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An empirical investigation of the determinants of the supply of bank loans to less developed countries
Journal of Banking and Finance 15 (1991) 535-557. North-Holland
An empirical investigation of the determinants of the supply of bank loans to less developed countries* Samanta B. Thapa Wesrern Kentucky University, Bowling Green, KY 42101, USA
Dileep R. Mehta Georgia State Unioersity, Atlanta, GA 30303, USA Received October 1989, fmal version received August 1990
The Third World debt held by private financial institutions has created a number of controversies in international finance. One of them revolves around the ability of LDCs (less developed countries) to attract hundreds of billions dollars of loans at a relatively ‘low’ spread over the last decade. Some general explanations have focused on, for instance. error of judgment by banks and the ‘herd effect’, i.e., ignorant followers rushing in while the more knowledgeable have already gotten out. An intriguingexplanationof the size of the sovereign loans has been olfered by Agmon and Dietrich (1983) in terms of political factors pertaining to the lender’s country: loans to LDCs are in effect global taxes imposed by LDCs on industrialized nations in terms of the nature of their political relationship. Unfortunately, the Agmon-Dietrich study was not subjected to any empirical test. The purpose of this study is to extend the Agmon-Dietrich framework and examine the impact of the political relationship (between the borrowing country and the lending bank’s government) on the supply of loans by commercial banks in recent times.
1. Introduction
Sovereign loans to less developed countries (LDCs) in recent years by private financial institutions have been one of the controversial issues in international finance. The fact that LDCs have been able to attract a ‘large’ volume of loan at a relatively ‘low’ spread over the last decade has been a *We are grateful to an anonymous referee for his valuable comments and suggestions. Research grant from the College of Business Administration, Georgia State University. is gratefully acknowledged. 0378-4266/91/SO3.50 0 Ml-Elsevier
Science Publishers B.V. (North-Holland)
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S.B. Thapa and D.R. Mehta. Determinants of supply of bank loans to LDCs
‘puzzling’ phenomenon. 1 Several general explanations have been offered for the large-scale sovereign lending by banks: error of judgment by banks [Edwards (1983, p. 726); Lessard (1983, p. 521)]; competition among banks the ‘herd’ effect [Aliber (1984, p. 675)]; critical informational issues [Diaz Alejandro (1983, p. 120)]; and use of *variable interest rate and syndication lending meeters (1983, p. 458)]. A study by Agmon and Dietrich (1983) introduces political factors (pertaining to the lender’s country) in its analysis of the supply of sovereign loans, and it argues that loans to LDCs are in effect global taxes imposed by the LDCs on the developed countries in the context of their political relationship. Their study thus assigns an active role to the borrowing nations and a passive role to banks in the decision-making process - an implication inconsistent with other explanations holding banks responsible for excessive loans to &DCs at relatively low spreads. Unfortunately, the Agmon-Dietrich study was not subjected to any empirical test. The purpose of this paper is to extend the Agmon-Dietrich framework and examine empirically the impact of the political relationship (between the borrowing country and the lending bank’s government) on the supply of sovereign loans by commercial banks during the last decade and a half. This paper is organized as follows: section 2 deals with the analytical formulation of the basic model. In section 3, definitions and measurement of the variables are presented. The empirical results are discussed in section 4. Predictive ability of various models is compared in section 5. Section 6 presents the concluding remarks. 2. The model Consider the case of a single representative bank (chartered in a developed country) extending loan to an LDC. It is assumed that the bank is an expected-profit-maximizer. At a given time, the bank can borrow unlimited amounts of funds from the national and offshore capital markets at a fixed interest rate. In keeping with the standard two-period analytical framework, it is assumed that the bank charges a fixed interest rate on LDC loans. The decision problem facing the bank is the loan size for a given interest rate. Let L be the size of the loan extended to an LDC at interest rate r, and i the cost of funds for the bank. Let 1 + r = R and 1 + i= I. The total contract amount ‘For example, Aliber (1984. p. 674) points out, ‘one of the puzzles of the last decade is that such countries as Argentina, Braxil and Mexico were able to greatly increase their debt denominated in U.S. dollars.. .*. Allegations of banks’ ‘overlending’ has been widespread. For example, Clint (1984, p. 113) states ‘... there is a need not only to avoid excessive lending (to LDCs) and some excessive lending undoubtedly did occur in 197Os...‘. Teeters (1983, p. 458) states ‘. . . during 1970s the supply of bank fina@ng to developing countries was quite elastic, at lending rates which on an cx post basis appear unjustiEably low in relation to the risks involved in such lending.. .‘. For similar criticism see also Wcintraub (1983, p. 6). Mclnik (1982 p. 468). Lessard (1983, p. 521).
S.B. Thapa arid: D.R. Mehta. Determhuutt& qfdtcppl), of btmk hmrir to LDCs
537
payable by the LDC will be LR. The total cost of funds fcr the bank wi!l be LI. Let 2 be the portion of foreign exchange assigned to external debt service by the borrowing country. Whenever Z< LR, the bank will receive only 2, and the bank will incur a loss of LR-2. The loss is, however, uncertain and depends upon the value of the random variable 2. Assume that Z is a continuous random variable with a probability density function f(Z) contained in the interval [0, co) and has a finite mean, cc, and variance, 2 Q.
Suppose the probability of bailout by the bank’s government (henceforth, lending country) is P(X), where X represents a vector of factors that affect the bailout. The gravity of the lending country’s strategic concern stemming from the borrower’s default may be one such factor [for a discussion of these issues, see Agmon and Dietrich (1983, p. 494)]. Similarly, the impact of bank failure on the stability of the lending country’s financial system may be another factor. The bailout efforts may be direct or through multilateral agencies such as the International Monetary Fund (IMF). Since likelihood of bailout stems primarily from the lending country’s national interest, it will be assumed that the likelihood of bailout is not influenced by Z, the foreign exchange reserves earmarked for debt service. (This assumption also makes the following analysis more tractable.) Now, define an uncertain loss function H such that r 0
for Z>LR, on P(X) on l-P(X)
for Z< LR.
Here H represents the loss contingent upon (a) the likelihood of an absence of bailout, and (b) inadequate generation of foreign exchange reserves purely due to economic factors. Taking expectations LR
E(H) = b J (LR -Z)f(Z)
dZ,
where b = 1 -P(X).
0
Let x be the profit on the loan. Then the expected profit is given by E(n)=LJR-I)-E(H) =L(R-I)-bb(LR-Z)f(Z)dz. 0 The
(1)
objective of the bank is to maximize the profit function given by (1)
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S.B. Thapa and
D.R. Mehta, Determinants of supply of bank loans to LDCs
by choosing the optimal loan size. The first order condition profit maximization is (see Appendix F=(R-Z)-bR)-y(Z)&=0
A)
for expected
(2)
0
or (3) where F(a) is a cumulative probability function. The second-order condition yields d*E(a) -= dL*
-bR*_f(LR)
since b, R, and f(LR) are positive quantities. Condition (4) ensures that the solution given by (3) is indeed a global maximum. The solution to the firstorder condition gives the optimal loan amount for a given interest rate. Although the explicit solution of (3) for L* is not possible, the impact of various parameters on L* may be established by using the implicit function theorem. Alternatively, assume a uniform distribution function for 2 in (2) such that
where 6 is the range and T is the mean. Thus, T=(R--I)-bR
“i 1/(26)dZ=O. c-6
The solution of the integral provides the optimal loan size L*=2S(R-Z)/(bR’)+(v6)/R.
(5)
Now the impact of p(X), the bailout probability, and the T, the mean level of foreign exchange, on L* can be readily established for the distribution dL* -=‘>O dr R
for R>O,
(6)
S.B. Thapa rind D.R. Mehta, Determinants ofsupply of bank loans to LDCs
dL*
x=-
26( R - I) <0 bfRf
for6>0,
539
R>I.
Since b = 1 -P(X), the lower the b, the higher the P(X), and the higher the L*. Thus a profit maximizing bank will be willing to lend an increasingly larger amount of loan to LDC, the higher the bank’s estimate of the probability of the LDC being bailed out during the adversity. During the latter half of the 197Os, a major portion of U.S. bank loans was concentrated in only a few countries, viz., Brazil, Mexico, Argentina, and Venezuela. Although important, conventional economic forces or the profit margin (spread) on these loans by themselves cannot explain the huge size of the loans to these countries. One possible explanation could be that these countries had greater likelihood of being, bailed out by the U.S. government in times of debt servicing difftculties. According to Agmon and Dietrich (1983), lending countries’ continued willingness to sponsor the flow of credits to LDCs would depend on the strength of the political relationship between the lending country and the LDC. Factors deemed important in determining the strength of political relationship are: geographic proximity, size of the borrowing country, dependence on borrowing country for strategic raw materials and political and strategic importance of the borrowing country. On the basis of these criteria, the LDC borrowers mentioned above had high probability of being bailed out by the U.S. government. In brief, consideration of this political factor, along with the conventional economic variables, provides a plausible explanation for the large volume of bank credits obtained by these few countries. Thus L* =f(~, P(X)]. For empirical testing, the model may be specitied as follows:
Lit'B,+B,X,,i,+B2X2.,,+"'+B~Xl,i*+Ui,,
(8)
where =volume of loan supplied to country i at time t; =proxies for the foreign exchange generating capacity of x, it’s a country and the likelihood of political bailout; B;s =parameters to be estimated; =l,...,k; i i =l,...,n; t = 1,..., T. Lit
3. Definition and measurement of variables The test of the model contained in eq. (8) requires specification of relevant variables.
54cl
3.1.
S.B. Thapa and D.R. Mehta, Determinants of supply of bank loans to LDCs
Volume of loan supply
The volume of loan supply is not directly measurable since it represents the borrower’s loan capacity from the lender’s viewpoint. The outstanding loan appears to be an attractive proxy for the loan capacity. However, it suffers from two mutually exclusive problems. First, a country may choose not to tap fully its borrowing capacity for a variety of reasons such as (a) tapping the reserve on a rainy day, and (b) avoiding the perception at home that the government is subservient to foreign private institutions. Second, when a country is unable (or at times unwilling) to service a loan, the lender may be unable or unwilling to either take a legal action or write off the loan. Instead, the lender may have to make a ‘new’ loan that covers or even exceeds the debt service on the existing loan. In such instances, the existing outstanding loan does not in any way reflect the lender’s assessment of the borrower’s capacity. Since our test period is rife with instances of the latter type (i.e., loan increases that cover or exceed the debt service amount), the outstanding loan volume was rejected as a reasonable proxy for loan capacity. Indeed, the empirical observation of a predominantly large portion of loans to a relatively few countries underscores the problem with this proxy. Country credit ratings published by the Institutional Investor (II) is another proxy. These ratings are based on surveys conducted among leading international banks that use a scale of O-100, with 0 representing the least, and 100 the most creditworthy country. More weight is given to the responses of the banks with higher world-wide exposure. Since the banks whose responses carry heavier weights have also been the major suppliers of credit to the LDCs, a higher credit rating for an LDC signifies a greater potential for that country to obtain a larger volume of loan. It should be noted that the ratings contain a potential for bias. For instance, ratings may reflect a ‘herd’ tendency, i.e., going along with the opinion of the day rather than a true assessment of the debt capacity. Because the banks carrying heavier weights in the ratings scheme have also been the managers or co-managers of loan syndications, the most prevalent form of loan extensions to LDCs, such a herd instinct is conceivable but unlikely. A more plausible scenario for bias may be that banks with exceedingly high exposure to a given country may choose to rationalize their presence or even hope to palm off their loan commitments to other banks through inflated ratings. Any consequent loss of credibility of the ratings, however, renders them useless. If the report by Institutional Investor’ in early 1981 that Venezuela’s future prformance in terms of these ratings would be a part of default clause is Bny indication, it can be safely inferred *Institutional Investor (Sept. 1981, p. 287) reports that in early 1981 Venezuela’sfuture performance in terms of these ratings was made part of the default clause in a loan awement.
