A New Look at Debt Rescheduling Indicators and Models

8/9/2019 A New Look at Debt Rescheduling Indicators and Models

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A New Look at Debt Rescheduling Indicators and ModelsAuthor(s): John B. MorganSource: Journal of International Business Studies, Vol. 17, No. 2 (Summer, 1986), pp. 37-54Published by: Palgrave Macmillan JournalsStable URL: http://www.jstor.org/stable/154582

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A NEWLOOKAT DEBT RESCHEDULINGINDICATORSAND MODELSJohn B. Morgan*Data Resources, Inc.

Abstract. This paper presents logit and discriminant models ofdebt rescheduling. The models incorporated new short-term debtdata and variables representing economic shocks. The 30-countrydatabase covered the period from 1975 to 1982 and containedthe largest number of debt rescheduling observations of any pre-vious database. Because of the new indicators and more recentdata, the models captured the effects of the changes that haveoccurred in the world economy since the oil price shocks and theperiod of rapid debt accumulation by the developing countries.Compared to the earlier research models, the models have provento be efficient in forecasting debt reschedulings.Following the wave of debt reschedulings by more than thirty countriesfrom 1981 to 1984, there was a pressing need to reassess country risktechniques used by international commercial banks. Before 1980, theamount of risk associated with lending to a country was usually discussedin general terms and was not quantified. Comparisons of risk betweencountries and regions were often very difficult. Upon reexamination ofcountry risk techniques, it became evident that economic indicatorsneeded to be found that distinguish countries that reschedule their debtsfrom other countries. Next, after those indicators were identified, a modelneeded to be constructed that would forecast countries that are likely toreschedule their debts.Earlier research on debt rescheduling helped to identify a few of theimportant indicators, such as the debt service ratio and the imports toreservesratio. However, the earliermodels of debt rescheduling [i.e., Frankand Cline (1971) and Feder and Just (1977)] did not appearto be repre-sentative of the current international debt environment. First, the earlier* John B. Morgan s the InternationalManagingConsultant n the Washington fficeof Data Resources,Inc. He is responsible or economicprojects,contractresearchandconsultingefforts involvingeconometricanddataapplications or a numberof agenciesincludingthe Departmentof State, Treasury,andCommerce, he IMF,andotherinter-national organizations.Previously,he was a Vice PresidentandInternationalEconom-ist at Texas CommerceBank,Houston.He receiveda Ph.D. from Rice University.Date Received: February 1985; Revised: October 1985; Accepted: December 1985.

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JOURNAL OF INTERNATIONAL BUSINESS STUDIES, SUMMER 1986

models were based on the period of the sixties and seventies which con-tained only a few cases of debt reschedulings and were estimated beforethe period of vast debt accumulation. Second, the earlier models did notreflect the important changes which occurred during the seventies in thestructure of the indebtedness in the developing countries and in theworld economy. For instance, the maturity structure of external debthad been altered with the rapid expansion of short-term debt. The usageof variable interest-rate loans had increased which made the debtorcountries more vulnerable to changes in international interest rates. Twooil price shocks had altered trade relationships and had distorted thebalance of payments of some non-oil developing countries. Hence, eventswhich caused debt reschedulings in the late sixties and early seventiesmay be entirely different from those which caused reschedulings in theearly eighties.Economic indicators used in some of the earlier research models may ormay not be useful in analyzing and forecasting debt reschedulings in theearly eighties. With the recent wave of debt reschedulings, a model util-izing the larger number of debt rescheduling observations and newly avail-able short-term debt data and other economic indicators could be moremeaningful and efficient in forecasting debt reschedulings. This model,in conjunction with other early warning techniques, then could be usedto manage country risk and to set commercial bank lending limits.' Know-ing that a country has a high probability of rescheduling can enable thelender to reduce credit exposure and to avoid costly and lengthy re-schedulings.This paper first reviews the results of the earlier debt rescheduling indi-cators and models. In the second section, logit models which utilize newshort-term debt data and variables representing economic shocks are pre-sented. The 30-country data base (see Table 1) used for the logit modelscovers the period from 1975 to 1982 and contains the largest number ofdebt reschedulings observations of any previous model. The results of thelogit models are then discussed in the third section. Different methodsof evaluating the logit scores are also examined. The logit models werefound to be very efficient in forecasting debt reschedulings. In the fourthsection, a discriminant model is derived utilizing the same database asthe logit models. The results of the two analytical methods were verysimilar.Concludingremarksare presented in the last section.

REVIEWOF EARLIERMODELSTherehave been severalattempts to estimate empirically debt service capa-city and to distinguish countries that are likely to reschedule or repudiatetheir debts from those unlikely to do so. Severalmethods have been usedto separate the two types of countries. Discriminantanalysis was used byFrank and Cline (1971), Sargen (1977), and Saini and Bates (1978). Logitanalysis was used by Feder and Just (1977), Saini and Bates (1978), Mayoand Barrett(1978), Feder, Just and Ross (1981), and Cline (1983).

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DEBT RESCHEDULING 39TABLE1

DevelopingCountriesRankedby BankDebt, December1982($ Millions)Country Amount Date of ReschedulingMexico $62,888 1982-83Brazil 60,45 3 1982-83Venezuela 27,474 1983Argentina 25,681 1976, 1982-83South Korea 23,245Philippines 12,554 1983Chile 11,610 1975, 1982-83Portugal 10,004Greece 9,988Indonesia 9,948Yugoslavia 9,821 1980, 1982-83Nigeria 8,527 1983Algeria 7,685Israel 6,669Malaysia 6,627Colombia 6,312Peru 5,353 1976, 1978-79, 1982-83Egypt 4,932Thailand 4,921Ecuador 4,488 1982-83Turkey 3,971 1978-80, 1982Morocco 3,882 1983Ivory Coast 3,387 1983India 2,603Uruguay 1,531 1982-83Costa Rica 1,261 1982-83Sudan 1,119 1979-83Tunisia 1,059Cameroon 1,049Bolivia 940 1981-83Source: Bank of International Settlements, "The Maturity Distribution of International BankLending;" Euromoney, August 1982; and WorldBank, WorldDevelopment Report 1983.

