# **6. Econometric Results**

## 6.1. **Results for IBRD Estimation**

## 6.1.1. The Panel Logit Model of Default

Table 5 presents the panel logit estimation results for default on IBRD loans. We first present the saturated model, which contains at least one element from each of the principal components found, and covers the main variables found in the literature. Only 58 countries had data available for the full set of variables considered. We see that the lagged default term is the most significant explanatory variable, and we thus reject the joint null of no state dependence and no heterogeneity. The estimated coefficients for the pooled logit and random effects logit are equal for the saturated model, a result of the extreme overfitting here. The second and third columns of Table 5 give the estimates, for the pooled logit and random effects logit respectively, for the preferred sub-model of this saturated model. A likelihood ratio test does not reject the submodel in favour of the saturated model. We consider only the Government Deficit/GDP percentage in our final model as a Chi-squared test of the null hypothesis that the Government Revenue/GDP and Government Expenditure/GDP percentages have equal and opposite effects on the propensity to default could not be rejected In this submodel we see that the proportion of the total variance contributed by the panel-level variance, denoted p, is 0.10, and a chisquared test does not reject that p=0. Thus the random effects and pooled estimators do not differ greatly, and efficiency concems therefore dictate that one should use the pooled logit estimator. Thus any country heterogeneity is captured by the lagged dependent variable and the other explanatory variables.

Chamberlain's test for state dependence was carried out for both the saturated and the submodel, using random effects estimators to condition on any individual country effects. In both cases we overwhelmingly reject the null of no state dependence, and hence need to include the lagged dependent variable in our model. The interpretation of this is that a country which has defaulted in the previous period exhibits different default behaviour to one that hasn't, _ceteris paribus._ This reflects not just the fact that a country in default will have built up IBRD arrears, which it must pay back to exit default, but also that once a country enters default, it suffers the reputational and creditworthiness consequences of this action, with a further year in default having a much smaller impact on creditworthiness perceptions than does entering into default.

The submodel shows that default to the IBRD can be modeled parsimoniously through six explanatory variables, all of which are significant at the 90% level. A country is more likely to default on its IBRD debt if it defaulted in the previous period, if it has a large private arrears to total debt ratio, and if it has been paying only a small proportion of its _total_ debt service due in the preceding period. These latter two debt ratios show that a country's debt-servicing performance with other creditors impacts on its ability to meet IBRD obligations. A large budget deficit relative to GDP makes a country more liable to default to the IBRD, reflecting the effects of a sovereign's demand for borrowing. Politics matters, especially the extent of military involvement in the politics of a country.

20