LDC default probabilities and U.S. commercial banks: An empirical investigation

LDC DEFAULT

PROBABILITIES

AND U.S.

COMMERCIAL

BANKS: AN EMPIRICAL

INVESTIGATION

GARY M. WOLLER

and KERK PHILLIPS

ABSTRACT This paper investigates the effects of default probabilities on the level of U.S. commercial bank loan exposure to lesser developed country (LDC) debtors. Specifically, the paper examines the level of LDC loan exposure among 61 U.S. commercial banks to nine highly indebted countries over the years 1983.1989, so as to test whether U.S. banks have tended to respond to high default probabilities by making defensive loans or by reducing their existing LDC loan exposure. It finds that default probabilities have significantly influenced U.S. bank behavior toward their LDC loans. However, evidence exists for both defensive lending and reduction strategies, such that it is not possible to choose one over the other. The study also finds that internal capital constraints and strategic behavioral incentives explain most of U.S. bank behavior over the period of the study, particularly among the largest banks.

I. INTRODUCTION A large body of empirical literature examines the foreign debt servicing capacity of lesser developed countries (LDCs) before and after the onset of the LDC debt crisis in 1982 (Abassi & Taffler, 1982; Avramovic, 1964; Cline, 1984; Dhonte, 1975; Edwards, 1984; Feder & Just, 1977; Feder, Just, & Ross, 1981; Frank & Cline, 1971; Grinols, 1976; Khato: Gary M. Woller and Kerk Phillips, Institute of Public Management, Provo, UT 84602-3 158

Direct all correspondence

Young University

Brigham

International Review of Economics and Finance, 4(4): 333-352 Copyright 0 1995 by JAI Press Inc. ISSN: 1059-0560 All rights of reproduction in any form reserved.

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GARY M. WOLLER AND KERK PHILLIPS

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ras, 1984; Mayo & Barrett, 1978; Saini & Bates, 1978; Sargen, 1977; Schmidt, 1984).’ Of particular concern to these empirical studies are the factors that may influence the probability that a country will default on, or be forced to reschedule, its foreign debts. Using a variety of statistical methodolgies, the studies were able to identify several macro-economic variables that were statistically linked to higher default probabilities in indebted developing countries.* Having established that certain macro-economic factors are systematically related to LDC default probabilities, an interesting question not addressed by the literature is how have the international creditor banks responded to these factors? This question has taken on even greater importance since 1982 as several indebted developing countries have found themselves unable to service their external debts at existing levels and terms. The purpose of this paper is to try to answer this question. Specifically, we test whether the LDC creditor banks in the United States have responded to high LDC default probabilities by increasing or decreasing their LDC loan exposure as predicted by competing theories of bank behavior. The remainder of our paper is as follows. Section II presents a brief overview of the two alternative theories of bank behavior in response to high expected LDC default probabilities. Section III describes our empirical model and the statistical methodology employed in the paper. Section IV presents our empirical results. Finally, Section V summarizes our findings and offers some concluding remarks.

II.

COMPETING

THEORIES

OF BANK BEHAVIOR

One theory that addresses the question of bank responsiveness to high expected LDC default probabilities became popular in the years immediately after the onset of the debt crisis. This theory, called the theory of defensive lending, posited that banks may have an incentive to relend a portion of the interest due, given high expected default probabilities, to prevent the country from defaulting in the short term, thereby protecting the value of the banks’ existing claims (see, e.g., Cline, 1984; Eaton, Gersovitz, & Stiglitz, 1986; Guttentag & Herring, 1989; Krugman, 1988). Defensive lending is a short-to-medium-term strategy which is designed to temporarily relieve the debt burden of an indebted country in response to high default probabilities so as to reduce the country’s debt service obligations to levels more compatible with economic recovery. The Baker plan, the first official U.S. LDC debt policy, was based on the premise that commercial banks would be willing, or could be convinced, to make defensive loans for this purpose. As several authors have pointed out (see, e.g., Guttentag & Herring, 1989; Krugman, 1988, 1989) defensive lending differs importantly from voluntary lending. Voluntary loans are made to countries that are currently servicing their existing debt and are expected to do so in the future. Such countries can obtain loans at market rates from new lenders as well as existing lenders. Defensive loans, on the other hand, are made at an expected incremental loss; the expected return of a defensive loan is the increased probability that the bank’s existing loans will be repaid. Since lenders without existing exposure to the country do not benefit from the improved prospect of repayment, they have no incentive to participate in a defensive loan. As such, banks are willing to engage in defensive lending only when the

