<mark>The IMF’s LIC DSF website highlights the revisions which “introduced reforms to ensure that the DSF remains appropriate for the rapidly changing financing landscape facing LICs and to further improve the insights provided into debt vulnerabilities.” In like mode, I</mark> MF (2017) provided rationale for reforming the LIC DSF based on:

1. The need to pay more attention to market-based financing risks from domestic and foreign (Eurobond) sources (mentioned throughout IMF 2017);

2. The recognition that debt projections have tended to underestimate debt burdens for both external and total public debt with unexpected changes in debt ratios driven primarily by “fiscal deviations and balance of payments (BoP) shocks” (IMF 2017 p. 9); 3. Underestimation of shocks from the primary fiscal deficit (IMF 2017 p.11) ; 4. Poor performance in identifying external debt distress (XDD) episodes out of sample. Performance deteriorated sharply when 7 extra years of data were included (IMF 2017 p. 18); 5. Limited use of staff judgment, which had been applied in only 25% of the DSAs since the inception of the DSF (IMF 2017 p 15). The feature introduced in the 2012 review to allow for a deeper assessment of total public debt and its impact on the overall risk rating had been “only sporadically used”. This needed to change to enable “attempts to disentangle risks”.

Drawing upon Annex III to IMF (2017), the main steps in implementing the 2017 DSF reforms were:

**Step 1:** Identifying a series of XDD episodes over the period 1970-2015 based on arrears or defaults on PPG external debt, Paris Club debt restructurings, large IMF programs or commercial debt restructuring. 98 episodes were identified based on the new methodology shown in in Figure AIII.1 (versus 76 under the old methodology).

**Step 2:** Running a probit model for each of 4 debt burden indicators, all pertaining to PPG XD and including: PV of XD to GDP or exports; and debt service (interest plus maturing principal on PPG XD) to revenue or exports. The probit model, which estimates the probability of XDD for a particular country in a particular year, used the following control or explanatory variables:

1. Debt burden indicator

2. World Bank Country Policy and Institutional Assessment (CPIA) 3. Country growth rate 4. Reserves (scaled by imports) 5. Reserves squared 6. Remittances (scaled by nominal GDP) 7. World growth rate (proxy for external shocks)

**Step 3:** Classifying countries as weak, strong, or medium based not just on the CPIA (as earlier) but on a new, composite indicator CI based on variables 2 to 7 in the list in Step 2 and using the coefficient for each variable averaged over the four probit regressions (one for each debt burden indicator). The classification is based on evenly spaced percentiles for the CI.

**Step 4:** Obtaining a threshold for each debt burden indicator by first choosing a cutoff probability of debt distress. This cutoff probability is chosen to strike a desired balance between Type 1 (missed crises) and Type 2 (false alarms) errors. The estimated probit regression equation is then inverted to obtain the debt burden threshold corresponding to the cutoff probability as a function of the CI (as shown on p 26 of Annex III, IMF 2017) and to value these thresholds at the same percentiles for the CI which define the country classification so as to obtain a numerical debt threshold for each country classification.

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