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default. Here ‘‘default’’ is defined as any country that has received a large, non-concessional IMF loan in excess of its 100% quota,85 or if Standard and Poor’s classifies the country as being in default, which occurs if a country’s government fails to pay interest or principal on an external obligation when it is due.

Table 6.7 is a partial reproduction of table 1 from Manasse et al. (2003) – 22 countries (including China and India) which did not default were dropped from the original table. It is notable that Russia is the only former Soviet bloc country that has defaulted. Some appear

Table 6.7 Country Debt Problems, 1970–2002

Data from 1993.

Source: Manasse et al. (2003), table 1. Note the table is not exhaustive – it does NOT include every country that defaulted during this period, only those the authors included in their study.

85 More explicitly, if a large non-concessional loan is approved and some of the loan is disbursed over the first year.

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to be chronic defaulters – South Africa was in default four times (though not since 1994) Argentina, Brazil, Jamaica, Peru, Uruguay and Venezuela have each had three episodes of default. Ecuador, Mexico and Turkey have defaulted twice and the majority have defaulted just once. The average length of time to resolve the problem is 5.5 years, though the Dominican Republic (22 years), Panama (14 years) and Costa Rica (10 years) took longer. Egypt, Guatemala, Thailand and Tunisia managed to settle the problem within a year, and only encountered difficulties of this magnitude just once.

Manasse et al. used a multinomial logit model to identify the variables significant in explaining why a country defaults, and once identified, to set up an early warning system (EWS). The use of logit is the most common econometric method used to assess sovereign risk.86 The logit approach is explained in detail in Chapter 7, but essentially it is an econometric procedure that deals with binary outcomes, that is, in situations where the event, default, happens or does not happen. In logit analysis, the dependent variable is the binary outcome and the objective is to identify the explanatory variables that influence the event. The right-hand side of the regression contains the explanatory variables.

In common with most of the work in this area, Manasse et al. test for the significance of a large list of possible explanatory variables, some of which are listed in Table 6.8. Column (3) gives the expected sign. If positive, it means a rise in the variable will raise the probability of rescheduling/default; if negative, an increase should reduce the probability of default. Manasse et al. also use two political indicators, a dummy for years with an election (in the country) and an index of freedom.

The list in Table 6.8 is not exhaustive, Manasse et al. test a total of 47 variables providing indicators of liquidity, solvency, external and public debt, and general macroeconomic indicators. Many of these are not independent of each other, and are tested in separate regressions – the researcher decides on the optimal set of variables based on the model’s diagnostics and the degree of significance of the variables tested.

The logit early warning system (or logit EWS) is estimated for 37 countries over the period 1976 – 2001, using a total of 594 observations. Acting on evidence suggesting that the defaults after 1990 differ in character to those in the 1970s and 1980s, the authors test the model in the two different periods. The variables found to be significant are reported in Table 6.9.

When it comes to prediction, the focus of attention is on the number of type I and type II errors. A type I error occurs when a country is not predicted to default but does; a type II error arises when a country is predicted to default and does not. Too many type I errors, and the value of the lender’s assets will fall. Too many type IIs, and profitable lending opportunities are missed. Normally, the average cost of a type I error is assumed higher than type II because losses will affect the bank’s balance sheet. The Manasse et al. logit EWS model correctly predicted 74% of the defaults for the whole sample, but post-1990 this drops to 69% with, respectively, 6% and 5% type II errors. However, for roughly half the error cases, default did occur two years later – the model is set up to predict a problem one year ahead.

86 In the review by Hoti and McAleer (2003), logit (or some variant of it) was used in 43 of the 50 studies they report on.

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political influences get far less attention, possibly because it is difficult to obtain objective measures. Yet the political risk aspect of banking in a foreign country (or even the home country) is a major concern to bankers. If the banks (or their clients) are exposed in lending to a foreign country, they will want to assess the effects of the political situation on the ability and/or willingness of the country to repay its debt. This section reviews the quantitative models that have been developed to test the influence of politics on the probability of a debtor country going into arrears or rescheduling.87

Brewer and Rivoli (1990) tested for the effects of political instability on banker perceptions of the creditworthiness of the country in question. They expected to find negative relationships between political stability and a country’s capacity to service its debt or perceived creditworthiness. They also wanted to identify the types of political stability which affect creditworthiness perceptions.

