notes that countries with currencies that have reserve currency characteristics are more highly rated, as they enjoy strong financial and policy flexibility. The structural features of the economy, including political risk, are weighted most highly by Fitch in their sovereign ratings analysis. Fitch (2014, 9) notes that “high-income and savings economies, open to international trade and finance with positive business environments tend to have highly rated sovereigns.” Fitch bases its assessment on the United Nations’ Human Development Index (HDI) and the World Bank’s Ease of Doing Business Survey and Governance Indicators. These allow for a determination of the relative strengths and weaknesses of the business environment, human capital and the overall level of governance. Fitch also uses the gross national income per capita as an imperfect proxy for the types of activities that labor is engaged in. A high per capita income suggests that labor is involved in high-value-added activities, making the economy better able to absorb adverse shocks. A high GDP per capita is similarly assumed to improve a country’s ability to respond to shocks, as it is positively correlated with the stock of human and physical capital, and the stock of financial assets. The soundness and supervision of the banking sector is considered to be particularly important to Fitch’s sovereign rating, as a weak financial sector can “undermine economic performance, macroeconomic stability and impose large fiscal costs (largely through contingent liabilities).”15 DOI: 10.1057/9781137391506.0006

Establishing the Determinants of Sovereign Debt Ratings



As with S&P and Moody’s, political risk is a key consideration for Fitch. It notes that “the political will and ability to mobilize resources necessary to honor their financial obligations is a key element of sovereign creditworthiness.”16 The political risk factors considered by Fitch include “the legitimacy of the political regime; the effectiveness of government (in terms of the formulation, implementation and credibility of policy); control of corruption; and an assessment of the likelihood of severe civil conflict and war risk” (2014,10). These are primarily reflected in Fitch’s model using the World Bank’s Governance Indicators. Fitch notes that a country that is highly divided by race, religion, region, or income distribution, will be closely considered, as such divisions may challenge the authority of the government and undermine its ability to effectively implement policies. Any recent incidence of debt default and rescheduling is also factored into Fitch’s analysis, but is moderated by a consideration of whether the default is symptomatic of continuing weakness in the political will and capacity to honor debt obligations. In addition, the country’s relations with the international community, including the IMF and World Bank can influence Fitch’s sovereign credit rating. The relationship with the IMF is considered as giving strong signals. Lower ratings are assigned to a country that is unwilling or unable to secure policy-conditional financing from the IMF in a distress scenario. Notwithstanding this, receipt of emergency financial support is viewed as a sign of distress and a weakening credit profile. A country’s management of its public finances is the second most highly weighted element of Fitch’s sovereign risk analysis. The factors considered therein include the sustainability of the public debt burden (both stock and debt service), the country’s stock of unencumbered and marketable financial assets, contingent liabilities and the net foreign debt/asset position of the sovereign.17 Fitch also examines the structure of public debt, with the maturity, interest rate and currency composition being important factors in its determination of market risk. High levels of financial intermediation are typically associated with a larger capacity to sustain high domestic debt burdens. A track record of accessing funding from international capital markets can also heighten the ratings received. The country’s fiscal track record also has a significant impact on Fitch’s assessment of sustainability. Fitch evaluates the vulnerability of public finances to external shocks, and so the degree of budgetary flexibility is important. Particular attention is paid to the share of interest payments in expenditure relative to revenue. DOI: 10.1057/9781137391506.0006

Sovereign Debt and Credit Rating Bias



Finally, Fitch considers the state of the country’s external finances in its analysis of sovereign creditworthiness. In this regard, it considers the “composition and stock of foreign assets and liabilities, as well as the capacity of the economy to generate foreign exchange.”18 The importance is obvious—foreign exchange is needed to service foreign-currency obligations. The current account of the balance of payments is examined to take account of the volatility and potential vulnerability of receipts from non-factor income and transfers. Vulnerability to terms of trade and other shocks is also considered by examining the degree of reliance on a single commodity or service. Large current account deficits are viewed as a source of risk, particularly if financed by volatile capital flows. Fitch (2014, 16) highlights a number of other factors as being determinants of the sustainability of any given level of external indebtedness:    

Willingness of non-residents to extend credit and purchase domestic assets; Share of current output and CXR (current account receipts) devoted to servicing external debt; Maturity and currency structure of foreign liabilities and assets; and Distribution of foreign liabilities and assets by sector.

A brief comparison of the variables considered by the big-three CRAs A clear finding from the above summaries is that each of the big-three CRAs has a substantially different method of combining the factors that they use to determine a sovereign’s credit rating. Moody’s rates its four key factors on a fifteen-point scale and combines them in three stages; S&P ranks its five key factors on an eleven-point scale and combines them in five stages; and Fitch includes its four broad factors as nineteen variables in a multiple regression model, from which it derives its weightings and preliminary ratings. Notwithstanding this, many of the key drivers of ratings are quite similar across all three agencies. Table 2.1 facilitates this comparison. Per capita GDP, the level and composition of debt, financial resources of the government, some indicator of political stability, the robustness of the financial sector, and the exchange rate regime and access to foreign exchange are key criteria utilized by all three (IMF 2010). The justification for including these variables is also quite similar across agencies. DOI: 10.1057/9781137391506.0006

DOI: 10.1057/9781137391506.0006

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DOI: 10.1057/9781137391506.0006

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3FTFSWF
BEFRVBDZ r
&YDIBOHF
SBUF
SFHJNFT r
*OEFYBUJPO
BOE
EPMMBSJ[BUJPO

r
&YDIBOHF
SBUF
SFHJNF r
*OEFYBUJPO
BOE
EPMMBSJ[BUJPO

Moody’s

Fitch

Continued

Source: IMF (2010, 101–102).

Other

Structural/ Institutional

Political

Exchange Rate

External Finances

table 2.1

Establishing the Determinants of Sovereign Debt Ratings



Differences are evident when one compares the number of variables considered by each agency, and the weight applied to each. Moody’s presents the shortest list of variables, seeming to place more emphasis on event risk, while S&P and Fitch put relatively more weight on the contingent liabilities of the government. Moody’s, however, along with S&P, seem to consider a broader set of factors than Fitch when analyzing the general economic structure. In this respect, Moody’s includes innovation and investment in human capital, and S&P includes income discrepancies and protectionist factors.19 The IMF (2010) also notes that the differences in the relative weighting of factors are evident not only across agencies, but also between types of countries. For all three agencies, the IMF observes that the relevance of each factor depends on the type of country being reviewed. As an example, it notes that for countries with a fixed or managed exchange rate, the level of reserves is a much more prominent factor.

Short list of empirically established determinants The list of variables presented by the big-three CRAs as potential determinants of sovereign credit ratings is quite long, and the respective explanations of the relationships between these variables and how they are combined to form a rating can be imposing. The comparison of variables across agencies, however, reveals a smaller subset of indicators that are viewed by all three agencies as highly important. The empirical literature on the determinants of sovereign credit ratings also consistently notes a small set of variables that can explain much of the ratings assignments of the big-three. Such studies have sought to reverse-engineer sovereign ratings from a list of theorized determinants. Because sovereign ratings summarize such a large volume of information, these empirical studies have sought to “predict country ratings based on a parsimonious set of economic variables.”20 Cantor and Packer (1996, 37) wrote the seminal paper in this line of research, and they noted that “our investigation suggests that, to a large extent, Moody’s and Standard and Poor’s rating assignments can be explained by a small number of well-defined criteria.” More recently, Basu et al. (2013, 2) confirmed this finding by noting that “despite the disturbance caused by the global financial crisis, a handful of macroeconomic, structural and governance variables are sufficient to predict nearly 90 percent of the variations in ratings.” Canuto et al. (2012, 2) similarly notes that “empirically most of the differences between DOI: 10.1057/9781137391506.0006

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Sovereign Debt and Credit Rating Bias

risk ratings of countries can be explained—insofar as sovereign risk is concerned—by a relatively small number of variables.” This section briefly reviews a sample of such studies to highlight the short list of variables that have been found to be important determinants of sovereign credit ratings. Table 2.2 presents a summary of the main studies on the determinants of sovereign ratings. The second column indicates that of the twelve studies reviewed, the number of significant explanatory variables table 2.2

Key empirical studies on the determinants of sovereign ratings

Authors & Agencies Examined

Statistically Significant Determinants

Cantor and Packer () Moody’s, S&P

GDP per capita (+), GDP growth (+), Inflation (–), External debt ratios (–), Economic development (+), Default history (–)

Mulder and Perrelli () Moody’s, S&P

Debt over exports (–), Rescheduling history (–), Fiscal balance (+), Output growth (+), Inflation (–), Investment to GDP (+)

Afonso () Moody’s, S&P

GDP per capita (+), External debt (–), Level of Economic development (+), Default history (–), Real growth rate (+), Inflation rate (–)

Rowland () Moody’s, S&P

GDP per capita (+), GDP growth (+), Inflation (–), External debt ratios (–), International reserves (+), Openness (+)

Rowland and Torres () Moody’s, S&P

GDP growth (+), Inflation (–), External debt ratios (–), International reserves (+), Openness (+)

Mellios and Paget-Blanc () Moody’s, S&P, Fitch

GDP per capita (+), Government income (+), Real exchange rate changes (+), Inflation (–), Default history (–), Corruption index (+)

Butler and Fauver () Moody’s, S&P

GDP per capita (+), Inflation (–), Foreign debt/GDP (–), Underdevelopment Index (–), Emerging Market dummy (–), Default dummy (–), Legal environment (+)

Afonso et al. () Moody’s, S&P, Fitch

GDP per capita (+), GDP growth (+), Government Debt (–), Inflation (–), External debt (–), External reserves (+), Sovereign Default (–), Government effectiveness (+), EU countries (+)

Archer et al. () Moody’s, S&P, Fitch

Trade (+), Inflation (–), GDP growth (+), Default (–)

Jaramillo () Moody’s, S&P, Fitch

External public debt (–), Domestic public debt (–), Political risk (–), Exports (+), Financial depth (+)

Afonso et al. () Moody’s, S&P, Fitch

Short-term impact: Changes in GDP per capita (+), GDP growth (+), Government debt (–), Government balance (+). Long-term impact: Government effectiveness (+), External debt (–), Foreign reserves (+), Default history (–)

Canuto et al. () Moody’s, S&P, Fitch

GDP per capita (+), Inflation (–), Economic growth (+), External debt/Current account receipts ratio (–), Central government gross debt/Total fiscal receipts ratio (–), Default events since  (–), Exports plus Imports/GDP (+)

Source: Studies referenced therein. DOI: 10.1057/9781137391506.0006

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ranges from four to eight, with six being the modal number. Inflation and external debt are the most commonly cited explanatory variables, being significant and having negative coefficients in all but two of the studies reviewed. Economic growth (9 studies), default history (9) and per capita GDP (8) are also clearly important determinants of sovereign ratings, being significant in the majority of studies. Indicators of openness (4 studies), international reserves (4) and the level of economic development (3) were also significant in multiple studies. In addition, if institutional and governance effectiveness are viewed broadly, then there are five studies in which indicators thereof are significant. While this short list of empirically established determinants clearly does not capture all the nuances of the ratings process for each of the CRAs, it is useful, as it can aid governments in focusing their attention on the variables that are most likely to have the greatest effect on their sovereign debt ratings.

Is there really room for bias? Red flags from the past The detailed and systematic presentation of the sovereign credit rating drivers and assumptions by each of the big-three CRAs in recent times raises the question as to whether there is really room for bias. Moody’s (2013, 1) makes the claim that users of their published methodology should be able to “estimate the likely credit rating for a sovereign within a three notch alpha-numeric rating range in most cases.” Fitch (2014, 6) notes that its sovereign rating model “uses empirical data, . . . does not allow for judgemental analyst input, and aims to provide a transparent, coherent framework for comparing sovereigns across regions and through time.” Such bold claims have come as each of the big-three have sought to respond to criticisms about the opacity, subjectivity and randomness of their sovereign debt ratings following the Asian financial crisis of the mid to late 1990s, and the more recent global financial crisis. The responses have been two-fold, the methodologies themselves have been revised, and, even more dramatically, the agencies’ have become much more transparent with respect to the methodologies used. This is a relatively new phenomenon, which needs to be understood in the context of the recent history of sovereign debt ratings. It also needs to be carefully evaluated to ascertain whether opportunities for bias persist under the newly applied veneer of objectivity and transparency. DOI: 10.1057/9781137391506.0006

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Historically, it must be noted that although the first sovereign credit ratings were issued before World War I, only a limited number of sovereigns were rated during much of the early period. Most of those ratings were suspended in the mid to late 1960s with the passage of the Interest Equalization Tax (IET) in the United States.21 The withdrawal of the IET in 1974 marked the start of the modern era of sovereign ratings. Throughout the rest of the 1970s and 1980s, sovereign rating activity steadily increased, but with an almost complete focus on industrialized countries. It is only since the 1990s that there has been a major increase in the ratings of emerging market and transition economies. Modern sovereign ratings are therefore a relatively new phenomenon, with the big-three CRAs having less than three decades of experience rating nonOECD countries (Bhatia 2002). Although this is a long enough period for empirical analysis,22 in the broader scheme of things it indicates that even the big-three have quite limited experience rating sovereigns in some parts of the world. This is particularly so for low income and lower-middle income countries in Sub-Saharan Africa. This is important, because since the 1990s, “countries are coming under increasing pressure to be rated, (as) . . . investors looking . . . at putting their money into out-of-the-way corners of the world want the comfort of a rating agency assessment” (Sinclair 2005, 139). The comfort they receive is because the CRAs purport to have, not only expert analytical capabilities, but also “local knowledge of a vast number of debt security issuers.”23 The extent to which this expert local knowledge could have been garnered in such a short period for so many countries has been questioned. Ferri (2004) notes that although rating agencies have a good track record in anticipating borrowers’ default in the developed countries where they have long been active, they have a poor track record in developing countries with which they have a relatively short history. So even though it is assumed that the big-three CRAs derive their credit ratings by using a “relatively small set of well-defined criteria with similar weights,”24 they often have disagreed about the ratings of sovereign debt, particularly in relation to the announcement of upgrades and downgrades.25 This is evidenced by marked differences in the frequency with which the CRAs announce rating changes, with S&P being the most frequent announcer of rating changes, followed by Moody’s and Fitch (Arezki et al. 2011). This is critical, as downgrades can severely curtail the access to financing and increase the cost at which such financing can be accessed, DOI: 10.1057/9781137391506.0006

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particularly if they push a country from investment to speculative grade. Because the sovereign rating generally sets the ceiling for the ratings assigned to domestic companies, it also affects private financing costs. It is for these reasons that the announcement of sovereign debt downgrades are so strongly contested. As an example, following Australia’s second downgrade in the space of three years, in 1989 the Australian minister of finance banned all contact with Moody’s officials, expressing exasperation about a difference of opinion regarding the relevant variables to be considered in determining the rating. A similar conflict was evident in Japan in the late 1990s, with Japanese analysts suggesting that only the narrow capacity to service debt should be considered in rating, rather than the broader scrutiny that was being meted out by Moody’s. The Japanese finance minister thus reportedly expressed a “strong feeling of displeasure” upon Japan’s loss of its top rating from Moody’s. This episode was repeated in Japan in 2002, culminating in the Japanese vice finance minister writing a letter that “attacked the qualitative explanation of Japan’s ratings, noting the absence of objective criteria” (Sinclair 2005, 144). These are just two of many examples in which country officials have challenged sovereign debt rating assignments on the basis of a lack of clarity about why the assignments were given. Such challenges have repeatedly arisen because, up until just recently, the CRAs were not specific about what determines their ratings and about their rating procedure (Mora 2006). In response to such challenges, or even simple questions about how ratings were derived, the CRAs have been characterized as high-handed or unresponsive. Sinclair (2005, 33) notes that this is part of a deeply entrenched culture of secrecy, wherein one of the “most secretive aspects of the rating business is the analytical process for producing bond rating judgements.” Moody’s in particular is noted to have had a history of a highly conservative and secretive corporate culture, resulting in it typically revealing very little about its ratings criteria. This had been justified on the basis that publication of criteria based on financial ratios could potentially distort expectations among issuers, while criteria based on qualitative information tended to confuse people when the ratings assigned did not conform to expectations based on the quantitative criteria. Such justifications were, however, not able to withstand the onslaught of scrutiny and criticisms following the Asian financial crisis of the mid to late 1990s. The big-three CRAs were forced to respond to public DOI: 10.1057/9781137391506.0006

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Sovereign Debt and Credit Rating Bias

sentiment by lowering the veil of secrecy, and by becoming more willing to present clear rationale for their ratings. As a result, since 2000, Moody’s publishes a Rating Methodology Handbook, which contains financial ratio appendices. S&P similarly publishes a number of criteria books that present guidelines on appropriate financial ratios and that link ratios with specific ratings.26 These trends in improved transparency were further propelled by the global financial crisis, with the IMF (2010, 94) noting the CRAs’ emphasis “on the publication of the underlying research, as well as revamped external websites to enhance transparency.” They further note that “there has been a particular emphasis on publishing better and more accessible research on sovereign creditworthiness.” Note, however, that this increased transparency has not necessarily translated into considerably enhanced clarity with respect to the determination of sovereign debt rating assignments. This is because, although the CRAs have provided additional information on the variables considered in their analyses, they have not been “explicit about the weights given to those variables in their final assessments” (Jaramillo 2010, 4). In addition, although there is much more transparency regarding the ratios utilized, the quantitative analysis is only one aspect of ratings determination. Sinclair (2005, 34), in reviewing the comments made by rating analysts, concluded that “rating mixes qualitative and quantitative data, producing a fundamentally qualitative result—a judgement.” This is clearly evidenced in the caveats included in the methodological outlines provided by each of the big-three CRAs. These are presented verbatim below, so as not to skew readers’ interpretation. The four rating factors in the scorecard may not in all cases constitute an exhaustive treatment of the considerations that are important for a particular sovereign rating, and the rating may differ from the one implied by the scorecard range. The use of supplementary adjustment factors is an attempt to capture idiosyncratic country-specific factors which may not be universally available or relevant. In addition, our ratings incorporate expectations around future metrics and risk developments, while the information that is used to determine the scoring is mainly historical. In some cases, our expectations around future credit developments may be informed by confidential information that we cannot publish or otherwise disclose. Rating outcomes may consider additional factors that are difficult to measure or that have a meaningful effect in differentiating credit quality only in some, but not all cases. While these are important considerations, it is not possible to express them precisely in the rating methodology scorecard without making it

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excessively complex and significantly less transparent. Ratings may also reflect circumstances in which the weighting of a particular factor will be substantially different from the weighting suggested by the scorecard. Moody’s Investor Service (2013, 26) These criteria represent the specific application of fundamental principles that define credit risk and ratings opinions. Their use is determined by issuer or issue specific attributes as well as Standard & Poor’s Ratings Services’ assessment of the credit and, if applicable, structural risks for a given issuer or issue rating. Methodology and assumptions may change from time to time as a result of market and economic conditions, issuer or issue specific factors, or new empirical evidence that would affect our credit judgement . . . A sovereign foreign-currency rating might differ by more than one notch compared with the indicative rating level if it meets one or more of the supplemental adjustment factors . . . Those supplemental adjustment factors are based on a forward-looking analysis. They are important because certain components of credit risk can, at times, dominate overall creditworthiness even if the other factors remain stable. The dominance of negative supplemental adjustment factors is based on our judgement that the supplemental risks can jeopardize debt service capacity more than positive developments can improve them. Standard and Poor’s Rating Services (2013, 8 & 41) Fitch’s sovereign analysts use the SRM (Sovereign Rating Model) as an important analytical tool and as one of a range of qualitative and quantitative inputs into the rating process. However, Fitch recognizes that no model can fully capture all the relevant influences on sovereign creditworthiness, meaning the actual rating determined by the sovereign rating committee can and does differ from that implied by the rating model. FitchRatings (2014, 7)

Reflected in each of the above quotations is an acknowledgment of a qualitative judgmental element to the ratings process. This tends to be underemphasized in the CRAs’ methodological outlines, which heavily feature the objective, quantitative aspect of the process, seemingly minimizing mention of the qualitative, judgmental elements. This may be misleading, as the IMF (2010) conducted a rigorous analysis of ratings and came to the conclusion that the qualitative judgmental element is an equally important driver of ratings as is the quantitative analysis. While the judgment of the rating committee allows for an important degree of flexibility, it also undeniably introduces an element of subjectivity. Sinclair (2005, 139) notes that “sovereign rating is one of the most

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Sovereign Debt and Credit Rating Bias

subjective areas of credit rating, (as) . . . it incorporates opaque, quality of life factors and what seem to be many overtly political variables.” While recent improvements in the objectivity and transparency of sovereign debt ratings cannot be denied, it is the remaining subjectivity in the process that leaves open the possibility of bias.

Notes       

         

 

See e.g. Robinson (2014) in King and Tennant, eds. (2014). IMF (2010). Canuto et al. (2012). Bhatia (2002), Canuto et al. (2012) and IMF (2010). FitchRatings (2014), S&P (2013) and Moody’s Investor Service (2013). FitchRatings (2014). Moody’s Investors Service (2013, 5) notes that they use an aggregation function to combine economic resiliency with fiscal strength. “The weight of fiscal strength is highest for countries with moderate economic resiliency. The rationale is that the credit worthiness of countries with high economic resiliency is less susceptible to changes in their debt metrics, whereas the creditworthiness of countries with moderate economic resiliency is more sensitive to changes in their fiscal strength. In contrast, the creditworthiness of countries with low economic resiliency tends to be weak irrespective of debt metrics.” S&P (2013, 6). S&P (2013). Ibid. Ibid., 6. Ibid., 18. Ibid., 31. Ibid. Fitch (2014, 11). Ibid., 10. “The sovereign net foreign asset position is defined as general government and central bank gross external debt (on a residency basis) less the international reserves of the central bank and foreign assets (debt and equity) of the government (e.g. in sovereign wealth and stabilization funds), expressed as a percentage of GDP and current account (of the balance of payments) receipts (CXR)” (Fitch 2014, 14). Fitch (2014, 15). IMF (2010).