S.B. Thapa and D.R. Mehta, betemrihonts ofsupply of bank tinhi to LBCs
541
that banks maintain their integrity in reporting their assessment. Still, potential for bias does exist, but following previous studies in the literature [i.e., Feder and Ross (1982); Short and Angeloni (1980); Heller and Frenkel (1982)], this paper will employ the Institutional Investor’s Country credit ratings as a proxy for a country’s creditworthiness and hence the loan capacity.3 3.2. Likelihood of political bailout Political bailout, a function of political alliance, is a complex, multidimensional phenomenon; any quantitative variable is unlikely to capture its essence. For instance, alliance may stem from national interest considerations (such as reliance for raw materials), cultural affinity or ideological affinity (commitment to democratic ideals). Of course, these dimensions are not always consistent. National interest considerations may override the negative ideological connotation of a dictatorial regime. Consequently, a fruitful avenue to explore is a search for indicators that manifest political commitment: military bases, trade intensity, military aid, membership in military alliance and government aid for economic development or political considerations are a few of such indicators. They are not mutually exclusive, although some are more comprehensive than others. For instance, military aid by the United States has been more comprehensive than the presence of military bases, as evidenced in cases of U.S. neighbors such as Honduras. Similarly, military aid is a part of the current balance of payments and it typically affects trade balances for both merchandise and invisibles. On the other hand, military aid may be ruled out by the United States due to political considerations such as the host government’s sensitivity and consequent opposition to military aid. The volume of aid by the United States Agency for International Development (USAID) is a useful indicator when military aid is not effective or possible. Naturally, as stated above, any variable selected is hardly likely to encompass fully the essence of political bailout. A broad classification scheme is thus in order. A binary variable, ALLIANCE, has been used to classify the sample of countries into two groups: group one consists of countries with a high probability of being bailed out, and group two consists of the rest. The classification of countries is essentially based on the criteria suggested by Agmon and Dietrich (1984) that the lending country’s continued willingness ‘Feder and Ross (1982, p. 680) state: ‘the Institutional Investor’s weighted scores are a reasonable measure of the market’s perceived default probabilities’. Similarly, Short and Angeloni (1980) conclude that ‘the best creditworthiness indicator is Institutional Investor’s Index’. Further, there are precedents in mainstream corporate finance in which S&P 500 and Moody’s corporate bond ratings have been extensively used as a measure of the bond default risk. Numerous econometric studies have been carried out to find the determinants of these ratings despite the potential biases arisina out of using ratings as a dependent variable.
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S.B. Thapa and D.R. Mehta, Determinants of supply of bank loans to LDCs
to sponsor the flow of credit to an LDC depends upon the strength of the political relationship between the two countries. In turn, this strength is reflected in factors such as military aid, geographic proximity, trade relationship, U.S. direct investment, the size of the borrowing country, lending country’s dependence on borrowing country for raw materials, and its GNP (a proxy for economic significance of the borrowing country). Grouping of countries according to these criteria is shown in Appendix B. All the countries in our sample have credit relationship in one way or other with U.S. banks. Each of the countries in the ‘high bailout prospects’ category has either (a) military alliance with the U.S., (b) received a large amount of military and/or economic aid, (c) geopolitical factors that may be strategically important for the U.S. because of geographic proximity, or (d) significant U.S. exports and/or direct investment. These countries cover Asia, Latin America, North Africa, and Europe. Thus, in the context of our sample, these countries have higher likelihood of being bailed out by the U.S. government during economic adversity. The second proxy for the probability of political bailout is the volume of the aid given by the U.S. government (USAID) to the LDCs. Such aid ‘is largely circumscribed by the political relationship between the United States and the LDC in question. Thus, the higher the USAZD amount provided to an LDC, the stronger the political relationship and hence the higher the likelihood of political bailout.
3.3. Measurement of the foreign exchange generating capacity of an LDC A country’s debt servicing capacity depends on the foreign exchange it can generate and allocate for servicing the external debt. Generation of foreign exchange, in turn, is affected by many factors, such as contribution of its export sector, overall domestic economic management, and the liquidity position of the country. Past studies related to the sovereign lending have used a number of variables as indicators of the debt servicing capacity of LDCs [see, for example, Edwards (1984); Feder and Just (1977); Feder et al. (1981); Taffler and Abassi (1984)]. This study uses the following economic variables as potentially affecting the debt servicing capacity of a country and hence its credit rating (see table 1 for the description of the variables). The signs within parentheses below indicate the direction of influence of these variables on ratings: l l l l
Per capita gross national product (GNPPC) (+). Ratio of exports to gross national product @X/GNP) (+). Export growth rate (EXGR) (+). Ratio of investments to GDP (ZNV/GDP) (+).
S.B. Thap&.&d D.R. Mehta, Drtermihonlj oftuppiy.ofbank
ioap
to LDCs
543
Table 1 11) , Per-capita Gross National Product (GNPPC): This variable has been used to indicate the relative level of the economic development of g country and is expected to impact the ratings positively. (2) Exports/Gross National Product (EX/GNP): The higher the ratio, the larger the amount of foreign exchange available for servicing the debt and the higher will be the ratings. (3) Export growth rate (EXGR): A country with high growth rate in exports in less likely to default than otherwise. So its impact on ratings is expected to be positive. (4) Investment/GDP (INVIGDP): The allocation of a higher share of GDP into capital formation increases the likelihood of future economic growth, thus improving the prospects of servicing and repayment of debt in the future. Its impact is expected to be positive on the ratings. (5) Total Debt/GNP (DBT/GNP): This ratio is assumed to indicate the degree of a country’s solvency. Its impact on ratings is expected to be negative. (6) Oil (OIL): The probability of default for an oil exporting country is expected to be less than for a non-oil country, other things remaining the same. A dummy variable will be used to separate the oil exporting countries from the rest. It will be given a value of 1 for all oil exporting countries, and 0 for the rest.
l .
Ratio of total debt to GNP (DBT/GNP) Oil (dummy variable) (OIL) (+).
(-).
3.4. The sample and the data sources The sample consists of 40 LDCs as shown in table 2. The sample size was limited to only 40 LDCs because requisite data were not available on other LDCs. The primary data sources used in this study are the World Debt Tables and World Development Report published by the World Bank. (A detailed description is provided in table 3.) Data on macroeconomic variables for the period 1979-1984 were compiled for these LDCs. Data for the country credit ratings were obtained from the Institutional Investor. Data for 5 years (19791983) were used to fit the regression model, and the 1984 data were used to test the predictive ability of the model.
4. Empirical results 4.1. Binary variable (ALLIANCE)
as a measure of the political bailout
Eq. (8) was initially estimated using the OLS procedure. The estimation results for the full model are presented in Panel 1 of table 4.4 The overall results are quite satisfactory in terms of goodness-of-fit of the equation, as *The full model includes the political bailout variable, whereas the restricted model does not.
S.B. Thapa and D.R. Mehta, Determinants of supply o/bank loam to LDCs
544
Table 2 Countries in the sample. Country 1. 2. 3. 4. 5.
6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.
Algeria Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador Egypt Ethiopia Greece India Indonesia Ivory Coast Israel Jamaica Kenya Korea Malaysia
Probability’ of bailout
country
High High High
21. 22. 23. 24. 25. 26.
LOW
27.
Low High High Low High Low High Low High Low Low High High
28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40.
Low High LOW
‘Rationale for assigning countries Appendix B.
Mexico Morocco Nicaragua Nigeria Pakistan Panama Paraguay Peru Philippines Sierra Leone Sudan Syria Thailand Trinidad-Tobago Tunisia Turkey Uruguay Venezuela Zaire Zambia
to the High-Probability-for-Bailout
Probability’ of bailout High Low Low LOW
High Low Low High High Low Low Low High High Low High High High Low Low group is given in
Table 3 Data sources. World Debt Tables: 1984-1986 editions and supplements. Data on the following variables were collected from World Debt Tables: (a) GNP (b) Total debt/GNP (c) Exports World Development Reports: 1979-1986 editions. Data on the following variables were collected from World Development Reports: (a) GNP per capita (b) Exports growth rate (c) Domestic investment/GDP Annual reports to the President and to the Congress: By U.S. National Advisory Council on International Monetary and Finana Policies: 1979-1986 editions of these reports. Data on USAID and Military Grants were collected from these reports.
well as the signs and statistical significance of the coeflicients. These results tend to support the hypothesis that country credit ratings and hence the cross-border lending decisions of banks are based on economic as well as political variables. In particular, the political variable ALLIANCE is posi-
S.B. Thapa and D.R. Mehta, Determinants
of supply of bank loans to L.DCs
545
Table 4 Full model estimation results. OLS
GLS(AUTO)b
GLS(AUTO-HET)
Variables
Panel 1
Panel 2
Panel 3
GNPPC
0.00206 (3.4412)d 0.7284 (5.6751) - 0.29564 (-9.3588) 0.90273 (8.4624) 3.6992 (2.1555) 6.8898 (4.1839) 9.3840 (3.9786) 17.233 (6.2397) 0.7432 9.2525
0.00149 (2.1772) 0.23002 (1.8735) -0.33569 (- 11.271) 0.78946 (9.5795) 5.4271 (2.8713) 3.6657 (1.9933) 9.7314 (3.4124) 23.921 (7.8944) 0.8694 6.5976
0.00117 (2.0676) 0.55024 (5.3104) -0.29985 (-11.256) 0.46548 (6.7610) 5.9242 (3.4575) 7.4062 (4.1718) 6.5658 (2.1571) 30.251 (12.513) 0.9197 5.1751
EXGR DBTJGNP INVJGDP ALLIANCE OIL
EX/GNP INTERCEPT
Adj. RZ SEE’
‘Dependent variable: country credit ratings. bGLS estimation (with autoregressive disturbances). ‘GLS estimation (with cross-sectionally heteroscedastic and time-wise autoregressive disturbances). dFigures in parentheses are t-values. ‘Standard error of estimate.
tively related to ratings and is statistically significant, endorsing the notion that banks assign high credit rating to the countries with perceived high chances for bailout. The magnitude of the coefficients and their statistical significance (as indicated by the t-statistic) can, however, be reliable only in the absence of autocorrelation and heteroscedasticity (table 5 contains the matrix of simple correlation coefficients). Unfortunately, the Durbin-Watson test indicated a positive autocorrelation.’ The presence of hereroscedasticity was also detected by the test proposed by White (198O).‘j In order to remove the problem of autocorrelation, the GLS procedure sThe lower and upper knits of the Durbin-Watson statistic are approximately 1.603 and 1.746 respectively. Since the computed value of this statistic (=0.6827) is below the lower limit of d (= 1.603). the null hypothesis of no positive serial correlation is rejected. 6For details, see White (1980). Operationally the test entails computing a test statistic which follows a &i-square distribution. The test statistic had a value of 165.249 and 33 degrees of freedom. So the null hypothesis that the errors are homoscedastic is rejected. Statistical package SAS (which has an option for this test) was used to calculate the test statistic.