The authors considered three types of political instability, including government regime change (the frequency of regime change, assumed to be inversely related to political stability), political legitimacy (as measured by the degree to which a country’s political system is democratic as opposed to authoritarian, arguing that while authoritarian regimes may be stable in the short run, they are unstable in the long run) and internal (civil wars) or external armed conflict. Several measures of political instability were used because, the authors argued, political risk is not a single phenomenon that can be measured by a single variable.

A least squares regression technique was used to test for the effects of proximate instability and chronic instability on perceived creditworthiness. The study employed 1986 data for the 30 most heavily indebted countries. The dependent variable, perceived creditworthiness of a country, was taken from the 1987 Institutional Investor (II) and Euromoney (EM). The II scores come from a survey of bankers who are asked to grade a country (0 to 100) according to the probability of it going into arrears.88 The EM ranking is based on the weighted average spread borrowers are able to obtain from the euromarket.89 Thus, the II scores represent banker assessments of the countries’ creditworthiness and the EM scores reflect the actual market conditions. The scores of II and EM were interpreted as probabilities, which allows logistic transformation of the credit rating.

The independent variables tested were as follows.

žPHI, CHI: The number of changes in the head of government between 1982 – 86 (proximate head instability) or, for CHI, 1967 – 86 (chronic head instability).

87Much has been written on the methods for assessing the risk of expropriation of foreign firms with branches or subsidiaries located in foreign countries. However, a detailed discussion of the literature on expropriation and political interference lies outside the scope of this chapter. It has a limited impact on banks, via their loan exposure in foreign branches or subsidiaries of a multinational firm. For more discussion of these aspects of country/political risk, readers are referred to Calverley (1990) and Shapiro (1988), Moran (2003) and Bouchet and Clark (2004).

88Their responses are weighted according to the bank’s exposure in a country and the level of sophistication in

volume is the volume of loans signed by country i during a given year, spread is the margin over LIBOR and maturity is the length of the loan to repayment.

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žPGI, CGI: The number of changes in the governing group between 1982 – 86 (proximate group instability) or, for CGI, 1967 – 86 (chronic group instability).

žPPL, CPL: Political rights scores for 1986 (for proximate instability) and 1975 – 86 (chronic instability). These variables are the proxy for political legitimacy and are taken from the annual reports on human rights.

žPAC, CAC: Armed conflict scores indicating proximate armed conflict at the end of 1986 (0, 1) and chronic armed conflict – the number of years the country had been involved in armed conflict.

žCAB: The 1986 current account balance (CAB) expressed as a percentage of GNP.

žTED: Total external debt as a percentage of GNP in 1986.

Brewer and Rivoli found perceptions of creditworthiness had a greater sensitivity to proximate head instability as measured by regime change, suggesting lenders focus on short-term recent changes rather than considering a country’s experience from a longer term perspective. Regime change was a better measure of political instability than either armed conflict or political legitimacy. The findings should be treated with caution, for two reasons. Compared to related published studies, the economic variables were not given much attention. Furthermore, perceptions of political risk may already be included in the

IIand EM scores.

In a second study, Rivoli and Brewer (1997) address these concerns. They employ a logit

model to test political and economic variables. The dependent variable is whether a country reschedules its debt in a given year. The political variables tested were changed to allow for short and long-term instability as follows:

žSCH, LCH: The number of times the head of government changes in 5 years (S) and 20 years (L).

žSPOLEG, LPOLEG: A political rights score for year t (S) and a score for the preceding 12 years (L).

žSCG, LCG: The number of times the governing group (e.g. political party or military) changes over the past 5 years (S) and past 20 years (L).

žSCONFL, LCONFL: A dummy variable indicating the presence or absence of armed conflict in year t (S) and the number of years the conflict had been taking place by the end of year t (L).

They also test a number of economic variables, drawn from variables found to be significant in other studies, plus a lagged value for the rescheduling variable.