Frank and Cline (1971) were the first to attempt to quantify the differentcountry risk and debt capacity indicators. They compiled data on eight in-dicators for 26 countries from 1960 to 1968. Eight countries (Argentina,Brazil, Chile, Ghana, India, Indonesia, Turkey, and Egypt) rescheduledtheir debt 13 times duringthe nine-year period. Of the eight variables test-ed, only the debt service ratio, the amortization to debt ratio, and the im-ports to reserves ratio were statistically significant at the 5 percent level.Utilizing only these three variables,their model was able to identify 10 ofthe 13 rescheduling cases and 118 of the 132 nonrescheduling cases.Sargen (1977) used data from 1960 to 1975 for 44 countries, in hisdiscriminant study. His results indicated that the change in consumerprices and the debt service ratio were by far the most important explana-tory variables. The final function also included the export growth rate,


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the money supply growth rate, the real GNP growth rate, and the devia-tion from the purchasing power parity value.Saini and Bates (1978) used nondebt variables n their discriminantanaly-sis study. The authors objected to the use of debt data because the datahad a two-year lag,did not include short-termdebt, and did not include pri-vate nonguaranteed debt. As a proxy for debt variables, Saini and Batesused the current account balance minus the increase in international re-serves divided by exports, the net foreign assets of the banking systemdivided by the money supply, and the rate of growth in international re-serves. The authors found that inflation, money supply growth, thecumulative current account balance to exports ratio, and growth in inter-national reserves consistently exhibited the greatest explanatory ability.Overall, their error rates were higher than in some of the earlier studies.The authors also ran their data using logit analysis. They concluded thatthere were no significant differences in the error rates or in the indicatorsfrom their discriminantanalysis and logit analysis.Feder and Just (1977) used logit analysis to determine a country's debtcapacity. They maintained that logit analysis was more suitable whenusing continuous data. With logit analysis, the probability of reschedulingshould increase as the date of the rescheduling approaches. Hence, thecountry would not suddenly become a member of the reschedulinggroup,as is the case with discriminant analysis. Their sample included data from41 countries from 1965 to 1972. There were 21 observations of countriesrescheduling during that timeframe. Generally, data for a country that re-scheduled were not included for at least two years after the rescheduling.Their results confirmed that the debt service ratio, imports to reservesand amortization to debt were significant. In addition, export growth, percapita income, and capital inflows to debt service were also significant.Using a cutoff of .40, they obtained 5 percent of Type I errors and 2.4percent Type II errors.In 1981, Feder, Just and Ross updated their previous logit analysis study.The database was expanded to cover the years 1965 to 1976 and contain-ed 56 countries. Out of the 580 observations, there were 40 cases of re-scheduling. Using a cutoff .20 percent, the error rates were 20 percentfor Type I and 6 percent for Type II.An extensive study was done for the EXIM Bank by Mayo and Barrett(1978). The database included 50 basic variables for 48 countries andcovered the period from 1960 to 1975 with projections extending to1980. The focus of the study was to predict the occurrence of reschedul-ings up to five years. In their final logit model, six variables(debt/exports,reserves/imports, imports/GDP, IMFreserveposition/imports, investment/GDP and the inflation rate) were found to be significant. No error rateswere given.A more recent study was done by Cline in 1983. Cline divided his re-scheduling indicators into either demand- or supply-related, althoughsome indicators were considered both. His logit model used data for 58

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DEBT RESCHEDULINGcountries for the period 1967 to 1982. During that time, there were 22cases of debt rescheduling, or 3 percent of the sample. Of the variablestested, the debt service ratio, reserves to imports, and LDCborrowingstoimports were the most significant indicators. Cline's errors were in the 10to 14 percentage range.The earliermodels made significant contributions to the country risk field.However, there are several problems associated with the studies and theirmodels. The models did not accurately forecast the debt reschedulingsofthe late seventies and early eighties. Using economic and debt data from1975 to 1982 of the 30 most indebted developing countries (see Table 1),the two seminal models of Frank and Cline (1971) and Feder and Just(1977) were tested. Using the more recent data, the model estimated byFrank and Cline had an overall accuracy of 62.1 percent using their criti-cal value of 5.295. The model overestimated the number of debt re-schedulings, predicting 125 debt reschedulings out of 240 observations.Raising the critical value to 7.665, which equalized the errorrates, yieldeda 75-percent accuracy rate (10 Type I errorsand 50 Type II errors).The overestimation problem associated with the Frank and Cline modelbasically reflects the changes in their indicators. The averagedebt serviceratio for the 30 most indebted countries has risen from 8.3 in 1965 to29.1 in 1982. (See Table 2.) Thus, what was thought to be the debt ser-vice threshold in 1971 by Frank and Cline was the norm in 1982. Theimports to reserves ratio also has risen dramatically since 1960. Duringthe period Frank and Cline tested, most developing countries had reservesequal to about one-third of their imports. By 1980, imports on averagewere 14 times greater than reserves. Note that the imports to reservesratio tends to distort the equation when reserves are extemely low. Forexample, many African countries have very low levels of reserves. As aresult, the Frank and Cline model usually predicted debt reschedulingsfor these countries. (Later studies have used the inverse of this ratio toavoid the problem.) The final variable used in their model, the amortiza-tion to debt ratio, also has increased in value since 1960. The trend isdue to the shortening of maturities of the debt of the developing coun-tries and larger interest payments resulting from higher interest rates inthe early eighties.The Feder and Just model underestimated the number of debt reschedul-ings from 1975 to 1982. Using a cutoff point of 0.40, their model pre-dicted only 8 of the 40 debt reschedulings observations. Again, the prob-lem with the forecasting record of the model related to the indicators.Feder and Just used per capita income (measured in current dollars) inthe model. As a result of increased real growth and inflation, the averageper capita income of the 30 most indebted countries increased from $330in 1965 to $1,809 in 1980, as shown in Table 2. Thus, the model tends tounderestimate debt reschedulings. (Feder, Just and Ross (1981) used theratio of per capita income to U.S. per capita income to avoid this prob-lem.) High export growth rates also led to forecasting problems. Export

