Appendix I. Overview of the LIC DSF
1. A model explaining the probability of external debt distress is at the core of the methodology underpinning the DSF. Given an approach to identify external debt distress episodes, the probability of experiencing such events is explained through probit models controlling for debt burden indicators, the strength of institutions and policies—measured by the CPIA—, and country growth.
2. Given an estimated model, countries are classified per their borrowing capacity in three categories: weak, medium, and strong performers. This categorization is exclusively based on the CPIA. Once countries are classified, debt thresholds derived for each category from the probit models are assigned.
3. As a first step for determining a risk rating, risk signals are obtained from the mechanical application of the framework. Based on key inputs—baseline macro projections and a battery of stress tests—forecast for five debt burden indicators (PV of PPG external debt to GDP, exports, and fiscal revenues, and PPG external debt service to exports and fiscal revenues) are produced. These in turn are compared against their respective debt thresholds, after which risk signals are determined based on the following aggregation rule:
(i) if none of the forecasted debt burden indicators exceed their corresponding thresholds under the baseline and stress test scenarios, the DSF would signal a low risk of debt distress;
(ii) if all baseline forecasts are below their thresholds but at least one forecast exceeds its threshold under the stress test scenarios, the DSF would signal a moderate risk;
(iii) if at least one baseline debt forecast exceeds its threshold, the DSF would signal a high risk;
(iv) significant or sustained breach of thresholds, actual or impending debt restructuring negotiations, or the existence of arrears would generally suggest that a country is in debt distress.
4. Next, the mechanical risk signals are combined with staff judgment. This helps bringing in country-specific considerations and/or technical elements (e.g., whether breaches are marginal and/or one-off) that cannot be captured by the core model to make a final determination of the risk rating of external debt distress (Low/Moderate/High/In Debt Distress).
5. In the 2012 review, new features were incorporated to the framework aimed to capture country heterogeneity where relevant. These included: i) the use of remittances-augmented thresholds, only applied to countries receiving sizable remittances;1 ii) the introduction of the “probability approach”, an alternative technical methodology, that was to be applied at “borderline cases”;2 and iii) qualification of risks stemming from total public debt or private external debt.
Abbas, S. M. A., and J. E. Christensen, 2007, “The Role of Domestic Debt Markets in Economic Growth: An Empirical Investigation for Low-Income Countries and Emerging Markets,” IMF Working Paper No. 07/127, (Washington: International Monetary Fund).
Abbas, S. M. A., N. Belhocine, A. El Ganainy, and M. Horton, 2010, “A Historical Public Debt Database,” IMF Working Paper No. 10/245, (Washington: International Monetary Fund).
Aisen, A., and D. Hauner, 2008, “Budget Deficits and Interest Rates: A Fresh Perspective,” IMF Working Paper No. 08/42. (Washington: International Monetary Fund).
Aron, J., R. MacDonald, and J. Muellbauer, 2014, “Exchange Rate Pass-through in Developing and Emerging Markets: A Survey of Conceptual and Policy Issues, and Empirical Findings,” Journal of Development Studies, 50(1): pp. 101–43.
Aslam, A., S. Beidas-Strom, R. Bems, O. Celasun, S. Kılıç Çelik, and Z. Kóczán, 2016, “Trading on Their Terms? Commodity Exporters in the Aftermath of the Commodity Boom,” IMF Working Paper No. 16/27, (Washington: International Monetary Fund).
Baldacci, E., I. Petrova, N. Belhocine, G. Dobrescu, and S. Mazraani, 2011, “Assessing Fiscal Stress,” IMF Working Paper No. 11/100. (Washington: International Monetary Fund).
Berg, A., E. Berkes, C. Pattillo, A. Presbitero, and Y. Yakhshilikov, 2014, “Assessing Bias and Accuracy in the World Bank—IMF’s Debt Sustainability Framework for Low-Income Countries,” IMF Working Paper No. 14/48. (Washington: International Monetary Fund).
Berg, A., E. Buffie, C. Pattillo, R. Portillo, A. Presbitero, and L. Zanna, 2015, “Some Misconceptions about Public Investment Efficiency and Growth,” IMF Working Paper No. 15/272. (Washington: International Monetary Fund).
Bom, P., and J. Ligthart, 2014, “What Have We Learned from Three Decades of Research on the Productivity of Public Capital?” Journal of Economic Surveys, 28(5): pp. 889–916.
Buffie, E., A. Berg, C. Pattillo, R. Portillo, and L. Zanna, 2012, “Public Investment, Growth, and Debt Sustainability: Putting Together the Pieces,” IMF Working Paper No. 12/144. (Washington: International Monetary Fund).
Calderon, C., E. Moral-Benito, and L. Serven, 2015, “Is Infrastructure Capital Productive? A Dynamic Heterogeneous Approach,” Journal of Applied Econometrics, 30(2): pp. 177–98.