546
S.B. Thapa and D.R. Mehta. Determinants o/supply
of bank loans to LDCs
Table 5 Matrix of simple correlation coefficients. 1
1. GNPPC 2. 3. 4. 5. 6. 7. 8.
EXGR DBTfGNP INVIGDP USAID ALLIANCE OIL EXIGNP
l.ooooo 0.13336 -0.19483 0.26101 0.1584 0.4032 1 0.19038 0.1734 5
2
3
-i%Zi 0:15832
-0.18070
0.3658 0.46576 - 0.09470 -0.16609
-0.01441 -0.37841 - 0.22671 0.31731
6
4
1.OOOOO
7
1.OOOOO 0.10025 0.17969 0.34638 0.21811 8
1. GNPPC 2. 3. 4. 5. 6.
EXGR DBTIGNP IN V/GDP USAID ALLIANCE 7. OIL
8. EXIGNP
l.OOOOO 0.33856
-0.1246 -0.0761
1.OOOOO 0.17545 -0.1228
1.OOcmO 0.0057 1.@loOO
[henceforth GLS(AUTO)] was used.’ The results are reported in Panel 2. Comparing with the OLS results, it is seen that the R2 increases from 0.7432 to 0.8694. The coeficients of all the variables are again significant and of correct signs. Standard error of estimate declines from 9.2525 to 6.5976. To overcome the problem of both heteroscedasticity and autocorrelation, a GLS procedure [henceforth GLS(AUTO-HET)] was undertaken.* The results are presented in Panel 3. The R2 increases to 0.9197 and the standard error of estimate decreases to 5.2464; further, all the estimated coefficients have the expected signs and are statistically significant. Henceforth, only the model in Panel 3 will be considered for further tests. Since pooled cross-sectional and time series data have been used in the estimation of the regression model, the Chow test was carried out to check the stability of the parameters. The data period was divided into two subperiods: 1979-1981 and 1982-1983. The test results indicate that, overall, there is no structural breakdown of the model, although some individual variables become insignificant in one or the other of the subperiods (see table 6). ‘In the GLS estimation it is assumed that the disturbances are generated according to the first-order autoregressive scheme. The statistical package SHAZAM mte (1978)] employed in this study uses the modsed Cochrane-Orcutt iterative process to estimate the first-order correlation coeficient. sFor the estimation of the model (with cross-sectionally heterosccdasticity and timewise autoregresive disturbances) see Kmenta (1971, p. 572). The statistical package SHAZAM uses this procedure.
S.B. Thapa tid D.R. Mehta, Determinants ofsiqply 4 bank loans to LDCs
547
Table 6 The Chow test GLS estimation results for the two subperiods.’ Explanatory variables GNPPC EXGR DBT/GNP INV/GDP ALLIANCE OIL EXIGNP INTERCEPT Adjusted R2 Residual sum of squares (RSS)
1979-1981
1982-1983
0.001926 (3.2867)b 0.5418 (3.7824) -0.29418 ( - 7.9267) 0.48773 (5.9416) 5.4525 (3.1623) 8.9788 (5.8654) 3.8484* (1.1504) 29.524 (10.216) 0.9221
0.002006 (2.4208) 0.65842 (4.567 1) -0.26873 (-9.3123) 0.76187 (7.7399) 2.1342* (0.92508) 7.8342 (3.4305) 7.6339 (4.0341) 19.518 (5.9676) 0.9054
2913.8
2018.4
‘Dependent variable: country credit rating. *Not significant at loo/, level of significance. bFigures in parentheses are t-values. ‘RRS for the whole sample period (1979-1983) = 5142.0. where P= [RSS(79-83)RSS(79-81) -RSS(82-83)1/K [RSS(79-81)+RSS(82-83)YN, + N, -2K ’ K = parameters to be estimated = 8; N,, N, = number of observations in the two subperiods = 120, 80 respectively. Computation gives F = 0.9784. Since F(critical 8,184)~ 1.98, the null hypotheses that regressions are the same cannot be rejected.
the two
While the results in table 4 clearly indicate that the political bailout variable significantly contributes to explaining the variation in credit ratings, its criticalness still remains to be established. For this purpose, a model without the political bailout variable was estimated. Its results are shown in table 7. The explanatory power of the restricted model in Panel 3 was then compared with that of the full model (table 4, Panel 3) with the help of an F-statistic. The F-statistic was calculated as follows: F
=
CSSEtr) - SSE(f)l/[d(r)- d(f)l SWfY4f) ’
S.B. Thapa and D.R. Mehta, Determinants of supply of bank loans to LDCs
548
Table I Restricted model estimation results.’
Variables
OLS
GLS(AUT0)
GLS(AUTO-HET)
Panel 1
Panel 2
Panel 3
(3.8256) la.52 (6.8043) 0.7384
0.00208 (3.101) 0.4055 (3.7073) - 0.3373 (-11.11) 0.7983 (9.496) 4.983 (2.746) 9.164 (3.17) 24.64 (8.098) 0.8645
9.3395
6.72
0.00161 (2.8046) 0.7050 (7.2393) -0.29553 (- 11.271) 0.48279 (7.009) 9.0593 (5.8746) 5.809 (1.9586) 30.534 (12.53) 0.9174 5.2464
-
GNPPC
0.0025 1 (4.4104)’ 0.84958 (7.2946) -0.30697 ( - 9.763) 0.88848 (8.2671) 7.7668 (4.822 1)
EXGR DBTJGNP INVIGDP OIL EXIGNP
9.093
INTERCEPT
Adj. R2 SEE’
-
‘Dependent variable: country credit ratings. bFigures in parentheses are r-values. ‘Standard error of estimate.
where SSE(t)
= error sum of squares for the restricted model;
= error sum of squares for the full model; SWf) d(f), d(r) = degrees of freedom for full and restricted models respectively. From estimations results, SSE(f) = 5,142, SSE(r) = $312.5, d(f) = 192, d(r) = 193, Computation
yields, F = 6.366,
F(critica1) 1,192- 3.84. The computed value of 6.366 clearly indicates the superiority of the full model over the restricted model in terms of its explanatory power. Later on,
S.B. Thapa and D.R. Mehta. Determinants of supply of bank loans to LDCs
549
importance of this variable will be re-examined by comparing the predictive ability of the full and restricted models.
4.2. U.S. aid as a measure of the likelihood of political bailout So far, a dummy variable, ALLIANCE, has been used in the estimation of the regression model to capture the effect of the likelihood of political bailout on the ratings. Such a grouping procedure, however, implies that the political bailout likelihood is the same for all LDCs within a group - which is implausible since it may significantly vary for LDCs even within each group. As a result, U.S. aid was selected as another proxy for this variable. A problem with this proxy, however, is that the U.S. government provides aid to LDCs for not only political reasons, but also humanitarian reasons. Thus the raw data on USAID will be very likely contaminated. In order to purge this meaurement error, the two-stage least squares method was employed. The method of two-stage least squares, the effect of measurement errors on the estimation, and the test for identification can best be explained by rewriting the regression model (8) in the following structural form: Y = B&SAID*
+ c BjX, + u f#2
USAID = USAZD* + v
(94 Pb)
USAZD* = C ajX, + W, j*2
where Y
= country credit rating;
USAID* = the true but unobservable value of the aid given by the U.S. to an LDC for political reasons only; USAID
= the raw data for USAZD,
X
= all other economic variables including constants;
V
=
measurement error.
The error terms u, 11,and w are assumed independent of each other with zero mean and constant variances. Eqs. (9a)-(9c) constitute a system of simultaneous equations in which Y and USAID are in effect endogenous and X,, are exogenous variables. ‘Two-stage least squares procedure essentially amounts to estimating USAID* in the first step and using the estimated values (instead of raw data for USAID) in (9a) in the second step.
550
S.B. Thapa and D.R. Mehta,
Determinants
of supply of bank loans to LDCs
Table 8 Two-stage regression results.‘.b. Explanatory variables GNPPC EXGR DBTIGNP IN V/GDP OIL USAID
EXIGNP INTERCEPT
Adjusted R2 Standard error of estimate
Coefftcients 0.00142 (2.4824) 0.66276 (6.7575) - 0.30766 (- 11.529) 0.48119 (6.1938) 9.2358 (6.1938) 0.000816 (2.0972) 6.4738 (2.1545) 30.041 ( 12.482) 0.9144 5.3422
Y$pendent variable: country credit ratings. order to avoid the problem of autocorrelation and heteroscedasticity, GLS estimation procedure was used in each stage instead of OLS. ‘Figures in parentheses are t-values.
In order to avoid the problem of under-identification,’ additional exogenous variables, GNP, geographic proximity of the LDC to the U.S., and U.S. military aid to the LDC were chosen for inclusion in the first stage (which were, however, excluded from the second stage). A binary variable was utilized ,to capture the effect of geographic proximity, with the value of 1 for all Latin American countries, and 0 for the rest). The two-stage results are shown in table 8. All variables including USAID are statistically significant and have correct signs. Goodness-of-fit of the regression model as measured by the R2 is 0.9144. 5. Predictive ability of the models
Both of the estimated models, with ALLIANCE
and USAID as proxy for
9The order condition for identification shows that eq. (9a) is underidentified because the number of excluded predetermined variables (which is zero, 6-6~0) is less than the endogenous variables less one (i.e., (2- 1= 1)) [see Johnston (1984, p. 455)]. Note that the number of exogenous variables in the system is 6. The number of exogenous and endogenous variables in eq. (9a) is 6 and 2, respectively.
S.B. Thapa and D.R. Mehta, Determinants ofsupply of
bank loans to
LDCs
551
Table 9 Predictive ability. Full model with Restricted model (Without the political variable)
Binary variable (ALLIANCE) as proxy for political bailout likelihood
USAlD as proxy for political bailout likelihood
RL between the observed 1984 ratings and its predicted values
0.69840
0.6780
0.7023
Theil inequality coefficient (U) Root mean square (RMS)
0.450 8.6944
0.487 9.3994
0.463 8.9435
the bailout variable respectively, were used to predict the credit ratings of 32 LDCs in 1984.” The summary prediction statistics for the restricted and full model with two different measures of the political bailout variable are given in table 9. These results indicate that the inclusion of this variable only marginally improves the predictive ability of the model. Furthermore, the Wilcoxon Matched-Pairs Signed Ranks Test and a r-test were carried out to check the dominance of one model over the other. In the Wilcoxon Matched-Pairs Signed Ranks test (a non-parametric test equivalent to the parametric r-test on the mean differences) a statistic, W, is calculated as follows: e-$N(N w=cc
+ l)]/[hN(N+
1)(2N + l)]*,
where xe is sum of ranks, and N is the number of observations. The test statistic W is distributed approximately as the standard normal. The sum of positive ranks=281; the sum of the negative ranks =247; N = 32. Using the sum of the positive ranks, the value of W is 0.3179. Since the 5% critical value of the test statistic is 1.96, the null hypothesis that the full model does not dominate the restricted model in terms of prediction accuracy cannot be rejected. The standard t-test also yielded similar results. These test results indicate that the prediction results do not improve significantly with the addition of the political bailout variable in the model. This predictive result, however, need not be a serious concern because the main objective of this study has been to explain the variation in ratings rather than developing a predictive model. Further, in the face of the problems associated with the measurement of the political bailout variable, *‘Requisite data for 1984 were available only for 32 LDCs.
552
S.B. Thapa and D.R. Mehta, Determinants of supply of bank loans ro LDCs
the results presented here may simply indicate the need for further refinement in its measurement.