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42 JOURNAL OF INTERNATIONALBUSINESSSTUDIES,SUMMER1986TABLE2

MajorDebt Rescheduling ndicatorsofThe 30 Most IndebtedCountries(UnweightedAverages)

Indicator Used by1 1960 1965 1970 1975 1980 1982Debt Service Ratio a, b, c, f,g NA 8.31 11.60 14.10 17.65 29.14Imports/Reserves2 a, c, d, e, f, g, h 2,20 2.95 3.68 8.81 14.41 18.05Debt Service/Debt3 a, c, g, h NA .087 .093 .106 .153 .183PerCapita GDP c, g $272 330 458 890 1809 1772(Current Dollars)Growth of PerCapita4 b, c, g 1.70 2.40 3.59 3.41 2.86 0.74Real GDP (4 year average)Export Growth Rate5 b, c, g 3.46 9.69 10.43 28.55 23.00 6.85(3 year average)Change in Consumer6 b, d, e 5.96 7.70 9.50 19.98 25.93 30.50Prices (3 year average)Debt/Exports7 d, g, h NA 1.13 1.49 1.29 1.16 1.47Current Debt Service Ratio h NA NA NA 38.19e 51.52 77.84Short-term Debt/imports h NA NA NA .17e .29 .36Real GDP Growth h 6.54 5.26 6.24 4.29 3.87 1.18(1 year)Exports/Imports h NA .94 .81 .78 .89 .77Notes:1. Key to Studies: (a) Frank& Cline (1971), (b) Sargen (1977), (c) Feder & just (1977), (d) Mayo& Barrett (1977), (e) Saini & Bates (1978), (f) Feder, Just & Ross (1981), (g) Cline (1983)and (h) Morgan (1986).2. Reserves include gold. Studies, (e), (f), (g) and (h) used the Inverseof the ratio.3. Also referred to as the amortization rate. Studies (g) and (h) included principal payments only

in debt service.4. Studies (b) and (g) used a 3 year average;study (c) used a 4 year average.5. Study (c) used an 8 year average.6. Debt includes medium- and long-term debt. Studies (g) and (h) included short-term debt intotal debt.7. Argentina and Chile were excluded from the average.Sources: IMF, International Financial Statistics Yearbook; World Bank, World Debt Tables andWorld Tables; and Bank of International Settlements, Maturity Dlstributlon of Interna-tional Bank Lending.

growth rates three-year average and measured in current dollars) averagedabout 28 percent in 1975 versus 9.6 percent in 1965. Many of the oilexporting nations experienced 30 to 40 percent increases in exports duringthe mid-seventies. One problem with this indicator is that it only tells oneside of the trade picture. It does not reflect changes in imports or the netof exports and imports. Variables such as the current account to exportsor exports/imports give a better indication of the trade position of a coun-try. Another variable that Feder and Just used was capital flows/debtservice. This variable tends to behave somewhat erratic. Because it mea-sures capital flows, it can go from positive to negative, year to year.


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DEBT RESCHEDULINGBesides the forecast record of the earlier models, there are other prob-lems. During the sixties and early seventies, there were only a small num-ber of countries that rescheduled their debts. For example, Feder andJust (1977) had only 21 observations of debt reschedulings, out of 238observations. Hence, they were testing for a rather unusual event. Con-versely in the early eighties, there were a number of debt reschedulingsand indicators that are common with the debt reschedulings are likelyto be different. Another problem relates to the pooling of observationsacross a number of years. For example, the Sargen study covered theyears from 1960 to 1976. The values of many of the indicators tendedto change over the years. For example, real GDP growth of 5 percent in1966 during the boom of the sixties is not comparable to a 5-percentgrowth in 1975 during a recession. Inflation rates also tended to increaseworldwide during the seventies, and the pooling of the nominal data (i.e.,per capita income) across time would not reflect that trend. Finally, therehave been structural economic changes (i.e., oil price shocks, increased in-debtedness of the LDCs and changes in their debt structures) which haveoccurred since the publication of the earlier studies. These factors havechanged the picture of indebtedness of the developing countries andaltered some of the relationships of the debt rescheduling indicators.These changes are not properly reflected in their models.