LDC Default

Probabilites

and US Commercial

Banks

preceived probability of default is high. To sovereign borrowers that have lost market access to voluntary funds, which during the 1980s included all of the so-called problem debtor countries, all new lending is defensive in nature. Toward the latter part of the 1980’s, however, it grew increasingly clear that the existing LDC debt obligations would not be fully repaid, nor were the banks prepared to lend the amount of new money envisioned by the Baker plan. Instead of making new loans, many banks were observed to reduce their LDC loan exposure. This observation suggested an alternative explanation of bank behavior: that the banks preferred response to high expected default probabilities was to write-off or forgive their existing LDC loan exposure. Loan write-offs lower the value of the loan as recorded on the bank’s balance sheet. They typically reflect the bank’s judgment that a portion of its loans to an individual debtor country is uncollectible.3 When writing-off a loan, the bank may continue to hold on to the loan, or it may sell the loan to a third party. Either way, the debtor country’s contractual liabilitiy remains intact.4 Hence, the primary incentive to write-off loans is if there exists some cost to maintain the account. We are able to demonstrate formally that this incentive increases with the probability of default (see Appendix). Second, the bank can forgive debt. Forgiveness is similar to loan write-offs, except that the bank actually reduces the present value of the country’s contractual debt liability.5 The theoretical justification for debt reduction is perhaps best demonstrated by Krugman’s (1989) debt-relief Laffer curve. Krugman’s curve, which has an inverted U-shape with the expected value of the external debt on the verticle axis and the nominal value of the debt on the horizontal axis, shows that the probability of default is an increasing function of the nominal value of outstanding debt. At very high levels of debt, the probability of repayment may become so low that reducing the country’s debt obligation may actually increase the expected value of debt collection. (See e.g., Sachs, 1986. We derive similar conclusions in the Appendix). This theory of debt forgiveness underlies the Brady plan, which since 1989 has made forgiveness the cornerstone of the official U.S. LDC debt policy.

III.

METHODOLOGY

AND MODEL

A.

SPECIFICATION

Data Set

To test which of the above theories has been the more accurate description of bank behavior, we regress U.S. commerical bank LDC loan exposure on 10 macro-economic indicators of LDC default probability and four control variables, two controlling for the bank’s internal capital constraints, one controlling for strategic incentives among LDC creditor banks, and the last controlling for yearly principal payments on outstanding debt. Our data set contains observations on 61 U.S. commercial banks and nine problem LDC debtors (Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, Philippines, and Venezuela) over the seven-year period of 1983 through 1989. All data is taken from the World Bank (1990-9 l), International Monetary Fund (1990a, b), Institute of International Finance (1990), Economic Commission for Latin American and the Caribbean (1990), and annual reports of the banks.

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AND KERK PHILLIPS

The countries and banks included in the data set are determined by the availability of the appropriate country-level and bank-level data. The availability of bank-level data, in turn, is a function of SEC reporting requirments. The SEC requires that banks report outstanding loans to individual countries only when such loans equal or exceed one percent of total assets.6 Because of this one percent reporting threshold, outstanding loan information across all seven years of the study is available only among a relatively small group of large creditor banks and a small group of large debtor countries. To overcome the econometric problems posed by the cross-sectional, time-series data set, we run the regressions using the Parks (1967) method. The Parks method is a random effects model that assumes first-order autoregression with contemporaneous correlation between cross sections. It produces estimators that are consistent and asymptotically normally distributed. Bank mergers in the data set are accommodated by combining the financial statements of the acquired and acquiring bank for the years t and t- 1. All monetary values in the data set are measured in real 1980 U.S. dollars, and all regressors take on year t- 1 values. The selection of the time period for the study reflects a number of factors. First, the LDC debt crisis did not erupt until late 1982. Second, the SEC did not require banks to report individual country exposure until starting in 1983. Consequently, detailed data on U.S. individual bank LDC exposure across a wide cross section of banks is not available prior to that time. Finally, the Brady plan was introduced in 1989, thus effectively ending any attempt to solve the LDC debt crisis through defensive lending. Despite the hundreds of creditor banks holding claims on any given debtor country, a relatively small number of “large,” strategically significant banks hold the large majority of U.S. commercial bank LDC claims. It is generally believed in the existing LDC debt literature that the incentive structures of the large and “small,” strategically insignificant banks differ in important ways. To put it another way, the large and small creditor banks are hypothesized to comprise different populations of banks. We tested this hypothesis by pooling the 6 1 sample banks in the data set into three separate categories according to size, LDC loan exposure, and strategic significance. For our pooling categories, we chose the categories already established by the Federal Financial Institutions Examination Council (FFIEC), which reports quarterly on aggregate U.S. commercial bank loan exposure to foreign countries. The categories established by the FFIEC are “money center” banks, “other large” banks, and “all other” banks.7 To test the hypothesis that the three groups of banks belong to different populations, we constructed an Fstatistic using the Chow test, which compares the sum-of-squares-error of the model regressed on the full data set with the model regressed on the segmented data sets. The resulting F-statistic of 16.70 was significant at the 0.01 level, indicating that the three groups represent different populations of banks. Consequently, we performed separate regressions on each of the three categories of banks.

B.

Dependent

Variable

LDC loan totals for our dependent variable are taken from the annual reports of U.S. banks. The dependent variable is defined as the log ratio of the sample bank’s loan exposure to the sample country normalized by the sample country’s gross domestic product (GDP).

LDC Default

Probabilites

and US Commercial

Banks

337

Since we have transformed the dependent variable by taking its natural logarithm, the coefficients generated by our regressions are interpretable as the percentage change in the bank’s exposure/GDP ratio due to a unit increase in the particular dependent variable. There are four reasons why the level of LDC loan exposure reported by the banks in their annual reports may vary from year to year: new loans, loan write-offs, debt forgiveness, and principal repayments. Our intent is to test for evidence of those movements over which the bank has control, specifically new lending, write-offs, and forgiveness. Ideally, we would prefer to observe each component separately, but the nature of the available data precludes this approach. Annual reports, which present the most detailed LDC loan information available, only rarely break down the annual components of outstanding loan variation. Consequently, we use the annual variation in outstanding loan amounts as proxy for these components. An increase in outstanding loan totals is seen as evidence of defensive lending and a decrease in outstanding loan totals as evidence of loan write-offs or debt forgiveness. The presence of principal payments in the data, however, complicates this approach. The payment of principal is something over which the banks generally have little control. Because the available data tends not to disclose yearly principal payments, we are unable to remove principal payments from our dependent variable. Instead, we control for principal payments by including total yearly principal payments by each country in each year as a regressor. This way we are able to capture the variation in the dependent variable that is explained by yearly principal payments. We believe this approach is appropriate since by contract each principal payment is allocated among the existing creditor banks according to each bank’s relative share of the outstanding loan total.