Rivoli and Brewer (1997) also looked at the question of whether the model is as effective in predicting rescheduling in the late 1980s and early 1990s as it was in the early 1980s. They estimate the model over the period 1985 – 89 to predict reschedulings between 1986 and 1990. These results are compared with estimates of the model for the period 1980 – 85. For this part of the investigation, only the economic variables are included. The economic variables found to be significant at the 1% level with the correct sign were the debt service ratio, the ratio of reserves to imports, and the ratio of external debt to GNP. The ratio of scheduled debt service to external debt is correctly signed and significant at the 10%

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level.90 A rescheduling variable (RESC), the dependent variable lagged by one year, was also included as an explanatory variable, and found to be significant at 1%, indicating the presence of positive serial correlation, Thus, if a country rescheduled one year, it is likelier to do so the next.

When the model is estimated for the later period, Rivoli and Brewer find its overall explanatory power falls. The parameter estimates are smaller, so their usefulness as explanatory variables is reduced, and the ratio of reserves to imports is no longer significant. The correct overall prediction rate was found to be 20% higher in the earlier period.

The next step of the investigation involved introducing the political variables. When added to the earlier model (1980 – 85), they are found to have little impact on overall performance. The only political variables found to be significant are the presence and length of armed conflict. According to the authors, armed conflict will place heavy demands on government budgets and often require large-sum hard currency expenditures, hence it could raise the probability of rescheduling. Adding the short and long-term political variables improved the prediction rate for rescheduling by 18% and 35%, respectively.

Overall, the explanatory power of the economic and political variables was greater for the early period, 1980 – 85, compared to the later period. However, by adding the political variables (a political economic model), the correct rescheduling prediction rate improved by 9% (short-term measures of political instability) and 12% (long-term measures) in the early period. For the later period (1986 – 90) the correct prediction rate rose by 18% (short-term measures) and 35% (long-term measures).

Recall that armed conflict was found to be the significant variable in the current study, which differs from the authors’ 1990 results, when government instability was found to affect bankers’ perceptions of a country’s creditworthiness and armed conflict did not. Part of the findings may be explained by the differences in dates of estimation: the early and late 1980s. Also, the dependent variable was different. More research is needed on how political factors affect a country’s probability of default.

Balkan (1992) used a probit91 model of rescheduling to examine the role of political (in addition to economic) factors in explaining a developing country’s probability of rescheduling. Two political variables were included in the model. A ‘‘political instability’’ variable is an index which measures the amount of social unrest that occurred in a given year. The ‘‘democracy’’ variable, reflecting the level of democracy, is measured by an index which, in turn, is captured by two components of the political system: participation (the extent to which the executive and legislative branches of government reflect popular will) and competitiveness (the degree of exclusion of political parties from the system and the ability of the largest party to dominate national elections). Balkan also included some standard economic variables in his model, such as the ratios of debt service to exports, interest payments to exports, and so on. In common with most studies all the explanatory variables were lagged by one year to minimise simultaneity problems. The sample period ran from 1970 to 1984 and used annual data from 33 developing nations. Balkan found

90The lower the ratio, the smaller the amount of external debt being repaid, which means interest accruals will be building up.

91Probit differs from logit in that it assumes the error terms follow a normal distribution, whereas in logit the cumulative distribution of the error term is logistic.

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the democracy variable was significantly negative: the probability of rescheduling fell as democracy levels rose. The probability of rescheduling rose with the level of political instability. The number of type I and type II errors fell when the political variables were included in the model.

6.5.5. Rescheduling and Debt Conversion Schemes

Once a country has defaulted on sovereign debt, banks can hardly foreclose on the loans, put the country into receivership or insist on collateral – some of the nation’s assets. Though ‘‘gunboat diplomacy’’ was not unheard of as a means of putting pressure on a sovereign state in earlier centuries, it has not been considered an acceptable way of resolving such matters for many years. Since the Mexican announcement in August 1982, many indebted countries have entered into or completed renegotiations for the repayment of their loans. The International Monetary Fund (and, to a lesser extent, the World Bank) plays a critical intermediary role. Rescheduling agreements share a number of features in common:

žThe agreement is reached between the debtor, the borrowing bank and the IMF. It typically involves rescheduling the total value of the outstanding external debt, with the debt repayment postponed.