6. Summary and conclusions Between 1973 and the early 198Os, a large volume of private bank loans were provided to LDCs. This unprecedented volume has created substantial interest in the cross-border lending behavior of the banks. There is a widespread feeling among observers that one factor which has played a crucial role in bank loans to LDCs is the likelihood of political bailout by countries of the lending banks to avert any imminent default by LDCs on their loan obligations. This study has examined the empirical role of the potential for political bailout on sovereign lending during the last decade. As hypothesized, country credit ratings were found to be positively related to the political bailout variable lending credence to the assertion that sovereign lending has been greatly influenced by political realities. This does not, however, mean that banks will continue to base their lending decisions primarily on the potential for political bailout, since every loan decision has a one-shot aspect to it and they may not make the same choice again. But during the last decade and a half, the possibility of political bailout appears to have played a major role in the flow of credits to the LDCs.
Appendix A E(n)=LJR-I)-bjR(LR-Z)f(Z)dZ. 0
Then, LR F=(R-I)-b&
j 0
(LR-Z)f(Z)dZ
If y=L, q=LR, p=O, f(x,y)=LR-Z,
1
and x=Z,
.
then
S.B. Thapa aid b.R. Mehta, Determinants of supply of bank loaits to LDCs
553
-& jR(LR-z)/(Z)dZ ]=T[(R-g)f(Z)+(LR-ZqdZ. 0
[
By delinition, SZ/SL=O and thus bf(Z)/GL =O. Hence, T=(R-I)-bTRf(Z)dZ=(R-I)-bI&(Z)dZ. 0
0
Appendix B. Rationale for classifying countries into high bailout probability group Political bailout, a function of political alliance, is a complex multidimensional phenomenon; any single quantitative variable thus is unlikely to capture its essence. We explored several avenues to find a suitable proxy for political bailout. We contacted the United States Agency for International Development (USAID) and the State Department of the U.S. government in Washington, D.C. to see if they have some sort of rankings for countries in terms of their political importance to the U.S. Unfortunately, they did not have pertinent information to report. As a result, we settled for the following indicators of political commitment. (1) (2) (3) (4) (5) (6) (7)
Military grant. U.S. exports. U.S. direct investment. Military alliance. Geographic proximity. Size of the economy (GNP). U.S. economic aid.
Table B.l shows rankings of countries on the basis of the above criteria. Although these rankings do not yield identical results, they show significant similarity, as indicated by the Spearman rank correlations (table B.2). Colume 2 of table B.l shows the assignment of the probability of bailout. Out of 40 countries, 20 were assigned to the High-Probability-for-Bailout (HIGH) group and the rest to the Low-Probability-for-Bailout (LOW) group. Military grant was the primary determinant of the classification scheme. It must be noted, however, that the countries in the HIGH group also show their strategic importance to the U.S. through either geographic proximity and/or strong economic relationship as indicated by their robust rankings in U.S. exports and/or direct investment. Although Venezuela, Mexico and Trinidad-Tobago rank low in military aid, they were included in the HIGH group because these countries were either contiguous or have had significant economic relationship, as indicated by their rankings in U.S. exports and
Tunisia Bolivia Zaire Dominican Republic Paraguay Nicaragua
Ecuador
Turkey GtWX Thailand Philippines Pakistan wpt Brazil Indonesia Ethiopia Peru Chile Colombia India Argentina Morocco Sudan Uruguay
Israel
Country
U.S. exportb
U.S. direct investmenV
24 25 26 27
18 19
;
L L L L
13 14 15 16 17
; L H L
: 22 23
87 9 10 11 12
:: H H L’ H
:: L L
zl
:
:
39.0 27.0 24.0 23.0
6375.0 4457.0 2603.0 1481.0 866.0 703.0 488.0 356.0 264.0 242.0 145.0 139.0 129.0 119.0 87.0 86.0 77.0 68.0 65.0 620 57.0 45.0
6606.0
22 39 26 24
;
2 19 27 12 6 21 10 4 5 36 16 15 7 14 13 28 35 33 20 29
9
527.56 48.44 400.11 496.52
1650.2 6316.69 698.20 364.76 1353.44 1866.37 593.60 1529.22 3371.54 2143.24 86.27 1006.06 1039.96 1764.35 1123.93 1202.14 334.07 106.91 115.95 646.67 253.67 198.77 85.96 19 36 35 3
3; 10 1 5 38 7 14 8 17 3 37 25 30 20 33 27 28
18 13 26 21 15
362.0 50.0 50.0 3758.0
415.0 704.0 183.0 326.0 485.0 t 247.0 106.CKl 1192.0 8253.0 1997.0 7.0 1941.0 581.0 1317.0 418.0 2640.0 45.0 183.0 127.0 343.0 93.0 160.0 159.0 ASEAN
NATO” NATO ASEAN’ ASEAN -
Amount Amount Amount Member military (millions (millions (millions Rank of U.S. S) Rank of U.S. $) Rank of U.S. S) alliance
:: H H
H’ H
Probability of bailout
Military grant’
Table B.1 Ranking of countries. Economic aid
0 0 0 0 0 0 0 0
35
E 39
z 23 25 34 31
19 6 8 11 14 12 15 16 1 5 32 20 18 13 3 9 21
21 37 28 27
20 18 12 3 30 13 22 38 32 15 19 14 6101.0 4029.0 2276.0 3566.0
29
4279.0
1 4 6 10 16 9 5 2 8 7 18333.0 23652.0 34366.0 163745.0.0 50324.0 15683.0 7550.0 8550.0 10979.0 8152.0 3859.0 5310.0
19088.0 17836.0 58542.0 38691.0 33870.0 35321.0 28271.0 25546.0 240073.0 72589.0
760.0 150.0 400.0 413.0
16542.0 8471.0 5407.0 2154.0 993.0 2465.0 5687.0 10260.0 2991.0 3273.0 386.0 832.0 888.0 1310.0 9844.0 365.0 1290.0 753.0 144.0 301.0 1133.0 874.0 1219.0
Amount Amount Geographic (millions (millions proximityd Rank of U.S. 8) Rank of U.S. $)
Size of economy (GNP)c
l
.
38 38
0.50 z9
;: $ 0.51
%5 3:o
:: ::
:;
i8.0 6.0
zo*o
;
28
11 38
31 18
40 ::
22: 1
34 8
3
11 29
962.46 76.64
40 ‘39 34
12 23 16 2 z
111.59 1749.10 464.17 504.10 9867.14 1465.26 122.24 6.58 183.23 738.69
4685.46
941.0 139.0
7.9 71.0
4.0
1987.0 121.0 837.0 249.0 446.0 5598.0 241.0 250.0
-
-
-
ASEAN _ -
::
::
:,
0 1 1
7
28 36
2: 40 22 10
17 37 38 2
29
35 34
6622.0 3469.0
t: ::, 40 39 31 17
;: 36
61047.0 6150.0 23790.0 3159.0 2630.0 165993.0 73996.0 7822.0 1086.0 14263.0 39786.0 209.0 262.0
289.0 487.0 178.0 488.0 475.0 1750.0 435.0 t 14.0 122.0 334.0 482.0
‘Military and Economic Aid (19~1983~ Source: Annual Report to the President and to the Congress: by the US. National Advisory Council on International Monetary and Financial Poiiiies, Washington, D.C, 1979-1986. bFive-year Average; Source: International Trade Statistics Yearbook, pubiish~ by the United Nations, New York, 1987. ‘Five-year Average; Source: Survey of Current Business, published by the U.S. Department of Commerce, Washington, D.C., November 1984. “Latin American and Caribbean countries are given a value of 1 to indicate geographic proximity. *Five-year average; Source: World Debt Tabteq publish~ by the World ~~k,,WasiRgton, D.C,, 1986, ‘H = High. ‘L = Low. ‘NATO=North Atlantic Treaty Organi~tion. tASEAN=Aseociation of South-East Asian Nations. INot nported in the original source because of small amount.
Algeria TrinidadTobago
Sierra J..conc Syria
Malaysia Costa Rica Jamaica Mexico Nigeria Ivory Coast
Kenya
Venezuefa
S.B. Thapa and D.R. Mehta, Determinants
556
of supply of bank loans to LDCs
Table B.2 Spearman rank correlation coefficients. Militaid’ Militaid
Econaidb
Exports’
FDId
0.71 (0.0001)
0.36 (0.021) 0.49 (0.001)
0.26 (0.105) 0.28 (0.082) 0.79 (0.000 1)
Econaid (&O, Exports
(&IO) FDI
GNP
0.55 (0.0002) 0.78 (0.0001) 0.51 (0.0007)
GNP $00, ‘Militaid = U.S. military aid. “Econaid = U.S. economic aid. ‘Exports = U.S. exports to the LDCs. dFDI= U.S. direct investment. ‘GNP= Gross national product. ‘Figures in parentheses are significance levels.
direct investments. Malaysia ranks low in military aid, but it has military alliance with the United States through its membership in Association of South East Asian Nations (MEAN) and also ranks high in U.S. exports and direct investment. Hence it was included in the HIGH group. At the same time, although India ranks quite high in all the indicators, it was excluded from the HIGH group because it has had a long political alliance with the Soviet Union and its relationship with the United States has been at best lukewarm. Simiarly, although Ethopia, Morocco and Sudan rank high in military aid, their rankings in other indicators do not justify their inclusion in the HIGH group. References Agmon. Tamir and Kimball J. Dietrich, 1983. International lending and income redistribution: An alternative view of country risk, Journal of Banking and Finance 7,483-496. Ahber, Robert 2.. 1984, International banking A survey, Journal of Money, Credit, and Banking 16,661-695. American Banker, January 15, 1982. Annual Report to the President and to the Congreasz The National Advisory Council on International and Monetary and Financial Policies, Washington, DC, 1979-1986 editions. Cline, W.R., 1984, International debt: Systematic risk and policy response (Institute of International Economics, Washington, DC). Daniel, W.W.. 1978, Applied nonparametric statistics (Houghton MilBn, Boston, MA). Diaz, A. and F. Carlos, 1984. Some ftnanciai issues in the North, in the South, and in between, in: T. Agmon and R.G. Hawkins, The future of international monetary system (Lexington Books, Lexington, MA) 119-143. Eaton, J. and M. Gerosowitz 1981, Debt with potential repudiation: Theoretical and empirical analysis, Review of Economic Studies 48.289-309.
