Political Risk in Credit Evaluation Empirical Studies and Survey Results

EKONOMI OCH SAMHÄLLE

Skrifter utgivna vid Svenska handelshögskolan Publications of the Swedish School of Economics and Business Administration

Nr 184

ANNIKA SANDSTRÖM

POLITICAL RISK IN CREDIT EVALUATION EMPIRICAL STUDIES AND SURVEY RESULTS

Helsinki 2008

< Political Risk in Credit Evaluation: Empirical Studies and Survey Results

Keywords: political risk, country risk, credit risk, export credit guarantees, export credit agencies, default probability estimation, survey results

Annika Sandström Swedish School of Economics and Business Administration Department of Finance and Statistics P.O.Box 479 00101 Helsinki, Finland

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Library Swedish School of Economics and Business Administration P.O.Box 479 00101 Helsinki, Finland

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ISBN 978-952-232-001-8 (printed) ISBN 978-952-232-002-5 (PDF) ISSN 0424-7256 i

ACKNOWLEDGEMENTS

Writing about the phenomenon of political risk in the context of international credit risk evaluation has been an exciting and stimulating, but at times exhausting and difficult endeavour. Not only are the definitions of political risk indeterminate, and more of a disciplinary perspective than objective statement, but to incorporate such phenomena in the academic literature on credit risk is far from an exact science. As in economics and in the social sciences more generally, the complexity and the historically changing character of the subject connected with the involvement of variable human influences make it impossible to find eternally valid laws and regularities. Moreover, deducing hypotheses from the nascent and discursive theories on political risk, and carrying out sufficient experiments to test such hypotheses in confrontation with real world data, seemed at first merely an overarching research task. The prospects of ensuring a meaningful contribution to the understanding of credit risk felt marginal. However, during the course of this research project I have been privileged to interact with a distinctive group academics and practitioners in the field of finance and related disciplines who have contributed substantially to my understanding on various issues concerning this research. Accordingly, this thesis would not appear in its present form without the kind assistance and support of the following individuals and organisations.

The main part of this work was carried out in Helsinki at the Department of Finance at the Swedish School of Economics and Business Administration. First of all, I would like to thank Professor Eva Liljeblom, Head of the Department, for providing me the facilities and the opportunity to carry out this study at the Department. Further, I wish to extend my sincere thanks to my advisor group, including Professor Mats Hansson, Professor Eva Liljeblom and Professor Gunnar Rosenqvist for their help, expert guidance and encouragement during my work. In particular, I am very grateful to my principal supervisor Professor Mats Hansson, for his continuous positive attitude, support and patience throughout this adventurous project. His advice, encouraging comments and practical help concerning the arrangements of the dissertation were highly valuable. During my time at the Department of Finance, I have been honoured to share Professor Eva Liljeblom’s experience and enthusiasm in the field of financial research as well as her efficient way of solving problems. I am thankful for all her support during these past few years. I have been privileged to have Professor Gunnar Rosenqvist as one of my teachers in statistics and owe him my special thanks for introducing me to more advanced statistical modeling approaches required for my complex data. I also wish to thank Lecturer Susanna Taimitarha and IT Special Planner Anna Therman, for the advice on implementing the survey for my third essay.

I wish to express my gratitude to the official reviewers of this thesis, Docent Esa Jokivuolle, Research Supervisor at the Bank of Finland, and Professor Nathan Jensen, University of Washington (St Louis) for their valuable and constructive criticism. I am especially grateful to Docent Esa Jokivuolle, who originally suggested the interesting and challenging thesis topic by introducing me to Finnvera plc, the official Export Credit Agency of Finland that consequently became the main data source for my empirical work. Docent Esa Jokivuolle has played an important role in the background during the course of this study, providing comments also on a previous paper ii

presentation. His experience, interest, and achievements in the field of credit risk and bank capital requirements, have always impressed me. Similarly, Professor Nathan Jensen’s work on political risk and foreign direct investment has greatly inspired this work and broadened my understanding concerning political risk research in the field of finance and investment. Professor Nathan Jensen’s friendly comments and constructive criticism on my work expanded my powers of scientific argumentation, and encouraged me greatly at the finishing stages of the dissertation.

Several ideas of this study developed in Stockholm, during my research visit at the Institute for Financial Research (SIFR) in 2004. Working as a Ph.D. student in Stockholm, taking courses at Handelshögskolan and Stockholm University, was a challenging but rewarding experience. In particular, the Development Economics course lectured by Professor Jacob Svensson inspired me to focus further on the debt related problems of developing countries. The encouragement, perceptive feedback, and new ideas by exceptionally talented scholars at SIFR, lifted the ambition level of this work considerably. Accordingly, I extend my sincere appreciation to Professor Magnus Dahlquist, Director of SIFR at the time, Professor Per Strömberg, current Director of SIFR, and others who helped me along the way and influenced the formation and understanding of the credit risk issues presented in this thesis.

This thesis could not have been undertaken without the co-operation initiated between the author and Finnvera plc. Accordingly, I would like to express my deepest gratitude to Mr Erkki Kontio, Vice President of Risk Management, for introducing me to the interesting world of export credits, for sharing his wide knowledge and expertise in the subject area, for providing access to Finnvera’s archival data and allowing his staff to help me in the data collection, and for all help in the subsequent compilation, computation and interpretation of the obtained data. I also want to thank Mr Pekka Karkovirta, Vice President of International Relations, for interesting discussions on recent developments in the export credit industry. Ms Raija Rissanen, Vice President of Research, and Senior Advisers Ms Outi Homanen and Ms Liisa Tolvanen are acknowledged for their expert, critical comments on this research. My sincere appreciation is also due to other staff of Finnvera for their assistance in the data collection process. For their time and well coordinated effort and helpfulness, I would particularly like to thank Ms Hanna Tamminen, Ms Päivi Tamminen, Mr Ilpo Jokinen, Mr Vesa Kuusinen, Mr Viljo Partanen and Ms Sinikka Witting.

During the course of this research project, I undertook a research visit to the United Nations Institute for Training and Research (UNITAR, Geneva, Switzerland) where I performed an internship under the ‘training programme in the legal aspects of debt, financial management and negotiation’. As a complement to my research work, the visit was intended as a hands-on experience in the training and skills development of financial experts, policy makers, and lawyers coming from government ministries and departments in developing countries, in related topics of debt management. I am extremely grateful to Mr Babar Kamal, Manager, Public Finance and Trade Programme, UNITAR, for giving me the opportunity to pursue this research project along with the internship, during which I was engaged in various high-profile training projects covering topics related to the research. I acknowledge the numerous interesting and friendly discussions with Mr Babar Kamal that contributed enormously to the genesis of this doctorate both in terms of intellectual input and of support and encouragement. iii

Back home in Finland, I wish to express my appreciation to the faculty associated with the Graduate School of Finance, especially Director Mikko Leppämäki and Professor Sami Torstila, both of whom have guided my research efforts by giving their comments to various parts of this thesis.

This thesis was finalised during the PhD Research Internship at the World Institute for Development Economics Research of the United Nations University (UNU-WIDER), in Helsinki, Finland. A research paper summarising the whole thesis work was prepared under the supervision of Senior Research Fellow Wim Naudé, who also reviewed the thesis manuscript before its final submission. I gratefully acknowledge the help, guidance and encouragement from Professor Naudé, who inspired the work further with new perspectives on the linkages between trade, finance and economic development.

I want to extend my special thanks to Ms Jo Molin, Western Australian Deparment of Agriculture and Food who took on an additional important role in the final writing stage. Ms Molin efficiently revised the English language of this thesis, and gave professional comments for the entire manuscript. Also her kind encouragement and support helped me to carry on in the final stage of the dissertation. A special thank you goes also to a friend and former colleague Mr Arun Seetulsingh at UNITAR and my French teachers Mr. Gauthier Duché and Ms Christine Beaurain-Heikinheimo, for their help in the French translation of the survey.

My thanks and warm hugs go to all my past and present fellow doctoral students and colleagues at the Department of Finance, the Graduate School of Finance (Helsinki, Lappeenranta, Oulu and Vaasa), SIFR in Stockholm, as well as other friends beyond academic circles. Among many others, I want to thank Jan Antell, Mari Hintikka, Olga Karakozova, Peter Nyberg, Nikolas Rokkanen, Hans-Kristian Sjöholm, Arto Thurlin, Sheraz Ahmed, Mika Vaihekoski and Hanna Westman, for sharing many inspiring discussions and fun moments during these years. I also want to thank Marianne Koskimies for her friendship, important support and good company.

I owe my love and gratitude to my parents, my mother Eeva Klapuri and my deceased father Jarl Sandström, for giving me a happy childhood and the inspiration for education. Finally, my warmest thanks belong to my love Kristian Pullola for his unreserved love and support, and for being my source of inspiration and happiness during the twelve years we have shared.

This thesis work was financially supported by the Jacob Palmstierna scholarship through the Stockholm Institute for Financial Research (SIFR), Center for Financial Research (CEFIR), Finnvera Oyj, Foundation for Economic Education, KAUTE Foundation and OP-ryhmän tutkimussäätiö.

Rosala, August 2008 iv

CONTENTS

1. Introduction 1

2. Political risk – concepts and definitions 6

3. The international export credit industry 11

4. Default probability estimation 18

5. Summary of the essays 20

6. Conclusions 23

Appendix 25

References 30

THE ESSAYS

POLITICAL RISK AND EXPORT CREDIT DEBT DEFAULT ...... 37-84

CORPORATE DEFAULT PROBABILITY AND POLITICAL RISK: A STUDY OF EXPORT CREDITS ...... 85-120

PERCEPTIONS OF POLITICAL RISK AND DEBT DEFAULT: SURVEY RESULTS FROM DEVELOPING COUNTRIES ...... 121-210 v

APPENDICES

INTRODUCTION Appendix Overview of the Finnvera data 25 ESSAY 1 Appendix 1 Summary statistics 68 Appendix 2 Collinearity diagnostics and principal component analysis 77 Appendix 3 Results 80 ESSAY 2 Appendix 1 The export credit industry 110 Appendix 2 Data and results 112 ESSAY 3 Appendix 1 Questionnaire design 156 Appendix 2 Response rate and geographical coverage 159 Appendix 3 Estimation results 165 Appendix 4 Political risk questionnaires (English and French versions) 172

TABLES

INTRODUCTION Table 1 World Export Credit Agencies 12 Table 2 Export credit guarantee statistics by country or economic region 25 ESSAY 1 Table 1 Export credit guarantees offered by Finnvera plc to public institutions in 1979-2006 68 Tables 2-4 Variable definitions 71 Table 5 Sample country descriptive statistics 75 Tables 6-7 Correlations among the explanatory variables 77 Table 8 Extraction of component factors 79 Table 9 Country solvency and export credit debt default 80 Table 10 Country liquidity and export credit debt default 81 Table 11 Country economic development and export credit debt default 82 Table 12 Country political risk and export credit debt default 83 Table 13 Institutional risk factors and export credit debt default 84 ESSAY 2 Table 1 Thirty main recipients countries of export credits 111 Table 2 Finnvera guarantees to private debtors between 1980-2007 112 Table 3 Study sample of private guarantees with financial information, 1985-2006 113 Table 4 Sectoral composition of the sampled corporations 114 Table 5 Employed accounting ratios in the analysis 115 Table 6 Correlation among accounting ratios 115 Tables 7-8 Employed political risk ratios and institutional factors in the analysis 116 Table 9 Correlation among political risk ratios and institutional factors 117 Table 10 Dynamic logit results: corporate default and firm financial variables 118 Table 11 Dynamic logit results: corporate default, firm financial variables and political risk 119 Table 12 Dynamic logit results: corporate default and the sub-components of the ICRG index 120 vi

ESSAY 3 Table 1 Description and measurement of individual characteristic variables 157 Table 2 Description and measurement of contract specific variables 157 Table 3 Description and measurement of country related variables 158 Table 4 Nationalities represented 159 Table 5 Debt distress experience and political risk rating by debt category 162 Table 6 Descriptive statistics and correlations among the explanatory variables 163 Table 7 External debt categories 164 Table 8 Collinearity diagnostics among the explanatory variables 164 Table 9 Debt distress and political risk perception – Individual characteristics 165 Table 10 Debt distress and political risk perception – Debt type 166 Table 11 Debt distress and political risk perception – Country characteristics 167 Table 12 Debt distress and political risk perception – Combined models 168 Table 13 Political risk ratings, comparison between local ratings and the ICRG 171 Table 14 Political risk ratings, shares in various risk categories 171

FIGURES

INTRODUCTION Figure 1 Structure of a buyer credit 13 Figure 2 World trade, export credit and investment insurance, 1993-2007 14 Figures 3-4 Guarantee-year observations and default-year observations 17 ESSAY 1 Figure 1 Defaulting and non-defaulting guarantee schemes 52 Figure 2 Scree plot of eigenvalues after principal component analysis 79 ESSAY 2 Figure 1 Export credit and investment insurance, The Berne Union members 110 Figure 2 Claims paid and recoveries received, The Berne Union members 110 Figure 3 Change in stock of export credits in 10-year intervals 111 Figure 4 Geographic coverage of the Finnvera data, private guarantees 113 Figure 5 Sectoral composition of the Finnvera data, all guarantees 113 Figure 6 Finnish exports of goods 1960-2006 114 ESSAY 3 Figure 1 Corruption evaluation matrix 128 Figure 2 Respondents by age and gender 140 Figure 3 Respondents by sector of work affiliation and work discipline 140 Figure 4 Schematic layout of the questionnaire 156 Figure 5 Respondents by geographic region 159 Figure 6 Countries covered by the survey 160 Figure 7 Global political risk map, 2007 160 Figure 8 Survey participant involvement in external debt related activities 161 Figure 9 Years of experience with external debt related activities 161 Figure 10 Political risk perception radars 169 Figure 11 Frequency and seriousness of corruption 170 Figure 12 Extent of corruptive offences 170 1

1. Introduction

Financing trade between economic agents located in different countries is affected by many types of risks that result from incomplete information about the creditworthiness of the trading partners involved, the problems of enforcing international contracts or the prevalence of other country risks. Political risk is a particular form of the country risk challenge that is at the heart of the world of modern finance. In today’s global economy, political risk matters to all players engaged in international trade and investment either directly or indirectly through the different transaction structures and financing mechanisms involved. Following the considerable increase in the volume of world trade and foreign investments and the consequent unprecedented levels of international capital flows being experienced in the 21st century, understanding country risk is increasingly important for both developing countries and international lenders and investors. Overall, trade is important for economic development and trade finance is an essential element in the integration of developing countries into world trade (see e.g. Toye, 2003 and Messerlin et al., 2005). Therefore, both the recipient countries of trade finance, as well as investors and lenders need to adopt processes to assess ongoing risks posed by political instability and unrest if they are to protect their growing investments1. As a result of many recent developments in the world political risk arena there is a renewed interest in economic and scientific analysis of the political risk associated with the assessment and analysis of different types of international investments.

Political risk originates from the relationships between political authorities and economic actors (Loikas, 2003). It refers to an uncertainty resulting from the changes in the political, social or economic environment that may affect negatively on investment and business climates2. This uncertainty affects investment decisions and/or the company’s mode of action by making economic agents lose reference to the likelihood of an event, the nature of that event, and, its impact on the corresponding economic undertaking. Examples of classical political risks include expropriation, currency convertibility and transferability and, political violence such as war, sabotage, or terrorism. However, this approach for defining political risk appears too narrow in the era of globalisation and integration where many new ways for conceptualising political risk have emerged. For example, Smith (1998) categorises political risk under regulatory risk, i.e. risks that arise from un-anticipated regulatory changes3 and quasi- commercial risks that may arise when contract parties in a project are state-owned and, possess a questionable ability and/or willingness to fulfill their contractual obligations. Overall, political risk related actions can range from subtle policy matters, like a change in licensing processes, to the full-scale nationalisation and expropriation of a business. The essential feature in any definition of political risk is the concept of discontinuity.

1In a recent survey by the Economist Intelligence Unit some 30% of the surveyed senior executives and risk professionals said their firms had been forced to cancel existing investments in emerging markets due to concerns about political risks (EIU, 2007). 2 As noted by Brink (2004), “Most authors in the field seem to agree that political risk broadly supposes the probability that business will either earn less money, or suffer losses in profit as a result of stakeholders within a political systems (in) actions or reactions to events, decisions and policies”. 3 i.e. the taxation of foreign investment or laws applicable either to industries or to the whole economy. 2

Political risk has for a long time been considered an ambiguous research phenomena largely due to the fragmented and incomplete information needed for its analysis. How does political risk affect cross-border investments and especially the credit risk in international settings? This is a highly fascinating but multifaceted research question due to the heterogeneous nature of political risk. The “dissimilarity” among political risk events implies that it is not easy to make good predictions about possible outcomes and their payoffs (as well as their associated probabilities) for economic undertakings with a political risk character. In the face of such uncertainty, any new event may cause marked revisions to beliefs and assessments, and any new event within the class of political risk can revise the definition of political risk itself (Hill, 1998). Along with new breeds of political risk comes therefore an intensified concern over how the new forms of risk are handled (see e.g. Minor, 2003).

In credit evaluation, the modeling process of risk includes the identification, mathematical description and estimation of influence factors on the credit risk. On the level of single credit counterparts, the process involves defining the default, the estimation of default probabilities, the calculation of credit exposures and, the estimation of losses given default. On the portfolio level, dependencies and interactions of debtors need to be modeled. Political risk may constitute one of such influence factors on credit risk. The individual default probability of debt may be evaluated by using various failure risk models. These include the classical statistical models based on multivariate discriminant analysis and the single and multi period logit models (see e.g. Altman, 1968; Ohlson, 1980; Shumway, 2001); the theoretical default risk models (i.e. the structural- or reduced form models by Merton, 1974; Black and Cox, 1976; Jarrow and Turnbull, 1995; Duffie and Singleton, 1999 and many others); or some alternative models for the credit risk analysis and scoring4. How to incorporate country risk in the analysis of individual failure probability is still challenging, as political, social, and macroeconomic conditions and events that are beyond the control of the individual borrower can still adversely affect otherwise good credit risks. Overall, despite the increased importance of political risk in world trade, there is relatively scant academic literature concerning the political (economy) dimension of borrowing5. Contributions in the field have focused mostly on modeling sovereign bond spreads or studying stock market reactions to political risk related events. The analysis has been focused predominantly on emerging markets from the viewpoint of an international investor, and political risk is usually covered with only one or few selected institutional variables6.

4 Alternative models include, for example, expert systems that attempt to duplicate a credit analyst’s decision making (e.g. the commercial application Moody’s RiskScore® ); hazard models (e.g. the CreditRisk+ product by Credit Suisse Financial Products); and the neural network survival analysis, especially applied for personal loan data (see e.g. Baesens et al. 2005). 5 Aizenman and Powell (1998), Santiso (2003) and Moser (2007) model the role of governments and their relation to international capital markets or debt servicing. 6 For example Pantzalis (2000), study the behaviour of stock market indices across 33 countries around political elections. They find positive abnormal returns in the two weeks prior to the election due to declining policy uncertainty. Block and Vaaler (2004) study whether the theory on political business cycles (PBC) is relevant in lending to developing countries. Together with Vaaler et al. (2005) they offer interpretations on the political business cycle theory (PBC) and find among other things that bond spreads are conditional on the partisan orientation of the incumbent government and its likelihood to stay in power. 3

Traditionally, the management of political risk has been handled with specific risk insurance instruments of state activity, including export credit guarantees and bilateral and multilateral trade and investment agreements. State Export Credit Agencies (ECAs) were established in the early 20th century to help domestic firms finance their foreign sales and to promote investment. Since then, changes in economic and financial markets have brought also private players into the field of political risk insurance. Financial innovations such as derivatives and risk transfer products have evoked new ways to address political risk. While economic agents seem better equipped than before to offset the negative implications of macroeconomic and asset price cycles, political risk insurance remains an important class of instruments for investors and their financiers. However, the role of political risk insurers, and export credit agencies in particular, is largely neglected in the literature. Exceptions where credit insurance and export promotion are treated on a theoretical level include Funatsu (1986), Abraham and Dewit (2000), and Rienstra-Munnicha and Turvey (2002). A strategic point of view is presented in Dominguez and Sequeira (1993) and some recent empirical contributions are Egger and Url (2006) and Moser et al. (2008) who study the trade promoting effect of public export credit guarantee schemes in Austria and Germany. This study continues this avenue, and looks into the related issues of export credit insurance and political risk.7 Such financing agreements transfer credit risk and are generally seen as an important engine to fuel and protect domestic and international trade.

In sum, recalling the series of major political and economic scares from the last decades, it is evident that political events matter to financial markets. Political risk is still inherent in cross-border transactions and constitutes one of the fundamental motivations for export credit. This study focuses on political risk, a sub-category of country risk8, that is still to a large extent managed by the institutional arrangements of the states (i.e. the export credit and investment insurance agencies). This research project has been afforded the unique opportunity to explore historical export credit guarantee data in cooperation with the Finnish Export Credit Agency, Finnvera plc. In addition, survey techniques have been used to address the problem of political risk, deriving research information from the work of practitioners, including finance professionals in developing countries faced with problems of political risk in their day-to-day work.

7Export credit insurance protects sellers of goods or services against the risk of buyer non-payment, in particular due to political reasons. Commercial risk refers to buyer insolvency and extended late payment (i.e. bankruptcy). Political risk involves non-payment due to actions by an importer's local government. 8 Bouchet et al. (2003) survey the concept of country risk. They define the term in its broader meaning, including any risk specific to a given country in the definition. While ’political risk’ is usually restricted to those risks that are exclusively political in nature, the definition depends on the underlying exposure. 4

Overview of the research design

The purpose of this research is to explore and discuss links between political risk and international debt defaults. The core hypothesis of the study is that country-specific political risk factors cause obstacles for debt repayment, both when the credit is extended to public institutions or to corporations in developed and developing countries. Due to the interdisciplinary nature of political risk, the project is designed to consist of two separate research endeavours. First, the study models default probabilities applying a previously unexplored type of credit data, i.e. the historical export credit guaranteed debt contracts, obtained from Finnvera plc, the official ECA of Finland. The unique dataset on export credit facilities between Finnish exporters and foreign buyers in a total of 145 countries is combined with a detailed set of political risk variables using data from Polity IV and the International Country Risk Guide. Empirical applications of basic credit scoring techniques (e.g. multi-level event history analysis) are applied in order to study the impact of various political risk variables on the event of debt default. Accordingly, the first two essays explore the linkages between political risk and export credit guaranteed debt default. The relationship between political risk and actual historical default events are modeled for two types of credit counterparts; governments or government controlled institutions (i.e. the public credit counterparts) and corporations (i.e. the private credit counterparts). In general, estimating default probabilities can be challenging due to limitations on data availability. This study is focused on the fundamentals-based models for estimating default probabilities, which rely on accounting variables, macroeconomic factors, and political risk ratings information9. To substantiate the empirical findings from the debtor point of view, the second part of this thesis brings insights into the question of political risk, through the results obtained from a web-based survey conducted among debt managers and finance professionals in 38 developing countries.

The first essay explores public debtors in 125 countries over the period 1980-2006 and includes country-specific macroeconomic, institutional, legal and political risk factors as explanatory variables determining the public default probability. The main motivation for this research is to develop an understanding of how various political risk factors realise in debt repayment difficulties, taking into account the different stages of economic development across countries. In the aftermath of the crisis of public debt in the countries of the Third World and the East, the study contributes to the understanding on what factors may be among the triggers for defaults, even if some of the debtors might have had the resources for repayment. Another motivation for the study is the concern of strategic debt service, i.e. the reputational arguments on why debtor countries may behave differently towards different creditors. While a country may have the technical ability to repay a debt it may still adopt a political decision not to do so (see e.g. Drazen, 2000). As a result this study may provide a platform for ECAs in other countries to analyse their own data for political and other risk factors.

The second essay focuses on corporate debt and the determinants of corporate failure. Two alternative ways in which a company may fail to meet its debt obligations are

9 The analysis is restricted to exclude the market based models which rely on security prices, as the majority of the credit counterparts in the export credit data do not have publicly traded securities or the secondary market prices from emerging markets are unreliable because of low liquidity. 5

considered. First, the traditional approach is to consider the financial position or, the so called commercial risk, that may result e.g. from a deterioration in a buyer/importer’s market, fluctuations in demand or from general buyer insolvency. The new approach developed in this study is to consider separately the impact of country political risk. In this framework, political risk realises in non-repayment of the export credit guaranteed debt due to the actions by the local government or other societal or institutional reasons in the country of the debtor. For example, the occurrence of contract violations, adverse measures taken by the government (e.g. expropriation, confiscation or transfer risk), conflict or other warlike events are considered. Various political risk factors are investigated as conceivable causes of corporate default using a dynamic logit specification of export credit contracts initiated between Finnish exporters and foreign buyers in 14 selected countries.

The third essay complements the earlier essays by providing survey evidence on debt default experiences and the impact of political risk on the debt distress, as perceived by finance professionals in developing countries. In order to explore the micro-foundations of political risk a web-based survey was conducted among 103 finance professionals representing 38 countries. The results provide unique information on default experiences, perceptions of political risk as well as on local political risk ratings. Using different logit specifications, the factors affecting both the default experience as well as the level of political risk perception are empirically tested. In addition, political risk ratings made by the local respondents are compared with the actual risk ratings of the International Country Risk Guide. Accordingly, this essay contributes to the thesis by eliciting borrowing experience and the spontaneous images associated with political risk, in particular from the debtholders point of view.

In its explicit form, the core hypothesis of the study is that the probability of debt default increases with increasing levels of country political risk. The three essays of this thesis support the central hypothesis from different angles of the credit evaluation process. The first essay takes the point of view of an international lender assessing the credit risk of a public borrower. The second looks at the question in the context of creditworthiness assessment of companies. Finally, the ‘other side of the coin’ is analysed and the third essay is devoted to an exploration of the borrower perspectives. Taken together, the findings suggest that various forms of political risk are associated with debt defaults and continue to pose great concerns both for international creditors and borrowers in developing countries.

The remainder of this introductory chapter is organised as follows. Section 2 introduces the concept of political risk and presents the background for studying it in the international lending environment. Section 3 discusses recent developments in the export credit industry and provides information on this specific type of credit data. Section 4 provides a general introduction to the empirical research design employed in the thesis. It also includes examples of the methods applied in credit evaluation more generally. An outline and the main findings of the essays are presented in Section 5. The conclusions from this research project are offered in Section 6. 6

2. Political risk – concepts and definitions

Country risk is a phenonena that attracts researchers from various disciplines and accordingly, there is a diversity of approaches to clarifying the concept. Within international finance and investment the concept usually relates to the ability or willingness of a country or a corporation to service its external obligations. Schroeder (2008) surveys the history and current status of country risk assessment. She notes that the broadest or, the so called “global” definition of country risk, involves evaluating the overall business or investment climate for foreign investors10. This contrasts with the more ‘traditional’ country risk assessment approach, where the goal is to evaluate the risk of default of a particular economic actor in a specific country (Bouchet et al., 2003).

The term country risk is often used interchangeably with sovereign risk that refers to situations when a government or a sovereign is in the central focus. Sovereign risk assessment is often used as a proxy for evaluating the overall health of an economy, providing a foundation from which the ratings of other issuers or economic actors in the same country are gauged. Three categories of variables are generally monitored in country risk assessment. The first category is the economic and financial state of the country and the second is the government’s overall management of that country’s economy. These two categories are thought to provide indicators of a country’s ability to pay. Variables under the economic state category may include GDP growth, GDP per capita, inflation and investment’s share of the GDP. The management category may include indicators of trade policies, indebtedness or the external debt situation, history of defaults and balance of payments positions. The third category of monitored variables includes political risk, or political and social stability related variables. Various political risk indices measure the extent of corruption, conflicts, unemployment, income distribution and other political and social stability related factors. Political risk is often contrasted with a country’s willingness to pay, but the distinction between ability and willingness remains elusive.

This research project represents one of the first attempts of incorporating country and political risk concepts of this size and scope into an academic study of credit risk. However, there exists no single consistent definition of ‘political risk’ as the identity of this concept crosses the boundaries of many different social sciences, including political science, economics, law and sociology (Simon, 1984). In philosophical terms, political risk can be seen as a problem of nominal definition11. Economists and political scientists usually define political risk as “the risk that a future political event will change the prospects of profitability of a given investment” (Haendel, 1979). Meanwhile, the implications of the term ‘political risk’ vary with the interest and need of the definer and ultimately, conceptualising the term depends on the business activity being contemplated. In the first part of this thesis, political risk is analysed from the viewpoint

10 Rating agencies or firms which compile the so called ”global assessments” or ratings include Business Environment Risk Intelligence (BERI), Economist Intelligence Unit (EIU), Euromoney, Institutional Investor (II) and International Country Risk Guide (ICRG). For more details, see e.g. Schroeder (2008). 11 The Aristotle's distinction between a nominal definition as opposed to real definition is outlined in Cohen (2005). A nominal definition gives a definition in name only while a real definition is one that states the function of the concept, the reason for conceiving it, and also indicates the essence, the underlying substance of its object. On further Aristotelian concepts, see e.g. Hintikka (1972). 7

of the international lender and the public export credit agency. In the second part, political risk is assessed from the viewpoint of the debtor. Both analyses are cognisant of the limitations that nominal definitions can place on empirical studies but continue in the endeavour to overcome the general skepticism that political risk is a too formless and subjective concept to be exposed to systematic quantitative analysis.

Historical evolution of the political risk paradigm

The topic of political risk has generated an extensive literature on its own, why introducing political risk in the study of credit risk involves a definitional challenge that is met here with an overview of the political risk paradigm following Loikas (2003). There were signs of interest towards political risk in the corporate finance literature as early as in the seventieth century (Barron et al., 1997). The early definitions of political risk concentrated on adverse governmental actions (Fitzpatrick 1983) but contained inherent problems. The scope of political risk was fairly narrow leading to inappropriate conceptualisation, problems in data selection, improper choice of analytical tools, as well as misinterpretation of the results (Oseghale 1993). The emphasis on government actions and political events excluded other causes of political risk, including internal, external and social sources (Simon 1982). In addition, the assumption that the orientation of political risk is necessarily negative was soon challenged (see e.g. Robock 1971 and Ting 1988). Fitzpatrick (1983) suggested that political risk should be viewed as a process that changes over time.

The scientific research on political risk has gone through different periods of transformations over the past three decades. According to Loikas (2003), the so called political risk paradigm emerged initially due to practical reasons; second, through the theoretical models developed; and third, as a response to political events in the international arena. Specific political events after the Second World War created a demand for risk analysis, which led to the scientific consciousness and reception of political risk in the economic literature. The period from the 1960s to the end of the 1970s was dominated by studies on multinational corporations and their exposure to political risk. Concepts like confiscation, expropriation and nationalisation became critical concerns for companies with foreign operations. This period was characterised by some independent countries that had just recovered their sovereignty from colonial powers and attempted to overcome their lack of capital by simply taking over foreign subsidiaries of multinationals. Another political event example from this period is the revolution in Iran in 1979, with its hostile acts against international companies, leading the researchers to add questions of political stability to the variables being examined.

The second transformation of political risk research took place in the 1980s with the international debt crisis devastating many developing countries. During this period, a large part of the literature was dedicated to the creditworthiness assessment. Quantitative risk assessment methods were developed along with the probabilistic interpretation of country and political risk. A refinement and professionalisation of the political risk concept emerged with the systematic use of the quantitative approaches on the corporate level. The crises in Mexico (1994), Asia (1997), Latin America (1999- 2002), as well as the Russian default (1998) are examples of the events forming the third evolution of the political risk paradigm. Through these events, political risk research shifted its’ attention to ‘financial crises’ and the identification of early warning 8

indicators for such crises. The changing role of political risk over the past twenty years, have obtained recently yet another dimension following September 11 and the “war on terrorism” (Minor, 2003). Following these developments, there is evidently a growing interest in the academic literature on the link between political institutions and political risks facing multinational corporations and investors12. A detailed treatment of the concept of political risk, its evolution and conceptualisation, is provided also in Jarvis (2008) who presents the utility in order to understand political events and processes that can threaten order, stability and continuity in international relations and disrupt the normal practices of inter-state investment, trade and commerce13.

A framework for defining and measuring political risk

As the above background suggest, the broad area of country and political risk assessment includes various definitions and risk components that are associated with claims against economic agents in a particular country and include but are not limited to the government of that country. The various definitions encompass a range of risk factors that arise from the economic, financial, political, legal, and social conditions of the country under investigation. The commonly applied political risk definitions in business contexts are presented below14.

The essential feature in any definition of political risk is the concept of discontinuity. A general definition of political risk is given by Haendel (1979) which is simple and flexible against the interest and need of the definer (e.g. being a corporation, a private insurance firm, an export credit agency, a bank or a multinational organization). According to this view, political risk may be defined as “the probability of the occurrence of some political event that will change the prospects for the probability of a given investment”. Caouette, Altman and Narayanan (1998), define political risk in a credit risk framework as “the possibility of delayed, reduced, or non-payment of interest and principal where the outcome is attributable to the country of the borrower”. In the context of political risk insurance, political risk is often defined as “the exposure to the risk of a political event that would diminish the value of an investment or a loan” (Alwis et al. 2006). Studying emerging markets, Bilson et al. (2001) describe political risk as "the risk that arises from the potential actions of governments and other influential domestic forces, which threaten expected returns on investment". The challenge of this particular research is to explore, which are those factors, e.g. the “political events”, “actions” or “forces” that the above definitions refer to.

12 A large part of this research is devoted on domestic institutions and FDI inflows, see e.g. Henisz (2002), and Jensen (2006). Another group of authors have investigated the relationship between political risk and credit, but so far, the focus has been mainly on sovereign borrowing, see e.g. Citron and Nickelsburg (1987), Balkan (1992), Edwards (1996), Brewer and Rivoli (1989) and Peter (2000). The relationship between democratic institutions and borrowing is discussed in Schultz and Weingast (2003) and Saiegh (2005). 13 Jarvis (2008) provides a profound organisation of the disparate literature that surrounds the concept of political risk. In this framework, the topic is rigorously applied as a social science method for understanding political events and their effects upon commercial and strategic activities. 14 See e.g. Kobrin (1979), Simon (1982), Fitzpatrick (1983) and Alon (1996). 9

The praxis application of political risk analysis relates to the negative consequences for the cross-border investment activities where political risk is seen to arise from actions taken by host governments, government agencies, or political actors in host countries. This approach falls into what Jarvis (2008) defines as the catalogue school, where practitioners simply develop lists of possible negative activities of governments in host countries that detract, or have the potential to detract, from business operations, value and profitability. Often cited political risk types include currency inconvertibility and exchange transfer, import restriction, unexpected currency devaluation or revaluation of non-floating currencies, delays in profit repatriation, political violence or war (including revolution, insurrection, politically motivated civil strife and terrorism). The list continues with concerns such as unfair tax laws, labour strikes and trade union power, production or export restrictions, contract repudiations, restrictions on local market access, expropriation or nationalization, confiscation of property, breach of contract, contract frustration or contract repudiation or restrictions on information flow.

A central but challenging issue in the analysis work is to distinguish political risk from commercial risk. The former relates to specific actions (or non-actions), usually under the control of the central government. A governmental entity is somehow related to a specific arrangement (e.g. investment law, tariff agreement, investment contract, etc) for the risk to be considered political. Actions taken by the government must likewise be specific and result in expropriation of value without payment of fair compensation. Commercial risks associated with the production process, changes in input and output prices, market demand, and/or the competitive environment are in this case, specifically excluded. According to the definition by Simon (1982), political risk is generally viewed as governmental or societal actions and policies originating either within, or outside the host country, that negatively affect either select groups or the majority of foreign business operations and investments.

Risk measures applied in this study

In this study, political risk is considered as a sub-category of country risk and is separated from economic and financial risk. A central feature of the research initiative is to include political risk measures directly into empirical default estimation models. For this purpose, country risk factors are categorized under 1) economic and financial risk, 2) political risk, and 3) institutional risk factors. Measures from all these groups are hypothesised to influence on the default probabilities, concerning both publicly held- as well as corporate debt. Some of the measures are also tested against the risk perceptions formed by developing country finance professionals. The economic and financial risk variables are assessed separately for governments and corporations as borrowers. Country specific macroeconomic measures are obtained from the Economist Intelligence Unit, while accounting information for corporations are collected from archives of Finnvera. The original sources for this latter type of financial information is Suomen Asiakastieto Oy, the leading business and credit information company in Finland, the Dun & Bradstreet Credit Bureau and company financial reports. 10

This study employs political risk indices developed by the International Country Risk Guide (ICRG) and compiled by the PRS Group15. Independently acclaimed and sourced by researchers, the IMF, the World Bank and a host of other international financial institutions, the ICRG has become one of the world’s most frequently used resources for evaluating and forecasting international risk. For example, Howell and Chaddick (1994) find that PRS indices are more reliable and are able to predict risk better than other major political risk information providers.

The aim of the ICRG political risk rating is to provide a means of assessing the political stability of countries on a comparable basis. This is done by assigning risk points to a pre-set group of factors, termed political risk components. The minimum number of points that can be assigned to each component is zero while the maximum number of points depends on the fixed weight that component is given in the overall political risk assessment. The lower the risk point total, the higher the risk, and the higher the risk point total, the lower the risk. Out of twelve components, four can be considered to directly impact the business environment, including: investment profile, corruption, law and order and bureaucracy quality. The others deal with government stability, socioeconomic conditions, internal/external conflict, military influence, religious/ethnic tensions and democratic accountability. By considering all these subcomponents this study captures the different dimensions of political risk. One drawback of using the ICRG is that it may suffer from potential perception bias, since it only draws information from one source16. For a discussion on the shortcomings with these type of data, see e.g. Svensson (2003).

This study also employs selected measures on the quality of a country’s political institutions. The quality dimension is first analysed in terms of the country’s exposure to democracy by employing the "Polity Index" from the Polity IV dataset17. This index measures the degree to which a nation is autocratic or democratic on a scale from -10 to +10. In the same vein, also the regime longevity measure by Polity IV is applied. To further control for the effects of legal risk, the Legal Rights Index and the Credit Information Index from the World Bank’s Doing Business Database is used. The former reflects the legal rights of borrowers and lenders and measures the degree to which collateral and bankruptcy laws facilitate lending. The latter measures credit information registries, i.e. the rules affecting the scope, accessibility and quality of credit information available through either public or private bureaus.

15 The PRS Group, Inc, in East Syracuse, New York has published its International Country Risk Guide which provides financial, political and economic risk ratings for 140 countries since 1980. See http://www.prsgroup.com/icrg/icrg.html. 16 Another critique with the ICRG is that it is primarily concerned with indicators related to economic development and foremost with issues of interest set by business corporations and potential investors. 17 The Polity IV database is maintained at the Center for International Development and Conflict Management (University of Maryland) and contains information on regime type and political structures of independent states in the world system between 1800 and 2006. 11

3. The international export credit industry

Export credit and investment insurance agencies (ECAs) have emerged collectively as the largest international financial institutions in the world today18. Categorised under bilateral finance agencies, most ECAs are public agencies providing assistance to corporations, for foreign direct investment or for export promotion. The forms of assistance include loan guarantees, insurance and credits. A generic rationale behind ECAs is that economic and political uncertainties, in particular in developing countries, are disincentives for foreign investment, international trade and project finance and, the insurance products offered should enhance the financial flows to these ‘high-risk’ countries19. Ascari (2007) reviews the prevailing economic rationales behind export credit, that involve incomplete insurance markets, moral hazard and adverse selection problems, as well as industrial policies. From a trade theory perspective, public export credit guarantees provide governments with an instrument to correct for market failures resulting from transaction costs due to asymmetric information and insecurity about the quality of the foreign borrower.

Industry overview

During the last century, a steadily increasing number of countries in both the developed and developing world have seen the establishment and development of an export credit insurance scheme as an important tool for promoting their export trade, increasing employment opportunities, and, improving the credit side of their balance of payments20. Today, every industrialised country and many developing countries have at least one ECA. These institutions are either fully government owned or have mixed (public and private) ownership structures. A range of financial services are provided by the ECAs, including for example; 1) credit where none is available, 2) loan guarantees and insurance to exporters or their banks as cover against non-payment, 3) loans by foreign customers, and 4) investment insurance (for commercial and political risk) to cover direct equity investments in foreign countries. Major ECAs in the world include the Exim Bank (Ex-Im) and the Overseas Private Investment Corporation in the USA, Coface in France, Euler Hermes in Germany and the Export Credits Guarantee Department (ECGD) in the UK. Table 1 provides a listing of multilateral and official ECAs and other private credit insurers and oversight groups21.

18 Berne Union, the organisation for the world's leading export credit and investment insurance agencies, announced a figure of $1.1 trillion in new business in 2006 (Berne Union Yearbook, 2008) 19 Other rationales advanced in support for the existence of ECAs include export credit as a counterbalance to economic distortions created by trade protection (Zhu 1996); export subsidies as a solution to the trade deficit problem (Cline 2001); ECAs as a repository of information about the creditworthiness of foreign firms (Stephens 1998); and export subsidies as a tool of strategic trade policy (Gillespie 1998). 20 For an interesting treatment of the history of the ECA, see the Berne Union Year Books. 21 The credit-insurance market is highly concentrated. Four credit-insurance groups, including Euler Hermes, Atradius (former German credit insurance group Gerling Credit and the Dutch credit insurer Nederlandsche Credietverzekering Maatschappij), Coface and Credito y Caucion, lead the global market, with combined revenues of $5.7bn in 2005. These groups compete with local credit insurers like Finnvera plc in Finland, EKN in Sweden, SACE in Italy, and CESCE in Spain, as well as with subsidiaries of international insurance groups like QBE Trade Credit and AIG Global Trade & Political Risk. 12

Table 1 World Export Credit Agencies

Multilateral ECAs, Official ECAs and Private Credit Insurers Source: The Project Finance Portal by Benjamin C. Esty and Harvard Business School (2008)

Name and type Abbreviation Country

Multilateral Export Credit Agencies African Export-Import Bank Afreximbank Africa Corporación Andina de Fomento CAF Andean Countries European Bank for Reconstruction and Development EBRD Central and Eastern Europe Inter-American Development Bank IADB Latin America Islamic Corporation for the Insurance of Investment and Export Credit ICIEC part of the Islamic Development Bank Multilateral Investment Guarantee Agency MIGA part of the World Bank

Official Export Credit Agencies Asuransi Ekspor Indonesia ASEI Indonesia Banco de Inversión y Comercio Exterior BICE Argentina Banco Nacional de Comercio Exterior SNC Bancomext Mexico BNDES-Exim Ex-Finamex Brazil Compagnie Francaise d. Assurance pour le Commerce Exterieur COFACE France Companhia de Seguro de Créditos S.A. COSEC Portugal Compañía Española de Seguros de Crédito a la Exportación, S.A. CESCE Spain Corporación Financiera Nacional Fondo de Promoción de Exportaciones CFN/Fopex Ecuador Credit Guarantee Insurance Corporation of Africa Limited CGIC South Africa Credit Insurance Zimbabwe Credsure Zimbabwe Croatian Bank for Reconstruction and Development HBOR Croatia ECICS Credit Insurance Ltd. ECICS Singapore Exgo, a division of State Insurance New Zealand Export Credit Bank of Turkey Turk Eximbank Turkey Export Credit Guarantee Agency ECGA Oman Export Credit Insurance Corporation Kuke Poland Export Credits Guarantee Department ECGD UK Export Development Canada EDC Canada Export Finance and Insurance Corporation EFIC Australia Export Guarantee and Insurance Corporation Egap Czech Republic Export-Import Bank of India I-Eximbank India Export-Import Bank of Korea Keximbank South Korea Export-Import Bank of the Russian Federation Eximbank Russia Export-Import Bank of Thailand Thai Exim Thailand Export-Import Bank of Trinidad & Tobago Eximbank Trinidad & Tobago Export-Import Bank of the United States EXIM US Export Credit Insurance Organization ECIO Greece Export Kredit Fonden EFS Denmark Export Risk Guarantee Agency ERG Switzerland Exportkreditnamnden EKN Sweden Finnvera plc Finnvera Finland Guarantee Institute for Export Credits GIEK Norway Hermes Kreditversicherungs-AG Hermes Germany Hong Kong Export Credit Insurance Corporation HKEC Hong Kong Hungarian Export Credit Insurance Ltd. MEHIB Hungary Israel Foreign Trade Risks Insurance Corporation Iftric Israel Istituto per i Servizi Assicurativi e il Credito all'Espotazione SACE Italy Japan Bank for International Cooperation (form. JExIm) Japan Korea Export Insurance Corporation KEIC South Korea KfW IPEX Bank part of the KfW Germany Malaysia Export Credit Insurance Berhad MECIB Malaysia Nederlandsche Credietverzkering Maatschappij NV NCM Netherlands 13

Table 1 (cont’d)

Norwegian Guarantee Institute for Export Credits Giek Norway Office National du Ducroire OND Belgium Oesterreichische Kontrollbank Aktiengesellschaft OeKB Austria Overseas Private Investment Corporation OPIC United States Segurexpo de Columbia Segurexpo Columbia Slovene Export Corporation SEC Slovenia Sri Lanka Export Credit Insurance Corporation SLECIC Sri Lanka Svensk Esportkredit SEK Sweden Uzbekinvest National Export-Import Insurance Company Unic Uzbekistan

Private Export Credit Insurance Coface North America CNA Credit USA Cox Insurance Political Risk Unit CPRU UK Crédito y Caución Spain EULER Group France Eurofactor France Exporters Insurance Company Bermuda Hiscox Trade Credit Insurance UK Seguradora Brasileira de Credito à Exportação SBCE Brazil

The insurance policies of the ECAs are often called “guarantees”.22 A financing package is granted directly by a bank (or a group of banks) to a foreign buyer (or a borrower acting on behalf of a buyer) who has signed a contract with an exporter. The forms of these financing packages vary, but the most common form is the buyer credit. This is a regulated loan, granted in accordance with international Organisation for Economic Cooperation and Development (OECD agreement), European regulations (for intra-community contracts), as well as national regulations. In most OECD countries and some non-OECD countries, such credits benefit from state export assistance in the form of credit risk insurance or a guarantee from the ECA (see Figure 1).

Figure 1 Structure of a buyer credit

22 The term “guarantees” may be misleading and it is important to note that they are not guarantees in the normal sense in which the word is used especially by commercial banks, as being fully unconditional and on-demand. For further discussion on different ECA market segments and the overall background of export credit globally, see e.g. Heinonen (2004). 14

Supplier credit is the other main type of credit insurance. For supplier credits, the credit involved in the transaction is extended by an exporter/supplier to the foreign buyer/importer and the terms of the credit are set out as part of the export contract. Export credits are generally designed for short-term (ST) or medium and long-term (MLT) export financing, with repayment periods differentiated under or above 2 years, respectively. Since the majority of world trade is conducted on the basis of cash- or short-term credit, the traditional and main product of export credit insurance is ST.

During the 1990s, ECAs in the world provided some US$ 100 billion per year in loans and guarantees for developing nations constituting roughly twice the level of official development assistance during the same period. The pattern has continued, and ECA- financed projects today account for approximately half of all developing country official debt23. The ECAs play an increasingly important role in international trade and investment flows and are outnumbering gross official development assistance and gross international financial institution lending (see Wang et al., 2005). Figure 2 illustrates the developments in world trade and export credit and investment insurance during the period 1993-2007.

Figure 2 World trade, export credit and investment insurance, 1993-2007

US$ billion US$ trillion 1 600 16 World export credits (new business, US$bn) 1 400 14 World exports (Total merchandise, US$tr) 1 200 12

1 000 10

800 8

600 6

400 4

200 2

0 0

3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 9 99 9 99 99 99 00 19 1 19 1 1 199 1 200 200 2 200 200 200 200 200

Source: Berne Union Yearbooks 2005-2008 and WTO’s online trade statistics database.