C.

Explanatory

Variables

As mentioned earlier, we include 10 measures of LDC default probability as explanatory variables in the model. Each of the 10 variables either has received strong empirical or theoretical support in past studies on LDC default probabilities. These past studies on LDC default probabilities have established that the default incentive is a function of a number of macro-economic factors, including the economic performance of the debtor country (Cohen & Sachs, 1986; Krugman, 1987, Nunnenkamp, 1989), the debtor country’s expected costs and benefits of default (Crawford, 1987; Eaton & Gersovitz, 1981; Krugman, 1989; Nunnenkamp & Picht, 1988; Sachs, 1984), and exogenous economic shocks (Nunnenkamp, 1989). Accordingly, Variables l-5 below measure the economic performance of the debtor country. Specifically, Variables l-3 measure the country’s international liquidity. Variables 4-5 measure the debtor country’s long-term economic prospects and long-term debt servicing capacity. Variables 6-8 measure some of the potential costs and benefits of default. Variables 9-10 present alternative measures of the effect of exogenous economic shocks on the probability of default. Poorer economic performance, higher (lower) expected benefits (costs) of default, and exogenous economic shocks are all generally associated with a higher expected probability of default. 1.

Interest Due/Exports (Avramovic, 1964; Cline, 1984; Edwards, 1984; Feder & Just, 1977; Feder, Just, & Ross, 1981; Frank & Cline, 1971; Sargen, 1977,

338

GARY M. WOLLER

2.

3.

4.

5.

6.

AND KERK PHILLIPS

Schmidt, 1984). This variable is the ratio of interest due on the country’s external debt to the exports of goods and services. It is the most common indicator of short-term debt servicing capacity. Debt service obligations are contractually fixed and cannot be easily adjusted in the short term. With new international capital flows to problem LDC debtors virtually shut off and exports relatively fixed in the short run, export shortfalls must be financed either by reducing imports or drawing down reserves. A higher ratio indicates a larger relative burden of import reduction for a given shortfall in export receipts. A higher interest due/exports ratio indicates a greater probability of default. International Reserves/Imports (Cline, 1984; Dhonte, 1975; Frank & Cline, 197 1; Feder & Just, 1977; Feder, Just, & Ross, 1981; Mayo & Barrett, 1978; Saini &Bates, 1978). This variable measures the country’s ability to accommodate short-term exchange earnings fluctuations. Since a high percentage of debtor country imports consist of capital and intermediate goods, foreign exchange shortfalls imply that any accommodating reduction in imports of goods and services can significantly worsen economic conditions and prospects for future growth. Drawing down reserves defers the need to reduce imports. The larger the reserves are relative to imports, the more reserves are available to service the country’s external debt, thereby making service payment interruptions less likely. A lower reserves/imports ratio indicates a higher probability of default. Current Account/Exports (Cline, 1984; Edwards, 1984; Saini &Bates, 1978). This variable is roughly equal to the country’s short-term new financing requirements. It is also an important measure of debtor country economic performance. The country’s current account balance is a short-term indicator of how successful the country has been in implementing the economic reforms favored by the international financial community. A lower current account/exports ratio is indicative of a higher probability of default. Investment/GDP (Avramovic, 1964; Edwards, 1984; Kharas, 1984; Mayo & Barrett, 1978). This ratio measures the country’s propensity to invest. The rate of investment will tend to reflect the country’s prospect for future economic growth. Lower rates of investment imply a higher probability of default. Inflation Rate (Abassi & Taffler, 1982; Avramovic, 1964; Dhonte, 1975; Edwards, 1984; Mayo &Barrett, 1978; Saini & Bates, 1978; Sargen, 1977). Inflation is a wellrecognized measure of acountry’s economic management. It is also argued (see, e.g., McDonald, 1982) that inflation exacerbates balance of payments problems by reducing the value of the country’s currency in the world market. Thus higher rates of inflation indicate a higher probability of default. GDP per Capita (Dhonte, 1975; Feder & Just, 1977; Feder, Just, & Ross, 1981; Frank&Cline, 1971; Saini &Bates, 1978; Schmidt, 1984). GDPpercapitameasures the country’s willingness to sustain large resource transfers abroad. Assuming a declining marginal utility of income, lower income countries will tend to be less willing to sustain large resource transfers abroad and, consequently, more willing to default on their foreign loans. This variable is also a measure of the debtor govemment’s policy flexibility. Supposedly, a higher relative income implies greater flexibility in dampening the demand for goods and services so as to generate additional

LDC Default

7.

8.

9.

10.

I I,

12.