THE SETUP OF THE MODELThe database of the logit model contained nine variablesfor 30 countriesand covered the period from 1975 to 1982. This timeframe was chosenbecause it contained the economic period since the two oil price shocksand the last two recessions. By limiting the timeframe of the model from1975 to 1982, the model captured this important economic transitionperiod. The time period from 1975 to 1982 also encompassed the build-up of commercial bank debt and utilized new data available on short-term debt.The study included the 30 largest debtor countries. Generally, commercialbanks consider loan requests only from the large, well-known developingcountries. Because banks have to monitor the economic and political situa-tions in each country in which loans are outstanding, there is a certainamount of economies of scale in concentrating their lending to the largerdeveloping countries. Also, there would be more lending opportunities inthe larger countries. Most of the indebtedness of the developing countriesis concentrated among a few borrowers. Five borrowers (Mexico, Brazil,Argentina, South Korea, and Venezuela) account for more than half ofthe total debt owed to international banks by the developing countries.The 30 countries included in the study account for 88 percent of the debtof the developing countries. Only countries that have complete economicdata and comparabledebt data were used. Because of data restrictions, thestudy excluded oil-rich countries such as Kuwait, U.A.E., Saudi Arabia,and Iran,and communist countries such as Poland, Hungary, Romania and

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China. Table 1 lists the 30 most indebted countries, the amount of bankdebts as of 1982 and the dates of reschedulings. Note the dates of re-scheduling begin when the country announces the intention to rescheduleand end after an agreementhas been signed.The variables which were tested can be divided among demand, supplyand exogenous shock indicators. On the demand side, the current accountbalance and international reserves are a function of the demand for ex-ternal borrowing. The ratio of exports to imports was used as a proxy forthe current account balance.2 Since it is possible that a country mightuse international reserves to fund a current account deficit (instead ofborrowing), the ratio of international reservesto imports was also includedin the model. Because of variations in its value, gold was not included ininternational reserves.On the supply side, four measures of debt were used. It was assumed thatbankers or lenders would be aware of these basic measures of debt andthat the indicators would affect their perceptions of risk in lending to thecountry. The four measures of debt used in the study are: total debt toexports, short-term debt to imports, principalpayments to total debt andthe current debt service ratio. Total debt (medium- and long-term publicand private debt plus short-termdebt) to total exports is a measure of theoverall size of debt compared to the country's ability to earn foreign ex-change. The ratio of short-term debt to imports usually is related to tradefinancing. This is important because excessive short-term debt of morethan 90-days worth of imports often indicates that short-term debt isbeing used to supplement medium- and long-term debt. Since short-termdebt must be rolled over or renewed periodically at the discretion of thelender, a country with a large amount of short-termdebt, in relation to itsimports, is vulnerable to a sudden change in international credit condi-tions. Many countries contracted largeamounts of short-term debt duringthe period of high interest rates in 1980 to 1982. The third indicator,ratio of principal payments to total debt (called the amortization rate),was found to be significant in the early studies by Frank and Cline (1971)and Feder and Just (1977). The amortization rate reflects the maturitystructure of a country's debt. Finally, the current debt service ratio wasincluded as the fourth measure of debt. This ratio, which is similarto thedebt service ratio, includes debt service from medium- and long-term debtplus short-term debt. The sum was then divided by total exports. With theinclusion of short-term debt, the current debt service ratio better reflectsthe country's total debt servicing requirements than the traditional debtservice ratio.In the Sachs and Cohen (1982) model of borrowing, shocks to the do-mestic economy cause sudden declines in the nation's income and maylead to a situation where the country cannot meet its debt service pay-ments. Thus, countries that are vulnerable to exogenous shocks are likelyto be candidates for debt reschedulings or defaults. Shocks to this modelwere incorporated by threeindicators. First, the percent of variable nterest-

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DEBT RESCHEDULINGrate loans to total medium- and long-term debt multiplied by the U.S.prime rate was used as a measure of interest-rate shocks to the country.If a large portion of the debt has a variable or floating rate feature, thendebt service payments increase directly with increases in interest rates.Note that variable interest-rate loans became more prevalent during thesample period. The percentage of floating rate loans to total loans rosefrom 15.7 percent in 1974 to 37.4 percent in 1981. Second, a decline ininternational lending would have been a shock to a country that wasaccustomed to ready access to international funds. Bank lending diddecline significantly from 1980 to 1982. The ratio of total bank lendingto the non-oil developing countries divided by the total current accountdeficits of the non-oil developing countries was used to capture thisshock. Finally, real GDP growth was used as an indication of a decline innational income or an economic recession. This indicator reflects externaland internal shocks that may disturb the country's debt servicing cap-abilities. It should be noted that the earlier models tended to use 3-to-5year averages for many of their indicators and thus did not incorporatethe concept of shocks to their models.The total database contained 240 observations of nine indicators.3 Therewere 40 observations (16.6 percent) of debt reschedulings, as shown inTable 1. All indicators were lagged one year. In other words, economicdata from 1982 were used to determine the probability of reschedulingin 1983. Logit analysis was used to estimate the probability of a countryrescheduling.

RESULTSOFTHELOGITMODELSTwo models (A and B) were derived from various combinations of thevariables. Model A, as shown in Table 3, includes two of the shock vari-ables (real GDP growth and bank lending), the exports to imports ratioand two debt measures (current debt service ratio and the amortizationrate). Model B contains three of the shock variables (real GDP growth,bank lending and the interest-rate sensitivity indicator), the reserves toimports ratio, and two debt indicators (total debt to exports and short-term debt to imports).All the variables in Model A were significant at the .95 level, except forthe exports to imports ratio.4 (The exports to imports ratio was signifi-cant at the .80 level.) Overall, the current debt service ratio was the mostsignificant variable in Model A. This ratio which includes debt service pay-ments and short-term bank debt represents the total annual external fi-nancing requirements in relation to the foreign exchange earnings.5 Coun-tries which rescheduled their debts in 1983 had current debt service ratiosof 82 percent, compared to 39 percent for other countries.Real GDP growth had a negative relationship to reschedulings. Hence, aslow growing or declining economy appearsto be more likely to reschedulethan a fast growingone. Real GDP growth rates of the developing countriesdid decline substantially in the early eighties. Slower or negative growth