The left axis of Figure 2 presents the export credit and investment insurance in US$ billion, including new insurance/guarantee commitments by the Berne Union members. The figures include both short and long term export credit loans as well as foreign investments insured/guaranteed against political risks. World exports are measured on the right axis in US$ trillion. These figures are obtained from the WTO, with merchandise trade defined according to its general trade definition. The export figures cover all types of outward movement of goods through a country or territory including movements through customs warehouses and free zones. Goods include all merchandise

23 Publicly available estimates are given e.g. by the IMF (see Wang, 2005). In 2006, the 51 members of the Berne Union exceeded for the first time US $1.1 trillion worth of business, which is about 10% of the world's total export trade (Berne Union Yearbook, 2007). 15

that either add to or reduce the stock of material resources of a country by leaving the country's economic territory. Overall, the demand for export credit guarantees fluctuates with the credit cycle, the funding requirements of large capital-intensive infrastructure projects, together with the reduced appetite of commercial lenders for uncovered term financing following e.g. financial crises.

Regulation of ECAs

The ECAs assess country risk and use the ratings generated in the process of assessing risk to help set premium rates for the insurance they provide. In an attempt to seek convergence of actual premium rates associated with export credits, the OECD implemented the Knaepen package in 1999, which contains a set of minimum premium rates reflecting country and sovereign credit risk. In addition to domestic regulation, ECAs are subject to international regulation, such as the ‘OECD Arrangement on Officially Supported Export Credits’ and the ‘WTO Agreement on Subsidies and Countervailing Measures’24. The role of the OECD involves the maintenance and development of disciplines, providing a forum for discussion and coordination of national export credit policies. In the Export Credit Division, rules are discussed in two setups with different legal status: “Participants” and “The Working Party on Export Credits and Credit Guarantees (ECG)". Participants is the formal negotiating forum for rules that are considered binding. The European Community as such is a participant, and other countries such as Australia, Canada, the Czech Republic, Japan, Korea, New Zealand, Norway, Switzerland and United States are also involved. The Participants administer and further develop the "Arrangement on Guidelines for Officially Supported Export Credits", which has the main purpose of providing a framework for the orderly use of officially supported export credits. In practice, this means providing a level playing field, whereby exporters compete on the basis of the price and quality of their products rather than the financial terms provided. The Participants work also to eliminate other trade distortions related to officially supported export credits. Whilst the Arrangement is a "gentlemen's agreement", the rules agreed upon are normally incorporated into Community law by way of Council Decision. The Working Party on Export Credits and Credit Guarantees (ECG)" is a sub-group under the OECD Trade Committee that deals with issues such as environment, bribery and unproductive expenditure.

The last two decades have witnessed dramatic changes in the landscape of the ECA industry and major challenges lie ahead for these organisations in the rapidly changing world economy. ECAs confront substantial changes in business environment trough competition from private insurers (especially in ST credit). In addition, more experience and technology is brought in by the private sector, as reflected in a shift from standardised products and basic business models to more sophisticated and tailor-made solutions. There is a sharper questioning from governments about proper role of public sector involvement, as well as an increasing pressure from NGOs on ECAs. In this environment, it seems that official ECAs are mostly left with longer maturities and riskier countries. In particular, the ECAs seem to remain critical financial partners/financiers in taking MLT risks in developing countries (see e.g. Kilicoglu,

24 See e.g. Canas and Scharf (1996) 16

2007). While the literature on export promotion is rather scant25, there is an ongoing discussion scrutinising ECA financing decisions from political-economic and sustainable development perspectives (see e.g. Harmon et al., 2005). Sustainable development initiatives have encompassed concerns regarding debt sustainability, responsible lending as well as environmental and social policies.

Finnvera plc and the export credit guarantee data

The export credit guarantee data for the first two essays of this thesis are obtained from Finnvera plc, the official Export Credit Agency of Finland26. Finnvera is a specialised financing company offering financing services to promote the domestic operations of Finnish businesses and to further exports and the internationalisation of these enterprises. Its’ product portfolio include various types of loans, guarantees and export credit guarantees. The latter group is aimed at export companies and their lenders to protect against political and commercial risks of export financing and international investment. Finnvera defines political risk as “…risks that arise from the economic or political situation in a country where a Finnish export company has customers. Commercial risks pertain either to the buyer or to the buyer's bank”. Finnvera’s clients comprise companies and domestic and international banks and financial institutions. The total outstanding commitments by Finnvera arising from export credit guarantees and special guarantees totalled EUR 4 980 million in 200727.

The export credit guarantee data for this research project was collected in co-operation with Finnvera during 2005-2007. This large-scale data collection procedure involved a massive systematic reviewing of both historical archives as well as current databases on the issued export credit contracts and the default events observed among these contracts. Several registries (both paper and electronic archives) of the issued guarantees as well as the corresponding default events were maintained by Finnvera during the 1980-2007 period. All these registries were delved and a significant part of the documentation was double checked from several sources in order to increase the data accuracy. The starting-point of the data collection was to document all guarantees initiated between Finnish exporters and foreign buyers (and their financiers and guarantors) between the time period 1980-2006. All guarantees are coded with an identification number, so the tracing between different registries was possible. The guarantee identification number also enabled the removal of names and other confidential information, in order to proceed with the exploration of this highly sensitive and confidential type of credit data. Detailed guarantee information collected from the various registries include guarantee initiation and length (i.e. the exact dates of the guarantee initiation and termination), origin of the buyer (i.e. the host country of the debt obligor), potential default episodes with exact dates, liability and indemnification values of the contract, guarantee type (mainly buyer credit, credit risk and letter of credit guarantees), contract length (ST or MLT), the public-private dimension as well as the industry sector of the buyer. In addition to this basic contract data, other credit details (i.e. guarantor status and

25 See e.g. Moser et al. (2008) 26 For a more detailed company presentation, see e.g. https://www.finnvera.fi, Finnvera (2008) 27 Finnvera was awarded the “Best Sovereign Export Credit Agency” in the TFR Awards in 2007 by the Trade & Forfaiting Review Magazine (see e.g. http://www.tfreview.com/) 17

potential recoveries) as well as the financial statements of selected buyers have been interrogated and used to strengthen the analytical capacity of the study.

The full data set on loan contracts and default events include guarantee information available for a total of 145 buyer countries (see Table 2 in the Appendix). The data represent 30 402 export credit guaranteed debt contracts initiated between Finnish exporters and foreign buyers between the 1975-2007 period. The study concentrates only on those contracts in force between the 1980-2006 period, since the data from earlier years is incomplete. Overall, the data offers a unique experience-based platform for studying the causal inferences between political risk and debt defaults. This type of credit data has not been previously addressed in similar credit risk or default prediction studies due to the confidentiality surrounding the ECAs.

Figure 3 Guarantee-year observations Figure 4 Default-year observations

14 000 ~ 1200 ~ Private 90 645 Private 1 756 12 000 Public 1000 Public

10 000 800 6 552 8 000 8 414 600 6 000 145 400 4 000 239 715 5 597 75 200 2 000 3 608 371 2 040 206 40 1 002 170 0 0 Low income Lower-middle Upper-middle High income Low income Lower-middle Upper-middle High income

Figure 3 represent the total number of guarantee-year observations and Figure 4 the default-year observations in the Finnvera database, categorised by country income group. The time period covers 1980-2006 with some guarantees initiated also between 1975 and 1980. “Private” and “Public” indicate for guarantees issued to corporations and governments, respectively. ~ denotes a shortened staple.

The data is assembled on a yearly basis. Figure 3 and figure 4 express the data in guarantee-year observations and default-year observations, illustrating the public- private dimension in the data. Guarantee indemnification is used as the “default indicator” which refers to the case when a debtor had missed a payment of interest or principal, violated against a covenant, attempted to restructure, or made any other declaration of insolvency. Any of these actions that have led the exporter (or creditor) to submit a notice of default for the loan in consideration, and that Finnvera had found to be in order and indemnified, are considered as defaults. Compliance with the Finnvera guarantee agreement and the general contractual terms ensured the guarantee holder the receipt of the Finnvera indemnification. The underlying debt is considered in a default state between the exact dates for the first and last indemnification. Overall, the data contains 4 461 (14.6%) cases of default. Evidently, the dataset is dominated by guarantees to private credit counterparts, especially in the upper-middle and high income countries. 18

4. Default probability estimation

This research builds upon the theory and methods developed under the theme of default probability estimation. During the last few decades, an entire theory has been developed within the credit risk literature on the estimation of the three main variables that affect the credit risk of a financial asset; i) the probability of default, ii) the loss given default, and iii) the exposure at default. In particular, the estimation of default probabilities has become more and more popular since credit derivatives, structured products and banking supervision make it necessary to use sound estimates28.

The literature of corporate default prediction begins with the seminal work of Beaver (1966, 1968) and Altman (1968). Beaver (1966) set the stage for multivariate attempts, and found that a number of indicators could discriminate between matched samples of failed and non-failed corporations five years prior to failure. While appealing in their simplicity, the models by Beaver suffered from the inability to account for the coexisting effects of many different indicators of default. Consequently, Altman (1968) used the control group method, whereby a number of defaulting and non-defaulting firms were compared. Today, the multiple discriminant credit scoring analysis (i.e. the Z-Score Model by Altman, 1968) is one of the most commonly used traditional credit risk measurement methodology. The model is a multivariate approach built on the values of both ratio-level and categorical univariate measures. These values are combined and weighted to produce a credit risk score that best discriminates between corporations that default and those that do not. From the original set of 22 variables, the final Z-Score model was the function of five accounting variables, including the working capital to total assets, retained earnings to total assets, earnings before interest and taxes to total assets, market value of equity to book value of total liabilities, and sales to total assets. Different empirical models have been used to predict default risk in spite of the absence of a unifying theory. After the multiple discriminant analysis methods, new analytical techniques emerged, including the logit and probit models as first applied in Martin (1977), Ohlson (1980) and Zmijewski (1984). Other statistical techniques employed in financial distress literature include e.g. recursive partitioning and multi-period logit models, also interpreted as hazard models (Shumway, 2001). Various research questions in finance and other disciplines have lead to the development of survival analysis (also called “time-to-event analysis” or “mortality analysis”). Over the last two decades, the research method of survival analysis was introduced and successfully employed also in the area of predicting financial distress.

Overall, there are two prominent groups of models among the modern methodologies of credit risk measurement. The structural models-group applies the option pricing methodology (see e.g. Merton, 1974); and the reduced-form models use data derived from credit markets to create default probabilities (see e.g. Jarrow and Turnbull, 1995 and Duffie and Singleton, 1999). The structural approach models the economic process of default, whereas reduced form models decompose risky debt prices in order to

28 The various strands of the literature concerning the statistical and stochastic analysis of default probabilities are summarised e.g. in Chakrabarti and Varadachari (2004). 19

estimate the random intensity process underlying default. A central concept under the former method is the distance to default (DD) that represents the number of standard deviations between current asset values and the debt liabilities. To convert the DD into a probability to default estimate, Merton (1974) assumes that asset values are lognormally distributed. Since this distributional assumption is often violated in practice, proprietary structural models use alternative approaches to map the DD into a probability to default estimate. For example, Moody’s KMV method is a popular commercial implementation of the structural credit risk model pioneered by Merton (1974), and estimates an empirical probability to default using historical default experience. The method prescribes an iterative algorithm which infers, from the firm’s equity price time series, the firm’s unobserved total asset value and unknown expected return and volatility, which are the quantities required for computing, for example, credit spread and default probability. The KMV uses a historical database of default rates to determine an empirical estimate of the default probability, denoted Expected Default Frequency (EDF). Because KMV EDF scores are obtained from equity prices, they are more sensitive to changing financial circumstances than external credit ratings that rely predominately on accounting data. Overall, the fundamental assumption behind the structural models is that the market values contain all the relevant information about the factors, which determine the default probability. There is no explicit recognition of the differentiating factors like industry, size and economy. Further, the predictive power of the model hinges directly on the assertion that the current value of the firm provides a good prediction on the future value of the firm (see e.g. Crosbie and Bohn, 2002). The alternative approach to predict default probability is based only on the prices of the firm’s traded liabilities. These methods do not depend on the balance sheet structure of the company and, hence, are commonly referred to as “reduced form” models. These models essentially find the probability of default from the term structure of yield spreads between risk-free and risky corporate securities. They use a stochastic method for both default and recovery, and do not require the same balance sheet information and the same assumptions about frictionless liquidation as the so called “contingent claims analysis”. For further details on the reduced form and structural models, see e.g. an excellent survey by Uhrig-Homburg (2002).

This thesis follows the accounting-based stream of research, usually applied when no publicly traded securities are available (see e.g. Altman, 1991). Selected empirical methods are developed using the survival and multi-level event history analysis methods. These ideas were developed by Shumway (2001) and Chava and Jarrow (2004) who argue that hazard models are superior to other types of models in default probability estimation. Shumway (2001) shows that with a discrete setup, one can reduce a hazard model estimation exercise to multi-period logit model estimation. To do so, one must include a function of time as one of the explanatory variables and rearrange the historical data, recording each time to event (either default or censoring) observation as a series of binary observations. Since logistic regression has become an industry standard for single period default modeling, such a discrete setup offers an easily implemented and interpreted platform for the analysis. 20

5. Summary of the essays

This research explores the links between political risk and international debt default. A central aim of the project is to gain a better understanding on how country-specific political risk factors impact on international credit, and in particular, to what extent these factors may cause obstacles for debt repayments. The research project was initiated in co-operation with Finnvera plc, the official Export Credit Agency (ECA) of Finland, with whom we have compiled an extensive historical database on export credits initiated between Finnish companies (or their financiers) and credit counterparts (the importers) in 145 countries around the world. In this data, detailed information on the debt contracts are documented from their initiation to termination, including any default events, for a time period between 1975 and 2006. Using multi-level event history analysis methods, the default probabilities induced from the observed default events in the guarantee data, are conditioned on country-specific macroeconomic variables, corporate-specific accounting information as well as political risk indicators from established international sources. Political risk indices employed include the subcomponents of the political risk rating by the International Country Risk Guide (PRS Group). This rating measures, among other things, the level of external and internal conflict, corruption, government stability and socioeconomic conditions in a particular country. In addition, regime and authority characteristics in a country are tested with a democracy indicator and a regime durability variable from the Polity-IV database. Further, indices on legal rights and credit information, as obtained from the World Bank’s Doing Business Database, are also included.

Essay 1 and essay 2 form the first quantitative part of the thesis, where statistical tests are carried out with the historical export credit data as well as data on political risk. The essays investigate whether various economic and political risk factors impact on the probability of default concerning debt held by either government controlled entities or corporations, reflecting the public-private dimension of the credit risk problem. Accordingly, the essays treat the involved credit counterparts separately with respect to theory and hypotheses. The overall results from this part of the study indicate that political risk, especially in the form of government stability and external and internal conflict in a debtor country, seem to explain some observed default patterns for both public and private credit counterparts. Finally, the empirical results are substantiated in essay 3 that deals with an extensive questionnaire survey to finance professionals in developing countries, seeking to identify risk factors that – from a practitioner view – influence on debt management and international debt repayments.

Essay 1 examines debt issued to government controlled institutions providing empirical evidence on the linkages between the probability of public debt default, political risk and country specific macroeconomic indicators. The theoretical fundament underlying the analysis revolves around the statistical refinements of the methods used, the advances in the economics of information and strategic interaction, as well as the emerging applications of country- and political risk paradigm. Following previous literature on the nature of credit relations between developed and developing countries, 21

public default probabilities are analysed against traditional measures of country solvency, liquidity and other macroeconomic fundamentals. In addition, 12 different political risk variables and 5 institutional risk factors are included in the estimations. The empirical methodology applied is the dynamic binary logit model, where the dependent variable is the guarantee-year observation from a particular country. Confirming previous research, important economic indicators of debt default appear to be the level of country indebtedness and the GDP growth rate. Further, it is shown that public defaults strongly respond to political risk in the form of government instability, socioeconomic conditions, investment climate and bureaucratic quality. In most of the model specifications, political risk variables show the expected signs and seem both jointly and individually statistically significant. The findings are in accordance with previous studies on political risk that have analysed other types of credit contracts (see e.g. Balkan 1992). A special contribution of the study is the comparison of political risk effects between countries at different stages of economic development. For this purpose, the countries are divided into low income, lower-middle income, upper-middle income, and high income countries according to the World Bank classification system, that are tracked over time. Among the solvency variables, we find that the total debt to GDP ratio exhibits a positive relationship with default probability, in particular for low income countries. This result is in line with numerous previous studies, i.e. the higher the debt burden, the larger the transfer effort a country will need to make over time to service its obligations (see e.g. Ades et. al. 2000). For countries in the higher income categories, several economic variables become less significant while the impact of political stability on default increase (significant conflict measures include government stability, external conflict, ethnic tensions and military in politics). Further, the higher the income group, the higher is the impact of legal rights. On the other hand, socioeconomic conditions, democratic accountability, bureaucratic quality, creditor rights and regime durability are particularly strong indicators of public debt default for the lower income countries. The upper-middle income countries exhibit a negative and significant relation between the level of democracy and the default probability of public debt. Meanwhile, the democratic accountability-variable, measuring how responsive the government is to its people, is a significant default predictor only for the low income and lower middle income countries.

Essay 2 constructs and compares models for corporate debt default with two sets of explanatory variables; the traditional firm specific accounting ratios and country specific political risk indicators. Here, the main research question is how and to what extent political- and legal risk environments affect firm performance and international debt repayments in particular, and whether political risk indices can be used as accurate predictors for corporate default. Various indicators on political risk are tested against default events, using dynamic logit specifications, with a specific focus on a selected group of fourteen target countries. Among these countries, corporate financial statement information are collected. Following previous research, a set of traditional financial ratios are included in the models to control for profitability, leverage, liquidity and solidity, or in other words, to control for corporate specific financial risk. Political risk is proxied with the composite ICRG political risk index, and separately also with the corresponding subcomponents of the ICRG index, level of democracy, and legal- and creditor rights. The results show that in addition to traditional measures on corporate 22

leverage, political risk indicators of a debtor country are significant predictors of corporate default. In a complementary analysis extended to include the Finnvera total private guarantee data, it was found that the general investment climate, external conflict as well as religion in politics are significant political predictors of corporate default. In repeated tests including only the fourteen selected target countries, i.e. countries that represent Finland’s most active, export countries among the developing countries29, the impact of military and religion in politics seem to play a considerable role. The conclusion from this study is that without assessing the political- and legal risk landscape of the debtor country, default probabilities may not be properly estimated using only (sometimes scarcely available) accounting data.

After having examined the contract specific, previously unexplored historical export credit data from the international lenders’ point of view, we finally turn our attention to the borrowers themselves. Essay 3 forms the qualitative part of this thesis and substantiates the obtained quantitative results through a questionnaire survey. A web- based survey on political risk is designed and conducted among finance professionals from developing countries. The survey is targeted in particular, to debt- and financial managers as well as lawyers working with international debt management, contracting and negotiation, at both public and private institutions, as well as at non-governmental organisations. Contact information to potential respondents were identified through Internet searches with the assistance from co-operating international credit institutions and an intergovernmental organisation. The findings from the survey, in which 103 professionals from 38 developing countries participated in, suggest that political risk associated with international debt pose great concerns – not only for international creditors, but also for the debt managers in developing countries. External borrowing experiences and the perceived impact of political risk are analysed jointly in limited dependent variable models, with the objective to find out whether individual-, contract-, and country specific determinants have predictive power in explaining either the extent of debt distress and/or the level of perceived political risk impact on debt distress. The results suggest that economic conditions measured e.g. by the GDP per capita, the level of agriculture in GDP, and the level of democracy are significant determinants of experienced debt distress. Also the respondents’ affiliation and experience do seem to matter for the political risk perception formation. Finally, the survey respondents are asked to do a political risk rating, according to the method developed by the International Country Risk Guide, both concerning their home- as well as one selected neighbouring country. Comparing the local home country ratings with the neighbour country ratings, reveals that the home countries are, in general, felt as more riskier. The same holds if the ratings are compared against the international rating agency ICRG performing and producing the original political risk assessment. In a follow-up study, selected respondents explain this finding by information availability locally, i.e. with a better understanding of the local prevailing economic- and political conditions.

29 The selected target countries include Argentina, Chile, Colombia, Indonesia, Mexico, Peru, Philippines, Poland, Saudi-Arabia, Singapore, Slovakia, South Africa, United Arab Emirates and Venezuela. 23

6. Conclusions

As a result of the rapid expansion of global trade and the increased diversification of investment opportunities in the global economy, evaluating the creditworthiness of countries and corporations has become an everyday fact for the financial community and an increasingly important research area of finance.

Modelling default probabilities for various types of debtors is a challenging task. This study contributes to the literature by the compilation and exploration of a new data set of export credit guaranteed debt contracts. In the analysis of the contracts initiated between Finland and public and private credit counterparts in 145 countries or economic regions over a 20-year period, the dependence of debtor country political risk on public and private debt default, are modeled. The definitional challenges of the political risk concept make it hard to distinguish between the separate influences of economic and political risk. However, to reach conclusions regarding the importance of various risk factors, we estimate two variants of the dynamic binary logit model, focusing separately on the public- and private dimension of the credit counterparts. We rely upon replications of the models with different variable settings and country groups, in order to distinguish the key determinants of the debt default. The results with respect to political risk are placed into perspective by controlling for macroeconomic and corporate specific financial risk, using traditionally employed explanatory variables in similar default prediction models. Previous studies on sovereign default and firm default-probability estimation, have not placed much, if any, emphasis on carrying on the analysis of political risk.

As a whole, the results of this thesis provide supportive evidence of a positive relationship between political risk and the incidence of international debt default. The study provides new insights on the importance of variable selection in country risk analysis, and how political risk is perceived and experienced in the riskier, often lower income countries of the global economy. The results confirm that political stability related factors are important determinants of debt default, in particular the measures on external conflict and government stability. Moreover, the high impact of bribery and corruption, as perceived by local finance professionals in developing countries, is striking. However, this form of political risk is extremely hard to quantify, and using the corruption estimates by the international rating agency in the default prediction models, does not confirm the relationship. Meanwhile, comparing the ratings produced by the international rating agency with those produced independently by the local professionals, shows that either the local professionals overestimate some of the local risk or, that the international rating agency fails to account for the full risk potential.

The estimated models predict that the inclusion of a composite political risk rating benefits the credit evaluation, concerning both public and private credit counterparts. The marginal effects are however small, i.e. in general, for a one unit increase in the political risk variable from its baseline (i.e. a decreasing risk), the probability of debt default is expected to decrease on average by 1-2%, holding all other variables constant. 24

Analysing the experiences and political risk perceptions by developing country finacne professionals suggests that both the experienced debt distress and the perceived political risk impact is a matter of individual experience, but is also underpinned by selected macroeconomic and political stability related country fundamentals. One explanation for this result is that the micro-foundations of political risk have not been identical in different countries over the last two decades. Thus, accounting for societal and governmental actions that can adversely affect lending, must be tailored to the historical and cultural background of the country.

Major events with political risk character from the last decades have affected both investors and lenders to a considerable degree, alerting both practitioners and academics to a renewed interest in the perils of political risk. The central results of this thesis underline the continued existence, importance and role of various political risk factors in the international lending environment. Overall, this research project is intended as a reference work for promoting further research in the areas of trade- and export credit, development finance, and political risk in particular. We desiderate further work on political risk and its relation to the international lending, and encourage new attempts to contrast the results in this study with other creditor countries, i.e. data from other export credit agencies or lending institutions in the world.

This research project was inspired by the question of what the main reasons are for developing countries, or corporations operating in these countries, to not pay back their debt, in particular in the case when resources for debt repayment are available. What is the expected probability of default when the loans and guarantees are issued to these countries? In order to find an answer, the theoretical foundations of sovereign debt and credit risk literature were interrogated, and empirical investigations on the relationship between political risk and debt default were performed. The central contribution of the thesis is the use of a previously unexplored type of historical export credit data as well as the survey results from developing country finance professionals. The results summarised above may have useful implications and insights for international lenders, investors and debtors in developing countries on ways to analyse and confront political risk. Some important findings may have also emerged for policy makers on the need to redefine approaches towards the changing nature of political risk. Structured feed-back from the debtors may also be used as input for further development of legal and regulatory frameworks or perhaps, as background for international debt related policy recommendations. 25

Appendix Overview of the Finnvera data

Table 2 Export credit guarantee statistics by country or economic region Source: Finnvera plc, Helsinki Finland (data retrieved between 5.4.2004 and 23.11.2007) This table lists the number of initiated and indemnified guarantees by country and contract type, as active between 1980-2006. Panel ‘all’ summarises all guarantees, and the subsequent panels ‘public’ and ‘private’ distinguish between public- (i.e. guarantees initiated with a government controlled institution as the credit counterpart) and private- (i.e. guarantees initiated with private or public corporations). Columns denoted ‘def’ list indemnified guarantees, and def.rate is the corresponding average default-rate of the country during the period. WB country income classification is the World Bank’s country income classification, representing countries categorised according to the GNI per capita in US$ (Atlas methodology). The groups include low income, lower middle income, upper middle, high income OECD (“HI-OECD”) and high income non-OECD (HI- other). Bank's fiscal year 2008 is applied with data from 2006. #ǻ is the number of classification changes during 1987-2007.

Export credit guarantees by Finnvera plc WB country income contracts initiated between 1980-2006 classification 1987-2006 All Public Private Country def. def. (or economic region) obs def rate obs def def. rate obs def rate WB FY08 1987 – 07 n n % n n % n n % income group #ǻ

EAST ASIA AND THE PACIFIC

Australia and New Zealand Australia 157 5 3.2 % 2 155 5 3.2 % HI (OECD) 1 New Zealand 32 1 31 HI (OECD) 1 Total 189 5 3 186 5

Eastern Asia Hong Kong 271 8 3.0 % 3 268 8 3.0 % HI (other) 1 China 212 9 4.2 % 182 4 2.2 % 30 5 16.7 % Lower middle 4 Korea, Rep. 120 6 5.0 % 65 55 6 10.9 % HI (OECD) 4 Japan 107 5 4.7 % 2 105 5 4.8 % HI (OECD) 1 Taiwan 46 1 2.2 % 12 34 1 2.9 % n.a. 1 Korea, Dem. Rep. 5 5 100 % 5 5 100 % Low 2 Mongolia 4 2 50.0 % 4 2 50 % Low 2 Total 765 36 273 11 492 25

South-eastern Asia Malaysia 93 3 3.2 % 17 76 3 3.9 % Upper middle 2 Thailand 85 3 3.5 % 21 2 9.5 % 64 1 1.6 % Lower middle 1 Singapore 82 2 2.4 % 10 72 2 2.8 % HI (other) 1 Indonesia 69 21 30.4 % 32 6 18.8 % 37 15 40.5 % Lower middle 4 Philippines 50 2 4.0 % 22 2 9.1 % 28 Lower middle 1 Viet Nam 17 17 Low 1 Myanmar 1 1 Low 1 Total 397 31 120 10 277 21

SOUTH ASIA

South-central Asia India 94 2 2.1 % 35 1 2.9 % 59 1 1.7 % Low 1 Pakistan 34 4 11.8 % 28 2 7.1 % 6 2 33.3 % Low 1 Bangladesh 14 12 2 Low 1 Sri Lanka 12 12 Lower middle 2 Maldives 2 2 Lower middle 2 Nepal 2 2 Low 1 Total 158 6 91 3 67 3 26

MIDDLE EAST AND NORTH AFRICA

Northern Africa Egypt 527 17 3.2 % 493 16 3.2 % 34 1 2.9 % Lower middle 3 Algeria 422 15 3.6 % 414 13 3.1 % 8 2 25.0 % Lower middle 2 Tunisia 300 250 50 Lower middle 1 Morocco 152 1 0.7 % 57 95 1 1.1 % Lower middle 1 Libya 113 16 14.2 % 107 16 15.0 % 6 Upper middle 1 Total 1 514 49 1 321 45 193 4 0

Middle East / Western Asia Iran 516 35 6.8 % 516 35 6.8 % Lower middle 2 Israel 315 4 1.3 % 50 265 4 1.5 % HI (other) 1 Saudi Arabia 188 9 4.8 % 45 4 8.9 % 143 5 3.5 % HI (other) 3 Lebanon 175 76 99 Upper middle 2 Iraq 110 28 25.5 % 110 28 25.5 % Lower middle 2 United Arab Emirates 72 3 4.2 % 32 40 3 7.5 % HI (other) 1 Jordan 45 1 2.2 % 14 1 7.1 % 31 Lower middle 1 Kuwait 41 3 7.3 % 25 1 4.0 % 16 2 12.5 % HI (other) 1 Syria 35 3 8.6 % 35 3 8.6 % Lower middle 1 Bahrain 13 1 7.7 % 7 6 1 16.7 % HI (other) 3 Oman 12 8 4 Upper middle 1 Yemen 11 6 5 Low 3 Qatar 3 3 HI (other) 1 Total 1 536 87 927 72 609 15

SUB-SAHARAN AFRICA

Eastern Africa Kenya 46 5 10.9 % 16 2 12.5 % 30 3 10.0 % Low 1 Zimbabwe 22 2 9.1 % 21 2 9.5 % 1 Low 2 Tanzania 21 7 33.3 % 21 7 33.3 % Low 1 Ethiopia 16 3 18.8 % 16 3 18.8 % Low 1 Mauritius 6 4 2 Upper middle 2 Zambia 5 5 100 % 1 1 100 % 4 4 100.0 % Low 1 Mozambique 3 2 66.7 % 3 2 66.7 % Low 1 Réunion 2 2 0 Madagascar 1 1 Low 1 Somalia 1 1 Low 1 Total 123 24 84 17 39 7

Middle Africa Cameroon 11 4 7 Lower middle 3 Gabon 8 1 7 Upper middle 1 Angola 4 3 75.0 % 4 3 75.0 % Lower middle 3 Central African Republic 3 3 Low 1 Total 26 3 12 3 14

Southern Africa South Africa 66 2 3.0 % 9 57 2 3.5 % Upper middle 6 Swaziland 4 1 3 Lower middle 1 Botswana 1 1 Upper middle 4 Total 71 2 11 60 2 27

Western Africa Nigeria 61 15 24.6 % 6 2 33.3 % 55 13 23.6 % Low 1 Côte d'Ivoire 34 1 2.9 % 16 18 1 5.6 % Low 2 Ghana 14 5 35.7 % 9 5 55.6 % 5 Low 1 Senegal 10 1 10.0 % 7 3 1 33.3 % Low 2 Liberia 4 4 Low 1 Benin 3 1 33.3 % 3 1 33.3 % Low 1 Mauritania 1 1 Low 1 Total 127 23 43 7 84 16

EUROPE AND CENTRAL ASIA

Western Asia Turkey 573 12 2.1 % 259 8 3.1 % 314 4 1.3 % Upper middle 6 Cyprus 61 1 1.6 % 5 56 1 1.8 % HI (other) 2 Kazakhstan 11 10 1 Upper middle 2 Armenia 1 1 Lower middle 3 Azerbaijan 1 1 Lower middle 3 Total 1 656 65 676 45 980 20

Eastern Europe Russia (f. Soviet Union) 550 65 11.8 % 190 41 21.6 % 360 24 6.7 % Upper middle 3 Poland 516 21 4.1 % 48 11 22.9 % 468 10 2.1 % Upper middle 2 Czech Republic29 304 2 0.7 % 127 177 2 1.1 % HI (OECD) 4 East Germany (f. DDR) 204 12 5.9 % 202 12 5.9 % 2 0 Hungary 197 3 1.5 % 22 175 3 1.7 % Upper middle 1 f. Yugoslavia 88 13 14.8 % 77 11 14.3 % 11 2 18.2 % 3 Romania 54 6 11.1 % 47 6 12.8 % 7 Upper middle 3 Slovakia 52 2 3.8 % 10 1 10.0 % 42 1 2.4 % Upper middle 2 Bulgaria 37 3 8.1 % 25 3 12.0 % 12 Upper middle 2 Ukraine 13 5 8 Lower middle 3 Uzbekistan 3 3 Low 2 Belarus 3 1 2 Lower middle 2 Total 2 021 127 757 85 1 264 42

Southern Europe Italy 1 493 81 5.4 % 2 1 491 81 5.4 % HI (OECD) 1 Spain 510 34 6.7 % 3 507 34 6.7 % HI (OECD) 1 Greece 398 19 4.8 % 8 1 12.5 % 390 18 4.6 % HI (OECD) 2 Portugal 381 13 3.4 % 6 375 13 3.5 % HI (OECD) 2 Slovenia 50 1 2.0 % 6 44 1 2.3 % HI (other) 2 Malta 22 22 HI (other) 8 Croatia 12 7 5 Upper middle 2 Andorra 11 1 9.1 % 11 1 9.1 % HI (other) 1 San Marino 4 4 HI (other) 3 Albania 2 2 Lower middle 5 Bosnia-Herzegovina 1 1 100 % 1 1 100 % Lower middle 3 Gibraltar 1 1 2 Total 2 885 150 35 2 2 850 148

29 Includes former Czechoslovakia. 28

Western Europe Germany 3 148 154 4.9 % 67 3 4.5 % 3 081 151 4.9 % HI (OECD) 1 France 1 351 71 5.3 % 6 1 345 71 5.3 % HI (OECD) 1 The Netherlands 1 109 34 3.1 % 89 1 020 34 3.3 % HI (OECD) 1 Austria 626 33 5.3 % 1 625 33 5.3 % HI (OECD) 1 Switzerland 512 13 2.5 % 11 501 13 2.6 % HI (OECD) 1 Belgium 365 17 4.7 % 365 17 4.7 % HI (OECD) 1 Luxembourg 13 13 HI (OECD) 1 Liechtenstein 6 6 HI (other) 1 Monaco 4 1 25.0 % 4 1 25.0 % HI (other) 1 Total 7 134 323 174 3 6 960 320

Northern Europe Sweden 4 195 228 5.4 % 14 4 181 228 5.5 % HI (OECD) 1 Norway 2 191 147 6.7 % 6 3 50.0 % 2 185 144 6.6 % HI (OECD) 1 United Kingdom 1 991 107 5.4 % 5 1 986 107 5.4 % HI (OECD) 1 Denmark 1 244 74 5.9 % 1 1 243 74 6.0 % HI (OECD) 1 Estonia 225 7 3.1 % 9 216 7 3.2 % HI (other) 4 Ireland 161 10 6.2 % 3 158 10 6.3 % HI (OECD) 1 Iceland 145 6 4.1 % 2 143 6 4.2 % HI (OECD) 1 Latvia 75 3 4.0 % 15 2 13.3 % 60 1 1.7 % Upper middle 3 Lithuania 55 2 3.6 % 3 52 2 3.8 % Upper middle 3 Total 10 282 584 58 5 10 224 579

NORTH AMERICA

North America Canada 302 19 6.3 % 6 1 16.7 % 302 19 6.3 % HI (OECD) 1 United States 809 81 10.0 % 803 80 10.0 % HI (OECD) 1 Total 1 111 100 6 1 1 105 99

LATIN AMERICA AND THE CARIBBEAN

South America Argentina 335 36 10.7 % 35 5 14.3 % 300 31 10.3 % Upper middle 3 Brazil 254 10 3.9 % 61 2 3.3 % 193 8 4.1 % Upper middle 5 Colombia 172 10 5.8 % 23 1 4.3 % 149 9 6.0 % Lower middle 1 Chile 171 3 1.8 % 27 144 3 2.1 % Upper middle 2 Peru 113 9 8.0 % 30 4 13.3 % 83 5 6.0 % Lower middle 1 Venezuela 82 11 13.4 % 14 2 14.3 % 68 9 13.2 % Upper middle 3 Uruguay 30 4 26 Upper middle 1 Ecuador 18 4 22.2 % 4 14 4 28.6 % Lower middle 1 Bolivia 2 1 1 Lower middle 1 Guyana 2 2 Lower middle 2 Paraguay 1 1 Lower middle 1 Suriname 1 1 Lower middle 2 Total 1 181 83 201 14 980 69 29

The Caribbean Cuba 23 7 30.4 % 22 7 31.8 % 1 Lower middle 1 Jamaica 11 11 Lower middle 1 Bahamas 7 1 14.3 % 1 6 1 16.7 % HI (other) 1 Barbados 7 7 HI (other) 8 Cayman Islands 5 3 60.0 % 5 3 60.0 % HI (other) 1 Puerto Rico 5 5 HI (other) 4 Netherlands Antilles 4 1 25.0 % 2 2 1 50.0 % HI (other) 2 Trinidad and Tobago 4 2 2 HI (other) 2 Dominican Republic 3 2 1 Lower middle 1 Guadeloupe 3 3 N/A Martinique 3 3 N/A Virgin Islands (UK) 2 2 HI (other) 1 Grenada 1 1 100 % 1 1 100 % Upper middle 2 Saint Lucia 1 1 Upper middle 2 Total 79 13 42 8 37 5

Central America Mexico 138 12 8.7 % 24 114 12 10.5 % Upper middle 2 Costa Rica 6 1 5 Upper middle 2 Guatemala 2 1 50.0 % 2 1 50.0 % Lower middle 1 Honduras 1 1 Lower middle 3 Nicaragua 2 1 1 Lower middle 3 Panama 7 1 6 Upper middle 3 Total 156 13 28 0 128 13

TOTAL 30 402 1 672 5.5 % 4 461 294 6.6 % 25 941 1 378 5.3 % 30

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POLITICAL RISK AND EXPORT CREDIT DEBT DEFAULT

ABSTRACT

This study explores the political determinants of export credit originated public debt default. It addresses the credit risk problem in a setting where sovereign credit counterparts are concerned by studying default events of public, i.e. government controlled borrowers in 125 countries between 1980 and 2006. The financial instruments of core interest are export credits that carry guarantees issued by the official export credit agency of Finland. The study finds support for the general hypothesis that political risk affects the default probability of public borrowers, also when macroeconomic- and institutional risk variables are controlled for. By employing a dynamic logit model, it is shown that information on a country's external debt position, the level of its creditor rights, as well as other stability measures of its political system are significant predictors for public debt default. The impact of the risk differs for countries at different stages of economic development. 38

1. INTRODUCTION

The assessment of country risk in the context of cross-border lending and investment is an essential activity for many international agencies, government bodies, financial institutions, multinational corporations and other investors. Accordingly, a body of literature addresses the risk that a country will not be able - or willing - to honour its financial commitments, bringing together a variety of disciplines and inter-related research questions. The literature spans from financial crisis theories and credit risk modeling to financial markets studies that deal with the impact of news on asset prices in the efficient markets hypothesis framework. Also sophisticated risk assessment and analytical techniques have been developed, including structured methodologies for assessing the strength or weakness of a country's financial system and techniques to factor in risks linked to the role of the IMF and other multinational agencies. Both in theory and in practice, country risk is often identified with sovereign risk, that is associated with the risk that a country cannot generate the earnings to keep up with debt service payments and/or that it does not have enough foreign exchange on hand to transmit earnings to foreign creditors (see e.g. Schroeder, 2008). Variables that are usually considered in country risk assessment can be categorised as indicators measuring 1) the economic and financial state of a country, 2) the government’s management of the economy, and 3) political risk. The former two groups refer to a country’s ability to pay while the latter group is often associated with a country’s willingness to pay. This paper discusses the links and empirically explores the relationship between political risk and public debt default. It focuses on export credit guaranteed debt where the credit counterpart is a sovereign, i.e. a government or a government controlled institution.

Political risk is a specific form of country risk that has emerged as an inherent impediment in cross-border transactions, in particular what concerns international trade. Traditional problems relating to political risk include government instability, currency non-convertibility, nationalisation and expropriation that have hindered efforts to retrieve outstanding payments. However, the concept of political risk has taken on a changing form over the past twenty years and a variety of risk forms have emerged. Examples include instability in political and societal systems, governance problems, ethnic or religious strifes, wars, changing power structures in international relations, or corrupt and ineffectual governments31. Meanwhile, the demarcation of the underlying causes of debt payment interruptions is challenging as the distinction between political and economic (or commercial) risk is not clear-cut. To what extent political risk impact on international debt service is therefore an intriguing research question, especially concerning developing countries where political tensions are more common. International lenders manage political risks by the institutional arrangements of the states or by the trade and investment insurance offered by the private sector. Specific instruments of state activity include export credit guarantees and bilateral and multilateral trade and investment agreements. The first contribution of this paper is to approach the research question by studying export credit financing arrangements, not previously analysed in similar empirical credit risk contexts.

31 Corrupt and ineffectual governments, or perhaps genuine anarchy still prevail in today’s world (see e.g. Minor, 2003) 39

The literature on sovereign debt uses game theory and optimisation techniques in specifying the incentives of countries to borrow and service their debt as well as the incentives of the investors (lenders) to provide the capital32. The economics of sovereign default is reviewed in Hatchondo et al. (2007) who describe the costs associated with a sovereign default episode to be imposed either by creditors (see e.g. Wright (2002); Gelos et al. (2006); and Arellano (2008)) or those implied by the information revealed by the default decision (see e.g. Sandleris 2006). Circumstances that are likely to lead to default episodes include periods when resources available to the sovereign are low; when borrowing costs are high; or, when there is a change in the political environment (see e.g. Tomz and Wright, 2007). Meanwhile, the empirical literature on sovereign borrowing centers mainly on explaining the determinants of secondary market spreads33. For example, Boehmer and Megginson (1990) distinguish between a country’s ability and its willingness to service its debt in an empirical specification. They proxied “ability to pay” with liquidity and solvency measures while “willingness to pay” was measured as the level of payment arrears and regulations on a debt conversion program34. Another example is Ferrucci (2003), who performs an empirical study on the determinants of emerging market economies’ sovereign bond spreads. He concludes that markets do take into account macro fundamentals when pricing sovereign risk but non-fundamental factors also play an important role. He asserts that the divergence between market determined spreads and model-based benchmarks might be due to the exclusion of political risk. Another group of studies relating to sovereign debt and political risk focuses on country ratings and the performance of major credit rating agencies (see e.g. Hoti and McAleer 2004). The purpose of the rating agencies is to reduce the informative asymmetry, concerning the creditworthiness of companies or countries. However, it has been shown that ratings tend to be sticky, lagging markets, and overreact when they do change. This overreaction may have aggravated financial crises in the recent past, contributing to financial instability and cross-country contagion (see e.g. Elkhoury, 2008).

In sum, the previous literature on sovereign debt indicates that sovereign default rarely is a solely economic matter, and fixing problems in the functioning of international institutions does not address, for instance, why countries under a common external shock react differently. This study adds to one of the least studied perspectives of country credit evaluation, by focusing directly on political risk as a conceivable cause of debt default. By using a combination of economic- and political risk ratings by leading country analysis firms, the study provides new perspectives on the linkages between public debt default and political risk.

32 A general constraint in the theoretical modeling of sovereign borrowing is that the debtor pays only as much as is in its enlightened self interest to pay recognising the consequences of default. The observation that willingness to pay restricts international capital flows, as articulated by Wallich (1943), was incorporated in formal models by Eaton and Gersovitz (1981). 33 Following Edwards (1984, 1986) who empirically studied the pricing of public and publicly guaranteed loans and bonds, the literature has been amended by several studies on the determinants of primary bond spreads (see e.g. Eichengreen and Mody (1998), Kamin and von Kleist (1999) and Min et al. (2003)). 34 The authors found that solvency indicators proved to be significant determinants of secondary debt prices, while liquidity indicators were not. Also regulations on debt conversion programs and the existence of arrears proved to be significant. 40

The central purpose of this paper is to identify key determinants of public, export credit guaranteed debt default. In the empirical analysis, the financial instruments of core interest are export credit guaranteed debt, comprising of credit contracts initiated between Finnish exporters (and their financiers) and public buyers (i.e. government controlled institutions) in a total of 126 countries. These credit contracts have been issued and guaranteed by Finnvera plc, the official export credit agency (ECA) of Finland between the time period 1975–2006. It is understood that this is the first study to analyse the risk of export credit guaranteed debt with a dataset of similar quality35. The data for the explanatory variables are obtained mainly from the Economist Intelligence Unit (EIU) and the International Country Risk Guide (ICRG). In addition, regime and authority characteristics are employed, using the Polity IV dataset36. Finally, the study controls also for other factors, such as the legal origin-, creditor rights-, and any recent election in the host country of the debtor. Treating economic-, political- and institutional risk separately in default probability estimation is expected to enhance the credit evaluation. The results of this study are salient for credit risk assessment especially concerning countries in their early stages of development, where political risk is generally considered as high.

The remainder of this paper is structured as follows. First, a detailed analysis of the theoretical and conceptual issues in sovereign debt- and political risk research is given. Second, the export credit framework is presented with a discussion on the promises and challenges this form of financing may provide in understanding political risk and in predicting debt default. The third section discusses the theoretical and methodological implications and presents the data set and the variables use in the empirical analysis. The fourth section presents and comments on the results and the final section summarises and presents the findings and conclusions of the study. Data definitions and the data sources are given in the appendix.

35 For detailed country statistics on this data, please refer to Table 1 in the Appendix. 36 The Polity IV dataset, housed at the Center for International Development and Conflict Management at the University of Maryland, contains coded annual information on regime and authority characteristics for most independent states in the global system. 41

2. THEORETICAL AND CONCEPTUAL FRAMEWORK

In formulating predictions about the economic, political and legal determinants of public default probabilities the study focuses on the decision of a public entity to default on its foreign debt. The analysis is divided into sections covering the economic-, legal- and political framework that impact on such decisions.

2.1. Sovereign debt default

Studies on sovereign default often commence with a listing of the theoretical underpinnings that provide the rationale for the overall existence of sovereign debt. Usually, also the analogy between corporate and sovereign debt is introduced in order to highlight the difference between government and private sector debt. In order to proceed with a discussion of the determinants or the set of states, that are likely to trigger public debt default, the underlying literature dealing with the public costs of borrowing is reviewed.

2.1.1. The costs of sovereign default37

Identifying the set of states that are likely to trigger a sovereign default is closely related with identifying how the costs of defaulting depend on these states. Overall, for sovereign debt to exist, it is necessary that at least in some circumstances it would be more costly for a sovereign to default than to pay back its debt. The different costs associated with sovereign debt, can be roughly divided into costs imposed by creditors and those implied by the information revealed by the default decision.

One of the pioneering works on sovereign debt is the reputational argument for debt repayment, first formalised by Eaton and Gersovitz (1981). They argue that a country has an incentive to make repayments in order to preserve its future access to international credit markets38. This type of financial exclusion threat might be strong enough to enforce repayment. Bulow and Rogoff (1989a) soon presented their critique to this framework, asserting that a pure reputation mechanism cannot support positive international debt, if defaulting countries can save at the same interest rate. If the government can invest existing borrowed funds in international markets, this cushion could be used to support current consumption should the sovereign be cut off from international borrowing following a voluntary default. Following this remark, the literature soon segmented along three broad paths. The first considered direct sanctions for repayment enforcement where creditors may litigate in foreign courts or interfere with a country’s current transactions, i.e. trade and payments. An example of this would

37 This section lends from the recent article by Hatchondo et al. (2007), who discuss thoroughly the economics of sovereign defaults and summarise the lessons from existing work on this issue. 38 Several authors have used the framework proposed by Eaton and Gersovitz (1981) to account for the business cycle regularities of emerging economies. For example, Wright (2002) and Amador (2003) analyse ways to reduce the range of a defaulting country’s saving mechanism so that reputation model can generate positive international lending. Comprehensive reviews of this earlier literature are found in Eaton and Fernandez (1995) and Obstfeld and Rogoff (1996). 42

be where trade sanctions are applied or trade credit denied. This group of studies model the credit negotiations explicitly (see e.g. Bulow and Rogoff (1989b) and Fernandez and Rosenthal (1990)). Further, some critiques have argued that increasing a defaulting sovereign’s borrowing costs requires good coordination among the holders of defaulted debt and all other potential lenders, which is questionable in competitive credit markets with large number of potential lenders.

Another group of studies presented the case in which default is costly because of the information it provides to the market.39 For example, default could signal some bad circumstances, poor economic conditions or provide confidential government information to market participants. The signaling costs are reflected as an increased perceived probability of a future default (and an increased cost of future borrowing) in contrast to a punishment imposed by other creditors. Default signals may also have other consequences, such as the borrowing government being considered untrustworthy in areas other than the credit relationship (Cole and Kehoe, 1998). Government default may affect private firms’ ability to borrow, thereby lowering the desired level of investment (Sandleris, 2006); drive large declines in foreign credit to domestic private firms (Arteta and Hale, 2006); create domestic financial crises (Kumhof and Tanner, 2005) and lead to banking problems (Kaminsky and Reinhart, 1999). In addition, the signals implied by a government’s default decision may have political consequences. They may demonstrate the politically motivated factors being the drivers of the issuance of government debt in the first place. For example, Alesina et al. (2008) show that a political agency can lead to excessive debt accumulation when voters are uninformed. It is easier for the authorities to enhance their own wealth during prosperous times, as enough resources are present to provide a sufficient supply of public goods to the citizens. Alesina et al (2008) present the argument that fiscal policies are more likely to be pro-cyclical in countries with higher corruption and weaker institutions and provide also empirical evidence to support this view.