Probabilites

and US Commercial

Banks

339

resources to meet shortfalls in foreign exchange necessary to service the country’s external debt. A lower per capita GDP implies a higher probability of default. Exports/GDP (Avramovic, 1964; Feder, Just, & Ross, 1981; Schmidt, 1984). This variable indicates the country’s stake in maintaining stable relationships with its trading partners. Countries with larger relative export levels will tend to be less willing to provoke retaliatory measures, such as increased trade barriers, by creditors and creditor country governments. It is also argued (see, e.g., Feder, Just, & Ross, 1981) that countries with larger exports relative to GDP will tend to have, more foreign exchange remaining after debt service, all else being equal, and thus will be less likely to default on their foreign loans. A lower ratio of exports to GDP indicates a higher probability of default. Total Debt/GDP (Edwards, 1984; Grinols, 1976; Schmidt, 1984). A country’s total external debt is approximately equal to its total expected resource transfer and, consequently, its total expected gain from default. Thus a higher level of debt relative to the country’s resources indicates a higher expected probability of default. Imports/GDP (Dhonte, 1975; Edwards, 1984; Frank & Cline, 1971; Mayo & Barrett, 1978; Saini &Bates, 1978). A country’s imports of good’s and services is a measure of the openness of its economy. A relatively open economy is generally believed to be more vulnerable to balance of payments crises and exogenous economic shocks. A higher ratio of imports to GDP implies a higher probability of default. Terms of Trade (Eaton, Gersovitz, & Stiglitz, 1986). The 1980’s was a decade of persistent deterioration in primary commodity prices of developing countries, a condition reflected in debtor countries’ terms of trade. It is expected that the wors ening of the terms of trade is positively related to the probability of default.’ Bank behavior, however, is undoubtedly influenced by numerous other factors not necessarily measured by the traditional macro-economic indicators used in past studies of LDC default probability. Accordingly, Variables 1l- 14 control for some of these factors. Several authors, for example, have argued that capital constraints have been an especially important determinant of U.S. commercial bank behavior since 1982 (see, e.g., Bird, 1989; Guttentag & Herring, 1989; Hay & Paul, 1991; Williamson, 1988). Capital is what protects the bank’s depositors from unexpected losses. Capital adequacy is the most widely-used indicator of a safe and well-run bank (see, e.g., Barge, 1985; Little, 1990), consequently bank behavior will be motivated by the desire to proctect its available capital. Variables 11 and 12 are two alternative measures of such capital constraints. Debt/Capital (Guttentag &Herring, 1989; Nunnenkamp, 1989). This variable measures the amount of the bank’s capital put at risk by loans to a single debtor country. The higher this ratio, the more capital the bank must write-off if the country defaults. Consequently, more-heavily-exposed banks are generally hypothesized to have a stronger incentive to lend defensively and vice versa. According to this hypothesis, we would expect the coefficient of the debt/capital ratio to be positive. Capital Margin (Guttentag & Herring, 1989). The capital/asset ratio is the most widely recognized measure of capital adequacy. The capital margin is the spread between bank’s capital/asset ratio and the regulatory minimum capital/asset ratio. The size of this margin measures the bank’s capital cushion and thus its flexibility to make decisions affecting capital. A reduction in the bank’s capital base associated

340

13.

GARY M. WOLLER

AND KERK PHILLIPS

with writing-off or forgiving loans potentially places the bank in danger of violating the minimum required level of capital. Thus less-heavily-capitalized banks are generally expected to be less willing to write-off or forgive their LDC claims. According to this hypothesis, we would expect to observe a negative coefficient for the capital margin. Bank behavior will also be influenced by strategic behavioral considerations. As is generally recognized, each bank has an incentive to do nothing and thereby free ride on the actions of the other banks.’ That is, while the collectivity of the banks might benefit from either new lending or debt reduction, each bank has an incentive to defect and let the other banks extend new loans or grant debt reduction. Variable 13 attempts to control for this incentive. Bank Loans/Total Loans.” This variable is the ratio of a bank’s loans to the sample country divided by total commercial bank loans to the country. It is generally believed that the incentive to free ride is a function of the bank’s relative loan exposure to the debtor country. Whereas the larger banks may recognize the need for additional lending, the smaller banks recognize that they can defect from any new lending agreement because their individual contributions are too small to affect the probability of default. On the other hand, a defection by any of the largest credtior banks could significantly affect the probability of repayment or could even cause the entire new lending agreement to collapse. Indeed, an empirical study by Spiegel (1992) tends to confirm this hypothesized relationship between the free riding incentive and relative bank exposure in new lending agreements. The free riding incentive has been observed to be the opposite direction when it comes to debt reduction. As Corden (1991) and others point out, banks have a disincentive to grant debt-relief because much of the benefits of relief would spill over Table 1 .

Variable

Interest due/exports

Interpretation

of Coefficient

Signs

Defensive

Debt

Lending

Reduction

+ + + +

Reserves/Imports Current Account/Exports Investment/GDP

+

Inflation

+ +

GDP per Capita Exports/GDP Total Debt/GDP Imports/GDP Terms of Trade

+ + + Hypothesized +

Debt/Capital Capital Margin LoanslTotal

Loans

Principal Payments

t NA

Sign

LDC Default Probabilites

and US Commercial

Banks

341

to other banks. This disincentive is presumably larger among the large banks for which the spill over effects would be larger. In addition, small banks tend have few or no long-term interests in problem debtor countries, tend to have fewer internal constraints to reducing their LDC exposure, and are generally believed to have higher carrying costs for their LDC loans. To the extent that these descriptions of the relative incentive structures of large and small banks are accurate, we would expect to observe a positive coefficient of the loans/total loans ratio. The final variable in the model controls for principal payments made by the sample country in each observation year. For the reader’s benefit, an interpretation of coefficient signs is provided in Table 1.