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Establishing the Determinants of Sovereign Debt Ratings

 Jaramillo (2010, 4).  The IET was a “15 percent levy on interest earned from foreign borrowers other than those domiciled in Canada” (Bhatia 2002, 5).  IMF (2010).  Sinclair (2005, 8).  Cantor and Packer (1996, 37).  Eijffinger (2012).  Sinclair (2005).

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

3

Resilience in Spite of Controversy: Conditions for Bias in the Credit Rating Industry Abstract: It has been argued that allegations of bias are baseless, as, if they were true, the reputation-dependent credit rating industry would not have been able to survive. This chapter summarizes a number of controversies that have recently surrounded the big-three CRAs. These controversies have severely tarnished their reputations, but have not threatened their survival. To explain this phenomenon, the chapter examines the nature of the credit rating industry, and presents a number of industry-specific factors that have allowed the reputation-dependent CRAs to be resilient to the controversies surrounding their activities. Whereas these factors by themselves do not constitute proof of biases, they do indicate their possibility. They also refute the argument that reputational capital would preclude the existence of any prolonged perpetuation of biases. Keywords: Bias; Controversies; Credit Rating Agencies; Developing Countries; Gatekeepers; International Capital Markets; Reputation; Sovereign Debt Tennant, David F., and Marlon R. Tracey. Sovereign Debt and Credit Rating Bias. New York: Palgrave Macmillan, 2016. doi: 10.1057/9781137391506.0007. 

DOI: 10.1057/9781137391506.0007

Resilience in Spite of Controversy

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Public perception of the big-three credit rating agencies (CRAs) has soured because of the recent sub-prime crisis and the Eurozone debt crisis, and because of lingering resentment from controversies surrounding the East Asian financial crisis and the Enron debacle. Over the past seven to eight years, popular media houses in the United States and the European Union have published scathing articles about the CRAs surrounding these controversies, many of which questioned not only their accuracy, but also their integrity. Some headlines include the following: “Credit Rating Agencies Fending Off Lawsuits from Subprime Meltdown”; “Suddenly, the Rating Agencies Don’t Look Untouchable”; “Will the Teflon Ratings Agencies Start Losing Fraud Suits?”; “S&P controversy fuels demands for ratings reforms!”; “Credit Rater S&P to be Banned for a Year from Biggest Part of Commercial Bond Market!”; “Now It’s Moody’s Turn for a Review: Justice Dept. Probing possible antitrust violations!” and “Ratings agencies suffer ‘conflict of interest’, says former Moody’s boss!”1 The reputation of the big-three CRAs has been severely tarnished. This is critical, as CRAs rely heavily on their reputation as providers of honest, accurate ratings, with the knowledge that providing objective and accurate ratings will improve future business opportunities (Duan and Van Laere 2012). Standard and Poor’s (S&P) has insisted that their reputation is more important than revenue generation, and Moody’s has emphasized the importance of reputational capital in their business model.2 The continued strength of the credit ratings industry and the dominance of the big-three in the aftermath of all these scathing attacks begs the question as to how could an industry that is so heavily reliant on the reputation of its key players survive the numerous controversies that have severely tarnished its reputation. Are there factors that have contributed to the resilience of the credit rating industry, and the bigthree in particular? This question is central to the theme of this book, as it has been argued that allegations of bias on the part of CRAs are ludicrous, as such biases would diminish their reputational capital, jeopardize their business model and eventually force them out of business. However, if there are factors that heighten the CRAs’ resilience to the reputational damage caused by controversy, then these same factors would allow biases to persist even in the reputation-dependent credit rating industry. The aim of this chapter is to determine whether any such factors exist in the credit rating industry. Whereas a history of the credit rating industry could be instructive in this respect, we are not interested in a simple chronicling DOI: 10.1057/9781137391506.0007

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of events, as this has been well-covered in the literature.3 Instead, this chapter examines the evolution of the industry, to identify factors that have led to the resilience of the big-three CRAs. These factors represent the conditions that have allowed the CRAs to flourish despite the damage done to their reputation and credibility by the numerous controversies surrounding their motives and practices. We argue that these conditions could allow biases to exist in the rating of sovereign debt. Whereas identification of such conditions does not constitute proof of biases, they do indicate their possibility and provide a refutation of the argument that reputational capital would preclude the existence of any prolonged perpetuation of biases. To highlight the resilience of the credit rating industry, the next section in this chapter presents a few of the controversies surrounding the CRAs that are particularly pertinent to this study. The conditions that have led to this resilience are then delineated.

Controversies The numerous and varied controversies surrounding the CRAs have been collated and summarized in, for example, Sinclair (2005) and Lynch (2010). This section focuses only on those controversies that are particularly relevant to establishing the possibility of bias in sovereign debt rating actions. As such, we delve more deeply into the conflict of interest controversy, highlighted in Chapter 1 as raising questions about the motives of the CRAs, and then examine a number of rating failures specifically related to sovereign debt. For each we present a summary of the allegations against the CRAs, the defense that has been mounted by the CRAs and their proponents, and evidence as to the eventual outcome.

Conflict of interest The allegation It has been alleged that fundamental conflicts of interest exist within the credit rating industry, that induce profit-seeking CRAs to assign unduly favorable ratings to securities—indicating a lower amount of default risk than actually exists. This occurs because the big-three CRAs earn the vast majority of their revenues from issuers which pay the CRAs to have their securities rated. It is estimated that approximately 80 to 90 DOI: 10.1057/9781137391506.0007

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

of the revenues generated by American CRAs are paid by issuers (Lynch 2010). This issuer-pays revenue model is analogous to food and drug safety agencies being remunerated by food and drug manufacturers. Or, more practically, to securities analysts being paid by “broker-dealers that do a substantial amount of investment banking business with the same companies about which the analysts publicly opine.”4 It is argued that CRAs would be reluctant to give poor grades to debt issuers for fear of losing business. The CRAs are alleged to be sensitive to the needs of the issuers—their paying clients—who want high and not necessarily accurate ratings. The CRAs are thus incentivized to issue higher ratings, whether or not the ratings are accurate. This clearly does not align with investors’ need for reliable ratings information, consequently exposing them to risk for which they are not being compensated (Lynch 2010). The situation is exacerbated by the CRAs’ practice of offering a preassessment of what the rating is likely to be, before issuing the formal rating and before payment is received. This precipitates shopping-around by issuers, wherein “the issuer solicits a rating from another CRA and searches the market to determine which CRA will provide the highest rating. Shopping around plays off rating agencies against one another and might ultimately result in further inflation of ratings, in turn contributing to the further deterioration of rating accuracy” (Duan and Van Laere 2012, 3241). The defense The CRAs and their defenders have acknowledged that the issuer-pays model has created a conflict of interest, but have downplayed its impact. They have argued that a CRA’s success is heavily dependent on its reputation among investors for issuing credible ratings. They are thus forced to manage the conflict such that the accuracy of ratings is not affected. This is in a bid to maintain their reputational capital, as “if the investing public were to come to believe that a credit rating agency was captured by issuers and was consequently issuing unreliable, suspect, or less than fully defendable opinions, the credit rating agency’s reputation would be tarnished. The investment community would then discount the value of its ratings, thus reducing market demand for its rating services. Revenues would consequently decrease. In the worst-case scenario, the credit rating agency would go out of business” (Lynch 2010, 250). This desire to maintain their good reputation is argued to provide sufficient incentive for the CRAs to exercise the requisite levels of due diligence DOI: 10.1057/9781137391506.0007

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in conducting risk analysis, so as to counter the effects of the conflict of interest. This is bolstered by compensation policies that the CRAs design to ensure that their analysts’ remuneration is based on the demonstrated accuracy of their ratings. The intention is to insulate the analysts from any inappropriate interference from issuers, and to ensure that their considerations are not influenced by the size of the issue being analyzed, or by any other factors that might impinge on objectivity.5 Finally, it is argued that the CRAs, particularly the big-three, are highly unlikely to be captured by any one issuer, as no single issuer accounts for a sufficiently large proportion of their revenues to enable them to manipulate the agency (Sinclair 2005). S&P, for example, has stated that “no single issuer or issuer group accounts for more than two percent of its total annual rating revenue” (Lynch 2010, 254–255). The evidence The reputation argument presented by the CRAs hinges on the assumption that when a CRA is captured by an issuer and/or is issuing inaccurate ratings, this will be readily apparent to the market and will result in an appropriate amount of reputational damage.6 This argument, however, ignores the fact that the CRAs only exist because they are more skilled and/or efficient than the market in collecting, interpreting and summarizing available information. This has important implications, as “the market cannot directly assess the quality of the ratings, with such assessments typically based on the accuracy of ratings issued in previous years” (Mattarocci 2013, xvii). So it is highly unlikely that the capture of a CRA by an issuer and/or inaccurate ratings will be readily apparent to the market, and thus reputational damage will either not result at all, or is likely to be significantly delayed. In this respect Lynch (2010, 253) notes that: It is not evident that the market is or has been particularly sensitive to any rating agency capture by issuers or to the ratings agencies’ failure to issue accurate ratings . . . Indeed, through shrewd management, marketing, and tacit or explicit cooperation with issuers and other industry players, it may be possible to . . . reduce the market’s sensitivity to any failures on the part of the rating agencies . . . So there is . . . reason to doubt that investors would be particularly sensitive to any but the grossest inaccuracies.

Evidence exists as to the presence of issuers’ influence on rating assignments. The results of a survey conducted by the Chartered Financial Analyst (CFA) Institute in 2008 found that “11 of about 2,000 finance DOI: 10.1057/9781137391506.0007

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professionals said that they had witnessed a CRA changing its rating under pressure from an outside party, for example, a bond underwriter.” Similarly, in the US congressional hearings on reforming CRAs in 2009, evidence was presented as to specific cases of inappropriate conduct by the CRAs, and, in 2008, the SEC Summary Report on Select CRAs provided support for the allegation that CRAs were captured by issuers of mortgage-backed securities (MBSs) in the period leading up to the sub-prime crisis.7 Duan and Van Laere (2012, 3241) thus conclude that “it is apparent that payments from issuers may influence ratings . . . [as]CRAs may negotiate ratings with regular customers.” Although damning, this has not led to reputational damage to the extent that the CRAs’ revenue has been significantly impacted. In fact, the Financial Times reported that as on May 2014, profits at S&P and Moody’s are at, or near, record highs.8 This is important, because once the CRAs remain profitable despite the controversies and reputational damage, there are few, if any, remaining safeguards that can effectively control their behavior. For example, whereas it was previously noted that analysts are encouraged by the CRAs to prioritize objectivity by tying their remuneration to the demonstrated accuracy of their ratings, a recent SEC report concluded that analysts’ bonuses were based on “individual performance and the overall success of the firm.”9 So the more business the CRA is able to attract, the higher the profits, and the greater the bonuses paid to analysts. CRA analysts have an implicit incentive to issue ratings that will attract more business. Even more controversially, in 2008 the Wall Street Journal published an article on Moody’s in which it alleged that analysts were pushed to engage prospective client-issuers in an effort to “find ways deals could get done within Moody’s methodologies.”10 It is reported that analysts would be transferred following issuer complaints, and that analysts who recommended lower ratings would be replaced with those who would give higher ratings. The article quoted a former Moody’s executive who observed that “the rating process became a negotiation.”11 The SEC Summary report highlighted numerous similar comments made in the internal reports of the CRAs. For example, a business manager wrote, “we are meeting with your group this week to discuss adjusting criteria for rating CDOs of real estate assets . . . because of the ongoing threat of losing deals.” And another said, “I had a discussion with the team leaders here and we think that the only way to compete is to have a paradigm shift in thinking, especially with the interest rate risk.”12 DOI: 10.1057/9781137391506.0007

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This anecdotal evidence is supported by empirical studies. Griffin and Tang (2011) showed that the big-three CRAs may have been overly aggressive in their assumptions in the lead-up to the sub-prime crisis, in an effort to attract issuers in the rising collateralized debt obligations market. He et al. (2011) also found that the mistakes made by the CRAs just before and during the sub-prime crisis were systematically correlated with issuer size and market conditions, with the CRAs being more positive about securities sold by large issuers during boom years. The SEC Summary Report (2008) concluded that “rating agencies do not appear to take steps to prevent considerations of market share and other business interests from the possibility that they could influence ratings or ratings criteria.”13 So despite the publicity surrounding this controversy and the weight of anecdotal and empirical evidence, nothing substantive has been done about the conflict of interest and the manner in which it is alleged to influence the CRAs.

Sovereign debt rating failures The allegations The big-three CRAs have been repeatedly criticized for (1) not giving adequate forewarning of crises; (2) exacerbating such crises by excessively downgrading the ratings of crisis-hit sovereigns and (3) failing to adequately upgrade ratings after the crises (Bhatia 2002). Sovereign debt ratings failures are evidenced by ratings downgrades that involve movement by several notches at a time, usually within a short timespan (IMF 2010). As an example, Bhatia (2002, 38) defines a failed rating as “one that is lowered or raised by three or more notches within 12 months.”14 This is based on the principle that any change in rating implies an acknowledgement by the CRA that its previous rating was (or has become) inappropriate. Rating instability over a certain threshold within a specified period thus indicates failure on the part of the CRA, as a series of downgrades or upgrades are viewed as corrections to failed ratings. The severity of each ratings failure is measured by the number of notches by which the failed rating is upgraded or downgraded within the specified period. Using such measures, it has been strongly argued that the CRAs failed in the past few major financial crises. This is particularly so for the East Asian crisis of the late 1990s, wherein both S&P and Moody’s are recorded as having seven rating failures between 1997 and 1998, with S&P’s failure severity being captured by average adjustments of 4.9 notches during DOI: 10.1057/9781137391506.0007

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this period, and Moody’s of 4.3 notches. As an example, S&P’s long-term foreign currency rating on Korea failed in 1997, “evidenced by ten notches of downgrades in less than two months,” implying that S&P’s rating of Korea was “too high going into the Asian crisis” (Bhatia 2002, 39). Ferri et al. (1999) similarly note that the East Asian crisis caught the CRAs by surprise, as Moody’s did not downgrade Thailand until April 1997 while the crisis was simmering, and S&P did not similarly act until September of that year. Korea was only downgraded in October/November of 1997, and Indonesia and Malaysia in December. It is not surprising therefore that the CRAs are blamed for not forecasting the East Asian crisis. The CRAs are, however, further blamed for exacerbating the crisis by excessively downgrading the affected sovereigns. With the exception of Malaysia, all the countries were downgraded from investment-grade to below investment-grade. Moody’s, for example, downgraded Indonesia and Korea by six notches, Thailand by five notches and Malaysia by four notches (Ferri et al. 1999). S&P’s ten-notch downgrade of Korea in less than two months followed by four notches of upgrades in just over eleven months is cited as evidence that the downgrades overshot the mark (Bhatia 2002). This unduly increased the cost of borrowing abroad and dramatically reduced the supply of international capital (Ferri et al. 1999). This has fuelled the assertion that the CRAs are procyclical, in that ratings are stricter (or overly pessimistic) during a crisis/recession relative to an expansionary period. This allegation has not only been raised during the Asian crisis. It was argued during the Mexican crisis of 1994–1995 that the CRAs reacted to events rather than predicting them. In the more recent Eurozone crisis, the CRAs are accused of being both late and overly aggressive in downgrading Eurozone countries’ creditworthiness, thus worsening the crisis.15 As an example, in the Eurozone crisis, Moody’s waited until December 2009 to downgrade Greek debt, having left it unchanged since 2003. Moody’s then downgraded it by nine notches in the following fifteen months, leaving it at a speculative grade (Jeon and Lovo 2013). Also of concern is the tendency towards herd behavior among the CRAs that has been observed in some studies. Bhatia (2002) notes not only a high degree of correlation between the sovereign credit ratings of S&P, Moody’s and Fitch (which is to be expected), but also a high correlation between the incidents of ratings failures across the three agencies. As an example, between 1997 and 2002, of the 17 ratings failures recorded by S&P and Moody’s, 14 were failures common to both agencies. This is problematic if the ratings failure of one agency induces failure by another agency. DOI: 10.1057/9781137391506.0007

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The possible causes of ratings failures are numerous. Basically, the quality and timeliness of the data used by the CRAs in their sovereign risk computations cannot be guaranteed, as they depend on each government’s statistical capabilities. The CRAs’ analysts have limited ability to corroborate official data, and governments have an incentive to mask credit weaknesses. This is exacerbated by the fact that as profit-seeking entities, the CRAs engage in rationing of analytical man-hours, with analysts free-riding on the research conducted by the IMF, academia, investment banks, and, possibly, other rating agencies. Bhatia (2002, 45) asserts that ratings failures were the result of “inattention, with insufficient resources devoted to data gathering, corroboration, and analysis.” This is not surprising, as the big-three CRAs are noted to collect relatively little revenue from the users of their sovereign ratings. Notwithstanding this, the sovereign ratings are important to the CRAs, as they typically act as a ceiling for issuers within that jurisdiction, and so affect subsovereign rating revenue. With virtually no price competition between the CRAs for sovereign rating services, this leads to strong incentives for generosity in sovereign ratings. Another incentive problem that the CRAs face is the fact that their clients’ unwillingness to bear the transaction costs precipitated by frequent rating changes, forces them to pursue ratings stability. This commitment to ratings stability has made the CRAs prone to procyclical ratings, as CRAs attempt to (1) rate through-the-cycle (TTC) rather than rating based on point in time (PIT) information;16 and (2) apply smoothing rules wherein they change ratings only if the anticipated change is expected to be persistent and/or is more than one notch.17 Such measures create a conflict between rating timeliness and stability (Altman and Rijken 2004). The IMF (2010) indicates that through these measures the CRAs have largely achieved the goal of rating stability, as, on average, 82 of A-rated sovereigns retained that rating at the end of each year. This stability, however, comes at the cost of accuracy/timeliness, as even though the CRAs have attempted to adapt their rating procedures to reflect a country’s ability to survive a crisis, it is argued that they have not been very effective in so doing, thus limiting their predictive power. The defense Following the East Asian crisis, the CRAs acknowledged mistakes and took some corrective action, noting that at the time, their forecasting models did not adequately account for sovereign debt crises being DOI: 10.1057/9781137391506.0007

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triggered by private sector vulnerabilities.18 So for example, S&P placed increased focus on private sector external debt, the quantification of off-budget and contingent liabilities, and the adequacy of reserves. Fitch began to more closely monitor countries with a large proportion of short-term external debt. The CRAs also attempted to reduce information risk by placing greater emphasis on the quality of fiscal data received, and increasing staffing in the sovereign ratings group, allowing them to conduct more thorough data authentication. More recently, as a result of the controversy surrounding their role in the global financial crisis, the CRAs have sought to publish better research on sovereign creditworthiness that is more easily accessible. They have also conducted ratings reviews, further updated criteria and models, and improved staff training.19 The CRAs have highlighted all of these efforts in rejecting the allegations of them being procyclical. They argue that because rating failures harm their reputation, they exert great effort to minimize such occurrences. This is particularly so for sovereign ratings, which tend to attract more media and market attention than other ratings. The evidence Many of the allegations about the procyclical nature of the CRAs’ ratings are based on the definition of ratings failure as numerous successive downgrades by several notches at a time. Note, however, that such definitions are imperfect, as they cannot distinguish between failed ratings and ratings changes that are justified by shifts in underlying creditworthiness. Econometric modeling is thus required. The empirical evidence from econometric studies is, however, split. For example, Ferri et al. (1999) base their assertion that the CRAs were procyclical on the results of an econometric study which showed that (1) before the East Asian crisis the high-growth economies of the region were rated more highly than economic fundamentals would warrant; and (2) during the crisis the CRAs downgraded these countries more than was justified by the worsening of their economic fundamentals. Mora (2006), while agreeing with Ferri et al.’s first conclusion, provided evidence suggesting that there is little support for their second assertion that CRAs were excessively conservative during the East Asian crisis. He instead found ratings to be sticky rather than procyclical during this period. More recent studies that have benefitted from an examination of the CRAs’ experiences with multiple crises, have, however, provided a more nuanced understanding of the issue. They suggest that the CRAs’ pursuit DOI: 10.1057/9781137391506.0007

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of rating stability may engender procyclical rating tendencies. Kiff et al. (2013, 1) note that because they are typically smooth and delay rating changes, the CRA’s TTC ratings are initially more stable, but exhibit inferior performance in predicting future defaults. They are also prone to cliff effects, wherein sudden rating downgrades of several notches may precipitate market disruption and forced selling. The IMF (2010, 111) similarly concludes that “the way that CRAs try to smooth their rating changes may make them prone to procyclical cliff effects.” So, in responding to the demand for ratings stability, the CRAs may have compromised the accuracy of their ratings, resulting in devastating effects for some sovereigns and significant damage to their own reputational capital.

Conditions contributing to resilience Diminished reputational capital in a reputation-based industry would normally precipitate bankruptcy and closures. Consider, for example, the banking industry, which is similarly based on the shaky foundation of reputational capital and consumer confidence. Throughout history, banking sectors in numerous countries have been rocked by several bank runs and dramatic closures that began with rumors which chipped away at consumer confidence. This has not occurred among the big-three CRAs. Despite the controversies outlined above, Moody’s has been publishing ratings since 1909, S&P since 1923 and Fitch since 1922. Revenues for all three in 2013 exceeded levels in the pre-global crisis years, causing the Economist to conclude that “Moody’s and S&P now look more attractive as businesses than do most other financial firms.”20 Although sovereign ratings are a relatively new phenomenon, note also that demand for these ratings has not diminished as a result of the controversies surrounding sovereign ratings failures. During the 1990s, the decade during which such failures gained the most notoriety, S&P increased the number of sovereigns rated from 35 to 83, and Moody’s from 33 to 108.21 This indicates a level of resilience that merits careful consideration. This section examines the conditions that have contributed to the resilience of the big-three CRAs, despite the controversies surrounding their operations.