Catão, L. A. V., and G. M. Milesi-Ferretti, 2014, “External Liabilities and Crises,” Journal of International Economics, 94: pp. 18–32.
Cespedes, L. F., and A. Velasco, 2013, “Was This Time Different? Fiscal Policy in Commodity Republics,” NBER Working Paper No. 19748.
Cruces, J. J., and C. Trebesch, 2013, “Sovereign Defaults: The Price of Haircuts,” American Economic Journal: Macroeconomics, 5(3): pp. 85–117.
Das, U. S., M. G. Papaioannou, and C. Trebesch, 2012, “Sovereign Debt Restructurings 1950–2010: Literature Survey, Data, and Stylized Facts,” IMF Working Paper No. 12/203. (Washington: International Monetary Fund).
Gelos, R. G., R. Sahay, and G. Sandleris, 2011, “Sovereign Borrowing by Developing Countries: What Determines Market Access?” Journal of International Economics, 83: pp. 243–54.
Giovanni, A., and M. de Melo, 1993, “Government Revenue from Financial Repression,” The American Economic Review, Vol. 83, No. 4, pp. 953–63.
International Monetary Fund-The World Bank, 2012, “Revisiting the Debt Sustainability Framework for Low-Income Countries,” (January) (Washington).
International Monetary Fund-The World Bank, 2013a, “Unification of Discount Rates used in External Debt Analysis for Low Income Countries: Staff Proposals,” (October) (Washington).
International Monetary Fund-The World Bank, 2013b, “Staff Guidance Note on the Application of the Joint Bank-Fund Debt Sustainability Framework for Low Income Countries,” (November) (Washington).
International Monetary Fund-The World Bank, 2015, “Public Debt Vulnerabilities in Low-Income Countries: The Evolving Landscape,” (November) (Washington).
International Monetary Fund, 2007, “The Changing Dynamics of the Global Business Cycle,” Chapter 5, World Economic Outlook (October). (Washington).
International Monetary Fund, 2012, “Commodity Price Swings and Commodity Exporters,” Chapter 4, World Economic Outlook (April). (Washington).
International Monetary Fund, 2015b, “Exchange Rates and Trade Flows: Disconnected?” Chapter 3, World Economic Outlook (October). (Washington).
International Monetary Fund, 2015c, “Where Are Commodity Exporters Headed? Output Growth in the Aftermath of the Commodity Boom”, Chapter 2, World Economic Outlook (October). (Washington).
International Monetary Fund, 2016, “Small States’ Resilience to Natural Disasters and Climate Change: Role for the IMF,” (November) (Washington).
International Monetary Fund, 2017, “Estimating the Stock of Public Capital in 170 Countries: January 2017 Update”. http://www.imf.org/external/np/fad/publicinvestment/pdf/csupdate_jan17.pdf.
Laeven, L., and F. Valencia, 2013, “Systemic Banking Crises Database,” IMF Economic Review, 61: pp. 225–70. (Washington: International Monetary Fund).
Ligthart, J., and R. Martin-Suarez, 2011, “The Productivity of Public Capital: A Meta-Analysis,” in Manshanden, W. and W. Jonkhoff, eds. Infrastructure Productivity Evaluation. (New York: Springer).
Mallik, G., and A. Chowdhury, 2001, “Inflation and Economic Growth: Evidence from Four South Asian Countries,” Asia-Pacific Development Journal, Vol. 8, Issue 1, pp. 123–35.
Manasse, P., and N. Roubini, 2005, “‘Rules of Thumb’ for Sovereign Debt Crises,” IMF Working Paper No. 05/42. (Washington: International Monetary Fund).
Manasse, P., N. Roubini, and A. Schimmelpfennig, 2003, “Predicting Sovereign Debt Crises,” IMF Working Paper No. 03/221. (Washington: International Monetary Fund).
Mauro, P. and M. Villafuerte, 2013, “Past Fiscal Adjustments: Lessons from Failures and Successes,” IMF Economic Review, 61(2): pp. 379–404. (Washington: International Monetary Fund).
Panizza, U., F. Sturzenegger, and J. Zettelmeyer, 2009, “The Economics and Law of Sovereign Debt and Default,” Journal of Economic Literature, 47:3, pp. 651–98.
Pennings, S., 2017, “Long-term Growth Model v4.0: Model Description,” available at http://globalpractices.worldbank.org/mfm/Pages/SitePages/MFM_Online_Tools.aspx
Rand, J., and F. Tarp, 2002, “Business Cycles in Developing Countries: Are They Different?” World Development, Vol. 30, No. 12, pp. 2071–88.
Reinhart, C. M., and K. S. Rogoff, 2009, “This Time is Different: Eight Centuries of Financial Folly,” Princeton University Press.