IV.

EMPIRICAL RESULTS

Tables 2-4 contain the results from regressing the empirical model separately on the set of money center banks, other large banks, and all other banks. To test the stability of the regression results, we ran additional regressions on several alternative specifications of the model. As can be seen in Tables 2-4, we found the empirical results to be generally consistent across several alternative specifications. 11 Looking first at the measures of default probability, the overall results, as determined by the statistical significance of the coefficients, are mixed. The regression results provide support for both theories of bank behavior in all three tables. Thus, the results provide insufficient evidence to conclude that either theory has been a better explanation of bank behavior than the other over the period of the study. The results are no less ambiguous when coefficient signs are examined within each of the three general categories of default probability indicators. Among variables measuring debtor country economic performance, the coefficient signs are consistent across all three groups of banks. The positive coefficient of the interest due/exports ratio and negative coefficient of the reserves/imports ratio support the conclusion that the banks increase their exposure when the probability of default, as measured by international liquidity, is higher. The positive coefficient for the current account/export ratio among all sizes of banks, however, suggests the opposite conclusion, although the effect of this variable appears somewhat weaker among the non-money center banks (it is statistically significant in only one of the model specifications). Among long-term economic performance measures, the negative coefficient of the investment/GDP ratio is consistent with defensive lending behavior, while the negative coefficient of the inflation variable indicates behavior more consistent with a write-off or forgiveness strategy. Regressing the model on measures of the expected benefits and costs of default yields different coefficient signs among the money center and non-money center banks when GDP per capita and exports/GDP are the regressors, although the direction of the overall relationship is again mixed within each category of banks. In Table 2, the coefficient of GDP per capita carries a positive sign, implying that lower (higher) expected costs (benefits) of default lead the money center banks to write-off their LDC loan exposure. Whereas in Tables 3 and 4, the coefficient of GDP per capita points in the opposite direction. The coefficient of the exports/GDP ratio in Table 2 is significant only once and is negative, suggesting an inverse but weak relationship between LDC loan expo-

Determinants

-7.242* (-21.969) 0.363 Debt Service Ratio (1.504) -0.178.t Reserves/Imports (-2.461) 0.208* Current Account/Exports (4.609) -1.185 Investment/GDP (-1.477) -0.017t Inflation (-2.218) 0.001* GDP per Capita (6.468) -1.422 Exports/GDP (-1.334) 1.322* Total Debt/GDP (2.413) 1.089 Imports/GDP (0.686) -0.104* Terms of Trade (-2.550) 0.036 Debt/Capital (0.125) -5.688* Capital Margin (-1.623 20.259* Bank Loans I Total Loans (6.648) 0.0471 Principal Payments (2.297) 244.000 DF 0.7823 Adj R2 F-statistic 67.208 Note: t-Statistics indicated in parentheses. ‘Inchcates parameter significant at 0.01 level. ‘Indicates parameter significant at 0.05 level. $lndicates at 0.10 level. parameter significant

Constant

Variable

Table 2.

(2.558) 247.000 0.2364 8.261

0.066t

-5.940* (-17.712) 1.608* (4.825) -0.097 (-1.043) 0.111 (1.326) -2.055t (-2.027) -0.033* (-3.388) 0.0003* (3.071) a.597 (4.337) 0.575 (0.868) 4.5.51* (2.718) -0.150* (-3.138) 0.384* (5.674) -6.739* (-9.872) 11.511* (7.171) 0.076* (7.361) 254.000 0.5977 96.836

(-17.202)

-5.166*

0.001* (6.926) -2.809t (-2.127) 2.352* (4.191) 1.252 (0.758) 4.150* (-2.852) 0.669* (4.044) -1.401 (-1.258) 25.628* (9.559 0.005 (0.3030) 249.000 0.7838 104.904

-7.789* (-18.285)

2.655* (3.520) -0.293* (-4.790) 0.534 (2.050) -0.850 (-0.351) 18.674* (7.926) 0.064$ (1.798) 247.000 0.6846 5 1.904

(-25.664) 1.699* (3.914) -.059 (-0.680) 0.299* (4.731) -1.206t (-1.957) -0.029* (-3.132)

-5.617*

of “Money Center Banks” LDC Loan Exposure: Parks Methods, 1983-89

78.000

0.229 (1.224) -5.171* (-3.162 18.263* (5.519) 0.046* (0.046) 246.000 0.7817

(2.580)

1.348*

-7.697* (-26.807) 0.354 (1.531) a.1351 (-2.067) 0.253* (7.535) -1.749t (-2.235) 4.026* (-5.262) 0.001* (9.645) -0.882 (-1.059)

2

c ;cI % -0 I r 1

z 0

%

E

5

T

9

N

z.-

Determinants

+x79* ( -6.524) 3.479* Debt Service Ratio (3.547) -0.842* Reserves/imports ( -2.480) Current Account/JSxports 0.435t (1.878) Investment/GDP -0.429 (4.148) Inflation -0.052$ (-1.686) -0.001t GDP per Capita ( -2.252) 11.745* Exports/GDP (4.677) 2.836 Total Debt/GDP (1.324) -3.019 Imports/GDP ( -0.856) -0.149 Terms of Trade (-0.839) -0.206 Debt/Capital ( 4.646) -24.478* Capital Margin ( -2.474) 115.156* Bank Loans /Total Loans (3.371) 0.072 Principal Payments (0.925) 216.000 DF 0.4416 Adj. R2 F-statistic 13.991 Note: t-Statistics indicated in parentheses. *Indicates parameter si&icant at 0.01 level. tIndicates parameter significant at 0.05 level. *Indicates parameter significant at 0.10 level.