žBridging loans often feature, as does an IMF guarantee of interbank and trade facilities, sometimes suspended when a country announced that it was unable to service its external debt.

žThe private banks normally agree to provide ‘‘new money’’ to allow the debtor country to keep up interest payments, raising the total amount of the outstanding debt. The IMF usually insists on increased exposure by the banks in exchange for IMF loans.

žThe debtor country is required to implement an IMF macroeconomic adjustment programme, which will vary according to the economic problems the country faces. Governments are required to remove subsidies that distort domestic markets, meet strict inflation and budget deficit targets, and reduce trade barriers. Note the country loses some ‘‘macroeconomic sovereignty’’ because its government is now limited in its choice of economic policy. Thus, ex post, it can be argued that sovereign borrowing exposed these countries to high interference costs.

Debt– equity swaps are another means of dealing with a sovereign debt problem, usually as part of or to complement an IMF rescheduling package. A debt – equity swap involves the sale of the debt by a bank to a corporation at the debt’s secondary market price. The corporation exchanges the debt for domestic currency through the central bank of the emerging market, usually at a preferential exchange rate. It is used to purchase equity in a domestic firm. It has proved unpopular with some countries because it can be inflationary, and the country loses some microeconomic sovereignty. Similar debt conversion schemes in the private sector have allowed firms to reduce their external debt obligations.

Other types of swaps include debt– currency swaps, where foreign currency denominated debt is exchanged for the local currency debt of the debtor government, thereby increasing the domestic currency debt. A debt– debt swap consists of the exchange of LDC debt by

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banks based in London and New York, as well as hedge funds and other institutional investors. The market allows banks to move the assets off their balance sheets, and for those with continuing exposure, it is possible to price these assets.

6.6. Conclusion

This chapter focused on several areas of banking in emerging market countries. The objective was to provide the reader with an insight into several key issues: attempts to resolve problems arising from financial repression through reform of banking systems, sovereign and political risk analysis, and a review of Islamic banking.

Until the last decade of the 20th century, almost half of the world’s population lived under communism. Communist regimes had extinguished the conventional, private sector, independent commercial bank. In country after country, throughout the former Soviet Union and its Warsaw Pact allies in Central and Eastern Europe, the 1990s were to see private banks return. China, still led by the Communist Party, also underwent profound financial changes as part of a broader economic reform, starting as early as 1979 and gathering pace in the new millennium. In India, the world’s largest democracy where banking has been subject to a high degree of state control, regulation and ownership, some cautious steps have been taken in the same direction as Russia and China.

If the demise (or reinterpretation) of communism and socialism has been a great victory for the concept of the conventional western bank, the later 20th century saw two other developments that posed it challenges. One of these is the growing perception in many Muslim countries – and beyond – that the whole basis of the conventional western bank’s operations, lending at interest, is inconsistent with religious principles. The other was the periodic but serious issue of how western banks should respond to many emerging market governments that could not or would not service or repay the debt owed to them: the problem of non-performing sovereign loans.

This chapter has chronicled these massive emerging market changes and their effects on the global financial landscape, especially banking. It began by analysing the phenomenon of financial repression, and exploring the question, a pressing policy issue for many countries, of whether foreign banks should be allowed to operate within their borders.

Next came a survey of financial systems of Russia, China and India. Each are classic, but different, examples of financial repression in the late 1990s. The key question was whether the reforms they introduced were enough to alleviate some of the more serious problems arising from financial repression. All three countries have enjoyed some degree of success. Though Russia experienced the economic equivalent of a roller coaster ride, it has gone the furthest in terms of financial liberalisation, followed by China, provided it lives up to its promises to allow foreign bank entry by 2007 and liberalises interest rates. India is the laggard here, with no clean plans to reduce state control of the banking sector, though other parts of its financial sector have been liberalised.

However, these countries are also experiencing a common problem: the difficulty each government faces in reducing or eliminating state ownership and control of banks. There is nothing wrong with state ownership per se, provided banks are free from government interference, have no special privileges which give them an unfair advantage, and have to