S._B. Thapa end OR. Mehta, Determinants
of supply of bank loans to LBCs
557
Edwards, S., 1984, LDC foreign borrowing and default risk: An empirical investigation, 19761980, The American Economic Review 74,726734. Feder, G. and R.E. Just, 1977, A study of debt servicing capacity applying logit analysis, Journal of Developmental Economics 4.25-38. Feder, G. and K. Ross, 1980, Risk assessments and risk premiums in the Eurodollar market (World Bank Staff Paper, Washington, DC). Feder, G., R. Just and K. Ross, 1981, Projecting debt servicing capacity of developing countries, Journal of Financial and Quantitative Analysis 16, 651-669. Feder, G. and K. Rosa, 1982, Risk assessment and risk premiums in the Eurodollar market, Journal of Finance 37, no. 3,679-691. Frank, C.R., Jr. and W.R. Cline, 1971, Measurement of debt servicing capacity: An application of discriminant analysis, The Journal of International Economics 1, 327-344. Gujarati, D., 1978, Basic economics (McGraw-Hill, New York, NY). Heller, R.H. and E.A. Frenkel, 1982, Determinants of LDC indebtedness, Columbia Journal of World Business, Spring, 28-34. Institutional Investor, Country credit ratings (various issues). Johnston, J., 1984, Econometric methods (McGraw-Hill, New York, NY). Kmenta, J., 1971, Elements of econometrics (Macmillan, New York, NY). Lessard, D., 1983, North-South, the implications for multinational banking, Journal of Banking and Finance 7,521-536. Melnik, A., 1983, Comments on: The role of banks in the international financial system, Journal of Banking and Finance 7,465-466. Saini, K. and P. Bates, 1984, A survey of the quantitative approaches to country risk analysis, Journal of Banking and Finance 8, 153-169. SAS Institute Inc., 1985, SAS User’s Guide: Statistics, version 5 edition (SAS Institute, Cary, NC). Short, B. and I. Angeloni, 1980, The impact of country risk assessment on Euro-currency interest rate spreads: A crossectional analysis (IMF Staff Paper, Washington, DC). Tamer, R.J. and B. Abassi, 1984, Country risk: A model for predicting debt servicing problems in developing countries, Journal of Royal Statistical Society, Series A, part 4, 541-568. Teeters, N.. 1983, The role of banks in the international financial system, Journal of Banking and Finance 7,453-463. Thapa, S., 1988. Theoretical and empirical analysis of the behavior of international banks in extending credit to less developed countries (LDCs) (Ph.D. Thesis, Georgia State University). United Nations, 1987, International trade statistics yearbook, New York. United States Department of Commerce, 1984, Survey of Current Business 64, no. 11, Nov., 24-27. Weintraub, R.E., 1983, International lending by U.S. banks: Practices, problems and policies (George Mason University, Fairfax, VA). White, H., 1980, A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica 48, 817-830. White, K.J., 1978, A general computer program for econometric methods SHAZAM. Econometrica 46,239-240. World Bank, World debt tables, Washington, DC (19841986). World Bank, World development report (Oxford University Press, New York) (1984-1986).
An empirical investigation of the determinants of the supply of bank loans to less developed countries* Samanta B. Thapa Wesrern Kentucky University, Bowling Green, KY 42101, USA
Dileep R. Mehta Georgia State Unioersity, Atlanta, GA 30303, USA Received October 1989, fmal version received August 1990
The Third World debt held by private financial institutions has created a number of controversies in international finance. One of them revolves around the ability of LDCs (less developed countries) to attract hundreds of billions dollars of loans at a relatively ‘low’ spread over the last decade. Some general explanations have focused on, for instance. error of judgment by banks and the ‘herd effect’, i.e., ignorant followers rushing in while the more knowledgeable have already gotten out. An intriguingexplanationof the size of the sovereign loans has been olfered by Agmon and Dietrich (1983) in terms of political factors pertaining to the lender’s country: loans to LDCs are in effect global taxes imposed by LDCs on industrialized nations in terms of the nature of their political relationship. Unfortunately, the Agmon-Dietrich study was not subjected to any empirical test. The purpose of this study is to extend the Agmon-Dietrich framework and examine the impact of the political relationship (between the borrowing country and the lending bank’s government) on the supply of loans by commercial banks in recent times.
1. Introduction
Sovereign loans to less developed countries (LDCs) in recent years by private financial institutions have been one of the controversial issues in international finance. The fact that LDCs have been able to attract a ‘large’ volume of loan at a relatively ‘low’ spread over the last decade has been a *We are grateful to an anonymous referee for his valuable comments and suggestions. Research grant from the College of Business Administration, Georgia State University. is gratefully acknowledged. 0378-4266/91/SO3.50 0 Ml-Elsevier
Science Publishers B.V. (North-Holland)
536
S.B. Thapa and D.R. Mehta. Determinants of supply of bank loans to LDCs
‘puzzling’ phenomenon. 1 Several general explanations have been offered for the large-scale sovereign lending by banks: error of judgment by banks [Edwards (1983, p. 726); Lessard (1983, p. 521)]; competition among banks the ‘herd’ effect [Aliber (1984, p. 675)]; critical informational issues [Diaz Alejandro (1983, p. 120)]; and use of *variable interest rate and syndication lending meeters (1983, p. 458)]. A study by Agmon and Dietrich (1983) introduces political factors (pertaining to the lender’s country) in its analysis of the supply of sovereign loans, and it argues that loans to LDCs are in effect global taxes imposed by the LDCs on the developed countries in the context of their political relationship. Their study thus assigns an active role to the borrowing nations and a passive role to banks in the decision-making process - an implication inconsistent with other explanations holding banks responsible for excessive loans to &DCs at relatively low spreads. Unfortunately, the Agmon-Dietrich study was not subjected to any empirical test. The purpose of this paper is to extend the Agmon-Dietrich framework and examine empirically the impact of the political relationship (between the borrowing country and the lending bank’s government) on the supply of sovereign loans by commercial banks during the last decade and a half. This paper is organized as follows: section 2 deals with the analytical formulation of the basic model. In section 3, definitions and measurement of the variables are presented. The empirical results are discussed in section 4. Predictive ability of various models is compared in section 5. Section 6 presents the concluding remarks. 2. The model Consider the case of a single representative bank (chartered in a developed country) extending loan to an LDC. It is assumed that the bank is an expected-profit-maximizer. At a given time, the bank can borrow unlimited amounts of funds from the national and offshore capital markets at a fixed interest rate. In keeping with the standard two-period analytical framework, it is assumed that the bank charges a fixed interest rate on LDC loans. The decision problem facing the bank is the loan size for a given interest rate. Let L be the size of the loan extended to an LDC at interest rate r, and i the cost of funds for the bank. Let 1 + r = R and 1 + i= I. The total contract amount ‘For example, Aliber (1984. p. 674) points out, ‘one of the puzzles of the last decade is that such countries as Argentina, Braxil and Mexico were able to greatly increase their debt denominated in U.S. dollars.. .*. Allegations of banks’ ‘overlending’ has been widespread. For example, Clint (1984, p. 113) states ‘... there is a need not only to avoid excessive lending (to LDCs) and some excessive lending undoubtedly did occur in 197Os...‘. Teeters (1983, p. 458) states ‘. . . during 1970s the supply of bank fina@ng to developing countries was quite elastic, at lending rates which on an cx post basis appear unjustiEably low in relation to the risks involved in such lending.. .‘. For similar criticism see also Wcintraub (1983, p. 6). Mclnik (1982 p. 468). Lessard (1983, p. 521).
S.B. Thapa arid: D.R. Mehta. Determhuutt& qfdtcppl), of btmk hmrir to LDCs
537
payable by the LDC will be LR. The total cost of funds fcr the bank wi!l be LI. Let 2 be the portion of foreign exchange assigned to external debt service by the borrowing country. Whenever Z< LR, the bank will receive only 2, and the bank will incur a loss of LR-2. The loss is, however, uncertain and depends upon the value of the random variable 2. Assume that Z is a continuous random variable with a probability density function f(Z) contained in the interval [0, co) and has a finite mean, cc, and variance, 2 Q.
Suppose the probability of bailout by the bank’s government (henceforth, lending country) is P(X), where X represents a vector of factors that affect the bailout. The gravity of the lending country’s strategic concern stemming from the borrower’s default may be one such factor [for a discussion of these issues, see Agmon and Dietrich (1983, p. 494)]. Similarly, the impact of bank failure on the stability of the lending country’s financial system may be another factor. The bailout efforts may be direct or through multilateral agencies such as the International Monetary Fund (IMF). Since likelihood of bailout stems primarily from the lending country’s national interest, it will be assumed that the likelihood of bailout is not influenced by Z, the foreign exchange reserves earmarked for debt service. (This assumption also makes the following analysis more tractable.) Now, define an uncertain loss function H such that r 0
for Z>LR, on P(X) on l-P(X)
for Z< LR.
Here H represents the loss contingent upon (a) the likelihood of an absence of bailout, and (b) inadequate generation of foreign exchange reserves purely due to economic factors. Taking expectations LR
E(H) = b J (LR -Z)f(Z)
dZ,
where b = 1 -P(X).
0
Let x be the profit on the loan. Then the expected profit is given by E(n)=LJR-I)-E(H) =L(R-I)-bb(LR-Z)f(Z)dz. 0 The
(1)
objective of the bank is to maximize the profit function given by (1)
538
S.B. Thapa and
D.R. Mehta, Determinants of supply of bank loans to LDCs
by choosing the optimal loan size. The first order condition profit maximization is (see Appendix F=(R-Z)-bR)-y(Z)&=0
A)
for expected
(2)
0
or (3) where F(a) is a cumulative probability function. The second-order condition yields d*E(a) -= dL*
-bR*_f(LR)
since b, R, and f(LR) are positive quantities. Condition (4) ensures that the solution given by (3) is indeed a global maximum. The solution to the firstorder condition gives the optimal loan amount for a given interest rate. Although the explicit solution of (3) for L* is not possible, the impact of various parameters on L* may be established by using the implicit function theorem. Alternatively, assume a uniform distribution function for 2 in (2) such that
where 6 is the range and T is the mean. Thus, T=(R--I)-bR
“i 1/(26)dZ=O. c-6
The solution of the integral provides the optimal loan size L*=2S(R-Z)/(bR’)+(v6)/R.
(5)
Now the impact of p(X), the bailout probability, and the T, the mean level of foreign exchange, on L* can be readily established for the distribution dL* -=‘>O dr R
for R>O,
(6)
S.B. Thapa rind D.R. Mehta, Determinants ofsupply of bank loans to LDCs
dL*
x=-
26( R - I) <0 bfRf
for6>0,
539
R>I.
Since b = 1 -P(X), the lower the b, the higher the P(X), and the higher the L*. Thus a profit maximizing bank will be willing to lend an increasingly larger amount of loan to LDC, the higher the bank’s estimate of the probability of the LDC being bailed out during the adversity. During the latter half of the 197Os, a major portion of U.S. bank loans was concentrated in only a few countries, viz., Brazil, Mexico, Argentina, and Venezuela. Although important, conventional economic forces or the profit margin (spread) on these loans by themselves cannot explain the huge size of the loans to these countries. One possible explanation could be that these countries had greater likelihood of being, bailed out by the U.S. government in times of debt servicing difftculties. According to Agmon and Dietrich (1983), lending countries’ continued willingness to sponsor the flow of credits to LDCs would depend on the strength of the political relationship between the lending country and the LDC. Factors deemed important in determining the strength of political relationship are: geographic proximity, size of the borrowing country, dependence on borrowing country for strategic raw materials and political and strategic importance of the borrowing country. On the basis of these criteria, the LDC borrowers mentioned above had high probability of being bailed out by the U.S. government. In brief, consideration of this political factor, along with the conventional economic variables, provides a plausible explanation for the large volume of bank credits obtained by these few countries. Thus L* =f(~, P(X)]. For empirical testing, the model may be specitied as follows:
Lit'B,+B,X,,i,+B2X2.,,+"'+B~Xl,i*+Ui,,
(8)
where =volume of loan supplied to country i at time t; =proxies for the foreign exchange generating capacity of x, it’s a country and the likelihood of political bailout; B;s =parameters to be estimated; =l,...,k; i i =l,...,n; t = 1,..., T. Lit
3. Definition and measurement of variables The test of the model contained in eq. (8) requires specification of relevant variables.
54cl
3.1.