Variable Constant

Table 3.

0.286* (8.430) 219.000 0.1772 5.503

-5.696* ( -8.722) 6.446* (19.380) -0.135 (-1.198) -0.184 (-1.360) -3.449* ( -2.784) -0.04ot (-2.330) -0.001* ( -3.825) 11.458* (10.270) a.981 (-1.218) 2.319 (1.398) -0.495* ( -4.492) -0.306 ( -0.749) -8.277 ( - 1.492) 117.299* (6.430) 0.169* (3.934) 226.000 0.3797 36.197

(-25.702)

+.a74*

-0.0003! ( -1.728) 6.325* (6.934) -0.261 ( -0.292) -5.156 (-1.350) -0.018 ( 4.383) 0.637t (2.332) 0.601 (0.978) 129.602* (10.965) 0.265* (7.895) 221.000 0.3967 17.801

-5.984* (-11.827)

7.071* (2.957) 0.043 (0.311) -0.052 (-0.079) -18.759t (-1.894) 89.328* (3.372) 0.130$ (1.805) 219.000 0.4061 15.297

-6.709* ( -6.491) 2.003 (1.592) a.113 ( -0.386) 0.252 (1.037) - 9.490* ( -4.045) -0.010 ( -0.382)

of “Other Large Banks” LDC Loan Exposure: Parks Methods, 1983-89

-0.046 (-0.055) -12.201 ( -0.882) 102.240* (3.485) 0.225* (4.920) 218.000 0.4570 17.129

-7.415* ( -6.056) 4.766* (3.714) a.319 (-1.387) 0.154 (0.990) -1.977 ( -1.307) +X078* (-3.571) -0.001* ( -2.760) 10.634* (5.215) 1.245 (0.935)

6.413* (a.041 I -0.772* (-3.943) -0.072 (-0.799) -0.981 (-0.749) -0.059’ (-2.803) -0.0006* C-3.883) 12.067* (3.728) 2.182* (1.848) -1.535 C-0.875) -0.306* (-2.565) -1.622 (-0.457) -13.525* (-3.235) 385.490: (2.462) 0.123* (3.151) ala.000 0.4456

*Indicates parameter s&uticant at 0.01 level. ‘Indicates parameter significant at 0.05 level. +lndicates parameter significant at 0.10 level.

(2.977) 821 .ooo 0.2474 25.858

o.i38*

(6.208) -0.095 (-0.411) 0.011 (0.047) 0.303 ( 0.709) -0.113* (4.448) -0.001* (-7.247) 14.093’ (6.034) 1.493 (1.415) -1.791 (-1.003) -0.375* (-3.005)

-10.366* (-11.940) 6.469*

0.894 (1.562) -23.877* (-18.034) 293.506* (I 7.813) 0.177’ (15.837) 828.000 0.3565 16.221

-8.548* (58.888)

(-1.524) 4.079’ (4.425) 1.932* (3.664) 0.613 (0.926) 0.007 (0.243) 2.744 (1.535) -0.212 (-0.077) 277.150* (5.680) 0.080* (3.695) 823.000 0.3595 52.886

-0.0001

-10.155* (-20.726)

8.032* (3.454) -0.044 (-0.377) 1.875 (0.536) -12.491* (-2.675) 206.917.J (1.602) 0.070f (1.806) 821.000 0.4133 54.273

3.322* (3.206) -0.245 (-1.071) 0.425t (2.248) -5.912* (-3.129) -0.071* (-4.046)

(-19.374)

-io.228*

of “All Other Banks” LDC Loan Exposure: Parks Method, 1983-89

-10.663*

Determinants

(-10.926)

DF Adj. R2 F-statistic 48.766 Note: t-Statistics indicated in parentheses.

Principal Payments

Bank Loans /Total Loans

Capital Margin

Debt/Capital

Terms of Trade

Imports/GDP

Total Debt/GDP

Exports/GDP

GDP per Capita

Inflation

Investment/GDP

Current Account/Exports

Reserves/Imports

Debt Service Ratio

Variable Constant

Table 4.