The service: Credit ratings as a necessity “Flawed or not, credit ratings are an integral . . . part of today’s increasingly integrated capital markets” (Bhatia 2002, 3). Some would even DOI: 10.1057/9781137391506.0007

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argue that they have moved beyond being integral to having become indispensable. No longer are they viewed as a luxury to be utilized by primarily large debt issuers, sophisticated institutional investors and rich sovereigns, but are now seen as a necessity for any entity (corporate or sovereign) seeking to borrow from or invest in the international capital markets. Just as the price elasticity of demand for any necessity tends towards inelasticity, we suggest that the demand for credit ratings responds in a relatively inelastic manner regarding changes in the CRAs’ reputational capital. Meaning that even major events that significantly tarnish the CRAs’ reputation tend to have relatively small adverse effects on the demand for credit ratings. An explanation for this phenomenon lies in understanding why credit ratings have come to be viewed as necessities. Supply designed to meet demand At the beginning of the 20th century, financial markets were functioning quite well without any CRAs. This is because the information requirements at the time were minimal, as securities trading was primarily limited to public bonds, with investors trusting governments to repay. Eventually, however, a corporate debt market developed in the United States, as the private sector needed financing for the construction of railroads. Investors needed information on these rapidly expanding investment options, and early credit reporting agencies sought to meet this demand by supplying information on the creditworthiness of American companies and on local economic conditions. From very early on, the credit reporting agencies sought to be flexible, adjusting their output to meet the most pressing information needs. For example, as railroad corporations became big business in the early 1800s, the American Railroad Journal was published in 1832 to provide information about the industry. By 1849, with Henry Poor as its editor, the journal shifted its attention to providing information that was particularly relevant for investors, such as on the ownership of railroads, their assets, liabilities and earnings. Recognizing investors’ need to chart the progress of a company over time, Poor’s Manual of the Railroads of the United States began in 1868 to provide financial and operating statistics for the major American railroads, covering several years. As the corporate debt market in America began to mature, by 1916 the Poor’s Company began to rate bonds, and in 1941 expanded its repertoire to industries well beyond railroads. The other CRAs evolved along a similar path, DOI: 10.1057/9781137391506.0007

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driven by responsiveness to market demands. In the early 1900s, Moody’s Analyses of Railroad Investments was expanded to include the assessment of industrial companies and utilities, other than railroads, and in 1913 the Fitch Publishing Company was formed to publish financial statistics on a variety of corporations in publications such as the Fitch Bond Book and the Fitch Stock and Bond Manual.22 A time-honored tradition of quickly adjusting supply to respond to market demand has allowed the CRAs to remain relevant in the 21st century. The relevance of the print publication-subscriber-pays model was threatened in the 1970s, as the proliferation of high-speed photocopying machines made it significantly easier for non-paying investors to access the CRAs’ manuals. The adoption of floating exchange rate regimes, increased international capital flows and increased bond issues in domestic and international markets all precipitated yet another key shift in the CRAs’ modus operandi. The “issuer-pays” model was adopted as a means of ensuring continued and increased revenues, as the CRAs sought to capitalize on the dynamism in the capital markets. Their ability to do so, was however, predicated on their ability to supply a product that met the information needs of this new global market dynamic. What were these needs? The investors required reliable assessments of the creditworthiness of a large number of increasingly complex bond issuers, and the issuers required these assessments to come from an objective, authoritative source of judgment. Both required the assessments to be presented in an easily understood and digestible format, and to be presented in a manner that allowed for global comparability. To remain relevant, the CRAs thus had to be able to help lenders to “pierce the fog of asymmetric information that surrounds lending relationships” and to “help borrowers . . . emerge from that same fog.”23 Despite the controversies, history would suggest that they succeeded in this regard. Bhatia (2002, 3) notes that “the widespread and longstanding use by private creditors of the credit ratings of S&P, Moody’s and Fitch testifies to their utility. This stems from the simplicity and comparability of the agencies’ risk-grading systems, with broad swathes of analysis condensed into a few bytes of data, and from the perceived analytical strength and independence of the agencies themselves.” Gonzalez et al. (2004, 8) thus note that “the use of ratings and the influence of the opinions of CRAs on securities markets have grown significantly, to the extent that ratings are now ubiquitous in financial markets.” This has become so ingrained that “preserving or achieving DOI: 10.1057/9781137391506.0007

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a desired rating is frequently incorporated into corporate goals and represents an integral part of the financing strategy of companies.” As an example, it is noted that “Swiss bankers are trained to believe that there is a higher goal than making profits. Their priority has been to retain triple A credit ratings, the badge of good banking.”24 The CRAs have recognized and responded to this incentive structure, by offering assessment/evaluation services to enable these companies to monitor the behavior of their ratings under different scenarios.25 Also very important to corporate debt issuers is the rating of the sovereign within which they operate, as sovereign bond yields serve as the zero-risk return benchmark against which returns on other domestic investments are compared. The sovereign rating often represents the ceiling for sub-sovereign bonds (IMF 2010). To this end, although sovereign rating is not considered to be highly profitable, the CRAs have significantly increased the number of sovereigns rated, with S&P, Moody’s and Fitch rating 125, 110 and 107 sovereigns, respectively, as at mid-2010.26 Changes in these sovereign ratings can have far-reaching implications both for the government and for the corporations that operate within it. The CRAs therefore not only provide critical information services that reduce information costs and increase the pool of potential borrowers, but also provide important monitoring services through which they influence issuers to take corrective actions to avert downgrades (IMF 2010). It is through the supply of these services that the CRAs have met key demands in the modern international capital markets and have positioned their ratings as necessities for issuers of bonds. Supply creating its own demand In the 1970s, increasing financial disintermediation, wherein bank loans were being substituted with securities, was also affecting the market for credit ratings. In the past, banks acted as the primary gatekeepers to external financing, as they issued loans based on their credit assessments. With disintermediation, however, investors began to invest directly in the capital markets, bypassing the banks. The extent to which financial disintermediation became a widespread phenomenon was, however, integrally linked to the supply of an alternate source of credit assessments. The services that CRAs now supply were then provided in the United States in a disjointed manner by the specialized business press that reported on business conditions for companies and industries, credit reporting agencies that evaluated the ability of merchants to meet DOI: 10.1057/9781137391506.0007

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their financial obligations, and investment banks that put their reputations at stake by underwriting debt. The first CRA, Moody’s, was formed on the principle that these functions would be more effectively supplied when consolidated (Harper 2011). The supply of such assessments of creditworthiness, (wherein the purveyors of the information—the CRAs—now put their reputation at stake, and claimed expertise based on analytical capabilities and local knowledge of debt security issuers) facilitated and expedited the disintermediation process. Disintermediation, in turn, heightened the role of the CRAs, as, with bank credit analysts becoming less important as gatekeepers, greater reliance was placed on the ratings provided by the CRAs.27 As an example, small- to medium-sized asset managers which are unable to develop reliable internal credit assessment systems, now rely heavily on the CRAs’ ratings, to the extent that such ratings play a central role in portfolio governance. Even large asset managers that have such capabilities rely heavily on the CRAs’ ratings, only utilizing their credit assessment systems to provide ratings that are not supplied by the CRAs, or to provide a resolution when the CRAs provide conflicting signals (Gonzalez et al. 2004). For many investors and portfolio managers, their reliance on the CRAs’ supply of timely ratings on a vast number of debt issuers, has been institutionalized. It is now a common practice for CRAs’ ratings to be used in portfolio governance and investment mandates, with, for example, ratings-based guidelines being used to determine the eligibility of assets. In the United States, a survey conducted by the Securities and Exchange Commission (SEC) in 2003 found that “most mutual funds, pension funds, insurance companies, private endowments, and foundations use credit ratings to comply with internal by-law restrictions or investment policies that require certain minimum standards.” Why is this so? It was opined that it is because the “external credit ratings constitute objective and easily verifiable third-party opinions.”28 The supply of an appropriately designed ratings product has thus created or enhanced the demand for the CRAs’ services by catalyzing financial disintermediation, and increasing and institutionalizing reliance on such services. This has also occurred more broadly in the international financial markets. While benefitting tremendously from these markets, it is also important to note that the CRAs have played an important role in their growth. Alcubilla and Del Pozo (2012, 5) note that the CRAs’ ability to “produce clear, internationally harmonized indicators of the risk of DOI: 10.1057/9781137391506.0007

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default was crucial to incentivize investments at an international level as many investors would not have been otherwise capable of assessing the credit risk of those securities on their own.” This became particularly important as financial innovation and complexity widened the information gap between investors and issuers. The CRAs’ supply of “qualified credit information” facilitated the accessibility of complex structured finance products, whose rapid expansion in turn increased reliance on the CRAs’ ratings. Bhatia (2002, 3) thus paints a very good picture of how the CRAs are intricately (and some would argue, inextricably) weaved into the overall global financial landscape. She notes that: The ratings agencies maintain what amounts to a global credit risk architecture. Combining empirical experience with economic, financial, and legal logic, they bring together a dynamic and interrelated web of ratings on preferred creditor institutions, national governments, and bilateral creditor organizations; regional, local and municipal administrations; public and private sector banks, insurers, corporations, and projects; and an increasingly complex array of credit-enhancing structured finance transactions. Like a jigsaw puzzle piece, each rating forms part of a larger picture.

Demand facilitated by state intervention Having become such an indispensable part of the global financial tapestry, it is difficult to perceive the ratings provided by the CRAs as anything but necessities. This is even more evident when one considers the part that the state has played in concretizing the role of the CRAs through regulations. This is particularly so in the United States, where ratingsbased regulations can be traced back to the 1930s. Credit rating activity benefitted tremendously with the passage of the Glass-Steagall Act of 1933, which separated banking and securities businesses. Many state governments shortly thereafter incorporated rating standards for securities into their prudential rules for investment by pension funds (Sinclair 2005). In 1936 a decree was passed prohibiting banks from investing in speculative investment securities, as determined by recognized ratings manuals. Insurance regulators also subsequently included references to ratings in their rules. In 1975 the SEC included two kinds of regulatory requirements that were linked to ratings: “rules that restricted the extent to which a firm could hold assets that fell below investment grade; and rules that linked capital requirements to the ratings on individual securities” (Alcubilla and Del Pozo 2012, 4). DOI: 10.1057/9781137391506.0007

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The extent to which US regulations have included references to CRAs (particularly those listed by the SEC as nationally recognized statistical rating organizations—NRSROs) has increased over time. By 1997 there were more than 1,000 such references in securities legislation, and approximately 400 citations each in pension, banking and real estate legislation. The credit ratings are being used in these regulations to determine regulatory capital, identify eligible investments or permissible asset concentrations, evaluate risks associated with assets purchased as part of securitization offerings and determine disclosure requirements (IMF 2010). White (2010, 214) thus notes that “taken together, these regulatory rules mean that the judgements of credit rating agencies became of central importance in bond markets. Banks and many other financial institutions could satisfy the requirements of their regulators by just heeding the ratings, rather than their own evaluations of the risks of the bonds.” Although ratings are most heavily utilized in financial regulation in the United States, Sinclair (2005) notes that ratings have become an increasingly important regulatory tool outside of America. He lists 21 OECD and APEC countries in which ratings are included in varied degrees in financial regulation. Even more broadly, under Basel II Capital Adequacy Requirements, “banks are required to hold capital against risk weighted assets. The risk weights can be either based on banks’ own internal ratings or on ratings produced by the big-three CRAs. Due to the complexity and cost associated with internal risk ratings, many banks, particularly those in emerging and developing markets, rely on the CRAs’ ratings.”29 These regulations all serve to create a captive market for the CRAs, making the demand for credit ratings more inelastic. Ironically, this demand inelasticity has become quite evident in the aftermath of the global crisis, as countries have sought to reduce rating reliance. In America, the financial sector reform bill of 2010 requires all federal agencies to remove references to or reliance on credit ratings, and to substitute an alternative standard of creditworthiness. The United Kingdom and Japan similarly adopted proposals seeking to reduce the use of credit ratings in the regulatory and supervisory framework. These efforts are important to the reduction of cliff effects, wherein the downgrade of securities forced banks to reduce or eliminate their holdings so as to remain regulation compliant, which further decreased asset values and initiated a downward spiral of losses.30 Reduced reliance on DOI: 10.1057/9781137391506.0007

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credit ratings, is, however, predicated on the existence of an alternative standard of creditworthiness. Countries thus sought to force institutions to conduct appropriate due diligence and rely more heavily on internal credit assessments. There are, however, factors that limit the effectiveness of such approaches. Small- and less-sophisticated investors often do not have the economies of scale or the requisite capabilities to conduct their own credit assessments. Even if they did, there is no guarantee that sufficient information will be readily available for the due diligence to be conducted in a timely manner. The IMF (2010, 93) thus concludes that “notwithstanding the current move toward reducing the regulatory reliance on credit ratings, CRAs and their ratings will inevitably continue to play important roles in financial markets.”

The suppliers: The big-three as an oligopoly The previous section attributed the relatively inelastic demand for credit ratings to a number of factors that have contributed to ratings being viewed as necessities. The elasticity of demand is, however, affected not only by the nature of the product/service, but also by the availability of substitutes. We have already established that there are very few substitutes for credit ratings, which could reliably serve as alternative standards of creditworthiness. This does not, however, explain the dominance of the big-three CRAs. Under normal circumstances, the absence of close substitutes for a product/service would imply a consistency of demand and potential profitability that would attract firms to the industry. So even if there are no close substitutes for the credit ratings as a service, why are not there more suppliers of this service from which consumers can choose? Why have not new CRAs been able to capitalize on the diminished reputational capital of the big-three by capturing significant shares of the market? The answers to these questions are provided below, and highlight two additional and important reasons for the resilience of the big-three. The credit rating industry as a natural oligopoly Across the world, only 68 new CRAs were created between the 1970s and 2000s, with only 4 being in existence before 1970. The largest number of new entrants was created in Asia in the 1990s.31 These smaller and younger CRAs tend to offer credit risk assessments in specialized fields, and they are not serious competitors to the big-three.32 There are also DOI: 10.1057/9781137391506.0007

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numerous strategic alliances in the rating sector, with several of the small CRAs being affiliated with the big-three through formal agreements often designed to reduce competition. When the rating agencies engaged in strategic alliances are excluded, the number of independent CRAs in the market is lower than 50 (Mattarocci 2013). Although there are more than 70 CRAs globally, the big-three capture the lion’s share of the global market, with more than 90 of the value of all issues rated worldwide.33 More specifically, Kruck (2011) notes that Moody’s and S&P have a combined global market share of 80, and together with Fitch their market share is more than 95. The credit rating industry is thus characterized by incomplete competition and an oligopolistic structure. Numerous studies have concluded that the credit rating market is a natural oligopoly, as the nature of the market makes it difficult for new CRAs to succeed.34 A number of factors contribute to this. Existing CRAs are able to take advantage of the large economies of scale in processing the information to assign ratings.35 The importance of this is underscored by the extent to which the big-three have been able to increase their size over time through merger and acquisition activity.36 New entrants have high start-up costs related to acquiring the requisite staffing, analytical tools and information technology systems, and few resources with which to meet these costs. By contrast, the large, established CRAs are able to hire more staff, with greater experience in the usage of the agency’s models and IT systems, and are thus able to more efficiently analyze large issuers involved in several complex transactions. It is reported that in the United States, the big-three CRAs employ 3,150 credit analysts, which is approximately 90 of the total number of analysts working for all of the NRSROs.37 It is not surprising, therefore, that the new entrants are often unable to attract large issuers because of issues with capacity. Even where capacity is not an issue, the economies of scale that the big-three benefit from, precludes the new entrants from engaging in any meaningful price competition, thus stymieing their efforts to gain market share. Another natural barrier to entry to the credit rating industry relates to the value of and difficulties associated with building reputational capital. The value of a credit rating depends on the credibility of the rater. A new entrant to the credit rating industry typically will not have the reputation needed to compete with the incumbents.38 Issuers only want ratings from CRAs that are recognized and respected by investors, and investors typically only utilize CRAs that have developed a good reputation by

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maintaining a history of accurate and timely ratings. To survive and make profits the new entrant has to attract clients, but doing so hinges on its ability to build a reputation for providing ratings that are more accurate than those of the incumbent.39 That reputation, however, cannot be built if the CRA is unable to attract clients. The issuer-pays model also works against new entrants in this respect, as a single fee-paying issuer may comprise a large portion of a new entrant’s total revenue. This causes investors to fear that the potential conflict of interest could be higher for the new entrant than for the established CRAs with a wide client base.40 Without being given the opportunity to establish a reputation to the contrary, the new entrant has no means of alleviating that fear. Thus, while economies of scale prevent new entrants from competing on the basis of lower prices, the reputational hurdle precludes competition on the basis of ratings accuracy. Because of these natural barriers to entry, the big-three CRAs are able to survive the controversies that tarnish their reputation, as there are very few competitors ready and able to exploit the opportunities thus created. Artificial barriers to entry to the credit rating industry The existence of strong natural barriers to entry means that the credit rating industry was never going to have hundreds of highly competitive small-scale service providers. Notwithstanding this, the American SEC created a potent artificial barrier to entry through its NRSRO designation, which further diminished the likelihood of heightened competition in the industry. As previously mentioned, there is a plethora of US regulatory requirements that make references to CRAs, particularly those listed by the SEC as NRSROs. This not only creates a captive market for the NRSROs, but effectively marginalizes any CRA that does not have the NRSRO designation. White (2010, 217) notes that “without the benefit of the NRSRO designation, any would-be bond rater would likely remain small-scale. New rating firms would risk being ignored by most financial institutions (the buy side of the bond markets); and since the financial institutions would ignore the would-be bond rater, so would bond issuers (the sell side of the markets).” Also troubling is the fact that there is much opaqueness surrounding the NRSRO designation. The SEC has neither defined the formal criteria that must be met for bestowment of the designation, nor has it established a formal application and review process.41 Criteria that have

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been unearthed from letters written by the SEC to CRAs that have been approved include the following: conflict of interest scrutiny; appropriate institutional separations to avoid mixing investment advice and rating; adequate financial resources; adequate staff and sufficient training. Concerns, however, have been raised about terms such as “adequate” and “sufficient” that have not been defined. Also, the designation requires CRAs to demonstrate that they are “nationally recognized”, but the SEC provides no codified process for demonstrating this recognition. Sinclair (2005, 42) thus notes that “the NRSRO concept remains vague and unspecified in law but significant in practice.” It is interesting to note that the capital adequacy requirements under Basel II similarly introduced an artificial barrier to entry to the credit rating industry that particularly affected emerging and developing markets. These requirements require banks to compute risk weights by either using internal ratings based on their own assessment of risk, or using a standardized approach that is based on the ratings produced by External Credit Assessment Institutions (ECAIs). Most banks in emerging and developing markets would choose the latter approach because of the costs associated with the former. Note, however, that only the bigthree CRAs have been granted ECAI status by all members of the Basel Committee. So even though regulators have the authority to recognize other CRAs, the Basel Committee has made it harder for new entrants to thrive because of its endorsement of the big-three.42 Following the involvement of the CRAs in the global crisis, there have been many calls for a reduction of the influence of the big-three, some of which have included advocacy for increased competition in the credit ratings industry. Note, however, that there is a growing body of literature which suggests that significantly increased competition in the credit ratings industry may not be desirable. It is argued that increased competition reduces ratings fees, which could have a number of undesirable effects. The reduced fees could diminish the CRAs’ incentives to invest in private information acquisition and/or make the CRAs more prone to bribery and capture. This could lead to reduced ratings accuracy.43 It has also been suggested that increased competition would create more opportunities for rating shopping, without necessarily enhancing the information content.44 Although there is no resolution as to whether increased competition in the credit rating industry is socially desirable, these arguments tend to make proponents for such competition more cautious. DOI: 10.1057/9781137391506.0007

Resilience in Spite of Controversy



Controversies and conditions: Summary and implications This book investigates the allegation that the big-three CRAs are biased against poor countries in their sovereign debt ratings. The CRAs assert that such allegations are ludicrous because they are a reputation-based industry, and any such practices would tarnish their reputations and drive them out of business. This chapter has presented numerous controversies that have surrounded the big-three CRAs, which have resulted in them being publicly pilloried and have tarnished their reputations. The controversies presented, while not nearly representing the total number of issues surrounding the CRAs, have focused on those that have questioned the motives of the CRAs, and also those in which the CRAs are noted to have made critical mistakes in their rating of sovereign debt. In each instance a balanced approach was taken, wherein the allegations were presented, followed by the CRAs’ defense and completed by the available evidence. Note, however, that public opinion is not always so balanced and does not frequently await the results of empirical studies. Because many of the controversial issues have caused a questioning of the CRAs’ basic motives, and have resulted in costly mistakes, the bigthree CRAs have been harshly judged in the court of public opinion, and have had their reputations sullied. Notwithstanding this, they remain resilient despite the controversies, with the demand for their services and their profit levels largely unaffected. This suggests that the demand for credit ratings responds in a relatively inelastic manner with respect to changes in the CRAs’ reputational capital. Even major events that significantly tarnish the CRAs’ reputation tend to have relatively small adverse effects on the demand for credit ratings. Why is this so? Numerous factors have been highlighted. Credit ratings have come to be viewed as necessities for any entity seeking to borrow from or invest in the international capital markets. This is because the ratings services have evolved over time, being designed to respond to market demands. This has been encouraged by financial disintermediation and the growth of the international capital markets. The supply of credit ratings has also created its own demand, by further propelling such disintermediation and catalyzing continued growth in global capital markets. Through ratings-based regulatory requirements the state has further concretized the role of credit ratings, making them even more inextricably woven into the global financial landscape. Such DOI: 10.1057/9781137391506.0007

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Sovereign Debt and Credit Rating Bias

regulations not only create a captive market for credit ratings, but also represent artificial barriers to entry to the ratings industry, thus contributing to the dominance of the big-three. Natural barriers to entry also exist. Economies of scale in information acquisition and analysis, and reputational hurdles, make it difficult for new entrants to compete in the credit ratings industry. The limited availability of substitutes for the ratings provided by the big-three, contributes to the relative inelasticity of demand with respect to changes in the CRAs’ reputational capital. These factors represent the conditions that have allowed the CRAs to flourish in spite of the damage done to their reputation and credibility by the numerous controversies surrounding their motives and practices. This is important for the purposes of this study, as it clearly indicates that an environment exists, even in the reputation-dependent CRA industry, wherein biases in the rating of sovereign debt can persist. Let us be careful to note that the identification of such conditions does not constitute proof of biases. The existence of the conditions, do, however, indicate their possibility and provide a refutation of the argument that reputational capital would preclude the existence of any prolonged perpetuation of biases. The findings from this chapter thus provide the launching pad for the rest of the book in which we empirically support the existence of a bias against certain categories of countries.