S.B. Thapa and D.R. Mehta, Determinants of supply of bank loans to LDCs
Volume of loan supply
The volume of loan supply is not directly measurable since it represents the borrower’s loan capacity from the lender’s viewpoint. The outstanding loan appears to be an attractive proxy for the loan capacity. However, it suffers from two mutually exclusive problems. First, a country may choose not to tap fully its borrowing capacity for a variety of reasons such as (a) tapping the reserve on a rainy day, and (b) avoiding the perception at home that the government is subservient to foreign private institutions. Second, when a country is unable (or at times unwilling) to service a loan, the lender may be unable or unwilling to either take a legal action or write off the loan. Instead, the lender may have to make a ‘new’ loan that covers or even exceeds the debt service on the existing loan. In such instances, the existing outstanding loan does not in any way reflect the lender’s assessment of the borrower’s capacity. Since our test period is rife with instances of the latter type (i.e., loan increases that cover or exceed the debt service amount), the outstanding loan volume was rejected as a reasonable proxy for loan capacity. Indeed, the empirical observation of a predominantly large portion of loans to a relatively few countries underscores the problem with this proxy. Country credit ratings published by the Institutional Investor (II) is another proxy. These ratings are based on surveys conducted among leading international banks that use a scale of O-100, with 0 representing the least, and 100 the most creditworthy country. More weight is given to the responses of the banks with higher world-wide exposure. Since the banks whose responses carry heavier weights have also been the major suppliers of credit to the LDCs, a higher credit rating for an LDC signifies a greater potential for that country to obtain a larger volume of loan. It should be noted that the ratings contain a potential for bias. For instance, ratings may reflect a ‘herd’ tendency, i.e., going along with the opinion of the day rather than a true assessment of the debt capacity. Because the banks carrying heavier weights in the ratings scheme have also been the managers or co-managers of loan syndications, the most prevalent form of loan extensions to LDCs, such a herd instinct is conceivable but unlikely. A more plausible scenario for bias may be that banks with exceedingly high exposure to a given country may choose to rationalize their presence or even hope to palm off their loan commitments to other banks through inflated ratings. Any consequent loss of credibility of the ratings, however, renders them useless. If the report by Institutional Investor’ in early 1981 that Venezuela’s future prformance in terms of these ratings would be a part of default clause is Bny indication, it can be safely inferred *Institutional Investor (Sept. 1981, p. 287) reports that in early 1981 Venezuela’sfuture performance in terms of these ratings was made part of the default clause in a loan awement.
S.B. Thapa and D.R. Mehta, betemrihonts ofsupply of bank tinhi to LBCs
541
that banks maintain their integrity in reporting their assessment. Still, potential for bias does exist, but following previous studies in the literature [i.e., Feder and Ross (1982); Short and Angeloni (1980); Heller and Frenkel (1982)], this paper will employ the Institutional Investor’s Country credit ratings as a proxy for a country’s creditworthiness and hence the loan capacity.3 3.2. Likelihood of political bailout Political bailout, a function of political alliance, is a complex, multidimensional phenomenon; any quantitative variable is unlikely to capture its essence. For instance, alliance may stem from national interest considerations (such as reliance for raw materials), cultural affinity or ideological affinity (commitment to democratic ideals). Of course, these dimensions are not always consistent. National interest considerations may override the negative ideological connotation of a dictatorial regime. Consequently, a fruitful avenue to explore is a search for indicators that manifest political commitment: military bases, trade intensity, military aid, membership in military alliance and government aid for economic development or political considerations are a few of such indicators. They are not mutually exclusive, although some are more comprehensive than others. For instance, military aid by the United States has been more comprehensive than the presence of military bases, as evidenced in cases of U.S. neighbors such as Honduras. Similarly, military aid is a part of the current balance of payments and it typically affects trade balances for both merchandise and invisibles. On the other hand, military aid may be ruled out by the United States due to political considerations such as the host government’s sensitivity and consequent opposition to military aid. The volume of aid by the United States Agency for International Development (USAID) is a useful indicator when military aid is not effective or possible. Naturally, as stated above, any variable selected is hardly likely to encompass fully the essence of political bailout. A broad classification scheme is thus in order. A binary variable, ALLIANCE, has been used to classify the sample of countries into two groups: group one consists of countries with a high probability of being bailed out, and group two consists of the rest. The classification of countries is essentially based on the criteria suggested by Agmon and Dietrich (1984) that the lending country’s continued willingness ‘Feder and Ross (1982, p. 680) state: ‘the Institutional Investor’s weighted scores are a reasonable measure of the market’s perceived default probabilities’. Similarly, Short and Angeloni (1980) conclude that ‘the best creditworthiness indicator is Institutional Investor’s Index’. Further, there are precedents in mainstream corporate finance in which S&P 500 and Moody’s corporate bond ratings have been extensively used as a measure of the bond default risk. Numerous econometric studies have been carried out to find the determinants of these ratings despite the potential biases arisina out of using ratings as a dependent variable.
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S.B. Thapa and D.R. Mehta, Determinants of supply of bank loans to LDCs
to sponsor the flow of credit to an LDC depends upon the strength of the political relationship between the two countries. In turn, this strength is reflected in factors such as military aid, geographic proximity, trade relationship, U.S. direct investment, the size of the borrowing country, lending country’s dependence on borrowing country for raw materials, and its GNP (a proxy for economic significance of the borrowing country). Grouping of countries according to these criteria is shown in Appendix B. All the countries in our sample have credit relationship in one way or other with U.S. banks. Each of the countries in the ‘high bailout prospects’ category has either (a) military alliance with the U.S., (b) received a large amount of military and/or economic aid, (c) geopolitical factors that may be strategically important for the U.S. because of geographic proximity, or (d) significant U.S. exports and/or direct investment. These countries cover Asia, Latin America, North Africa, and Europe. Thus, in the context of our sample, these countries have higher likelihood of being bailed out by the U.S. government during economic adversity. The second proxy for the probability of political bailout is the volume of the aid given by the U.S. government (USAID) to the LDCs. Such aid ‘is largely circumscribed by the political relationship between the United States and the LDC in question. Thus, the higher the USAZD amount provided to an LDC, the stronger the political relationship and hence the higher the likelihood of political bailout.
3.3. Measurement of the foreign exchange generating capacity of an LDC A country’s debt servicing capacity depends on the foreign exchange it can generate and allocate for servicing the external debt. Generation of foreign exchange, in turn, is affected by many factors, such as contribution of its export sector, overall domestic economic management, and the liquidity position of the country. Past studies related to the sovereign lending have used a number of variables as indicators of the debt servicing capacity of LDCs [see, for example, Edwards (1984); Feder and Just (1977); Feder et al. (1981); Taffler and Abassi (1984)]. This study uses the following economic variables as potentially affecting the debt servicing capacity of a country and hence its credit rating (see table 1 for the description of the variables). The signs within parentheses below indicate the direction of influence of these variables on ratings: l l l l
Per capita gross national product (GNPPC) (+). Ratio of exports to gross national product @X/GNP) (+). Export growth rate (EXGR) (+). Ratio of investments to GDP (ZNV/GDP) (+).
S.B. Thap&.&d D.R. Mehta, Drtermihonlj oftuppiy.ofbank
ioap
to LDCs
543
Table 1 11) , Per-capita Gross National Product (GNPPC): This variable has been used to indicate the relative level of the economic development of g country and is expected to impact the ratings positively. (2) Exports/Gross National Product (EX/GNP): The higher the ratio, the larger the amount of foreign exchange available for servicing the debt and the higher will be the ratings. (3) Export growth rate (EXGR): A country with high growth rate in exports in less likely to default than otherwise. So its impact on ratings is expected to be positive. (4) Investment/GDP (INVIGDP): The allocation of a higher share of GDP into capital formation increases the likelihood of future economic growth, thus improving the prospects of servicing and repayment of debt in the future. Its impact is expected to be positive on the ratings. (5) Total Debt/GNP (DBT/GNP): This ratio is assumed to indicate the degree of a country’s solvency. Its impact on ratings is expected to be negative. (6) Oil (OIL): The probability of default for an oil exporting country is expected to be less than for a non-oil country, other things remaining the same. A dummy variable will be used to separate the oil exporting countries from the rest. It will be given a value of 1 for all oil exporting countries, and 0 for the rest.
l .
Ratio of total debt to GNP (DBT/GNP) Oil (dummy variable) (OIL) (+).
(-).
3.4. The sample and the data sources The sample consists of 40 LDCs as shown in table 2. The sample size was limited to only 40 LDCs because requisite data were not available on other LDCs. The primary data sources used in this study are the World Debt Tables and World Development Report published by the World Bank. (A detailed description is provided in table 3.) Data on macroeconomic variables for the period 1979-1984 were compiled for these LDCs. Data for the country credit ratings were obtained from the Institutional Investor. Data for 5 years (19791983) were used to fit the regression model, and the 1984 data were used to test the predictive ability of the model.
4. Empirical results 4.1. Binary variable (ALLIANCE)
as a measure of the political bailout
Eq. (8) was initially estimated using the OLS procedure. The estimation results for the full model are presented in Panel 1 of table 4.4 The overall results are quite satisfactory in terms of goodness-of-fit of the equation, as *The full model includes the political bailout variable, whereas the restricted model does not.
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Table 2 Countries in the sample. Country 1. 2. 3. 4. 5.
6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.
Algeria Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador Egypt Ethiopia Greece India Indonesia Ivory Coast Israel Jamaica Kenya Korea Malaysia
Probability’ of bailout
country
High High High
21. 22. 23. 24. 25. 26.
LOW
27.
Low High High Low High Low High Low High Low Low High High
28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40.
Low High LOW
‘Rationale for assigning countries Appendix B.
Mexico Morocco Nicaragua Nigeria Pakistan Panama Paraguay Peru Philippines Sierra Leone Sudan Syria Thailand Trinidad-Tobago Tunisia Turkey Uruguay Venezuela Zaire Zambia
to the High-Probability-for-Bailout
Probability’ of bailout High Low Low LOW
High Low Low High High Low Low Low High High Low High High High Low Low group is given in
Table 3 Data sources. World Debt Tables: 1984-1986 editions and supplements. Data on the following variables were collected from World Debt Tables: (a) GNP (b) Total debt/GNP (c) Exports World Development Reports: 1979-1986 editions. Data on the following variables were collected from World Development Reports: (a) GNP per capita (b) Exports growth rate (c) Domestic investment/GDP Annual reports to the President and to the Congress: By U.S. National Advisory Council on International Monetary and Finana Policies: 1979-1986 editions of these reports. Data on USAID and Military Grants were collected from these reports.
well as the signs and statistical significance of the coeflicients. These results tend to support the hypothesis that country credit ratings and hence the cross-border lending decisions of banks are based on economic as well as political variables. In particular, the political variable ALLIANCE is posi-
S.B. Thapa and D.R. Mehta, Determinants
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545
Table 4 Full model estimation results. OLS
GLS(AUTO)b
GLS(AUTO-HET)
Variables
Panel 1
Panel 2
Panel 3
GNPPC
0.00206 (3.4412)d 0.7284 (5.6751) - 0.29564 (-9.3588) 0.90273 (8.4624) 3.6992 (2.1555) 6.8898 (4.1839) 9.3840 (3.9786) 17.233 (6.2397) 0.7432 9.2525
0.00149 (2.1772) 0.23002 (1.8735) -0.33569 (- 11.271) 0.78946 (9.5795) 5.4271 (2.8713) 3.6657 (1.9933) 9.7314 (3.4124) 23.921 (7.8944) 0.8694 6.5976
0.00117 (2.0676) 0.55024 (5.3104) -0.29985 (-11.256) 0.46548 (6.7610) 5.9242 (3.4575) 7.4062 (4.1718) 6.5658 (2.1571) 30.251 (12.513) 0.9197 5.1751
EXGR DBTJGNP INVJGDP ALLIANCE OIL
EX/GNP INTERCEPT
Adj. RZ SEE’
‘Dependent variable: country credit ratings. bGLS estimation (with autoregressive disturbances). ‘GLS estimation (with cross-sectionally heteroscedastic and time-wise autoregressive disturbances). dFigures in parentheses are t-values. ‘Standard error of estimate.
tively related to ratings and is statistically significant, endorsing the notion that banks assign high credit rating to the countries with perceived high chances for bailout. The magnitude of the coefficients and their statistical significance (as indicated by the t-statistic) can, however, be reliable only in the absence of autocorrelation and heteroscedasticity (table 5 contains the matrix of simple correlation coefficients). Unfortunately, the Durbin-Watson test indicated a positive autocorrelation.’ The presence of hereroscedasticity was also detected by the test proposed by White (198O).‘j In order to remove the problem of autocorrelation, the GLS procedure sThe lower and upper knits of the Durbin-Watson statistic are approximately 1.603 and 1.746 respectively. Since the computed value of this statistic (=0.6827) is below the lower limit of d (= 1.603). the null hypothesis of no positive serial correlation is rejected. 6For details, see White (1980). Operationally the test entails computing a test statistic which follows a &i-square distribution. The test statistic had a value of 165.249 and 33 degrees of freedom. So the null hypothesis that the errors are homoscedastic is rejected. Statistical package SAS (which has an option for this test) was used to calculate the test statistic.