The strategic debt argument or the strategic use of budget deficits is presented in Persson and Svensson (1989) with a model where a conservative and a liberal party have different preferences over the amount of public goods to provide in each period. The idea is that the liberal party receives more utility from the public good than the conservative one. A conservative government that anticipates the possibility of its defeat in the next election runs a budget deficit to increase public debt and reduce future public spending. The larger the debt, the larger the fraction of the budget spent on interest payments. On the other hand, a liberal government that anticipates the possibility of its defeat at the next election runs a budget surplus so as to reduce public debt and raise future public spending. In this model, conservative governments run budget deficits and liberal governments run budget surpluses if they anticipate to be voted out at the next election. Respecting contracts with a political motive is studied also in Amador (2003). In his model, governments undersave because they know that they may lose power, but at the same time wish to retain access to capital markets since they count on being returned to power eventually.

39 Sandleris (2006) provides a good review on the issue of asymmetric information in sovereign lending. 43

2.1.2. Incentives to repay

The classic theory of sovereign debt may be broadly summarised with the different incentives to repay the sovereign debt, including: 1) credit market incentives, 2) avoiding reductions in trade and legal harassment, and 3) broader reputation concerns. Concerning the latter, the domestic politics of international debt repayment may in fact be influenced by reputation benefits that compliance brings. A government that repays on time will preserve its image in the eyes of international lenders, increasing the likelihood of future loans and other foreign investment (Kletzer and Wright, 2000).

Tomz (2007) addresses the puzzle why governments repay their debts to private foreign lenders, and what gives the bondholders and banks the confidence to lend billions of dollars abroad each year. To assist in the analysis Tomz (2007) develops a theory of reputation. According to this theory39, “the preferences of governments are heterogeneous: some governments assign greater value than others to maintaining good relations with creditors, and their preferences can change over time. Investors cannot fully know the preferences of a foreign government, but they do have beliefs about the government’s “type.” Those beliefs, which constitute the government’s reputation, evolve as investors observe behaviour in context—as they review the government’s record of repayment during good times and bad. Borrowers understand this reputational logic and take it into account when deciding whether to default”. Tomz (2007) tests his theory finds strong support for it while challenging prevailing views about sovereign debt. His study shows that, across centuries, reputations have guided lending and repayment in consistent ways. Tomz and Wright (2008) further extend the analysis to foreign direct investment (FDI) and lending to study how behaviour in one area of international relations, such as expropriation of FDI spills over to affect reputations in other areas, such as sovereign debt. The authors assess more general models of investor-government cooperation, in which investors choose among different types of assets and governments decide which contracts to respect. The authors show that the relationship between output and default is conditional on the degree of slack in international capital markets and on political conditions in the borrowing country. Thus this significant piece of work do not only speak to the theoretical literature about sovereign debt, but also have policy implications for forecasting defaults and designing appropriate institutions to address them.

The reputation argument differs from the two other incentives that contend that compliance is necessary to avoid direct sanctions such as lawsuits, trade embargoes, or diplomatic and military pressure. All these incentives in domestic politics lead also to another prediction; if the repayment decision is based on the rewards for repaying the debt (e.g. continued access to foreign capital) creditor countries that provide most of the funding should be among the first to see the repayments being made. Depending on official creditor seniority, the same creditor might be treated differently depending on the structure of the country's debt and the relative power of other creditors to impose costs on governments.

39 The explanation of the theory lends from Michael Tomz “Research and Teaching Statement” (2007). See e.g. (www.stanford.edu/~tomz/TomzResearchandTeachingStatement-2007-12a.pdf ) 44

2.1.3. Previous empirical findings

In support of the above presented theories and to explore additional explanations for sovereign default behaviour, the previous empirical literature in sovereign default probability estimation is revisited. In this review, we focus on studies that have tried to model also a political dimension.

Following the seminal work by Avramovic (1964) who undertook a systematic study of factors influencing a country’s ability to service its foreign debt an extensive empirical literature on the determinants of sovereign defaults has developed. These studies have usually involved some form of dichotomous dependent variable techniques to predict the likelihood of a country defaulting on a given loan. The early studies focused on the probability of rescheduling, and the optimal level of lending in terms of credit ceilings, using external debt ratios and other macroeconomic data as explanatory variables as essential elements in the different models. Meanwhile, the variable to be explained depended on the default definition as well as on the statistical procedure employed. Examples among the earlier studies are Frank and Cline (1971) and Citron and Nickelsburg (1987). Frank and Cline (1971) used discriminant analysis to test eight external debt ratios, associated with debt servicing difficulties, on a binary value dependent variable that consisted of rescheduling and non-rescheduling cases from 26 debtor nations from 1960 to 1968. Their experiments show that the debt service ratio and the average maturity of debt are the best predictors of debt servicing capacity. Citron and Nickelsburg (1987) were among the first to consider political instability, which they proxied by the number of changes of government that occurred over a five- year period. Using a logit model they determined that their political stability index was a statistically significant determinant of the probability that a borrower defaults. Among the later studies Kutty (1990), Oral et al (1992) and Balkan (1992) have used logit regression techniques. Oral et al. (1992) are among the few that control for country heterogeneity and allow for fixed country effects in their model. Balkan (1992) test the impact of two political risk proxies on rescheduling through a probit model, controlling for the classical economic variables. The sample consists of 33 countries over 1970-84. The political variables considered are the level of democracy and political instability. The findings suggest significant, opposite effects of democracy and political instability (negative and positive respectively) on the likelihood of rescheduling. Balkan (1992) further supports the inclusion of the two quantified proxies of political risk for improving the in-sample forecasting performance of the model.

Cantor and Packer (1995, 1996) studied the effects of rating announcements by S&P and Moody’s on ‘relative spreads,’ i.e., the differential between yields on sovereign dollar-denominated eurobonds and on comparable U.S. treasury, using data from 1987 until 1994. The conclusions of these studies were that an announcement of a change in sovereign risk assessment appears to be preceded by a similar change in the markets’ assessment. Cantor and Packer (1996) find that sovereign credit ratings strongly respond to macroeconomic factors, such as the GDP growth rate and per capita income, and assert that defaults may be triggered by low liquidity or solvency positions. In addition, when a relatively large fraction of the sovereign’s debt is denominated in foreign currency and when the country revenues rely heavily on the taxation of non-tradable goods a devaluation of the local currency may cause further debt defaults. 45

Overall, various attempts have modeled the factors that drive the subjective or perceived risk assessment. In such studies, the dependent variable is usually a measure of risk as perceived by the lending community, such as the spread over London Interbank Offered Rate (LIBOR) (see e.g. Feder and Just 1977, Edwards 1984 and 1986, Eichengreen and Mody 1998) or some risk rating composed from a survey of lender opinions (e.g. Feder and Uy (1985), Brewer and Rivoli (1990) and Cosset and Roy (1991)). Many of these papers rely on econometric modeling (without an explicit model) aiming to evaluate each variable’s net effect over credit risk. Explanatory variables in these studies include any measurable influences on the lender’s perception of borrower risk. For example, Feder and Just (1977) used logit analysis on a sample of 30 countries for the period 1965 to 1972. They found six variables: per capita income; capital inflows/debt service payments ratio; real export growth rate; imports/reserves ratio; debt service ratio and, amortization/debt ratio to be statistically significant in explaining the probability of default. Results by Brewer and Rivoli (1990) and Cosset and Roy (1991) indicate that, although political instability are taken into account in evaluating a country’s creditworthiness, it appears that these perceptions may perhaps, be largely reflected in a country’s economic performance, which is expected to indicate longer term political stability. Accordingly, there is no general agreement on whether political factors empirically a have significant impact on sovereign credit risk. On the other hand, Haque et al. (1996) mention that omitting political variables, when studying the determinants of sovereign credit ratings, can induce bias in the parameter estimates for the economic variables.

Esty and Megginson (2003) investigate the syndicate structure of project finance loans, and support that political risk is reflected in the spread of the loan. In countries with high political risk, i.e. weak creditor rights and poor legal enforcement, they find that syndicates are particularly large and diffuse in order to deter strategic default. In contrast, in countries with strong and enforceable legal rights, syndicates are structured to ensure monitoring and low-cost re-contracting. This holds especially for commercial banks. Esty and Megginson (2003) find thus support for their deterrence hypothesis. This finding would imply that commercial banks too could influence the host government and thus reduce political risk. In a related study, Hainz and Kleimeier (2003) identify three broad categories of “political risk”. The first category includes the risks of expropriation, currency convertibility and transferability, and political violence, including war, sabotage or terrorism. The second category covers risks of unanticipated changes in regulations or failure by the government to implement tariff adjustments because of political considerations. The third category includes quasi-commercial risks arising when the project is facing state-owned suppliers or customers, whose ability or willingness to fulfil their contractual obligations towards the project is questionable. Finally, studies on legal institutions and legal rights include mainly investigations of the determinants of transaction costs and the level of investor participation, i.e. the participation in stock markets41. Overall, the vast empirical evidence on sovereign default indicates for a central finding that a country tends to be more likely to default in periods of low available resources42.

41 For example, La Porta, Lopez-De-Silanes, Shleifer and Vishny (1997, 1998). 42For further discussion and good summaries of previous studies analysing sovereign defaults, see e.g. Aylward and Thorne (1998) and Peter (2000). 46

2.2. Hypothesis development

Assessing the risk of sovereign debt usually starts with determining the current status of a country’s economic health. Previous research demonstrated that there are mainly three types of economic variables that have predictive power for actual and perceived creditworthiness of a country: performance in international trade and measures of leverage and liquidity. This study hypothesises on 1) economic risk, measured by variables representing country solvency, liquidity and debt service; 2) selected measures on overall economic development as well as 3) political risk factors, as potential determinants of public debt default. The explanatory variables employed in the analysis are presented in Tables 2-4 in appendix 1, which also provide a detailed list of variable definitions, their expected signs and a description of the data sources used.

2.2.1. Solvency

Solvency variables relate to a country’s long-term ability to pay its debt. Trade performance is one form of solvency which may be measured by the current account balance. This study expects a negative relationship between trade performance and the default probabilities of public borrowers. A strong performance in trade suggests that a country is better able to generate the hard currency necessary to maintain debt servicing while a large negative trade balance means that, either more borrowing or the sale of assets will be required for debt servicing to be maintained.

H1: A good solvency position reduces the dependence on foreign savings, slows down the increase in the foreign (net) debt and thus, reduces the likelihood of a debt default.

Accordingly, the larger the 1) current account deficit relative to GDP, the higher is the probability of experiencing debt-servicing difficulties (see e.g. Berg and Sachs, 1988). Other variables under the category of solvency include, 2) debt-to-GDP ratio; 3) arrears- to-total foreign debt; 4) exports-to-debt; 5) debt-to-exports ratio; and 6) the budget balance. The debt burden figures measure the transfer effort a country will need to make over time to service its obligations. Overall, a lower the ratio of country indebtedness, the better is the economy's financial position.

H2: An increasing debt stock compared to the resource base increases the likelihood that the debt is unsustainable and, hence, of probability of default.

Thus, a higher debt burden is expected to relate to a higher risk of default. Using the debt to exports is motivated by the importance of exports to foreign exchange and consequently a smaller vulnerability to external shocks when it comes to servicing the debt. Also the build-up of arrears has destroyed many countries credibility as a debtor. Thus,

H3: The higher the stock of already accumulated arrears on interest payments and principal repayment, the higher is the risk of further defaults. 47

2.2.2. Liquidity and debt service

In mapping the country’s financial risk and the composition of its debt, various indicators on international reserves and debt service are employed. External debt profile measures are used as liquidity variables in the attempt of measuring a country’s financial risk, i.e. the ability of the country’s national economy to generate enough foreign exchange to meet payments of interest and principal on its foreign debt. In previous research (see e.g. Hawkins and Turner, 2000) such variables have usually included exchange rate stability, levels of foreign debt, foreign debt service as a percentage of exports, and net liquidity measured in terms of months of import cover.

High liquidity, in general, suggests that international reserve assets are available in the event of short-term difficulties. In such case, foreign-currency debt has to be paid out of the international reserves. Therefore in combination with the debt service-ratios, the international reserves ratios (reserves-to-total debt and reserves-to-imports) are among the most crucial variables in measuring liquidity. A negative relationship is expected between the import cover ratio and the probability of default as this ratio measures the country's ability to maintain import levels with current cash in hand.

H4: The higher the international reserves ratio and longer the import cover period, (measured as international reserves to imports of goods and non-factor services), the lower is the default probability.

International reserves are expected to signal a better borrower standard. However, as the Lucas Critique suggests, this question cannot be answered without understanding the underlying factors that determine a country’s choice of international reserve holdings. For example, Aizenman and Marion (2004) emphasise for a greater attention to the role of political-economy factors in explaining the demand for reserves and the functioning of buffer stocks. They claim that international borrowing and international reserve accumulation are the simultaneous outcome of optimising decisions.

A country’s debt service position and the composition of ifs overall debt, are assessed with 1) debt-service-to-GDP, 2) debt-service-to-exports, 3) debt-service-to-reserves, 4) short-term-debt-to-reserves, and 5) average time to maturity of debt.

H5: A high debt-service burden indicates that a country may face large amortizations that could be difficult to roll over, which increases the default probability.

The debt service is contrasted against the country GDP, exports as well as reserves. Exports are a major source of foreign exchange, and countries with large exports are normally less vulnerable to external shocks when it comes to servicing their debt. Also, since the foreign debt has to be serviced out of international reserves, the debt-service- to-reserves ratio is another important measure of a country’s debt-service capability. Finally, the debt service is, dependent on the composition of the debt. A large fraction of short-term debt will increase the current debt service when this debt matures and a short time to maturity implies a large proportion of short-term debt. 48

2.2.3. Level of economic development

The analysis continues by using inflation (the GDP deflator and change in consumer prices), the GDP growth rate and the GDP per head as measures of a country’s overall economic development. Following the results e.g. by Min (1998) and Cantor and Packer (1996), it is hypothesised that;

H6: A high rate of inflation is indicative of structural problems in the government’s finances. Used as a measure of government discipline, high inflation is expected to lead to a higher default rate. Further, public dissatisfaction with a high inflation rate may generate political instability.

H7: A high economic growth rate normally generates a stronger fiscal position that suggests that the country’s debt burden will become easier to service over time.

H8: The GDP per capita has a negative relationship with the probability of default

As a measure of the relative size of a country's economy it has been argued by many authors that GDP per capita should have a negative relationship with the probability of rescheduling external debt (see e.g. Feder and Just, 1977).

2.2.4. Determinants of political stability

Political risk is expected to play a crucial role in the event of debt default43. It could be assumed that the political environment in a country affects its credit risk for many different reasons. First of all, a government's ability to service external debt depends upon its ability to extract the necessary resources from its citizens (see e.g. Sharpio, 1981). There are several factors affecting this ability, all usually grouped under the generic term 'governmental stability'. It is necessary to distinguish between different forms of stability, as these will have various effects on actual and perceived creditworthiness (see e.g. Brewer and Rivoli, 1990).

Political instability is often cited as one factor leading to debt defaults. Accordingly, three political instability factors are considered, including: 1) political legitimacy, 2) governmental regime change; and, 3) external or internal conflict. Political stability is defined in terms of the "legitimacy" of the government or political system more generally. Legitimacy is based upon the degree to which a country's political system is democratic versus authoritarian, and on the extent to which its citizens therefore enjoy various political rights. In this study, political risk is analysed following the components of the International Country Risk Guide’s political risk index44.

H9a: Government stability: a government's improved ability to carry out its declared program(s) and to stay in office is associated with a lower default probability.

Sociopolitical risk is both conceptually distinct and empirically independent from the government policy risk. Accordingly:

43 A more formal treatment of political risk theory, is provided e.g. in Ciarrapico (1992) 44 For a description of the index, see e.g. Bouchet, Clark and Groslambert, 2003 49

H9b: Socio-economic conditions: the collective actions from organisations such as trade unions, non-governmental organisations, or more informal sets of people that, peacefully or not, democratically or not, lobby the local authorities and/or directly the foreign firms, in order to influence their policy and/or their actions are expected to increase the default probability.

Also other ICRG subcomponents are examined, including internal and external conflict, the military in politics, religion in politics, ethnic tensions, law and order, ethnic tensions, bureaucratic quality and the level of corruption. Intuitively, like the other political instability factors they are all expected to increase the likelihood of default.

H9c: All conflict related political risk measures are expected to impact positively on the propensity of debt default.

Corruption, in particular, is a threat to foreign lending for several reasons. It distorts the local economic and financial environment reduces the efficiency of government and business by enabling people to assume positions of power through patronage rather than ability and, it introduces an inherent instability into the political process. Alwarez-Plata and Brück (2008) show that many war-affected countries face rising debt arrears and deteriorating relations with creditors. From a historical perspective, the post-conflict environment poses special challenges and experiences unique circumstances concerning debt, arrears and aid as well as trust and institutional relations.

2.2.5. Institutional factors

The study explores certain regime characteristics and institutional factors that are expected to impact on the public default probability. Van Rijckhegem and Weder (2004) use a non-parametric model containing both macroeconomic and political variables to study debt default. Their analysis concludes that political conditions play a major role in debt default noting different default probabilities for democratic and non- democratic regimes. They suggest that the conditions leading to default differ in democratic and non-democratic countries. Whereas in non-democratic regimes, no political variable seems to insure safety from default; in democracies, the existence of a parliamentary system, the length of tenure and the number of veto players seem crucial in preventing default on foreign debts. Following the "democratic advantage hypothesis”, which states that democratic countries have a greater ability to make credible commitments to repay their debts (see e.g. Schultz and Weingast, 1996), it is expected that the democratic countries have a lower probability of defaulting on debt.

H10a: Non-democratic regimes default more frequently on their foreign debts than the democratic ones.

This hypothesis relates to a recent political economy model of sovereign debt, developed and empirically tested by Alichi (2008). He follows the reasoning that the main incentive for repaying sovereign debt is to maintain access to international capital markets. However, in a democracy, one generation may choose to default regardless of its consequences for future generations. An old generation with little concern for its country’s access to capital markets can force a default on debt if it has the majority of voters. On the other hand, if the younger generation is more numerous, it can force 50

repayment of previously defaulted debt. He further notes that other voter heterogeneities, such as in income, can generate similar results.

Further, the frequency of regime change, or alternatively “regime longevity” is considered as an indicator of government policy risk. Any changes in the policy of local authorities may result in unanticipated detrimental actions, such as expropriation, nationalisation, breach of contract, loan repudiation, foreign exchange controls or trade restrictions.

H10b: A shorter regime durability and periods of election are expected to increase the default probability.

Durability of regime is an (inverse) indicator of country stability and as a result a negative relationship with default probabilities can be expected.45 However, when changes in government are regular there can be less doubt about the instability. As a complement to the durability variable this study also tested whether periods of election are associated with higher default probabilities.

Finally, country legal rights and the credit information availability are investigated as probability factors in deterring debtor default. The execution of debt default can take several years, which is not necessarily related to enforcement issues per se, but rather to the legal framework itself. The laws in a country may be outmoded and not suited to modern business practices. Thus, debtors in countries with high political risk, realised in weak creditor rights or poor legal enforcement, are expected to show higher default probability. The strength of the legal framework is captured by measuring the extent of legal rights and the depth of credit information.

H10c: The greater the level of legal rights in a country the lower the probability of default

H10d: The more credit information available from either a public registry or a private bureau to facilitate lending decisions the lower is the probability of default.

The process of deriving the above hypotheses illustrated that rather few papers have previously analysed the links between debt defaults and political risk, even though intuition may have suggested that politics, government instability or institutional factors play an important role in the process of debt default.

45 This argument may be reflected with the ”opportunistic” Political Business Cycle theory (see e.g. Block, Schrage and Vaaler, 2003), in which opportunistic politicians use expansionary fiscal, monetary and related policies during elections to boost their chances of retaining office. We expect PBC’s to have an impact on the default probability coming from shifts in the economic policy over short horizon. 51

2.3. The export credit framework

Officially supported export credits are government-supported financing assistance to countries that import products from the country providing the support. Export credit is defined as a financing arrangement that allows a foreign buyer of exported goods and/or services to defer payment over a period of time. It refers to credit extended by exporters to importers (i.e. supplier credit) or medium to long term loans made by banks used to finance projects and capital goods exports (i.e. buyer credit). It includes credit extended both during the period before goods are shipped or projects completed and the period after the delivery or acceptance of the goods or completion of the project (Malcolm, 1999). The contracts are generally divided into short-term (usually two years or less), medium term (usually two to five years) and long-term (usually more than five years).

Export credit finance is commonly found in transactions or projects in developing regions and emerging economies where political or other risks are considered to be high. In project finance contexts this is particularly important as the involvement of multilateral organisations and export credit agencies are primarily needed to mitigate political risks that private sector investors cannot control (see e.g. Griffith-Jones and De Lima, 2004). Their task is to cover political risks so as to remove the ‘sovereign rating ceiling’, thereby enabling projects to obtain ratings reflecting their true commercial risk and to provide access to a larger pool of funds. Among developing countries, export credit financing represents some 20 percent of total debt and 50 percent of total debt to the official sector. As export credit agencies play a major role in international financing, trade and investment flows46, these products constitute a well-grounded research material for assessing the relationship between the risk of financial assets and the broader macroeconomic and socio-political environment. Also in view of the recent changes in commercial lending47, it is desirable to study the risk and risk drivers of export credits in a general credit risk framework.

As this study is concerned with default probability estimation, it is important to define what is meant by the ‘default’. The default criterion used in this study is the observed contract specific insolvency, validated by the indemnification by the export credit agency. The indemnification procedure to be followed for receivables covered by a state export credit guarantee is basically divided into three phases. The phase of a potential claim starts when receivables are in danger of becoming overdue and requires that the risk-increasing circumstances are reported by the exporter/creditor. Indemnification then implies that the underlying credit has defaulted in the sense that there has been a payment interruption either of interest or principal. The exporter has submitted a notice of default for the underlying loan, and claims that the issuing export credit agency have found to be justified according to the contract, have been indemnified. In this study, the default is specified according to the indemnification period of the credit contract, as documented by Finnvera plc.

46 In monetary terms, world export credit and investment insurance collectively exceeds in size the World Bank Group and official development assistance. It is estimated that ECA-backed export credits and foreign investment from industrialised countries towards developing countries amount to $100 to $200 billion annually (The Berne Union 2002-2005). 47 With regard to calculating regulatory capital requirements for credit risk, the “standardised approach" by Basel II proposes to measure credit risk based on external credit assessments provided by rating agencies and export credit agencies. 52

3. DATA AND METHODOLOGY

The data underlying our analysis includes export credit guarantees issued by Finnvera, the official export credit agency of Finland. To assist in this research, a database has been developed that include all export credit guaranteed debt contracts initiated between Finnish exporters (and their financiers) and international foreign buyers during the time period 1980-200647. The complete, individual guarantee-level database consists of 30,402 guarantees from 137 countries, from which the “public” guarantees are selected. These include guarantees that have been issued to governments or government controlled institutions as the credit counterparts48. In other words, the underlying debt contract for which the default probability is considered is an external obligation of a public debtor. By this procedure, the public study sample is condensed to 4,461 guarantees of which 294 (6.6%) have experienced a state of default. The observations are spread over 125 countries or economic regions in a total of 17 larger macro areas. Detailed country statistics are illustrated in Table 1 in Appendix 1. Summary statistics on the explanatory variables for the corresponding countries are presented in Table 5 of Appendix 1. These are obtained from the Economist Intelligence Unit, The PRS Group, Polity IV and the World Bank.

3.1. Empirical specification

An exposition of different guarantee schemes in the data is provided in Figure 1 that also illustrates the procedure used for defining the default. Accordingly, the sample contains both "failed" (or defaulted) guarantees for which indemnification has occurred and "survived" guarantees for which no indemnification case has arisen.

Figure 1 Defaulting and non-defaulting guarantee schemes

Guarantee A i) no interruption/default

granting Guarantee A ii) no interruption / default - and still effective filing

granting

Guarantee B Indemnification ongoing interruption granting filing Defaulting guarantees

Guarantee C Indemnification ongoing interruption granting filing

Year 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

47 The study period includes all guarantees that have been active (i.e. recorded in the Finnvera stock of guarantees) during the specified time period. A small fraction of the guarantees have been issued before year 1980, but are included in the sample from the beginning of 1980. The study period is restricted to commence in 1980 as data from earlier time periods are incomplete. 48 Debtors classified as “private” have been excluded and will be examined in a further study. These include private- and publicly listed companies not under government control. As these institutions face unique risks and form topics of their own it is required that they are treated separately with respect to theory and hypotheses. 53

For each guarantee the exact dates for the initiation (granting) and expiration (filing), and the potential dates for the first and last indemnifications are known. For contracts with payment interruptions (i.e. guarantee types B and C in the figure), the underlying loan is considered in a default state between the first and last indemnification (i.e. the gray shaded area in the figure), or if available, between the first notice of payment interruption and the last indemnification date. During this specified time, the borrower is considered inable or unwilling to comply with the agreement of the debt contract, i.e. to meet the scheduled debt service payments49.

3.1.1. The dynamic logit model

The study employs the conventional failed/non-failed dichotomy, where the dependent c variable, yit is a binary choice variable that takes the value one if loan i in country c defaults during year t, and zero otherwise50.

c 1 if loan i is in a default state during year t, ( yit * > 0 ), and c yit = 0 otherwise

The interest here is in estimating the probability of default in dependence of selected c country specific explanatory variables xt−1 . Lagged values of the explanatory variables are considered in order to reflect the assumed direction of causation. Following c Hajivassiliou (1989), the technique involves a latent variable yit * that can be interpreted as representing country c’s underlying creditworthiness, which depends c upon observable economic and political characteristics, measured by the vector xt−1 . The model involves also an unobserved country effect, denoted αc and an error term uit.

In this case, we have

c c β α yit * = xt−1 ’ + c+ uit (1)

When the propensity to default exceeds the normalized threshold value of 0, we observe that loan i defaults. By making specific assumptions about the distribution of the error term uit in equation (1), the probability of default occurring can be calculated. In this estimation framework, the logistic distribution is used to specify the link function for the unknown probability of default (PD) to a linear function of the explanatory variables.

49 The default definition is the result of a careful analysis of the data that was performed with Finnvera expert advice during the data compilation procedure. Consistent measures were undertaken to distinguish between the various interruption cases in the data. For example, in the full dataset, 180 guarantees were documented as default as these had exhibited a clear notice of “ payment interruption”. For various reasons, these interruptions were cancelled later, or the indemnification by Finnvera was denied. 50 Basel II specifies the time horizon for the future probability of default (PD) as one year, which is consistent with the use in the banking practice and prevalent in credit risk models. 54

c c exp(x − 'ȕ) PD = P ( y= 1⏐ xc ) = it 1 it t−1 + c {1 (xit−1'ȕ)} (2) or equivalently51

M c logit { P ( y= 1)⏐ xc } = β + β xc (3) it t−1 0 ¦ m t−1 m=1

This is the probability that a public debtor will fail on its debt payment schedule. In principle, once the function has been estimated from historical data, new data can be substituted into the function to estimate the current probability that a borrower from a particular country will experience payment difficulties.

Shumway (2001) proves that a multiperiod logit model is equivalent to a discrete-time hazard model. The dependent variable in the prediction model is each guarantee-year’s default status (0, 1) in a given sample year. In a hazard analysis, for a default guarantee, the dependent variable equals 1 for the year in which a payment interruption is noticed, and the dependent variable equals 0 for all sample years prior to the interruption year. The non-default guarantees are coded 0 every year they are in the sample.

This approach assumes that the outcome for each guarantee-year is an independent observation, given the explanatory variables. It is acknowledged, that this might not always be the case in practice as it is conceivable that a borrower who has experienced a default event under study in the past are more likely to experience the event also in the future compared to other borrowers who have not experienced any default states. Thus, the conditional probability that a borrower will default in the future is thus a function of past experience. Heckman (1991) lists two explanations for this type of phenomena in discrete panel data applications which he refers to as 1) true state dependence and 2) observed or unobserved heterogeneity among the objects of the study. In separate tests, the role of state dependence is analysed by the inclusion of a lagged dependent variable. Further, it is understood that any unobserved individual country characteristics denoted 52 by αc, may reflect persistent heterogeneity amongst nations .

3.1.2. Principal component analysis

Principal components analysis (PCA) and factor analysis are the two most widely used linear dimension reduction methods based on second-order statistics (Fodor, 2002). Before the actual logit regressions, a principal components analysis is performed to produce components that can explain as much variance as possible in the variables. Thus, the amount of explanatory variables is reduced in order better assist in identifying the important factors affecting the event of public default. PCA helps to reduce the dimensionality of the data set, and is appropriate when the primary concern is the

51 C Here it is assumed that xt−1 includes a constant and that it is (m+1) dimensional 52 Heterogeneity may result from differing country characteristics such as colonial histories and political, financial and religious institutions (Hajivassiliou, 1989). 55

minimum number of factors needed to account for the maximum proportion of the variance represented in the original set of variables (see e.g. Hair et al. 2003)54. Variables determined to be highly correlated and members of the same factor (component) will be expected to have similar profiles in our analysis. Therefore, the objective is to summarise and reduce most of the original information (variance) to a minimum number of factors for prediction purposes. The rational for latent root technique is that any individual factor should account for the variance of at least a single variable if it is to be retained for interpretation. Thus, only factors having latent roots or eigenvalues greater than 1 are considered significant. The scree test is used to identify the optimum number of factors that can be extracted before the amount of unique variance begins to dominate the common variance structure.

3.2. Models summarised

Using different combinations of the explanatory variables, we test for five different model specifications for the probability of export credit debt default (PD):

c PD = P( yit = 1| X) (4)

Here, X represents the groups of explanatory variables measuring either country solvency, liquidity and debt service, economic development, political instability or institutional characteristics. Accordingly, the five different model specifications include;

Model 1 Solvency55 c c c c c PD1 = \ ( E0 + E1 TDPYt1 + E2 ARTDt1 + E2 PEXPt1 + E3 CARAt1 + E4 PSBRt 1 )

Model 2 Liquidity and debt service56

c c c c c c PD2 = \ ( E0 + E1 IRTDt1 + E2 MCOVt1 + E2 TSPYt1 + E3 TSPX t1 + E4 TSPRt1 + E5 EFMTt1 )

Model 3 Economic development57

c c c PD3 = \ ( E0 + E1 DCPN t1 + E2 DGDPt1 + E2 YPCAt1 )

54 In contrast, common factor analysis is appropriate when the primary objective is to identify the latent dimensions or constructs represented in the original items. 55 Solvency variables are: Total debt-to-GDP (tdpy); Arrears-to-total foreign debt (artd); Exports-to-GDP (pexp); Current Account balance (cara); and Budget balance (psbr). 56 Liquidity and debt service variables are: Reserves-to-total debt (irtd); Reserves-to-imports (mcov); Debt-service-to-GDP (tspy); Debt-service-to-exports (tspx); Debt-service-to-reserves (tspr); Average time to maturity of debt (efmt). 57 Economic development variables are: Consumer prices, % end-period (dcpn); GDP Growth (dgdp); GDP per head (ypca). 56

Model 4 Political risk58

c c c c c c PD4 = \ ( E0 + E1 STABt1 + E2 SOCt1 + E3 INVt1 + E4 INTERt1 + E5 EXTERt1 + E6 CORt1 c c c c c c + E7 MILt1 + E8 RELIGt1 + E9 LAWt1 + E10 DEM t1 + E11 ETHN t1 + E12 BURQt1 )

Model 5 Institutional risk factors59 c c c c c PD = \ ( E0 + E1 ELECt1 + E2 LEGALt1 + E2 CREDITt1 + E3 POLITYt1 + E4 DURABLEt1 ) where \ ( ) represents the respective distribution function for each estimation method. Models 1-3 are tested separately also with the composite political risk index by the ICRG, that is included as an additional explanatory variable. The various models are analysed separately between countries in different income categories. During the post- debt crisis period, there has been a substantial reduction in the low-income countries’ access to private capital markets in favour of official lending. In the middle- and high- income countries, the decline in commercial bank lending has been replaced by bonds, portfolio, and FDI flows (see e.g. Evrensel, 2004).

Classified countries/economic regions in the sample60

Low-income economies (35): Angola, Armenia, Azerbaijan, Bangladesh, Benin, Central African Republic, Cameroon, China, Cote d'Ivoire, Ethiopia, Ghana, Honduras, India, Indonesia, Kenya, Liberia, Madagascar, Mauritania, Mongolia, Mozambique, Myanmar, Nepal, Nicaragua, Nigeria, North Korea, Pakistan, San Marino, Senegal, Somalia, Taiwan, Tanzania, Viet Nam, Yemen, Zambia, Zimbabwe. Lower-middle income economies (38): Albania, Algeria, Belarus, Bolivia, Bosnia-Herzegovina, Bulgaria, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, Egypt, Guatemala, Guyana, Iran, Iraq, Jamaica, Jordan, Kazakhstan, Latvia, Lithuania, Maldives, Morocco, Paraguay, Peru, Philippines, Romania, Russia, South Africa, Sri Lanka, Suriname, Swaziland, Syria, Thailand, Tunisia, Ukraine, Uzbekistan, Yugoslavia (former). Upper-middle income economies (30): Argentina, Bahrain, Barbados, Botswana, Brazil, Chile, Croatia, Czech Republic, Estonia, Gabon, Grenada, Guadeloupe, Hungary, Lebanon, Libya, Malaysia, Mauritius, Mexico, Oman, Panama, Poland, Puerto Rico, Saint Lucia, Saudi Arabia, Slovakia, South Korea, Trinidad and Tobago, Turkey, Uruguay, Venezuela. High-income economies, OECD (24): Australia, Austria, Belgium, Canada, Denmark, East Germany (former), France, Germany, Gibraltar, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States. High-income economies, non-OECD (18): Andorra, Bahamas, Cayman Islands, Cyprus, Hong Kong, Israel, Kuwait, Liechtenstein, Malta, Martinique, Monaco, Netherlands Antilles, Qatar, Reunion, Singapore, Slovenia, United Arab Emirates, Virgin Islands.

58 Political risk variables are: government stability (stab); socioeconomic conditions (soc); investment climate (inv); internal conflict (inter); external conflict (exter); corruption (cor); military in politics (mil); religion in politics (relig); law and order (law); democratic accountability (democ); ethnic tensions (ethn); bureaucratic quality (burq). 59 Institutional variables are: Elections (elec); Legal rights (legal); Creditor rights (credit); Level of democracy (polity); Durability of regime (durable). 60 Income group: Economies are divided according to GNI per capita, as specified by the Atlas mehtod by the World Bank. The classification that represents the situation as of 7/2005, is retrieved from Global Development Network Growth Database (New York University, The Development Research Institute). In the analysis, the income classification is tracked over time. 57

4. RESULTS

4.1. Correlation and principal components analysis

While the initial selection of the explanatory variables used in this analysis were based on previously published work, the multicollinearity problem restricts the use of a a too opulent a set of variables. Thus, an identification of the main dimensions of the data set through correlation analysis and principal components analysis is attempted. Table 6 and Table 7 in Appendix 2 present two-way correlations, and tests for collinearity between the economic and political variables. Variables that indicate a correlation higher than 0.8 among the groups are marked as bold and subsequently excluded. The excluded variables include the debt-to-exports ratio as this is highly correlated with total-debt-to-GDP. The application of only one of these variables should be sufficient in capturing the solvency dimension of a country. Among the liquidity and debt service ratios, the short-term-debt-to-reserves-ratio is excluded due to its high correlation with debt service-to reserves. Concerning the other economic variables, both the GDP deflator and consumer prices (% avg change) are excluded. Thus, inflation is measured with only one variable, i.e. the end-period percentage change in consumer prices. Among the ICRG-related variables only two subcomponents show higher correlation than 0.7. This is found between individual pairs “law and order” and “internal conflict”, which is expected as these are basically derived from the same fundamentals. The subcomponent measuring democratic accountability is highly correlated with the Polity IV- index (measuring level of democracy), which are accordingly not tested jointly.

A principal components analysis (PCA) was undertaken in an effort to further examine multicollinearities and capture the dimension of the data. The objective of PCA is to find the unit-length linear combinations of the variables with the greatest variance. Each of the principal components represent an independent dimension of variation. For the full set of 45 explanatory variables, it was found that 9 components (factors) have eigenvalues greater than 1, accounting for 77% of the total variability in the data (see Table 8). The analysis then continued with the identification of the most significant factor loadings, in order to determine the dimension of each of the 9 factors (components) and from each dimension select a predetermined amount of variables to be included in the model, which should be sufficient in explaining the whole dimension. While the interpretation of these dimensions is not straightforward, it was found that one of the factors could be a political one, with most of the high and significant factor loadings centered on components pointing towards various conflict variables. Factor loadings were significant also around variables representing “external liquidity”, “inflation”, “GDP growth” and the “socioeocnomic conditions”. Further, the most commonly used varimax rotation that maximises the variance of the loadings was employed. The rotation is an attempt to put the factors in a simpler position with respect to the original variables, which aids in the interpretation of factors. It places the factors into positions so that only the variables with are distinctly related to a factor will be associated. Interestingly, seven political indicators (including the legal rights indicator) were found to be the most significant for the first and second principal components, which explained 40 percent of the variation in the sample data. 58

4.2. Logit results

The results of the dynamic logit estimations on public export credit debt are presented in Tables 9-13 of Appendix 3. The effect of economic risk, political risk and institutional factors are scrutinised separately for different country income groups, tracking the country classification over time61. In these tables, Country Group I presents the results for all countries in the sample, while Country Groups II-V present the tests performed separately for low income, lower middle income, upper middle, and high income countries. These groupings are referred hereafter to as LICs, LMICs, UMICs and HICs. The impact of the overall country solvency is analysed in Table 9. Then the various indicators on liquidity and economic development are examined in Table 10 and 11, respectively. In Tables 9-11, the composite ICRG political risk rating is included as an additional explanatory variable in the Country Group I, assessing all sample countries jointly. Finally, Table 12 and Table 13 examine the impact of political risk factors and institutional risk.

The results on solvency variables suggest that for all countries combined and in particular for the LICs (Country Group II), the total debt to GDP ratio exhibits a positive relationship with default probability. For example for the LICs, the change in the predicted probability of default increases 2% with an increase of one standard deviation in this indebtedness-ratio, while holding all other independent variables constant at their means. This result is intuitive and supports numerous previous studies that conclude that the higher the debt burden the larger the transfer effort a country will need to make over time to service its obligations (see e.g. Ades et. al. 2000). Meanwhile, for the wealthier countries, the variable is less significant. After the severe debt crises in 1980s and 1990s, the overall debt stock seems still a crucial source of crisis for many low and middle income countries. Except for the LICs, the probability of default becomes significantly higher when total arrears on both interest and principal to creditors increase. Debt payment arrears have been the main cause of the persistent increase in the debt stock for many developing countries, and have disrupted normal channels of trade credit. Overall, the finding may reflect the notion that debt ratios may be symptoms of debt repayment difficulties rather than their causes.

The potential adverse effects of budget deficits on debt service is investigated both with the effect of deficits on the current account balance and then, with the effect of the overall budget balance. It was hypothesised that a greater current account surplus relative to GDP is expected to reduce the probability of default, i.e. more global funds, that are available for borrowing dominate any effect from a reduction in exports on the propensity to default. The varying sign of the current account ratio between different country income groups seems to reflect more complex economic underlying forces. Countries at different stages of the economic development incur sovereign debt for varying reasons, including consumption-smoothing purposes, investment purposes and adjustment purposes based on current account sustainability62. The varying effect of the

61 This corresponds to the standard World Bank classification, documented since 1987. 62 Aguiar and Gopinath (2004) examine the dynamics of emerging market debt defaults, interest rates and the current account. They note that interest rate movements are driven by changes in the default rate and the steepness of the interest rate schedule makes it challenging even qualitatively to match the positive correlation between interest rates and the current account. 59

current account position (which implies a varying effect of the net foreign asset position) between country income groups could be an indication of the Paris Club in operation. This refers to the extension of the repayment periods for debts exhibited by many of the sample countries studied during the study period62. For example, the change of the sign of this variable from LMICs to UMICs, reflects the differing case for higher income countries. Unlike the poorer countries, the UMICs may have better access to private credit, and retain also better degree of policy capacity with access to the market. Poor countries that depend on official creditors rarely have this option. In addition, the effect of the current account measure may be influenced by other, more complex exchange control mechanisms63.

The budget balance is a significant indicator for debt default for all country groups except for the HICs64. This results accords with the finding by Ades et. al. (2000). Hovever, in most previous studies, the budget balance measure has not been found to significantly impact either on the spread or the credit ratings of countries. Cantor and Packer (1996) explained this by suggesting that fiscal policy might, in fact, in many cases be endogenous. In other words, countries trying to improve their credit standings may opt for more conservative fiscal policies. Meanwhile, because the deficit rises automatically when economic activity slows, and vice versa, the budget deficit in a given year may offer a misleading impression of underlying fiscal policy. Overall, the results in this study support the notion that debt and currency crises are interlinked through the government’s budget constraint. Budget surplus seem to result in decreasing debt levels, and in this case, also in decreasing default probabilities. Countries classified in the low to upper-middle income groups are more likely to default rather than inflation/devaluation for financing, while a high money stock reduces the probability of debt defaults. A closer look at the marginal effects of each variable indicates that solvency variables do not have a particularly large impact on the probability of default. However, most of the variables are significant with the expected sign, and should thus not be disregarded in credit evaluation.

Table 10 presents results on indicators of international reserves and the maturity structure of debt and debt-service ratios that are expected to impact on the default propensity. The coefficient of the ratio of reserves to total debt is, as expected, negative but not statistically significant for any of the country groups. Meanwhile, the reserves to imports-ratio is negative and significant only for LMICs, a result consistent e.g. with Balkan (1992). More mixed results are found among the debt service ratios. For example, the debt service-to-GDP is found negative for the wealthier countries (UMICS and HICs), but for LICs this ratio is positive and significant, as expected. That is, for LICs heavier permanent ratios of debt service to GDP influence the risk of default because more domestic resources are required to meet the outstanding debt and thereby,

62 This happened, for example, in Poland and Egypt in 1991. For LMICs and UMICs, the Houston Terms were introduced in 1990, which allow for deferral of payments but do not provide any debt reduction. The terms apply to LMICs and provide for rescheduling over 20 years for ODA debt and up to 15 years for non-ODA debt, with the exception of Russian Federation which has received better terms. UMICs receive debt rescheduling on what are known as standard terms, which reschedule debt on market-related terms. 63 More detailed analysis on the related advanced topics within International Economics are beyond the scope of this study, and are left as a topic for further research. 64 Numerous authors have examined the dynamic macroeconomic interrelationship between budget balances and public debt using a range of mathematical methods (see e.g. Fischer and Easterly, 2000). 60

all else equal, less resources are available for real consumption and investment. Increasing average time to maturity of debt increases the default propensity for LICs and LMICs.

Apart from the build-up in debt and lowering of reserves, a look at the structural factors such as GNP per capita growth rates, the inflation rate and GPD per head is provided in Table 11. It is expected that the probability of default depends on this group of factors through their impact on our debt and financial ratios. The outstanding significant explanatory variable in relation to economic development is the economic growth rate. This is negative and significant for all country groupings, except for the HICs. This results is in accordance with Eichengreen and Mody (1998) and indicates that a higher permanent real GDP growth in a country implies higher domestic savings and investment, and hence, a lower critical capital stock ratio to meet a given borrowing program.

Including the composite political risk rating in the model specifications (Tables 9, 10 and 11), results in a significant and negative coefficient of this (inverse) risk ratio65. The intriguing research question is thus, whether political risk endogenous to the economic situation. Further examinations of political risk- and institutional variables are presented in Table 12 and Table 13. The influence of the separate political risk components are presented in Table 12. For all country income categories except for the HIC’s the component measuring government stability is significant. For LICs, other important political risk factors explaining default probability include socioeconomic conditions (significant at 1%, with a marginal increase of 3% on the default probability for an increase of one standard deviation in the risk level). Also democratic accountability and bureaucratic quality seem equally as important predictors of debt default for these countries. For LMIC’s, also the level of external conflict and military in politics become significant and negative. For UMICs, especially military in politics and ethnic tensions show significant coefficients with the default probability. Meanwhile, the higher the country income level, the higher becomes the impact of external conflict. For HICs, mainly the general investment climate and worries about external conflict, appear to endanger the repayments of export credit guaranteed debt.

Studying institutional factors (Table 13) reveals an interesting finding concerning democracy. A negative and significant relationship between the level of democracy and default probability is demonstrated only for UMICs. Also the timing of elections seems to affect default probabilities only in UMICs. These results are contrasted with some previous findings concerning the level of economic development and the level of democracy. Most quantitative cross-national research in this field suggests that the largest gains in democracy are experienced by countries at intermediate levels of development (see e.g. Muller, 1995). This result may also be a reflection of the level of democratisation of the countries involved. Only a few stable democracies are represented in the group, and most of these countries record either declining in democracy scores (e.g. Argentina, Chile and Mexico) or the scores were low as the political system remained authoritarian (e.g. Iran and South Africa). Finally, the level of creditor rights is a significant and negative determinant of debt default in LIC’s and LMIC’s, while legal rights are predominant for UMICs and HICs.

65 A negative coefficient implies that a higher risk rating (lower risk) lowers the default probability. 61

5. CONCLUDING REMARKS

There is substantial argument that political risk has an important and increasing influence in the international financial landscape and it is obvious that it plays a crucial role in the process of export credit default. Previous research has focused on the role of political instability mainly in the context of explaining other economic phenomena such as differences in patterns of economic growth, inflation, investment, and fiscal policy among countries. Overall, little empirical work exists on default risk assessment that takes a clear perspective on political risk. This research contributes in filling that gap.

In this paper, new empirical evidence is documented on the determinants and importance of political risk, along with country specific macroeconomic indicators, related to public export credit debt default. Despite using a new type of credit data, similar results as found in selected previous findings are documented. The most important economic indicator of public default appears to be country indebtedness, along with various forms of political risk. In most of our model specifications, political risk variables show the expected sign and seem both jointly and individually statistically significant. For low-income countries, the investment climate, socioeconomic conditions, democratic accountability, bureaucratic quality, as well as creditor rights are among the most important factors determining default. On the other hand, legal rights, ethnic tensions and other measures on conflict are predominant predictors of default in upper-middle and high income countries. The closeness to an election or the level of democracy seems to affect default probabilities mainly in the upper-middle income countries.

Various additional tests were undertaken during the course of this study. For example, the Cox proportional hazards model was estimated to assess the relationship between the explanatory variables to contract survival time. The results from the Cox regressions indicated similar type of patterns as the results from our dynamic logit models. Further, separate tests were carried out to check for any differences between the level of political risk between countries with the English Common Law legal origin and countries with French Civil Law legal origin. For example, the English Common Law legal origin countries, with less assumed state power, show a slightly higher (lower risk) score than the French Civil Law legal origin countries for all measures except for ethnic tensions, democratic accountability and polity (level of democracy). While many developing countries have come a long way since the military regimes in the past, some of them are still struggling with relations between civilian governments and the armed forces.

Several avenues for further research are suggested by the paper. It is clear that there is room for additional work on the statistical refinements of the default probability estimation models that are better able to predict future default probabilities with input data, perhaps including also stock market data. This work should also include more detailed treatment of the effects of important social and political events in the debtor country and of the events surrounding the issuance of the credit contract. It would also be desirable to find new ways of dealing with the role and impact of international financial institutions in bailing out countries in debt crisis. Finally, a more detailed, country-specific analysis on political risk and its impact on debt default is proposed as an area for further research. 62

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APPENDIX 1 SUMMARY STATISTICS

Table 1 Export credit guarantees offered by Finnvera to public institutions (1980-2006)

This table presents summary statistics on export credit guarantees issued by Finnvera to public institutions in different economic regions during the 1980-2006 period. The first column presents the overall average stock of official export credits, suppliers’ credits and bank credits officially guaranteed or insured by an export credit agency in the particular country. Source: Finnvera plc and The Economist Intelligence Unit, 2006.

