and random effects logit estimates are once again identical to each other, but we see more variables show up as significant in this saturated model than in the IBRD case. A test of p=O did not reject the null in the submodels either, and we hence report only the pooled logit estimates for the submodels. It appears that any heterogeneity amongst countries is controlled for by the explanatory variables used in these models.

In the third column of Table 9 we report our preferred model for Paris Club default, which was exhaustively tested against alternatives following the steps used in modeling default to the IBRD. The resultant model includes several of the variables most often found to be significant in the literature. We find that the higher a country's short-term debt as a percentage of total debt, the higher its total debt stock relative to exports, the greater its arrears to private creditors, and the lower its reserves relative to imports the more likely the country is to be in default to Paris Club creditors. These financial variables show that it is not just total debt, but also its composition which is important to creditworthiness. The strongest effect is once again the rescheduling history, reflecting state dependence once again. More exports relative to GDP and less imports relative to GDP have a weak effect on reducing the likelihood of default. We find two external variables to have highly significant effects on the likelihood of default to Paris Club creditors. In common with the IBRD default model, a G7 current account surplus, reflecting increased liquidity, lessens the chance of any country defaulting. Secondly, we also find that an increase in the real LIBOR rate raises the likelihood of default to Paris Club creditors. This effect is closely related to the current account effect, although both variables show up as significant in the model. On the political front, we once again find that greater involvement of the military in politics raises the likelihood of default, but this effect is offset to a degree by the total political score. For a given level of military involvement, a reduction in total political risk actually increases the likelihood of default. This somewhat counterintuitive result reflects the fact that we measure Paris Club default by means of rescheduling agreements, which require both an IMF program and negotiations with multiple creditors. An unstable political system may prevent these from occurring, reducing the probability of a default being _measured._ This suggests that one may wish to supplement Paris Club rescheduling data with arrears data to obtain a more inclusive definition of Paris Club defaults.

The higher incidence of default indicates that we should use a higher cutoff threshold for determining predicted default, and we suggest 0.30 to be an appropriate level. Again this is set at a level slightly below the sample mean incidence of defaults, reflecting a greater concern with Type I errors. The within sample type I and type II error rates are both low, at 5.4% and 6.5% respectively, suggesting this model provides a good fit in determining creditworthiness to Paris Club creditors.

# 6.3. **Results for Commercial Bank Default Estimation:**

Following our estimation of the determinants of default to Paris Club creditors, we now turn our attention to Commercial Bank lending. Once again we do not reject the hypothesis of no random effects, and hence Table 10 reports the pooled logit estimates used to model default to this group of creditors. We see that in common with the Paris Club default models, the saturated model has a lot more significant variables than in the 25