Notes  See the following articles: Graybow (2008), http://www.insurancejournal. com/news/national/2008/07/14/91841.htm; Segal (2010), http://www.nytimes. com/2010/05/23/business/23rating.html?_r=1; Field (2010), http://www. dailyfinance.com/2010/04/26/will-the-teflon-ratings-agencies-start-losingfraud-suits/; SHALAL-ESA (2011), http://www.reuters.com/article/2011/08/11/ us-financial-regulation-creditraters-idUSTRE77A03S20110811; http://www. businessweek.com/stories/1996-04-07/now-its-moodys-turn-for-a-review; Robinson (2015), http://www.bloomberg.com/news/2015-01-20/sec-to-suspends-p-from-rating-part-of-cmbs-market-for-year.html; Neate (2011), http://www. theguardian.com/business/2011/aug/22/ratings-agencies-conflict-of-interest.  As cited in Duan and Van Laere (2012).  See e.g. Sinclair (2005) and Alcubilla and del Pozo (2012).  Lynch (2010, 248) notes that such conflicts of interest earlier this decade “generated extensive federal, state, and industry investigations and resulted in criminal charges against several Wall Street investment firms.”

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Resilience in Spite of Controversy



 Lynch (2010).  Ibid.  This report was officially entitled “Summary Report of Issues Identified in the Commission Staff ’s Examination of Select Credit Rating Agencies” as cited in Lynch (2010).  Grene (2014).  Lynch (2010, 254).  As cited in Lynch (2010, 258).  Ibid.  Ibid., 260.  Ibid., 261.  Excluding downgrades or upgrades into, out of, within, or between the ratings categories from “CCC” or “Caa” downward.  IMF (2010) and Council on Foreign Relations (2015).  Kiff et al. (2013, 3&8) define a TTC rating as a two-step process in which “ex-ante ratings are calculated conditional on a stress scenario for the cyclical component (and) ex-post rating changes are smoothed and thus not adjusted immediately.” “The PIT approach can be thought of as using current information when computing the default risk metrics that are mapped into ratings. Credit ratings assigned under the PIT approach should provide the most accurate estimate of future default probabilities and expected losses.”  Eijffinger (2012, 914) explains by noting that “the TTC approach smoothes cyclical effects on ratings in normal times by taking into account cyclical changes in addition to fundamental characteristics of the issuer. In contrast . . . PIT rating focuses on the current conditions of the issuer, regardless of the state of the business cycle. In normal times, the TTC approach smoothes ratings as a recession does not lead to severe downgrades (since cyclical changes are taken into account). However, when it becomes clear that the recession is due to a crisis, and thus is no longer cyclical, the rating has to be adjusted down quickly by a large amount. This development is called a cliff effect.”  Ferri et al. (1999).  IMF (2010).  http://www.economist.com/news/finance-and-economics/21601020-ratingsindustry-has-bounced-back-financial-crisis-credit-where  Bhatia (2002).  Alcubilla and Del Pozo (2012).  White (2001, 4).  Preston, as quoted by Sinclair (2005).  Gonzalez et al. (2004, 8).  IMF (2010).  Sinclair (2005).

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Sovereign Debt and Credit Rating Bias

 IMF (2010, 92).  United Nations (2013, 5).  The United Nations (2013, 5) notes that another possible cliff effect “arises from the general principle of a sovereign ceiling, where the credit rating of banks cannot exceed their sovereign rating. This is embedded in the standardized approach which may leave a trail of implicit downgrades for private sector banks in the wake of a sovereign downgrade, resulting in capital outflows and higher borrowing costs.”  Mattarocci (2013).  Kruck (2011).  IMF (2010) and Mattarocci (2013).  Alcubilla and Del Pozo (2012, 6).  Ferri et al. (1999).  Mattarocci (2013).  Alcubilla and Del Pozo (2012).  Mattarocci (2013).  Alcubilla and Del Pozo (2012).  Ibid.  White (2010).  United Nations (2013).  Jeon and Lovo (2013).  IMF (2010).

DOI: 10.1057/9781137391506.0007

4

Trends in Sovereign Debt Ratings: Are There any Preliminary Signs of Bias? Abstract: This chapter highlights trends in rating levels and actions that raise questions about potential bias. The chapter uses descriptive techniques and trends to analyze foreign currency sovereign debt ratings for 132 countries over the period from 1997 to 2011. The analysis highlights interesting distinctions between the rating actions taken for rich countries (defined as high income) and poor countries (defined as lower-middle and low income), and between regional groupings of poor countries. While interesting, the trends identified in this chapter are not used to make conclusive statements about bias, as the descriptive and trend analysis does not allow for such conclusions. It, however, highlights the need for the more rigorous methodology and econometric evidence that are presented in subsequent chapters. Keywords: Africa; Bias; Credit Rating Agencies; Developing Countries; International Capital Markets; Latin America & the Caribbean; Sovereign Debt Tennant, David F., and Marlon R. Tracey. Sovereign Debt and Credit Rating Bias. New York: Palgrave Macmillan, 2016. doi: 10.1057/9781137391506.0008. DOI: 10.1057/9781137391506.0008





Sovereign Debt and Credit Rating Bias

The previous chapters have shown that despite recent improvements, there remains a possibility of bias in sovereign debt ratings. This is because an element of subjectivity persists in the determination of rating assignments, and conditions for bias exist in the credit rating industry. Notwithstanding this, allegations of bias are often viewed as emotive responses by affected parties to adverse rating actions. As an example, when Italian Prime Minister Silvio Berlusconi berated Standard and Poor’s (S&P) downgrade of his country in September 2011, by asserting that their assessments “appear dictated more by newspaper articles than reality and appear to be tainted by political considerations,”1 S&P calmly responded with the standard defense that “our ratings are apolitical. We provide investors with an independent view of how political risk and policy initiatives, among other factors, may impact future creditworthiness.”2 Increasingly, however, allegations of bias are not so easily dismissed. Whereas for decades the frequent and sudden downgrades of non-OECD sovereigns have been begrudgingly but meekly accepted, the downgrade of numerous developed countries in the aftermath of the global financial crisis and Eurozone debt crisis have been met with vocal and searing criticisms of the credit rating agencies (CRAs). The president of the European Commission even went as far as directly accusing the CRAs of bias when evaluating European sovereign debt issues. To date, however, such allegations of bias have not been thoroughly investigated, with very few academic studies exploring the issue. This chapter uses descriptive techniques and trends to analyze foreign currency sovereign debt ratings for 132 countries from 1997 to 2011. This analysis is so as to ascertain whether there are any preliminary signs of bias. A list of all sovereigns in our sample by development level and developing region is provided in Table A4.1 of this chapter’s Appendix.

Distribution of sovereigns and their rating classes The external debt for most sovereigns has been rated by at least one of the big-three CRAs. Table 4.1 presents the 21 alphanumeric rating classes assigned by S&P, Moody’s and Fitch for external sovereign debt, along with the distribution of sovereigns by rating class for our sample. S&P and Fitch use the same notation to denote their rating classifications. Whereas AAA and D (and gradations thereof) are the highest and lowest rating classifications, respectively, for S&P and Fitch, Moody’s ratings DOI: 10.1057/9781137391506.0008

Trends in Sovereign Debt Ratings



table 4.1 Distribution of rating categories for each rating Rating class Characterization of debt and issuer

 of sovereigns by rating class

S&P/Fitch Moody’s

Rating Scale

S&P

Moody’s

Fitch

AAA

Aaa



.

.

.

High quality

AA+

Aa



.

.

.

Upper-medium grade

Medium-grade, Adequate payment capacity

Investment Grade

Best quality

Small payment capacity, uncertain position Poor standings, possibility of default on obligations

Speculative grade

Moderate payment capacity

Highly speculative Lowest quality

AA

Aa



.

.

.

AA–

Aa



.

.

.

A+

A



.

.

.

A

A



.

.

.

A–

A



.

.

.

BBB+

Baa



.

.

.

BBB

Baa



.

.

.

BBB–

Baa



.

.

.

BB+

Ba



.

.

.

BB

Ba



.

.

.

BB–

Ba



.

.

.

B+

B



.

.

.

B

B



.

.

.

B–

B



.

.

.

CCC+

Caa



.

.

.

CCC

Caa



.

.

.

CCC–

Caa



.

.

.

CC D

Ca C

 

. .

. .

. .

Notes: In our sample, the D category comprises of SD for S&P and DDD, DD, RD for Fitch. Source: Computed by authors from data provided on each agency’s website.

range from Aaa to C. The rating classes with relatively high frequencies are B to BB- for S&P (26.3), BB+ to BBB- for Moody’s (16.6) and BB+ to BBB for Fitch (23.1). There is just about an even split in the proportion of sovereigns assigned to investment and speculative grades for S&P and Fitch. Moody’s has a slightly higher proportion of sovereigns in the investment grades (55.5). Table 4.1 also provides a rating scale that transforms the alphanumeric notations to numeric values such that 21 corresponds to the highest DOI: 10.1057/9781137391506.0008

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Sovereign Debt and Credit Rating Bias

rating class and 1 to the lowest class. This linear transformation allows for further descriptive analyses. It is evident that, as of 2011, the CRAs’ geographical coverage was quite wide. In our sample, North America constitutes 1.5 of the total number of sovereigns rated by at least one of the big-three CRAs, Europe & Central Asia 34.1, Middle Eastern countries 7.6, Asia-Pacific countries 15.9, African countries 20.4 and Latin America & the Caribbean (LAC) 20.5. This wide coverage is due to a trend of increasing numbers of non-OECD sovereigns rated since the 1990s. As a result of this trend, the CRAs moved from having most of their sovereigns being investment-grade, to a large proportion now falling in the speculative grade category. This reflects riskier emerging market and developing countries gaining access to debt markets.3 Table 4.2 presents a snapshot of the regional distribution of ratings for each of the big-three CRAs, averaged over the period from 1997 to 2011. The regional patterns observed are largely common across the three agencies. The most highly rated sovereigns are located in North America and Europe & Central Asia. Sovereigns in the other regions tend to have lower average ratings, with all the CRAs assigning LAC countries significantly lower ratings, and S&P and Fitch assigning similarly low ratings to African countries. Moody’s, however, had an average rating for African countries which was about three notches higher than S&P and Fitch. It must be noted, though, that these regional averages conceal significant heterogeneity in ratings across countries, with large gaps between the ratings assigned to the highest and lowest ranked sovereigns

table 4.2 Regional distribution of sovereign ratings S&P

Moody’s

Fitch

Region

mean max min mean max min mean max min

North America

.





.





.





Europe & Central Asia

.





.





.





Middle East

.





.





.





Asia-Pacific

.





.





.





.





.





.





.





.





.





Africa Latin America & Caribbean

Source: Computed by authors from data provided on each agency’s website.

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Trends in Sovereign Debt Ratings



table 4.3 Distribution of sovereign ratings by development level S&P Development level LMLIC UMC HIC

Moody’s

Fitch

mean

max

min

mean

max

min

mean

max

min

.





.





.





. .

 

 

. .

 

 

. .

 

 

Source: Computed by authors from data provided on each agency’s website.

in all regions except North America. Also noteworthy is the fact that even the most highly ranked sovereigns from Africa and LAC were quite a distance from the best possible rating of 21, achieved only by sovereigns in the North America, Europe & Central Asia and Asia-Pacific regions. As indicated in Table 4.3, the CRAs’ ratings seem to be closely linked to the sovereign’s development level. When the average ratings for the period from 1997 to 2011 is computed for each of the big-three CRAs, lower-middle and low income countries (LMLICs) have a mean rating of about 8, which translates to a letter grade of B+/B1. This means that LMLICs typically exhibit a small payment capacity that places them in an uncertain position. LMLICs’ ratings do not exceed the lower boundary of investment grade. Upper middle income countries (UMCs) have an average rating of about 10, that is, a letter grade of BB/Ba2, indicating that they have moderate payment capacity. Although some UMCs have attained ratings up to AA–, none of the sovereigns at this development level were able to achieve the best quality bond rating. Not surprisingly, high income countries (HICs) have average ratings of about 18, translating to letter grades of A+/A1, which place them in the upper medium or high quality grades.

Trends in rating actions Table 4.4 presents the frequency distribution of rating changes for the big-three CRAs. Despite the attention that rating changes attract, it is evident that rating stability is much more commonplace. Also, despite the furor that typically accompanies multiple-notch downgrades, it is clear that they occur far less frequently than single-notch adjustments.

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Sovereign Debt and Credit Rating Bias

table 4.4 Frequency distribution of rating changes Change in ratings – – – – – – – – – – – –         within one notch  downgrades  upgrades

S&P . . . . . . . . . . . . . . . . . . .  . .

Moody’s . . . . . . . . . . . . . . . . . . .  . .

Fitch . . . . . . . . . . . . . . . . . . .  . .

Source: Computed by authors from data provided on each agency’s website.

The percent of observations within a one notch change across all CRAs is more than 90. As such, for the remainder of the chapter we simply analyze rating actions in terms of whether a sovereign is downgraded, upgraded or experienced no change in ratings. Note that for all three CRAs, upgrades tend to outnumber downgrades. Figure 4.1 provides a breakdown of rating actions taken by the bigthree by levels of development. Ratings stability is again evident across all agencies, with HICs having more stable ratings than UMCs and LMLICs. For each of the agencies, UMCs had a higher proportion of upgrades than HICs and LMLICs. UMCs also had a higher proportion of downgrades than HICs. It is only for S&P that UMC downgrades were larger than LMLIC downgrades. For Moody’s and Fitch, LMLICs had a slightly larger proportion of downgrades than UMCs. Notwithstanding this, in all three agencies, downgrades were more frequent for LMLICs than for HICs. DOI: 10.1057/9781137391506.0008

% of observations

Trends in Sovereign Debt Ratings



90 80 70 60 50 40 30 20 10 0 LMLIC

UMC

HIC

LMLIC

S&P

UMC

HIC

LMLIC

Moody's Downgrade

No change

UMC

HIC

Fitch Upgrade

figure 4.1 Distribution of rating changes by levels of development Source: Computed by authors from data provided on each agency’s website.

Figure 4.2 focuses more closely on the rating actions taken in regions in which developing countries are concentrated. The comparison is limited to developing countries so as to better reflect regional differences that are not confounded by differences in development level. Because it was shown in Table 4.2 that sovereigns in LAC and Africa tend to be assigned relatively low ratings, we have distinguished between these two regions and other developing regions (ODR). It is evident that the sovereign debt for African countries is more stable than that of the other regions. Correspondingly, for all of the agencies, downgrades and upgrades were lower for Africa than for the other regions. The comparison across LACs and ODRs revealed very little differences for Moody’s and Fitch. However, for S&P, although LACs had a slightly higher proportion of downgrades, ODRs had a considerably higher proportion of upgrades. The averages for 1997 to 2011 utilized above, however, mask the effect of two important events during the period—the end of the Asian crisis between 1997 and 1999, and the start of the global crisis between 2008 and 2011. Figures 4.3 to 4.5 present the annual distribution of rating changes by development level for each of the big-three CRAs. The effect of these two crises is evident, as during each there was a spike in the number of downgrades. As would be expected, for the Asian crisis, UMCs had the largest number of downgrades, reflecting the income category of the crisis-hit countries. For the global crisis, particularly in the latter stages, HICs had the highest number of sovereign downgrades. Before the global crisis no advanced economies were assigned a non-investment grade rating, but by mid-2013, Greece, Cyprus, Portugal, Ireland and Slovenia had all been downgraded to non-investment grades.4 DOI: 10.1057/9781137391506.0008

% of observations

100 90 80 70 60 50 40 30 20 10 0 LAC

AFRICA

ODR

LAC

S&P

AFRICA

ODR

LAC

Moody's Downgrade

No change

AFRICA

ODR

Fitch Upgrade

figure 4.2 Distribution of rating changes by developing regions Source: Computed by authors from data provided on each agency’s website. 30

Number of Sovereigns

25 20 15 10 5 0 (5) (10) (15) (20)

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 LMLIC: Downgrade LMLIC: Upgrade

UMC: Downgrade UMC: Upgrade

HIC: Downgrade HIC: Upgrade

figure 4.3 Distribution of rating changes by development level by year for Moody’s Source: Computed by authors from data provided on each agency’s website. 25 Number of Sovereigns

20 15 10 5 0 (5) (10) (15)

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 LMLIC: Downgrade LMLIC: Upgrade

UMC: Downgrade UMC: Upgrade

HIC: Downgrade HIC: Upgrade

figure 4.4 Distribution of rating changes by development level by year for Fitch Source: Computed by authors from data provided on each agency’s website.

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Trends in Sovereign Debt Ratings



30

Number of Sovereigns

20 10 0 (10) (20) (30)

1997

1998

1999

2000

2001

2002

LMLIC: Downgrade LMLIC: Upgrade

2003

2004

2005

UMC: Downgrade UMC: Upgrade

2006

2007

2008

2009

2010

2011

HIC: Downgrade HIC: Upgrade

figure 4.5 Distribution of rating changes by development level by year for S&P Source: Computed by authors from data provided on each agency’s website.

In the eight intervening years (2000–2007), it is interesting to note that Moody’s only downgraded one HIC sovereign debt in one year, while downgrading at least one UMC debt in each of seven years and at least one LMLIC debt in each of six years. Moody’s also upgraded at least two HICs in each of the eight years, with the total number of HIC upgrades exceeding that of UMC upgrades. By contrast, LMLICs were only upgraded in five years, with the total number of LMLIC upgrades being significantly smaller than that of either the HICs or UMCs. S&P and Fitch exhibited a similar trend in the intervening years, with a lower number of downgrades and a significantly higher number of upgrades for HICs relative to LMLICs. When the period before the crisis is compared with the global crisis years, a marked shift in this trend is however exhibited. Whereas before the crisis HICs seem to have on average benefited from favorable rating changes relative to LMLICs and to a lesser extent UMCs, during the crisis the HICs bore the brunt of the negative actions. Note, however, that by 2013 this trend began to reverse, as a number of HICs, particularly those in Western Europe, received improved ratings, while some emerging market sovereigns received rating downgrades in 2014.5 Fitch (2015, 1–2) therefore notes that “sovereign rating convergence between developed markets and emerging markets, evident since the outbreak of the global financial crisis, ceased in 2014 as sovereign creditworthiness in developed markets stabilized, whereas emerging markets encountered a more challenging global economic environment.”

DOI: 10.1057/9781137391506.0008

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Sovereign Debt and Credit Rating Bias

Comparison of odds ratios In Table 4.5, odds ratios are computed to highlight the relative risk of one type of rating change as against another. They are compared across levels of development and developing regions, and significance testing is based on a null hypothesis of an odds ratio of one (no effect). In the third column, a significant odds ratio of 1.912 for Moody’s indicates that, compared with UMCs, LMLICs are approximately two times more likely to be downgraded rather than upgraded. This odds ratio is, however, only weakly significant for Moody’s, and is not significant for either S&P or Fitch. There are also no statistically significant differences between LMLICs and UMCs when comparing downgrades versus no change. Consistently highly significant relationships are however derived when comparing upgrades versus no change for these two categories of countries. A highly significant odds ratio of 0.58 for S&P suggests that LMLICs are 42 less likely than UMCs to receive a ratings upgrade rather than experiencing no changes in their ratings. The results for Moody’s and Fitch can be interpreted similarly. In the fifth column, the comparison between UMCs and HICs also produce several statistically significant differences. While there are no significant differences between downgrades and upgrades, the differences between upgrades and no change are highly significant. table 4.5 Comparison of odds of rating change across different country groupings Level of Development

Rating Change

Downgrade vs Upgrade

Upgrade vs No change

Downgrade vs No change

Rating Agency

LMLIC vs UMC

LMLIC vs HIC

Developing Regions

UMC vs HIC

LAC vs AFRICA

LAC vs ODR

AFRICA vs ODR

S&P

.

.

.

.

.

.

Moody’s

.*

.**

.

.

.

.

Fitch

.

.

.

.

.

.

S&P

.***

.*

.***

.***

.

.***

Moody’s

.**

.

.***

.**

.

.**

Fitch

.**

.

.***

.***

.

.***

S&P

.

.*

.***

.**

.

.**

Moody’s Fitch

. .

.** .

.** .*

.* .**

. .

.* .**

Notes: A value in a cell of the table represents the odds ratio corresponding to the respective categories. *, **, *** indicates that the odds ratio is significantly different from 1.0 (no effect) at the 10, 5 and 1 level, respectively. Source: Computed by authors.

