_domestic debt distress_ using a panel of MIC and LIC in 1971-2007. A broad spectrum of repayment problems identify debt distress episodes: (i) large arrears on public external debt; (ii) a debt negotiation with the Paris Club; (iii) a large IMF non-concessional financing package (on a disbursement basis); and (iv) an episode of domestic debt default or restructuring, as listed by credit rating agencies, academic papers, and IMF staff reports. Parsimonious probit models include one total public debt indicator, the CPIA score, and a few macroeconomic (control) variables. Debt indicators and policy-related variables turn out to be good predictors of sovereign defaults towards foreign creditors as well as towards domestic creditors. IMF-IDA (2012) calibrate the value of the total public debt indicator that matches an arbitrarilychosen value of debt-distress probability and fixed values of other explanatory variables. This delivers a debt threshold associated with the level of risk implicit in the arbitrarily-chosen debt-distress probability—which is set around 15 percent. Thresholds and long-term projections of total public debt indicators can be also compared against each other to assess risk of debt distress. However, for reasons laid out in IMF-IDA (2012), the IMF and the World Bank have decided to maintain the total public debt thresholds as indicative benchmarks that are not used to inform the risk rating system for LIC.

# _Debt thresholds for Mauritania_

IDA-IMF (2012) postulates a probit model whereby the probability of a country experiencing repayment problems of any type described above over a one-year horizon depends on four variables: the public debtto-GDP indicator, an interaction term between a MIC dummy and the public debt-to-GDP indicator, the CPIA score, and the real GDP growth (as a proxy of economic shocks). Thus, = 1) = 0 1 2 M 3 4 ) , where the binary indicator IItt equals 1 if the country _i_ experiences debt distress episode in period _t_ , and 0 otherwise. PP(YY is the IItt IItt IItt IItt IItt IItt GG(ββcumulative density function of the standard normal distribution. The covariates to the right are self- + ββ DDEEBBTT + ββ CC DDEEBBTT + ββ CCPPMMAA + ββ GGSSG TTGG YY explained and measured in the year preceding the debt distress episodes in order to mitigate endogeneity GG(. ) issues. The estimated parameters are reproduced in Table AIII.1. Thresholds on the Mauritania’s public debt-to-GDP indicator are calculated by _inverting_ the estimated plugging the Mauritania’s CPIA score and real GDP growth. The threshold equation, fixing levels of risk of debt distress—i.e., choosing values of debt-distress probability—and �������� for an arbitrarilychosen probability _P(debt distress)_ is calculated using �������� IItt )�− DDEEBBTT 0 3 4 1. This paper considers Mauritania’s average CPIA score in 2011-IItt DDEEBBTT = �GG<sup>−1</sup> �PP(ddeed ddiid rreed 13 (equivalent to 3.23) and its average real GDP growth in the next ten years (7 percent per annum). IItt IItt (ββ + ββ CCPPMMAA + ββ GGSSG TTGG )�/ββ Thus, if the tolerable probability (risk) of debt distress is 15 percent, then the threshold on public debt-toGDP is 75 percent. If the tolerable risk is 20 percent, instead, then the threshold raises to 95 percent.

## **Table AIII.1. Probit model and debt thresholds.**

|**Explanatory variables**|**Coef. **|<sup>**Estimated**</sup><br>**value**|**Signif.**|
|---|---|---|---|
|Public debt-to-GDP indicator(%)|β1|1.0080|***|
|MIC dummy * Public debt-to-GDP indicator(%)|β2|0.0214||
|CPIA score(1 to 6)|β3|-0.5260|***|
|Real GDP growth(%)|β4|-6.1500|***|
|Constant|β0|0.3360||
|No. of observ.||597||
|Pseudo R-squared||0.138||
|Sign. ***p<0.01,**p<0.05,and *p<0.10||||

Source: IMF-IDA (2012) 39