Official export credits Finnvera guarantees and indemnifications Study period Stock value Total liability # guarantees # defaults start end duration USDm USD n % n % year year months (avg) (avg) (avg)

Australia and New Zealand Australia 10 145 38 027 2 66.7 - - 1997 1999 15 New Zealand n/a 9 500 000 1 33.3 - - 1983 1987 60 sub-total 3 100.0

Eastern Africa Ethiopia 2 782 1 437 094 16 19.1 3 18.8 1980 2006 50 Kenya 20 455 2 977 935 16 19.1 2 12.5 1980 2003 44 Madagascar 309 10 700 000 1 1.2 - - 1980 1984 60 Mauritius 463 1 299 020 4 4.8 - - 1989 2003 84 Mozambique 6 613 7 684 657 3 3.6 2 66.7 1978 1985 65 Somalia 2 031 196 290 1 1.2 - - 1981 1985 60 Tanzania 1 865 5 742 071 21 25.0 7 33.3 1978 2004 55 Zambia n/a 88 000 1 1.2 1 100.0 1978 1980 25 Zimbabwe 572 4 392 404 21 25.0 2 9.5 1980 2003 40 sub-total 84 100.0 17

Western Africa Cote d'Ivoire 2 132 210 730 16 37.2 - - 1994 2001 7 Ghana 11 132 4 444 039 9 20.9 5 55.6 1971 2005 64 Liberia 62 2 504 208 4 9.3 - - 1980 1984 33 Mauritania 606 1 2.3 - - 1981 1985 60 Nigeria 3 078 1 186 859 6 14.0 2 33.3 1979 1996 36 Senegal 1 262 72 524 7 16.3 - - 1980 1985 24 sub-total 43 100.0 7 16.3

Middle Africa Angola 809 2 344 309 4 33.3 3 75.0 1980 1992 41 Central African Republic 1 578 5 401 201 3 25.0 - - 1980 1996 38 Cameroon 4 660 7 103 533 4 33.3 - - 1986 1997 61 Gabon 1 608 708 748 1 8.3 - - 1984 1990 74 sub-total 12 100.0 3 25.0

Northern Africa Algeria 14 237 1 344 957 414 31.3 13 3.1 1977 2007 11 Egypt 10 141 2 510 574 493 37.3 16 3.2 1977 2002 19 Libya 1 440 6 905 361 107 8.1 16 15.0 1978 2004 32 Morocco 2 780 1 077 167 57 4.3 - - 1983 2002 12 Tunisia 3 630 229 108 250 18.9 - - 1979 2003 7 sub-total 1 321 100.0 45 3.4

Southern Africa Botswana 128 67 111 1 9.1 - - 1991 1991 5 South Africa 1 363 19 900 000 9 81.8 - - 1991 2003 52 Swaziland 12 342 1 992 492 1 9.1 - - 1986 1998 147 sub-total 11 100.0 69

Official export credits Finnvera guarantees and defaults Study period Stock value Total liability # guarantees # defaults start end duration USDm USD n % n % year year months (avg) (avg) (avg)

North America United States 3 737 986 115 6 100.0 1 16.7 1980 2004 27 sub-total 6 100.0 1 16.7

Central America Argentina 6 336 6 086 945 35 15.3 5 14.3 1974 2006 56 Bolivia 128 323 256 1 0.4 - - 2000 2002 22 Brazil 19 197 3 391 296 61 26.6 2 3.3 1976 2005 28 Chile 1 297 1 632 747 27 11.8 - - 1980 1998 45 Colombia 1 717 6 734 640 23 10.0 1 4.3 1978 2001 41 Costa Rica 248 28 100 000 1 0.4 - - 2001 2012 137 Ecuador 1 195 382 893 4 1.8 - - 1980 1993 19 Honduras 5 000 8 088 025 1 0.4 - - 2002 2020 224 Mexico 693 8 682 040 24 10.5 - - 1978 2011 53 Nicaragua 8 817 364 071 1 0.4 - - 1998 1999 13 Panama 14 200 11 600 000 1 0.4 - - 2004 2012 97 Paraguay 442 10 000 1 0.4 - - 1982 1983 24 Peru 947 2 991 339 30 13.1 4 13.3 1973 2006 39 Suriname 1 652 561 488 1 0.4 - - 1986 1990 60 Uruguay 3 878 2 261 068 4 1.8 - - 1980 1987 58 Venezuela 12 751 6 422 961 14 6.1 2 14.3 1980 2010 41 sub-total 229 100.0 14 6.1

Caribbean Bahamas 1 871 2 420 074 1 2.4 - - 1991 1992 18 Cuba 2 604 20 600 000 22 52.4 7 31.8 1978 1991 48 Dominican Republic 572 4 612 479 2 4.8 - - 1984 2003 96 Grenada 1 731 1 775 028 1 2.4 1 100.0 1981 1989 107 Jamaica 24 199 5 068 412 11 26.2 - - 1980 2014 70 Netherlands Antilles 9 207 3 817 063 2 4.8 - - 1983 2003 68 Saint Lucia 2 138 1 2.4 - - 1980 1984 60 Trinidad and Tobago 643 537 288 2 4.8 - - 1983 1984 24 sub-total 42 100.0 8 19.0

Eastern Asia China 16 180 7 747 827 182 66.7 4 2.2 1980 2020 73 Hong Kong 3 910 3 275 990 3 1.1 - - 1992 2006 33 Japan 27 917 265 671 2 0.7 - - 1995 1996 5 Mongolia 6 346 4 820 198 4 1.5 2 50.0 1987 2003 108 North Korea 1 514 653 246 5 1.8 5 100.0 1968 1991 67 South Korea 2 437 703 902 65 23.8 - - 1980 2006 12 Taiwan 5 516 355 102 12 4.4 - - 1982 2001 23 sub-total 273 100.0 11 4.0

South-eastern Asia Indonesia 5 039 11 700 000 32 26.7 6 18.8 1980 2006 35 Malaysia 395 1 598 606 17 14.2 - - 1980 2001 27 Myanmar 6 061 142 205 1 0.8 - - 1982 1983 24 Philippines 4 046 14 500 000 22 18.3 2 9.1 1979 2010 55 Singapore 33 569 99 742 10 8.3 - - 1994 2001 18 Thailand 8 298 42 300 000 21 17.5 2 9.5 1980 2003 69 Viet Nam 397 2 259 970 17 14.2 - - 1994 2020 69 sub-total 120 100.0 10 8.3 70

Official export credits Finnvera guarantees and defaults Study period Stock value Total liability # guarantees # defaults start end duration USDm USD n % n % year year months (avg) (avg) (avg)

South-central Asia Bangladesh 478 1 001 294 12 1.9 - - 1990 2006 6 India 926 6 510 647 35 5.7 1 2.9 1979 2006 35 Iran 4 133 1 581 552 516 83.2 35 6.8 1973 2008 23 Kazakhstan 33 800 1 799 947 10 1.6 - - 2002 2011 32 Maldives 4 166 2 324 768 2 0.3 - - 1994 2010 106 Nepal 4 905 3 701 977 2 0.3 - - 1994 2008 93 Pakistan 6 764 3 303 835 28 4.5 2 7.1 1980 2006 38 Sri Lanka 8 065 7 071 256 12 1.9 - - 1980 2020 71 Uzbekistan 12 672 2 188 540 3 0.5 - - 1997 2010 52 sub-total 620 100.0 38 6.1

Western Asia Azerbaijan 4 263 17 700 000 1 0.2 - - 2006 2006 1 Bahrain 57 553 133 7 1.0 - - 1983 2001 20 Cyprus 101 1 047 824 5 0.7 - - 1980 2001 28 Iraq 2 842 4 872 539 110 16.3 28 25.5 1978 1993 34 Israel 4 995 476 185 50 7.4 - - 1979 2004 22 Jordan 23 859 1 399 148 14 2.1 1 7.1 1978 2006 28 Kuwait 8 831 1 456 533 25 3.7 1 4.0 1980 2004 25 Lebanon 1 013 383 051 76 11.2 - - 1988 2007 10 Oman 5 707 703 091 8 1.2 - - 1981 1999 9 Qatar 808 532 888 3 0.4 - - 1979 1988 36 Saudi Arabia 11 990 2 432 627 45 6.7 4 8.9 1979 2003 30 Syria 524 5 402 692 35 5.2 3 8.6 1979 1987 28 Turkey 6 439 1 927 113 259 38.3 8 3.1 1975 2011 21 United Arab Emirates 6 427 1 456 215 32 4.7 - - 1976 2007 22 Yemen 255 1 088 261 6 0.9 - - 1981 1989 42 sub-total 676 100.0 45 6.7

Eastern Europe Belarus 1 943 248 359 1 0.2 2006 2007 1 Bulgaria 1 486 4 323 740 25 3.7 3 12.0 1977 2006 37 Czech Republic 2 230 4 377 343 127 18.8 1978 1998 36 East Germany, DDR 1 676 955 970 202 29.8 12 5.9 1977 1993 28 Hungary 2 522 3 833 585 22 3.3 1979 1998 48 Poland 3 933 6 048 646 48 7.1 11 22.9 1974 2004 47 Romania 1 521 3 004 035 47 6.9 6 12.8 1976 2007 37 Russia 9 655 7 899 578 137 20.2 16 11.7 1979 2010 25 Slovakia 26 054 2 420 111 10 1.5 1 10.0 1981 1997 48 Soviet Union 53 7.8 25 47.2 Ukraine 9 117 406 427 5 0.7 1998 2007 20 sub-total 677 100.0 74 10.9

Western Europe Austria 11 832 110 178 1 0.6 1993 1996 31 France 3 459 1 222 121 6 3.5 1979 1996 28 Germany (and form. BRD) 6 307 667 260 67 38.5 3 4.5 1977 1985 27 Netherlands 12 911 890 850 89 51.2 1980 2006 10 Switzerland 9 141 506 078 11 6.3 1980 2005 14 sub-total 174 100.0 3 71

Official export credits Finnvera guarantees and defaults Study period Stock value Total liability # guarantees # defaults start end duration USDm USD n % n % year year months (avg) (avg) (avg)

Northern Europe Denmark 2 280 1 137 734 1 1.7 1987 1988 24 Estonia 51 490 161 9 15.5 1996 2003 23 Iceland 2 943 4 699 102 2 3.5 1984 1985 24 Ireland 4 665 1 706 623 3 5.2 1982 1985 24 Latvia 11 346 539 517 15 25.9 2 13.3 1997 2001 13 Lithuania 292 665 639 3 5.2 1999 2000 5 Norway 7 331 150 964 6 10.3 3 50.0 1980 1995 13 Sweden 5 915 14 400 000 14 24.1 1980 1997 30 United Kingdom 9 214 755 062 5 8.6 1991 2006 14 sub-total 58 100.0 5 8.6

Southern Europe Albania 0 15 100 000 2 1.8 1979 1981 24 Bosnia-Herzegovina -1 778 219 624 1 0.9 1 100.0 1998 1999 9 Croatia 869 382 685 7 6.3 1996 2001 6 Greece 2 457 950 123 8 7.1 1 12.5 1980 1994 40 Italy 25 439 196 589 2 1.8 1992 1998 16 Portugal 19 234 771 374 6 5.4 1984 1997 29 Slovenia 8 588 2 668 372 6 5.4 1989 2000 38 Spain 5 317 611 487 3 2.7 1983 1996 30 Yugoslavia (form.) 2 586 4 285 879 77 68.8 11 14.3 1978 1996 35 sub-total 4 285 879 112 100.0 13 11.6

Total (all countries) 4 461 294 6.6

Table 2 Variable definitions – economic and financial risk

Source: The Economist Intelligence Unit, Country Data 2006-2007

Variable name Label Measure Sign Definition Source

Solvency ratios

Total debt-to-GDP tdpy % + Total external debt at end-period EIU: World Bank (Global as a percentage of nominal GDP. Development Finance) and IMF (International Financial Statistics) Arrears-to-total foreign debt artd % + Stock of unpaid interest charges EIU: World Bank (Global due, divided by total external Development Finance). debt stock67 Exports-to-GDP pexp % - Value of exports of goods and Different sources by the EIU: e.g. IMF non-factor services expenditure (International Financial Statistics and as a percentage of GDP.68 IMF Staff Papers). Debt-to-exports tdpx % - External debt stock as a EIU: World Bank (Global percentage of exports.69 Development Finance) and IMF (International Financial Statistics) Current account-to-GDP cara % - Current-account balance as a EIU: Derived from the IMF percentage of GDP. (International Financial Statistics)

Budget balance psbr % - General government receipts EIU: Derived from the IMF, minus central government (International Financial Statistics) outlays, as a percentage of GDP.

67 Total external debt stock comprises public and publicly guaranteed long-term debt, private non-guaranteed debt, use of IMF credit and short-term debt, at end of period. 68 Value calculated at current market prices. 69 Exports of goods, non-factor services, income, and workers’ remittances. 72

Variable name Label Measure Sign Definition Source

Liquidity: international reserves

Reserves-to-total debt irtd % - Total international reserves as a EIU: World Bank (Global percentage of total external debt Development Finance) and IMF stock. (International Financial Statistics) Reserves-to-imports mcov months - Total international reserves EIU: World Bank (Global divided by imports of goods and Development Finance) and IMF non-factor services. (International Financial Statistics)

Debt service and composition of debt

Debt-service-to-GDP tspy % +/- Total external debt service paid EIU: World Bank (Global as a percentage of nominal GDP. Development Finance) and IMF (International Financial Statistics) Debt-service-to-exports tspx % +/- Total external debt service paid EIU: World Bank (Global as a percentage of exports69. Development Finance) and IMF (International Financial Statistics) Debt-service-to-reserves tspr % +/- Repayments made on MLT debt, EIU: World Bank (Global IMF debits (incl. interest) divided Development Finance) and IMF by stock of foreign reserves. (International Financial Statistics) Short-term-debt-to-reserves stdr % + External debt having an original EIU: World Bank (Global maturity up to one year, divided Development Finance) by foreign reserves70. Avg. time to maturity of debt efmt years + Total MLT debt in the previous EIU: World Bank (Global year divided by MLT principal Development Finance) repayments paid for the current year.

Inflation

GDP deflator gdfd % - Percentage change in GDP EIU: national or international sources. (avg ǻ) deflator index in local currency, period average (1996 = 100). Consumer prices I/II dcpi % - Percentage change in consumer EIU: national or international sources. (avg ǻ) price index (local currency, period average), previous year. Consumer prices II/II dcpn % (end- - Percentage change in consumer EIU: national or international sources. period ǻ) price index (end-period), over previous year.

Economic development

GDP Growth dgdp % - Percentage change in real GDP, Different sources by the EIU: e.g. over previous year. World Bank (World Development Indicators). GDP per head ypca USD - Nominal GDP divided by EIU: Derived from the IMF, population. (International Financial Statistics)

69 Exports of goods, non-factor services, income and workers’ remittances 70 Stock of foreign reserves includes gold (national valuation, end-period). 73

Table 3 Variable definitions – political risk

Source: The PRS (Political Risk Services) Group. International Country Risk Guide, Researchers Dataset, 2006.

Variable name Label Measure Sign Definition Source

Political risk

The ICRG Political Risk Index icrg 0 - 100 - Composite index of the components listed below.

- government stability stab 0 - 12 - Government’s ability to remain in office by carrying out declared policy plans. Measured by government unity, legislative strength and popular support.

- socioeconomic conditions soc 0 - 12 - Pressures that conspire to constrain government action or to fuel social dissatisfaction. Includes unemployment, the degree of consumer confidence and the level of poverty.

- investment climate inv 0 - 12 - Risk to investment not covered by other political, economic and financial components and is made up of contract viability and expropriation, profit repatriation, and payment delays.

- internal conflict inter 0 - 12 - Political violence in a country and its impact on governance. The subcomponents of this risk factor are civil war or coups threat, terrorism or political violence, and civil disorder.

- external conflict exter 0 - 12 - Non-violent external pressure (i.e. diplomatic pressure, withholding of aid, trade restrictions and sanctions) and violent external pressure (cross- border disputes and all-out war).

- corruption cor 0 - 6 - An internal assessment of the political system; whether the economic and financial environment is distorted by the reduced efficiency of government and business caused by corruption.

- military in politics mil 0 – 6 - The military are not democratically elected and their involvement in politics is a decrease of accountability. Military participation may represent a symptom rather than a cause of higher political risk.

- religion in politics relig 0 – 6 - The domination of society and or governance by a single religious group that seeks to replace civil law and order by religious law. Other religions are excluded from the political and social process.

- law and order law 0 – 6 - The law and order components are assessments of the strength and impartiality of the legal system and popular observance of the law respectively.

- ethnic tensions ethn 0 – 6 - Ethnic tensions relate to racial, nationality or language divisions where opposing groups are intolerant and unwilling to compromise.

- democratic accountability dem 0 – 6 - How responsive government is to its people. The less responsive, the greater the chance that the government will fall. (The change is expected to be peaceful in a democratic country but possibly violent in a non- democratic country). - bureaucratic quality burq 0 – 4 - A measure that reflects the revisions of policy when governments change. Low risk applies to bureaucracies that have a degree of autonomy from political pressure (an independent mechanism for e.g. recruitment etc.) 74

Table 4 Variable definitions – institutional risk factors

Sources: Wikipedia, World Bank and The Polity IV Database.

Variable name Label Measure Sign Definition Source

Institutional factors

Elections elec dummy + Presidential-, parliamentary-, Subtracted from Wikipedia general-, federal-, municpal, or / The Politics Series legislative elections or referendums were held in the country during the year. (http://en.wikipedia.org)

Legal Rights Index lega 0-10 - The degree to which collateral The World Bank and bankruptcy laws facilitate lending. (Doing Business -database) Creditor Rights Index credit 0-6 - The rules affecting the scope , - '' - access, and quality of credit information.

The Polity IV-Index polity -9 to 9 - Status of democracy or autocracy The Polity IV Database University of Maryland

(http://www.cidcm.umd.edu/) Durability of regime durab years - Time since last regime change. - '' - Table 5 Sample country descriptive statistics, economic, political and institutional risk

Source: The Economist Intelligence Unit, The PRS Group, Wikipedia, World Bank and The Polity IV Database.

Country group Low income countries Low to middle income countries Upper middle income countries High income countries

Solvency ratios obs mean stdev min max obs mean stdev min max obs mean stdev min max obs mean stdev min max Total debt-to-GDP 1705 42.6 33.4 0.0 231.5 4776 65.2 77.3 3.0 792.4 2660 35.7 34.9 2.0 292.2 337 30.5 18.7 1.5 104.0 Arrears-to-total foreign debt 1805 0.0 0.1 0.0 0.4 4961 0.0 0.0 0.0 0.3 1926 0.0 0.0 0.0 0.1 334 0.0 0.0 0.0 0.0 Exports-to-GDP 1794 22.6 10.6 2.5 69.0 4790 27.3 11.7 8.3 85.4 2674 26.4 17.9 4.1 124.6 821 51.1 30.8 7.2 198.7 Debt-to-exports 1801 200.8 244.2 32.1 2.09 k 4836 216.7 406.6 10.9 6.81 k 2480 128.4 116.1 4.9 692.6 311 59.3 54.6 1.8 247.5 Current account -to-GDP 1806 -0.4 4.4 -26.9 50.0 4778 -2.1 7.1 -43.6 21.2 2577 -1.8 9.4 -35.3 31.9 752 3.0 14.7 -244.4 54.2 Budget balance 1595 -4.0 3.9 -37.5 18.5 4225 -4.0 4.8 -42.6 13.7 1776 -6.2 6.3 -23.7 9.5 621 -3.1 7.0 -19.4 37.2

Liquidity: international reserves Intl. reserves-to-total debt 1806 36.0 28.1 0.1 160.8 4718 42.9 68.8 0.2 602.5 1712 78.4 185.4 0.7 1.16 k 330 191.9 275.4 11.6 1.37 k

Reserves-to-imports 1893 5 3 0 13 4663 5 4 0 28 1627 7 5 0 32 769 3 2 0 8 75

Debt service and composition of debt Debt-service-to-GDP 1703 4.1 3.0 0.6 21.1 4763 6.5 4.2 0.0 24.5 2273 6.0 5.9 0.4 29.2 291 4.6 2.7 1.2 21.2 Debt-service-to-exports 1801 15.9 8.2 2.3 41.4 4829 21.2 17.8 0.0 303.0 2133 19.8 18.6 1.2 117.2 272 8.8 8.5 0.6 57.2 Debt-service-to-reserves 1805 1.6 11.9 0.1 475.6 4745 1.3 2.2 0.0 62.3 1721 30.0 150.7 0.0 843.3 291 0.3 0.4 0.0 3.3 ST debt-to-reserves 1805 10.4 71.2 0.0 2.23 k 4745 2.0 3.5 0.0 104.6 1732 28.2 140.4 0.0 788.6 304 1.0 1.1 0.0 8.1 Time to maturity of debt 1795 18 16 0 208 4729 16 15 1 129 2302 10 8 1 104 287 8 8 1 40

Inflation GDP deflator 1408 10.5 8.01 -2 84 3616 59.2 233.8 -8.7 3.04 k 1499 103.5 393.6 -2.3 2.24 k 538 19.7 55.2 -4.7 383.3 Cons. prices (% avg ǻ) 1799 1771 74 k -10 3154 k 4437 43.4 182.3 -59.2 3.08 k 2324 78.6 333.6 -31.5 2.95 k 751 13.5 44.1 -3.2 362.7 Cons. prices (% end-period) 1210 3670 127 k -1 4419 k 3347 41.4 252.9 -61.1 4.93 k 1221 90.0 321.7 -12.6 2.48 k 437 4.1 4.5 -2.0 20.7

Growth and economic development

GDP Growth 1804 6.5 4.98 -15.7 30.0 4691 4.0 6.37 -58.8 40.0 2563 0.7 10.62 -39.6 41.7 715 2.8 10.4 -58.8 162.1

GDP per head 1689 510 195 88 1.32 k 4599 1 512 790 377 4.3 k 2560 3 619 2.6 k 272 12.5 k 748 18.8 k 8.4 k 3.2 k 48.5 k 76 1 6 10 10 6.0 6.0 6.0 6.0 6.0 6.0 6.0 4.0 4.3 k 4.3 194 96.1 96.1 11.0 11.0 12.0 12.0 12.0 max max 96.1 k 96.1 k 32.2 k 24.0 321.3 k 321.3 0 0 0 0 0 2.1 2.1 2.9 1.8 3.0 2.0 0.0 2.0 1.0 1.0 1.0 1.0 0.6 -10 -10 min min 34.1 34.1 -4.6 k -3.2 k 0.02 k 0.02 -496.9 k -496.9 5.9 k 5.9 k 1.9 k 0.9 15.7 15.7 1.95 1.78 2.33 2.50 2.47 1.36 1.16 1.78 1.21 1.44 1.76 0.67 0.42 2.23 1.36 8.32 30.6 k 30.6 k 24.2 stdev 34.05 34.05 8.2 8.2 8.1 8.2 9.9 4.4 5.1 4.6 4.9 4.7 4.6 3.4 0.2 5.9 4.7 4.1 2.4 k 2.4 k 3.3 k 0.3 k 0.8 76.2 76.2 10.2 45.1 High income countries 10.9 k 10.9 mean obs 276 342 785 639 639 639 639 639 639 639 639 639 639 639 639 639 975 977 1002 1002 1002 1002 1002 1 9 6 10 85 5.0 5.0 6.0 6.0 6.0 6.0 6.0 4.0 7.1 k 7.1 86.6 86.6 11.3 10.1 11.3 12.0 12.0 max max 28.5 k 28.5 k 11.7 k 31.9 104.5 k 104.5 0 0 0 0 0 0 2.2 2.2 2.0 3.0 2.0 0.0 1.0 0.0 0.0 1.0 0.7 0.0 0.0 -10 -10 min min 24.4 24.4 -1.9 k -21.2 k -21.2 k -30.3 7.3 k 7.3 k 5.4 k 4.9 k 1.2 2.22 2.22 1.61 1.79 3.05 3.75 0.93 1.40 1.91 1.37 1.47 1.22 0.83 0.46 1.90 2.08 6.72 17.1 k 17.1 stdev 15.40 15.40 19.68 6.9 6.9 5.3 6.2 8.1 8.2 3.2 3.5 3.9 3.3 3.5 3.4 1.9 0.3 3.7 3.2 2.9 k 2.9 k 6.5 k 2.3 k 0.4 -2.0 57.5 57.5 17.6 -0.4 k mean Upper middle income countries obs 3608 2287 2312 2154 3608 2205 2205 2205 2205 2205 2205 2205 2205 2205 2205 2205 2205 2205 3608 3608 3608 3537 3595 1 9 6 10 74 5.0 5.0 6.0 6.0 6.0 6.0 6.0 3.5 3.9 k 3.9 79.1 79.1 12.0 10.0 11.5 12.0 12.0 max max 13.9 k 13.9 k 79.1 44.7 k 44.7 124.8 k 124.8 0 0 0 0 0 0 8.5 8.5 1.0 1.0 1.1 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -10 -10 min min -0.6 k -12.1 k -12.1 k -12.6 2.6 k 2.6 k 0.6 2.36 2.36 1.40 1.74 2.67 1.66 0.91 1.65 1.65 1.17 1.26 1.29 0.66 0.48 1.78 1.60 6.22 13.7 k 13.7 k 10.1 k 13.6 stdev 10.20 10.20 17.78 7.6 7.6 5.1 6.1 8.0 9.6 2.7 3.0 3.4 3.5 3.8 3.3 1.9 0.4 3.2 3.5 2.7 k 2.7 k 0.6 k 5.3 k 0.3 -1.6 57.9 57.9 18.0 10.1 k 10.1 mean Low income to middle countries obs 5597 4814 4762 4526 5597 4540 4540 4540 4540 4540 4540 4540 4540 4540 4540 4540 4540 4540 5597 5597 5597 5459 5524 1 8 5 10 99 9.0 9.0 4.5 6.0 6.0 5.0 6.0 6.0 3.0 7.5 k 7.5 k 1.3 43.8k 28.7k 72.4 72.4 11.6 10.0 12.0 12.0 max max 43.8 k 43.8 0 0 0 0 0 0 -9 1.5 1.5 0.5 1.0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 min min 21.8 21.8 -4.6 k -24.7 k -24.7 -20.1 k -20.1 0.3 k 0.3 8.3 k 8.3 k 5.0 2.15 2.15 1.42 1.43 2.48 1.95 1.03 1.20 1.76 1.15 1.35 1.19 0.69 0.41 1.79 1.45 5.49 10.8 k 10.8 k 15.0 stdev 10.08 10.08 17.78 7.3 7.3 5.6 5.8 9.0 9.8 3.1 2.6 4.5 3.5 3.7 3.0 2.0 0.2 3.8 3.1 2.2 k 2.2 k 9.8 k 9.7 k 0.2 -3.9 59.9 59.9 30.9 -0.1 k Low countries income mean obs 2040 1745 1770 1802 2040 1771 1771 1771 1771 1771 1771 1771 1771 1771 1771 1771 1771 1771 2040 2040 2040 2039 2040 Country group Tade Trade balance (BOP) Export credits Flow of export credits Inward direct investment trade with Finland Import Political stability ICRG political risk index - government stability - socioeconomic conditions climate - investment - internal conflict - external conflict - corruption - in politicsmilitary - religion in politics - law and order - ethnic tensions - democratic accountability - bureaucratic quality Institutional factors Elections Legal rights Creditor rights level Democracy Durability of regime 77 2.25 2.25 6.14 11.29 11.29 11.29 95.61 38.04 Cond.nr Cond.nr Cond.nr Cond.nr Mean VIF Mean VIF Mean VIF 0.25 0.25 0.84 0.62 0.25 0.77 0.70 1.00 1.47 2.04 2.77 4.66 5.44 7.68 1.00 1.25 1.98 3.59 3.93 Cond.Index Cond.Index Cond.Index 0.25 0.25 0.84 0.62 0.25 0.77 0.70 4.14 1.91 0.99 0.54 0.19 0.14 0.07 2.94 1.89 0.75 0.23 0.19 Eigenv. Eigenv. Eigenv. 2 2 2 R R R 0.75 0.75 0.16 0.38 0.75 0.23 0.30 0.43 0.33 0.46 0.46 0.94 0.94 0.12 1.00 1.00 0.75 0.07 0.02 VIF Factors VIF Factors VIF Factors Tol. Tol. Tol. 0.25 0.25 0.84 0.62 0.25 0.77 0.70 0.57 0.67 0.54 0.54 0.06 0.06 0.88 0.00 0.00 0.25 0.93 0.98 2.01 2.01 1.09 1.27 1.98 1.14 1.20 1.32 1.22 1.36 1.36 4.21 4.14 1.06 15.1 15.7 2.02 1.04 1.01 Sqrt.VIF Sqrt.VIF Sqrt.VIF 226 246 VIF VIF VIF 4.03 4.03 1.19 1.60 3.93 1.30 1.43 1.75 1.50 1.86 1.84 17.7 17.1 1.13 4.06 1.07 1.02 - 1 - efmt efmt 1 1 - stdr psbr psbr 0.040 0.040 1 1 1 tspr cara ypca 0.445 0.445 0.960 0.022 1 1 1 tspx tdpx dgdp 0.137 0.137 0.014 -0.200 -0.200 -0.274 -0.091 -0.127 1 1 1 tspy Correlation Correlation Correlation pexp dcpn 0.284 0.284 0.255 0.626 0.143 0.023 -0.263 -0.263 -0.128 -0.166 -0.023 1 1 1 artd dcpi mcov mcov 0.136 0.136 0.852 -0.213 -0.213 -0.112 -0.259 -0.232 -0.144 -0.144 -0.062 -0.114 -0.137 -0.020 1 1 1 irtd tdpy tdpy gdfd gdfd 0.222 0.222 0.041 0.803 0.528 0.998 0.838 -0.152 -0.152 -0.305 -0.386 -0.360 -0.099 -0.036 -0.235 -0.125 -0.018 tdpy tdpy artd pexp tdpx cara psbr irtd mcov tspy tspx tspr stdr efmt gdfd dcpi dcpn dgdp ypca ) ǻ Variable Solvency ratios Total debt-to-GDP Arrears-to-total foreign debt Exports-to-GDP Debt-to-exports Current Account balance Budget balance service Liquidity and debt Reserves-to-total debt Reserves-to-imports Debt-service-to-GDP Debt-service-to-exports Debt-service-to-reserves Short-term-debt-to-reserves Avg. time to of debt maturity Economic development GDP deflator prices (% avg Consumer prices (% end-period) Consumer GDP Growth GDP per head APPENDIX 2 COLLINEARITY DIAGNOSTICS AND PRINCIPAL COMPONENT ANALYSIS Table 6 variables and among the financial economic explanatory Correlations 78 1.00 1.00 3.37 7.52 9.01 2.06 1.00 3.37 7.52 9.01 1.11 6.26 10.13 10.13 11.78 12.20 14.25 15.36 17.93 21.41 23.32 25.01 10.13 Cond.Index Cond.Index 0.99 0.99 0.20 0.14 0.11 0.08 0.08 0.06 0.05 0.03 0.02 0.02 0.99 0.20 0.14 0.11 11.21 11.21 11.21 Eigenv. Eigenv. Cond.nr Cond.nr Cond.nr Mean VIF Mean VIF R2 R2 0.57 0.57 0.47 0.59 0.74 0.44 0.45 0.37 0.49 0.61 0.42 0.09 0.40 0.57 0.47 0.59 0.74 0.44 VIF Factors VIF Factors Tol. Tol. 0.43 0.43 0.53 0.41 0.26 0.56 0.55 0.63 0.51 0.39 0.58 0.91 0.60 0.43 0.53 0.41 0.26 0.56 1.52 1.52 1.37 1.56 1.97 1.34 1.35 1.26 1.40 1.59 1.32 1.05 1.29 1.52 1.37 1.56 1.97 1.34 Sqrt.VIF Sqrt.VIF VIF VIF 2.32 2.32 1.88 2.43 3.90 1.79 1.82 1.58 1.96 2.54 1.73 1.10 1.67 2.32 1.88 2.43 3.90 1.79 - 1 burq burq - 1 0.22 0.22 dem dem - 1 ethn 0.00 0.00 0.33 - 1 law 0.54 0.54 0.10 0.50 - 1 0.48 0.48 0.32 0.11 0.37 relig - 1 mil 0.36 0.36 0.37 0.26 0.15 0.42 - 1 Correlation Correlation cor 0.42 0.42 0.30 0.33 0.31 0.15 0.43 1 1 dur dur extc 0.26 0.26 0.26 0.49 0.52 0.46 0.16 0.39 1 1 pty pty intc 0.60 0.60 0.38 0.52 0.67 0.72 0.56 0.08 0.46 0.13 0.13 1 1 inv cred 0.03 0.03 0.21 0.15 0.42 0.31 0.08 0.28 0.34 0.34 0.27 0.14 -0.07 1 1 leg soc 0.50 0.50 0.31 0.38 0.33 0.31 0.45 0.43 0.04 0.41 0.51 0.51 0.38 0.04 -0.10 1 1 stab elec 0.23 0.23 0.66 0.25 0.25 0.05 0.08 0.35 0.27 0.06 0.13 -0.25 0.05 0.05 0.12 -0.12 -0.16 stab soc inv intc extc cor mil relig law ethn dem burq elec leg cred pty dur Variable Political Stability government stability conditions socioeconomic climate investment internal conflict external conflict corruption in politics military religion in politics law and order ethnic tensions democratic accountability bureaucratic quality Variable Institutional factors Elections Legal rights Creditor rights level Democracy Durability of regime Table 7 and variables among the political institutional explanatory Correlations 79

Table 8 Extraction of component factors

FACTOR Eigenvalue Difference Proportion Cumulative

Comp 1 9.11437 3.86115 0.25320 0.25320 Comp 2 5.25322 2.17986 0.14590 0.39910 Comp 3 3.07336 0.43374 0.08540 0.48450 Comp 4 2.63962 0.62627 0.07330 0.55780 Comp 5 2.01335 0.34562 0.05590 0.61370 Comp 6 1.66773 0.22241 0.04630 0.66000 Comp 7 1.44532 0.16982 0.04010 0.70020 Comp 8 1.27550 0.11581 0.03540 0.73560 Comp 9 1.15969 0.22605 0.03220 0.76780 Comp 10 0.93365 0.06585 0.02590 0.79380 Comp 11 0.86780 0.14581 0.02410 0.81790

Note: Principal component factors: 9 factors retained with eigenvalues above 1.

Figure 2 Scree plot of eigenvalues after pca 10 8 6 4 Eigenvalues 2 0

0 10 20 30 40 Number 80 ndent ndent in ǻ prob. 0.01 0.01 0.01 0.01 -0.01 -0.01 ** ** ** ** ** ** ** (6) % I + ICRG 0.01 0.01 0.01 0.04 6816 -0.05 -0.02 -3.44 14.13 14.13 94.59 (0.00) (1.85) (0.00) (0.01) (0.01) (0.01) (0.32) All + the ICRG Index 0.0961 0.0961 -1294.5 -1294.5 275.206 275.206 in ǻ prob. n.a. n.a. n.a. n.a. n.a. * * (4) % High income 256 2.84 2.84 -0.13 -0.10 -0.01 -0.07 Country group V 97.66 97.66 (0.06) (0.05) (0.04) (0.06) (3.18) -20.29 -20.29 16.326 16.326 0.2869 dropped dropped in ǻ prob. 0.01 0.02 0.00 0.02 -0.01 % (5) ** ** ** * * Upper-middle Upper-middle 0.02 0.02 0.00 0.09 1471 -0.06 -4.47 15.70 15.70 85.75 95.04 Country group IV (0.00) (4.00) (0.01) (0.03) (0.03) (0.30) 0.1476 0.1476 -247.52 -247.52 ion equation including all countries. ion equation including solvency ratios as independent variables. Models 2-5 replicate the results in different country replicate the results in ratios as independent variables. Models 2-5 different country solvency % and 1% levels. Bold figures denote statistically significant variables with the expected% and 1% levels. Bold figures denote statistically signs. in ǻ prob. 0.01 0.02 0.01 0.01 -0.01 ** ** ** * ** ** (5) % Lower-middle 0.01 0.01 0.03 0.03 4167 -0.06 -4.52 18.11 18.11 94.74 Country group III (0.00) (2.33) (0.01) (0.01) (0.01) (0.23) 0.0909 0.0909 -772.34 -772.34 154.508 154.508 in prob. indicates for change in the predicted probability of default for an increase of one standard deviation in each indepe deviation in of one standard for an increase default predicted probability of prob. indicates for change in the in ǻ in ǻ prob. 0.02 0.00 0.00 0.00 -0.01 ** ** ** (5) % Low income 0.02 7.16 0.01 1497 -0.02 -0.08 -4.41 93.45 (0.00) (7.34) (0.01) (0.03) (0.03) (0.38) Country group II 79.937 0.1156 -305.77 in ǻ prob. 0.01 0.02 0.01 0.01 -0.01 ** ** ** ** ** ** (5) % All 0.01 0.01 0.01 0.04 7396 -0.05 -4.14 16.32 16.32 248.6 94.73 (0.00) (1.76) (0.00) (0.01) (0.01) (0.12) Country group I 0.0819 0.0819 -1394.1 -1394.1 Country group nr nr group Country classification Country Variables Solvency Total debt-to-GDP Arrears-to-total foreign debt Exports-to-GDP Current Account balance Budget balance ICRG Constant Observations Log Likelihood LR chi Pseudo R2 % correctly predicted variable, holding all other independent variables constant at their * and ** indicate statistical significance at the 5 variable, holding all other independent means. Source: Finnvera plc, the Economist Intelligence Unit, the PRS Group and the Bank World APPENDIX 3 RESULTS Table 9 and credit export debt default solvency Country and selected variable dependent variable binary ‘public default’ as the logit regressions using the This table presents dynamic errors are presented in parentheses. Robust standard Note: Entries are logit coefficients. groupings according to the World Bank country classification system. BankModelgroupings according to the World country classification system. 6 adds the ICRG Political Risk Index in the regress 81 ndent ndent in ǻ prob. 0.00 0.00 -0.02 0.00 -0.01 -0.01 0.01 -0.02 % ** ** ** * (7) I + ICRG 0.00 0.00 0.00 0.03 0.71 7178 -0.15 -0.02 -0.04 -0.05 94.48 94.48 (0.00) (0.02) (0.03) (0.01) (0.04) (0.00) (0.01) (0.35) All + the ICRG Index 0.1212 0.1212 -1366.4 -1366.4 377.037 377.037 in ǻ prob. 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 % * (6) High income 272 0.51 0.51 0.12 0.15 0.61 -0.02 -0.77 -0.94 -25.1 Country group V 97.43 97.43 (0.01) (0.31) (0.37) (0.26) (2.69) (0.55) (3.40) 14.939 14.939 0.2296 in ǻ prob. 0.01 0.01 -0.01 -0.03 -0.01 0.01 0.00 * ** ** (6) % Upper-middle Upper-middle 0.00 0.00 0.14 0.01 1471 -0.04 -0.23 -0.02 -1.71 94.77 94.77 Country group IV (0.00) (0.03) (0.06) (0.01) (0.12) (0.01) (0.44) -273.9 -273.9 56.396 56.396 0.0933 ion equation including all countries. ion equation including liquidity ratios as independent variables. Models 2-5 replicate the results in different country Models 2-5 replicate the results in different country liquidity ratios as independent variables. % and 1% levels. Bold figures denote statistically significant variables with the expected% and 1% levels. Bold figures denote statistically signs. in ǻ prob. -0.03 -0.02 -0.01 0.00 0.00 0.01 ** ** ** (6) % Lower-middle 0.02 0.02 4467 -0.02 -0.22 -0.06 -0.01 -0.07 -1.69 94.78 94.78 Country group III (0.01) (0.06) (0.03) (0.00) (0.04) (0.01) (0.37) -779.4 -779.4 0.1481 0.1481 271.047 271.047 in prob. indicates for change in the predicted probability of default for an increase of one standard deviation in each indepe deviation in of one standard for an increase default probability of predicted prob. indicates for change in the in ǻ in ǻ prob. -0.03 0.00 0.01 0.00 -0.11 0.01 ** ** * ** (6) % Low income 0.02 0.02 0.15 0.01 0.03 1696 -0.05 -0.27 -2.80 Country group II 93.99 93.99 (0.03) (0.10) (0.05) (0.03) (0.10) (0.01) (0.94) -316.8 -316.8 0.1783 0.1783 137.506 137.506 in 0.00 0.00 -0.02 0.00 -0.01 -0.01 0.02 ǻ prob. ** ** ** (6) % All 0.00 0.00 0.00 0.03 7906 -0.15 -0.01 -0.04 -2.56 Country group I 94.70 94.70 (0.00) (0.02) (0.02) (0.01) (0.02) (0.00) (0.18) 0.0801 0.0801 -1507.2 -1507.2 262.588 262.588 Country group nr nr group Country classification Country Variables service Liquidity and debt Reserves-to-total debt Reserves-to-imports Debt-service-to-GDP Debt-service-to-exports Debt-service-to-reserves Avg. time to of debt maturity ICRG Constant Observations Log Likelihood LR chi Pseudo R2 % correctly predicted Source: Finnvera plc, the Economist Intelligence Unit, the PRS Group and the Bank World variables constant at their * and ** indicate statistical significance at the 5 variable, holding all other independent means. Table 10Table default credit debt and export liquidity Country variable ‘publicvariable and selected binary default’ as the dependent the logit regressions using This table presents dynamic errors are presented in parentheses. Robust standard Note: Entries are logit coefficients. groupings according to the World Bank country classification system. BankModelgroupings according to the World country classification system. 6 adds the ICRG Political Risk Index in the regress 82 ndent ndent in ǻ prob. -1.00 -0.02 0.01 -0.01 * ** ** ** (4) % Group I + ICRG 0.00 0.00 0.00 5835 -0.10 -0.05 -0.12 95.05 95.05 (0.00) (0.01) (0.00) (0.01) (0.29) All + the ICRG Index 0.0972 0.0972 -1035.7 -1035.7 223.127 223.127 in ǻ prob. 0.00 0.00 0.04 ** ** (3) % High income 417 0.04 0.04 0.01 0.00 -6.56 -78.9 Country group V 94.00 94.00 (0.06) (0.03) (0.00) (0.81) 25.769 25.769 0.1403 in ǻ prob. -0.02 -0.02 0.00 * ** ** % Upper-middle Upper-middle 0.00 0.00 0.00 1221 -0.09 -2.73 94.92 94.92 Country group IV (0.00) (0.02) (0.00) (0.35) -230.6 -230.6 29.089 29.089 0.0593 measures on economic development as independent variables. Models 2-5 replicate the development as independent measures on economic all countries. equation including ical Risk Index in the regression % and 1% levels. Bold figures denote statistically significant variables with the expected% and 1% levels. Bold figures denote statistically signs. in ǻ prob. 0.00 -0.02 -0.02 ** ** ** (3) % Lower-middle 0.00 0.00 0.00 3245 -0.16 -1.71 96.15 96.15 Country group III (0.00) (0.02) (0.00) (0.29) -485.4 -485.4 88.374 88.374 0.0834 in prob. indicates for change in the predicted probability of default for an increase of one standard deviation in each indepe deviation in of one standard for an increase default probability of predicted prob. indicates for change in the in ǻ in ǻ prob. -1.00 0.00 0.00 ** * (3) % Low income 0.00 0.00 0.00 1210 -0.23 -0.94 Country group II 93.55 93.55 (0.00) (0.03) (0.00) (0.41) -253.6 -253.6 87.378 87.378 0.1470 in ǻ prob. -1.00 -0.02 0.00 ** ** ** (3) % All 0.00 0.00 0.00 6093 -0.10 -2.74 Country group I 95.19 95.19 (0.00) (0.01) (0.00) (0.09) 0.0483 0.0483 -1115.4 -1115.4 113.275 113.275 Country group nr nr group Country classification Country Variables Indicators of economic development prices Consumer GDP Growth GDP per head ICRG Constant Observations Log Likelihood LR chi Pseudo R2 % correctly predicted variable, holding all other independent variables constant at their * and ** indicate statistical significance at the 5 variable, holding all other independent means. Table 11Table debt default credit export and development economic Country variable ‘public logit regressions binary default’ as the dependent variable and selected using the This table presents dynamic errors are presented in parentheses. Robust standard Note: Entries are logit coefficients. . results in different country groupings according to the results in different country groupings World Bank country classification system. Model 6 adds the ICRG Polit Source: Finnvera plc, and the Economist Intelligence Unit 83 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.01 0.01 0.01 in -0.02 -0.03 -0.01 ǻ prob. prob. ** ** ** (12) High income 639 0.01 0.01 0.17 0.14 0.11 2.10 0.33 0.43 1.00 -0.80 -0.97 -0.50 -0.24 -5.87 Country group V (0.17) (0.33) (0.23) (0.25) (0.27) (0.31) (0.38) (0.42) (0.62) (0.42) (0.26) (0.61) (3.17) -91.81 -91.81 76.224 76.224 0.2933 94.52 % 0.00 0.00 0.01 0.00 0.02 0.03 0.01 in -0.04 -0.01 -0.01 -0.02 -0.03 -0.01 ǻ prob. prob. ** ** ** ** * ** ** (12) Upper-middle Upper-middle 0.06 0.06 0.20 0.35 0.75 0.22 1.71 2205 -0.56 -0.15 -0.09 -0.10 -0.53 -0.84 -0.25 Country group IV (0.08) (0.10) (0.12) (0.09) (0.06) (0.15) (0.09) (0.10) (0.15) (0.09) (0.11) (0.20) (0.62) 0.2233 0.2233 -455.47 -455.47 261.933 261.933 92.52 % omponents of the ICRG Political Risk Rating as independent variables. Models 2-5 replicate ICRG Political Risk Rating as independent of the omponents or Tables 9-11. or Tables 0.02 0.02 0.01 0.01 0.01 0.00 0.01 in -0.01 -0.02 -0.02 -0.01 -0.01 -0.02 ǻ prob. prob. * ** ** ** * * * * * ** ** ** (12) Lower-middle 0.16 0.16 0.25 0.14 0.12 0.19 1.01 4540 -0.11 -0.33 -0.30 -0.11 -0.14 -0.10 -0.74 Country group III (0.05) (0.06) (0.07) (0.05) (0.05) (0.10) (0.06) (0.06) (0.07) (0.05) (0.06) (0.10) (0.39) 0.1829 0.1829 -937.07 -937.07 419.582 419.582 92.93 % 0.02 0.02 0.01 0.00 0.01 0.01 0.01 in -0.03 -0.01 -0.01 -0.01 -0.02 -0.02 ǻ prob. prob. ** ** * ** ** ** (12) Low income 0.35 0.35 0.11 0.03 0.31 0.15 0.18 1771 -0.56 -0.16 -0.28 -0.19 -0.41 -0.78 -0.60 Country group II (0.07) (0.07) (0.08) (0.10) (0.09) (0.15) (0.10) (0.11) (0.15) (0.15) (0.11) (0.18) (0.70) 0.2790 0.2790 -360.01 -360.01 278.666 278.666 92.43 % in 0.01 0.01 0.00 0.00 0.02 0.00 0.00 0.01 ǻ -0.01 -0.03 -0.02 -0.01 -0.01 prob. prob. All * ** ** ** ** * ** ** (12) - 0.05 0.05 0.20 0.08 0.10 0.64 Country group I 9304 -0.06 -0.31 -0.22 -0.08 -0.04 -0.03 -0.06 -0.31 92.81 92.81 (0.03) (0.04) (0.04) (0.03) (0.02) (0.06) (0.04) (0.04) (0.05) (0.04) (0.05) (0.07) (0.23) 0.1099 0.1099 524.019 524.019 Country group nr nr group Country classification Country Variables government stability conditions socioeconomic climate investment internal conflict external conflict corruption in politics military religion in politics law and order accountability democratic ethnic tensions bureaucratic quality Constant Observations Log Likelihood LR chi Pseudo R2 % correctly predicted Source: Finnvera plc and the PRS Group Table 12 risk and political Country export credit debt default variable and the subc dependent variable ‘publicbinary default’ as the the logit regressions using This table presents dynamic the results in different country groupings according to the World groupings according to the the results in different country Bank as f classification Same entries apply country system. 84 0.00 0.00 0.01 0.02 0.02 -0.02 in ǻ prob. ** * ** ** ** (5) High income 975 0.30 0.30 0.09 0.02 -0.27 -0.34 -4.07 Country group V (0.41) (0.06) (0.15) (0.02) (0.00) (0.69) 41.844 41.844 0.1278 -142.82 -142.82 96.00 % 0.02 0.02 0.00 0.02 -0.02 -0.01 in ǻ prob. ** ** ** ** ** (5) Upper-middle Upper-middle 1.00 1.00 0.01 0.02 3537 -0.26 -0.05 -2.99 (0.20) (0.05) (0.05) (0.02) (0.00) (0.21) Country group IV 0.0798 0.0798 -722.91 -722.91 institutional factors as independent variables. Models 2-5 replicate the results in different variables. as independent factors institutional 125.333 125.333 94.18 % 0.00 0.00 0.00 0.00 0.00 -0.02 in ǻ prob. ** ** (5) Lower-middle 0.10 0.10 0.01 0.01 0.00 5459 -0.18 -2.17 Country group III (0.11) (0.04) (0.03) (0.01) (0.00) (0.18) 25.692 25.692 0.0095 93.28 % -1332.31 -1332.31 0.00 0.00 0.00 0.01 -0.03 -0.03 in ǻ prob. ** ** ** * (5) Low income 0.16 0.16 0.05 0.05 Country group II 2039 -0.38 -0.04 -0.76 (0.20) (0.05) (0.06) (0.02) (0.01) (0.30) 0.1826 0.1826 -478.25 -478.25 213.626 213.626 91.56 % 0.01 0.01 0.01 0.01 0.00 -0.03 in ǻ prob. All ** * ** ** ** (5) 0.24 0.24 0.04 0.02 0.00 Country group I -0.27 -2.14 (0.08) (0.02) (0.02) (0.01) (0.00) (0.11) 12493 12493 0.0276 0.0276 -2916.3 -2916.3 165.695 165.695 93.52 % Country group nr nr group Country classification Country Variables Elections Legal rights Creditor rights level Democracy Durability of regime Constant Observations Log Likelihood LR chi Pseudo R2 % correctly predicted Source: Finnvera plc, Polity IV Project, Bank’s Doing Business database. Wikipedia, and the World Table 13Table default debt credit and export factors risk Institutional and selected the dependent variable as ‘public default’ variable binary the logit regressions using dynamic This table presents country groupings according to the Bank country country groupings World classification system. Same entries as for apply Tables 9-12. 85

CORPORATE DEFAULT PROBABILITY AND POLITICAL RISK: A STUDY OF EXPORT CREDITS

ABSTRACT

Despite the increased presence and importance of political risk in the world economy, the impact of political risk on corporate default has not been rigorously incorporated into credit risk analysis. In this study, political risk factors are investigated as conceivable causes of corporate default using a dynamic logit specification to a new dataset of export credit guaranteed debt contracts from Finland. Corporate default probabilities are modeled considering 14 countries identified as the most active non- western export partners of Finland during the 1985-2005 period. Apart from the usually employed firm specific financial ratios, the study looks at a vector of country specific political and legal risk indicators as potential determinants of the default probability. These include various indicators of political instability, the level of democracy as well as legal and creditor rights. The results indicate that, in addition to traditional measures of firm leverage, political instability in the form of external and internal conflict in a host country is a significant predictor for corporate default. At an aggregate level, supportive evidence is found of a positive relationship between political risk and corporate default. In circumstances of scarcely available, or unreliable financial information, adding such measures improves the credit evaluation process. 86

1. INTRODUCTION

Identifying actual and potentially financially distressed (or bankrupt) firms starts with the analysis of the corporate financial statements. Among the first classifications of companies into healthy and distressed firms based on their accounting is Fitzpatrick (1932), who compared financial ratios between successful industrial enterprises from those that failed. He found that the probability of default was related to the individual characteristics of firms. Later, when developing the famous Z-Score, Altman (1968) used five financial ratios weighted differently to attribute a credit score to firms. Similar accounting based models are still applied when no publicly traded securities are available and when the structural models71 combining market data and finance theory as developed by Merton (1974), and, Black and Scholes (1973) can not be used. Extensions to Altman’s multiple discriminant analysis model include linear or nonlinear regression models (usually logit or probit) that can explicitly model nonlinearities between financial variables and the score. The models compute the probability of default directly (see e.g. Shumway (2001) and Chava and Jarrow (2004)) with the largest variations in the default probabilities usually stemming from ratios capturing firm profitability, level of indebtedness, and liquidity.