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Table 5 Matrix of simple correlation coefficients. 1
1. GNPPC 2. 3. 4. 5. 6. 7. 8.
EXGR DBTfGNP INVIGDP USAID ALLIANCE OIL EXIGNP
l.ooooo 0.13336 -0.19483 0.26101 0.1584 0.4032 1 0.19038 0.1734 5
2
3
-i%Zi 0:15832
-0.18070
0.3658 0.46576 - 0.09470 -0.16609
-0.01441 -0.37841 - 0.22671 0.31731
6
4
1.OOOOO
7
1.OOOOO 0.10025 0.17969 0.34638 0.21811 8
1. GNPPC 2. 3. 4. 5. 6.
EXGR DBTIGNP IN V/GDP USAID ALLIANCE 7. OIL
8. EXIGNP
l.OOOOO 0.33856
-0.1246 -0.0761
1.OOOOO 0.17545 -0.1228
1.OOcmO 0.0057 1.@loOO
[henceforth GLS(AUTO)] was used.’ The results are reported in Panel 2. Comparing with the OLS results, it is seen that the R2 increases from 0.7432 to 0.8694. The coeficients of all the variables are again significant and of correct signs. Standard error of estimate declines from 9.2525 to 6.5976. To overcome the problem of both heteroscedasticity and autocorrelation, a GLS procedure [henceforth GLS(AUTO-HET)] was undertaken.* The results are presented in Panel 3. The R2 increases to 0.9197 and the standard error of estimate decreases to 5.2464; further, all the estimated coefficients have the expected signs and are statistically significant. Henceforth, only the model in Panel 3 will be considered for further tests. Since pooled cross-sectional and time series data have been used in the estimation of the regression model, the Chow test was carried out to check the stability of the parameters. The data period was divided into two subperiods: 1979-1981 and 1982-1983. The test results indicate that, overall, there is no structural breakdown of the model, although some individual variables become insignificant in one or the other of the subperiods (see table 6). ‘In the GLS estimation it is assumed that the disturbances are generated according to the first-order autoregressive scheme. The statistical package SHAZAM mte (1978)] employed in this study uses the modsed Cochrane-Orcutt iterative process to estimate the first-order correlation coeficient. sFor the estimation of the model (with cross-sectionally heterosccdasticity and timewise autoregresive disturbances) see Kmenta (1971, p. 572). The statistical package SHAZAM uses this procedure.
S.B. Thapa tid D.R. Mehta, Determinants ofsiqply 4 bank loans to LDCs
547
Table 6 The Chow test GLS estimation results for the two subperiods.’ Explanatory variables GNPPC EXGR DBT/GNP INV/GDP ALLIANCE OIL EXIGNP INTERCEPT Adjusted R2 Residual sum of squares (RSS)
1979-1981
1982-1983
0.001926 (3.2867)b 0.5418 (3.7824) -0.29418 ( - 7.9267) 0.48773 (5.9416) 5.4525 (3.1623) 8.9788 (5.8654) 3.8484* (1.1504) 29.524 (10.216) 0.9221
0.002006 (2.4208) 0.65842 (4.567 1) -0.26873 (-9.3123) 0.76187 (7.7399) 2.1342* (0.92508) 7.8342 (3.4305) 7.6339 (4.0341) 19.518 (5.9676) 0.9054
2913.8
2018.4
‘Dependent variable: country credit rating. *Not significant at loo/, level of significance. bFigures in parentheses are t-values. ‘RRS for the whole sample period (1979-1983) = 5142.0. where P= [RSS(79-83)RSS(79-81) -RSS(82-83)1/K [RSS(79-81)+RSS(82-83)YN, + N, -2K ’ K = parameters to be estimated = 8; N,, N, = number of observations in the two subperiods = 120, 80 respectively. Computation gives F = 0.9784. Since F(critical 8,184)~ 1.98, the null hypotheses that regressions are the same cannot be rejected.
the two
While the results in table 4 clearly indicate that the political bailout variable significantly contributes to explaining the variation in credit ratings, its criticalness still remains to be established. For this purpose, a model without the political bailout variable was estimated. Its results are shown in table 7. The explanatory power of the restricted model in Panel 3 was then compared with that of the full model (table 4, Panel 3) with the help of an F-statistic. The F-statistic was calculated as follows: F
=
CSSEtr) - SSE(f)l/[d(r)- d(f)l SWfY4f) ’
S.B. Thapa and D.R. Mehta, Determinants of supply of bank loans to LDCs
548
Table I Restricted model estimation results.’
Variables
OLS
GLS(AUT0)
GLS(AUTO-HET)
Panel 1
Panel 2
Panel 3
(3.8256) la.52 (6.8043) 0.7384
0.00208 (3.101) 0.4055 (3.7073) - 0.3373 (-11.11) 0.7983 (9.496) 4.983 (2.746) 9.164 (3.17) 24.64 (8.098) 0.8645
9.3395
6.72
0.00161 (2.8046) 0.7050 (7.2393) -0.29553 (- 11.271) 0.48279 (7.009) 9.0593 (5.8746) 5.809 (1.9586) 30.534 (12.53) 0.9174 5.2464
-
GNPPC
0.0025 1 (4.4104)’ 0.84958 (7.2946) -0.30697 ( - 9.763) 0.88848 (8.2671) 7.7668 (4.822 1)
EXGR DBTJGNP INVIGDP OIL EXIGNP
9.093
INTERCEPT
Adj. R2 SEE’
-
‘Dependent variable: country credit ratings. bFigures in parentheses are r-values. ‘Standard error of estimate.
where SSE(t)
= error sum of squares for the restricted model;
= error sum of squares for the full model; SWf) d(f), d(r) = degrees of freedom for full and restricted models respectively. From estimations results, SSE(f) = 5,142, SSE(r) = $312.5, d(f) = 192, d(r) = 193, Computation
yields, F = 6.366,
F(critica1) 1,192- 3.84. The computed value of 6.366 clearly indicates the superiority of the full model over the restricted model in terms of its explanatory power. Later on,
S.B. Thapa and D.R. Mehta. Determinants of supply of bank loans to LDCs
549
importance of this variable will be re-examined by comparing the predictive ability of the full and restricted models.
4.2. U.S. aid as a measure of the likelihood of political bailout So far, a dummy variable, ALLIANCE, has been used in the estimation of the regression model to capture the effect of the likelihood of political bailout on the ratings. Such a grouping procedure, however, implies that the political bailout likelihood is the same for all LDCs within a group - which is implausible since it may significantly vary for LDCs even within each group. As a result, U.S. aid was selected as another proxy for this variable. A problem with this proxy, however, is that the U.S. government provides aid to LDCs for not only political reasons, but also humanitarian reasons. Thus the raw data on USAID will be very likely contaminated. In order to purge this meaurement error, the two-stage least squares method was employed. The method of two-stage least squares, the effect of measurement errors on the estimation, and the test for identification can best be explained by rewriting the regression model (8) in the following structural form: Y = B&SAID*
+ c BjX, + u f#2
USAID = USAZD* + v
(94 Pb)
USAZD* = C ajX, + W, j*2
where Y
= country credit rating;
USAID* = the true but unobservable value of the aid given by the U.S. to an LDC for political reasons only; USAID
= the raw data for USAZD,
X
= all other economic variables including constants;
V
=
measurement error.
The error terms u, 11,and w are assumed independent of each other with zero mean and constant variances. Eqs. (9a)-(9c) constitute a system of simultaneous equations in which Y and USAID are in effect endogenous and X,, are exogenous variables. ‘Two-stage least squares procedure essentially amounts to estimating USAID* in the first step and using the estimated values (instead of raw data for USAID) in (9a) in the second step.
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Determinants
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Table 8 Two-stage regression results.‘.b. Explanatory variables GNPPC EXGR DBTIGNP IN V/GDP OIL USAID
EXIGNP INTERCEPT
Adjusted R2 Standard error of estimate
Coefftcients 0.00142 (2.4824) 0.66276 (6.7575) - 0.30766 (- 11.529) 0.48119 (6.1938) 9.2358 (6.1938) 0.000816 (2.0972) 6.4738 (2.1545) 30.041 ( 12.482) 0.9144 5.3422
Y$pendent variable: country credit ratings. order to avoid the problem of autocorrelation and heteroscedasticity, GLS estimation procedure was used in each stage instead of OLS. ‘Figures in parentheses are t-values.
In order to avoid the problem of under-identification,’ additional exogenous variables, GNP, geographic proximity of the LDC to the U.S., and U.S. military aid to the LDC were chosen for inclusion in the first stage (which were, however, excluded from the second stage). A binary variable was utilized ,to capture the effect of geographic proximity, with the value of 1 for all Latin American countries, and 0 for the rest). The two-stage results are shown in table 8. All variables including USAID are statistically significant and have correct signs. Goodness-of-fit of the regression model as measured by the R2 is 0.9144. 5. Predictive ability of the models
Both of the estimated models, with ALLIANCE
and USAID as proxy for
9The order condition for identification shows that eq. (9a) is underidentified because the number of excluded predetermined variables (which is zero, 6-6~0) is less than the endogenous variables less one (i.e., (2- 1= 1)) [see Johnston (1984, p. 455)]. Note that the number of exogenous variables in the system is 6. The number of exogenous and endogenous variables in eq. (9a) is 6 and 2, respectively.