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

With odds ratios of 2.518, 1.606 and 1.948 for S&P, Moody’s and Fitch, respectively, it is clear that the sovereign debt of UMCs is considerably more likely than that of HICs to be upgraded rather than remaining stable. The converse is, however, also true. Statistically significant odds ratios of 2.023, 1.778 and 1.587 for S&P, Moody’s and Fitch, respectively, indicate that relative to HICs, UMCs are also considerably more likely to be downgraded rather than remaining stable. The countervailing ratings actions that were quite evident when comparing UMCs and HICs are not as consistent in the few statistically significant differences between LMLICs and HICs. For example, in the fourth column, S&P is shown to be both more likely to upgrade and to downgrade LMLICs relative to HICs rather than keeping their ratings unchanged (with weakly significant relationships of 1.459 and 1.594, respectively). By contrast, Moody’s is shown to be twice more likely to downgrade rather than upgrade the sovereign debt of LMLICs relative to HICs (with a highly significant odds ratio of 2.117). It is also almost twice more likely to downgrade LMLICs relative to HICs rather than keeping their ratings unchanged (odds ratio of 1.856). There is, however, no statistically significant tendency for Moody’s to be more likely to issue upgrades for LMLICs relative to HICs. In fact, although not statistically significant, the odds ratio of 0.877 would suggest that LMLICs are less likely than HICs to be upgraded rather than remaining stable. The last three columns of Table 4.5 compare the odds ratios across developing regions. The results indicate no statistically significant effect of regional differences on the odds of being downgraded versus upgraded. The statistically significant odds ratios are, however, quite consistent across CRAs. In the sixth column, LAC sovereigns are shown to be much more likely than sovereigns in Africa to be upgraded than remaining stable, but also more likely to be downgraded rather than remaining stable. In the final column, Sub-Saharan African (SSA) sovereigns relative to those from ODR are shown to be less likely to be upgraded and less likely to be downgraded rather than remaining unchanged. These results all confirm the relative stability of ratings assigned to SSA sovereigns.

Selected economic fundamentals as explanators The trends presented above are describing rating levels and actions, with the implicit assumption that they are exogenously driven by development DOI: 10.1057/9781137391506.0008

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Sovereign Debt and Credit Rating Bias

level or regional grouping. A more reasonable assumption is that these groups vary in their ability and willingness to repay debt, thus making particular rating actions more likely for particular groups. Simply put, the observed differences in rating actions across sovereigns of different development levels or regional groupings may be justified by economic fundamentals. Table 4.6 presents summary statistics on a few typical economic fundamentals used in the credit rating literature. Comparisons across level of development and developing regions will allow for preliminary assessments of the factors behind the ratings assigned. In the next chapter, we present our full set of theorized indicators of debt quality. HICs have the highest average government debt to GDP ratios (52.5), followed closely by LMLICs (50.5) and at some distance by UMCs (46.1). Levels of indebtedness tend to be particularly high in the LAC region, with the average government debt exceeding that of Africa and the ODR. Sovereign indebtedness is often driven by fiscal and current account imbalances. In this respect, the figures clearly indicate the positive positioning of the HICs, which have very low average fiscal deficits (–1.5) and a current account surplus (1.1). By contrast, LMLICs and UMCs have much larger average fiscal deficits (–13.8 and –8.8, respectively), and have current account deficits of –4.6 and –2.6, respectively. Despite their relatively high levels of indebtedness, LAC sovereigns have on average lower fiscal deficits (–10.5) than those of ODR (–11.3) and Africa (–11.0), and have a lower average current account deficit (–3.9) table 4.6 Selected economic fundamentals by development level and developing regions LEVEL OF DEVELOPMENT LMLIC Mean Gov’t Debt Fiscal Balance C/A balance

SD

UMC Mean

SD

Mean

.

. .

. .

–.

. –.

. –.

–.

.

–.

.

.

Inflation

.

.

.

.

.

Real GDP growth Countries

.

.

.

.

.





DEVELOPING REGIONS

HIC

LAC SD

Mean

ODR

SD

Mean

SD

Mean

SD

. .

.

.

.

.

.

. –.

. –.

. –.

.

. –.



AFRICA

.

.

.

. 

.

–.

.

–.

.

.

.

.

.

.

.

.

.

.



. 

Notes: The letter ratings were linearly transformed by assigning discrete values from 1 to 21, where 1 correspond to default ratings. To compare the means, export was normalized by GDP. Source: Computed by authors. DOI: 10.1057/9781137391506.0008

Trends in Sovereign Debt Ratings



than that of Africa (–4.1). Note, however, that current account imbalances seem to be problematic for LAC and Africa, as their average deficits are significantly larger than that of ODR (–2.8). In Chapter 2, it was noted that inflation has been used by the CRAs as a proxy for the credibility of a country’s monetary policy and was shown to be one of the most consistent predictors of ratings assignments in empirical studies. It is not surprising therefore that the summary statistics presented in Table 4.6 indicate that HICs have significantly lower average inflation rates than UMCs and LMLICs. The relative ratings instability of UMCs could be partially driven by their high average inflation rate of 10.7. By contrast, the average inflation rate for African sovereigns of 6.3, which is relatively low by developing country standards, could help to explain the rating stability exhibited in that region. The real growth rate for developing countries, particularly for LACs and African countries, tends to be higher than that for HICs. It is however quite likely that this positive indicator of debt quality is being outweighed by the adverse trends in the other economic fundamentals, with the realized outcome being low ratings.

Bias in the balance: Weighing the preliminary evidence When rating assignments and actions are compared across levels of development and across regions within the developing world, three broad trends are identified: (1) rating assignments for HICs are higher and more stable than those for UMCs and LMLICs; (2) among developing countries, African sovereigns and LACs tended to have low ratings, but with African sovereigns having greater rating stability and (3) rating actions taken outside of the periods of the Asian and global crises have tended to favor HICs relative to LMLICs, in that they received more sovereign debt upgrades and fewer downgrades. The summary statistics for the few selected economic fundamentals have helped to explain some of these trends, as differences were exhibited between developed and developing countries and across the developing regions, that were consistent with a priori expectations about how they affect a country’s capacity and willingness to repay debt. Notwithstanding this, it is important to note that the summary statistics do not and cannot provide all the explanations. For example, they do not explain the differences revealed when average ratings assigned DOI: 10.1057/9781137391506.0008



Sovereign Debt and Credit Rating Bias

to different regions were compared across the big-three CRAs. Why did S&P and Fitch have an average rating for African sovereigns that was approximately three rating classes lower than that of Moody’s? GultekinKarakas et al. (2011) highlight allegations that some CRAs have regional biases. Does this result confirm such allegations? Let us be clear, the trends and statistics described in this chapter cannot be used to provide evidence as to whether biases exist. There are two reasons for this: (1) The figures used in providing these preliminary results are highly aggregated and should only be used to highlight areas for further study; and (2) Showing the existence of a bias requires a more rigorous methodology than was used in this chapter to effectively control for theorized quantitative and qualitative indicators of debt quality. The next chapter in this book outlines such a methodology, and the penultimate chapter presents the requisite empirical evidence.

Appendix table a4.1 List of sample sovereigns by development level, developing region and rating agency LMLIC LAC

Bolivia El Salvador Guatemala Honduras Nicaragua Paraguay St. V.G Suriname AFRICA Benin Burkina Faso Cameroon Cape Verde Egypt Gambia Ghana Kenya Lesotho Madagascar Malawi Mali Morocco Mozambique Nigeria

UMC F F F

F F F F F F F F F F F F F F

M M M M M M M M

M M M

M M

S LAC S S S

Argentina Belize Brazil Chile Colombia S Costa Rica D. R. S Ecuador S Grenada S Jamaica S Mexico S Panama S Peru Uruguay S Venezuela S AFRICA Angola Botswana S Gabon Libya S Mauritius S Namibia S South Africa S Tunisia

HIC F F F F F F F F F F F F F F

M M M M M M M M M M M M M M M M

F F F F F

M M M M

S S S S S S S S S S S S S S S S S S S

Aruba Australia Austria Bahamas Bahrain Barbados Belgium Canada Croatia Cyprus Czech Rep. Denmark Estonia Finland France Germany Greece Hong Kong Hungary Iceland Ireland S Israel S Italy

F S F M S F M S M S F M S M S F M S F M S F M S F M S F M S F M S F M S F M S F M S F M S F M S F M S F M S F M S F M S F M S F M S

Continued DOI: 10.1057/9781137391506.0008

Trends in Sovereign Debt Ratings

table a4.1

Continued

LMLIC

ODR



UMC Rwanda Senegal Seychelles Uganda Albania Armenia Bangladesh Cambodia Georgia India Indonesia Moldova Mongolia Pakistan Philippines Sri Lanka Ukraine Vietnam

F M F F F

F F F F F F F F F

M M M M M M M M M M M M M M

S ODR S S S S S S S S S S S S S S S

Azerbaijan Belarus B.H Bulgaria China Fiji Jordan Kazakhstan Lebanon Lithuania Macedonia Malaysia Montenegro P.N.G Romania Russia Serbia Thailand Turkey Turkmenistan

HIC F

F F

F F F F F F F F F F F F

M M M M M M M M M M M M M M M M M M

S S S S S S S S S S S S S S S S S S S

Japan Korea, Rep. Kuwait Latvia Luxembourg Macao Malta Netherlands New Zealand Norway Oman Poland Portugal 2BUBS Saudi Arabia Singapore Slovak Rep. Slovenia Spain Sweden Switzerland T &T U.A.E United Kingdom United States

F F F F F F F F F F F F F F F F F F

F F

M M M M M M M M M M M M M M M M M M M M M M M M M

S S S S S S S S S S S S S S S S S S S S S S S

Notes: St. V.G. – St. Vincent & Grenadines, D.R. – Dominican Republic, B.H. – Bosnia &Herzegovina, P.N.G. – Papua New Guinea, U.A.E. – United Arab Emirates, T&T – Trinidad &Tobago, F – Fitch, M – Moody, S – Standard & Poor Source: Computed by authors.

Notes  Fuchs and Gehring (2013, 2).  Financial Times. http://www.ft.com/intl/cms/s/0/0a332b2a-e35c-11e0-8f47÷÷øûûGFBCED÷IUNMҮBY[[úL1%0*2Tù  Moody’s Investor Service (2015)—Sovereign Default and Recovery Rates.  Moody’s (2015) notes that only the latter two regained investment-grade ratings by April 2015.  Moody’s (2015).

DOI: 10.1057/9781137391506.0008

5

Introducing Greater Rigor— Methodological Approach Abstract: This chapter presents an approach for identifying whether biases exist. Some methodological improvements to the current credit rating literature are introduced, which bore in mind the criticisms of the existing empirical studies. The approach controls for: (1) a core set of theorized and quantifiable indicators of debt quality; (2) a complex qualitative aspect of the credit rating process—the behavior of CRAs in trying to balance rating timeliness and rating stability and (3) fixed effects such as time invariant political and institutional characteristics of sovereigns within an ordered response framework. This framework allows us to estimate a lower threshold below which debt quality changes can lead to a downgrade and an upper threshold above which debt quality changes can lead to an upgrade. We allow the data to determine whether the upper threshold systematically varies across different groups of sovereigns. This forms a new modeling approach for more rigorously assessing the existence of bias. Keywords: Bias; Credit Rating Agencies; Developing Countries; International Capital Markets, Nonlinear Ordered Response Framework; Panel Data Models; Sovereign Debt Tennant, David F., and Marlon R. Tracey. Sovereign Debt and Credit Rating Bias. New York: Palgrave Macmillan, 2016. doi: 10.1057/9781137391506.0009. 

DOI: 10.1057/9781137391506.0009

Introducing Greater Rigor—Methodological Approach



Though the previous chapter reveals some descriptive trends in rating assignments and their relation to a few popular empirically established determinants, it provides no clear evidence for or against the existence of biased ratings. In this chapter, we attempt to generalize the analysis of credit ratings, beyond the use of purely descriptive tools and the methods of existing empirical studies. We present a simple but more rigorous empirical approach that we believe captures some essential aspects of credit rating agencies’ (CRAs’) decisions about rating assignment that have not been addressed in previous studies. This approach allows for more convincing evidence of bias. Studies that have attempted to estimate rating bias tend to use panel data to estimate either fixed effects linear regressions (e.g. the UniCredit Report 2014; Fuchs and Gehring 2013) or pooled/random effects ordered response models (e.g. Ozturk 2014; Gultekin-Karakas et al. 2011). In studies of this nature, there are likely fixed effects, that is, systematic unobserved time-invariant factors that affect debt quality. Rather than assuming away such effects as simply random (and relegate them to an error term), effectively controlling for fixed effects produces consistent estimates. However, the studies that use fixed effects, also linearly transform credit ratings to a numerical measure so as to estimate a linear regression. This linear approach implicitly assumes that the change in debt quality between any two adjacent rating classes is the same for all such adjacent classes. For instance, it is hard to claim that the difference in debt quality between AA+ and AAA is the same as between BB+ and BBB-. In this respect, ordered response (non-linear) techniques are useful for estimating thresholds for each rating class, which are not necessarily equally spaced. Indeed, Afonso et al. (2011) find unequally spaced thresholds for each of the big-three CRAs based on their random effects ordered probit model. These empirical studies are also criticized by CRAs for using regression techniques that are too simple, for not capturing the complexity of the sovereign debt ratings process, and for not controlling for the complete list of variables used by their analysts. While we address to some degree the first two criticisms, our approach for including variables in our model should not be likened to the actual quantitative analysis conducted by CRAs in making rating decisions. Our empirical model for establishing bias is premised on theorized observed and unobserved indicators of sovereign debt quality and on a key behavioral trait of CRAs. Specifically, we are concerned with simply controlling for factors DOI: 10.1057/9781137391506.0009



Sovereign Debt and Credit Rating Bias

that should theoretically determine a sovereign’s debt quality. We then assess whether it is more difficult for some sovereigns to be upgraded, for any given change in debt quality induced by those theorized factors, in a realistic setting where CRAs exhibit a need for rating stability. If the results indicate that this is the case, we class those sovereigns as being subjected to bias. The bias is therefore credible to the extent that our model’s premise is reasonable. Our empirical approach, which, to the best of our knowledge, is unlike any other study, is presented in four parts. First, we account for a core set of theorized and quantifiable indicators of debt quality. These are discussed in the next subsection. Second, we combine a first difference approach to control for fixed effects with a nonlinear ordered response framework. Specifically, by focusing on changes in debt quality, we can effectively control for time invariant political and institutional characteristics of sovereigns, without assuming them away as random effects. The use of the ordered response framework allows us to estimate a lower threshold below which debt quality changes can lead to a downgrade and an upper threshold above which debt quality changes can lead to an upgrade. Third, we model a complex qualitative aspect of the credit rating process—the behavior of CRAs in trying to balance rating timeliness and rating stability. Finally, we allow the data to determine whether the upper threshold systematically varies across different groups of sovereigns. The second to fourth parts together form a new modeling approach for more rigorously assessing the existence of bias, which is formally described in the final subsection of this chapter.

Theorized and quantifiable indicators of debt quality The main purpose of CRAs is to assign sovereigns to a rating class based on their debt quality. A sovereign’s debt quality is expected to be objectively assessed based on a weighted combination of indicators that reflect the sovereign’s financial capacity and its willingness to repay debt. Such indicators include economic, financial and institutional variables, some of which cannot be easily quantified. We rely on a theorized set of quantifiable indicators of debt quality. We take advantage of the existing empirical literature, summarized in Chapter 2, which points to a growing consensus around a short list of empirically established determinants of sovereign debt ratings. As such, we conceptualize the indicators of DOI: 10.1057/9781137391506.0009

Introducing Greater Rigor—Methodological Approach



debt quality by making a simple distinction between the factors affecting either a country’s ability or its willingness to repay debt, as follows: DEBT QUALITY = f (WILL, ABILITY) where: WILL = f (IQ)

(5.1) (5.2)

ABILITY = f (SOL, LIQ) where: SOL = f (DEBTstk, PRODcap, FISCAL) LIQ = f (DEBTsrv, FOREX, INFL)

(5.3) (5.4) (5.5)

We first discuss the concept of ABILITY, which reflects the fact that a sovereign’s debt quality is impacted by its ability to repay its debt. As indicated in equation 5.3, this is in turn impacted by the country’s TPMWFODZ
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-*2 1 Solvency refers to a country’s longterm ability to pay its debt.2 Equation 5.4 shows that it is a function of a country’s stock of debt (DEBTstk), productive capacity (PRODcap) and fiscal space (FISCAL). The government debt to GDP ratio (Gov’t Debt) and external debt stock to export ratio (External Debt) are used to measure a sovereign’s total and external indebtedness, respectively, in relation to their ability to repay. More broadly, the ratio of domestic credit issued by banks to GDP (Domestic Credit) is also used to indicate heightened leverage of a country if borrowed funds are increasingly relied on to propel production. An increased total debt burden is expected to correspond with a higher risk of default.3 Negative relationships with DEBT QUALITY are thus expected, as increased debt burdens, holding all else the same, signals poor quality debt. The impact of a sovereign’s capacity to repay its debt is investigated through the inclusion of a number of productive capacity indicators. Most traditionally, we include real per capita GDP (real GDP/pop) and economic growth (real GDP growth) since the greater the potential tax base of the country and the faster it grows, the more likely a government will be able to repay its debt. The level of investment in the country also affects the potential tax base. Lower gross capital formation as a percentage of GDP (Investment), and net inflow of foreign direct investment as a percent of GDP (FDI), are both expected to reduce the government’s ability to repay its debt. The current account balance as a percentage of GDP (C/A Balance) is also used (albeit tentatively) as an indicator of productive capacity. Most studies view a high and increasing current account deficit as a signal of an economy’s tendency to over-consume and rely heavily on funds DOI: 10.1057/9781137391506.0009



Sovereign Debt and Credit Rating Bias

from abroad, thus leading to growth in foreign indebtedness, which may become unsustainable in the long run.4 Afonso et al. (2011), however, highlight an alternative view of increasing current account deficits reflecting accumulation of investment, which should lead to higher growth and improved sustainability over the medium term. A positive relationship with debt quality will provide evidence for the more traditional interpretation of C/A balance, while a negative relationship will support Afonso et al.’s (2011) alternate perspective. Fiscal balance (Fiscal—measured as the general government balance as a percent of revenue) is another important theorized determinant of a country’s solvency. Higher fiscal deficits absorb private domestic savings, signal macroeconomic disequilibria and indicate that the government lacks the ability to increase taxes to cover expenses or to service its debt.5 Larger deficits also imply a higher likelihood that external shocks may generate a default (Rowland and Torres 2004). A positive relationship with debt quality is thus expected. As indicated in equation 5.5, a country’s liquidity refers to its shortUFSN
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of the country’s debt service requirements (DEBTsrv), its capacity to earn foreign exchange (FOREX) and the inflation rate (INFL). A higher debt service ratio (Debt Service) indicates greater financial obligations imposed by sovereign indebtedness and thus is expected to have a negative relationship with debt quality. When a sovereign’s debt is denominated in a foreign currency, access to foreign exchange through Exports and reserves (Reserve/Imports) signals the ability to meet short-term obligations (hence expected positive relationships with debt quality). Inflation is traditionally theorized to have a negative relationship with a country’s creditworthiness, as increasing inflation is symptomatic of macroeconomic problems, particularly when the government resorts to inflationary financing of the fiscal deficit in lieu of increasing taxes or cutting spending (Cantor and Packer 1996).6 A country’s ability to repay its debt is also impacted by the quality of its institutions, as institutional quality affects the effectiveness of a country’s governance arrangements. These arrangements in turn impact, for example, the extent to which governments maintain fiscal discipline, resist the urge to over-borrow, and maintain adequate reserves, all of which are captured in the model.7 Note, however, that Butler and Fauver (2006) found that a country’s institutions have a strong and independent effect on sovereign debt ratings, over and above the country’s ability to pay. They thus DOI: 10.1057/9781137391506.0009

Introducing Greater Rigor—Methodological Approach



concluded that institutional effectiveness also affects sovereign debt quality through a willingness to pay channel. We include a variable that explicitly NFBTVSFT
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XF
VUJMJ[F
an aggregate of three of the World Governance Indicators: rule of law, government effectiveness and regulatory quality, which are typically found to be significant in empirical work (such as Afonso et al. 2011; Biglaiser and Staats 2012; Ozturk 2014). This variable is defined as respect for institutions that govern economic interaction and property rights, and the capacity to effectively formulate and implement sound policies. We expect a positive relationship between institutional quality and debt quality, as higher institutional quality should enhance willingness to repay debt. Data for all variables were collated from the official databases for the International Monetary Fund, the United Nations and the World Bank. A full description of the variables and their sources are presented in Tables A5.1 and A5.2 in the Appendix to this chapter.