However, research on bankruptcy and default prediction has not been conclusive as to which variables are good predictors of corporate default. The forward default probability within a given time horizon conditional on survival until the beginning of that horizon, are usually explained with a set of corporate-specific covariates. Some studies additionally include industry or economy-wide factors, or stock market information. Recent contributions employ discrete duration models or, equivalently, multi-period logit models (see e.g. Shumway (2001), Chava and Jarrow (2004), Hillegeist et al. (2002) and Campbell et al. (2006)). A variable that measures a firm’s current leverage is usually included and found to have significant explanatory power72. In this study, country-specific political risk factors are investigated in order to determine if these are useful in the credit evaluation process, especially in situations when corporate financial information is unavailable or is of poor quality. Credit decisions involving developing countries are often made under similar circumstances.

The general approach of this study is to estimate corporate default probability directly from failed and non-failed debt contract observations, given historical data using annual financial statements. While financial statements of a company are the basis for evaluating its credit profile, non-financial and environmental characteristics seem necessary to complete the picture. The first contribution of this paper is to identify linkages between corporate default probability and political risk in the country where the borrowing company operates. To what extent do politically unstable environments constitute a risk for foreign lenders that might not otherwise be reflected in the financial statements of the borrowing company? Is there a way to distinguish between political and commercial risk factors in the credit evaluation process? The paper thus builds on

71 These models have their origins in options theory and model debt as an option. 72 Duffie et al. (2007) and Lo et al. (2008) study the dynamics of leverage through time series models inspired by the pricing model of Collin-Dufresne and Goldstein (2001). Model parameters such as target leverage or speed of adjustment are estimated within a first-order vector auto-regressive time series model (Duffie et al. (2006)) or directly taken from the capital structure literature (Lo et al. (2008)). Both studies conclude that modeling the dynamics of leverage increases predictive accuracy. 87

the renewed interest in the academic literature on the link between political risks and political institutions facing multinational corporations73. There are no previous studies that have incorporated political risk factors directly into analysis of corporate defaults, as attempts on political risk analysis (see e.g. Citron and Nickelsburg, 1987; Balkan, 1992; Li, 1992; Rivoli and Brewer 1997, and Peter, 2002) have mainly focused on the public sector borrowing, testing the significance of some political variables in the context of estimating sovereign defaults.

The second contribution of the study is the use of a new type of credit data, that is, the export credit guaranteed debt contracts between Finnish exporters and foreign buyers in 14 selected target countries. The paper empirically explores the relationships between political risk factors and the default probability, observed in these export credit guaranteed debt contracts issued to foreign corporations by the Finnish Export Credit Agency, Finnvera plc. Thus, the study is a new attempt to model the default probabilities from historical realised payment interruptions74. An export credit is a financing arrangement that allows a foreign buyer of exported goods and/or services to defer payment over a period of time, usually until the time after the product has been delivered. Technically, these contracts can be viewed as credit default swaps, which are not traded on financial markets.

Export credit contracts constitute an interesting research material for assessing the relationships between the risk of financial assets and the broader macroeconomic and political environment. Overall, the use of ECA data is warranted, as there are otherwise only few empirical attempts within the framework75. World export credit agencies (ECAs) have played a critical and strategic role in international financing during the last decade, and are especially important for developing countries. Figure 1 and Figure 2 in Appendix 1 illustrate the development of business volumes, claims and recoveries for the members of the Berne Union76. The level of aggregate new business for all members increased by 36% and broke the US$1 trillion for the first time at the end of 2005. Overall, the average growth rate of new business is 10.5% between 1993 and 2006. During this period, the claims paid were at their highest between 1993-1995, 1999 and 2002, whereas recoveries have reached their peak only recently (Figure 2). The developments reflect, among other things, Argentina’s financial crisis, and the general prevailing credit cycle. The amount of investment that ECAs support globally is growing significantly greater than the total amount of lending from the World Bank, IMF, and other multilateral institutions combined (Boote and Ross, 1998).

73 See e.g. Henisz (2000, 2002) and Jensen (2002, 2006) for domestic institutions and FDI flows. The relationship between democratic institutions and sovereign borrowing are investigated in Schultz and Weingast (2003) and Saiegh (2005) 74 This type of data has not been previously addressed in similar credit risk- or default probability studies, due to a general secrecy surrounding Export Credit Agencies and consequently, the unavailability of data. 75 As noted by Ascari (2007); “while the theoretical underpinnings and empirical results of ECA’s activities at times have been subject of scrutiny, economic literature on this line of research has almost disappeared over the last two decades”. 76 Berne Union is the international union of credit and investment insurers, whose members help promote world trade by supporting exports and/or investments to both highly developed and emerging markets. Berne Union members are major players in cross border trade; collectively, their business volumes amount to about 7% of world trade. 88

Also the recent changes in the regulation of commercial lending set the stage for an interesting research area. The Standardized Approach under Basel II, allows banks to use risk assessments of credit rating agencies, known in bank regulator jargon as External Credit Assessment Institutions (ECAIs) for the calculation of their capital requirement against credit risk. Only the credit assessments of an eligible ECAI, may be used by banks and investment firms to determine risk weights to calculate the minimum capital needed to absorb surprise losses from defaults. Banks using the simpler Basel II approaches to measuring the chances of borrowers defaulting on their loans will rely on the judgments by ECAIs, while banks using more sophisticated methods of measuring their credit risk will use their own internal assessments of creditworthiness. In recognition of the light market penetration of the major rating agencies the Basel Committee has incorporated the recognition of country risk scores assigned by Export Credit Agencies (ECAs). Banks may use the risk scores published by individual ECAs recognised by national supervisors or the consensus risk scores published by the OECD in the “Arrangement on Guidelines for Officially Supported Export Credits”. This will allow for greater ratings coverage for sovereign exposures.

This study investigates export credit guaranteed debt contracts issued to 14 selected target countries between the 1985-2005 period. The countries included in the sample are Argentina, Chile, Colombia, Indonesia, Mexico, Peru, Philippines, Poland, Saudi Arabia, Singapore, Slovakia, South Africa, the United Arab Emirates and Venezuela. These countries were chosen on the basis of data considerations and should serve as representative examples of developing countries from different economic regions. The countries are the most active of Finland’s (non-western) export credit clients in terms of issued guarantees as well as default events during the study period. Since the associated data collection of accounting ratios consisted of manual search in old archives of records available only in paper format, the selection of countries with a sufficient amount of observations as well as financial information was essential. To check the countries representative credentials the countries selected were also identified as being among the largest export credit recipients in the world (see Table 1 in Appendix 1).

Political risk can encompass many factors and events that appear in a remote manner on corporate undertakings and operating conditions. Examples include exchange controls, expropriation or nationalisation of property and resources, non-convertibility of currency, war damage, civil strife, actions against personnel (e.g. kidnapping), limits on remittances, government interferences with contractual terms, discriminatory taxation, politically-based regulations on operations and the loss of copyright protection (see e.g. Howell and Chaddik, 1994). Changes in operating conditions may arise from the political process, either directly through war, insurrection, or political violence, or through changes in government policies that affect the asset ownership or perhaps, impact the debt repayment abilities of the company78. In this study, political risk is proxied largely by the risk ratings provided by the International Country Risk Guide (ICRG). The ICRG political risk index are prepared by market analysts in each country and are subjective incorporating the analyst’s opinions of potential changes in the constituent elements of the index. The ICRG indices can be interpreted as measures of “varying degree of political risk”. It is therefore assumed that the ICRG indices incorporate both the current political climate and expectations of the future political

78 Jodice (1985) notes that political risk can be conceptualised as events in national and international environments that can affect the physical assets, personnel and operations of the firms. 89

climate in each country. Corporate default probabilities are likely to be influenced by both of these factors.

The remainder of this paper is organised as follows. Section 2 provides a brief literature review on default probability estimation and introduces and discusses potential channels through which political risk may impact on firm default. After formulating our main research hypothesis, the research methodology and data are presented in Section 3. The main results from the empirical investigation are presented in Section 4. Section 5 presents the conclusion the research. 90

2. CORPORATE DEFAULT AND POLITICAL RISK

There are two schools of thought in the use of statistical methods to predict corporate default. One holds that default is modeled using market, or price information, whereas the other recommends using accounting data. This study is the first attempt to include political and legal risk factors among the explanatory variables in a firm default prediction model. Before presenting our hypotheses and variables to be used, the concepts of default probability and political risk are defined. Whereas this study considers mainly unlisted corporations the literature review concentrates on default probability estimation under the so called fundamentals based models78.

2.1. Default probability estimation

The total credit risk (or the risk premium) of corporate debt may be described in terms of currency risk, reflecting the risk of a depreciation or devaluation of the domestic currency79; default risk, that mirrors the financial health (solvency) of the borrower under consideration and compensates for the risk that the borrower may default. In addition, a jurisdiction risk can occur when domestic financial regulations and international legal standards differ. In the literature, the sum of the default premium and the jurisdiction premium is often called the country premium or simply country risk.

Among the first empirical attempts to utilise financial information to predict bankruptcy is Beaver (1966) and Altman (1968). Beaver (1966) concluded that the cash flow to debt ratio was the single best predictor of firm default. Beaver’s univariate approach of discriminant analysis led the way to a multivariate analysis by Altman (1968) who adopted a multivariate discriminant analysis (MDA) framework in his effort to find a bankruptcy prediction model. This became the Z-score model, where the financial ratios used are: 1) working capital over total assets; 2) retained earnings over total assets; 3) earnings before interest and taxation over total assets; 4) market value of equity over book value of liabilities; and 5) sales over total assets. The Z-score correctly classified 94% of the bankrupt companies and 97% of the non-bankrupt companies one year prior to bankruptcy. Later, Altman (2000) revised the model for private firms by substituting book value for market value in the calculation of the ratio of market value of equities to the book value of liabilities80. The popularity of the Altman Z-score is explained by its parsimony and ease of interpretation. Recently, Moody’s KMV has developed a model for assessing the expected default frequency (EDF) for private companies using only financial statement information (see e.g. Dwyer et al 2004).

A large number of financial ratios can be used as explanatory variables in the accounting based models. Usually, the variables selected are those with the highest discriminating power for explaining the default frequency after performing univariate analysis. There is also a risk of “over fitting”, that is, the model functions only on the sample data but fails to engage with real world data that it has not “seen” before. A variety of statistical techniques have been used to assess the default probability,

78 For a good review of the market based models, see e.g. ChanLau (2006). 79 As noted by Peter and Grandes (2005), here the currency risk do not refer to the exchange risk that can arise as a result of an investor’s risk aversion and/or because of covariance with exchange rates. 80 A summary of the methodologies is given in Chuvakhin & Gertmenian (2003) 91

including also more advanced econometric models, such as the k-nearest neighbour classifier, neural networks, and support vector machine classifier (see ChanLau, 2006). Due to problems with the assumptions of the initially employed discriminant analysis, discrete dependent variable econometric models (i.e. logit or probit models), have become the more popular tools. Ohlson (1980), and Platt & Platt (1990) present some of the early studies using the logit81. In sum, previous work has focused mainly on the appropriate statistical methods used to develop the model, finding the financial variables which best discriminate between the failed and non-failed firms, and demonstrating the performance of the prediction model by examining the percentage of firms predicted correctly.

2.2. Political risk

The concept of political risk is generally regarded as rather controversial, because its identity crosses the boundaries of specific social sciences: political science, economics, law and sociology (Simon, 1984). The field as such attracts a variety of people from different disciplines, ranging from economists, finance professionals, experts in business management and organisational behaviour to political scientists, public policy makers as well as military and intelligence professionals. The analysis of political risk bridges various methodologies and approaches and centres broadly on the question on how political environments of economic action differ from one another between societies, and how those differences, can be managed by states and companies (see e.g. Loikas, 2003). Hill (1998) crystallises the problem of political risk definition and measurement by revisiting the standard finance theory treatment of risk. Already in the classic book by Knight, (1921) risk and uncertainty were distinguished by whether we can make good predictions by extrapolating from sufficiently similar (or homogeneous) past events, or whether the extrapolation is to be made from dissimilar (heterogeneous) events. In the latter case, the quantification is far more difficult depending on the appreciable degree of confidence and precision in the prediction of possible outcomes, payoffs and associated probabilities. The more heterogeneous the events, as is the case with political risk, the less we know about the distribution of possible outcomes. In addition, as the world changes the profile of political risk changes and a new event within the class of political risk may revise the definition of political risk itself.

Though signs of interest towards political risk in the corporate finance literature existed as early as in the seventieth century (see e.g. Baskin and Miranti, 1997), this field of scientific research has gone through periods of transformations only recently. Loikas (2003) reviews the various political risk paradigms. This area of research has been generally very practical, emerging as a response to political events in the international arena. In particular, specific political events after the Second World War created a demand for risk analysis, which led to the scientific consciousness and reception of political risk in the economic literature. The period from 1960s to the end of 1970s was dominated by studies on multinational corporations (MNCs) and their exposure to political risk. Concepts like confiscation, expropriation and nationalisation became critical concerns for companies with foreign operations as some independent countries that had just recovered their sovereignty from colonial powers tried to overcome their lack of capital by simply taking over the foreign subsidiaries of multinationals. Another

81 See e.g. Laitinen (1999), who use automatic selection procedures to select the set of variables to be used in logistic and linear models. The authors test these variables also thoroughly out-of-sample. 92

example from this period was the revolution in Iran in 1979, with its hostile acts against international companies, which led the researchers to add questions of political stability to the variables being examined. The second transformation of political risk research took place in the 1980s with the international debt crisis in many developing countries, when a large part of the literature was dedicated to the creditworthiness assessment. Quantitative risk assessment methods were developed along with the probabilistic interpretation of country and political risk. A refinement and professionalisation of the political risk concept emerged with the systematic use of these quantitative approaches on the corporate level. The crises in Mexico (1994), Asia (1997), Latin America (1999- 2002) as well as the Russian default (1998) formed the third evolution of the political risk paradigm. Through these events, political risk research shifted its attention to “financial crises” and the identification early warning indicators for such crises. Overall, there is an evident growing interest in the academic literature on the link between political institutions and political risks facing multinational corporations. A significant line of research is devoted on domestic institutions and FDI inflows (see e.g. Henisz 2002, and Jensen 2006). Meanwhile, only few authors have investigated the relationship between political risk and credit, and the focus has been mainly on sovereign borrowing (see e.g. Citron and Nickelsburg 1987, Balkan 1992, Edwards 1986, Brewer and Rivoli 1989 and Peter 2000) and the relationship between democratic institutions and borrowing (see e.g. Schultz and Weingast 2003 and Saiegh 2005).

How broadly political risk is ultimately defined is determined by the interests of the definer as well as on the type of investment involved. Haendel (1979) defines political risk as “the probability of the occurrence of some political event that will change the prospects for the probability of a given investment”. Caouette et al. (1998), defines political risk in a credit risk framework as “the possibility of delayed, reduced, or non- payment of interest and principal where the outcome is attributable to the country of the borrower”. In the context of political risk insurance, political risk can be defined as the company’s exposure to the risk of a political event that would diminish the value of an investment or a loan (see. e.g. Alwis et al. 2006). The major political risk classes, usually considered in the insurance framework are: 1) Currency inconvertibility and exchange transfer; 2) Confiscation, expropriation and nationalization; 3) Political violence or war (including revolution, insurrection, politically motivated civil strife, terrorism); 4) Breach of contract, contract frustration or contract repudiation.

2.3. Hypotheses on political risk

The core hypothesis in this study is that the event of debt default by corporations are uncertain not only because of the corporation as an individual obligor, but also due to surrounding political and legal risk factors, over which the corporation has little or no control. Wagner (2000) distinguishes on how political risk may affect firm’s performance. There is a difference between firm-specific political risks and country- specific political risk, sometimes also referred as the “micro” and “macro” risks. Micro political risks are risks directed at a particular company, and are discriminatory by nature. A firm may be able to reduce both the likelihood and impact of firm-specific risks by imposing strong arbitration language into a contract or by enhancing onsite security to protect against terrorist attacks. On the other hand, macro or country-specific political risks are not directed at a firm but may still affect its performance. Examples include a government’s decision to forbid currency transfers or the outbreak of a civil 93

war within the host country. Wagner (2000) also distinguishes between government and instability risks. The former arise from the actions of a governmental authority, whether that authority is used legally or not. The latter emerges from political power struggles. The ultimate challenge is to determine whether any particular type of political event poses a threat to a firm’s financial performance. We divide our political risk hypotheses under political instability and quality of political institutions.

2.3.1. Political instability

Government stability risk involves the traditional risks of expropriation or confiscation or the risk of currency inconvertibility and transferability, for which we expect a positive relation with corporate default. If government stability hampers, a corporation may be unable to obtain foreign exchange in order to repay its debt, i.e. send payments out of the country due to governmental restrictions or foreign exchange transfer.

Also social stability is an important risk factor, as it may put capital investments at risk. Overall, social stability is the basis for any successful investment. This involves the state taking responsibility for workers' welfare. By creating jobs in the public and private sectors, the state ensures subsidised housing, education and the health of the population. Unfortunately, the assessment of social stability is notoriously subjective and it is not easy to obtain assessments that are comparable among different countries. However, the difficulty does not diminish the importance of taking this factor into account, and at a minimum, the assessments should consider the following factors: socioeconomic conditions, religious tensions, ethnic tensions, internal conflict, law and order and military in politics.

The stability dimension is related also with political violence, such as war, sabotage, or terrorism. We assume that situations with worsened social risk conditions may cause companies, which would otherwise be willing to pay the creditor, to be unable to do so. Concerning the external political risk dimension, we evaluate how the risk of a war through foreign policies and military build-ups, may translate in uncertainty in a country and consequently, to corporate defaults. A country that promotes good relations with its neighbours, is better placed to promote its foreign trade and to attract foreign investment. The political system prevailing in a country shapes its external policy. Dictatorships, theocratic states, monarchies and democracies, each based on different legal systems and political culture and ethics, may also influence on corporate default.

2.3.2. Quality of political institutions

Countries in the world operate under varying political and legal systems, under varying levels of economic development as well as financial strength. Quality of governance can is closely related to political and social stability since bad governance can easily lead to political instability. Meanwhile, a nation’s quality of governance goes beyond questions of stability in the assessment how well governed the country is, i.e. how predictable its business environment is. This aspect includes, among other things, the rule of law, regulatory framework, corruption, government effectiveness, bureaucracy and democratic accountability. Many of the same information sources as listed previously offer information about quality the country governance. A country’s political system is hypothesised to have direct or indirect impact on corporate defaults through the quality of a country’s political institutions in terms of the exposure to democracy. According to the “democratic advantage” argument (see e.g. Shultz and Weingast 2003), democracies 94

in general pay lower interest rates for the sovereign debt than authoritarian regimes, because they are better able to make credible commitments. We test if this argument is reflected also on the corporate level and expect a negative relationship between the default probabilities and the level of democracy in the country where the borrowing company operates.

Further, good legal environments foster higher volumes of credit (see e.g. Jappelli et al. 2002) and in such environments, also smaller firms have greater access to formal financing opportunities (see e.g. Chemin, 2005). We test whether legal- and creditor rights in a country affect the corporate default probability. We argue that legal costs may prevent the borrower to incur a so called “strategic default”, i.e. the case when the corporate fails to pay the amount stipulated in the debt contract even though it possesses resources to do so. On the other hand, with costly liquidation, creditors may prefer to forgive part of the debt, which may result in equity holders’ incentives to default opportunistically (see e.g. Davydenko and Strebulaev, 2003).

Recently, some studies have also explored the mechanisms through which legal origin of a country influences on its financial development (see e.g. Beck et al. 2003). Legal families that can be explained by the “political channel”, i.e. differences in giving priority to private property rights of the state, could also be crucial in the context of corporate performance. Beck et al. (2003) found that there is less supreme court power in Civil Law than in Common Law countries so the influence by the state is therefore regarded as higher in the Civil Law legal family. One may also argue that legal families respond differently to changing socioeconomic circumstances, and as confirmed by Djankov et al (2005) the case law approach by the Common Law is seen as more flexible whereas Civil Law is regarded as inherently more formalistic and rigid. Following previous cross-country evidence, that suggests a link between judicial quality and corporate financing, we compare the results between different legal families following legal origin categorization by Djankov et al. (2003, 2005). We expect countries with Civil Law origin (higher state influence) to have a larger impact on corporate default through the political risk channels.

2.4. Political risk measures

We include political risk measures from three sources, including the ratings by the International Country Risk Guide (ICRG)82, to measure government- and instability risks. The level of democracy is measured using the Polity IV dataset. Legal and creditor rights indices are taken from the World Bank’s Doing Business Database. All political risk variables employed are further described in Table 8 in the appendix.

2.4.1. ICRG Political Risk Rating

The composite ICRG rating comprises 22 variables in three subcategories of risk: political, financial and economic. We use the political risk rating (“the polrisk-index”) that contributes 50% of the composite rating and aims at gauging the country’s degree of political stability. These ratings are obtained from subjective assessment of ICRG

82 Founded in 1980, the International Country Risk Guide was initially published in the newsletter International Reports. Like Political Risk Services (PRS) it has been, since 1992, a product of the PRS Group. ICRG covers about 140 countries. See http://www.icrgonline.com for more details. 95

editors that transform qualitative information into numerical scores. The polrisk-index is calculated as the sum of 12 social and political qualitative components. Among the components, the Investment Profile is an indicator of whether a government will take precipitous actions such as expropriation. Law and Order is seen as an indicator of the stability and transparency of the legal system and an indication of whether contracts might be abrogated. Ethnic tensions are considered as preliminary conditions to strife that may lead to political violence against investors and creditors. The index varies between zero and 100 points, with higher rating indicating for lower risk. As a general guide to grouping countries on the basis of comparable risk, below 50 is considered as very high risk; 50-59.9 as high risk; 60-69.9 as moderate risk; 70-79.9 as low risk; and 80-100 as very low risk. Thus, we expect an inverse relationship between the index or any of its subcomponents (when analysed separately) with corporate default probabilities.

2.4.2. Polity IV

The Polity Index from the Polity IV dataset83, measures the degree to which a nation is either autocratic or democratic on a scale from 10 (strongly autocratic) to +10 (strongly democratic). It contains information on regime type and political structures of independent states in the world system since 1800 (see Marshall and Jaggers, 2004). As a complement to measure political stability, we include also the regime durability variable that measures the years since the most recent regime change or the end of a transition period defined by the lack of stable political institutions in a particular country.

2.4.3. Legal rights and creditor rights

To capture the effects of legal risk, we include two additional indices from the World Bank’s Doing Business Database84. The Legal rights index reflects the legal rights of borrowers and lenders and measures the degree to which collateral and bankruptcy laws facilitate lending. It has a scale from 0 to 10, including 3 aspects related to legal rights in bankruptcy and 7 aspects found in collateral law. Higher scores indicate that collateral and bankruptcy laws are better designed to expand access to credit. The Credit Information Index measures credit information registries; the rules affecting the scope, accessibility and quality of credit information available through either public or private bureaus. The index ranges from 0 to 6 featuring the credit information system and the credit information is available.

83 http://www.systemicpeace.org/polity/polity4.htm 84 http://www.doingbusiness.org/ 96

3. DATA AND METHODOLOGY

3.1. Estimation method

We model corporate default probabilities using a dynamic binary estimation method where the dependent variable is 1 if the borrowing firm defaults in a specific year and 0 otherwise. The estimated default probabilities are conditioned over a vector of lagged explanatory variables including both country specific political risk indicators as well as firm specific financial ratios.

The dependent variable yit is the binary discrete variable indicating whether firm i has defaulted or not in year t. The general representation of the model is

= β k + yi,t f ( k , X it−1 ) eit (1)

k where X it−1 represents the values of the k explanatory variables of firm i, (or the corresponding country risk index) one year before the evaluation of the dependent variable. To examine the likelihood of firm default, we estimate

k pi,t = Pr ( yi,t = 1) = E ( yi,t | X it−1 ) (2)

where pi,t is the probability that firm i will default in period t, conditional on the k observed covariates X it−1 in the previous period. The functional form selected for this study is thus a dynamic logit model. We assume that the variable yit ∈ {0,1} is related to an unobservable index yi* by a linear function of the lagged explanatory variables xi1, xi2, …. , xik , and the random term uit such that

yi* = ȕ0 + ȕ1 xi1 + ȕ2 xi2 + … + ȕk xik + uit (3)

yi = 1 if yi* > 0 yi = 0 otherwise

By this structure, we have

P(yi,t = 1| ȕ‘Xi,t-1 ) = P(ui > - ȕ‘Xi,t-1 ) (4)

= 1 - F( ȕ‘Xi,t-1 ) with F( ) being the cumulative logistic distribution for u. Our methodology is an attempt to respond to the concerns of a single period logit approach85 (see e.g. Shumway, 2001). In the model framework, lagged accounting ratios of the debtor as well as political risk and institutional risk variables from the debtor country, are tested both separately as well as jointly as predictors of default probability.

85 Concerns include a sample selection bias from using only one, non-randomly selected observation per defaulting firm, and 2) a failure to model time-varying changes in the underlying or baseline risk of default that induces cross-sectional dependence in the data. 97

3.2. The data

Guarantee level credit contract information on export credit guarantees, as well as available accounting figures for the underlying debtors, was compiled within fourteen countries, namely Argentina, Chile, Colombia, Indonesia, Mexico, Peru, Philippines, Poland, Saudi Arabia, Singapore, Slovakia, South Africa, United Arab Emirates and Venezuela. Figure 3 in Appendix 1 illustrates the change in stock of official export credits in 10-year intervals in these countries. In general, exports to newly emerging markets and developing regions rely heavily on export credit financing. On average, Asian and Eastern European destinations accounted for more than one third of the total contract portfolio of official export insurance credit agencies in the early 1990s, and the share of Asian contracts continued to increase through the 1990s (see e.g. Boote and Ross, 1998). The choice of the sample countries should reflect these developments, although it is acknowledged that, political uncertainty in these countries have been severe during many occasions during the study period. Thus, one may actually worry that the country selection leads to a source bias in the empirical analysis. It is, however, worth noting that the central idea of this study is to focus and restrict the analysis on emerging economies, in order to analyse how debt contracts of private corporations in these high-risk environments are affected by political risk. Accordingly, among the selected sample countries, accounting information for the debtor companies was collected systematically, in order to test jointly for political risk and the firm specific financial factors.

3.2.1. Export credit guarantees

The credit data is obtained from Finnvera plc, the official Export Credit Agency (ECA) of Finland. For the purposes of this study, we compiled and developed an extensive database containing all export related financing agreements between Finnish exporters and foreign buyers (debtors) between 1979 and 2006, which have been guaranteed by the Finnish state through Finnvera plc (see e.g. Table 2 in the Introduction of this thesis). The database was intended to serve in two different investigations: one studying public debtors and the other a set of corporations, the present study. Thus, detailed credit default statistics and other information on companies in various countries was collected in order to develop a default prediction model. The register based data contains detailed information for each guarantee contract, including the country of origin of the buying firm (i.e. the debtor country); the initiation and ending dates of the underlying loan; type and size of the liability; and the details of possible payment interruptions that the creditor may have experienced with respect to the particular loan concerned. A detailed description of how the guarantees are spread along the years is reported in Table 2 of Appenxdix 2. Panel A of Table 2 lists newly issued guarantees and defaults per year. Some of the guarantees in the database were initiated before 1980, but were still active in the later years in the study period. Thus, they are recorded for in the study period that is limited to the years 1985-2006. This rationale of including all active guarantees during the 20 year period is presented in Panel B of Table 2, which lists the stock of active guarantees and number of defaults for each year. Throughout the study, we consider the stock of guarantees per year and divide our credit observations in two groups: those that do not experience a default and those that eventually face financial troubles (and default) under the year in consideration. In this setting, it is possible that a guarantee that have experienced a default, may recover (i.e. manage to 98

repay the missed debt repayment tranche), and is in some of the later years considered again as a non-default guarantee observation. Data is measured in firm years and the time horizon for the future probability of default is specified as one year, consistent with the use in the banking practice. Finnvera indemnification is used as the “default indicator” which implies that the debtor has missed a payment of interest or principal, has violated against a covenant, attempted to restructure, or made any other declaration of insolvency. Any of these actions that have led the exporter to submit a notice of default for the loan in consideration to Finnvera, and that had subsequently been indemnified, are considered as “defaults” in our study86.

3.2.2. Details of the sample selection procedure

Figure 4 in Appendix 2 illustrates how all private guarantees and defaults are spread in larger geographical areas in the complete Finnvera dataset87. Included countries in the study sample have at least 5 guarantee-year observations, and have also political-and legal risk information available from our main sources88. The study sample is chosen for the period 1985-2006 due to unavailability of data for earlier years. Thus, the selection of fourteen target countries is based on choosing Finland’s most active (non-western) export countries, representing developing countries from the five different world regions. As indicated, these countries represent type examples of countries that have received a major part of world export credit (see Table 1 and Figure 3 in Appendix 1). For example, in 1996, Argentina, Indonesia, Mexico and Poland were among the top ten countries, that together accounted for 30 percent of the industrial country ECA contribution. The financial crisis affected Latin American region is represented by Argentina, Chile, Colombia, Mexico and Peru, while Saudi Arabia and United Arab Emirates represent oil-dependent Arab nations. The Philippines and Singapore are examples from Asia, while Poland and Slovakia are representatives from the East- European emerging markets. Due to limited access to accounting data from the African continent, it was possible to include only one country, South Africa, from this region. At the policy level, there are of course no obvious reasons to compare these countries as such. Meanwhile, the interest lies in the various political risk landscapes that might be distinguished with the different default patterns. Overall, this study consider these countries as type examples of regions facing various economic, commercial and political risks, where an increasing number of western businesses may still find it desirable to export and invest in. Table 3 of Appendix 2 illustrates the number of guarantees and the default-rates as well as the corresponding figures measured in guarantee-years (corp-year) for the selected countries.

3.2.3. Firm financial variables

Next, detailed financial information was collected for the importing debtor companies in the selected target countries. In lending to high risk countries, there is usually a special concern regarding the availability and reliability of the financial information. The selection of the financial variables is largely based on how the decision process of

86 We do not possess information on the dates for the loss claim decision, which would be more appropriate for a more precise default definition. However, as the data is analysed in yearly intervals, we believe this approximation is justified. The underlying debt in considered to be in a default state between the exact dates for the first and last indemnifications. 87 These figures exclude Western European countries with 17184 guarantee observations. 88 The main sources include 1) The ICRG Researchers Dataset; 2) The Polity IV Database and 3) the World Bank’s Doing Business Database. 99

the credit contract was initially undertaken, i.e what information was available when the credit was granted. Following previous literature, we select ratios that measure different dimensions of companies’ financial health, including size, turnover, profitability, leverage, solidity and liquidity. Table 5 provides summary statistics and lists the selected financial variables and ratios that are being used. This accounting information is obtained from various original sources, including Suomen Asiakastieto Oy, the leading business and credit information company in Finland; the Dun & Bradstreet Credit Bureau as well as company financial reports compiled by Finnvera plc. A sample of 217 annual, end-of-year corporate financial statement summaries are extracted for buyer companies in the selected target countries. The data is dived and presented separately for the non-defaulting and defaulting corporations. One can see that the average turnover is slightly higher for non-defaulting credit counterparts (1.89 versus 1.12). The profitability seems to be slightly higher for non-defaulting credit counterparts, while the ratio on leverage is clearly higher for defaulting credit counterparts. In the data, there is a large variation in corporate size, as measured e.g. by total assets. As firms increase the book value of their assets, they may become more correlated with the general economic and political environment in their respective countries. For this reason, we test separately for the size effect in our further analysis. Table 6 presents the correlations between firm financial variables, showing merely a moderate correlation between the variables. Summary statistics of political risk variables are given in Table 7 and the institutional ratios in Table 8. A distinguishing feature is that all political risk variables are consistently higher (i.e. a lower risk level) for the non-defaulting credit counterparts. Further, the default-events correspond to country-years with, on average, lower level of democracy, longer regime durability.

3.3. Industry sector statistics

The target countries include a total of 1 740 guarantees of which 286 (4.9%) indicate for a default. In corporate years, this amounts to 5,551 “default-free” observations and 286 “defaulting” observations. Figure 5 of Appendix 2 presents the sectoral composition of the sampled corporations, indicating that the heavy industry seem to represent the largest share in the data. In addition, the textile, confectionery, and leather goods industries show a rather large share of the total number of guarantees in the sample. However, in these industries, the export contracts are significantly smaller in size. Overall, the sample reflects the sectoral distribution of total Finnish exports, as illustrated in Figure 6. The main sectors representing Finland’s export include telecommunications, energy, forestry, the wood industry, environmental technology and shipbuilding. Also high-technology or technology-intensive products make a major contribution. Within the sample countries of this study, major industries represented include e.g. forestry, paper and plywood, energy, cold storage and consultancy. Table 4 of Appendix 2 presents the distribution of the guarantees and default events among major industry sectors. The largest share of the default events are observed in the capital intensive industries, including wood, chemical and metal. 100

4. RESULTS

4.1. Default probability and corporate financial information

Table 10 report the results for models, where the financial ratios are tested alone, grouped under profitability ratios (Models 1-4) and liquidity ratios (Models 5-8), or in combined format (Models 9-11). A negative coefficient implies that an increase in the ratio means less risk, i.e. reduces the probability of corporate default. Measured alone, the size variable shows a positive and significant coefficient, indicating that smaller firms may have stronger tendency for default. The result is in line e.g. with Altman (1993) who argues that young companies are more likely to fail than experienced companies.

Corporate sales turnover, measured by sales over total assets, is significant and negative in all model specifications. The sales turnover was also one of the significant explanatory variables in the Altman’s Z-score (Altman, 1968). In order to better read the outputs, the tables report also the change in the predicted probability for default, as each explanatory variable changes from 1/2 standard deviation below base to 1/2 standard deviation above base. For example, for one unit increase in the standard deviation of the sales turnover variable, the default probability should decrease for about 2-4% (the change in predicted probabilities is reported next to the coefficients). The more effective the corporate is at using its assets in generating sales revenue, the lower is its probability of debt default.

Profitability, measured either by EBITDA, i.e. earnings before interest, depreciation and amortization to total sales, or net profits to total sales, seem to have only a weak explanatory power of corporate default probability. The net profit-ratio shows significant negative values in Model 4 and Model 9. The negative relationship is intuitive as it predicts that a firm with higher profitability would have lower default probability, and vice versa. Meanwhile, corporations with lower profit margins may still have higher asset turnover, which would instead indicate for lower default probability. While the EBITDA is considered by many financial analysts to be a meaningful indicator of an entity's ability to meet its future financial obligations, using it as a single measure of earnings or cash flow can be misleading. While showing more profit than just operating profits, EBITDA has become the metric of choice for highly leveraged companies in capital-intensive industries. A company can make its financial picture more attractive by touting its EBITDA performance, shifting investors' attention away from high debt levels and unsightly expenses against earnings. In every case, the sign of the EBITDA is as expected: the more loss the firm made in the past year, the more it was likely to default on its debt.

Leverage, measured by total liabilities to total assets, has the expected positive and significant influence on default probability (see Models 5-8). Also the equity ratio has the expected negative sign, and is significant at 1% or 5% in Models 7 and 8. This indicates that an increasing use of debt, moves ownership from equity to debt holders, increasing the corporate’s probability of default. The finding is consistent with previous statistical default prediction models, where a company’s current leverage ratio is one of the core characteristics of credit quality. Current and quick ratios, measuring solidity or liquidity are not significant in the model specifications. 101

While the significances of the individual coefficients concerning financial ratios in the models measuring corporate performance are indicative, they do not provide evidence concerning the collective group significance of accounting information. In table 10, the composite ICRG political risk index is tested along with financial statement information. Together with turnover and profitability, the index on political risk is not significant. However, in other model specifications it is negative and significant at 1% as expected. With the leverage ratios (Model 5 and Model 6) the average change in predicted probabilities is 2% for one standard deviation increase political risk rating. In separate tests, we also analysed the impact of institutional factors, including the level of democracy, regime durability, legal rights and creditor rights in the country of the borrower. In these tests, only the Polity IV-index, measuring the level of democracy, emerged as a significant and negative predictor of default probability (in particular concerning Model specifications 2, 3, 7, 8 and 11).

4.2. Default probability and political risk

Table 11 presents the findings on the relation between political risk and corporate default, by presenting separate tests on the subcomponents of the ICRG political risk index. In this analysis, the full set of Finnvera private guarantees from non-western countries are used, in order to detect more general relationships, not necessarily connected to the specifics of the 14 sample countries in this study. This data selection covers 91 countries with 5,682 country-year observations available with the ICRG data.

The most significant political predictors of corporate default seem to be the overall investment climate and selected conflict measures, including external conflict, internal conflict, military in politics as well as religion in politics. However, ‘investment climate’ is a vague term that necessarily includes issues of governance, transparency and facility to trade, including infrastructure. The concept is recognised as particularly important to attract market-seeking foreign direct investment, especially in the services sector. The International Country Risk Guide defines the investment profile component with factors related to the risk of investment that are not covered by other (financial and economic) risk components. These include contract viability (expropriation), profits repatriation and payment delays. As such, it provides a relevant measure of the regulatory environment facing e.g. foreign lenders and investors. Low contract viability is taken as an indication that the host-country judicial system is not likely to enforce contracts made between the foreign investor and its domestic associates, and that there is a risk of unjustified expropriation of assets by the host-country government. Risk for profits repatriation signifies that foreign parent-companies are relatively free to repatriate profits and that the level of taxation on repatriated funds is relatively low. Increased risk of overall payment delays, signals the existence of legislative protection against undue delay of payment for services or products rendered. Under this methodology, it was found that the investment profile is negatively correlated with corporate default probabilities.

Except for military in politics, the conflict measures are negative and significant at 1% level in most of the models. Carrying out the tests separately between different country groups of legal origin, it was found that conflict measures do not explain the default probabilities to a similar degree in countries with Socialist or Nordic legal origin. In the full sample, the positive sign of the corruption measure (cor) might be explained with 102

the large occurrence of observations from the Nordic and German legal origin countries. The external conflict measure is an assessment both of the risk to the incumbent government from foreign action, ranging from non-violent external pressure (diplomatic pressures, withholding of aid, trade restrictions, territorial disputes, sanctions, etc) to violent external pressure (cross-border conflicts to all-out war). Thus, it may affect businesses adversely in many ways, ranging from restrictions on operations, to trade and investment sanctions, to distortions in the allocation of economic resources. Any interpretation of the external conflict variable should therefore be adjusted to the country and the circumstances in question. Generally, our results could be interpreted as the more external pressure a country has, the more the firms operating in that country are likely to default on their foreign obligations. Logit regressions on the democracy variable, the legal rights- and creditor rights do not indicate for clear-cut relationships.

Overall, it seems that our target countries exhibit similar patterns as was indicative from the full sample. In separate tests with the fourteen sample countries, it was found that the same indicators as above, i.e. the investment climate and conflict measures, show significant and negative coefficients with respect to corporate default probability. In addition, also the subcomponent measuring government stability, socioeconomic conditions and bureaucratic quality are significant.

4.3. Selected notes about the sample countries

It is acknowledged that the choice of our 14 sample countries (and the data from only one export credit agency) may involve various issues related to sample selection, as the default history may be driven by other external factors. Below, selected country characteristics are discussed, that may influence on the obtained results.

The Argentine economic crisis affected Argentina's whole economy during the late 1990s and early 2000s, with a currency collapse, the world’s largest sovereign debt default, and pervasive bank and corporate failures. A particularly high level of Argentinean defaults in 2001 and 200289 in our sample reminds about this situation that was more complicated, as almost all companies operating in the country were affected by the crisis. Compared to the debt, exchange rate and fiscal aspects of the crisis, systemic bank and corporate failure in Argentina has received relatively little attention. Interestingly, due to renegotiations of the debt contracts, and with the probable help from larger (some foreign) more solid owners, many companies survived the difficult period. The impact of the IMF bailouts and other forms of debt restructurings mechanisms are left as a topic for further work.

Indonesia has also been a crisis affected country during the whole study period, and was hit hardly by the 1997-98 Asian financial crisis. This fact is well shown in the default history in our data. Also other patterns of Indonesia may be reflected in our results, including the heavy borrowing from official creditors during the 1980s. Overall, Indonesia’s political institutions and democracy have a relatively short history, increasing the risk of political instability in the country. In the past, the country has faced political and militant unrest within several of its regions, and has also experienced acts of terrorism, predominantly targeted at foreigners. Corruption and the perceived lack of a rule of law in dealings with international companies in the past may have

89 In the full data, 95% and 67% of the stock of guarantees were in a default state during these years. 103

discouraged much needed foreign direct investment. Accordingly, these issues are expected to reflect from our estimation results. Many economic development problems remain for Indonesia, including high unemployment, a fragile banking sector, endemic corruption, inadequate infrastructure, a poor investment climate, and unequal resource distribution among regions. Further, throughout the post-war history of Indonesia, the military have played a key role in the politics of the country.

Poland experienced a transition from a centrally planned economy to a market orientated economy, some decade ago. The number of issued guarantees as well as defaults in Poland reflect the general positive developments in the country during the 1990s as well as the fact, that the country had rejoined international capital markets and regained favourable credit ratings, triggering investment inflows. Also Slovakia has mastered much of the difficult transition from a centrally planned economy to a modern market economy during the study period. Saudi Arabia and United Arab Emirates represent examples of oil dependent Arab economies that have strong government controls over major economic activities. The region has been for long the scene of both internal crises and external conflicts. On several occasions, these crises have affected either the flow energy exports or the development of energy production and thus, the import capacity of the country. Thus, the significance of the external conflict measure draws the attention also to specific issues within this region. 104

5. CONCLUSIONS

In this study, we have constructed and compared models for firm default probability with two sets of explanatory variables: the traditional firm specific accounting ratios and country specific political- and legal risk indicators. This study is the first of its kind, assessing how and to what extent political- and legal risk environments may affect firm performance and international debt repayments in particular, and whether political- and legal risk indices can be used as accurate predictors for firm default.

The results of this study are applicative especially in situations, when market data is unavailable and when accounting data is unreliable, as might be the case for private firms operating in many developing countries. The models were tested using novel export credit guaranteed debt data from Finland, consisting of a sample of 14 importing debtor countries from different legal origins from the period 1985-2005. Various indicators on political risk were tested using dynamic logit specifications. To control for firm specific effects, we collected firm financial accounting information for the companies operating in respective countries. Following previous research, we included traditional firm specific accounting ratios measuring profitability, leverage, liquidity and solidity as explanatory variables to control for firm specific risk. Political risk was proxied with various indicators measuring government stability, investment climate, conflict and war, corruption, level of democracy, and legal- and creditor rights, using indices from well established sources and country risk experts.