Reinhart, C. M., and K. S. Rogoff, 2010, “From Financial Crash to Debt Crisis,” American Economic Review, Vol. 101, No. 5, pp. 1676–706.
Reinhart, C. M., and K. S. Rogoff, 2011, “The Forgotten History of Domestic Debt,” The Economic Journal, Vol. 121, Issue 552, pp. 319–50.
Reinhart, C. M., and M. B. Sbrancia, 2015, “The Liquidation of Government Debt,” IMF Working Paper No. 15/7. (Washington: International Monetary Fund).
Spatafora, N., and I. Samake, 2012, “Commodity Price Shocks and Fiscal Outcomes,” IMF Working Paper No. 12/112. (Washington: International Monetary Fund).
Yehoue, E., 2005, “International Risk Sharing and Currency Unions: The CFA Zones,” IMF Working Paper No. 05/95. (Washington: International Monetary Fund).
The technical work of the review has been informed by a broad external consultation process. This has included dialogue with authorities from developing countries and staffs of multilateral development banks (including at the 2016 (Lusaka) and 2017 (Vienna) DMF Stakeholders’ Forums, the 2016 African Caucus in Cotonou, at the 2016 and 2017 Spring Meetings -including bilateral discussions with Pacific Islands Governors and Ministers—, and the 2017 Multilateral Development Bank Meeting on Debt Issues in Washington, DC), and with members of the Paris Club. Staff have also sought feedback from civil society organizations (including through an open web-based consultation and different events during the 2016 Annual Meetings).
Debt service indicators have relatively much smaller unexpected changes. The share of DSAs with unexpected changes above 15 percentage points (in absolute value) ranges between 0 and 8 percent.
Among DSAs with sizable unexpected changes in debt, the share of those underestimating debt outcomes rises from about 55 percent to more than 80 percent when moving from the 1- to the 7-year projection horizon.
This relationship between historical and actual primary balances continues to hold when the sample is expanded to cover the longer period of 2000–15.
The highly-skewed distribution of remittances implies that only countries receiving sizable remittances would benefit from the estimation of remittances-augmented thresholds based on the full sample of countries. Not surprisingly, countries that have benefited from the use of remittances-augmented thresholds received remittances flows as percentage of GDP and exports way in excess of the eligibility cutoffs (averages of 30 percent of GDP and 150 percent of exports).
In case of individual debt indicators, missed crises are defined as cases in which debt distress is observed but the associated debt threshold was not breached, while false alarms are defined as cases in which debt distress is not observed but the associated debt threshold was breached. Type I and II errors are missed crises and false alarms as a share of all cases in which debt distress is observed and non-observed, respectively.
The rates of false alarms and missed crises are measured using historical data during the sample period (i.e., 1970– 2014). It is not straightforward to assess how policy responses to high risk ratings may have affected the number of false alarms, but any such impact may be only present in the few last years of the sample period in which such ratings were produced (2008–2014).
Such vulnerabilities are signaled in relation to the relevant benchmark of the PV of total public debt to GDP ratio.
The DSF relies on three distress signals to identify conditions under which a country is experiencing external debt difficulties: i) cumulative IMF disbursements from the General Resource Account (GRA)—under Stand-By Arrangements (SBA) and Extended Fund Facilities (EFF)—exceeding 50 percent of the member’s quota; ii) restructuring of claims held by Paris Club creditors; and iii) accumulation of arrears on external PPG debt in excess of 5 percent of the outstanding stock of external PPG debt.
Since 2005, 14 LICs have issued 29 Eurobonds worth US$20 billion. Of the 29 issuances, 25 or US$17 billion had bullet payments. Principal repayments of sovereign external debt (in percent of exports of goods and services) by frontier LICs are projected to exceed those of the 17 largest EMs over the next five years (see IMF-WB, 2015).
This helps rule out episodes associated with one-off and temporary occurrence of external arrears (including for technical reasons) that do not necessarily signal debt distress.
See Box AIII.2 and AIII.3 (Annex III) on data sources and issues, including a discussion of reserve measurement in currency unions.
Sub-indices of the CPIA could not be considered due to insufficient data.
The determinants of the country-specific composite indicator would be shown in the DSA output to facilitate understanding of what is driving the classifications and where any changes have come from.
Forecasts of the additional variables in the composite indicator are routinely produced in the WEO database and individual DSAs. Recognizing its slow-moving nature, the forecast for the CPIA rating would consist of its most recent value.
As highlighted in the 2012 review, threshold effects occur when small changes in the predictors of debt distress lead to discrete jumps in debt thresholds. For instance, in the current framework, a weak-performer (say with a CPIA score of 3.24) would face a debt-to-GDP threshold of 30 percent, whereas a medium performer (say with a CPIA score of 3.26) would face a threshold of 40 percent (see IMF (2012), Appendix 1).