1.190 (0.352) -14.476: (-4.571) 263.0001 (1.617) 0.105” (1.617) 820.000 0.4419 55.904

-11.342* (-10.059) 5.642* (5.599) -0.643% (-4.027) -0.012 (-0.105) -2.300+ (-1.741) -0.070* (-3.685) -0.0004t (-2.349) 10.752* (3.736) 2.206 (1.371)

LDC Default Probabilites and US Commercial Banks

345

sure and the probability of default. In Tables 3 and 4, however, the coefficient of exports/ GDP is significant in all relevant specifications of the model and carries a positive sign in each case. The coefficient of the total debt/GDP ratio is positive among money center and all other banks, implying a positive relationship between the level of LDC loan exposure and the probability of default, but is not found to be statistically important among the other large banks. Turning next to the effect of exogenous economic shocks, the results in Tables 2-4 are again ambiguous. In each table, the positive coefficient of the imports/GDP ratio implies a positive relationship between the expected probability of default and LDC loan exposure, while the negative coefficient of the terms of trade variable indicates an inverse relationship. Judging by the number of significant coefficients, the importance of the imports/GDP ratio appears fairly weak across the three groups of banks, and the effect of the terms of trade appears much more robust among the money center banks. Among the four control variables, the results are more straightforward. As expected, the coefficients of the debt/capital ratio and the capital margin are positive and negative respectively. This result lends evidence to the hypothesis that the incentive to lend defensively is positively related to risk posed by the country to the bank’s capital position. This finding, however, appears primarily limited to the money center banks. The results also tend to confirm the hypothesis that loosening capital constraints will encourage debt reduction at the margin. The coefficient of the bank loans/total loans ratio is statistically significant in every case except one and is, as expected, positive. This finding is consistent with the hypothesized free rider incentive of the large and small banks-banks with a higher relative exposure appear more likely, all else equal, to engage in defensive lending, while the banks with a smaller relative exposure appear, else equal, to be more inclined to write-off their LDC loan exposure. Finally, the coefficient of the principal variable is found to be consistently positive and significant. This finding perhaps implies that the banks reserve defensive loans for “good’ problem debtor countries, while write-offs and forgiveness tend to be reserved for “bad” problem debtors. Despite the ambiguous relationship between default probabilities and LDC loan exposure found in the study, some interesting and important trends emerge from Tables 2-4. One interesting result that stands out is that, with some exceptions and excluding cases where coefficient signs differ, coefficients sizes are roughly inversely related to bank size across all categories of regressors. Thus it appears that the smaller banks tend to be more responsive to changes in the explanatory variables than the larger banks, whether the variable measures the probability of default, capital constraints, or strategic behavioral factors. Judging by the adjusted R2’s, the overall explanatory power of the model is moderate to strong among all three groups of banks. It is strongest among the money center banks where it accounts for over 70 percent of the total variation in the dependent variable. The explanatory power of the model also differs importantly across the different specifications of the model. Of particular interest are the first three regressions in each table, in which the dependent variable is regressed on the full model, default probability indicators only, and bank-specific variables only respectively (each specification includes the control variable for principal payments). In each case, we find that capital constraints and strategic behavioral incentives explain most of the variation in LDC loan exposure among the banks,

GARY M. WOLLER

346

accounting for over 50 percent of the total variation in outstanding the sample banks.

V.

CONCLUDING

AND KERK PHILLIPS

LDC loan totals among

REMARKS

This paper has analyzed the relationship between expected LDC default probabilities and the level of U.S. commerical bank LDC loan exposure to test whether U.S. commercial banks have tended to respond to high expected default probabilities with new lending or by writing-off or forgiving their existing LDC claims. The empirical analsysis has used loan data from 61 U.S. commercial banks and nine LDC debtor countries over the years 1983 through 1989. The empirical results fail to find reasonably unambiguous support for either theory of bank behavior; rather, both appear to offer some help in explaining bank behavior over the time period in question. It appears that bank behavior toward their problem LDC loans is more complex than implied by either of the two theories analyzed here. The paper has also examined the effect of capital constraints and strategic behavioral incentives on U.S. bank behavior toward their problem LDC loans. The results show that both have significantly influenced bank behavior. Specifically, the results show that the level of LDC loan exposure is positively related to internal capital constraints. From a policy perspective, this result implies that loosening capital constraints would greatly facilitate the level of debt reduction under the Brady plan. Finally, the results find that capital constraints and strategic behavioral incentives have been more powerful in explaining yearly changes in U.S. bank LDC loan exposure than traditional macro-economic measures of default probability. This finding is particularly strong among the money center banks. The results suggest that future empirical research on bank behavior and LDC lending might want to explore more fully the effect of bank-specific characteristics and strategic behavioral considerations on bank behavior as opposed to the traditional macro-economic indicators of LDC default probabilities.

APPENDIX This Appendix forgiveness.

illustrates

the incentives

for defensive

lending,

loan write-offs,

and debt

Defensive Lending Banks may make new loans to debtor countries if such new loans cause the probability of repayment to rise. This will increase the value of the country’s liabilities, but the positive responsiveness of the probability of repayment to new loans may be large enough to offset the negative responsiveness to higher debt. The bank’s problem is given by equation (A. 1): Max r

7L:(Lx)

(V + L) - L (1 + PI

(A.11

LDC Default

Probabilites

and US Commercial

347

Banks

where 7c is the probability of repayment, L is the amount of new loans, other factors which affect 7~:(by assumption xL > 0 and xx > O), V is standing balance prior to any new loans, and p is the difference between being charged the debtor country and the world market interest rate. (If same then p = 0) The solution to this problem is defined by equation (A.2):

nL(L,x)(v+L)= Comparative

l+p-rc((L,X).

statics shows that the responsiveness

aL

X is a vector of the current outthe interest rate the rates are the

(A.2)

of new loans to exogenous

shocks is:

n&V+L)+n,

ax = ?c,,(V+

64.3)

L)+ 2x,’

As long as rcL~ and 71~~ are close to zero, (A.3) is negative. Intuitively, this says that the responsiveness of the probability of repayment to new loans issued does not change very much when the amount of new loans, L, or other conditions, X, change. Hence, as conditions for repayment improve, the amount of defensive lending falls.