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JOURNALOF INTERNATIONAL USINESSSTUDIES,SUMMER1986TABLE3

Estimatesof LogitModelsA and BVariable ModelA ModelBExports o Imports -2.70(1.98)Reserveso Imports -6.64(2.66)TotalDebtto Exports 1.45(0.42)Short-termDebtto Imports 1.96(1.87)AmortizationRate -14.75(6.70)CurrentDebtServiceRatio 3.96(0.89)InterestRateSensitivityIndicator .0014(.0008)BankLending -.062 -.058(0.022) (0.024)RealGDPGrowthRate -.29 -,22(0.073) (0.081)Constant 3.08 -1.15(1.94) (1.30)Logof LikelihoodFunction -56.12 -50.07Standard rrorsare in parentheses.

rates might also indicate a sudden shock to national income that wouldincrease a country's external borrowing needs, as hypothesized by Eatonand Gersovitz (1981). Feder and Just (1977) and Cline (1983) also founda strong relationship between countries rescheduling and economicgrowth.6The amoritzation rate of Model A was negatively related to debt resched-ulings. This negative relationship was also found by Feder and Just (1977).Bank lending, as apercentageof the aggregatecurrent account deficits, wassignificant. When commercial bank lending decreased, as was the casefrom 1980 to 1982, the number of reschedulingsincreased.7Countries that rescheduled their debts had lower exports to importsratios (.75 versus .84 in nonrescheduling countries). Thus, countriesthat rescheduled were under a great deal of pressure to find additionalforeign exchange, on averageequivalentto a third of their export revenues.Model B was devised to highlight other variables. It is also of interestbecause it was used in the discriminant model presented in the nextsection. Four of the indicators in Model B (reservesto imports, total debt

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DEBT RESCHEDULINGto exports, bank lending and real GDPgrowth) were significant at the .95percent level. The interest sensitivity rate was significant at the .90 level,while short-termdebt to imports was significant at the .70-percent level.Total debt to exports was the most significant variable in Model B.Countries that rescheduled their debts in 1983 had total debt to exportsratios which averaged 254.7 percent in 1982 versus 136.7 percent forother countries. The total debt to exports ratio has been used more inrecent analyses as an indicator of a country's ability to service its debtand as an indicator of the likelihood of debt rescheduling.The reserves to imports ratio was found to be significant in Model B. Thisratio is inversely related to debt reschedulings. International reserves canbe used as a substitute for borrowing, as hypothesized by Eaton andGersovitz (1980). The lower the level of reserves, the more likely thecountry is to reschedule. The ratio was first used by Frank and Cline(1971) and has been found to be significant by most later studies.The interest-rate sensitivity indicator was somewhat significant in ModelB. Countries that rescheduled their debts had a higher portion of theirdebt based on floating interest rates. Wheninterest rates increasedin 1980to 1982, their interest payments increased significantly. For example, itwas estimated by Mexican officials in 1983 that every one percentagepoint increase in the prime rate increased Mexico's annual debt servicepayments by $650 million.Table 4 lists the logit scores for Model A. The scores are stated as proba-bilities of rescheduling. This table is arrangedby data years. For example,based on the 1981 data, Brazil had a .93 probability of rescheduling in1982. For some countries, the probability of rescheduling increased sub-stantially as the date of rescheduling approached. For example, economicdata in 1976 indicated that Argentinahad a .04 probabilty of reschedulingits debts in 1977. That probability continued to rise, reaching .54 in datayear 1980 and .97 in data year 1981.There are a number of ways to evaluate the logit scores and determinethe errors of the model. For example, assume .50 is selected as the cutoffpoint. If the score was above .50, the model predicted a debt rescheduling.If a country rescheduled its debts and scored below .50, a Type 1 errorwas recorded. If a country had a score above .50 and did not reschedule,a Type II error was recorded. Using .50 as a cutoff point, Model A had anoverall accuracy rate of 89.6 percent with 19 Type I and 6 Type II errors.The cutoff point of .50 yields a greater number of Type I errors thanType II errors. By altering the cutoff point, the distribution of the errorscan be changed, as shown in Table 5. For example, if an equal number oferrors (in this case 10) is desired, the cutoff point would be .39. If oneassumes that Type I errors (misclassifying a country that rescheduled) istwice as costly for the lender as Type II, then the cutoff point wouldbe .33. This would yield 7 Type I errors and 14 Type II errors with anoverall accuracy of 91.3 percent.8 Another method of selecting the

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JOURNAL OF INTERNATIONAL BUSINESS STUDIES, SUMMER 1986TABLE4

Logit ProbabilityScores,ModelA

DATA YEARSCOUNTRY 1975 1976 1977 1978 1979 1980 1981 1982* | , ' ~~~~~ ~~~~~ ~~~~~~~1,t ' -' - '' - .' I .

