The first wave of debt reschedulings started in the early 1980s, when a number of African and small Latin American countries began to experience liquidity problems. The number of countries rescheduling their debt suddenly jumped from 2 or 3 per year to 8 in 1980 and 11 in 1981. In 1983, 29 countries rescheduled their debts, valued at $68.8 billion. One explanation of the record number of reschedulings is that it was the result of a number of adverse economic shocks such as the severe recession, the escalation of real interest rates, oil price fluctuations, declines in commodity prices and a decrease in world trade. The decline in bank lending restricted the supply of funds available for international lending to the developing countries and was a factor causing the debt reschedulings.
There were two major areas of research in this study. First, the model of borrowing with default risk used by Eaton and Gersovitz (1981) was analyzed and updated to include data from 1977 and 1981. The inclusion of the new data changed some of the early conclusions of Eaton and Gersovitz. It was found that the probability of a country being credit constrained fell substantially in 1977 and 1981. Also, it was determined that the model covering the period from 1970 to 1981 was inferior to a model composed of subsets of the sample. Estimation of the model by individual years revealed that the coefficients and their significance varied from year to year. The explanatory value of the model declined after 1970. This raised the possibility that the model was not stable and could be misspecified.
A logit model covering the years 1975 to 1982 and containing 30 countries was constructed to find indicators that distinguish between countries that reschedule their debts from other countries. Four variables (real GDP growth, debt to exports, the current debt service ratio, and reserves to imports) consistently appeared to be significant. The amount of bank lending to the developing countries was also significant. The logit model was found to be efficient in forecasting debt reschedulings.