Appendix IV Public Debt Ratios and Probabilities of Debt Crises
- Harald Finger, and Mauro Mecagni
- Published Date:
- April 2007
We analyze the relationship between the probability of a crisis and the ratio of public debt to GDP using a pooled probit model on an unbalanced sample of 55 low- and middle-income countries over 1971–2002. The countries in the sample are Algeria, Argentina, Bangladesh, Bolivia, Brazil, Burkina Faso, Cameroon, Chile, Colombia, Costa Rica, Côte d’Ivoire, the Czech Republic, the Dominican Republic, Ecuador, Egypt, El Salvador, Ethiopia, Gabon, Ghana, Guatemala, Haiti, Honduras, Hungary, India, Indonesia, Jamaica, Jordan, Kenya, Korea, Malaysia, Mexico, Morocco, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Panama, Paraguay, Peru, the Philippines, Poland, Russia, South Africa, Senegal, Sri Lanka, Tanzania, Thailand, Togo, Tunisia, Turkey, Ukraine, Uruguay, Venezuela, and Zimbabwe.
The binary dependent variable (CRISIS) represents the occurrence of a public debt crisis. It takes the value of 1 when a country is classified as being in default by Standard & Poor’s. Independent variables are the lagged ratio of public debt to GDP (DEBT1), lagged GDP growth (GGDP1), and the lagged ratio of short-term external debt to GDP (ST_EXTDEBT_GDP1). All coefficients have the expected sign. DEBT1 and GGDP1 are significant at the 1 percent level, while ST_EXTDEBT_GDP1 is significant at the 10 percent level (see Table A4.1). The goodness of fit, as measured by the McFadden R-squared, is fairly low at 0.17, not untypical for this type of pooled probit regression.
|Dependent Variable: CRISIS|
|Variable||Coefficient||Standard Error (SE)||Prob.|
|McFadden R-squared||0.17||SE of regression||0.41|
We compute in-sample forecasts from this equation to yield predicted crisis probabilities associated with the sample data points. From this, we plot inferred crisis probabilities against lagged debt ratios (see first figure in Box 1). In order to be able to associate a single probability with any given level of debt, we fit a polynomial through the data (see Table A4.2).
|Dependent Variable: DEBT1|
|Variable||Coefficient||Standard Error (SE)||t-Statistic||Prob.|
|Adjusted R-squared||0.76||SE of regression||17.91|
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This analysis is confined to comparing outcomes with IMF staff projections made after the restructurings. The staff’s analysis may have differed from private sector market participants’ views.
These may include, for example, the cost of bank recapitalization following a financial sector crisis, or principal reduction during a restructuring. The decomposition and methodology are described in more detail in IMF (2002a).
Definitions of the fiscal sector used for program monitoring differ across countries. Russia: general government; Pakistan: federal and provincial governments; Ukraine: consolidated government; Ecuador: nonfinancial public sector; and Uruguay: public sector. In all cases except Russia, projections made before the restructuring assumed a greater role for fiscal constraint than it actually played.
The medium-term projections included in the post-restructuring staff report on Ukraine are not detailed enough to allow for an analysis.
For the purpose of the debt-sustainability analysis, the primary balance including grants is considered.
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