Subject to certain limitations with our sample selection, the results presented in this study suggest that indicators of political risk seem to explain the firm default patterns. In particular, measures of external conflict and the measured general investment climate in a country appear highly significant in repeated tests within various settings. Within our target countries, that represent developing countries and countries in transition from different parts of the world, the impact of military and religion in politics as well as religion in politics seemed to play a significant and substantial role. The conclusion from this study is that without assessing the political- and legal risk landscape, default probabilities may not be properly estimated using only (sometimes scarcely available) accounting data from developing countries. Relying only on firm financial variables in the analysis suggest that measures of indebtedness may indicate for future payment difficulties. The overall presentiment of the above results is that for the best estimate of firm default probabilities, both financial figures as well as political risk indices should be used in the credit evaluation of emerging market firms. Meanwhile, the political, economic, and legal risk dynamics that shape threats to international credit contracts are complex. Understanding the factors behind these diverse forces as well as future trends, needs detailed assessment of each country in question, taking each conceivable variable into careful consideration. 105

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APPENDIX 1 THE EXPORT CREDIT INDUSTRY

Figure 1 Export credit and investment insurance

New business, Berne Union members US$ bn (1993-2006)

1 200 Investment Insurance (INV)

1 000 Medium/Long Term Export Credit Insurance and Lending (MLT)

Short Term Export Credit Insurance (ST) 800

600

400

200

0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

Source: Berne Union Yearbooks (2002-2008)

Figure 2 Claims paid and recoveries received

Total figures for ST, MLT and INV US$ bn (1993-2006)

Premium income 30 Claims paid 25 Recoveries received

0 n/a 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year

Source: Berne Union Yearbooks (2002-2008) 111

Table 1 Thirty main recipient countries of export credits

This table presents summary statistics on the stock of official export credits, suppliers' credits and bank credits officially guaranteed or insured by an export credit agency. Countries are arranged in descending order according to the average stock value for the time period 1980-2007. The values are reported in USD millions. Countries marked with * represent countries that are included in the study sample.

Source: The Economist Intelligence Unit (2006-2007)90

Country Stock Average Min Max Country (year Stock Average Min Max (year / period) 2007 1980-2007 1980-2007 1980-2007 / period) 2007 1980-2007 1980-2007 1980-2007

Russia 13 228 28 692 8 070 44 718 * Peru n.a. 4 310 2 042 6 782 Israel 46 814 18 559 5 175 46 814 Greece 5 000 4 304 2 509 5 000 * Poland 28 236 17 956 6 824 33 020 Hong Kong 10 195 4 234 0 10 195 China 34 550 14 349 0 34 550 Thailand 2 627 4 089 1 318 12 211 * Nigeria 15 950 13 915 4 495 20 935 Morocco 1 539 3 930 0 8 878 Brazil 4 333 13 768 0 28 471 Cuba 5 215 3 924 2 732 5 215 * Indonesia 25 543 13 031 4 849 25 543 Pakistan 8 572 2 845 0 8 572 Algeria 3 005 11 610 2 971 19 681 * Venezuela 4 156 2 804 745 7 750 * Mexico 5 960 10 455 3 397 26 686 *South Africa 1 773 2 796 640 5 086 Egypt 12 383 10 434 0 21 026 *Colombia 1 527 2 188 744 3 908 Turkey 12 299 7 985 0 13 932 Côte d'Ivoire 1 963 2 086 0 3 617 * Argentina 9 538 6 790 0 12 514 Cameroon 4 177 2 048 0 4 177 India 11 494 5 174 1 666 11 494 * Saudi Arabia 2 842 1 950 914 3 865 Iran 3 441 4 874 0 13 474 Malaysia 262 1 643 0 4 373 * Philippines 1 947 4 497 1 947 9 751 Gabon 1 878 1 582 606 2 539

Figure 3 Change in stock of export credits in 10-year intervals

This table presents the change in the stock of official export credits, suppliers' credits and bank credits officially guaranteed or insured by an export credit agency. Selected countries are included in the study sample, and are arranged in descending order according to the stock value in 2005. The values are reported in USD millions. The figures for Peru (year 2005) and Singapore are not available.

Indonesia

Poland

Argentina

Mexico

United Arab Emirates 2005 Venezuela 1995 Philippines 1985 Saudi Arabia

So ut h Afr ica

Colombia

Slovakia

Chile

Peru USDm

0 5 000 10 000 15 000 20 000

Source: The Economist Intelligence Unit

90 Derived from World Bank, Global Development Finance; IMF, International Financial Statistics; and the OECD, External Debt Statistics. 112

APPENDIX 2 FINNVERA DATA AND THE STUDY SAMPLE

Table 2 Finnvera guarantees to private debtors between 1980-2006

This table presents summary statistics of the yearly issued guarantees to private debtors by Finnvera plc in a total of 114 countries. Panel A illustrates the number of initiated guarantees per year and the corresponding amount of defaults from the corresponding yearly guarantee data. Panel B describes the yearly Finnvera stock of active guarantees as well as stock of defaults/guarantees under indemnification. The last column of Panel B presents the number of new defaults/indemnification cases per year.

Source: Finnvera plc

Panel A. Private guarantees by initiation year Panel B. Yearly stock of private guarantees

# of initiated % of # of % of # of active # of defaults # of new Year Year guarantees total defaults total guarantees in stock defaults

before 1980 394 1.5 104 7.5 before 1980 - - 4 1980 814 3.1 45 3.3 1980 1 115 57 53 1981 195 0.8 71 5.2 1981 1 082 67 17 1982 385 1.5 96 7.0 1982 800 51 23 1983 880 3.4 65 4.7 1983 1 513 110 84 1984 741 2.9 56 4.1 1984 1 885 167 85 1985 144 0.6 52 3.8 1985 1 040 136 71 1986 436 1.7 76 5.5 1986 713 93 68 1987 966 3.7 75 5.4 1987 1 577 84 57 1988 942 3.6 63 4.6 1988 2 166 88 61 1989 5 325 20.5 200 14.5 1989 6 560 112 87 1990 1 874 7.2 44 3.2 1990 7 609 81 61 1991 1 436 5.5 65 4.7 1991 8 622 89 73 1992 2 796 10.8 74 5.4 1992 9 958 89 79 1993 2 397 9.2 78 5.7 1993 10 424 116 99 1994 1 767 6.8 44 3.2 1994 10 774 94 76 1995 1 137 4.4 40 2.9 1995 9 962 105 78 1996 1 334 5.1 43 3.1 1996 8 477 119 72 1997 903 3.5 33 2.4 1997 6 963 105 51 1998 265 1.0 20 1.5 1998 5 614 96 45 1999 104 0.4 5 0.4 1999 4 215 113 52 2000 106 0.4 9 0.7 2000 930 73 17 2001 135 0.5 9 0.7 2001 452 47 21 2002 112 0.4 2 0.1 2002 414 40 22 2003 158 0.6 5 0.4 2003 463 22 5 2004 92 0.4 3 0.2 2004 362 24 12 2005 25 0.1 0 0.0 2005 243 20 2 2006 78 0.3 1 0.1 2006 209 17 3 Total 25 941 100 1 378 100 113

Figure 4 Geographic coverage of the Finnvera data, private guarantees

Issued guarantees Defaults

No rthern and No rthern and Australia and Aus tra lia a nd Middle Africa Middle Africa New Zealand New Zealand 2 % 2 % 2 % Other Africa Other Africa 1 % 5 % Southern 2 % Southern Europe Eastern Europe Eastern 32 % Europe 33 % Europe 14 % 8 %

Asia 14 %

Asia 21 % North Central North America America Central America 13 % 18 % America 21 % 13 %

Table 3 Study sample of private guarantees with financial information, 1985-2005

# of default- obs percent defaults percent default-rate Country guarantees rate (corp-year) (corp-year) (corp-year) (corp-year) (corp-year)

Argentina 254 16 % 831 14.2 47 16.4 5.7 % Chile 137 31 % 580 9.9 43 15.0 7.4 % Colombia 141 22 % 521 8.9 32 11.2 6.1 % Indonesia 36 58 % 173 3.0 37 12.9 21.4 % Mexico 105 15 % 376 6.4 19 6.6 5.1 % Peru 63 24 % 181 3.1 16 5.6 8.8 % Philippines 29 10 % 129 2.2 3 1.1 2.3 % Poland 469 2 % 1 707 29.2 9 3.2 0.5 % Saudi-Arabia 125 32 % 374 6.4 44 15.4 11.8 % Singapore 63 8 % 233 4.0 5 1.8 2.1 % Slovakia 43 2 % 154 2.6 1 0.4 0.6 % South Africa 53 4 % 186 3.2 2 0.7 1.1 % United Arab Emirates 34 26 % 103 1.8 14 4.9 13.6 % Venezuela 58 16 % 289 5.0 14 4.9 4.8 % Total 1 610 15.2% 5 837 100 286 100 4.9 %

Figure 5 Sectoral composition of the Finnvera data, all guarantees

4 %3 % 2 % Consumer goods, 30% 5 % Industrial forestry, 22% 30 % 6 % Metals and Engineering, 19% Construction, 9% 9 % Chemicals, 6% Telecom and Electronics, 5% Transportation / Shipping, 4% Medical, 3% 19 % 22 % Power generation, Resources and Infrastructure, 2% 114

Table 4 Sectoral composition of sampled corporations

Source: Finnvera plc

Observations Percent Default-rate Industry sector (corp-year) (corp-year) (corp-year)

Wood, pulp and paper products default 67 23.4 % 3.2 % non-default 2 102 37.9 % Chemical products default 33 11.5 % 3.8 % non-default 866 15.6 % Metal and engineering default 136 47.6 % 8.7 % non-default 1 566 28.2 % Electrotechnical products default 18 6.3 % 3.8 % non-default 480 8.6 % Textile default 12 4.2 % 8.0 % non-default 150 2.7 % Construction default 13 4.5 % 3.7 % non-default 354 6.4 % Food products default 6 2.1 % 22.2 % non-default 27 0.5 % Shipping default 0 0.0 % 0.0 % non-default 3 0.1 % Other consumer durables default 1 0.3 % 33.3 % non-default 3 0.1 %

Total default 286 5.2 % non-default 5 551

Figure 6 Finnish exports of goods

100 % 90 % 80 % Wood products 70 % Pulp, paper and paper products 60 % Basic metals and metal products 50 % Machines, machinery and vehicles 40 % Electronics / electrotechnical products 30 % Chemicals 20 % Other goods 10 % 0 % 1960 1970 1980 1990 2000 2002 2003 2004 2005 2006

Source: Tekes and National Board of Customs (www.tekes.fi) 115 8.9 8.9 2.0 max 9.73 9.73 1.20 0.50 0.13 1.50 0.80 0.2 0.2 0.8 min 0.18 0.18 1.00 0.00 -1.30 -1.53 -0.30 (corp-year) 2.1 2.1 0.6 2.24 2.24 0.09 0.31 0.28 0.50 0.25 st.dev 1.7 1.7 1.3 4.98 4.98 1.12 0.14 0.00 0.91 0.32 mean mean 7 7 Defaulting guarantees 39 28 33 35 38 28 obs 2.4 2.4 max 9.79 9.79 0.70 1.30 2.30 1.20 83.3 27.80 27.80 0.2 0.2 0.4 min (corp-year) 0.04 0.04 0.00 -1.20 -1.30 -5.43 -0.50 0.4 0.4 2.42 2.42 2.81 0.22 0.38 0.37 0.23 10.0 st.dev 3.1 3.1 1.1 4.14 4.14 1.89 0.17 0.05 0.55 0.46 mean mean 1 Non-default guarantees obs 784 376 643 723 784 818 670 221 quick quick 1 0.2473 0.2473 current 1 er name name size rev_ta ebitda prof_rev debt er current quick 0.5250 0.5250 0.1995 1 debt exp. sign (+) (-) (-) (-) (+) (-) (-) (-) -0.3930 -0.3930 -0.6415 -0.1262 1 0.0470 0.0470 0.0736 0.1143 -0.1376 -0.1376 value USDm % % % % % % % prof_rev 1 -0.080 -0.080 ebitda 0.3401 0.3401 0.2801 0.1782 0.4243 1 rev_ta 0.2513 0.2513 0.2958 -0.1955 -0.1955 -0.1285 -0.6676 -0.0185 1 size size 0.0533 0.0533 0.0178 0.1945 -0.1365 -0.1365 -0.0599 -0.3894 -0.0713 91 Total assets Sales / Total assets EBITDA Net profit (or loss) / Sales Total liabilities / Total assets ratio Equity / Total assets) (Book value of equity Current ratio (Current assets / Current liabilities) Quick ratio (Cash + Accounts receivable / Current liabilities) Variable definitions Accounting measures Size Turnover Profitability Profit Leverage Solidity Liquidity size rev_ta ebitda prof_rev debt er current quick Earnings before interest, taxes, depreciation and amortization divided by sales. divided by and amortization Earnings before interest, taxes, depreciation Table 5Table analysis in the ratios accounting Employed financial statements, Suomen Asiakastieto Oy, Source: Corporate Dun Bradstreet. & Table 6 ratios among accounting Correlation 91 116 max 10.8 10.1 11.5 12.0 12.0 6.0 6.0 6.0 6.0 6.0 6.0 3.5 min 2.0 2.0 2.9 3.0 5.0 0.0 0.0 1.0 1.0 1.0 2.0 0.0 (corp-year) st.dev 2.0 1.6 1.9 2.8 1.8 1.0 1.6 1.8 1.3 1.6 1.1 0.8 mean mean 6.3 5.3 5.7 6.9 9.2 2.9 2.9 3.8 3.2 4.3 3.5 2.3 Defaulting guarantees obs 263 263 263 263 263 263 263 263 263 263 263 263 max 11.0 10.7 12.0 12.0 12.0 6.0 6.0 6.0 6.0 6.0 6.0 4.0 min 1.0 2.0 2.9 2.8 5.0 0.0 0.0 1.0 1.0 0.7 0.0 0.0 (corp-year) st.dev 1.8 1.2 1.8 2.5 1.3 1.1 1.6 1.0 1.4 1.1 1.2 0.7 mean mean 7.4 6.2 7.1 9.8 11.1 3.5 4.5 4.9 4.2 5.2 4.2 2.7 Non-default guarantees obs 5419 5419 5419 5419 5419 5419 5419 5419 5419 5419 5419 5419 name name govstab socec invest intcon extcon cor mil religion law ethnic demac burq exp. sign (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) value (0-12) (0-12) (0-12) (0-12) (0-12) (0-6) (0-6) . (0-6) (0-6) (0-6) (0-6) (0-4) Government unity, legislative strength and popular support. support. legislative strength and popular unity, Government confidence and poverty. consumer Unemployment, Contract viability, expropriation, profits repatriation and delays. payment Civil war, terrorism/political violence and civil disorder. war, Function of and foreign pressures. cross-border conflict has a government been in power continuously. Length of time harmony. to live in and the of religious groups Degree of religious freedom, capacity observance of the law. Strength / impartiality of the legal system, and the popular The degree of tolerance and compromise between various ethnics. to its people. Degree of responsiveness a government Ability of the local bureaucracy to administrate the country. Risk component and the derivation Political stability stability Government conditions Socioeconomic profile Investment Internal conflict External Conflict Corruption Military in Politics Religious Tensions Law and Order Ethnic Tensions AccountabilityDemocratic Bureaucracy Quality Table 7Table analysis the in risk ratios political Employed Source: The PRS Risk Guide. Group / The International Country 117 1 67 6.0 6.0 max 10.0 10.0 10.0 credit 0 1 2.0 2.0 2.0 min -10.0 -10.0 (corp-year) legal -0.3508 -0.3508 7.6 7.6 1.2 1.3 20.3 20.3 1 st.dev 0.2350 0.2350 durable -0.0688 -0.0688 0.8 0.8 3.9 4.7 21.3 21.3 mean mean 1 Defaulting guarantees polity obs 0.0092 0.0092 248 248 251 251 -0.6461 -0.6461 -0.2756 1 burq burq 0.2582 0.2582 0.2016 -0.0745 -0.0745 -0.2303 81 6.0 6.0 max 10.0 10.0 10.0 1 0 2.0 2.0 2.0 demac demac 0.2631 0.2631 0.7813 min (corp-year) -0.5886 -0.5886 -0.3102 -0.0160 -10.0 -10.0 1 5.6 5.6 1.7 1.2 17.0 17.0 ethnic st.dev 0.4323 0.4323 0.3076 0.4875 0.2229 defined by the lack of stabledefined political institutions) -0.2574 -0.2574 -0.2279 1 5.7 5.7 3.8 4.5 15.2 15.2 law mean mean 0.4175 0.4175 0.1460 0.2954 0.0335 0.0844 -0.2792 -0.2792 -0.0081 Non-default guarantees obs 5189 5200 5271 5271 1 0.2814 0.2814 0.5414 0.5038 0.1242 0.5146 0.3095 -0.4692 -0.4692 -0.0017 religion 1 mil 0.3454 0.3454 0.6116 0.4340 0.3106 0.5318 0.2083 0.0250 -0.3039 -0.3039 -0.2041 name name polity durable legal credit 1 cor 0.5992 0.5992 0.2947 0.5300 0.4210 0.3496 0.5695 0.4277 -0.4702 -0.4702 -0.0272 -0.1587 1 exp. sign (-) (-) (-) (-) extcon 0.4294 0.4294 0.4204 0.5745 0.4093 0.4035 0.4836 0.1203 0.5237 -0.5751 -0.5751 -0.0880 -0.0541 1 value (-10 / +10) years (0 -10) (0 - 6) intcon 0.5649 0.5649 0.5111 0.7721 0.4872 0.7689 0.4496 0.3943 0.3024 0.1975 0.0358 -0.3100 -0.3100 -0.0983 1 inv 0.1865 0.1865 0.0876 0.2834 0.0898 0.0608 0.0941 0.3367 0.2715 0.1087 0.0368 -0.0063 -0.0063 -0.0816 -0.0416 1 socec 0.1793 0.1793 0.1115 0.1375 0.0030 0.1430 0.0028 0.1230 0.1124 0.1256 0.2943 0.0604 -0.0800 -0.0800 -0.2790 -0.2891 1 0.6829 0.6829 0.2711 0.0638 0.2600 0.1715 0.1847 0.1275 0.1664 0.1978 0.0314 0.0139 0.1009 0.0011 -0.0288 -0.0288 -0.0481 govstab Levels of democracy. Levels of democracy. The degree to which a nation is either autocratic or democratic. Years since the recent most regime change (defined by a three point change in the POLITY score over a period of three years (or less) or the end of a transition period The degree to laws facilitatewhich collateral lending. and bankruptcy Measures rules affecting the scope, access and quality of credit information. Risk component Institutional factors Polity IV index Polity IV durable Legal rights index index Credit Information govstab socec inv intcon extcon cor mil religion law ethnic demac burq polity durable legal credit Table 8Table analysis factors in the institutional Employed Bank) Source: Polity IV / Doing Business Database (World 9Table factors and institutional risk ratios among political Correlation 118 ǻ 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 ** 11 ) 320 8.06 28.1 98 % 0.417 -3.05 -6.96 (0.32 -0.68 -0.08 (3.54) (1.71) (9.51) -19.63 -18.01 -18.01 (0.86) (5.55) (4.09) -10.17 -10.17 ǻ 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 ** 10 320 7.93 28.1 -3.1 98 % 0.417 -9.90 -7.19 (3.52) -19.63 (0.88) (5.56) (4.20) (1.74) (9.59) -0.63 -18.00 ǻ 0.00 0.00 -0.02 -0.02 ** * 9 320 13.0 98 % 0.193 (0.71) 3.78 3.78 -27.19 (1.53) -1.08 -25.6 -2.57 -0.96 (0.23) (2.93) (10.3) ǻ 0.00 0.00 0.00 -0.01 ** ** 8 228 6.12 6.12 0.72 22.2 -16.1 -16.1 -1.84 97 % 0.16 0.16 0.356 0.356 (2.05) (0.64) (1.13) (1.93) (6.04) -20.15 -20.15 ǻ -0.02 0.01 0.01 -0.01 ** ** ** 7 696 -3.6 -2.9 1.66 1.66 39.3 -0.03 95 % 0.168 0.168 (0.48) (1.11) (0.02) (0.63) -97.73 -97.73 ǻ 0.02 -0.01 ** ** 6 819 -1.6 -3.4 1.30 1.30 25.7 96 % ificance at the 5% and 1% levels.. 0.089 0.089 (0.34) (0.84) (0.51) -131.8 -131.8 ǻ 0.02 ** ** 5 819 -4.2 1.56 1.56 21.3 (0.31) 96 % 0.074 0.074 (0.30) -133.9 -133.9 ǻ 0.00 -0.01 -0.02 ** ** ** 4 327 -0.7 -2.9 4.66 4.66 11.7 -21.6 -21.6 -28.0 98 % 0.173 0.173 (0.21) (4.44) (8.25) (0.85) ǻ -0.04 -0.01 * ** 3 2.3 2.3 327 -0.8 -2.3 -3.12 -32.7 98 % 0.034 0.034 (0.21) (4.79) (0.91) ǻ -0.03 ** ** 2 2.1 2.1 -0.79 383 -2.9 (0.14) -33.9 -33.9 98 % 0.031 0.031 (0.43) ǻ 0.01 0.01 ** * 1 4.3 4.3 823 -3.6 0.14 0.14 95 % 0.014 0.014 (0.06) (0.32) -154.8 -154.8 Size Sales-to-TA EBITDA Profit-to-Sales Tot. liabilities-to-TA ratio Equity Current ratio Quick ratio Indebtness Profitability Profitability Model nr Variables Constant Observations Log Likelihood LR chi Pseudo R2 % correctly predicted Table 10Table financial variables firm and default logit results: corporate Dynamic Source: Finnvera plc, the Economist Intelligence Unit, the PRS Group and the Bank World standard errors and ** indicate statistical sign are presented in Note: Entries are logit coefficients. Robust parentheses. * 119 ǻ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 ** ** 11 98 % 318 0.05 0.05 4.68 -18.2 -18.2 -0.06 0.419 -3.20 -9.67 -7.07 -0.70 (1.66) (9.66) (0.34) 28.2 28.2 (1.04) (5.26) (4.34) (0.09) (5.96) -19.56 -19.56 ǻ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 ** ** 10 98 % 318 -3.2 -18.3 0.419 0.05 0.05 4.55 28.2 (1.70) (9.67) -9.44 -7.27 -0.65 (1.05) (5.19) (4.33) (0.09) (5.86) -19.56 2 ǻ 0.00 0.00 0.00 -0.0 -0.01 ** ** * 9 98 % 0.202 318 3.62 3.62 4.14 13.6 -1.15 -25.7 -2.28 -0.07 (0.26) (2.83) (9.47) (1.40) (0.03) (2.38) -26.85 -26.85 ǻ 0.00 0.00 ** ** 8 0.11 227 22.9 22.9 5.68 5.68 0.13 0.74 (0.09) -17.1 -17.1 -9.35 96 % (2.00) (6.32) (0.58) (1.12) 0.366 0.366 (5.72) -19.82 -19.82 ǻ 0.01 -0.01 -0.01 -0.01 * ** ** ** 7 685 -0.09 -0.1 2.89 2.89 59.5 1.39 1.39 (0.02) -2.54 96 % (0.51) (1.27) (0.02) 0.269 0.269 (1.48) -80.83 -80.83 ǻ 0.01 0.00 -0.02 ** ** 6 1.12 1.12 -0.38 -0.08 807 0.97 0.97 42.4 (0.41) (0.97) (0.02) 96 % 0.157 0.157 (1.03) -113.5 -113.5 ǻ 0.01 -0.02 ** ** 5 807 -0.08 0.94 0.94 42.2 1.18 1.18 (0.02) (0.34) 96 % 0.157 0.157 (1.02) -113.6 -113.6 ǻ -0.01 0.00 0.00 0.00 0.00 0.00 -0.03 4 ** ** * ** * 4.51 4.51 325 -0.1 -0.1 -0.78 -22.5 12.5 12.5 (0.21) (4.19) (7.90) -27.5 -27.5 98 % 0.186 0.186 (2.33) (0.03) ǻ -0.01 0.00 0.00 0.00 -0.04 ** 3 2.4 2.4 325 -0.8 -3.31 -0.02 -0.02 -32.6 98 % 0.035 0.035 (0.22) (4.88) (0.02) (2.43) ǻ 0.00 0.00 -0.03 ** 2 2.2 2.2 381 0.01 0.01 0.01 -0.78 -33.8 98 % 0.032 0.032 (0.14) (0.01) (1.03) ǻ 0.01 0.01 -0.02 -0.02 * ** ** 1 813 0.15 0.15 38.3 -0.10 -0.10 96 % 0.130 0.130 (0.07) (0.02) (1.04) -128.2 -128.2 Size Sales-to-total assets EBITDA Profit-to-sales Tot. liabilities-to-TA ratio Equity Current ratio Quick ratio Profitability Model nr Variables Indebtness ICRG-index Constant Observations Log Likelihood LR chi Pseudo R2 % correctly predicted Table 11Table variables and political risk firm financial default, corporate results: Dynamic logit Source: Finnvera plc, the Economist Intelligence Unit, the PRS Group and the Bank World 120 0.00 0.00 0.00 0.01 0.00 ǻ 0.00 0.00 0.00 0.00 0.00 -0.01 -0.01 -0.01 -0.02 ** ** ** ** ** ** * 11 0.14 0.14 0.04 0.54 5.36 0.06 0.06 0.03 0.00 5682 -0.13 -0.29 -0.32 -0.37 -0.17 -0.53 499.3 499.3 (0.09) (0.08) (0.08) (0.06) (0.11) (0.14) (0.57) (0.05) (0.07) (0.08) (0.06) (0.06) (0.10) 0.2344 0.2344 95.4 % -815.33 -815.33 ǻ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 -0.01 -0.01 ** ** * ** ** 10 -0.3 -0.4 -0.2 -0.2 0.01 0.01 0.03 0.05 5.01 5682 -0.12 -0.06 -0.01 95 % 468.7 468.7 (0.05) (0.07) (0.07) (0.06) (0.06) (0.10) (0.08) (0.07) (0.08) (0.06) (0.53) -830.6 -830.6 0.2201 0.2201 ǻ 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 -0.01 -0.01 ** ** * * ** 9 -0.3 -0.4 0.01 0.04 0.00 5.05 5682 -0.11 -0.06 -0.20 -0.18 95 % 468.1 (0.05) (0.07) (0.07) (0.06) (0.06) (0.10) (0.08) (0.07) (0.08) (0.52) -830.9 0.2198 ǻ 0.00 0.00 0.00 0.00 0.00 -0.01 -0.01 -0.01 ** ** * ** ** 8 -0.3 -0.4 -0.2 0.01 0.04 5.05 5682 -0.11 -0.06 -0.18 95 % 468.1 (0.05) (0.07) (0.07) (0.04) (0.06) (0.10) (0.08) (0.07) (0.53) -830.9 0.2198 ǻ 0.00 0.00 0.00 0.00 -0.01 -0.01 -0.01 7 ** ** * ** -0.3 0.00 5.34 5682 -0.05 -0.07 -0.47 -0.07 -0.18 95 % 459.2 (0.05) (0.07) (0.07) (0.04) (0.04) (0.09) (0.07) (0.52) -835.4 0.2156 ǻ 0.00 0.00 0.00 -0.01 -0.01 -0.01 6 ** ** ** ** -0.5 5.60 5682 -0.02 -0.06 -0.31 -0.13 -0.16 95 % 453.0 (0.05) (0.07) (0.07) (0.03) (0.04) (0.08) (0.51) -838.5 0.2127 ǻ 0.00 0.00 0.00 -0.01 -0.01 -0.01 5 ** ** ** ** -0.3 0.01 5.27 5682 -0.07 -0.15 -0.47 95 % 448.9 0.211 (0.05) (0.07) (0.07) (0.03) (0.04) (0.49) -840.6 ǻ 0.00 0.00 -0.01 -0.02 * ** ** ** 4 -0.3 -0.3 0.01 1.82 5682 -0.14 95 % 331.5 0.156 (0.04) (0.06) (0.06) (0.02) (0.35) -899.3 ǻ -0.01 -0.02 -0.01 ** ** ** ** 3 -0.4 1.52 5682 -0.16 -0.21 95 % 189.7 0.089 (0.05) (0.06) (0.05) (0.39) -970.1 ǻ -0.02 -0.02 ** ** ** 2 -0.3 -0.5 1.61 5682 95 % 173.8 0.082 (0.03) (0.06) (0.39) -978.1 ǻ -0.02 ** * 1 -0.3 89.2 89.2 5682 -0.69 95 % 0.042 0.042 -1020 -1020 (0.04) (0.27) Model nr Variables government stability conditiosns socioeconomic climate investment internal conflict external conflict corruption in politics military religion in politics law and order ethnic tensions democratic accountability bureaucratic quality Constant Observations Log Likelihood LR chi Pseudo R2 % correctly predicted Table 12 risk political of the index default and ICRG sub-components the Corporate logit Dynamic resutls: 121

PERCEPTIONS OF POLITICAL RISK AND DEBT DEFAULT: SURVEY RESULTS FROM DEVELOPING COUNTRIES

ABSTRACT

Little specific information exists to explain the types of political risks that are important in assessing developing country debt repayment capacity. To address this need and support the empirical research on political risk and debt default of earlier studies a comprehensive survey of senior staff involved in the industry was undertaken. This paper documents the survey evidence describing the impact of political risk on debt distress, as experienced by senior staff of borrower organisations, including legal and financial advisors from the public and private sectors in developing countries. Conducted across 38 countries, the survey provides an unique understanding of risk perceptions and a locally deduced political risk rating. Exploring the effects of individual and country-specific factors, it was found that both the experienced “debt distress” and the perceived “political risk impact” is a matter of individual experience, but is also underpinned by selected macroeconomic and political stability related country fundamentals. Bribery and corruption were found to be among the most important factors affecting international debt management. Among the macroeconomic variables, the share of agriculture in GDP appears an important indicator of potential default with the debt managers reporting the prevalence of political risks as lower where agriculture’s share of GDP is higher. The study also provides evidence that international country risk ratings may underestimate some of the risk factors associated with political stability as compared with local views. 122

1. INTRODUCTION

Political risk has received recently an increased interest in economic and finance circles, largely due to many events of political character in the world that have adversely affected on equity investments and cross-border lending transactions92. Political risk is shown to be a important driver of various economic phenomena, including, for example, expected returns and international portfolio investment (Korbin, 1979 and Erb et al. 1996); international capital volatility (Chang, 2006 and Demir, 2007); foreign direct investment (Jensen, 2003 and Busse and Hefeker, 2007); and cross country correlations of the risk (Clark and Tunaru, 2001). Overall, research in international finance focusing on political instability centers on the global asset allocation decisions of private investors. A wide range of fields are covered, including measurement of risk premiums in equity returns to measuring country creditworthiness (see e.g. Feder and Uy, 1985; Bailey and Chung, 1995, Rivoli and Brewer, 1997; Bilson et al., 2002).

Among debt related studies referring to political risk, most research has focused on sovereign debt and the link between political risk and sovereign risk as proposed originally in the Eaton and Gersovitz (1981) framework. This framework distinguishes between a country’s ability to pay and a country’s willingness to pay its debt, with the latter often influenced by politics93. The research is mainly focused on the determinants of sovereign debt rescheduling or on the risk premiums on borrowing rates. Citron and Nickelsburg (1987) find political instability as a statistically significant determinant of the probability of default. They proxy political instability by the number of changes of government over a five-year period. The impact of regime instability is further analysed in Brewer and Rivoli (1990), who find that changes in the head of government and changes in the governing group are statistically significant variables in explaining the probability of sovereign default. Balkan (1992) employs a political instability index that measures the level of support for the government and social unrest in the country, as proxied by the number of government crises, riots, assassinations, general strikes, and anti-government demonstrations. He finds the index statistically significant in explaining default probabilities. Studies discussing political risk and debt default (rescheduling) in the context of developing or emerging market countries include e.g. Rivoli and Brewer (1997), Reinhart et al. (2003) and Meyersson (2006). Further, the risk premiums on borrowing rates and political risk is analysed e.g. by Moser (2006) who studies the Latin American countries. He finds that political instability, as measured by cabinet reshuffles increases sovereign bond spreads. Another group studies the relationship between democratic institutions and sovereign borrowing is discussed e.g. in Schultz and Weingast (2003) and Saiegh (2005). According to the “democratic advantage” argument, electoral accountability makes democracies more likely to honour their debts than non-democratic countries. Saiegh (2005) shows the opposite, i.e. that democracies are more likely to reschedule their debts than dictatorships. A central overall conclusion from the previous research is that political risk factors are at least as important as economic variables in explaining countries’ creditworthiness. While such research, together with the financial press and major rating institutions, confirm that

92 For example, Argentina was hit by a financial crisis in 2002 that led to one of the largest political risk insurance claims in history. National and state governments broke contracts and restricted the capital transactions of foreign firms (see e.g. Moran, 2003). 93 Two main explanations are generally offered for why governments may want to repay: reputation (exclusion from future credit) and direct sanctions. 123

political events do matter to financial markets, empirical evidence on the influence of various forms of political risk on debt is still rather limited. Importantly, the issue is no longer if there is a risk premium component associated with political risk, especially in emerging markets. Rather, the focus is now on how to identify the sources of political risk that influence debt management and that may even lead to debt default. Little specific information exists to explain the types of political risks that are important in assessing debt repayment capacity for developing countries. To address this need and support the empirical research on political risk and debt default of earlier studies a comprehensive survey of senior staff involved in the industry was undertaken.

This paper provides survey evidence to describe and explain political risk, as experienced and perceived by finance professionals in developing and emerging market countries. Against the background of the heterogeneous and interdisciplinary nature of political risk, qualitative survey research methods that offer tools to measure experience are employed. Thus, the survey provides insight on attitudes and perceptions as to how the ‘large and amorphous category of risk’ (Hill, 1998), concerns international debt management94. Web-based, the survey was directed at an extensive group of finance professionals located in developing countries, to document their views on the impact of various political risk factors on international debt management. The target group for the survey consist of public- and private sector finance professionals, government officials and representatives from nongovernmental organizations (NGOs), who are involved with various debt related undertakings, e.g. in international debt management, negotiations and contracting, or with the development of international financial- and securities markets. Contact details for this target group were obtained through Internet searches with the help from selected financial institutions and an intergovernmental organization.95 The survey was conducted over a period of two months (April–May, 2007) and more than 2400 surveys were sent to experts in a total of 118 countries.

The results of this survey are analysed across various dimensions of the political risk concept96. This concept refers to the complications businesses, investors and governments may face as a result of what are commonly referred to as political decisions or “any political change that alters the expected outcome and value of a given economic action by changing the probability of achieving business objectives” (DiPiazza and Bremmer, 2006)97. Thus, political risk encompasses many factors that might politically stabilise or destabilise a country. It is influenced by the passage of laws, arbitrary or discriminatory actions taken by leaders, political groups, or individuals in home- or host governments. Political risks may have an adverse impact on international trade or investment transactions, but the significance of any given underlying risk factor is dependent upon the context of the investment. In this study, political risk factors are analysed tracking the sub-components used in the political risk index distributed by the International Country Risk Guide (ICRG).

94 The case for the scientific investigation of opinions about risk was introduced by Slovic et al (1982). 95 Due to the sensitiveness of the topic, the survey was completely confidential to all collaborating partners, including the end respondents and the institutions providing us a starting list of professional e- mail contacts. All these institutions are major actors in the international financial sector, and are involved either with international debt training; or with the insurance or reinsurance of political risk. 96 Political risk is generally defined as the ‘possibility that political decisions or societal events in a country will affect the business climate in such a way that investors will lose money or not make as much money as they expected when the investment was made’ (see e.g. Howell, 2001) 97 A generic definition is provided by wikipdedia.org (see e.g. http://en.wikipedia.org/wiki/Political_risk#cite_note-0, accessed March 14, 2008) 124

The empirical analysis in this study is divided in three parts. In the first part, survey data is used to understand the impact of political risk on external debt distress, as perceived and experienced by the local debt managers. In this context debt distress is defined as some sort of negative credit event, e.g. a failure of debt repayment (principal or interest) or the occurrence of outright default, insolvency or bankruptcy. Logistic regression models including a partial proportional odds model are used to predict the likelihood for distress experience and the perceived impact of political risk, in the context of external debt classified under four heads98. The approach allows a comparison of the odds of facing financial distress among these different groups of debt contracts, as well as to predict political risk impact on the debt distress, while controlling for individual characteristics and country-specific macroeconomic and political stability variables. Analysing 679 ‘average experiences’ of debt cases, and the corresponding 368 default experiences among the survey sample99, we find that better economic conditions, a higher agricultural base, as well as a more stable government, seem to reduce the extent of debt distress. The respondents rate political risk impact on debt distress as ”high” or ”very high” for 232 (43%) debt cases analysed. The empirical models designed to predict political risk impact on debt distress, include variables such as the respondent’s affiliation and experience; selected vulnerabilities in the economy (i.e. the volatility in GDP growth; the share of agriculture in GDP and volatility in trade terms); the overall debt burden of the country as well as the time since last regime change.

In the second part, radar chart methods are used to analyse how important and influential the respondents regard selected political- legal- and macroeconomic risk factors, in the context of international debt operations, i.e. the borrower's debt management and the negotiating and structuring of its debt transactions. According to their assessments, macroeconomic problems and a dysfunctional legal system are among the most important attributes impacting on debt management. Of particular concern is bribery and corruption - factors that stand clearly out as major concerns both for short and long-term debt contracting. Further, more detailed questions on corruption reveal that ties between politics and business, kickbacks and nepotism100, are among the particularly serious and frequent offences in the financial sector of the respondents’ countries. The majority of respondents indicate that corrupt activities occur among “some officials occasionally”, including both government officials and higher level representatives from the private sector. Nationalisation and expropriation are still seen as long term political threats, while such risks are more generally seen as minor, or occurring with less frequency and ferocity nowadays.

The third empirical part documents the discrepancies between political risk ratings made by the respondents on their own country and on a chosen neighbouring country, as well as the actual risk ratings by the International Country Risk Guide (ICRG). While the assessments are fairly coherent, some interesting differing views are observed between the home- and neighbour country ratings, with the latter being consistently less

98 The analysed debt groups include 1) public and publicly guaranteed debt; 2) private non-guaranteed credits; 3) central bank deposits and loans due to the IMF, and 4) official development assistance. 99 The respondent may describe the “case” for each specified debt type only once. That is, even if the respondent may have experienced many similar cases under certain debt type, his or her experience appears only once in the data. Thus, the cases are indicated as ‘average’ observations. 100 A kickback is an official's share of misappropriated funds allocated from his or her organization to an organization involved in corrupt bidding. Nepotism is favouring relatives, based upon that relationship, rather than on an objective evaluation of ability or suitability. 125

risky. In addition, compared with the actual ICRG ratings, local finance professionals perceive their home country situation as less risky concerning the specific political risk categories of socio-economic conditions, corruption and religious tensions. A significantly higher local risk perception is discovered for government stability, external conflict and democratic accountability.

Overall, the survey results show that, even after controlling (and surveying) for many economic factors that are expected to impact on international debt distress, operating in a politically unstable environments represent still many additional concerns for the finance professionals. According to the respondents, there is not a clear separation of the debt management function from political sphere and monetary policy responsibilities i.e. public debt management office. One respondent describes; “Bank collapse and insolvency is often linked to politicians. Compliance problems with Central Bank regulatory guidelines, huge NPL portfolios and often poor risk mitigation dynamics exposes banks to political influence. State owned banks and banks having significant state shareholding have big problems with government debts that remain unpaid. State presence in banks project political instability to banks, with bank management being subjected to the whims of the government of the day. Professionalism in management is compromised, and frequent changes in management boards hamper efficiency, independence of action and continuity of policies”.

It is important to note that the results from the survey should be viewed in the light of factors such as response rates across regions and across countries. The study should be seen as a unique snapshot of the personal opinions of the 103 respondents, but the overall survey response rate is relatively low. This is attributed to the specialised nature of the survey questions and topic area and may also indicate that political risks are little understood and paid attention to, though significant in impact (Tillmann, 2007). Keeping in mind these limitations, the results shed new light on the important and uncharted area of international credit risk. The results provide insight into the impact of political risks on debt management and the perceived magnitude of various risk categories on debt default. It supports international lenders and investors in prioritising and analysing country risks.

The remainder of this paper is structured as follows. Section 2 provides a discussion of the political risk concept and a presentation of the central steps undertaken in the survey design, including questionnaire construction and hypothesis development. Following this background, Section 3 presents the empirical methods including the sampling procedure, along with the estimation methods applied in the result analysis. Descriptive statistics of the survey data as well as the estimation results are reported in Section 4. A conclusion is provided in Section 5. 126

2. SURVEY DESIGN

The survey questionnaire was developed with the aim of collecting data that would enable i) an exploration of the relationships between political risk and external debt distress as perceived and experienced by developing country finance professionals; ii) a mapping of the perceptions concerning various forms of political risk in the international borrowing environment; and iii) a comparison between locally induced country risk ratings with the actual risk ratings by an international risk rating agency101. Accordingly, the overall purpose of the consecutive survey analysis is to identify the forms of ‘political risks’ considered as most harmful for international debt management. The rating comparison is expected to highlight some major areas of agreement and disagreement between international creditors (i.e. the rating institutions) and the local finance professionals, about the economic and political situation in the target countries.

2.1. Political risk definition

A starting point of the survey design was to reduce the scope for variation in the respondents understanding of the term ‘political risk’ as applicable to this study. As economists and political scientists usually define political risk very broadly, i.e. ‘the risk that a future political event will change the prospects of profitability of a given investment’102, the concept requires an interpretation that clears up potential ambiguity. Usually, political risk definition is accompanied by a descriptive enumeration of various political risk concerns representing various political stability elements. For international lenders, political risks may include the classic “sovereign risk” i.e. the risk that a government will fail to honour its sovereign obligations; “transfer risk” i.e. the risk of restrictions on the international transfer of funds; and more recently “collective debtor risk” i.e. some countrywide event that will cause simultaneous default by a large number of private debtors (Wilkin, 2004). Three mechanisms were put in place to ensure that the survey respondents were applying the same definition of political risk. First, an explanatory paragraph was offered at the beginning of the survey instrument to establish the “debt management / debt contract” focus of the term being used in the questions.

“... political risk is defined as any change in the economic or political environment caused by political powers that had an effect on the corresponding debt contract. Examples of political risk include e.g. government restrictions of foreign currency transfer, contract repudiation/frustration, war or civil disturbance etc.”

Second, the respondents were given many options to further specify (in separate fill-in questions or spaces for elaboration) the form of risk encountered when assessing various components of political risk. Third, via a series of 24 tick-box questions, the respondents were asked indirectly about the definitional aspects of political risk. Five separate categories with examples were put forward as potential sources of political

101 We apply the rating system developed by the International Country Risk Guide (ICRG), that offer a political risk index based on a poll of expert opinions and ratings of twelve political risk indicators. These political risk factors, or sub-categories of political risk, are a central focus in our rating comparison. 102 This definition appeared originally in Haendel (1979). 127

risk.103 Accordingly, what risk elements or risk sources are ultimately included or excluded in the definition, are left as the explicit judgement of the survey respondent.

2.2. Constructing the survey instrument

Given the study’s objectives and resources, it was determined that an observational study with a cross-sectional design type would be an effective tool for analysis. The questionnaire was designed to elicit comparative data on various angles of political risk while avoiding difficulties that often accompany lengthy questionnaires, such as respondent fatigue, which may lead to over-simplistic or stereotyped responding. The target participants to this survey consisted of a broad group of finance professionals drawn from both the private and public sectors. Non-governmental organisations (NGOs) and other institutions seen as important in this sector were also canvassed. Given the broad scope of this group an important consideration in the survey design was to harmonise the questions in a manner appropriate and accessible to the entire spectrum of target respondents.

2.2.1. Structure of the survey questionnaire

A schematic layout of the developed questionnaire is illustrated in Figure 1 in Appendix 1 and the complete survey instrument in its original (English and French) versions is presented at the end of the appendices. The questionnaire is divided into two main sections with five sub-sections. Section A presented the respondents with a set of standardised questions on socio-demographics, such as gender, age and level of education. After this general background identification, each respondent are directed to their own set of specific questions on their professional status, depending on their public, private or NGO dimension of work affiliation. Section B of the questionnaire constitutes the main foundation for the empirical analysis. It treats the issue of political risk under three different headings. Here, the questions are again common to all respondents and are designed in accordance with the three specific aims of the survey.

In sub-section 3, the questions were aimed at eliciting external borrowing experience and the spontaneous images associated with the issue of political risk impact on debt distress. The respondents are first asked to indicate their familiarity with different types of international debt instruments and their total years of experience within external borrowing. This establishes their proficiency among the survey sample. Next, the respondent faces a set of detailed questions on external debt transactions and political risk experiences. In this context, the setting consists of three sequential stages where each respondent evaluates a set of external debt contracts in which he or she has participated104. The evaluation is performed as follows. At stage one, the respondent expresses whether he or she has participated in a particular type of external debt transaction (e.g. been involved in debt negotiations, surveillance, legal drafting or execution of any money transfers). At stage two, the respondent is asked to recall and

103 These categories are identified from previous country risk literature, and include 1) Macroeconomic problems; 2) A dysfunctional legal system; 3) Political stability; 4) Government policies and 5) Other. Under category “Other”, we expressed specific topics such as Terrorism, Financial crises, External institutions limiting actions, Anti-foreign bias and Lack of commitment to international treaties. 104 A total of 15 different external debt types are specified under the four generally acknowledged external debt groups, among which the respondents may choose to describe their experience. 128

indicate whether that particular lending transaction experienced a state of financial distress. Finally, at the third evaluation stage, the respondent is asked for an opinion regarding the impact of political risk on the experienced debt distress. This assessment employs a five-point Likert scale, measuring either positive or negative responses to the general argument that “political risk affects debt distress”.

Professional views on the importance of different political-, legal-, and macroeconomic risk factors are further examined in sub-section 4 of the questionnaire. The respondents are asked to rate a list of risk factors on a four-point rating scale, considering both a short and a long-term time horizon for debt repayment105. To avoid potential central tendency bias Likert scales in a forced choice method are used with the middle option of “neither agree nor disagree” not available. A verbal specification of three factors the respondents consider as the most important sources of political risk impacting negatively on trade and investment are requested.

The topic of corruption is treated separately, by questioning whether this particular form of political risk exists in the financial sector of the respondents’ country. The sensitivity of this issue is acknowledged and in this question a functional matrix is employed whereby the respondent may position 13 illicit behaviours (forms of corruption) in a more sensitive and neutral fashion106.

Figure 1 Corruption evaluation matrix

A two-dimensional matrix (figure 1) measures the extent of ‘seriousness’ and ‘frequency’ on the different axes. While corruption is a multifaceted phenomenon with multiple causes and effects, its complexity is reduced in this part of the survey by requesting a response about a selected array of corruption producing activities along these dimensions. Inquiries as to the perceived extent of corruption in eight different public and private sectors in the economy (ranging from the office of the president to civil servants and from private sector senior executives to employees) are made.

105 Contracts that must be fulfilled usually within a year or two at the most, are considered as short-term. 106 This technique is available form the Webropol-service (the technical survey tool used in this study). 129

The survey ends with a political risk rating in sub-section 5, where the respondents are asked to rate selected political, social and economic factors in their own and one neighbouring country, according to the rating system by the ICRG. The components of the ICRG political risk rating have a minimum score of 0 points and a maximum score of an even number (4, 6 or 12), so the full scale is an odd number with a neutral point. The presence/absence of the neutral point is a widely debated problem in studies measuring attitudes or opinions, and so a re-scaled version of the ICRG rating system that omits this neutral point was used. The neutral point in the scaling is often eliminated, assuming that 1) it attracts people who are careless or have no opinion; 2) respondents tend toward one of the two nearest alternatives; and 3) respondents who really are neutral, randomly choose a polar alternative (Schuman and Presser, 1996). When analysing and comparing the obtained answers, a condensed rescaling of both the respondent’s ratings as well as the original ICRG risk-rating is employed. The categories are aggregated together under four favourable or unfavourable headings; 1) Very high risk; 2) High risk 3) Low risk and 4) Very low risk.

2.2.2. Profile of the target participants

Target participants of the survey include public and private sector finance professionals, government officials, and representatives from non-governmental organizations (NGOs) who are involved with external debt, or with the development of international financial and securities markets. The focus is on developing countries107, so the aim is to reach respondents either working in these countries or, who have otherwise a strong proven work related connection, corresponding to one of these countries. The job function of the participant may vary, but should revolve around the broad categories of debt managers, financial analysts, economists and lawyers. The sampling frame for the Internet-based survey envisaged both 'internal' and ‘external’ candidates whereby respondents were found both on the Internet itself (among listings of email addresses) as well as from outside sources.