.231 .724.032 .252.002 .022.043 .006.001 .004

.021 .053.023 .009.005 .024.004 .017.008 ,007

.002 ,003.000 .198.003 .033.045 .084.000 .001

.010 .018

.469 .808.007 .018.002 .009.002 068

.011 .475.009 .050.001 .010.010 .003.003 .017

.014 .014

.242 .116.004 .043.006 .032.019 .161

.049 .016 .093 .373 .903.323 .176 .240 .931 .983.275 .134 .097 .146 .697.060 .023 .541 .973 .970.001 .006 .389 .037 .170

.019 .027 .165 .377 .911.016 .008 .027 .447 .999.019 .006 .017 .168 .330.006 .008 .059 .190 .460.003 .028 .002 .010 .160

.003 .002 .015 .039 .107.128 .006 .018 .138 .499.013 .003 .004 .013 .074.028 .019 .041 .064 .405.000 .000 .004 .018 .094

.003 .007 .057 .302 .787

.561 .023 .050 .157 .920.005 .005 .003 .027 .136.011 .006 .015 .004 .121.114 .005 .117 .466 .922

.251 ,159 .343 .063 .110.050 .027 .051 .504 .420.003 .013 .016 .236 .554.005 .089 .008 .024 .189.000 .005 .039 .363 .816

.008 .036 .407 .823 .956

.495 .449 .397 .529 .953.005 .005 .007 .007 .137.014 .005 .013 .035 .068.210 .432 .379 .646 .961

cutoff point used by Cline (1983) is to equalize the percentage of errors.Using a cutoff point of .16, gave 15 percent Type I and Type II errors.Analyzing where errors occurred yields several important points. First,several of the errors occurred in the timing of the rescheduling. Using .39as the cutoff point, the model basedon 1980 data predicted that Argentinashould have rescheduled in 1981 (as well as 1982 and 1983). The modelalso predicted prematurely that Morocco should have rescheduled in1982. The second major source of errors centered around three countries(Turkey, Yugoslavia, and Peru) that had rescheduled several times.9 Forexample, based on data from 1977, the model had a probability of .47that Turkey would reschedule in 1978. However, the probability declinedafter that, reaching .06 in the data year 1981. (Turkey rescheduled part ofits debt in 1978, 1979, 1980, and 1982 and its debt service payments

MexicoBrazilVenezuelaArgentinaSouth KoreaPhilippinesChilePortugalGreeceIndonesiaYugoslaviaNigeriaAlgeriaIsraelMalaysiaColombiaPeruEgyptThailandEcuadorTurkeyMoroccoIvory CoastIndiaUruguayCosta RicaSudanTunisiaCameroonBolivia

.222.195,000.154.026

.020.984.262.024.032

.004.005.035.068.043

.067

.512.007,024.019

.017.021.009.004.013

.065

.600.003.017.046

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DEBT RESCHEDULINGTABLE5

ErrorDistribution,ModelA

Type I Type 11 OverallCutoff Point Number Percent Number Percent AccuracyPercet -Acc--trac.9 28 70,0 1 0.5 87.9.8 26 65.0 1 0.5 88,8.7 25 62.5 3 1.5 88.3.6 23 57.5 4 2.0 88.8.5 19 47.5 6 3.0 89.6.4 11 27.5 10 5.0 91.3.3 7 17.5 16 8.0 90.4,2 6 15.0 25 12.5 87.1.1 2 5.0 46 23.0 80.0

' #I I' - -'. I' ,t n I Ii ' I: - -- 1F.. tI-r * . , -*-x_ - v - -

were altered several times.) The model was not sensitive to the debt prob-lems of Yugoslavia. The highest probability of rescheduling (.11) was indata year 1982. One reason might be Yugoslavia'srelatively low currentdebt service ratios, low levels of short-term debt and seemingly adequatelevels of international reserves. In the case of Peru, the errors occurredmainly in the timing of the reschedulings. Peru rescheduled portions ofthe debts in 1976, 1978-79, and 1982-83. The model showed high prob-abilities of rescheduling from 1976 to 1979 and in 1983, but low prob-abilities from 1980 to 1982.One method of reducing some of the errorsin the timing of a reschedulingis to drop the country from the sample for two years after a rescheduling.This procedure was followed by Feder and Just (1977) and Cline (1983).Presumably the reason for dropping a country after a debt rescheduling isthat the rescheduling alters the economic and debt statistics of the coun-try. For example, principal payments are usually rescheduled for 5 to 12years. This would effectively lower the current debt service ratio and theamortization rate for the country. Since the adoption of a stabilization orausterity program is usually required with a debt rescheduling, changes inthe nation's trade patterns might affect the reserves to imports and ex-ports to imports ratios. Real GDP growth may also be affected by a stabil-ization program. Following a procedure of dropping a rescheduled coun-try for two years, Models A and B were reestimated. The new database(called the adjusted database) consisted of 211 observations of which 21observations (10 percent) were of countries rescheduling their debts.Table 6 shows the estimates and standard errorsof Models A and B.As a result of the reestimation, the relationship between the variableschanged. For example in Model A, the amortization rate lost its signifi-cance. The t-ratio of the coefficient fell from -2.20 to -0.63. The currentdebt service ratio also lost some of its significance. Comparingthe resultsof the two methods yields several important observations. The overallaccuracy rates did not vary much between the two sampling methods.

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JOURNAL OF INTERNATIONAL USINESSSTUDIES,SUMMER 986TABLE6

Estimatesof LogitModelsA and B, AdjustedDatabase

Variable Model A Model BExports to Imports -4.70

(2.41)Reserves to Imports -6.52(2.95)Total Debt to Exports 1.01(2.03)

Short-term Debt to Imports 2.86(2.03)Amortization Rate -4.32(6.83)Current Debt Service Ratio (3.47)(1.03)Interest Rate Sensitivity Indicator .0027

(.0027)Bank Lending -0.074 -0.068(0.026) (0.026)Real GDP Growth -0.31 -0.29(0.095) (0.098)Constant 4.31 -0.40(2.37) (1.20)

Log of Likelihood Function -68.37 -68.37Standard errors are in parentheses.