While the tool would ideally use cyclically-adjusted primary balances, these are difficult to compute for LICs given the high degree of uncertainty in estimating output gaps. At the same time, measuring fiscal adjustment as the change in headline primary balances across all LICs may include cases in which improved fiscal performance was driven by exogenous factors (e.g., coming on stream of natural resource projects).
This issue will be addressed in the Staff Guidance Note.
A more detailed analysis may be done outside the confines of the DSF, as a means of informing Fund-supported programs, World Bank growth diagnostics, and the policy dialogue more generally. Available tools supported by Fund and Bank staff for this purpose include the IMF’s Debt-Investment-Growth model (see Buffie and others, 2012), and the World Bank’s Long-Term Growth model (see Pennings, 2017).
While a useful check for the consistency of growth and primary balance projections is to uncover the growth path consistent with a neutral fiscal stance and assess its realism, such an approach is challenging in the case of LICs. This is because LICs’ borrowing capacity is very weak in the absence of fiscal adjustment, casting doubts about the likelihood of such a counterfactual.
This stress test reflects a scenario where multiple shocks hit the economy at the same time. It applies half the magnitudes of all stand-alone shocks, and incorporates individual macro interactions as assumed under each individual shock.
This is based on the average increase in debt-to-GDP ratios observed for 44 banking crisis episodes for LICs since the 1980s, as in Laeven and Valencia (2013).
This stress test would help eliminate a disincentive to achieve full and transparent disclosure of public sector contingent liabilities.
If a user yet wished to consider one of the dropped stress tests, this would also offer a channel to do that.
This is particularly important in countries where external debt is defined on a currency denomination basis, which seems to be the norm in LICs, given data limitations on debt by residency (a Fund’s staff survey in 2016 revealed that more than 50 percent of LICs report their external debt on currency basis).
Data on the PV of total public debt is the sum of the PV of external PPG debt and nominal domestic public debt, which is assumed to be contracted on market terms.
The use of probit models for analysis of public debt vulnerabilities would produce a composite indicator different from the one derived in the external debt risk assessment—potentially leading to a classification of countries’ debt-carrying capacity different from that developed in analyzing external debt distress. This would create considerable conceptual confusion, while also increasing the complexity of the general framework.
Benchmarks were rounded up to align the strong classification category with the MAC DSA high-risk benchmark.
This is even more compelling in those cases where non-residents have increased their participation in local and regional debt markets, blurring the distinction between domestic and external debt.
Actual or impending debt restructuring negotiations, the existence of arrears, or a significant or sustained breach of thresholds would generally suggest that a country is in debt distress (see IMF-WB, 2013b). The Staff Guidance Note will elaborate further.
See “Unification of Discount Rates Used in External Debt Analysis for Low-Income Countries” (IMF-WB, 2013a).
The CIRR overstates the risk-free rate, thus underestimating the required investment, as the different export credit agencies (ECAs) agreed to set the CIRR as the average of market rates for long-term government debt in their own currency, plus one percent. This was designed to ensure that ECAs do not unfairly subsidize trade by setting their lending rates too low.
The median dollar nominal GDP growth rate is 7 percent when based on (i) 5 years of history and 5 years of projections, (ii) 10 years of history and 10 years of projections, or (iii) 5 years of history and 10 years of projections, using data submitted in the latest LIC DSAs. The median population growth rate is 2.2 when based on (i) 5 years of history and 5 years of projections or (ii) 10 years of history and 5 years of projection, using WEO data. A longer-term average has the benefit of smoothing out cyclical components and avoiding abrupt changes in the discount rate.
During 2016–17, 175 representatives from 40 countries participated in one-week training sessions held in several regional centers and individual countries around the world, the bulk of which were financed through the Debt Management Facility. Most of them benefited from accessing the IMF-WB online module on the LIC DSF ahead of their face-to-face training. Overall, over the same period, nearly 850 government officials familiarized themselves with the online LIC DSF materials, through the IMF Massive Open Online Course on debt sustainability and debt management.
Missions conducted before the DSF enters into effect will produce DSAs based on the old framework, as will the ensuing staff documentation. In cases where multiple missions are needed to complete the relevant documentation for Board meetings, it will be the timing of the final mission that determines whether the new or old frameworks should be applied.
Each year, IDA determines its grant/allocation mix for the next 12 months based on the external risk ratings available by end-June.
Countries eligible to incorporate remittances into the DSF mechanical analysis have remittances flows greater than 10 percent of GDP and greater than 20 percent of exports of goods and services. Both ratios are measured on a backward-looking, three-year average basis.
A borderline case is defined as one where the largest breach, or near breach, of a threshold under any scenario falls within a 10-percent band around the threshold.