Econometric Framework Let R*it be the true quality of debt for sovereign i at time t. The prior discussion suggests that it should be measured as: K

Rit*  £^ 1 j $X ijt  h t  ] i  eit ,

t  1,2,z,T ; i  1,2,z, n

(5.6)

j 1

where Xit are the theorized and quantifiable indicators of debt quality, which have a unique weight of ^1. Equation 5.6 also includes unmeasured indicators of debt quality that are decomposed into three types. The first type ht includes events that vary across time and affect all countries, such as global crisis periods. The second type ]i denotes features that vary by sovereign but not over time, such as social capital and the nature of the political system. The third type eit represents an unexplained part of debt quality, which we assume is sufficiently unimportant to be considered as a normal random error. Although ]i is unobserved, we can control for it by assessing changes in debt quality over time for each of the n sovereigns, assuming the structure of equation 5.6 remains the same over the T time periods. If there are also observed factors that are time-invariant (such as whether a sovereign has a default history), those are also controlled for DOI: 10.1057/9781137391506.0009

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Sovereign Debt and Credit Rating Bias

by considering changes in debt quality over time. Similarly, the termht, since it is a period-specific general trend, we can control for it by using a set of period dummy variables. Since eit is a random error, its effect is zero on average.8 So, if we let ΔR*it be the change in debt quality from t–1 to t for sovereign i, then it is measured as: K

T

j 1

j 3

$Rit*  ^ 0  £ ^ 1 j $X ijt  £ ^ 2 j p jt  $eit , t  2,z,T ; i  1,2,z, n

(5.7)

Here pjt is the jth period dummy with period-specific effect ^2j, and the general trend over a reference period is ^0.9 We now propose that CRAs determine a band within which a change in debt quality ΔR*it cannot practically be considered a downgrade or an upgrade. However, if there are non-negligible changes in debt quality, CRAs can choose to assign a downgrade or an upgrade. Formally, the lower limit of the band is q1 and it is the threshold that separates the downgrade category from the no-change category. The upper limit of the band is q2 and it is the threshold that separates the no-change category from the upgrade category. The no-change band thus ranges from q1 to q2. We consider two important features of this no-change band: (1) the proportion of sovereigns within the band; and (2) the width of the band. We relate the first feature to the behavior of CRAs, as observed by investors. The second feature, we propose, varies for different groups of sovereigns. The proportion of sovereigns within the no-change band can be determined by two processes. First, as equation 5.7 suggests, if there is little or no change in the indicators of willingness and ability to pay debt or no global shocks, reflecting practically no change in debt quality, then that would warrant no change in ratings. Second, a no-change outcome may be determined by a separate process that is governed by CRAs’ desire for rating stability. Regarding this second process, Altman and Rijken (2004) argue that, while CRAs try to respond quickly to events that affect debt quality, their response is sometimes offset by their desire for rating stability, which they achieve by taking a long-term perspective. Rating migrations only occur when there are significant, permanent changes in debt quality. This is so as to reduce the effect of the business cycle and any short-term fluctuations. In light of this desire for rating stability, we form two expectations to characterize it. First, we expect that such inertia in the behavior of CRAs is more likely to affect those sovereigns already at relatively low rating DOI: 10.1057/9781137391506.0009

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levels and those already at relatively high ones. That is, sovereigns are more likely to be assigned a no-change as their rating class approaches the lowest (highest) level, irrespective of whether debt quality has worsened (improved). Second, we expect that CRAs may be reluctant to downgrade high-rated sovereigns in response to a decline in debt quality, given their creditable reputation of maintaining high debt quality. It is not expected that there will be such reluctance to upgrade low-rated sovereigns in response to an increase in debt quality. As, despite their poor credit standing, an increase in debt quality would suggest that their ability and willingness to repay debt has increased.10 Formally, let CRAs’ desire for rating stability be defined in terms of the probability of a final no-change decision (denoted nc), given a change in debt quality, that is, Pr(nc|ΔR*it ≤ q1 or ΔR*it ≥ q2). If we let the rating classes in Table 4.1 be denoted by R, that probability can be written as a function of a sovereign’s rating class at year t–1, Rit–1. Specifically, based on our expectations, Pr(nc|ΔR*it ≤ q1) is a quadratic function of Rit–1 and Pr(nc|ΔR*it ≥ q2) is a linear function of Rit–1. In the spirit of Greene et al. (2013), these functions are referred to as tempered equations, because the need for an upgrade or downgrade is tempered by CRAs’ desire for stable ratings. We now consider the width of the no-change band. In so doing, we define what constitutes a bias in rating assignments. Suppose the width of the no-change band varies such that the upper threshold q2 increases or decreases based on the regional or developmental grouping of sovereigns. Specifically, as in Terza (1985), the upper threshold becomes qi2 = q2 + G9if, where for sovereign i, Gi denotes the group that it belongs to and f is the amount by which the upper threshold changes for that group. This means that for sovereigns in one group the upper threshold is q2 + fversus q2 for sovereigns in another group. Clearly, if f0, then for the same increase in debt quality R*it, it would be harder for those sovereigns with an upper threshold of q2 + f to achieve an upgrade. Such behavior constitutes a bias against sovereigns in one group versus those in another. At this point, we discuss how the parameters are estimated, in particular the parameter that indicates bias, f. To do so, we need to compute the probability of an actual downgrade, an actual upgrade and an actual no-change, each conditional on changes in debt quality, the tempering effects of CRAs’ desire for rating stability and the threshold effects of regional or developmental grouping. Probability of actual downgrade: The likelihood that CRAs actually downgrade a sovereign depends on how likely it is that a decline in debt DOI: 10.1057/9781137391506.0009

Sovereign Debt and Credit Rating Bias

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quality indicates the need for a downgrade and CRAs respond with a final downgrade decision. That is, Pr(actual downgrade) = Pr(final downgrade decision | ΔR*it ≤ q1) × Pr(ΔR*it ≤ q1). It should be noted that when a sovereign’s debt quality has deteriorated, CRAs can either choose to make a final downgrade decision or choose to keep ratings unchanged. That is, Pr(final downgrade decision | ΔR*it ≤ q1) = 1 – Pr(nc|ΔR*it ≤ q1). Probability of actual upgrade: The likelihood that CRAs actually upgrade a sovereign depends on how likely it is that an increase in debt quality indicates the need for an upgrade and CRAs respond with a final upgrade decision. That is, Pr(actual upgrade) = Pr(final upgrade decision | ΔR*it ≥ q2) × Pr(ΔR*it ≥ q2). Note that when a sovereign’s debt quality has improved, CRAs can either choose to make a final upgrade decision or choose to keep ratings unchanged. That is, Pr(final upgrade decision | ΔR*it ≥ q2) = 1 – Pr(nc|ΔR*it ≥ q2). Probability of actual no-change: The likelihood of CRAs actually assigning a no-change depends on three possible mutually exclusive cases:  



Case 1: The likelihood of a true no-change outcome when there is little or no change in debt quality, computed as Pr(q1 < ΔR*it < q2). Case 2: The likelihood that a decline in debt quality indicates the need for a downgrade and CRAs desire rating stability in response to such a decline, computed as Pr(nc|ΔR*it ≤ q1) × Pr(ΔR*it ≤ q1). Case 3: The likelihood that an increase in debt quality indicates the need for an upgrade and CRAs desire rating stability in response to such an increase, computed as Pr(nc|ΔR*it ≥ q2) × Pr(ΔR*it ≥ q2).

Cases 2 and 3 account for some of the large number of no-change outcomes that we observe in the data, as argued by Altman and Rijken (2004). In other words, when CRAs desire rating stability, it creates excess no-change outcomes beyond those truly due to little or no change in debt quality. Formally, Pr(actual no-change) = Pr(q1 < ΔR*it < q2) + [Pr(nc|ΔR*it ≤ q1) × Pr(ΔR*it ≤ q1)] + [Pr(nc|ΔR* ≥ q ) × Pr(ΔR* ≥ q ). it

2

it

2

We form a likelihood function that represents the overall probability of all the actual downgrades, actual upgrades and actual no-changes in the sample data, conditional on changes in debt quality, the tempering effects of CRAs’ desire for rating stability and the threshold effects. By maximizing that likelihood function, we can estimate the effects of the observed indicators of willingness and ability to pay on debt quality (^1 from DOI: 10.1057/9781137391506.0009

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

equation 5.6), the parameters of the tempered equations, and importantly, the threshold effects (f) of regional or developmental groupings. In the next chapter, an estimate is provided for f, among other parameters, about which we make inferences regarding their statistical significance. Specifically, if there exists a bias against poor countries, or against specific regional blocks of poor countries, then the sample data should support f0. In such a case, we would have established bias.

Appendix table a5.1 Description of variables and their sources VARIABLE Solvency Gov’t Debt External Debt† Domestic Credit Real GDP/pop Real GDP growth Investment FDI C/A Bal Fiscal Bal Liquidity Debt Service † Export Reserve/Import Inflation Institutional Quality

DESCRIPTION

SOURCE

Gross general government debt ( of GDP) External debt stocks ( of exports of goods, services, income) Domestic credit provided by banking sector ( of GDP) GDP per capita (constant  US) GDP growth (annual ; at market prices) Gross capital formation ( of GDP) Foreign direct investment, net inflows ( of GDP) Current account balance ( of GDP) General government balance as  of revenue

IMF PDD, WEO WB WDI WB WDI WB WDI WB WDI WB WDI WB WDI WB WDI, UNdata IFS IMF WEO

Debt service ( of exports of goods, services and net income) ) Exports of goods and services (US BN) Total reserves in months of imports (total reserves/import/) Inflation, consumer prices (annual )

UNSTAT MDGI

Measured as the aggregate of three World Governance Indicators: government effectiveness, regulatory quality and rule of law (each measured on scale: –. to .)

WB WGI

WB WDI WB WDI IMF WEO, WB WDI

Notes: Definition of acronyms: WB: World Bank, WGI: World Governance Indicators, IMF: International Monetary Fund, PDD: Public Debt Database, WEO: World Economic Outlook, WDI: World Development Indicators, UNdata: United Nations Data, IFS: International Financial Statistics. †Data not available for developed countries. UNSTAT MDGI: United Nations Statistics Division, Millennium Development Goals Indicators.

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table a5.2 Simple descriptive statistics VARIABLE

MEAN

SD

MAX

Solvency Gov’t Debt External Debt Domestic Credit Real GDP/pop (‘) Real GDP growth Investment FDI C/A Bal Fiscal Bal

. . . . . . . –. –.

. . . . . . . . .

. . –. . –. . –. –. –.

. . . . . . . . .

. . . .

. . . .

. . . –.

. . . .

.

.

–.

.

Liquidity Debt Service Export Reserve/Import Inflation Institutional Quality

MIN

Source: Computed by authors.

Notes  This is based on an adaptation of Rowland and Torres’ (2004) framework.  Although insolvency of a sovereign issuer is not a well-defined concept, solvency, as defined in this context is intuitive and aids in the comprehension of the manner in which many of the variables used in previous studies impact on a country’s creditworthiness and hence on changes in its sovereign debt rating (Rowland and Torres 2004).  Cantor and Packer (1996).  See e.g. Cantor and Packer (1996), and Rowland and Torres (2004).  Cantor and Packer (1996), Afonso et al. (2011) and Rowland and Torres (2004).  Afonso et al. (2011), however, suggest that there is some ambiguity in the potential impact of this variable, as it could also lead to improved creditworthiness as “it reduces the real stock of outstanding government debt in domestic currency, leaving more resources to cover foreign debt obligations.”  Afonso et al. (2011).  We assume it follows a standard normal distribution.  In estimating the final model, ^0 is normalized to zero for identification purposes.  We accounted for such a CRA’s response and it resulted in poor convergence issues, perhaps due to the relative paucity of data on low ratings.

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6

Are Poorer Countries Disadvantaged by the CRAs? Empirically Establishing a Bias Abstract: In this chapter, we survey the findings of extant empirical studies on bias, before reporting the results of our own methodological approach for assessing bias. Our results provide clear empirical evidence of the existence of bias. The results indicate that S&P, Moody’s and Fitch all find it more difficult to upgrade poor countries relative to rich countries, for any given improvement in ability and willingness to repay debts. S&P and Fitch are further shown to find it more difficult to upgrade African countries relative to other developing countries, for any given improvement in ability and willingness to repay debts. These results are taken as a strong indication of bias, as they are highly significant even though we controlled for the key observed economic and institutional determinants of sovereign debt ratings, unobserved country-specific fixed effects and the CRAs’ desire for rating stability. Keywords: Africa; Bias; Credit Rating Agencies; Developing Countries; International Capital Markets; Latin America and the Caribbean; Sovereign Debt Tennant, David F., and Marlon R. Tracey. Sovereign Debt and Credit Rating Bias. New York: Palgrave Macmillan, 2016. doi: 10.1057/9781137391506.0010. DOI: 10.1057/9781137391506.0010

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Sovereign Debt and Credit Rating Bias

Previous chapters have done just about everything but provide evidence that the big-three credit rating agencies (CRAs) are biased. We have examined the ratings process and ratings industry and have observed that there are opportunities for bias to be introduced. We have highlighted trends in rating levels and actions that have precipitated questions about potential bias. We have even outlined a method for determining the existence of bias. But, up until now, we have not provided any evidence to support that such biases actually exist. Such evidence is critical, as the big-three CRAs have been quite dismissive of allegations of bias. Even econometric studies that point to bias are treated with scant regard.1 In this chapter, we review the recent empirical studies that have explicitly addressed the issue of bias, before providing our own evidence. Having applied the methodology described in Chapter 5, we have derived results that strongly indicate that the big-three CRAs are biased against poorer countries in the ratings actions taken. We show that Standard and Poor’s (S&P), Moody’s and Fitch are all more disinclined to upgrade poor countries relative to rich countries, for any given improvement in willingness and capacity to repay debt.

What have the existing empirical studies shown? An increasing number of econometric studies have been examining the issue of bias in sovereign debt ratings. The UniCredit Report (2014), for example, highlighted the fact that the big-three CRAs’ ratings are based on an objective assessment of measurable fundamentals, as well as the subjective judgment of their in-house ratings committees. They found that in some cases the committee significantly overruled the signals from the hard data. In their regression analyses, they estimated the objective component of ratings that depend on the measurable fundamentals of creditworthiness, and then computed the subjective component as the residual. The results revealed that on average, the subjective component added no value to the predictability of default until less than one year before the actual event. They thus concluded that “history is littered with sovereigns having been over and underrated by the subjective component overruling the objective component from the macro fundamentals.”2 As an example they pointed to the Eurozone debt crisis, noting that during the extreme 2009–2011 period,

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Are Poorer Countries Disadvantaged by the CRAs?

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the big-three downgraded the Eurozone periphery numerous times, as ratings committees repeatedly overruled the macroeconomic signals with “severely negative subjective assessments.” During the same period, the ratings committees overruled the macroeconomic signals for the “Fragile Five” emerging markets, but in a positive direction. In both instances, the report notes that the CRAs have been forced to correct their mistakes. In rejecting the claims of the UniCredit Report, S&P (2014b) attacked the simplicity of the regression analysis that was utilized, the limited number of variables that were included as determinants and the lack of an analytical justification for the inclusion of an advanced economy dummy variable. Although the UniCredit Report (2014) speaks to the existence of bias, it is not explicit about the source of the bias and against whom it is directed. Gultekin-Karakas et al. (2011) are clearer on the second point. They examined the determinants of Moody’s sovereign debt ratings for the period from 1999 to 2010 by estimating separate ordered probit models for developed and emerging market economies. Their results indicated that “the CRAs give higher ratings to developed countries regardless of their macroeconomic fundamentals.”3 They thus concluded that credit ratings favor developed countries, and, as such, are biased against emerging market economies. Although they do not give an explanation for the bias, they cite articles which highlight the importance of politics and value judgments of the analysts. This is not without controversy, as Archer et al. (2007) explicitly examined the effect of politics on the sovereign debt ratings of 50 developing countries from 1987 to 2003, and found that most political factors have little effect. This was, however, later refuted by Biglaiser and Staats (2012) who found that rule of law, strong and independent courts and protection of property rights have significant positive effects on ratings. Fuchs and Gehring (2013) controlled for political fundamentals when they investigated whether there is any empirical evidence for a home bias in sovereign ratings. A home bias is defined as occurring “if a rating agency gives preferential treatment to its home country and to countries with close economic, political and cultural ties to it.” They estimate this bias as “a deviation of the actual rating level from what would be predicted solely by the sovereign’s economic and political fundamentals.”4 Using monthly dyadic panel data on nine CRAs’

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Sovereign Debt and Credit Rating Bias

sovereign ratings between 1990 and 2013, they provided evidence of a home bias, which became more pronounced following the start of the global crisis in 2008. They found that some agencies provided significantly better ratings to their home country than was justified based on their relative economic and political fundamentals, while others provided better ratings to countries to which home-country banks had relatively higher risk exposure. Other agencies were shown to be affected by cultural distance, which they measured by linguistic differences. S&P (2014a) however completely rejected Fuchs and Gehring’s (2013) findings. As they did for the UniCredit Report, they attacked the simplicity of the regression analysis as being unable to “account for the subtleties of a sovereign’s policymaking and institutional behavior.”5 This is both because of the small number of variables used, and because of a reliance on solely quantitative factors to the exclusion of qualitative factors that S&P regards as being critical. Soudis and van Hoorn (2013) addressed at least one of S&P’s criticisms by emphasizing the importance of institutions in their study of home bias in sovereign debt ratings. They sought to test their hypothesis that “ratings among agencies based in different countries will have systematic differences”, against the standard view of ratings as “objective valuations of default probability.”6 To do this, they examined whether there were systematic non-random differences in the valuation of sovereign credit risk by US-based Moody’s and S&P and Chinese-based Dagong. They found that while the ratings of Moody’s and S&P were consistent, they both deviated significantly from those of Dagong. Specifically, the results indicated that “Western high income countries, receive on average a one notch higher valuation by the US agencies as compared to Dagong’s ratings. All other regions of the world receive valuations of up to 3 notches lower by the US agencies.” Their results further showed that the strongest predictor of the differences is the “political proximity to the rating agency’s home country.” Since political proximity has been shown to lower investor risk and thus increase investment, Soudis and van Hoorn (2013, 3) conclude that the CRAs “produce risk valuations most suitable to their home country investors.” Despite the strident refutations from S&P, there has been, since the global financial crisis, an upsurge in the number of studies that claim to provide empirical evidence of sovereign debt rating bias. Recognizing this trend, Ozturk (2014) utilized ordered response models to investigate whether what other studies had labeled as biases were actually caused DOI: 10.1057/9781137391506.0010

Are Poorer Countries Disadvantaged by the CRAs?



by cross country variations in the quality of institutions. He utilized a number of governance indicators to capture institutional quality and found that most of those indicators had a positive effect on rating decisions. In particular, he found that government ineffectiveness and poor regulatory quality were the two key factors that explained the low ratings for developing country sovereign debt. Ozturk (2014) is not unique in this respect, as other studies have found positive correlations between economic development and sovereign debt ratings, but did not conclude that the CRAs were biased. Cantor and Packer (1996), for example, found positive correlations between indicators of economic development and credit ratings, after controlling for per capita GDP and other traditional determinants of ratings. They, however, explained the correlations by arguing that the level of development acts as an instrument for the degree of a country’s integration into global markets, with less integration implying lower incentives for repaying debt, as the disruptions to linkages from default will be minimal. Similarly, Mellios and Paget-Blanc (2006), found positive correlations between indicators of economic development and credit ratings, but did not present these results as evidence of biased ratings. They asserted that the impact on sovereign debt ratings was accounted for by the strong relationship between corruption and economic development.

Our approach: Addressing the criticisms of previous studies Despite the increasing attention that is being placed on the issue of sovereign debt ratings bias, there is to date no consensus in the empirical literature as to whether such biases exist. Although it is unlikely that S&P or any of the other CRAs would accept any evidence proffered, the criticisms which they make must be taken into account to allow for the most objective and rigorous empirical testing of this important issue. The primary criticism of the previous studies on bias is that the regression techniques used are too simple and do not capture the complexity of the sovereign debt ratings process. Chapter 5 detailed the methodological approach used in this book and highlighted the methodological improvements to the current credit rating literature that we have introduced. Our empirical framework is simple but sophisticated enough to DOI: 10.1057/9781137391506.0010

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Sovereign Debt and Credit Rating Bias

capture a realistic component of the credit rating process, and thereby provide convincing evidence of bias. Regarding the variables included in our model, we explicitly account for a core parsimonious set of variables, which are firmly grounded in economic theory, and econometrically handle a number of key unobserved factors. Our empirical model also characterizes a realistic aspect of the credit rating process, which is the observed tendency of CRAs to attempt to achieve a balance between rating timeliness and rating stability. It is only after factoring in all of these considerations that we seek to ascertain whether biases exist by examining threshold heterogeneity. We consider, for each of the big-three CRAs, whether a bias exists which makes it harder for poorer countries to receive an upgrade, for any given improvement in ability and willingness to repay. In our first set of regressions, we examine whether biases exist because of a country’s development level. We use the World Bank’s income classifications to distinguish between high income countries (HIC), upper middle income countries (UMC), and lower middle and low income countries (LMLIC). Because of the low rating levels for countries in Africa and the Latin America and Caribbean region (LAC), we utilize a second set of regressions to ascertain whether biases exist against these regions. In this set of regressions we focus on developing regions to alleviate the confounding effect of development level.

Are the big-three CRAs biased against poor countries? Table 6.1 separately provides the estimation results for S&P, Moody’s and Fitch. For each CRA, we estimated two specifications. The first specification includes all variables, and the second excludes the three variables (external debt, domestic credit and fiscal balance) that have the most missing values, to show how the results change. In the first panel, we present the results from estimating the impact on debt quality of the observed indicators of willingness and ability to pay. Although all are theorized indicators, the first panel shows that some are more empirically relevant than others. The estimated effects are largely consistent with theory and previous empirical work. The second and third panels present the parameter estimates of the tempered equations that characterize CRAs’ desire for rating stability. The significant estimates justify the need to account for this aspect of the credit rating process. The DOI: 10.1057/9781137391506.0010

Are Poorer Countries Disadvantaged by the CRAs?



fourth panel, importantly, presents the results on bias from estimating the threshold effects of developmental groupings.