Loan Write-Offs

Loan write-offs lower the value of the loan as recorded on the banks balance sheet, but do not lower the debtor country’s liabilities. Hence, the primary incentive to writeoff loans is if there exists some cost to maintaining the account. We let this cost be C(v!w, where V is the initial book value of the bank’s loans and W is the amount of the write-off. By assumption C, > 0, Cw < 0, and C,, > 0. The bank’s problem is given by (A.4):

M+

71

(x) (V-w)

-

c mv

(A.4)

where again rc is the probability of repayment and X is a vector of exogenous The solution which defines the optimal amount of write-offs is:

c,wv =- Nx). As before, comparative offs is given by:

shocks.

(A.5)

statics show that the response of the optimal amount of write-

aw ax

=

-ew.o.

Hence the amount of write-offs fall as conditions

for repayment

64.6) improve.

GARY M. WOLLER

348

AND KERK PHILLIPS

Debt Forgiveness Debt forgiveness is similar to loan write-offs, except that the bank actually reduces the present value of the country’s contractual debt liability. As a result, forgiveness can increase the probability of repayment. Under the right conditions, the expected value of the loan may actually rise when part of the loan is forgiven. This phenomenon is captured in the debt-relief Laffer Curve. The bank’s problem is given by: (A.7) Note that the probability of repayment now depends on V also; by assumption xv < 0 and xx> 0. The bank can choose any new value of outstanding loans less than the current value simply by forgiving part of the debt. Increasing the debt would require new loans, which was addressed earlier in the Appendix. The optimal amount of debt, v * the highest point on the Debt-Relief Laffer Curve is given by solving (A.7) for V x&pJv

The amount of forgiveness

= - 7c(w).

(A.81

if V*
(A.9)

is given by:

F=

V-V* 0

Once again, comparative statics shows the response of the optimal amount of debt outstanding to exogenous shocks: C3v*

x

-

%JxV+*x

- -.nvvv+

2Z:,’

(A.lO)

We get results similar to those for defensive lending in that as long as 7cw and 7cvx are close to zero, (A. 10) is positive. This condition says that the responsiveness of the probability of repayment to the size of the outstanding debt does not change very much when either the debt, v or other conditions, X, change. This means that the optimal value of the debt left outstanding is higher, and hence, debt forgiveness is lower when conditions for repayment are favorable.

NOTES 1. See McDonald (1982), Saini and Bates (1984), and Eaton, Gersovitz, and Stiglitz (1986) for a review of the empirical literature on LDC default probabilities. 2. We define the probability of default in this paper as the probability that the banks are repaid less than the face value of the debt.

LDC Default Probabilites and US Commercial Banks

349

3. One possible exception to this rule occurs when the bank is required by the supervisory authorities to recognize a specific loss on loans to a given country. The supervisory authorities are empowered by law to mandate loan losses in the form of an Allocated Transfer Risk Reserve. The supervisory authorities, however, have tended to use this power sparingly. 4. If, for example, the bank sells a loan in the secondary market, it writes-off the loan and retains no claim on the country for that loan. Instead, the country is now obligated to repay the purchaser of the loan. Alternatively, the bank might choose to write-off the loan on its books but continue to hold the loan hoping for eventual repayment. Upon any repayment of the loan, the bank would reverse the write-off loss and record the payment. 5. Forgiveness can take several forms, such as debt/equity swaps, bond swaps, or concerted debt-relief packages. For example, in a debt/equity swap, the bank swaps a loan at a discount for the currency of the debtor country which the bank then uses to purchase an equity interest in a firm located in the country. 6. Within these reporting rules, reporting practices varied widely across banks. Some banks adhered strictly to the formal reporting rules, while other banks reported loan exposure to individual countries even if it did not exceed the one percent of assets threshold. 7. The nine money center banks hold approximately 80 percent of outstanding U.S. LDC loans, the 12 other large banks 12 percent, and the remaining 150 to 220 banks (depending on the reporting period) the remaining 8 percent. Each of the nine “money center” banks is included in the data set, nine of the twelve “other large” banks, and 43 of “all other” banks. 8. The terms of trade used in this paper are real exchange rates vis-a-vis the G7 nations derived from the Penn World Table (Mark 5) (Summers & Heston, 1991). The values used express the number of G7 goods per unit of domestic goods. Thus a low value for the terms of trade indicates that foreign goods are relatively less expensive than G7 goods. By this definition, a “worsening” of the terms of trade would be a increase in its value. 9. This strategy, of course, offers a third alternative to the banks besides new lending or loan write-offs. Even though free riding has been a presistent problem throughout the debt crisis, few banks have pursued this strategy to the complete exclusion of the other two. Consequently, we have chosen to concentrate our efforts on the lending vs. write-off/forgiveness decision. 10. We would like to thank an anonymous referee for suggesting this variable to us. 11. Tests for multicollinearity using the method described by Belsley, Kuh, & Welsch (1980) find no serious multicollinearity problem in any of the regressions results.

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