The cutoff point in Model A, where the errors were equal, dropped from.39 to .28 with the adjusted database. Likewise, the cutoff point wherethe percentage of errors was equal also dropped from .16 to .14 with theadjusted database. Thus, the model estimated from the adjusted databaseappeared to be less sensitive than the original model. Finally, comparingthe errorsbetween the two databasesyielded the conclusion that the sameerrors were repeated. In sum, the procedure of dropping a country fromthe database for two years after a rescheduling does not appearto improvethe efficiency of the model.

A DISCRIMINANTMODELSince many of the earlier studies used discriminant analysis [Frank andCline (1971), Sargen (1977) and Saini and Bates (1978)], it was useful toreestimate the model using discriminant analysis. The results of the logitmodel can be compareddirectly with the results of the discriminantmodel.The same database from the logit model was used in estimating the dis-criminantmodel.10The discriminantfunction included the six variablesof ModelB, as shown inTable 7. Two sets of coefficients aregiven. The unstandardizedcoefficients

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DEBT RESCHEDULINGTABLE7

Estimatesof DiscriminantModel B

Standard zed UnstandardizedVariable Canonical Coefficient Canonical CoefficientReserves to Imports -.247 -1.42Total Debt to Exports .559 .73Short-term Debt to Imports .147 .81Interest Rate Sensitivity Indicator .159 .00049Bank Lending -.317 -.03Real GDP Growth Rate -.448 -.119Constant .862

were used for computing the Z score. The standardizedcoefficients showthe relative contribution of a variable to the function. The sign denotedwhether the variablewas making a positive or negative contribution. Totaldebt to exports and the real GDP growth rate were the most significantvariables, as shown by the large value of the standardized coefficient.Bank lending was also important. Note that the signs of the coefficientsare the same as the logit model.The discriminant model had an accuracy of 85.4 percent. As shown inTable 8, there were 27 Type II errorsand 6 Type I errors. The distributionof errors from the discriminant Model B and the logit Model B was verysimilar. For the logit model, a cutoff point of .16 was selected in order toequalize the percentage of Type I and Type II errors.For the logit model,there were 6 Type I errors and 26 Type II errors. Both models had thesame six Type I errors.Of the Type II errors,the discriminantmodel con-tained 22 of the 26 errorsfound in the logit model. In the few cases wherethe errorswere different, the marginof errorwas small. In sum, the differ-ences between the discriminantModel B and the logit Model B were slight.

CONCLUDING REMARKSThe logit model presented in this paper is more useful than the earliermodels discussed in the first section for a number of reasons. First, themodels of Frank and Cline (1971) and Feder and Just (1977) were foundto be inefficient in forecasting debt reschedulings during the period from1975 to 1982. Frank and Cline's model overestimated the number of re-schedulings, while Feder and Just's model underestimated the number ofreschedulings. Second, the model captures the changes that have occurredin the world economy since the first oil price change in 1974 and incor-porates the period of rapid debt accumulation of the developing countries.Studies by Feder, Just and Ross (1981), Saini and Bates (1978) and Cline(1983) which covered the late sixties to the late seventies probably would

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JOURNALOF INTERNATIONALBUSINESSSTUDIES,SUMMER1986

TABLE8Comparison f Errors,ModelB

Discriminant Model Logit ModelErrorType Country Year Country Year

I Argentina 1975 Argentina 1975Yugoslavia 1979 Yugoslavia 1979Turkey 1977, 1978, 1981 Turkey 1977, 1978, 1981Uruguay 1981 Uruguay 1981Mexico 1976, 1977 Mexico 1976, 1977, 1980Brazil 1977-1980 Brazil 1980Venezuela 1980, 1981 Venezuela 1980, 1981Argentina 1980 Argentina 1980South Korea 1980, 1982 South Korea 1980, 1982Philippines 1981 Philippines 1981Chile 1975 Chile 1975Portugal 1982 Portugal 1981, 1982Greece 1981, 1982 Greece 1981,1982Israel 1982 Peru 1976Colombia 1982 Egypt 1982Peru 1976 Turkey 1980, 1982Egypt 1982 Morocco 1981Turkey 1980 Ivory Coast 1981Morocco 1981 Costa Rica 1980Costa Rica 1980 Sudan 1975-1977Sudan 1975-1977 Bolivia 1978Bolivia 1978

have been more accurate if divided into separatesample periods. In otherwords, because of the structural changes in the world economy, eventsthat caused debt reschedulings in the late sixties and early seventies arelikely to be different than events that caused debt reschedulingsin the lateseventies and early eighties. Third, the inclusion of short-term debt andother variables gives a better indication of the indebtedness of the devel-oping countries. Countries with a high level of short-termdebt in relationto imports are vulnerable to sudden changes in international credit condi-tions. Finally, because of the large number of countries that have re-scheduled since 1981, this logit model had the benefit of more observa-tions of reschedulings. There were 40 observations of debt reschedulings,or 16.6 percent of the total sample, which was the highest percentage ofany of the studies presented in the second section. Presumably this im-proved the efficiency of the models.Three variables always seemed to be significant in distinguishingcountrieswhich reschedule their debts from other countries. Real GDP growth wasnegatively related to debt rescheduling. Whenreal GDP growth fell, or be-came negative, the country was more likely to reschedule. In the Sachsand Cohen (1982) model, a country was likely to find itself facing the

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DEBT RESCHEDULINGdecision to repudiate or reschedule following a sudden and unexpecteddecline in income. The results of the logit model point to this same con-clusion. The second most important variablewas a measure of debt, eithertotal debt divided by exports or the current debt service ratio. Because ofthe-high correlation of the two variables, only one could appear in themodel at a time. Finally, the variable of reserves to imports was also use-ful in distinguishing countries likely to reschedule. Generally, countrieswith low levels of international reserves were more vulnerable to exo-genous shocks and declines in foreign exchange earnings.In this paper, several empirical methods of predicting debt reschedulinghave been examined. Both logit and discriminant models were used withonly slight differences in the results. The earlier practice of changing thesample by dropping a country after rescheduling was shown to have littleeffect on reducing the errors of the model. The models presented in thispaper capture the changes that have occurred in the world economy sincethe oil price shocks and the period of debt accumulation by the develop-ing countries. The models, which have proven in actual usage to be effi-cient in forecasting countries which rescheduled their debts, can be usedto forecast the probability of future debt reschedulings.