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LDCs
551
Table 9 Predictive ability. Full model with Restricted model (Without the political variable)
Binary variable (ALLIANCE) as proxy for political bailout likelihood
USAlD as proxy for political bailout likelihood
RL between the observed 1984 ratings and its predicted values
0.69840
0.6780
0.7023
Theil inequality coefficient (U) Root mean square (RMS)
0.450 8.6944
0.487 9.3994
0.463 8.9435
the bailout variable respectively, were used to predict the credit ratings of 32 LDCs in 1984.” The summary prediction statistics for the restricted and full model with two different measures of the political bailout variable are given in table 9. These results indicate that the inclusion of this variable only marginally improves the predictive ability of the model. Furthermore, the Wilcoxon Matched-Pairs Signed Ranks Test and a r-test were carried out to check the dominance of one model over the other. In the Wilcoxon Matched-Pairs Signed Ranks test (a non-parametric test equivalent to the parametric r-test on the mean differences) a statistic, W, is calculated as follows: e-$N(N w=cc
+ l)]/[hN(N+
1)(2N + l)]*,
where xe is sum of ranks, and N is the number of observations. The test statistic W is distributed approximately as the standard normal. The sum of positive ranks=281; the sum of the negative ranks =247; N = 32. Using the sum of the positive ranks, the value of W is 0.3179. Since the 5% critical value of the test statistic is 1.96, the null hypothesis that the full model does not dominate the restricted model in terms of prediction accuracy cannot be rejected. The standard t-test also yielded similar results. These test results indicate that the prediction results do not improve significantly with the addition of the political bailout variable in the model. This predictive result, however, need not be a serious concern because the main objective of this study has been to explain the variation in ratings rather than developing a predictive model. Further, in the face of the problems associated with the measurement of the political bailout variable, *‘Requisite data for 1984 were available only for 32 LDCs.
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the results presented here may simply indicate the need for further refinement in its measurement.
6. Summary and conclusions Between 1973 and the early 198Os, a large volume of private bank loans were provided to LDCs. This unprecedented volume has created substantial interest in the cross-border lending behavior of the banks. There is a widespread feeling among observers that one factor which has played a crucial role in bank loans to LDCs is the likelihood of political bailout by countries of the lending banks to avert any imminent default by LDCs on their loan obligations. This study has examined the empirical role of the potential for political bailout on sovereign lending during the last decade. As hypothesized, country credit ratings were found to be positively related to the political bailout variable lending credence to the assertion that sovereign lending has been greatly influenced by political realities. This does not, however, mean that banks will continue to base their lending decisions primarily on the potential for political bailout, since every loan decision has a one-shot aspect to it and they may not make the same choice again. But during the last decade and a half, the possibility of political bailout appears to have played a major role in the flow of credits to the LDCs.
Appendix A E(n)=LJR-I)-bjR(LR-Z)f(Z)dZ. 0
Then, LR F=(R-I)-b&
j 0
(LR-Z)f(Z)dZ
If y=L, q=LR, p=O, f(x,y)=LR-Z,
1
and x=Z,
.
then
S.B. Thapa aid b.R. Mehta, Determinants of supply of bank loaits to LDCs
553
-& jR(LR-z)/(Z)dZ ]=T[(R-g)f(Z)+(LR-ZqdZ. 0
[
By delinition, SZ/SL=O and thus bf(Z)/GL =O. Hence, T=(R-I)-bTRf(Z)dZ=(R-I)-bI&(Z)dZ. 0
0
Appendix B. Rationale for classifying countries into high bailout probability group Political bailout, a function of political alliance, is a complex multidimensional phenomenon; any single quantitative variable thus is unlikely to capture its essence. We explored several avenues to find a suitable proxy for political bailout. We contacted the United States Agency for International Development (USAID) and the State Department of the U.S. government in Washington, D.C. to see if they have some sort of rankings for countries in terms of their political importance to the U.S. Unfortunately, they did not have pertinent information to report. As a result, we settled for the following indicators of political commitment. (1) (2) (3) (4) (5) (6) (7)
Military grant. U.S. exports. U.S. direct investment. Military alliance. Geographic proximity. Size of the economy (GNP). U.S. economic aid.
Table B.l shows rankings of countries on the basis of the above criteria. Although these rankings do not yield identical results, they show significant similarity, as indicated by the Spearman rank correlations (table B.2). Colume 2 of table B.l shows the assignment of the probability of bailout. Out of 40 countries, 20 were assigned to the High-Probability-for-Bailout (HIGH) group and the rest to the Low-Probability-for-Bailout (LOW) group. Military grant was the primary determinant of the classification scheme. It must be noted, however, that the countries in the HIGH group also show their strategic importance to the U.S. through either geographic proximity and/or strong economic relationship as indicated by their robust rankings in U.S. exports and/or direct investment. Although Venezuela, Mexico and Trinidad-Tobago rank low in military aid, they were included in the HIGH group because these countries were either contiguous or have had significant economic relationship, as indicated by their rankings in U.S. exports and
Tunisia Bolivia Zaire Dominican Republic Paraguay Nicaragua
Ecuador
Turkey GtWX Thailand Philippines Pakistan wpt Brazil Indonesia Ethiopia Peru Chile Colombia India Argentina Morocco Sudan Uruguay
Israel
Country
U.S. exportb
U.S. direct investmenV
24 25 26 27
18 19
;
L L L L
13 14 15 16 17
; L H L
: 22 23
87 9 10 11 12
:: H H L’ H
:: L L
zl
:
:
39.0 27.0 24.0 23.0
6375.0 4457.0 2603.0 1481.0 866.0 703.0 488.0 356.0 264.0 242.0 145.0 139.0 129.0 119.0 87.0 86.0 77.0 68.0 65.0 620 57.0 45.0
6606.0
22 39 26 24
;
2 19 27 12 6 21 10 4 5 36 16 15 7 14 13 28 35 33 20 29
9
527.56 48.44 400.11 496.52
1650.2 6316.69 698.20 364.76 1353.44 1866.37 593.60 1529.22 3371.54 2143.24 86.27 1006.06 1039.96 1764.35 1123.93 1202.14 334.07 106.91 115.95 646.67 253.67 198.77 85.96 19 36 35 3
3; 10 1 5 38 7 14 8 17 3 37 25 30 20 33 27 28
18 13 26 21 15
362.0 50.0 50.0 3758.0
415.0 704.0 183.0 326.0 485.0 t 247.0 106.CKl 1192.0 8253.0 1997.0 7.0 1941.0 581.0 1317.0 418.0 2640.0 45.0 183.0 127.0 343.0 93.0 160.0 159.0 ASEAN
NATO” NATO ASEAN’ ASEAN -
Amount Amount Amount Member military (millions (millions (millions Rank of U.S. S) Rank of U.S. $) Rank of U.S. S) alliance
:: H H
H’ H
Probability of bailout
Military grant’
Table B.1 Ranking of countries. Economic aid
0 0 0 0 0 0 0 0
35
E 39
z 23 25 34 31
19 6 8 11 14 12 15 16 1 5 32 20 18 13 3 9 21
21 37 28 27
20 18 12 3 30 13 22 38 32 15 19 14 6101.0 4029.0 2276.0 3566.0
29
4279.0
1 4 6 10 16 9 5 2 8 7 18333.0 23652.0 34366.0 163745.0.0 50324.0 15683.0 7550.0 8550.0 10979.0 8152.0 3859.0 5310.0
19088.0 17836.0 58542.0 38691.0 33870.0 35321.0 28271.0 25546.0 240073.0 72589.0
760.0 150.0 400.0 413.0
16542.0 8471.0 5407.0 2154.0 993.0 2465.0 5687.0 10260.0 2991.0 3273.0 386.0 832.0 888.0 1310.0 9844.0 365.0 1290.0 753.0 144.0 301.0 1133.0 874.0 1219.0
Amount Amount Geographic (millions (millions proximityd Rank of U.S. 8) Rank of U.S. $)
Size of economy (GNP)c
l
.
38 38
0.50 z9
;: $ 0.51
%5 3:o
:: ::
:;
i8.0 6.0
zo*o
;
28
11 38
31 18
40 ::
22: 1
34 8
3
11 29
962.46 76.64
40 ‘39 34
12 23 16 2 z
111.59 1749.10 464.17 504.10 9867.14 1465.26 122.24 6.58 183.23 738.69
4685.46
941.0 139.0
7.9 71.0
4.0
1987.0 121.0 837.0 249.0 446.0 5598.0 241.0 250.0
-
-
-
ASEAN _ -
::
::
:,
0 1 1
7
28 36
2: 40 22 10
17 37 38 2
29
35 34
6622.0 3469.0
t: ::, 40 39 31 17
;: 36
61047.0 6150.0 23790.0 3159.0 2630.0 165993.0 73996.0 7822.0 1086.0 14263.0 39786.0 209.0 262.0
289.0 487.0 178.0 488.0 475.0 1750.0 435.0 t 14.0 122.0 334.0 482.0
‘Military and Economic Aid (19~1983~ Source: Annual Report to the President and to the Congress: by the US. National Advisory Council on International Monetary and Financial Poiiiies, Washington, D.C, 1979-1986. bFive-year Average; Source: International Trade Statistics Yearbook, pubiish~ by the United Nations, New York, 1987. ‘Five-year Average; Source: Survey of Current Business, published by the U.S. Department of Commerce, Washington, D.C., November 1984. “Latin American and Caribbean countries are given a value of 1 to indicate geographic proximity. *Five-year average; Source: World Debt Tabteq publish~ by the World ~~k,,WasiRgton, D.C,, 1986, ‘H = High. ‘L = Low. ‘NATO=North Atlantic Treaty Organi~tion. tASEAN=Aseociation of South-East Asian Nations. INot nported in the original source because of small amount.
Algeria TrinidadTobago
Sierra J..conc Syria
Malaysia Costa Rica Jamaica Mexico Nigeria Ivory Coast
Kenya
Venezuefa
S.B. Thapa and D.R. Mehta, Determinants
556
of supply of bank loans to LDCs
Table B.2 Spearman rank correlation coefficients. Militaid’ Militaid
Econaidb
Exports’
FDId
0.71 (0.0001)
0.36 (0.021) 0.49 (0.001)
0.26 (0.105) 0.28 (0.082) 0.79 (0.000 1)
Econaid (&O, Exports
(&IO) FDI
GNP
0.55 (0.0002) 0.78 (0.0001) 0.51 (0.0007)
GNP $00, ‘Militaid = U.S. military aid. “Econaid = U.S. economic aid. ‘Exports = U.S. exports to the LDCs. dFDI= U.S. direct investment. ‘GNP= Gross national product. ‘Figures in parentheses are significance levels.
direct investments. Malaysia ranks low in military aid, but it has military alliance with the United States through its membership in Association of South East Asian Nations (MEAN) and also ranks high in U.S. exports and direct investment. Hence it was included in the HIGH group. At the same time, although India ranks quite high in all the indicators, it was excluded from the HIGH group because it has had a long political alliance with the Soviet Union and its relationship with the United States has been at best lukewarm. Simiarly, although Ethopia, Morocco and Sudan rank high in military aid, their rankings in other indicators do not justify their inclusion in the HIGH group. References Agmon. Tamir and Kimball J. Dietrich, 1983. International lending and income redistribution: An alternative view of country risk, Journal of Banking and Finance 7,483-496. Ahber, Robert 2.. 1984, International banking A survey, Journal of Money, Credit, and Banking 16,661-695. American Banker, January 15, 1982. Annual Report to the President and to the Congreasz The National Advisory Council on International and Monetary and Financial Policies, Washington, DC, 1979-1986 editions. Cline, W.R., 1984, International debt: Systematic risk and policy response (Institute of International Economics, Washington, DC). Daniel, W.W.. 1978, Applied nonparametric statistics (Houghton MilBn, Boston, MA). Diaz, A. and F. Carlos, 1984. Some ftnanciai issues in the North, in the South, and in between, in: T. Agmon and R.G. Hawkins, The future of international monetary system (Lexington Books, Lexington, MA) 119-143. Eaton, J. and M. Gerosowitz 1981, Debt with potential repudiation: Theoretical and empirical analysis, Review of Economic Studies 48.289-309.
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557
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