S&P For S&P, eight of the thirteen ability to pay indicators are statistically significant. The solvency measures that seem to be important to debt quality are government debt, real per capita GDP, investment, current account balance and domestic credit. Liquidity measures such as exports, the inflation rate and reserves (in the second specification) also influence debt quality. All these significant indicators have the expected relationships with debt quality. It is only for debt service that the relationship with debt quality is not that which was expected a priori. Note, though, that this variable only captures the effect for developing countries. For a large majority of the sovereigns rated by S&P (75), the debt service ratios are not at a level typically considered to be unsustainable. Increasing debt service ratios within sustainable levels, holding the quantity of debt and inflation rates constant, may be viewed by S&P as a positive signal for debt quality, as the debt will be repaid more quickly. Institutional quality is shown to have the expected positive impact on debt quality and is strongly significant. Since this impact is independent of the countries’ ability to pay, like Butler and Fauver (2006), we interpret this as being an indication of willingness to pay. In the effort to isolate any genuine biases against developing countries’ sovereign debt rating, we not only control for the above indicators of ability and willingness to pay, but also use tempered equations to account for the tendency of CRAs to leave a sovereign’s rating unchanged as the rating level approaches the highest (lowest) rating level, irrespective of whether the sovereign was supposed to be upgraded (downgraded). The results for the tempered equations are presented in the second and third panels of Table 6.1. In the second panel, the negative coefficient on Ratingt–1 indicates that S&P is less likely to make a final decision to upgrade high-rated sovereigns (versus leaving ratings unchanged) as their rating levels increase, even if an improvement in their debt quality suggests that they should have been upgraded. The positive coefficient on Ratingt–1 in the third panel similarly indicates that, for low-rated sovereigns, the probability of a final downgrade decision decreases as rating level decreases, even if the fundamentals suggest a decline in debt quality that warrants a

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Sovereign Debt and Credit Rating Bias

downgrade. These two results characterize S&P’s desire for rating stability as being stronger when low-rated sovereigns should be downgraded and when high-rated sovereigns should be upgraded. The third panel also controls for the possibility of CRAs being reluctant to downgrade highly rated countries in response to adverse changes in economic fundamentals, given their reputation for maintaining high quality debt. The results indicate a quadratic relationship between prior rating level and the propensity to be downgraded versus a no-change. The highly significant and negative quadratic coefficient indicates that there is indeed a tempering effect on S&P’s decision to downgrade sovereign debt when ratings are high. This suggests that even when trends in economic and institutional fundamentals indicate the need for a downgrade, the probability of S&P actually downgrading (versus leaving the rating unchanged) falls when a bond becomes investment grade and even more so as ratings improve towards the highest level. This result further reflects S&P’s desire for rating stability among high-rated sovereigns that have a reputation of maintaining high quality debt. Conditional on changes in debt quality and the tempering effects of CRAs’ desire for rating stability, we can now check for bias against developing countries. This is done by allowing the data to determine if the upper threshold, the level that a sovereign’s debt quality must surpass to be considered for an upgrade, differs based on development level. The results indicating heterogeneous thresholds are presented in the fourth panel of Table 6.1. For S&P, the positive coefficients on the LMLIC and UMC dummies corroborate our prediction of a default bias towards developing countries. While the UMC dummy is not significant at conventional levels, there is strong statistical evidence that S&P finds it harder to upgrade LMLICs relative to HICs, for any given favorable change in ability and willingness to repay debts. This is clear, as the upper threshold is significantly larger for LMLICs relative to HICs. For instance, based on specification (2), the upper threshold for HICs is 0.859, and thus for LMLICs the upper threshold is 0.859+0.620 = 1.479.7

Moody’s The results for Moody’s are quite similar to those for S&P. Based on specification (1), five of the ability and willingness to pay indicators are

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Are Poorer Countries Disadvantaged by the CRAs?



significant. Real per capita GDP, investment, current account balance, exports, reserves and institutional quality all have the expected effect on debt quality. In specification (2), government debt also has a statistically significant and negative impact on debt quality, when the sample size is increased after removing variables with many missing values. The results for the tempered equations in panels 2 and 3 are also very similar to those for S&P, particularly in specification (2). The data supports the importance of modeling the behavior of Moody’s in pursuing rating stability. It is shown that rating stability will be pursued even when changes in debt quality suggest otherwise. Finally, the results for the heterogeneous thresholds for Moody’s also clearly support a bias against LMLICs, as the positive and highly significant coefficient indicates that Moody’s finds it harder to upgrade LMLICs relative to HICs, for any given favorable change in ability and willingness to repay debts. Based on specification (2), Moody’s upper threshold for HICs is 0.967, which is significantly smaller than the upper threshold for LMLICs, which is 1.693 (i.e. 0.967+0.726).8

Fitch Fewer ability and willingness to repay indicators are significant for Fitch. Still, government debt (in specification 2), real GDP per capita, investment, current account balance and institutional quality are the dominant factors, all of which have the expected effect on the quality of debt for rated sovereigns. In addition, the negative and statistically significant coefficient on debt service suggests that a higher debt service ratio is a signal of worsening debt quality for Fitch. Similar to S&P and Moody’s, the significance of the coefficients in panels 2 and 3 also reveal Fitch’s desire for rating stability, depending on the level of ratings. Conditional on these results, we also find for Fitch evidence that the upper threshold beyond which an upgrade can be attained is higher for developing countries (though statistically insignificant for UMCs). In particular for LMLICs, the statistically significant higher upper threshold makes it harder for them to achieve an upgrade, for any given improvement in debt quality (when there are favorable changes in fundamentals). Fitch’s upper threshold for HICs, based on specification (2), is 0.747, which is significantly smaller than the upper threshold for LMLICs of 1.915 (i.e. 0.747+1.168).9

DOI: 10.1057/9781137391506.0010



Sovereign Debt and Credit Rating Bias

Are the big-three CRAs biased against specific regional groupings of poor countries? The results above clearly indicate a bias against poorer countries in the big-three’s sovereign debt ratings. In Table 6.2, we delve deeper to investigate whether this bias is applicable to any specific regional grouping of developing countries. We thus reduce the sample of sovereigns to focus specifically on countries in the developing world and allow for the upper thresholds of debt quality changes to vary by selected regional classifications rather than by development levels. We focus on LAC and African countries, given their tendency to receive on average lower ratings relative to other regions. The analysis is restricted to developing countries to alleviate the confounding effects of development level and to maintain a sufficiently large sample size. We, however, still control for any remaining differences in development level among developing countries in our analysis of the thresholds. The first panel in Table 6.2 reveals that in general the indicators maintain the same relationship with debt quality. Although a few variables lost their significance, this is to be expected given the large restriction in sample size. Notably, real GDP per capita, current account balance and institutional quality and reserves (for S&P and Moody’s) appear to be highly robust indicators of debt quality. As in the previous set of regressions, panel 2 of Table 6.2 indicates that even if ability and willingness to repay indicators suggest that an upgrade is warranted, the desire for rating stability will make it more likely for S&P, Moody’s and Fitch to leave a sovereign’s rating unchanged when its rating level is relatively high. Panel 3 shows that the desire for rating stability at the lower extremity of the ratings distribution (when there is a decline in debt quality), is, however, only applicable to S&P. Our estimates are unstable for Moody’s and Fitch because of the relatively low number of observations of downgrades that are available for estimation when ratings are low, especially with the restriction in sample size. Note that we do not include a quadratic term in panel 3 to capture reputational effects (i.e. when ratings remain stable despite a decline in debt quality for high-rated sovereigns). This is because such reputational effects are not expected to be present among developing countries. Having controlled all these factors, our findings in panel 4 do not suggest a bias against countries in LAC. The coefficient for the LAC DOI: 10.1057/9781137391506.0010

Are Poorer Countries Disadvantaged by the CRAs?



dummy, though positive, is highly insignificant for all three agencies. Moody’s also does not appear to show any bias against countries in Africa, as the coefficient on the AFRICA dummy is insignificant. This, however, is not true for S&P and Fitch, both of which have positive and highly significant coefficients for the AFRICA dummy. This suggests that these two agencies find it more difficult to upgrade African countries relative to other developing countries, for any given improvement in ability and willingness to repay debts. The finding is apparent even after removing differences in development levels among developing countries. This result is quite instructive as it helps to explain the tendency highlighted in Chapter 4 for S&P and Fitch to assign considerably lower ratings to African sovereigns than did Moody’s. In that chapter we questioned whether this could be due to a regional bias on the part of the former two agencies. Our results point to evidence supporting this suspicion.

The evidence speaks: Summary and conclusions This chapter has sought to address criticisms of the previous studies on bias in sovereign debt rating actions by applying a more credible methodology to a well-established and theoretically sound economic model of the determinants of sovereign debt quality. Before we even consider bias, we control for the key economic and institutional determinants of sovereign debt ratings; unobserved time-invariant political and institutional characteristics of sovereigns; and a realistic setting where CRAs desire rating stability. Even after rigorously controlling for all of these factors, we still find evidence of a bias against poorer countries in the big-three’s sovereign debt rating changes. Our results showed that S&P, Moody’s and Fitch all find it harder to upgrade LMLIC relative to HIC, for any given improvement in ability and willingness to repay debts. We further showed that S&P’s and Fitch’s rating actions are also impacted by a regional bias against developing countries of Africa. The results indicate that these two agencies find it more difficult to upgrade African countries relative to other developing countries, for any given improvement in ability and willingness to repay debts. The implications of these results are huge. In the final chapter of this book, we conclude our investigation of bias in sovereign debt rating actions by discussing what is to be done with these findings. DOI: 10.1057/9781137391506.0010

DOI: 10.1057/9781137391506.0010

Inflation

Reserve/Import

ln Export

Liquidity (–HIC)* Debt Service

Fiscal Balance

C/A Balance

FDI

Investment

real GDP Growth

ln real GDP/pop

Domestic Credit

(–HIC)* External Debt

Solvency Gov’t Debt

Ability & Willingness to Pay

.* (.) .*** (.) . (.) –.*** (.)

–.*** (.) . (.) –.* (.) .*** (.) –. (.) .** (.) –. (.) –.*** (.) –. (.)

()

S&P

.** (.) .*** (.) .** (.) –.** (.)

.*** (.) –. (.) .* (.) –. (.) –.*** (.)

–.*** (.)

()

()

Moody’s

. (.) .** (.) .** (.) –. (.)

–. (.) –. (.) –. (.) .*** (.) –. (.) .** (.) . (.) –.** (.) . (.)

table 6.1 Estimation results – thresholds varying by development level

. (.) .*** (.) .** (.) –. (.)

.*** (.) –. (.) .*** (.) . (.) –.** (.)

–.** (.)

()

–.* (.) . (.) . (.) –. (.)

–. (.) . (.) . (.) .*** (.) –. (.) .* (.) –. (.) –.** (.) . (.)

()

Fitch

–.* (.) . (.) . (.) –. (.)

.*** (.) –. (.) .** (.) –. (.) –.** (.)

–.** (.)

()

DOI: 10.1057/9781137391506.0010

.*** (.) . (.) .** (.) –.*** (.)   –. .

. (.) –. (.) –. (.)

–.*** (.) .** (.)

.*** (.)

Source: Computed by authors.

Notes: Cluster robust standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01

Observations Countries Log Pseudo Lik. Pseudo R

q

q

UMC

.** (.) . (.) .** (.) –.*** (.)   –. .

.*** (.) –.*** (.) –. (.)

Pr(final downgrade decision | ΔR*it ≤ q) .*** Rating t– (.) [Rating t–] –.*** (.) Constant –.* (.)

.*** (.) . (.) .** (.) –.*** (.)   –. .

–.*** (.) .*** (.)

Pr(final upgrade decision | ΔR*it ≥ q) –.*** Rating t– (.) Constant .*** (.)

Threshold Effects LMLIC

.*** (.)

.*** (.)

Institutional Quality

.*** (.) . (.) .*** (.) –.*** (.)   –. .

.** (.) –.** (.) –.** (.)

–.*** (.) .*** (.)

.*** (.)

.*** (.) . (.) .*** (.) –.*** (.)   –. .

. (.) –. (.) –. (.)

–.** (.) . (.)

.*** (.)

.*** (.) . (.) .** (.) –.*** (.)   –. .

. (.) –.* (.) –. (.)

–.*** (.) .** (.)

.*** (.)

DOI: 10.1057/9781137391506.0010

Inflation

Reserve/Import

ln Export

Liquidity (–HIC)* Debt Service

Fiscal Balance

C/A Balance

FDI

Investment

real GDP Growth

ln real GDP/pop

Domestic Credit

(–HIC)* External Debt

Solvency Gov’t Debt

. (.) .*** (.) .*** (.) –.*** (.)

–. (.) . (.) –. (.) .** (.) –. (.) . (.) . (.) –.*** (.) . (.)

()

S&P

. (.) .*** (.) .*** (.) –.** (.)

.*** (.) –. (.) . (.) . (.) –.*** (.)

–.* (.)

()

Estimation results – thresholds varying by developing regions

Ability & Willingness to Pay

table 6.2

. (.) . (.) .*** (.) –. (.)

. (.) –. (.) . (.) .** (.) –. (.) .* (.) –. (.) –.** (.) . (.)

()

Moody’s

. (.) . (.) .** (.) –. (.)

.** (.) –. (.) . (.) –. (.) –. (.)

. (.)

()

–.** (.) . (.) . (.) –. (.)

–. (.) –. (.) –. (.) .*** (.) –. (.) . (.) . (.) –.*** (.) . (.)

()

Fitch

–. (.) . (.) . (.) –. (.)

.*** (.) –.* (.) . (.) . (.) –.* (.)

–.** (.)

()

DOI: 10.1057/9781137391506.0010

. (.) .** . .* (.) .* (.) –.* (.)   –. .

. (.) .** . .** (.) .* (.) –.** (.)   –. .

. (.) . . .* (.) . (.) –. (.)   –. .

–. (.) . (.)

–.*** (.) . (.)

.*** (.)

Source: Computed by authors.

Notes: Cluster robust standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

Observations Countries Log Pseudo Lik. Pseudo R

q

q

LMLIC

AFRICA

LAC

.*** (.) –. (.)

–.*** (.) .** (.)

Pr(final upgrade decision | ΔR*it ≥ q) –.*** Rating t– (.) Constant .** (.)

Pr(final downgrade decision | ΔR*it ≤ q) .** Rating t– (.) Constant –. (.) Threshold Effects

.*** (.)

.** (.)

Institutional Quality

–. (.) . –. .* (.) .* (.) –. (.)   –. .

. (.) . (.)

–.** (.) . (.)

.*** (.)

. (.) .*** . . (.) . (.) –.*** (.)   –. .

.*** (.) –.*** (.)

–.** (.) . (.)

.*** (.)

. (.) .*** . . (.) . (.) –. (.)   –. .

–. (.) . (.)

–.** (.) . (.)

.** (.)

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Sovereign Debt and Credit Rating Bias

Notes  In response to the UniCredit Report’s (2014) econometric results on the damaging bias of sovereign ratings, S&P (2014b, 1) has noted that “claims that sovereign ratings have a damaging bias . . . are baseless . . . UniCredit’s regression analysis is flawed . . . We will continue to rigorously apply our transparent and publicly available sovereign criteria. That approach has served investors well.”  UniCredit Report (2014, 5).  Gultekin-Karakas et al. (2011, 81).  Fuchs and Gehring (2013, 3).  S&P (2014a, 1), https://www.globalcreditportal.com/ratingsdirect/ renderArticle.do?articleId=1256886&SctArtId=213686&from=CM&n sl_code=LIME.  Soudis and van Hoorn (2013, 3).  These upper thresholds should be considered relative to the fixed (by assumption) lower threshold of -0.899.  These upper thresholds should be considered relative to the fixed (by assumption) lower threshold of -1.318.  These upper thresholds should be considered relative to the fixed (by assumption) lower threshold of -1.030.

DOI: 10.1057/9781137391506.0010

7

Now That We Have Found Bias, What Are We Going to Do with It? Abstract: Having provided strong evidence of bias against poor countries in the big-three’s sovereign debt ratings, we conclude the book by asking—What are we going to do with this finding? With an acknowledged bias towards action, we use the discourse in the literature on the causes of bias in credit ratings to guide our suggestions on how to address the bias that we have evidenced. The studies reviewed strongly indicate that problems with the quantity and quality of information received from poor country governments, and issues with how that information is perceived or interpreted by the CRAs both contribute to the bias against poor countries. We thus conclude the book by suggesting a few ways that these issues should be addressed. We suggest actions for poor country governments, regulators and the credit rating agencies. Keywords: Africa; Bias; Credit Rating Agencies; Developing Countries; International Capital Markets; Latin America and the Caribbean; Sovereign Debt Tennant, David F., and Marlon R. Tracey. Sovereign Debt and Credit Rating Bias. New York: Palgrave Macmillan, 2016. doi: 10.1057/9781137391506.0011. DOI: 10.1057/9781137391506.0011

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Sovereign Debt and Credit Rating Bias

This book has investigated allegations of bias against the big-three credit rating agencies (CRAs) in their assignment of sovereign debt rating upgrades and downgrades. We established the importance of this issue in the first chapter by highlighting the fact that by effectively serving as gatekeepers to the international capital markets, these agencies dictate the terms at which countries (and firms operating within them) can access funds. Sovereign debt downgrades have been shown to reduce the potential pool of investors, increase interest costs, precipitate decreased investment by firms operating within the economy and to have contagion effects across countries and financial markets. The CRAs thus have enormous power, which they are expected to use judiciously in their rating assignments. The prospect of any sort of bias in this context is daunting. The allegations of bias have nonetheless been persistent and have elicited different types of responses from the industry. Chapters 2 and 3 of this book examined two of the strongest and often repeated responses, as summarized below.

The CRAs’ rigorous and transparent determination of ratings does not leave room for bias CRAs argue that they assign ratings only after applying an objective, consistent, transparent and rigorous process. The very possibility of bias in rating assignments has been questioned because of the CRAs’ detailed and systematic presentation of sovereign debt rating drivers, methodologies and assumptions. Note however, that the provision of such detailed and clear information is a very recent phenomenon that belies the culture of secrecy which previously characterized the big-three. More importantly, as we showed in Chapter 2, this increased transparency has not necessarily translated into considerably enhanced clarity with respect to the determination of sovereign debt rating assignments. The big-three CRAs have each presented a very long list of variables that they deem to be potential determinants, and even more imposing explanations of the relationships between these variables and how they are to be combined to form a rating. Although empirical studies have produced a much more easily-digestible short list of macroeconomic and institutional fundamentals that explains most of the variation in sovereign debt rating assignments, it is widely agreed that the CRAs are not adequately explicit about the weights given to the variables. In addition, DOI: 10.1057/9781137391506.0011

Having Found Bias, What Should be Done About It?

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a number of studies have shown that the subjective judgments of the CRAs’ in-house ratings committees are given just as much weight as the objective signals provided by the fundamentals, and, at times, are used to overturn the initial assignments dictated by the fundamentals. Although underemphasized in the methodological outlines made publicly available by the CRAs, it is clear, even from the caveats that they provide, that the qualitative, judgmental element remains highly important in the determination of rating assignments and actions. While this allows for a necessary degree of flexibility, it undeniably introduces an element of subjectivity, which, we argue leaves open the possibility of bias.

Biases would diminish the CRAs’ reputational capital and force them out of business CRAs rely heavily on their reputation as providers of accurate ratings, as it is this reputation that provides them with future business opportunities. It has therefore been argued that allegations of bias are baseless, as, if they were true, the credit rating industry would not have been able to survive. Biases would diminish the CRAs’ reputational capital, jeopardize their business model and eventually force them out of business. Note, however, that allegations of bias are not the only factors that have caused the CRAs reputational damage. Chapter 3 summarized a number of controversies involving highly publicized conflicts of interest and ratings failures that have surrounded the big-three CRAs recently. These controversies have severely tarnished their reputations, but have not threatened their survival or even caused a prolonged reduction in demand for their services or in their profitability. To explain this phenomenon, we examined the nature of the credit rating industry, and presented a number of industry-specific factors that have allowed the reputation-dependent CRAs to be resilient to the controversies surrounding their activities. We presented numerous demand-side reasons for why credit ratings are now viewed as a necessity for any entity desirous of borrowing on the international capital markets. On the supply-side, factors were highlighted that establish the credit rating industry as a natural oligopoly, the effects of which have been concretized by regulations which represent artificial barriers to entry to the credit ratings industry. The result has been that virtually no other firms have been able to capitalize on the diminished reputational capital of the big-three by capturing significant shares of the market. The limited availability of substitutes for the ratings provided by the big-three contributes to the relative inelasticity of demand with DOI: 10.1057/9781137391506.0011

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Sovereign Debt and Credit Rating Bias

respect to changes in the CRAs’ reputational capital. The existence of these conditions refutes the argument that dependence on reputational capital precludes the prolonged perpetuation of biases. Having established that there are opportunities for bias to be introduced in the credit ratings process and industry, Chapter 4 highlighted trends in rating levels and actions that raised questions about potential bias. We highlighted interesting distinctions between the rating actions taken for rich countries (defined as high-income) and poor countries (defined as lower-middle and low income), and between regional groupings of poor countries. We, however, were careful not to make any conclusive statements about bias in that chapter, as the descriptive and trend analysis did not allow for such conclusions. In Chapter 5 we presented the methodology that allowed us to rigorously identify biases and confidently label them as such. Numerous methodological improvements to the current credit rating literature were introduced, which bore in mind the criticisms of the existing empirical studies. In Chapter 6 these techniques were applied to a well-established and theoretically grounded economic model of sovereign debt rating determination. The resulting empirical evidence was clear and conclusive. The results indicated that Standard and Poor’s (S&P), Moody’s and Fitch all find it more difficult to upgrade poor countries relative to rich countries, for any given improvement in ability and willingness to repay debts, and irrespective of the rating level of the sovereign. S&P and Fitch were further shown to find it more difficult to upgrade African countries relative to other developing countries, for any given improvement in ability and willingness to repay debts. These results are taken as a strong indication of bias, as they are highly significant even though we controlled for the key economic and institutional determinants of sovereign debt ratings, unobserved country-specific effects, the CRAs’ desire for rating stability, and the possibility that CRAs are mindful of the reputation of highly rated sovereigns and so are reluctant to downgrade them in response to adverse changes in fundamentals. In a popular song that was initially recorded by the O’Jays in 1973, the question was asked: “Now that we’ve found love, what are we going to do with it?” Having provided this strong evidence of bias against poor countries in the big-three’s sovereign debt ratings, we are forced to ask the same question about bias. What are we going to do with this finding? In this respect we are motivated by Indira Gandhi who is quoted as saying, “Have a bias toward action—let’s see something happen now.” DOI: 10.1057/9781137391506.0011

Having Found Bias, What Should be Done About It?

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We want to have a bias towards action. But knowing what to do about a problem is predicated on an understanding of the cause of the problem. Our results, while empirically providing evidence of a bias against poor countries, do not provide an explanation for why the bias exists. To shed some light on this area, the next section in this chapter summarizes the discourse in the literature on the causes of bias in credit ratings and uses that discussion to guide our suggestions on how to address the bias that we have evidenced.