2.3. Hypothesis development

Explanatory variables analysed in the context of external debt distress and the perceived impacts of political risk are grouped under 1) individual characteristics; 2) contract specific factors; and 3) country characteristics. Tables 1-3 in the Appendix summarise the set of variables to be employed in the models.

2.3.1. Individual characteristics

Hypotheses for the first set of explanatory factors, including the individual characteristics, are derived partly from Cultural Theory and partly from the psychometric research on risk perception (see e.g. Sjöberg 1998 and Douglas and Wildavsky, 1982). Explaining political risk perception can draw upon many different

107 In this context, ‘developing countries’ refer to the low- through upper middle income countries, as classified by the World Bank. Also current ‘transition countries’ e.g. the central and Eastern European countries and the independent states of the former Soviet Union, are considered in this group. 130

factors related to the person, which we measure with gender, age, education, affiliation and work experience.

Gender differences in economic behaviour are considered beyond the scope of this paper108. However, the substantial body of risk research indicating that women and men differ in their perceptions of risk is acknowledged. For example, many surveys have found that women typically report higher perceptions of risk than men and tend also to show higher levels of concern regarding environmental and technological hazards. This effect is shown empirically robust across a number of different studies (see e.g. Davidson and Freudenberg, 1996; and Gustafson, 1998) but the current literature fails to offer properly theorised explanations of why the observed relationships might occur. In order to make a comparison, it can be expected that male responses are negatively related to higher levels of perceived political risk. The recent increased women’s political participation and visibility in the political arena (e.g. on the African continent) support this view as an increased proportion of women in politics should also increase women’s awareness of various types of political risks.

Controlling for an age-variable is motivated by the dynamics of the “senior citizen” (see e.g. Campbell, 2005) and the general notion that older people tend to be highly engaged in political participation (e.g. vote at higher rate than younger people). In studies of different cohorts over the years, older people tend to be more generally knowledgeable about politics than younger people. Older people can be expected to rate risks as greater than do younger people (see e.g. Sjöberg, 2004) and thus, the age variable is expected to be positively related to both the degree of experienced defaults as well as the perceived impact of political risk.

Two interlinked variables to age; education and experience are also controlled for in the group of individual characteristics. As education is often associated with qualifications that are required in proper risk assessment, a negative relation with the level of education and debt distress can be expected. Experience, on the other hand, measures the degree of perceived control and familiarity of the risks faced. Some of the experts in our sample probably perceive that they have more control over some of the risks and a longer experience may have even habituated some of them to these risks. Thus, it can be expected that work experience in the respective institutional environments of the respondents to bring greater comfort with political uncertainty and a sense of control over government related outcomes. When queried about their experience, participants were asked to respond to a list of 11 international debt-related activities, and indicate their corresponding familiarity with these activities. In addition, they were asked for their total experience in years (combined for all activities) on Likert type 7-point scale. From these figures, an aggregate experience-variable can be created by multiplying the time variable by the square-root of the number activities for which each participant has been involved109. This experience variable is expected to be negatively related with political risk perception. Meanwhile, sign predictions on relationship with distress experience are more difficult to forecast. While a longer and a more diverse career may have increased respondents exposure to adverse economic cycles and more numerous

108 A small but growing empirical literature looks at how the increasing political and economic influence of women may have contributed to changes in economic outcomes in recent years. For example, research on the microfinance movement of the last decades has shown that with surprising regularity across cultures, women have superior repayment rates compared to those of men (Kevane and Wydick, 2001 and Wydick, 2002). 109 With this square-root transformation, we emphasise on the length of the experience. 131

distress events, it could also reduce the relative number of the distress experiences, along with the induced proficiency of handling events. Finally, differences in public and private sectors employment may also capture differences in the preferences, decision making and the practices regarding political risk. Also explored is the impact of the public (or NGO) professional status of the respondent on political risk perception. Public servants are expected to be more exposed to political risk concerns.

2.3.2. Contract specific factors

The analysis concerning different debt groups is grounded on the supposition that there are important characteristics between different external debt types that differentiate them with regard to proclivity to distress, political risk and/or other political intervention. In this respect, the analysis on distress experiences and political risk perceptions is undertaken with contracts categorised under four external debt classes, as defined by the creditor quality110. The applied classification for external debt is presented in Table 6.

Private non-guaranteed external debt (class I) include obligations that are not guaranteed for repayment by a public entity, and are further classified according to the creditor; banks and other sectors. Public and publicly guaranteed debts (class II) are broken down according to creditor quality: official and private. Official creditors’ resources for lending are coming from the public budgets, and can be further classified under “bilateral” (i.e. single governments) and “multilateral” (i.e. various governments’ contributions through a multilateral agency, such as the World Bank). Private sources for lending guaranteed by a public sector may include private banks, bonds and others. Financial arrangements such as “buyer credits” and “supplier credits” (with the involvement of export credit and insurance agencies) are included under this class.

Support loans are loans usually referred to as the “IMF bailouts” to countries that face balance of payments crisis (class III). These IMF Credits and Central bank lending include the Standby Arrangements (SBA)111 and the Extended Fund Facilities (EFF)112 by the IMF. The Poverty Reduction and Growth Facilities (PRGF) of the IMF constitute low-interest lending facilities and are one source of long term development finance for low- countries. The IMF credits have been criticised for generating moral hazard in international financial markets, when other governments as creditors become more willing to lend at lower rates in the knowledge that the IMF is the lender of last resort113. This criticism has become very influential in policy circles, particularly since the emerging market crises of the 1990s. Meanwhile, the IMF loans have exhibited a habit of being repaid in full. Although some countries have gone into arrears, almost all have eventually repaid the IMF and the actual realised historical default rate is virtually nil (see e.g. Rogoff, 2002).

110 The classes are defined following the debt classification system by the World Bank. 111 SBAs are the most common loans granted by the IMF. They are short term loans intended to provide support to member countries facing short-term balance of payments problems. 112 EFF refers to assistance given to IMF member nations with economies suffering from serious balance of payments difficulties caused by structural imbalances in production, trade and prices. (See e.g. the UC Atlas of Global Inequality, http://ucatlas.ucsc.edu/sap/current_imf.php) 113 the "moral hazard hypothesis" was originally proposed in Vaubel (1983) 132

Official development assistance (class IV) refers to loans from multilateral and bilateral creditors that can be further divided into Official Development Aid (ODA) and Other Official Flows (OOF). ODA loans, undertaken by the official sector of the creditor country, have as their main objective the promotion of economic development and welfare. Unlike conventional commercial transactions, these loans must include an element of subsidy amounting to at least 25 per cent and as a result incorporate subsidised loans and non-repayable grants. OOF loans are transactions, which do not meet the conditions for eligibility as Official Aid, either because they are not primarily aimed at development, or because they have a grant element of less than 25 per cent.

Analysing different debt groups require applying a complex set of interrelated ideas about debt sustainability, a topic outside the scope of this research114. To structure and narrow down a wide area the study focuses on the guaranteed/non-guaranteed dimension of the debt contract, and expect the impact of political risk to be higher for the latter. Guaranteed contracts are thus expected to attenuate the impact of political risk. Also the public debtors are expected to be in a more direct exposure of governmental actions and political risk.

2.3.3. Country specific macroeconomic factors

The causal relationship between economic- and political risk factors is ambiguous and political risks are often found to be highly correlated with a country’s macroeconomic factors115. Thus, adverse economic conditions are expected to play an important role in the political risk perception formation. Components of the overall country risk are thus included as explanatory variables, grouped under macroeconomic- and political stability indicators.

Among the macroeconomic indicators country size, as measured with the logarithm of the country GDP, is used to test the general claim that political risk is perceived less severe in smaller countries than in larger countries116. The role of country size merits a particular attention also in relation to debt distress, as larger countries may be less vulnerable to abrupt economic imbalances, and more likely to receive external support form the international community in the event of a crisis. The relationship between poverty, debt distress and perceived political risk impact is tested using GDP per capita. Countries with small per-capita s are more likely to experience a fall below a critical subsistence threshold, thus leading to more debt distress and creating a more fertile environment for political risk. There is a widespread view that poverty creates political risk particularly in the form of terrorism (see e.g. Kahn and Weiner, 2002)117.

114 See e.g. Kraay (2006) for an examination of the implications of debt distress in developing countries for the lending policies of official creditors and the borrowing strategies of low income debtor countries. 115 For example, Hale (2002) shows that macroeconomic factors (e.g. foreign debt to GDP, debt service to exports ratio, real exchange rate appreciation and real interest rate) affect the perceived country risk by emerging market borrowers, as evident from their choice of the debt instrument. 116 This reasoning follows e.g. Treisman (1999) and Fisman and Gatti (2002) who have examined the impact of country size on the quality of governance concluding that larger sized countries tend to have governments that are more corrupt than governments in smaller nations. 117 Also the existing literature on the economics of conflicts support this view. For example, the results in Alesina et al (1996a) suggest that poor economic conditions increase the probability of political coups. 133

Vulnerability to shocks is measured from three different angles including volatility in growth; share of agriculture in GDP; and the Terms of Trade. Countries with high output volatility, as measured with the variation in the yearly growth rate of the GDP, are expected to be more vulnerable for situations when the government may face severe obstacles in servicing its debt. It has been shown that negative exogenous shocks in economic growth increase the likelihood of civil conflict (see e.g. Miguel, Satyanath, and Sergenti, 2004). Accordingly, we expect the perceived political risk to increase with output volatility.

The agricultural argument follows Berg and Sachs (1988) who found that the share of agriculture in GDP is a significant determinant of debt rescheduling. According to them, the extent to which agricultural versus urban interests influence the political decisions over economic policymaking is another important dimension of the political system. Berg and Sachs (1988) reason; since the work by Huntington (1968), political scientists have stressed that in developing countries, urban politics tends to be a cauldron of instability and populist policies. Governments are most secure which find a significant base of support in the agricultural sector, which tends to favour more conservative and stable policies. While the agricultural markets can be volatile, as they are subject to global supply and demand factors, geopolitical risks, and currency fluctuation risks, a high agricultural base in a country is expected to decrease political instability and offer a better environment for timely repayment of external debt. A country that has its political support mostly in the agricultural sector is expected to be politically more stable and, by extension, less subject to external debt distress.

The final vulnerability measure considers the volatility in the terms of trade, often associated with slower growth and a worse distribution of . Shocks in terms of trade and poor macroeconomic policy responses to these shocks are widely regarded as major a source of external vulnerability and imbalance for low- and middle- developing countries because of their dependence on the export of primary commodities. The less economically diversified a country is the more vulnerable it is expected to be to terms of trade shocks, that increase not only economic, but also political volatility118. Further, when the risks of commodity price shocks are borne by the public sector (e.g. in the oil- exporting economies), concerns arise that the collective political decision making process raises the possibility that the shock will be mismanaged and its destructive economic impact greatly magnified. Terms of trade is measured with the ‘export price index to the import price index ratio’. Volatility of this measure is calculated as the standard deviation of the measure over twenty years between 1985 and 2005.

Following previous research results (e.g. Kraay, 2006), higher levels of external debt are expected to increase the proclivity to distress. In addition, debt burden is hypothesised to be positively associated with the perception of political risk. This is explored using the debt service due-figure that includes all current interest and principal repayments, divided by exports of goods and services that represent the value of all goods and other market services provided to the rest of the world. It can be expected that countries with higher debt-service due exhibit larger political disputes and pressures on how to carry that burden. For developing countries, the interpretation of debt service figures rests on

118 Within the literature of external debt sustainability, Rodrik (1999) investigated the interaction among external shocks and domestic political resistance, and tested the argument that shocks lead to crisis only when domestic social conflicts are high. He showed that shocks in the terms of trade alone cannot predict crisis, but can do so together with indicators of social conflict. 134

a body of economic theory supporting the "debt overhang hypothesis” i.e. situations where the debt of a country exceeds its future capacity to pay it (e.g. Krugman, 1998). Debt service measures include interest payments on all debt and amortization payments on long-term debt only. The assumption is that short-term debt is normally rolled over. A more comprehensive measure of debt service should include all amortizations. Debt service is commonly computed on a cash basis instead of an accrual basis . If a country is in arrears on its debt payments, the debt service paid undercounts the true obligation. Thus a better measure is debt service due instead of debt service paid.

2.3.4. Political stability indicators

Finally, certain regime characteristics are included in the analysis of the survey respondents’ debt distress experiences and political risk perceptions. In general, democracies, or systems in which leaders must win the support of a plurality of eligible voters, are more likely to provide public services than non-democracies (Stasavage 2005). Governments sell loans internationally to generate revenue needed to fund domestic spending projects. In this context, especially democracies enjoy a considerable advantage over non-democracies due to the greater credibility of their commitments to repay their debts (see e.g. Schultz and Weingast, 2003). Thus, respondents from more democratic countries are expected to experience less debt distress. Following the results by Jensen (2006), who found that democratic institutions greatly reduce the risks of government expropriation and contract disputes, a higher level of democracy in a respondent’s country is expected to be associated with a lower political risk perception. The level of democracy is measured by the Polity-IV index of the Polity IV project (Marshall and Jaggers 2000, 2001) that measures the level of democracy on a scale from -10 (pure autocracy) to +10 (perfect democracy). Eventually, it can also be claimed that a longer time since last regime switch is associated with more favourable fundamentals and thus, a lower political risk perception. This claim is tested using the Polity IV Durable -variable, that measures the number of years since the last regime transition. 135

3. EMPIRICAL METHODOLOGY

Two central aspects of survey design; appropriateness and cost-effectiveness lead the way in the process of approaching potential target respondents. A primary prerequisite for conducting the survey was to obtain appropriate contact details (email addresses) for target participants.

3.1. Survey technicalities

3.1.1. Contact details

Email contacts were identified through extensive Internet searches with the assistance from cooperating partners, including selected international financial institutions and an intergovernmental organisation (these have requested to remain anonymous due to sensitiveness of the topic). They are major players in the international financial sector with operations closely related to developing countries and international debt finance. A primary list of contact details, obtained from these sources was eventually amended with extensive Internet searches involving systematic reviewing of homepages of the regular financial institutions, mainly central banks (CBs) and ministries of finance (MOFs)119 in a total of 157 developing countries. Individuals that correspond with the target participant denominators and who had a personal email address available were listed and any overlapping names were removed. The combined database of finance professionals contained a total of 2407 names from 118 countries.

3.1.2. Design features of the web survey

The survey was conducted over a period of two months (April-May 2007) through an "URL embedded questionnaire", whereby the group of potential respondents were invited to participate in completing the web-based survey, and their responses were submitted electronically by means of the Internet. The email request for participation had a URL embedded in the message, so that respondents could simply click on this hypertext link, which then evokes their web browser, presenting the reader the web- based questionnaire120 . The respondents were recruited for participation by this email invitation followed by two rounds of email reminders. The questionnaire and the invitation email were furnished with a set of confidentiality rules, to prevent disclosure of any information deemed confidential and to lower the barriers to answering the more sensitive questions. Another benefit of gathering data online was the capability of using a simultaneous language translation. In order to reach more participants given the French-speaking base of some developing countries the survey could be answered either in English or in French.

119 Both the CB’s and MOF’s have somewhat different names and institutional formats in different countries. We use the notation “CB” to denote various Central Banks, National Banks and Reserve Banks that were identified in the target countries. Similarly, the “MOF” denotes various ministries related to country finances, such as “Ministry of Planning and Finance”, “Ministry of Finance and Development Planning”; “Ministry of Economic Affairs and Finance” etc 120 The questionnaire was put available using the Internet-based survey software provided by Webropol RTA (see www.webropol.com ) 136

3.1.3. Response rate and the sample

Clearly, it is not possible to survey the entire target population as identified in the survey objectives. However, when choosing the smaller sample, attempts were made to avoid any easily accompanying bias. Out of the 2407 email invitations, 358 returned with an error message of failed delivery. In addition, 20 respondents were out-of-office for the whole survey period. A total of 2029 emails were usable providing the target sample of the present survey. The total number of completed and qualified questionnaires received at the end of the survey period was 103 (from a total of 38 countries), corresponding to a response rate of 5.1%. Against a general declining trend of survey response rates over recent decades (see e.g. Dillman et al, 2008) as well as the sensitive topic that is expected to lead to higher drop-out rates (see e.g. Knapp and Heidingsfelder, 2001), a low figure had been anticipated.121.

The problems involved with inferential statistics with low response rates are recognised. When interpreting the results, it is necessary to consider both who responded to the survey and who did not. In particular, self-selection is common problem similar a type of internet designs because participants who respond may be especially motivated or interested in the research topic, exacerbating the problem of sample representativeness. However, by looking at the profile of the people who responded to this survey there is convincing evidence that they are about the same as people who “did not respond” and correspond fairly well with the target population of “developing country finance professionals”. Thus the survey design provides a sample enough ‘randomly drawn’ to enable the construct of reasonably good point estimates in the analysis.

3.2. Estimation methods

Instead of applying complex survey sampling methodologies the focus of this study is on detailed reporting of the obtained answers in an analytical sample survey framework. In this framework, the attention centers on the estimation of relationships between (dependent and independent) variables, for a ‘conceptually infinite population’. The analysis seeks to center on making inferences about the process generating political risk perceptions or acting on the population, not, about the population itself.

The structure of the empirical analysis follows the three specified aims stated for the survey. In the first part, a model framework is developed for the process of debt distress experience and political risk perception. Here, the reported distress cases and perceptions of political risk are analysed against individual country and contract specific characteristics. As a result survey response data including information on respondent background and contract type is combined with country data from the Economist Intelligence Unit and the Polity IV Database. The empirical analysis employs a logit and ordered logit approach. In the second part, respondent ratings on selected risk indicators are analysed with a radar chart approach, that is a special analytical tool that have been developed in connection with benchmarking in the private and public sectors. Finally, a comparison between different ratings is made using the Wilcoxon matched-pairs ranks test since the data do not meet the rigor associated with a parametric test.

121 In web-based surveys, completed by respondents who agree to complete a survey in response to an e- mail invitation to participate, the non-response is usually a big concern (see e.g. Couper, 2000) 137

3.2.1. Model framework for the political risk assessment

The setting consists of three sequential stages of the political risk assessment process:

Stage one: A respondent indicates whether he or she has participated in a particular type of external debt transaction.

Stage two: For each debt type in which participation has occurred; the respondent indicates whether he/she has experienced a state of debt distress.

Stage three: The respondent makes an assessment, to what extent he/she considers that political risk had an impact on the distress period.

Equation (1) below relates the various determinants to the probability of an individual experiencing debt distress while equation (2) describes the political risk assessment. In this model framework, DISTR is a binary zero-one variable indicating whether respondent i has faced distress or not with the particular type of debt transaction t, given that he or she has participated (PART) in the transaction. POLRISK is an ordinal assessment of the political risk impact on the debt distress occurred, on a five-point Likert scale. INDIV represents a vector of respondent characteristics; and TYPE the external debt class under consideration for the particular transaction. COUNTRY encompasses macroeconomic and political risk variables that are specific to the respondents’ country. Building on this framework, a series of statistical models are proposed to estimate how distress experience and the perceptions of political risk vary, depending on individual; contract; and country characteristics.

Prob (DISTRi,t » PART ) = F (INDIVi, TYPEt, COUNTRYi) (1)

Prob (POLRISKi,t » DISTRESSi,t =1) = F (INDIVi, TYPEt, COUNTRYi) (2)

3.2.2. Logistic and ordered logistic regression

Three different logistic regression techniques are used to investigate the research questions. Model 1 makes use of equation (1) and the basic logit regression technique, in which the left-hand side variable is a dummy variable equal to one if the respondent has experienced distress, and zero otherwise. The standard logit is chosen as it provides results which can be easily interpreted and the method is relatively simple to analyse. In addition, reported are ordered logit models on political risk perception, where the dependent variable of interest is the probability that respondent i chooses political risk level POLRISKi,t = r from R mutually excluding alternatives. Thus, in Model 2, the probability that an individual will rate political risk at the rth level is estimated applying the standard ordered logit estimator122. Along with both these techniques, also the percentage changes in odds are reported123. Although political risk impact was evaluated on a scale of one to five in the survey (i.e. R was set to include five ordered categories124), for the analysis purposes, the political risk rankings are condensed to

122 When the dependent variable is measured on an ordinal scale, Freese and Long (2003) suggest using “models that avoid the assumption that the distances between categories are equal” 123 See e.g. Long (1997), Greene (2003) or Wooldridge (2002) for a more formal review of some of the basic properties of logit and the ordered logit model. 124 Original categories in the questionnaire are 1=no impact, 2=low impact, 3=medium impact, 4=high impact, 5=very high impact. 138

three categories: low impact (1-2), medium impact (3), and large impact (4-5). The dependent variable is rescaled due to the small sample size which caused a lack of variation in this variable. Further, as it is not possible to ensure that all respondents use the same criteria to interpret what each choice on the five-point scale means, merging five categories into three eliminates some of this subjectivity.

3.2.3. Underlying assumptions and checks

Efficient estimation of the ordered logit demands that the data adhere to the “parallel regression assumption125 ” or the claim that the independent variables have roughly the same effect on the likelihood of each dependent variable outcome. This assumption is checked with the approximate likelihood-ratio test of proportionality of odds (LR-test) and the Brant test of parallel regression assumption, for each coefficient individually (Brant test). For most of our model specifications126, the LR-tests give significant p- values providing evidence to reject the null hypothesis that the coefficients are equal across categories. Also the Brant test statistic shows significant values for most of our model specifications and variables, providing evidence that the parallel regression assumption has been violated. Dealing with this type of problem (the very restrictive and often violated assumption), calls for the use of a non-ordinal alternative instead, like the multinomial logit model. However, the multinomial logit has a complicated interpretation as it generates many more parameters than the ordinal logit, as all variables are now freed from the proportional odds constraint. Accordingly, in Model 3 we re-run Model 2 in the “partial proportional odds model” framework, where the parallel lines assumption is relaxed only for those variables where it is not justified. In this respect, the user-written gologit2 Stata9-program for generalised ordered logit models offers a powerful statistical tool. This program can estimate models that are less restrictive than the parallel lines models estimated by the ologit-command but more parsimonious and interpretable than those estimated by a non-ordinal method, such as multinomial logistic regression127.

As the research engages in two interrelated empirical questions, and observes political risk ratings mainly from participants that experienced distress, there is a possibility for the Heckman (1976, 1979) sample selection problem. The variable on political risk perception is incidentally truncated, that is, for some observations (those that have not experienced distress), the risk perception is zero128 . A sample selection bias is tested for by running the models with the probit and ordered probit, in order to incorporate the Inverse Mills ratio from the first probit selection model on distress experience as a correction factor into the ordinal probit model for political risk perception. The significance of this correction factor is a form of test for the sample selection bias.

125 Or the “proportional odds (or parallel lines) assumption” (see e.g. Long and Freese (2003, 2006) 126 The exception is the model specification which includes only dummies for the external debt types. 127 For further details on gologit2, see e.g. StataCorp. (2005), Williams (2005 and 2006). Through a stepwise process, the method first tests all independent variables, and then it estimates the model holding some variables constant across dependent outcomes and allows others to vary. Variables whose effects do not significantly differ across equations, have proportionality constraints imposed. 128 For example, Verbeek (2000) notes that sample selection bias may occur if some of the unobserved factors affecting selection into the sample also affect the phenomenon under investigation. 139

4. DATA AND RESULTS

4.1. Descriptive statistics

4.1.1. Geographical coverage

The survey information represents 38 countries in total, but the geographical coverage is highly centered on the African continent (See Figure 2 in Appendix 2). In particular, Western African countries (e.g. Nigeria and Ghana) exhibit a high representation in terms of initial target participants which may be a reflection of the high indebtedness of this sub region. These countries represent also a relatively higher share of the initial group of potential survey participants. This may be due to the fact that these countries have recently put great effort and human resources on the implementation of development strategies for poverty alleviation and debt management129. Also the rapid development of the West African internet network130 is a contributor, increasing the availability of email contact details. Table 4 in the Appendix presents the response rates by countries, illustrating an initial concern with the present survey; the variation in the number of participants approached per country. While the response rates from many countries are at an overall average, the relatively high representation of the West African finance professionals needs to be carefully considered in the analysis.

Countries represented in the survey are further compared with the global political risk ratings by Euromoney Plc for 2007 (see Figure 3 and Figure 4 in Appendix 2). The high-risk rated countries represent only 9.7% of the total sample, including countries like Togo, Sierra Leone, Zimbabwe, Malawi, and Kyrgyz Republic. There are no responses from the war-affected Central Africa. Among the financial crisis-affected countries, Latin America is represented with five respondents from Argentina, Brazil and Mexico and only one respondent from Russia. Asia has a relatively high share of the total sample (17%) with the majority from Pakistan and Indonesia

4.1.2. Demographic information and affiliation of the survey respondents

79% of the sampled finance professionals are male and most of the respondents are situated either in the age group 26-35 (49%) or 36-45 (38%). In each geographic region, some 20-30% of the respondents are women, except for Latin America with no women participation. Despite the traditionally rather male dominated field of finance, the 21% female representation is in line with figures reported in the Gender Empowerment Measure, which describes how active women take part in economic and political life131.

129 For example, the West African Institute for Financial and Economic Management (WAIFEM), established in 1996 by the central banks of The Gambia, Ghana, Liberia, Nigeria and Sierra Leone, has brought substantial improvements in the debt management functions in most of its constituent member countries. 130 See e.g. Bernard (2003) 131 The gender empowerment measure is an indicator used by the United Nations Development Programme (UNDP 2006) in its annual Human Development Report. It is a composite index measuring gender inequality in three basic dimensions of empowerment including economic participation and decision-making; political participation and decision-making; and power over economic resources 140

Figure 2 Respondents by age and gender

56-65 below 26 Female 46-55 2 % 1% 21 % 10 %

36-45 26-35 38 % 49 % Male 79 %

58% of the respondents are working in the public sector but the private sector and NGOs are also represented132. Public sector respondents come from Ministries, Central Banks, the President’s office and Parliaments, while the private sector respondents work mainly for Law Firms and Management Consulting bureaus. 53% of the respondents have received a higher education (Master level or PhD) and many had also earned a combination of degrees.

Figure 3 Respondents by the sector of work affiliation and work discipline

Other NGO 20 % Econom. 14% 36 %

Private Public Lawyer 28% 58% 24 % Financial Analyst 20 %

Different work disciplines are represented in the sample. They appear as lawyers (24%), economists (36%), financial analysts (20%) and other (20%). Among other disciplines, are diplomats, engineers, accountants, political scientists, managers and administrative staff. The majority (87%) of the respondents have work related experience within external borrowing. About one third of the respondents have experienced debt rescheduling (e.g. related to the Paris Club), and are familiar either with external borrowing by state enterprises; administrating funds from foreign donors; participating in financial market transactions in the foreign exchange and bond markets and/or doing transactions with international financial institutions and bilateral creditors. Less than one fifth indicated experience with private or public non-guaranteed debt, trade or structured finance transactions (e.g. with ECA involvement), or the issue of guarantees. Figure 5 and Figure 5 in Appendix 2 provide statistics on the length and diversity of these experiences, illustrating the typical length of the per person combined experience in year groups, and the total number of different external debt related activities in which the participants have been involved. From these figures, an aggregate experience

132 For example, exchanges, multilateral development institutions, chanceries and regulatory bodies. 141

variable is created by multiplying the 7-point Likert scale time variable by the square root of the number activities each participant has been involved with.

4.1.3. Average debt distress experience and political risk impact rating

Table 5 in Appendix 2 illustrate how external debt distress and political risk was experienced and rated among the respondents and their different sub-samples. A total of 679 debt cases are identified, where a respondent had been involved with a certain type of external debt category. For some of the respondents, “involvement” might mean several transactions under the same debt category, why the cases per se can not be treated as observations, but rather as “average experiences” concerning a particular debt category. We identify a total of 368 (54%) average experiences where a state of distress was experienced.133 A higher proportion of men have experienced distress (56%) compared to women (48%), and a relatively higher share of men tend also to give higher ratings for political risk. Further, most of the younger respondents have experienced distress and up to 70% of the “medium” to “very high” political risk ratings belong to these younger respondents134. While the difference between age groups is significant the similar age structure of the full study sample is acknowledged. Respondents from the public sector exhibit a larger concern for political risk and account also for a higher share of the combined default experiences.

A 5-point rating on the impact of political risk on debt distress concerning a particular debt type is given for 545 cases, by a total of 86 individual respondents. A “high” or “very high” political risk rating is given for 232 cases, corresponding 42.6% of all responses. Panel A explores respondent experiences of financial distress within External Debt Classes I-IV (class I-IV) and Panel B and illustrates to what extent these distress events were perceived as a result of political risk. Class I and class II represent the largest groups both concerning the extent of overall experience as well as distress cases. The highest frequencies are found for class I, i.e. for the debt type Private banks and other financial institutions 27 non-distressed and 44 distressed cases are observed. Class III, or IMF facilities show the lowest distress rate which can be expected as these loans are generally obtained when a country already finds itself in a “crisis situation”, for example as a result of a sudden shock to its economy or poor macroeconomic planning. As these loans are seen as 'sacrosanct' (i.e. a country cannot default but is required to embark on an IMF monitored economic reform program in return for the IMF's help), a “distress period” might be hard to identify for the respondent.

The highest ratings on political risk are found for external debt classes I and II, which exhibit an average political risk rating of 2.21 and 2.23, respectively. Similar to the default experience, the average ratings on political risk are somewhat lower for class III (1.79) and class IV (2.00). Almost two thirds of the respondents point out government restrictions on foreign currency transfer as the main type of political risk encountered, in particular with class I. Other pointed risk factors include frustration or repudiation of contracts, government policies, and the weakness of regulatory institutions.

133 We find “non-distressed” and “distressed” external debt experiences from 36 and 31 different countries, respectively. “Distress” is defined as some sort of negative credit event, e.g. failure of a debt repayment, default, insolvency or bankruptcy. 134 43.6% of all distress experiences are in the age group 26-35 and 44.2% in the age group 36-45. 142

While the dimension for government backing is totally different between class I and class II types of debt, only a small difference exists between these and what concerns the political risk rating135. The top-rated political risk factors (chosen freely by the respondents) include government restrictions on foreign currency transfer and contract repudiation. However, some respondents add economic mismanagement and budgetary constraints as political concerns. Also civil disturbance and war-related factors are referred to as major problems leading to debt distress. The low political risk rating for class III may be partly explained by IMF conditionality that ties future loan disbursements to specific economic policies, as well as, the sanctions for non- performance posed by the IMF. One respondent cites that the impact of political risk is seen as mostly regulatory relating to changes to export regimes and support measures.

4.2. Cross-sectional regressions

The joint logistic regression estimates are reported in Tables 9 - 12 in the Appendix. These tables split the survey data is used in four different model specifications, in order to determine how individual, contract, or country specific characteristics affect on distress experience and on political risk perceptions. The last table (Table 12) combines all characteristics in the same estimation. The results are reported in three different panels, depending on the methodology applied. Panel A. presents logit regression estimates of the probability that a respondent experiences debt distress. Estimates on “political risk impact perception” are presented in Panel B, using the ordered logit and, in Panel C, with the generalized ordered logit estimates.

The obtained estimates are analysed jointly. The tables present regular logit coefficients with their corresponding z-values, for which one can interpret only the sign and significance but not the size of effects. An intuitive method of interpreting both the logit and ordered logit effects is to examine how changes in the independent variables affect the likelihood that a respondent will report a higher or lower value for the dependent variables. Accordingly, in Panel A, the % change in odds display the percent change in the odds of experiencing distress per one unit increase in the independent variable. Similarly, in Panel B, the % change in odds, is the percentage factor change in odds for unit increase in the independent variable. Finally, the generalised ordered logit estimates are presented in Panel C. Here, those variables for which the proportional odds assumption hold (and as a result have constant coefficients over the range of outcomes) are marked as grey.

4.2.1. Logistic- and ordered logistic regression estimates

Panel A of Table 8 reports the logit regression coefficients of experienced debt distress by using individual characteristics as the explanatory variables. Here, the respondent age, in addition to public- or NGO-affiliation seems to increase the tendency of having experienced debt distress. On the contrary, experience decreases distress tendency on a 1% significance level and has also a negative impact on political risk perception136.

135 Class I includes private debtors without any form of guarantee from the debtor's government while Class II is the long term external obligations of public debtors and the external obligations of private debtors, guaranteed for repayment by a public entity. 136 In separate tests, the observations are clustered either by the individual or country . These tests reveal that the age- and affiliation variables become insignificant but the effects on experience are still robust. 143

Panel B reports the coefficients obtained from the ordered logit regression on political risk perception. For example, if the responses are obtained from a male or public sector respondent, the odds of rating political risk one step higher increases by 24% and 25%, respectively. However, among the individual characteristics, only the experience variable is a significant indicator of the political risk perception. Table 9 presents the impact of different debt classes. As expected, debt class III seems to be significantly and negatively associated with distress. However, any of the debt groups do not seem to impact on the ordered logit results of the perceived political risk.

Analysing country characteristics in Table 10, we find that better economic conditions concerning poverty, a higher share of agriculture in GDP, as well as more stable government (both in terms of democracy and regime durability), seem to reduce the extent of debt distress experience. Level of democracy and the agricultural base are significant at a 1% level, and for a one unit increase in these figures, there is an average decrease in the odds for debt distress, at 6.7% and 11.7%, respectively. The share of agriculture in GDP seems also to decrease the political risk perception, while more volatile trade terms increases the perception. The combined analysis from Panel A and Panel B in Table 11, reveal the following central results. When controlling for all factors identified as impacting on the debt distress, it was found that respondent experience, better economic conditions (GDP/capita), share of agriculture in GDP and the level of democracy in a country are all statistically significant, negative predictors of debt distress. Further, it seems that men tend to have experienced more debt distress than women. In the standard ordered logit specification (Panel B) economic conditions, share of agriculture, level of democracy as well as the age variable seem to reduce the perception of political risk.

4.2.2. Results form the partial proportional odds model

The Generalised Logistic Regression specification of the political risk perception model (Panel C of Tables 8-12) confirm and strengthen the obtained results. From Table 8, it is indicative that the effects of the constrained variables (gender and education) are not significant. Meanwhile, now the differences for the other individual variables from before are largely a matter of degree. Older people seem unlikely to rate political risk in the lowest category (y=0) as is the case for public and NGO affiliated persons. The public sector respondents are in fact more likely to rate political risk impact as high. Experience is found to be negatively associated with the overall political risk rating, i.e. the more experienced professionals are less likely to rate political risk in the lowest category. In Table 9, Debt class III shows up as the only non-constrained variable in the partial proportional odds model. However, there is a tendency that this group is not likely to be rated in the lowest political risk category, perhaps a reflection of the IMF "bail-out" mechanism, and the decisions that are shaped by various domestic and international political interests137. Table 10 reveal that in countries that are either larger; have more volatile GDP growth; or have longer time since last regime change, a low political risk impact rating becomes highly unlikely. A high debt burden of a government appears to be associated with a tendency towards a lower political risk impact rating. Perhaps, because of an already heavy debt burden, governments have

137 Looking separately at the alternative parameterisation by the gamma option, it can be seen that the coefficient for debt class III in the y>1 model is 0.66 larger. However, the effect is not significant. 144

lower incentives to influence external debt management, in order to increase the chances for debt relief, for example through rescheduling.

From the combined model specification (Table 11), it is noticed that the public sector and NGO representatives, as well as the more experienced respondents are not likely to rate political risk impact as low. The same holds for respondents from countries that are larger, have a higher share of agriculture in GDP, or, in a longer time since recent regime change. Variables that are held constant over the three political risk outcome specifications, include: gender, age, education, debt class I, debt class II, GDP/capita and the polity-index. Overall, the generalized ordered logit analyses suggest that individual affiliation and experience, debt class III, the size of the economy, certain vulnerabilities in the economy (the share of agriculture in GDP) and regime change are particularly powerful in moving respondents away from rating political risk as low.

4.2.3. Differential non-response and data weighing

As the study sample was highly concentrated on countries in Sub-Saharan Africa, the responses rate cannot be considered as uniform across all subgroups or countries. This may introduce non-response and/or undercoverage bias, which may be to some extent reduced by sample balancing, i.e. weighing a sample to control totals. In this study, the tests and the regressions are recalibrated by weighting the data to even out the responses between the respondent countries. This is performed by restricting observations in random order so as to include no more than 35 debt cases from each country. This corresponds to 546 observations, where each country represents fewer than 7% of the observations in the study sample. After performing this weighing the above enumerated models were reran.

In most cases, the results of this study still holds as presented in the above paragraphs. Among individual characteristics, both the public affiliation and experience clearly stand out as significant determinants of experienced debt distress, as well as the extent of perceived political risk impact. One of the differences noticed is that none of the debt groups no longer show predictive power for either of the models. However, a higher share of agriculture in GDP is again a negative indicator of debt distress, and reduces also the general political risk perception. Further, the level of democracy and better economic conditions reduce the extent of debt distress. The latter also decreases the likelihood of respondents rating the political risk as very high. As a suggestion for further research, a new country-specific analysis framework could be developed to study the specific circumstances in all of the sample countries.

4.2.4. Selection bias and endogeneity

As this research engages in two interrelated empirical questions, and observes political risk ratings mainly from participants that experienced distress, there is a possibility for the Heckman (1976, 1979) sample selection problem. The variable on political risk perception is incidentally truncated, that is, for some observations (those that have not experienced distress), the risk perception is zero138. Often, this type of sample selection

138 For example, Verbeek (2004) notes that sample selection bias may occur if some of the unobserved factors affecting selection into the sample also affect the phenomenon under investigation. 145

bias is tested for by incorporating the Inverse Mills ratio from the first ‘selection model’ as a correction factor into the ordinal model for risk perception. The significance of this correction factor is the test for sample selection bias. Also in this study, a two-step estimation was incorporated in the analysis to check for the selection bias. Initially it was selected to employ a logit model on distress (first stage) and the ordered logit on political risk perception (second stage). However, the inverse mills ratio is derived from the normal distribution, while logit and ordered logit use the logistic distribution, Thus, the approach would seem more plausible with the probit and oprobit-, since these use the normal distribution. In order to perform the two step estimation properly, the models were reran with logit now changed to probit and ordered logit to ordered probit. Qualitatively, logit and probit models gave similar results, but the estimates of parameters of the two models are not directly comparable. Meanwhile, the ordered probit on political risk perception was now tested with the same explanatory variables plus the inverted mills ratio calculated from the first stage using probit. For all models, the parameter estimates for the inverse mills ratio are not statistically significant, so we fail to reject the null of no sample selection bias.

Further, another statistical complication is that the ordered responses are clustered as multiple ratings are observed from the same professional who rate different debt contracts. This can skew standard errors and affect significance levels, if the ordered responses are positively correlated. To account for this problem, the models were estimated also with additional versions of robust standard errors, in which individual responses were allowed to cluster either on the respective individual or on the corresponding country139. In addition to the insignificant Mill’s ratio obtained, a series of preliminary diagnostic checks including the correlation matrix presented in Table 6 and collinearity diagnostics presented in Table 7 were performed. These indicate that the data complies with the basic assumptions of the ordered logit model.

Finally, the potential problem of endogeneity resulting from participation brings another element in the discussion. As participation in a certain debt contract may be affected by political risk perception in the first place, one may envisage that a highly risk averse person abstains from participating in transactions he or she perceives highly risky. Among other things, this issue was re-examined up in a follow-up survey, sent to those respondents who had requested for a free example of the survey summary report. A total of 82 respondents were contacted in June 2008 with a confidential status report that was published to survey participants summarizing the central results of the study until that time. All participants willing to receive this survey report, were also invited to answer 10 follow-up questions and to discuss the central important findings of the survey. Concerning risk perception, the participants were asked to indicate in what type of transactions they would be willing to participate, i.e. how much risk they would usually be willing to accept when participating in a debt transaction. A total of 15 respondents replied (response-rate 18.9%), out of which the risk aversion was divided as follows: very risky (6.7%), risky (0%), somewhat risky (6,7%), neutral (20%), somewhat safe (26,7%), safe (26,7%), only very safe (13,3%). Accordingly, the risk aversion seem to be skewed to the safe side, i.e. a considerable portion of the ‘zero observations’ of the

139 The standard errors were adjusted for non-independence using the "cluster" procedure of Stata 146

political risk perception, seem to be due to higher than average risk perceptions among the initial group of survey respondents140.

4.3. Political risk perception radars

In figures 7 a-f) radar charts are laid out in a circular fashion, to display the obtained multi-parameter responses on various macroeconomic- legal- and political risk factors. Indicators in the radar chart are arranged around the circular chart and are grouped by theme, or in this case, by risk category. The method enables at a glance analysis across a range of indicators for a selected area. It makes the comparison of profiles for different areas quick and easy.

Five analysed categories of risk include 1) macroeconomic problems; 2) functionality of the legal system; 3) general political stability; 4) government policies and 5) war related factors / international politics. The groups with corresponding “sub-risks” are derived from previous literature on country risk assessment, distinguishing between economic and financial-, legal-, governmental-, and political instability risks. The respondent’s scores on how important and influential they consider these risk factors to be for international debt operations are plotted on the axis lines and all points are connected to visually represent the gap between short and long-term impact assessment. The radar charts consist of axis lines representing a scale starting from 2 in the center of a circle, and extending to its margin of a maximum 3.5. This type of scaling is used in order to illustrate the direction and intensity between specific risk items. In the benchmarking, the data is standardised to depict multiple risk concerns with comparable risk data on the same scale. While acknowledging the potential central tendency error in the data, reflecting a reluctance on the part of respondents to give extreme responses even when they exist, the polygon formed by the connecting the data points on the radar is considered a composite illustrative indicator of the overall political risk perception.

Figure 7a) summarises all five categories, and shows that macroeconomic problems and a dysfunctional legal system seems to weigh the most in respondent assessments. Among the macroeconomic problems (set apart in Figure 7b), it is recognised that monetary and fiscal policy and inflation are seen as the most important and influential factors for international debt operations, both in short and long-term debt contracts. Fiscal policy choices related to debt may include choices on maturity structure, whether to issue nominal or indexed bonds, and whether debt payments should be contingent on other variables such as output. Inflation is rated particularly high in the short-term, possibly reflecting concerns on the standards of deferred payment. For debt denominated in a particular monetary currency, changes in the valuation of that currency can change the effective size of the debt considerably.

In the legal group (Figure 7c), bribery and corruption stand out clearly as the highest risk concern, both for short and long term debt contracts. Judicial independence and legal transparency are also seen as important particularly in the long run. All political stability factors (Figure 7f) centre around a rating of 3, with the impact from various factors seen as somewhat higher in the long term. ‘Traditional political’ risks (including e.g. nationalisation and expropriation), are also seen as long term threats, while such

140 Reasons for abstaining from dubious transactions included protecting personal reputation. 73.4% of the respondents indiacte they abstain often or very often from transactions due to political reasons. 147

risks are more generally seen as minor, or occurring with less frequency and ferocity nowadays. Detailed questions on corruption reveal that ties between politics and business, kickbacks and nepotism are among the particularly serious and frequent offences found in the financial industry in the respondents’ countries (see Figure 8). The least frequent and serious forms are blackmail and extortion, perhaps more often associated with the management of foreign direct investment, or in suspicious debt collection activities. The majority of respondents indicate that corrupt activities occur among “some officials occasionally”, including both government officials and higher level representatives from the private sector (see Figure 9). “All, or, almost all,” public sector officials are thought to be involved with corrupt activities by at least 20 respondents in our sample.

4.4. Political risk rating

Finally, the respondents are asked to rate their ‘home countries’ and one ‘neighbour country’ according to the risk rating system developed by the International Country Risk Guide (ICRG). The same components and sub-components are employed as by the ICRG but the scales are presented without the zero-alternative, in order to avoid central tendency bias or the “0”-alternative being treated as a non-response. Comparing the ratings, both the original ICRG and local ratings are re-scaled into four categories, to comply with ICRG’s verbal interpretation (very high, high, very low, or low risk). Table 13 in the Appendix shows descriptive statistics from this risk rating exercise illustrating the number of ratings, average ratings and the standard deviations, separately for “home countries”, “neighbour countries” as well as the actual “ICRG141” for the corresponding countries. In the two latter panels, the rating difference is tested between the “home countries” and the “neighbour countries” as well as between “home countries” and the “ICRG”. This is achieved by applying the Wilcoxon matched-pairs signed-ranks test which is the nonparametric analogue of the paired t-test. The inverse interpretation of the rating implies that a higher rating corresponds to a lower risk in the corresponding category of political risk.

Among home country ratings, the risk of External conflict is perceived the lowest among the sub-categories (with an average rating of 2.57) while Corruption stands clearly as the highest risk factor (average rating of 1.74). Among neighbour country ratings, the lowest risk perception is on Military in politics (average rating of 2.85) and the highest risk perception is on Socio-economic conditions (average rating of 2.20). According to the actual ICRG-rating, the lowest risk concern in the corresponding countries is on External conflict (average rating of 3.28) and the highest concern is on Socio-economic conditions (average rating of 1.44). It was found that all components for neighbour countries are rated as higher, i.e. the risk is perceived lower in the neighbouring country. The hypothesis that the median difference between pairs or “neighbour” and “home country” ratings are zero, is rejected for Government stability on a 5% significance level, as well as for Corruption, Military in politics, Law and order, Democratic accountability, and Bureaucracy quality on a 1% level. Comparing home country ratings with the ICRG, it is observed that the ICRG rates the risk with Socio-economic conditions, Corruption and Religious tensions as higher. (a 1% significant difference is found for the Socio-economic conditions component).

141 The analysis employs the rating from April, 2007 (Source: the PRS Group Inc) 148

Meanwhile, the ICRG rates the risks with Government stability, External conflict and Democratic accountability as lower.

Finally, table 14 presents the distributions of the ratings, again grouped under the “home”, “neighbour” and “ICRG” panels. Rating classes that represent at least 30% of the total number of ratings, are shown in bold. Confirming the previous results it can be seen that, among home country ratings, the highest concern falls on sub-components Corruption and Military in Politics. These have 33% and 32% of all ratings in the “very high risk” category. Corruption shows a particularly low share in the “low” or “very low” risk categories, a pattern observable also by the ICRG. The ICRG rates 83% of the countries in the “high risk” category. All components for the neighbouring countries, except for Socio-economic conditions and Corruption, have more than 30% of the ratings in the “very low” risk rating category. 149

5. CONCLUSION

The findings of this research suggest that political risk associated with international debt pose great concerns – not only for international creditors, but also for debt managers and other finance professionals in developing countries.

The first objective of the survey was to analyse external borrowing experiences and the perceived impact of political risk. In a joint analysis framework two limited dependent variables were analysed. These were the binary occurrence of experienced debt distress, and, the corresponding ordinal political risk impact assessment. The objective was to find out whether specific individual, contract, and, country specific determinants had predictive power in explaining either the extent of debt distress and/or the level of political risk impact on debt distress. Analysing 679 debt cases and 545 political risk ratings, the results suggest among other things that more experienced public sector representatives, and respondents from larger countries with a longer time since recent regime change are unlikely to rate political risk impact in the lowest risk category. Further, economic conditions in a country, as measured by the GDP per capita, the level of agriculture in GDP, and the level of democracy are significant determinants of experienced debt distress and seem also to reduce the perceived impact of political risk on debt distress. The GDP per capita is a common measure a country's level of development and is often used in country risk analysis. Our results correspond with the claim by Frank and Cline (1971) that assert that countries with lower GDP per capita makes them less able to solve debt servicing difficulties by implementing austerity programs. Thus, countries with low GDP per capita are therefore more likely to default, so a higher debt distress experience from such countries is also expected. Similar to the results in Alesina et al (1996a and 1996b) who suggest that poor economic conditions increase the probability of political coups, the results from this study indicate that finance professionals in wealthier countries are likely to rate the political risk impact on debt distress as lower. Meanwhile, the order of causation is not clear. For example, the 'poverty trap' model suggests that elements in a country's political system, and its economic structure, may be instrumental in perpetuating a state of poverty (see e.g. Mosley et al., 2006).

The finding on the agricultural base, i.e. that a higher the share of agriculture in GDP in a country actually seems to reduce the proclivity to debt distress and the perception of political risk, relates to the question of agricultural transformation for economic development. The classical agrarian question, especially in the developing country context hinges upon the progressive decline in the importance of agricultural sector in an economy and increasing urbanisation of its society, supported by industrialisation processes. Meanwhile, with rising rural-urban disparities in the transforming countries comes also a new source of political tensions. The related and topical concept, food security, also influences the political stability of countries, especially in the wake of the global food crisis. Thus, the finding adds an interesting feature in the discussion of agriculture’s shifting political power base in the world economy.