NOTES1. The models presented in this paper were the result of research done by the author while atTexas Commerce Bank. The models provided the research basis for designing a country risk ratingsystem. See John B. Morgan, "Assessing Country Risk at Texas Commerce," The Bankers Maga-zine, May/June 1985.2. One of the earlier versions of the model contained the ratio of the current account balancedivided by exports. Since the exports to imports ratio was repeatedly found to be more signifi-cant than the current account balance, the latter was dropped from the list of variables.3. The World Bank, World Debt Tables, was the data source for all the debt data except short-term debt in which data were used from the Bank of International Settlements, Maturity Distri-bution of International Bank Lending. Balance of payments and economic statistics came fromthe International Monetary Fund, International Financial Statistics. Estimates of short-term debtin 1975 and 1976 were based on the relationship of short-term debt to debt from private creditorsin 1977 and 1978. In the estimation process, the logit option of the SHAZAM was used.4. To determine the significance of the variables, divide the maximum likelihood estimate by itsstandard error. The null hypothesis is 3 = 0. Using a 0.95 level of significance, the null hypothesisis rejected if the sum is greater than 1.96 for a large sample. The test statistic for 0.90 percent levelof significance is 1.645.5. In the earlier estimations of the model, both the current debt service ratio and the debt serviceratio were tested. Because of the inclusion of short-term debt, the current debt service ratio wasfound to be more significant than the debt service ratio.6. In Cline's study, per capita real GDP growth was used. Feder and Just used per capita real GDPgrowth over a 4-year period in their study. Although the variable was somewhat significant, theypreferred to use export growth over an 8-year period.7. Cline used a similar variable in his study. He defined global credit as the total net external bor-rowing by all non-oil developing countries divided by their total imports.8. A Type I error (misclassifying a country that reschedules) is more costly to a commercial bankthan a Type II error (misclassifying a country that did not reschedule) for several reasons. Debtrescheduling is a costly exercise for a bank. It increases loan monitoring costs and ties up funds fora much longer than anticipated time. Also there is an opportunity cost, associated with rescheduledloans. A Type II error means that the bank refrained from lending to a marginal country and lostsome revenue. In short, most banks would prefer to err on the conservative side and would assign ahigher cost to a Type I error than a Type II error.

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JOURNALOF INTERNATIONALBUSINESSSTUDIES,SUMMER19869. It is interesting o note that Sargen 1977) also had problems lassifyinghesethree countries.10. A stepwisemethod from the discriminantanalysisoption of SPSSwas used in constructingthe discriminant unction. At each step the variablewith the highestF-ratiowas selecteduntil theremaining ariableshad F-ratios ess than 1.0. The cost of errorswas assumed o be equal,andnopriorprobabilitiesweregiven.All of the variableswere entered nto the analysis.

REFERENCESCline, W. "A Logit Model of Debt Rescheduling, 1967-1982." Institute for International EconomicsWorkingPaper, June 1983.Eaton, J. & Gersovitz, M. "LDC Participation in International Financial Markets." Journal of De-velopment Economics, 7 (1), 1980, 3-21.

-. "Debt with Potential Repudiation: Theoretical and Empirical Analysis." Review of Eco-nomic Studies, 48 (2), 1981, 289-309.Feder, G. & Just, R. E. "A Study of Debt Servicing Capacity Applying Logit Analysis." Journal ofDevelopment Economics, 4 (1), 1977, 25-38.Feder, G., Just, R, E., & Ross, K. "Projecting Debt Servicing Capacity of Developing Countries."Journalof Financial ndQuantitativeAnalysis,16 (5), 1981, 651-669.Frank,C. R. &Cline,W. R. "Measurementf DebtServicingCapacity:An Applicationof Discrim-inantAnalysis." ournalof InternationalEconomics,1 (3), 1971, 327-344.Mayo,A. L. & Barrett,A. G. "An Early-Warning odelfor AssessingDevelopingCountryRisk."In S. H. Goodman (ed.), Developing Countries'Debt. Symposium presented by the Export-ImportBank of the United States, New York, August 1977.Morgan, J. B. "The Second Wave of LDC Debt Problems." The Bankers Magazine, July/August1984, 14-17.

. "AssessingCountryRisk at Texas Commerce."The BankersMagazine,May/June1985,23-29.Sachs, J. D. & Cohen, D. "LDC Borrowing with Default Risk." National Bureau of Economic Re-search, Working Paper No. 925, Chicago, July 1982.Saini, K. & Bates, P. "Statistical Techniques for Determining Debt-Servicing Capacity for Develop-ing Countries:AnalyticalReview of the Literatureand FurtherEmpiricalResults."FederalRe-serveBankof NewYork,ResearchPaperNo. 7818, NewYork,September1978.Sargen,N. "Economic Indicatorsand Country Risk Appraisal."FederalReserve Bank of SanFranciscoEconomicReview,Fall 1977, 19-39.World Bank. WorldDevelopment Report. Oxford, England: Oxford University Press, 1983.

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A New Look at Debt Rescheduling Indicators and Models

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