Possible reasons for bias against poor countries In attempting to provide an explanation for the existence of a home bias in sovereign debt ratings,1 Fuchs and Gehring (2013) and Soudis and van Hoorn (2013) posit a number of possible causes, which we broadly group into two categories: (1) Preferential treatment to countries based on economic interests, geopolitical interests and institutional embeddedness; and (2) Preferential treatment to sovereigns that are culturally similar to the home country because of the comfort which comes from familiarity. This is particularly applicable in a context of information deficiency. Although home bias is not the subject of this book, these explanations are explored to ascertain whether they can be applied as we seek to explain the bias against poor countries.

Economic interests, geopolitical interests and institutional embeddedness Fuchs and Gehring (2013) argue that governments and lobby groups might put pressure on CRAs so as to advance the economic and geopolitical interests of the home country, which could lead to preferential treatment of certain sovereigns. Due to the multiple adverse effects of sovereign debt downgrades, it is clear why governments would want to pressure the CRAs to offer preferential treatment to their own country or to countries with which they have significant ties. Not as clear is the source of power from which such influence can be exerted on the privately owned CRAs. One possibility is the power that home governments have to issue officially recognized designations. In the United States, designation as a Nationally Recognized Statistical Rating Organization (NRSRO) is required if a CRA is to be able to tap into the most lucrative corporate markets. It is argued that fear of refusal or removal of such designation DOI: 10.1057/9781137391506.0011

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Sovereign Debt and Credit Rating Bias

opens up the CRAs to governmental influence.2 Another source of influence is through the banks and other financial institutions that are major shareholders of the CRAs, which also have an interest in protecting the value of their bond holdings. So, for example, shareholders might try to obtain preferential treatment for countries wherein, by virtue of the composition of their loan or investment portfolios, they are exposed to large risks. This typically would be either in the home country or countries to which they have close ties. Soudis and van Hoorn (2013) identify a channel of influence that while not as explicit as either of the above two, may be just as or even more influential. Using an institutional perspective, they conceptualize CRAs as organizations that are embedded in their home-countries’ institutional environment. This environment establishes the norms and values that dictate not only the way things are done, but also “defines the goals and legitimate means available to achieving desired ends.”3 The CRAs, as competitive organizations that struggle for survival in such an environment, align their interests to those of the home country investors, thus gaining legitimacy. Soudis and van Hoorn (2013, 1&6) therefore argue that the CRAs’ ratings “are not an objective valuation of relative default probability, but reflect the risk that investors originating from the agencies’ home countries face when investing abroad.” Specifically with respect to Moody’s and S&P, they assert that “their valuation of risk with regard to investment in different countries is likely to be influenced by a partial view that emphasizes the risk of US investors.” Note, however, that whereas these political economy/institutional explanations may seem plausible, there are a number of factors which suggest that they are not likely to be the main causes of the bias evidenced in Chapter 6. Regarding governments’ influence on the CRAs through their power to refuse or remove official designation, it is clear that this is much more likely to be applicable to new, small CRAs than to the well-established big-three that are the subject of our investigation. Also, although the CRA’s shareholders are more likely to have greater influence, empirical studies have shown that Japanese rating agencies are more vulnerable to influence by its shareholders than are the American CRAs that are the subject of this book.4 Fuchs and Gehring (2013, 36) further note that their study provided “no robust empirical support that geopolitical ties . . . play a significant role in rating outcomes.” Soudis and van Hoorn (2013), however, provided contradicting evidence which points to the importance of a sovereign’s political proximity to the CRA’s DOI: 10.1057/9781137391506.0011

Having Found Bias, What Should be Done About It?

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home country as a key driver of rating differences. Although intriguing, it is important to note that this result also does not answer our question as to possible causes of a bias against poor countries, as political affinity among such countries (even within the narrower grouping of African countries) varies significantly.

The role of culture, familiarity and information asymmetry If the source of bias against poor countries is not from economic and geopolitical interests or from institutional embeddedness, then what is it? In the context of investment decisions made by private and corporate investors, a number of studies have identified a home bias and have explained it by reference to familiarity-related effects. People and firms prefer to invest in or lend to entities that are domiciled in countries that are culturally closer to their home country, because they are familiar with how things are done in those contexts.5 How is this applied to the CRAs’ rating decisions, and particularly so for the big-three, which purport to be global institutions with offices and expertise all over the world? Information received Fuchs and Gehring (2013) note that it is important to begin by recalling that the CRAs base their assessments on limited and incomplete information. The information utilized is acquired either from public sources or from the sovereigns themselves. The CRAs thus have concerns about the reliability and accuracy of the data, and have to find ways of coping with these concerns. One possibility is to expend the necessary resources to check the veracity of the information received, and, where necessary, to acquire additional information. The CRAs therefore face a tradeoff between the benefit of the additional information and the costs of acquiring it. It is argued that the information acquisition costs are higher for more culturally distant sovereigns. For example, linguistic differences raise transaction costs, causing agencies to collect less information. This often translates into lower ratings for less familiar countries, as estimations of ability and willingness to repay are less precise, leading to greater reliance on the subjective judgments of the ratings committees, which are likely to err on the side of perceived caution in such circumstances. Soudis and van Hoorn (2013) present a similar explanation using a bounded rationality perspective. They argue that although ratings are an objective, rational valuation of risk, bottlenecks in the flow of information reduce the accuracy of the prediction. They assume that this lack of DOI: 10.1057/9781137391506.0011

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Sovereign Debt and Credit Rating Bias

information will almost certainly result in a lower rating, because of the aversion that CRAs have to falsely high ratings. Tennant et al. (2015) develop a theoretical model of the sovereign debt rating decisions of the CRAs, which not only provides strong support for the intuitive explanations provided above, but also presents a clear explanation of bias based on a country’s level of economic development. They formulate a loss function, wherein a CRA seeks to minimize the reputational loss associated with incorrect estimations of the probability of default, while simultaneously minimizing the costs incurred in acquiring the information required to increase the accuracy of its estimations. They assume that such costs are inversely related to a country’s level of development, as institutional quality, which is highly correlated with economic development, is a major determinant of the quantity and quality of data that is available from a country. Therefore, the cost of acquiring the information needed to estimate the likelihood of default is likely to be higher in poor countries that typically have low quality institutions. Through the model they show that, because information acquisition costs are higher for poor countries, and because reputational loss is asymmetric (such that a default by a country with a good rating is more harmful to the CRA’s reputation than the failure to default by a country with a poor rating), it is optimal for CRAs to be biased against poor countries in their sovereign debt ratings. Tennant et al. (2015) thus assert that the higher information acquisition cost in poor countries is what drives the CRAs’ bias against them. Information perceived Note, however, that in the portfolio investment literature, it has been argued that bias does not necessarily depend on the quantity or quality of information possessed, it often suffices that investors simply perceive the information differently. This often constitutes an optimism bias wherein people have more optimistic expectations about domestic investments, as they feel more confident about investment possibilities at home. By contrast, other studies have shown that cultural distance leads to lower levels of bilateral trust, which lessens economic exchange between countries.6 Fuchs and Gehring (2013, 16) suggest that this can affect sovereign debt ratings, as analysts “could perceive the same economic information from a more familiar source as more reliable and its bonds as less risky.” Furthermore, they assert that bilateral trust might “not only matter for how analysts perceive the available information about the sovereign’s DOI: 10.1057/9781137391506.0011

Having Found Bias, What Should be Done About It?

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ability to pay, it could also affect beliefs about a sovereign’s willingness to pay its debt.” They therefore argue that it is not farfetched to believe that CRAs will have a more optimistic assessment of a government’s willingness to repay debt if there is a greater degree of bilateral trust between that government and the CRA’s home country. This degree of trust is also likely to be inversely related to a country’s level of economic development. Poor countries with a reputation of low-quality institutions are highly unlikely to elicit the same amount of confidence and trust as rich countries that have benefitted from a long tradition of high-quality institutions.

What can be done to reduce the bias against poor countries? There is clearly a need for more rigorous studies that seek to explain the CRAs’ bias against poor countries in their sovereign debt ratings. Notwithstanding this, the studies summarized above strongly indicate that problems with the quantity and quality of information received from poor country governments, and issues with how that information is perceived or interpreted by the CRAs, both contribute to the bias against poor countries. We conclude this book by suggesting a few ways that these issues should be addressed as governments seek to confront the ratings bias against poor countries.

What can poor countries do? The bias evidenced in this book most directly and adversely affects poor countries, particularly those in Africa. These countries therefore have the greatest interest in finding a solution to the problem. The good news is that at least a part of the solution lies within their control. This is because there are strong indications that the CRAs’ bias against poor countries is rational and even optimal, based on the higher information acquisition costs in these countries. This is good news, because if the bias is driven by rationality and the factor that causes it to be optimal is known, then addressing that factor will make the bias sub-optimal and the rational course of action for the CRAs will be to remove the bias. So what can the governments of poor countries do? Reduce the costs associated with acquiring information about their creditworthiness. Low-hanging fruit will involve empowering and insisting that agents of the state are more forthcoming with information when dealing with DOI: 10.1057/9781137391506.0011

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Sovereign Debt and Credit Rating Bias

the CRAs. This, however, is unlikely to create monumental changes, as the problems affecting the accuracy and timeliness of information provision from state agencies in poor countries have much deeper institutional roots. Some of these countries do not have any agencies that are given the specific responsibility of collecting data and ensuring reliability. Even where they exist, they often are not adequately financed, staffed and equipped with modern technology. A deeply rooted culture of secrecy within state agencies, lax reporting requirements, weak checks and balances, and, at times, a willingness to bend to political imperatives, all affect the accuracy of the information provided by poor-country governments, and the degree to which they can be trusted. Properly correcting these problems will require a commitment to properly designed, comprehensive public sector and institutional reform. Such reforms are, however, costly, and are viewed by some governments as low on their list of priorities. Many developing countries have had a long and largely unsuccessful history of donor-supported public sector reforms. Even where successful outcomes were achieved, doubts were expressed about whether they would be sustained. Schacter (2000, 7) notes that this is as a result of the technocratic approach traditionally taken by donors, which has failed to account for the fact that public sector reform is a “social and political phenomenon driven by human behaviour and local circumstances. It is a long and difficult process that requires public servants to change, fundamentally, the way they regard their jobs, their mission and their interaction with citizens.” Moving forward, it is argued that successful public sector reform will require local leadership and ownership.7 Without veering too deeply into the issues of public sector reform, we tentatively suggest that the results presented in this book could incentivize such local ownership. Increasingly, poor countries are seeking access to the international capital markets to finance their fiscal deficits and fund developmental projects. However, because of the bias against them, they are less likely to receive the benefit of increased access to lower cost funds associated with improved ratings. If indeed public sector and institutional reforms can aid in removing this bias and improving the flow of cheaper funds, some governments may consider it to be a worthwhile investment.

What should the regulators do? All of the responsibility for removing the bias cannot, however, be placed on the poor countries. By virtue of the role that they play, CRAs DOI: 10.1057/9781137391506.0011

Having Found Bias, What Should be Done About It?

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are reasonably expected to expend the necessary resources to acquire the information needed to accurately estimate the creditworthiness of the sovereigns that contract them. Ferri (2004, 79) notes that in theory, concerns about their reputational capital should induce the CRAs to “make the socially optimal level of investment in collecting and processing information on issuers.” This, however, does not happen because the almost monopolistic structure of the credit rating industry, hinders the smooth functioning of the reputation mechanism. What results is that CRAs underinvest in information gathering for non-OECD sovereigns, exerting less effort despite greater opaqueness. Ferri (2004, 97) thus concludes that “incentives should be devised to induce rating agencies to increase their effort in less developed countries.” He, however, does not specify what these incentives should be. In highlighting a similar problem regarding the CRAs’ rating of developing country bonds, Elkhoury (2008, 15) notes that there is a “need for a mechanism to take over if reputation fails.” This is one of the bases for the arguments in favor of greater regulatory oversight of the credit rating industry, which is now a heated, and, to date, unresolved issue in academic and policy circles. Gavras (2012) presents a useful summary of the varied options open to regulators, ranging from simple regulatory enhancements that would modify existing rules while keeping the CRAs in essentially the same role, to regulating the CRAs so extensively that they practically become public utilities, to bringing one or more of the private CRAs under public control, or excluding them all from regulatory activity and replacing them with a new public agency. Clearly the expected benefits of each of these options must be weighed against the likely costs. There is no magic bullet or painless panacea, as there will be costs and risks associated with regulation, irrespective of the option chosen. As Gavras (2012) however concludes, these costs and risks should not become an excuse for inaction. Regulatory reform is needed. This book’s finding of a bias against poor countries in the CRAs’ sovereign debt ratings provides one more piece of evidence for why this is so. While we will not delve into the debate as to the specific form that the regulatory reform should take, we argue that at the very least, the hardwiring of CRAs’ opinions in financial regulation should be removed, as instead of supplementing informed decision making, it often takes the place of due diligence. We, however, believe that this is a necessary but not sufficient action, and thus encourage the continuation of the discussion on regulatory reform, with, as Gandhi states, a bias towards action. DOI: 10.1057/9781137391506.0011

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Sovereign Debt and Credit Rating Bias

What should the CRAs do? Finally, is there any role for the CRAs? Based on their responses to previous studies on biases in their rating assignments, we expect the CRAs to react to this study (if at all) in a defensive manner. We would, however, encourage consideration of a thought proffered by the late English painter Benjamin Haydon—“fortunately for serious minds, a bias recognized is a bias sterilized.”8 We have presented an explanation for the bias evidenced against poor countries as being a rational and possibly even optimal decision on the part of the CRAs faced with high information acquisition costs. It would be useful for the CRAs to consider this, along with the likely devastating impact of the bias on the already impoverished countries, with a view to working with them to ascertain ways through which they can cooperate to reduce the information deficiencies. It is possible that through this avenue the bias may be removed. Again, in this respect there should be no excuse for inaction.

Notes  Fuchs and Gehring (2013, 10) identify a home bias as occurring if “a sovereign deviates from what would be justified by a sovereign’s economic and political fundamentals in favour of the home country (or countries aligned with it).”  Fuchs and Gehring (2013).  Soudis and van Hoorn (2013, 6).  Shin and Moore (2003) as cited in Fuchs and Gehring (2013).  See e.g Grinblatt and Keloharju (2001), Sarkissian and Schill (2004) and Giannetti and Yafeh (2012), all as cited in Fuchs and Gehring (2013).  See e.g Kilka and Webber (2000), French and Poterba (1991) and Guiso et al. (2009), all as cited in Fuchs and Gehring (2013).  See also Fatile and Adejuwon (2010).  2VPUF
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DOI: 10.1057/9781137391506.0011

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DOI: 10.1057/9781137391506.0012

Index Note: t, n denote table and note adverse market conditions, 11 Africa disadvantaged by CRAs, 87, 92, 96–97 evidence of bias, 30, 97, 106, 109, 111 trends in ratings, 64–75, 101t artificial barriers to entry, 55–56 Asia disadvantaged by CRAs, 12, 14n natural oligopoly, 53 trends in ratings, 64–67 Asian Crisis, 7, 9, 12, 29, 31, 37, 42–45, 67, 73 balance of payments, 22, 24, 26t, 34n banking sector, 25t, 22, 46, 85t banking sector risk, 19, 22 Big-Three CRAs an oligopoly, 53–60, 105. See also Credit Rating Agencies capital flow, 2, 24, 26t, 48 market, 2–3, 5–7, 9–13, 21, 23, 25t, 47–49, 57, 104–105, 112 comparison of odds ratios, 70–71 DOI: 10.1057/9781137391506.0013

conditions for bias, 36–60, 62 conflict of interest, 7–8, 37–42, 55–57, 58n contingent liability, 19, 21–23, 25t, 27, 45 controversy, 36–60, 89 corruption, 8, 23, 26t, 28t, 91 credit rating agencies accountability, 6, 20 flawed gatekeepers, 7–14, 46, 102n gatekeepers, 1–14, 49–50, 104 necessity, 46–56 origin, 3–6, 47–53 power, 6, 13, 44, 104, 107, 108 reliability, 6, 20, 109, 112 role, 6–8, 13, 45, 50–53, 57, 108, 112, 114 transparency, 2, 9, 16, 20, 26t, 29, 32, 34, 104 Credit Rating Agency Reform Act, 9 credit rating industry, 4–5, 8, 13, 21–24, 32–34, 36–60, 62, 105, 113 creditworthiness, 43, 45, 47–48, 50, 52–53, 62, 69, 80, 86n, 88, 111, 113 culture of secrecy, 31, 104, 112 current account, 24–26, 28, 34n, 72–73, 79–80, 85t, 93, 95–96 



Index

debt burden, 19–23, 79 default, 3, 17–19, 22–23, 28–30, 46, 51, 59n, 63t, 72n, 75n, 80, 90–91, 94, 108, 110, 116 over exports, 28 repayment, 25t service, 25t, 33, 80, 85t, 86t, 93, 95, 98, 100 debt-development dilemma. See Poor countries default probability, 90, 108, 110, 116–117 risk, 5, 7, 17, 38, 59n, 79, demand facilitated by state intervention, 51–53 demand for local currency, 25t ratings reform, 37, 39, 44 rating stability, 44, 46, 65, 73, 76, 78, 82–84, 92, 94–97, 106 sovereign debt, 3 determinants of debt ratings empirical, 27–29 theorized, 17–27 developing countries, 1–5, 30, 64, 67, 73, 87, 102, 106, 112. See also Poor countries distribution of rating classes, 62–69 economic development, 28–29, 91, 110 economic framework, 81–87. See also Nonlinear ordered response framework economic growth, 3, 18, 20–21, 25, 29, 79 emerging markets, 11, 28t, 30, 64, 89 estimation results, 92–100 Europe debt crisis, 12 disadvantaged by CRAs, 12, 37, 62 trends in ratings, 64–69 exchange rate, 22, 24, 26–28, 48 external accounts, 26t debt, 24–29, 34n, 45, 62, 79, 85–86, 92, 98, 100t shock, 4, 18, 23, 80,

vulnerability, 19, 26t financial market stability, 12 fiscal balance, 20, 28t, 72t, 80, 92, 98t, 100t deficit, 72, 80, 112 policy, 25–26 Fitch. See Credit Rating Agencies flawed processes, 8–10 foreign debt to GDP, 28t, 86n. See also debt under External GDP growth, 28t, 72t, 79, 85–86, 98t, 120t per capita, 19, 22, 25t, 28t, 85t, 95–96 geopolitical interests, 107–109 global crisis, 3–5, 7, 46, 52, 56, 67, 69, 81, 90, 109 government effectiveness, 28t, 81, 85t income, 4, 25t, 28t liquidity, 6, 11, 13, 19, 79–80, 85–86, 93, 98, 100t HICs, 65–75, 92, 94, 95, 97, 99t high income countries. See HICs home bias, 89 human capital, 22, 26–27 IMF, 2, 6, 17, 23–27, 32, 34n, 35n, 42, 44, 46, 49, 52, 53, 59n, 60, 85t impact of sovereign debt ratings, 10–14, 114 indicators of debt quality, 25t, 72, 74, 76–96 See also Determinants of debt ratings inflation, 18, 21–22, 28–29, 72–73, 80, 85–86t, 93, 98t, 100t information asymmetry, 9, 109–111 institutional embeddedness, 107, 109 quality, 80–81, 85–86, 91, 93, 95–96, 99t, 101t, 110 international capital markets. See Capital markets International Monetary Fund. See IMF international reserves, 22, 28t, 29, 34n DOI: 10.1057/9781137391506.0013

Index

LAC (See Latin America and the Caribbean) Latin America and the Caribbean disadvantaged by CRAs, 101t trends in sovereign debt ratings, 61, 64–75, 92, 96 LMLICs, 65–75, 92, 94–97, 99t lower middle income countries. See LMLICs Middle East, 64 monetary policy, 20–21, 25–26, 73 nonlinear ordered response framework, 76–76–78, 90. See also Economic framework North America, 39, 47, 52, 55, 64–67 openness, 26t, 28–29 output growth. See growth under GDP Panel data models, 77, 89 political risk, 19, 22–23, 26t, 28t, 62, poor countries bias against, 57, 85, 87–102, 106–114. See also Developing countries debt-development dilemma, 4 procyclical rating tendencies, 46 rating accuracy, 37, 39–42, 44, 46, 55–56, 109–112. See also Resilience action, 12, 16, 38, 61–62, 65–69, 72, 73, 97, 106 assignments, 2, 13, 27, 31, 32, 40, 62, 73, 77, 83, 104–105, 114 change, 9–11, 30, 44, 46, 59n, 65–70, 97 decision, 9, 77, 91, 109, 110 factors comparison, 24–27 Fitch, 21–24 Moody’s, 18–19 S&P, 19–21 failures, 38, 42–46 model, 21, 29, 33 process, 9–10, 15–16, 33, 41, 76, 78, 92 DOI: 10.1057/9781137391506.0013



services, 20, 33, 39, 44 stability, 42, 44, 46, 65, 73, 76, 78, 82–84, 87, 92, 94–97, 106 trends, 61–75, 88, 106 real growth rates. See growth under GDP reason for bias, 107–111 regional distribution of ratings, 64 regression analysis, 89, 90, 102n reputation, 7–8, 36–41, 50, 53–55, 57–58, 83, 94, 96, 105–106, 110–111, 113 resilience 18–19, 36–60. See also Controversy risk. See risk under Default role of credit agencies. See role under Credit Rating Agencies role of culture, 109–111 skewed motives, 7–8, 19 sovereign bond issuer, 7, 48, 55 market, 37, 52, 55 prices, 11, 55 ratings, 31, 65. See also Ratings yield, 49 sovereign debt rating failures. See failures under rating Standard and Poor’s. See Credit Rating Agencies supply creating demand, 49–51 designed to meet demand, 47–49 S&P. See Standard and Poor’s trade, 22, 24–26, 28t UMCs, 65–75, 92, 94–95, 99 Underdevelopment Index, 28t upper middle income countries. See UMCs willingness to repay, 4, 16, 18, 72–73, 78–79, 81, 83, 87, 92, 94–97, 106, 109, 111 World Bank, 17–18, 22–23, 81, 85n, 92 World Governance Indicators, 81, 85