The second aim of the survey was to perform a descriptive analysis on various macroeconomic-, political-, and legal risk factors, in order to distinguish which are the factors that developing country finance professionals see as most pervasive on debt management. The results from the radar chart analysis indicate that local debt managers 150

perceive the legal environment (and problems with corruption in particular), among the most important forms of political risk that affect international debt operations. Among the corruptive activities identified among various officials both in the public and private sectors, the respondents point out kickbacks, bribes, and ties between politics and business as the most serious and frequent.

The third and final aim of the survey was to do an international rating comparison between local ratings between home country political risk ratings and corresponding ratings on neighbour countries, in addition to a comparison between home country political risk ratings and the actual political risk rating by the ICRG. The analysis revealed that locals rate most of the risk factors higher in their home countries, as compared both with the neighbour view as well as the ICRG. For example, local professionals do not consider the socio-economic constraints in their countries as high as does the ICRG-rating agency, but rate risk factors representing government stability, external conflict and democratic accountability, as significantly higher. A central question that remains from this finding is whether the locals overestimate some of the risk, or, whether the international rating agency underestimates some of the risk. Based on responses to a set of open-ended follow-up questions, the finding was further analysed by the following discoveries. First of all 73% of the respondents in the follow- up indicated they were not familiar with the ICRG Political Risk Rating at the time of performing the local rating. Thus, the local assessment should be fairly objective and independent. As the socio-economic conditions in many of the home countries is actually very severe, the finding seems fairly accurate. A better understanding of the home country situation, i.e. less knowledge about what happens in the neighbouring countries and the availability of inside information in the form of networking among local professionals, are pointed out as some of the reasons for the higher assessment of the risk for home countries than for neighbour countries. One respondent refers: “the realities and the situation are better known from the social and historical point of view. There are more details known in the home country than about the neighbour countries”.

Despite the fact that the topic of political risk seems more relevant than ever, there has been relatively little effort to investigate political risk’s impact on debt defaults. This survey has provided new independent information that contributes to the understanding of finance professionals’ experiences of debt default and their perceptions of political risk, both in developing countries, and in the international rating environment. Since the initial survey was distributed, the political risk situation has remained volatile in many developing countries. Some respondents describe the political risk situation as deteriorating, while others point to an improving situation. There have been campaigns against corruption that has caused many important political figures arrested, charged and condemned in court. Others point to changing constitutions, improving rule of law, obedience to civil society by the military, enhanced accountability and transparency of media and public. Also a high rate of unemployment and a high inflation rate are listed among the major concerns. Many believe that the environment is still very risky for business purposes though there is a rising inflow of foreign resources for investment. As the impact of political risk varies across countries, across the different debt forms, and changes over time, it may be practically hard to achieve a general understanding of political risk and its impacts on international debt. However, this study indicates that both international creditors and local debtors are on the same path regarding many risk categories, including the destructive phenomenon of corruption. 151

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APPENDIX 1 QUESTIONNAIRE DESIGN

Figure 4 Schematic layout of the questionnaire

1. Respondent data - nationality / geographic location of workstation - age and gender - education level, education location, field of training and current affiliation

2. Professional background by affiliation

Public sector Private sector NGO / other Section A

Type of organization represented

Professional area / job function

Area of expertise / specialisation

Other optional specifications, i.e. job title

Public sector Private sector NGO / Other

3. External borrowing, debt distress and political risk c) Familiarity with international debt instruments, total years of experience d) Experience of debt distress and assessment of the impact of political risk

4. Perceptions on political risk Section B a) Rating of political risk factors; impact on short- and long term debt contracts b) Selection of the most important sources (factors) of political risk a) Views on corruption b) Perceived extent of corruption in the financial industry of home country

5. Country risk rating a) Selection of countries to be rated b) Political risk rating according to the International Country Risk Guide 157

Table 1 Description and measurement of the individual characteristic variables

Individual characteristics

Category Variable Hypothesis Measurement Expected sign

distress polrisk Gender MALE Women report higher perceptions of political Male-dummy created from n.a. - risk than men. survey data (background info).

Age AGE Age is negatively related to the direction of 5-point categorial variable + + debt managers' political risk perception. obtained from survey data. 1 = under 26; 2 = 26-35; 3 = 36-45; 4 = 46-55; and 5 = 56 and above

Education EDUC A negative relation is expected between the 6-point categorial variable - + / - level of education and experienced debt obtained from survey data distress. 1 = high school; 2 = beyond high school; 3 = college graduate; 4 = honours; 5 = master; 6 = PhD

Experience EXP More experience with external debt is expected Square-root of the number of + / - - to reduce the assessment of political risk international debt-related impact on debt distress. activities multiplied by time (years of experience)

Public affiliation PUBLIC Affiliation is expected to influence on debt Public/private -dummy +/- managers’ political risk perception. 1 = public sector servant; 0 = private sector professional

NGO affiliation NGO NGO-dummy +/- - ‘’- 1 = NGO representative 0 = other

Table 2 Description and measurement of contract specific variables

Contract characteristics

Category Variable Hypothesis Measurement

External debt class 1 DEBT 1 There is a positive relation between private, Dummy variable equal to 1, for the following debt types: non-guaranteed debt type and distress and the - Private banks and other financial institutions Private perceived political risk impact on debt distress. - Foreign partners, affiliates and exporters non-guaranteed debt - Official (governments and international)

External debt class 2 DEBT 2 There is a negative relation between public- Dummy variable equal to 1, for the following debt types: and publicly guaranteed debt and distress. - Multilateral/bilateral Public- and publicly guaranteed debt A positive relationship is expected between - Buyer- /Suppliers credits perceived political risk, and public- and - Bonds publicly guaranteed debt types.

Dummy variable equal to 1, for the following debt types: External debt class 3 DEBT 3 The IMF credits are negatively related to the experience of debt distress. - Stand-by arrangements SBAs IMF Credits and other liabilities of the central - Extended Fund Facilities EFFs The impact of political risk is perceived lower bank - Poverty Reduction and Growth Facilities, PRGFs for the IMF credit types. - Other liabilities of the central bank

Dummy variable equal to 1, for the following debt types: External debt class 4 DEBT 4 There is no relationship between distress, perceived political risk, and ODA loans. - Official development aid (incl. grant element >25%) Official development assistance - Other official loans (incl. grant element <25%)

158

Table 3 Description and measurement of country related variables

Country characteristics

Category Variable Hypothesis Measurement Expected sign

distress polrisk

Macroeconomic indicators

Size GDP Country size is negatively related to distress. Gross domestic product at -- purchasing power parity (PPP) Political risk is perceived less severe in in US$ billion. smaller countries than in larger countries.

Economic development GDP / capita Poverty creates a more fertile environment for Nominal GDP divided by -- (poverty) debt distress and is reflected also in higher population. Derived using political risk perceptions. period-average exchange rate.

Volatility in GDPVol A stable environment for productivity and Standard deviation of yearly + solvency, reduces the perceived political risk. growth in real GDP (10yrs)

- % agriculture in GDP AGRI Higher share of agriculture decreases the Agricultural value-added, incl. level of perceived political risk. livestock, forestry and fishing, as percentage of nominal GDP at factor cost

Terms of trade TRADE Higher volatility in terms of trade increases the Standard deviation of the + perceived political risk export price index to the import price index-ratio

Debt burden DUE High debt service burden is associated with Total external debt service + increased political risk perception. paid/due as a percentage of exports of goods, non-factor services, and workers' remittances.

Political stability indicators

Polity IV Index POLITY More democratic regimes are associated with Combined 20-pt score lower political risk perceptions. (PolityIV). Difference between _ a measurement of democracy (0-10) and autocracy (0-10).

Polity IV Durable DURABLE Longer time since the last regime transition, is Number of years since the last associated with more favourable fundamentals regime transition _ and a lower political risk perception. (3pt or greater). 159

APPENDIX 2 RESPONSE RATES AND GEOGRAPHICAL COVERAGE

Table 4 Nationalities represented (N=101)

Nationality was set as an optional fill-in question. For 2 respondents, this information is not available. 86% of the respondents indicated his or her current work location is the same as his or her nationality.

Country # of respondents Country # of respondents

# in Response % of # in Response % of sample rate sample sample rate sample

Argentina 2 9 % 2 % Mauritius 1 11 % 1 %

Bangladesh 2 10 % 2 % Mexico 2 10 % 2 %

Bosnia and Herzegovina 1 100 % 1 % Namibia 1 7 % 1 %

Botswana 1 3 % 1 % Nigeria 27 4 % 26 %

Brazil 1 4 % 1 % Pakistan 5 8 % 5 %

Cameroon 5 12 % 5 % Papua New Guinea 1 8 % 1 %

Egypt 1 3 % 1 % Russia 1 14 % 1 %

Eritrea 7 8 % 7 % Sierra Leone 3 3 % 3 %

Ethiopia 2 3 % 2 % Sri Lanka 2 17 % 2 %

Ghana 3 2 % 3 % St Lucia 1 25 % 1 %

Haiti 1 33 % 1 % Tajikistan 1 100 % 1 %

India 1 1 % 1 % Tanzania 1 2 % 1 %

Indonesia 4 22 % 4 % The Philippines 2 4 % 2 %

Kenya 5 5 % 5 % Togo 1 17 % 1 %

Kyrgyz Republic 2 50 % 2 % Uganda 6 7 % 6 %

Malawi 2 8 % 2 % Zambia 3 4 % 3 %

Malaysia 1 7 % 1 % Zimbabwe 2 5 % 2 %

Figure 5 Respondents by geographic region (N=103)

Note: ‘Location’ is based on the ‘current work station’ of the respondent.

Eastern Africa M iddle Africa Northern Africa Europe Pacific 2 % 2 % Southern Africa 5 % 3 % 2 % 20 % 7 % Western Africa Asia Caribbean 17 % 6 % South America Northern America 4 % 2 % Central Asia 3 % Eastern Asia Latin America 11 % Southern Asia 8 % 3 % Africa South-Eastern Asia 67 % 2 % Eastern Europe 33 % Northern Europe Western Europe Australia and NZ Melanesia 160

Figure 6 Countries covered by the survey

This map indicates the countries to which the responses to the “Political Risk Questionnaire” pertain. Dark shaded areas represent countries with at least one respondent.

Figure 7 Global political risk map, 2007

Source: Sovereign Risk Insurance Ltd (2007). The shaded areas represent different country risk ratings by Euromoney Plc. (Dark green = low risk; Dark red=high risk). 161

Figure 8 Survey participant involvement in external debt related activities142

Number of activities

0 5 10 15 20 25 30 35 # of respondents

Figure 9 Years of experience with external debt related activities

Total years of experience

> 20 years 5.9%

15 - 2 0 ye a r s 7.9%

10 - 15 ye a r s 12 . 9 %

5-10 years 28.7%

2-5 years 27.7%

< 2 years 4.0%

no experience 6.9%

no answer 5.9 %

010203040 # respondents

142 Different types of external debt related activities include 1) External borrowing by the government; 2) External borrowing by state enterprises, guaranteed by the government; 3) Non-guaranteed borrowing by state enterprises; 4) Private, guaranteed debt; 5) Private, non-guaranteed debt; 6) Financial market transactions (e.g. foreign exchange and bond markets); 7) Transactions with International Financial Institutions (IFI's) and bilateral creditors; 8) Debt rescheduling (e.g. related to the Paris Club); 9) Administrating funds from foreign donors; 10) Trade- or structured finance transactions (e.g. with an Export Credit Agency involvement); and 11) Issue of guarantees. 162 16 % 10 % 17 % 12 % 13 % 19 % 12 % 25 % 21 % 23 % 21 % 25 % 17 % 24 % 18 % 20 % 13 % 36 % 36 % 31 % 31 % 27 % 32 % 34 % 34 % 33 % 32 % 45 % 34 % 28 % 27 % 26 % 26 % 33 % 33 % 30 % 27 % 26 % 26 % 33 % 12 % 18 % 19 % 23 % 19 % 19 % 14 % 14 % 15 % 18 % 15 % 22 % 9 % 20 % 19 % 17 %

Political risk impact rating (0=low; 4=high) 98 71 144 138 94 138 144 98 71 35 % 35 % 36 % 26 % 30 % Panel B. Impact of political risk on default - rating 545

47 41 23 % 40 20 % 25 15 % 15 % 29 12 % 5 % 182 21 % 5 % 7 % 26 13 % 12 % 22 21 % 7 % 7 % 24 % 107 27 19 % 32 25 % 9 % 16 % 74 15 % 25 % 20 % 25 % - 19 % 13 % n 0 1 2 3 4 n 63 37 11 % 33 19 % 49 13 % 12 % 182 19 % 14 % 14 % 18 % 24 % 8 % 14 % 19 % 20 % 19 % 40 34 10 % 21 % 25 % 15 % 21 % % 62 % 62 % 43 % 61 % 58 % 57 % 58 % 57 % 47 % 58 % 56 % 45 % 36 % 51 % 47 % 45 % 59 % 55 % 57 % distress-rate - # 44 28 19 34 30 26 26 15 19 17 12 23 18 70 33 24 57 125 116 368 distress cases of - # 27 17 25 22 91 23 19 20 17 14 93 21 21 22 20 84 23 20 43 no 311 distress - n 71 45 44 56 53 45 46 32 33 38 33 45 38 56 44 216 209 154 100 679 Panel A . Default experiences by external debt type External Debt Class I External Debt Private banks and other financial institutions Foreign partners and affiliates Foreign exporters and other private sources and international) Official (governments Sub-total / average Class II External Debt Multilateral agencies Bilateral debt Private banks Bonds Other Sub-total / average Class III External Debt Stand-by arrangements (SBAs) Extended Fund Facilities (EFFs) Poverty Reduction and Growth Facilities (PRGFs) Other liabilities of the central bank Sub-total / average Class IV External Debt aid (incl. grant Official development element >25%) Other official loans (incl. grant element <25%) Sub-total / average Total (n) Average Table 5Table risk rating by debt category and political experience Debt distress 163 8 1 0.19* 0.19* 8 1 0.24* 0.24* -0.09* -0.09* 8 1 0.01 0.01 -0.40* -0.40* -0.22* 8 1 -0.02 -0.33* -0.33* -0.13* -0.29* 8 1 0.03 0.03 0.11* 0.11* 0.29* -0.30* -0.30* -0.09* 8 1 0.27* 0.27* 0.47* 0.28* -0.16* -0.16* -0.51* -0.23* 8 1 0.06 0.06 0.01 0.40* 0.40* 0.23* 0.26* -0.46* -0.46* -0.42* 8 1 0.00 0.00 0.04 0.02 -0.02 -0.02 -0.06 -0.01 -0.08* -0.08* 8 1 0.04 0.04 0.03 0.02 0.01 -0.00 -0.04 -0.03 -0.00 Correlations -0.36* -0.36* 7 1 0.01 0.01 0.02 0.04 0.01 0.07 0.01 -0.01 -0.01 -0.47* -0.47* -0.36* 6 1 0.02 0.02 0.01 -0.01 -0.01 0.12* 0.12* 0.24* 0.22* 0.27* -0.17* -0.17* -0.10* -0.12* 5 1 0.01 0.01 0.00 0.01 0.01 -0.05 -0.05 0.07* 0.07* 0.13* 0.07* -0.13* -0.13* -0.16* -0.18* 4 1 0.04 0.04 0.02 0.02 0.01 -0.05 -0.06 -0.06 -0.00 0.19* 0.19* 0.11* -0.49* -0.49* -0.15* -0.10* 3 1 0.05 0.05 0.02 0.01 0.02 -0.04 -0.04 -0.01 -0.07 0.16* 0.16* 0.24* 0.12* 0.16* -0.36* -0.36* -0.48* 2 1 0.04 0.04 0.05 0.02 -0.02 -0.08 -0.06 0.13* 0.13* 0.46* 0.22* 0.08* 0.23* -0.09* -0.09* -0.08* -0.16* -0.11* 1 1 0.06 0.06 0.04 0.01 0.06 0.06 -0.07 -0.03 -0.03 -0.04 -0.01 -0.03 -0.03 0.18* 0.18* -0.30* -0.30* -0.34* -0.19* 1 5 6 1 1 1 1 1 21 10 57 7.55 7.55 9.02 16.0 76.9 22.4 Max. Max. 108.2 108.2 0 1 1 0 0 0 0 0 0 0 -7 0.16 0.16 5.18 0.87 1.50 4.17 1.10 Min. 0.44 0.44 0.79 1.19 0.49 0.35 4.68 0.47 0.47 0.41 1.71 0.95 2.50 13.8 40.8 4.69 4.98 12.69 12.69 St.dev 0.74 0.74 2.75 4.34 0.59 0.15 6.19 0.32 0.32 0.22 4.13 6.81 3.90 23.0 39.6 6.37 2.61 10.36 10.36 Mean Variable 1. Gender 2. Age 3. Education 4. Public affiliation 5. NGO affiliation 6. Experience 7. External Debt Class 1 8. External Debt Class 2 9. External Debt Class 3 of 10. Logarithm GDP 11. GDP / Capita 12. GDP volatility 13. Share of agriculture 14. Trade terms 15. Debt service ratio, due 16. Polity IV Index 17. Durability of regime Table 6Table variables the explanatory among and correlations statistics Descriptive 164

Table 7 External debt categories

External Debt Class I. Private non-guaranteed debt External Debt Class III. IMF Credits

Private banks and other financial institutions Stand-by arrangements (SBAs) Foreign partners and affiliates Extended Fund Facilities (EFFs) Exporters and other private sources Poverty Reduction and Growth Facilities (PRGFs) Official (governments and international) Other liabilities of the central bank

External Debt Class II. Public and publicly guaranteed debt External Debt Class IV. Private non-guaranteed debt

Multilateral agency Official development aid (incl. grant element of >25%) Bilateral (e.g. the source of budget is a single government) Other official loans (grant element < 25%) Private banks (e.g. "buyers credits") Bonds Others (e.g. "suppliers credits")

Table 8 Collinearity diagnostics among the explanatory variables

Variable Collinearity diagnostics Condition VIF Sqrt -VIF Tolerance R-Squared Eigenvalue Index 1. Gender 1.14 1.07 0.88 0.12 11.65 1.00 2. Age 1.40 1.18 0.71 0.29 1.07 3.30 3. Education 1.49 1.22 0.67 0.33 1.00 3.41 4. Public affiliation 1.84 1.36 0.54 0.46 1.00 3.41 5. NGO affiliation 1.64 1.28 0.61 0.39 0.86 3.68 6. Experience 1.24 1.12 0.80 0.20 0.58 4.49 7. External Debt Class 1 2.20 1.48 0.45 0.55 0.39 5.48 8. External Debt Class 2 2.33 1.53 0.43 0.57 0.34 5.85 9. External Debt Class 3 2.20 1.48 0.45 0.55 0.30 6.22 10. Logarithm of GDP 2.16 1.47 0.46 0.54 0.24 6.97 11. GDP / Capita 3.41 1.85 0.29 0.71 0.18 8.15 12. GDP volatility 2.42 1.56 0.41 0.59 0.13 9.36 13. Share of agriculture 3.57 1.89 0.28 0.72 0.11 10.37 14. Trade terms 2.81 1.68 0.36 0.64 0.07 13.04 15. Debt service ratio, due 5.02 2.24 0.20 0.80 0.04 17.50 16. Polity IV Index 1.80 1.34 0.56 0.44 0.03 20.03 17. Durability of regime 1.68 1.29 0.60 0.40 0.01 32.49 Mean 2.26 165

APPENDIX 3 ESTIMATION RESULTS

Table 9 Debt distress and political risk perception – Individual characteristics

This table presents joint logistic regression estimates for debt distress (distress) and for political risk impact on debt distress (polrisk). Panel A. presents logit regression estimates of the probability that a respondent experiences distress. The ordered logit results of political risk impact are presented in Panel B, and the generalised ordered logit estimates are presented in Panel C. In panel A the dependent variable is equal to 1 if distress occurred, and zero otherwise. In panel B the dependent variable is equal to 0 if there was no perceived impact from political risk, 1 for medium perceived impact, and 2 for a large perceived impact. The total number of contract observatios is 1545, defined as external debt “experiences”. Observations are skipped for missing values in the dependent or independent variables. The proportional odds assumption hold for the grey shaded variables. These have constant coefficients over the range of outcomes in Panel C.

Method Panel A. Logit Panel B. Ordered logit Panel C. Generalised logistic

Model Model 1 Model 2 Model 3 Dependent variable distress distress polrisk polrisk polrisk polrisk

Variables Coef. % ǻ Coef. % ǻ 0 1 (Z-value) in odds (Z-value) in odds

Individual characteristics Gender 0.37 44.2 0.21 23.9 0.16 0.16 (1.63) (0.98) (0.72) (0.72) Age group 0.25 * 28.0 -0.18 -16.8 -0.63 ** 0.11 (2.01) (1.52) (4.04) (0.82) Education level -0.08 -8.1 -0.08 -7.3 -0.06 -0.06 (1.19) (1.02) (0.80) (0.80) Public sector 0.60 ** 82.2 0.22 24.8 -0.83 * 0.69 ** (2.64) (1.04) (2.34) (2.81) NGO representative 0.63 * 87.4 -0.05 -5.1 -1.05 * 0.55 (2.18) (0.18) (2.34) (1.66) Experience -0.09 ** -9.0 -0.05 * -4.5 -0.09 ** -0.02 (4.26) (2.05) (2.97) (0.94)

Constant -0.30 - 4.74 * (0.66) - (7.25)

Observations 589 487 487 Log (pseudo)likelihood -391.3 -500.7 -473.9 Pseudo R2 0.04 0.01 0.07 Wald chi2/ LR chi2 (df) 29.11 (6) 14.79 (6) 77.51 (10) % correctly predicted 54 % - - 166

Table 10 Debt distress and political risk perception – Debt type

Method Panel A. Logit Panel B. Ordered logit Panel C. Generalised logistic

Model Model I Model II Model III Dependent variable distress distress polrisk polrisk polrisk polrisk

Variables Coef. % ǻ Coef. % ǻ 0 1 (Z-value) in odds (Z-value) in odds

Debt Type Debt I 0.04 3.6 0.24 27.3 0.25 0.25 (0.18) (0.95) (0.98) (0.98) Debt II -0.06 -5.9 0.14 15.0 0.15 0.15 (0.28) (0.55) (0.57) (0.57) Debt III -0.46 * -37.1 -0.32 -27.3 -0.72 * -0.05 (2.11) (1.13) (2.27) (0.18)

Constant 0.28 - 1.57 ** (1.10) (6.70)

Observations 679 545 545 Log (pseudo)likelihood -465.1 -563.2 -559.8 Pseudo R2 0.01 0.01 0.01 Wald chi2/ LR chi2 (df) 6.36 (3) 6.21 (3) 13.89 (4) % correctly predicted 56 % - - 167

Table 11 Debt distress and political risk perception – Country characteristics

(Same definitions apply as in Tables 9-10).

Method Panel A. Logit Panel B. Ordered logit Panel C. Generalised logistic

Model Model I Model II Model III Dependent variable distress distress polrisk polrisk polrisk polrisk

Variables Coef. % ǻ Coef. % ǻ 0 1 (Z-value) in odds (Z-value) in odds

Macroeconomic variables Size (GDP) -0.13 -12.1 -0.18 -16.0 -1.09 ** 0.03 (1.39) (1.65) (4.28) (0.23) Poverty (GDP/Capita) -0.39 * -32.4 -0.39 -32.6 0.44 -0.79 ** (2.11) (1.85) (1.36) (3.18) Volatility (GDP growth) -0.04 -3.5 -0.03 -2.5 -0.28 * 0.24 * (0.41) (0.27) (2.04) (1.97) % of agriculture in GDP -0.07 ** -6.7 -0.04 * -3.7 -0.06 ** -0.06 ** (4.60) (2.35) (3.40) (3.40) Trade terms 0.00 0.4 0.01 ** 1.0 0.02 ** 0.00 (1.19) (3.12) (4.60) (0.52) Debt service ratio, due 0.03 2.6 0.07 7 0.31 ** -0.07 (0.83) (1.94) (4.67) (1.38) Government stability Level of democracy -0.12 ** -11.7 -0.04 -3.7 -0.01 -0.09 ** (4.98) (1.53) (0.45) (2.94) Durability of regime -0.02 * -2.1 0.00 -0.2 -0.03 * 0.01

Constant 5.41 ** 3.58 6.08 ** (3.65) (1.81) (3.44)

Observations 469 397 397 Log (pseudo)likelihood -292.2 -394.8 -367.6 Pseudo R2 0.10 0.05 0.11 Wald chi2/ LR chi2 (df) 73.56 (8) 40.44 (8) 80.46 (15) % correctly predicted 62 % - - 168

Table 12 Debt distress and political risk perception – Combined models

(Same definitions apply as in Tables 9-10).

Method Panel A. Logit Panel B. Ordered logit Panel C. Generalised logistic

Model Model I Model II Model III Dependent variable distress distress polrisk polrisk polrisk polrisk

Variables Coef. % ǻ Coef. % ǻ 0 1 (Z-value) in odds (Z-value) in odds

Individual characteristics Gender 0.94 ** 155.6 0.51 66.6 0.34 0.34 (3.13) (1.92) (1.26) (1.26) Age group -0.06 -5.9 -0.39 * -31.9 -0.30 -0.30 (0.32) (2.10) (1.63) (1.63) Education level -0.26 -22.6 -0.22 -19.4 -0.21 -0.21 (1.48) (1.59) (1.43) (1.43) Public sector -0.07 -6.3 0.02 2.1 -3.68 ** 0.65 (0.18) (0.07) (5.29) (1.85) NGO representative 0.78 118.6 -0.10 -9.2 -3.58 ** 0.65 (1.67) (0.25) (4.43) (1.40) Experience -0.10 ** -9.7 -0.02 -1.6 -0.17 ** 0.06 (3.09) (0.56) (3.74) (1.59) Debt type Debt I -0.14 -12.9 0.20 22.3 0.35 0.35 (0.41) (0.65) (1.04) (1.04) Debt II -0.28 -24.6 0.24 27.6 0.35 0.35 (0.92) (0.79) (1.06) (1.06) Debt III -0.62 -46.3 -0.39 -32 -1.02 * 0.31 (1.96) (1.11) (2.20) (0.78) Macroeconomic variables Size (GDP) -0.01 -0.5 -0.05 -4.5 -2.78 ** 0.22 (0.05) (0.38) (5.49) (1.51) Poverty (GDP/Capita) -0.66 ** -48.4 -0.63 * -46.8 -0.69 * -0.69 * (3.09) (2.54) (2.53) (2.53) Volatility (GDP growth) 0.00 -0.2 0.07 7.5 0.76 ** 0.22 (0.02) (0.65) (3.76) (1.61) % of agriculture -0.09 ** -8.4 -0.06 ** -5.9 -0.36 ** -0.06 * (4.08) (3.03) (6.58) (2.53) Trade terms 0.00 0.2 0.00 0.4 0.01 * 0.00 (0.53) (1.03) (2.25) (0.24) Debt service 0.00 0.1 0.00 0.2 0.26 ** -0.08 (0.02) (0.05) (3.21) (1.44) Government stability Level of democracy -0.16 ** -14.6 -0.08 * -7.5 -0.14 ** -0.14 ** (4.26) (2.41) (3.85) (3.85) Durability of regime 0.01 0.5 0.01 0.6 -0.05 * 0.02 (0.48) (0.52) (2.48) (1.83)

Constant 8.58 * 30.43 ** 5.17 (3.48) (6.94) (1.93)

Observations 436 379 379 Log (pseudo)likelihood -250.9 -365.5 -312.3 Pseudo R2 0.17 0.08 0.21 Wald chi2/ LR chi2 (df) 64.13 (17) 61.05 (17) 95.71 (17) % correctly predicted 73 % - - 169

Figure 10 Political risk perception radars

a) Summarized categories b) Macroeconomic problems

Monetary or fiscal policy Macroeconomic problems 3.5 3.5

3.0 3

2.5 Other 2.5 Legal system

Hard currency 2.0 Economic problems 2

Government policies Political stability Infla t io n

Short Term Lo ng Te r m Short-term Lo ng - t e r m

c) Dysfuncitonal legal system d) Political stability

Frequent leadership changes 3.50 Contract rights law 3.5 3.00

2.50 Judicial independence 2.5 Legal transparency Strikes or major labour 2.00 Abrupt change in policy 2 disruptions

Bribery and corruption Theft o f intellectual p ro p erty

War, revolution or social unrest

Short-term Lo ng - t e r m Short-term Lo ng - t e r m

e) Government policies f) Miscellaneous

Terrorism 3.50 Nationalisation of assets 3.50

3.00

3.00 Trade embargo or sanctions Expropriation Lack of commitment to 2.50 Macroeconomic crises 2.50 international treaties

2.00 2.00

Taxes or tariffs Currency inconvertibility

Ext ernal ins t itut io ns limit ing Anti-foreign bias Currency transfer restrictions government action

Short-term Long-term Short-term Lo ng - t e r m 170

Figure 11 Frequency and seriousness of corruption

Seriousness 4.0 Ties between politics and business

Bribes Nepotism Kickbacks Political 3.5 contributions Excessive gifts Patronage Favo r fo r 'favo rs '

Extortion Theft Job reservations 3.0 F a ls ific a tio n o f documents

Blackmail

Frequency 2.5 2.5 3.0 3.5 4.0

Figure 12 Extent of corruptive offences

4=all or almost all 3=some occasionally 2=very few Office of the President 1=no ne o r almost none

Cabinet Ministers

Senior Government Officials

Members of the Parliament

Civil servants and other officals

Private sector - senior executives

Private sector - middle managers

Private sector - other employees

0 102030405060 171 ** ** ** ** 4 3 % 0 % 7 % 22 % 17 % 23 % 11 % 37 % 37 % 33 % 83 % 33 % 33 % 2.96 2.96 0.02 0.95 0.06 -4.94 -1.89 -1.51 -4.59 -0.90 -0.13 -3.33 -0.81 Z-value 3 13 % 15 % 11 % 28 % 21 % 61 % 61 % 76 % 64 % 45 % 32 % 43 % 46 % 0.45 0.45 0.68 0.53 0.51 0.38 0.41 0.77 0.92 0.56 0.84 0.75 0.66 st.dev ICRG rating 2 2 % 1 % 3 % 2 % 23 % 43 % 43 % 83 % 52 % 46 % 48 % 45 % 37 % avg 2.88 2.88 1.44 2.63 2.69 3.28 1.65 2.19 2.49 2.26 2.39 2.65 2.12 ICRG rating (test of difference) n 1 94 94 94 94 94 94 94 94 94 94 94 94 0 % 1 % 0 % 0 % 6 % 3 % 0 % 0 % 0 % 1 % 6 % 41 % 41 % * ** ** ** ** ** 4 24 % 28 % 34 % 34 % 40 % 40 % 49 % 56 % 39 % 39 % 39 % 41 % 39 % -2.11 -1.93 -1.65 -1.29 -1.16 -3.51 -3.50 -1.03 -2.76 -1.10 -2.43 -2.93 Z-value 3 26 % 24 % 24 % 27 % 24 % 14 % 23 % 26 % 19 % 21 % 21 % 31 % 31 % 1.01 1.01 1.04 0.97 1.09 1.09 1.04 1.11 1.06 0.96 0.99 1.01 1.00 st.dev 2 21 % 24 % 17 % 22 % 30 % 34 % 34 % 35 % 32 % 31 % 31 % 35 % 35 % avg 2.49 2.49 2.20 2.69 2.53 2.75 2.28 2.82 2.65 2.65 2.52 2.63 2.59 Neighbour country rating country Neighbour n 1 82 82 77 78 78 79 79 80 80 80 80 80 6 % 8 % 8 % 9 % 8 % 4 % 8 % 8 % 5 % 16 % 12 % 16 % Neighbour country rating (test of difference) rating (test country Neighbour 0 0 0 0 0 0 0 0 0 0 0 0 4 22 % 17 % 16 % 28 % 24 % 28 % 23 % 31 % 31 % 33 % 45 % 35 % 34 % 0.96 0.96 1.01 0.97 1.12 1.11 1.04 1.21 1.10 0.96 1.07 1.03 1.07 3 st.dev 19 % 29 % 25 % 21 % 12 % 16 % 23 % 22 % 19 % 17 % 22 % 35 % 35 % avg 2.18 2.18 1.87 2.40 2.45 2.57 1.74 2.09 2.54 2.23 2.37 2.29 2.11 2 29 % 30 % 20 % 24 % 33 % 33 % 40 % 39 % 31 % 45 % 33 % 43 % 33 % Home country rating Home country rating n 92 94 90 87 89 92 92 91 92 91 92 90 1 11 % 23 % 11 % 11 % 13 % 11 % 10 % 14 % 11 % 21 % 33 % 33 % 32 % Sub-components of political risk rating stability Government Socioeconomic conditions profile Investment Internal conflict External conflict Corruption Military in politics Religious tensions Law and order Ethnic tensions AccountabilityDemocratic Bureaucracy Quality Sub-components of political risk rating stability Government Socioeconomic conditions profile Investment Internal conflict External conflict Corruption Military in politics Religious tensions Law and order Ethnic tensions AccountabilityDemocratic Bureaucracy Quality Table 13Table ICRG and the between local ratings Political risk ratings, comparison 14Table categories in various rating shares Political risk ratings, CEFIR, Center for Financial Research, Swedish School of Economics and Business Adminsitration, Helsinki FINLAND

Questionnaire on Political Risk

for Developing Country Finance Professionals

Background information

This questionnaire survey is part of a doctoral research project exploring the impact of political risk on international credit. The purpose is to provide new independent information about different forms of political risk that may affect international credit operations. The specific aim of this particular survey is to gather experiences and perceptions of political risk, from the viewpoint of local debt managers and finance professionals in developing countries.

The whole study is being conducted for research purposes only and no material of this research will be used for investment or any other purposes. A top priority is to protect the confidentiality of the respondents, so the survey is completely anonymous.

Undertaken within the framework of The Center for Financial Research (CEFIR) at the Swedish School of Economics and Business Administration (HANKEN, Finland), this survey is expected to yield important and impartial insights for future research initiatives, for debtors in developing countries as well as for international lenders, on how to confront and analyse the perils of political risk.

Invitation

You are cordially invited to take part in this international survey to make your views part of a comprehensive review on the topic of political risk. The survey is being targeted to a large group of finance professionals in the public and private sectors as well as for non-governmental organizations and other institutions, and is being conducted in more than 100 developing countries.

The estimated time for completing this questionnaire is 15-30 minutes.

By completing the survey, you will have the option to receive a free confidential tailored summary report of the results showing how specific economic regions compare along key political risk dimensions as well as regional professional assessments of the experienced and current risk situations. The results will allow you to compare your own views to other finance professionals and provide you with new insights on how to confront the changing nature of political risk in the context of international debt. Confidentiality

All answers provided will be held in the strictest confidence. The following confidentiality rules are applied to prevent disclosure of any information deemed confidential.

● The results of this survey will be only reported as group summaries. This means that no names, individuals, institutions or countries will be identifiable.

● Responses on various issues on political risk weill be analysed only in country groups by macro geographical (continental) regions or selected economic groupings or ratings.

● A large number of countries (and respondents within each country) will be approached, including various institutions, positions and disciplines. This enables the grouping of data in order to prevent direct or residual disclosure of any identifiable data.

● The questionnaire is not coded, which enables the identity of the respondent to remain uncovered. All answers will be disposed once they have been analysed for the purposes of this study.

Start the Survey!

The survey starts when you click on the "Next" button below.

Thank you in advance for your valuable contribution and time spent on this questionnaire!

Next --> 1 RESPONDENT DATA

(Section 1/5)

1 a) Please indicate your nationality and the geographic location of your current workstation.

Your Nationality (fill-in, optional)

Geographic location or region of your current work. Choose below

Name of the country, where you are currently working and living in (fill-in, optional)

1 c) What is your age? Choose below

1 d) What is your gender? Choose below

1 e) Please state your education level. i.e. the highest academic qualification obtained.

nmlkj Basic education, not a high shchool graduate

nmlkj High school graduate / Matric

nmlkj Some schooling beyond high school

nmlkj College graduate / Bachelor

nmlkj Honours

nmlkj Master

nmlkj Doctoral degree

nmlkj Post-doc position 1 f) Where did you obtain your main education?

Geographical region::::::::::::::::::: Specification of country and institution (optional):::

nmlkj Africa

nmlkj Asia

nmlkj Europe

nmlkj United States

nmlkj Some other part of the world

1 g) What is the main field of your training?

nmlkj Economist

nmlkj Financial Analyst

nmlkj Lawyer

nmlkj Other, please specify

1 h) On which sector is your current affiliation?

Please note: After this question, you will be directed to the sub-section corresponding your current affiliation.

nmlkj Public sector (The Government)

nmlkj Private sector

nmlkj Non-governmental Organization (NGO) or other

<-- Previous Next --> 2 PROFESSIONAL BACKGROUND - PUBLIC SECTOR

(Section 2/5)

2 a) Which type of organization do you represent?

Tick mark the one that apply.

nmlkj Treasury, Ministry of Finance

nmlkj Ministry of Trade

nmlkj Ministry of Planning

nmlkj Ministry of Foreign Affairs

nmlkj Accountant-General's Office

nmlkj Attorney-General's Office

nmlkj Parastatal corporation

nmlkj Central bank

nmlkj Public bank

nmlkj Other, please specify

2 b) Which of the following categories best correspond your profession?

Tick mark the one that describes best your job function.

nmlkj Financial manager

nmlkj Debt manager

nmlkj Policy advisor or regulator

nmlkj Examiner

nmlkj Economist

nmlkj Accountant

nmlkj Lawyer, counsel or judge

nmlkj Administrative official or budget officer

nmlkj Operations or enterprise officer

nmlkj Statistician

nmlkj Assistant or trainee

nmlkj Other, please specify 2 c) Please specify your area of expertise

Tick mark all that apply.

gfedc Accounting

gfedc Monetary and/or fiscal policy

gfedc Bond issuance, capital market operations

gfedc Auditing, oversight or other control functions

gfedc Legal issues

gfedc Tax issues

gfedc Other, please specify

2 d) Please specify your job title

Fill-in (optional)

<-- Previous Next --> 2 PROFESSIONAL BACKGROUND - PRIVATE SECTOR

Section 2/5

2 a) Which type of organization do you represent?

Tick mark the one that apply.

nmlkj Listed corporation

nmlkj Privately held corporation

nmlkj Family-run business

nmlkj Stock Exchange

nmlkj Private bank

nmlkj Partnership or association

nmlkj Please specify the industry

2 b) Which of the following categories best correspond your profession?

Tick mark the one that best describes your job function.

nmlkj Management; highest-ranking executive position

nmlkj Board of directors, supervisory board or some other supervisory function

nmlkj Operations manager, General manager, Principal

nmlkj Director of Finance, Head of Finance/Investment/Treasury, Chief (Finance)

nmlkj Assistant Manager or Officer

nmlkj Councel, Advocate, Lawyer or Solicitor

nmlkj Advisor, Analyst, Consultant or Specialist

nmlkj Secretary functions

nmlkj Student or Trainee

nmlkj Other, please specify 2 c) Please specify your area of expertise

Tick mark all that apply.

gfedc Accounting

gfedc Financial control

gfedc Loans and credits

gfedc Treasury functions

gfedc Capital market operations

gfedc Information systems

gfedc Law and finances

gfedc Business development

gfedc Other, please specify

2 d) Please specify your job title

Fill-in (optional).

<-- Previous Next --> 2 PROFESSIONAL BACKGROUND - NGO AND OTHER

2 a) Which type of organization do you represent?

Tick mark the form of your organization that is the most applicable.

nmlkj Operational NGO - relief oriented

nmlkj Operational NGO - development oriented

nmlkj Advocacy NGO - religious

nmlkj Advocacy NGO - non-religious

nmlkj University or other research initiative

nmlkj Environment or infrastructure project

nmlkj Other, please specify

2 b) If you are working for an NGO, please specify whether it is

nmlkj Community-based

nmlkj National

nmlkj International

2 c) Briefly describe your area of expertise and your responsibilities within your organisation. i.e. how is your work is related to debt or financial management?

2 d) Please specify your job title

Fill-in (optional)

<-- Previous Next --> 3 EXPERIENCE WITH EXTERNAL BORROWING

(Section 3/5)

3 a) Have you been involved with the international financial community through the following activities?

Type of international activitity (please tick-mark all that apply)

gfedc External borrowing by the government

gfedc External borrowing by state enterprises, guaranteed by the government

gfedc Non-guaranteed borrowing by state enterprises

gfedc Private, guaranteed debt

gfedc Private, non-guaranteed debt

gfedc Financial market transactions (e.g. foreign exchange and bond markets)

gfedc Transactions with International Financial Institutions (IFI's) and bilateral creditors

gfedc Debt reschedulings (e.g. related to the Paris Club)

gfedc Administrating funds from foreign donors

gfedc Trade- or structured finance transactions (e.g. with Export Credit Agency involvement)

gfedc Issue of guarantees

Total years of your experience (combined for all activities) Please choose below 3 b) Please indicate your experience of financial distress within the following External Debt Classes (I-IV). To what extent was the distress a result of political risk factors?

Instructions (and definitions) for the tables below:

Please tick-mark the box on the left if you have participated in a corresponding transaction (e.g. been involved in debt negotiations, surveillance, legal drafting or execution of money transfers). Then, please make an assessment on the level and type of political risk impact on any financial distress period, that you may have experienced in this connection.

On the right, please indicate i) whether the particular lending transaction experienced a state of financial distress (Distress)

"financial distress" = some sort of negative credit event, for example, failure of debt repayments (principal or interest), occurrence of default, insolvency or bankruptcy. ii) if yes; please rate to what extent you regard the distress as a result of political risk? (Impact of political risk)

Categories: 1=No impact, 2=Low impact, 3=Medium impact, 4=High impact, 5 = Very high impact iii) what was the main type of political risk you encountered? (Type of political risk)

In this context, political risk is defined as any change in the economic- or political environment caused by political powers, that had an effect on the debt contract. Examples of political risk include e.g. government restrictions of foreign currency transfer; contract repudiation/frustration; war or civil disturbance etc. Feel free to name any.

External Debt Class I: Private non-guaranteed debt Distress Impact of political risk Type of political risk* Yes No 1 2 3 4 5 gfedc Private banks and other financial institutions nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Foreign partners and affiliates nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Foreign exporters and other private sources nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Offficial (governments and international) nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

*) you may elaborate on any further causes of the financial distress in the space provided below.

Comments on External Debt Class I.

External Debt Class II: Public / publicly guaranteed Distress Impact of political risk Type of political risk* Yes No 1 2 3 4 5 gfedc Multilateral agency nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Bilateral nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Private banks (e.g. "buyers credits") nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Bonds nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Others (e.g. "suppliers credits") nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

*) you may elaborate on any further causes of the financial distress in the space provided below.

Comments on External Debt Class II.

External Debt Class III: IMF Credits / Central bank Distress Impact of political risk Type of political risk* Yes No 1 2 3 4 5 gfedc Stand-by arrangements (SBAs) nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Extended Fund Facilities (EFFs) nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Poverty Reduction and Growth Facilities (PRGFs) nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

gfedc Other liabilities of the central bank nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

*) you may elaborate on any further causes of the financial distress in the space provided below.

Comments on External Debt Class III.

Ext. Debt Class IV: Official development assistance Distress Impact of political risk Type of political risk* Yes No 1 2 3 4 5 gfedc Official development aid (incl. grant element nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj >25%) gfedc Other official loans (incl. grant element <25%) nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

*) you may elaborate on any further causes of the financial distress in the space provided below.

Comments on External Debt Class IV.

<-- Previous Next --> 4 POLITICAL RISK - THE PERCEPTIONS

(Section 4/5)

4 a) In the context of your own working environment and experience, how important and influential do you consider the following risk factors to be for international debt operations?

Periods:

Short term (ST) defines contracts that must be fulfilled usually within a year or two at most.

Long term (LT) is used for contracts with longer payback periods.

Categories:

1=no impact (not important), 2=some impact (low importance), 3=medium impact (important), 4=significant impact (very important).

Macroeconomic problems:::::::::::::::::::::::::::::::::::::::::::::::::::::: ST impact/importance LT impact/importance <-low high-> <-low high-> 1 2 3 4 1 2 3 4 gfedc Unsound monetary or fiscal policy nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc General economic problems of the country nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc High inflation nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Hard currency shortage nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

A dysfunctional legal system::::::::::::::::::::::::::::::::::::::::::::::::: ST impact/importance LT impact/importance <-low high-> <-low high-> 1 2 3 4 1 2 3 4 gfedc Contract rights law nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Lack of legal transparency nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Theft of intellectual property nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Bribery and corruption nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Lack of judicial independence nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

Political stability::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: ST impact/importance LT impact/importance

<-low high-> <-low high-> 1 2 3 4 1 2 3 4 gfedc Frequent leadership changes nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Abrupt change in policy / ruling party nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc War, revolution or social unrest nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Strikes or major labour disruptions nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Government policies::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: ST impact/importance LT impact/importance <-low high-> <-low high-> 1 2 3 4 1 2 3 4 gfedc Nationalisation of assets nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Expropriation / "creeping expropriation" nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Currency inconvertibility nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Restrictions on transfer of the credit currency nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Taxes or tariffs nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Trade embargo or sanctions nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

Other ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: ST impact/importance LT impact/importance

<-low high-> <-low high-> 1 2 3 4 1 2 3 4 gfedc Terrorism nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Macroeconomic crises nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc External institutions limiting government action nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Anti-foreign bias nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj gfedc Lack of commitment to international treaties nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

4 b) Please outline three factors that you consider as the most important potential sources of political risk in your country, that may impact on international trade and investment.

The factors may include (but are not limited) to the previously listed.

Factor 1

Factor 2

Factor 3

Please elaborate on this question both considering the situation as of today as well as going forward.

<-- Previous Next -->

Please note: If you can not get the table working (i.e. ticking in marks in the matrix), please use the conventional rating system below. Some browsers may not support the above matrix-system. If the table is working properly, please skip the rating below and continue with question 4d.

:::::::Frequency::::::: :::::::Seriousness::::::: 1=not frequent 5=very frequent 1=not serious 5=very serious 1 2 3 4 5 1 2 3 4 5 Special payments or bribes nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Patronage nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Nepotism nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Job reservations nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj 'Favor-for-favors' nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Political contributions nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Close ties between politics and business nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Excessive gifts and gratuities nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Kickbacks nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Falsification of documents nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Extortion nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Blackmail nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Theft nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj

4 d) How many officials do you think have been involved with any of these offences in the following sectors of your country?

1=none or almost none, 2=very few, 3=some occasionally, 4=all or almost all 1 2 3 4 Office of the President nmlkj nmlkj nmlkj nmlkj

Cabinet Ministers nmlkj nmlkj nmlkj nmlkj

Senior Government Officials nmlkj nmlkj nmlkj nmlkj

Members of the Parliament nmlkj nmlkj nmlkj nmlkj

Civil servants and other officals from government offices and ministries nmlkj nmlkj nmlkj nmlkj

Private sector - senior executives nmlkj nmlkj nmlkj nmlkj

Private sector - middle managers nmlkj nmlkj nmlkj nmlkj

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4 e) What do you understand by the word "corruption"?

What does it mean to a person like yourself?

<-- Previous Next --> 5 COUNTRY RATING

(Section 5/5)

In this final part, you are invited to make an experimental assessment (a rating) of the political risk situation in your country and for comparison - in one of your neighbouring countries!

A benevolent and anonymous baseline comparison will be performed between ratings provided by you (the local experts) and international rating institutions.

5 a) Please state which countries you intend to evaluate.

Country 1 = Country where you are currently working in

Country 2 = A chosen neighbouring country

Country 1

Country 2

5 b) Please assign a numerical rating within risk types i-vi) according to the predetermined range.

Please observe the inverse rating: The higher rating, the lower risk!

i) Government stability

Short definition: governments ability to carry out its declared program and its ability to stay in office.