metadata: authors: 0: author: Baghdassarian,William
1: author: Mele,Gianluca
2: author: Pradelli,Juan
count: Mauritania
docna: 0: docna: Assessing public debt sustainability in
            Mauritania with a stochastic framework
docty: Policy Research Working Paper
subtopic: Macro-Fiscal Policy,Economic Theory & Research,Economic Adjustment and Lending,Industrial Economics,Public Sector Economics,External Debt,Economic Growth,Public Finance Decentralization and
            Poverty Reduction,Debt Markets
teratopic: Governance,Macroeconomics and Economic Growth,Public Sector Development
abstracts: cdata!: This work presents a stochastic
            framework for assessing public debt sustainability and
            applies it to the case of Mauritania. The sustainability
            assessment projects solvency and liquidity indicators --
            public debt stock and gross financing needs relative to GDP
            -- for 2014-23. The analysis uses deterministic scenarios
            and stochastic simulations to analyze policy options and
            fiscal risks. The study relies on simple econometric models
            to generate forecasts of key macroeconomic variables driving
            the public debt dynamics and to compute debt-distress
            probabilities and debt thresholds. The study builds on basic
            techniques to determine optimal portfolios suitable as
            benchmarks for public debt management. A main result is
            that, if Mauritania maintains a strong growth performance
            and pursues sound policies to balance the budget and take
            advantage of concessional financing opportunities, it could
            reduce the public debt from 74 percent of GDP in 2013 to 30
            percent by 2023, and the gross financing needs from 12
            percent of GDP to 4 percent. Further scaling up capital
            spending is likely to deteriorate public debt sustainability
            because the estimated (marginal) growth-dividend is small. A
            more promising avenue would be to improve the quality of
            public investment and institutions, as opposed to the volume
            of capital expenditure. Different debt strategies can
            significantly affect the liquidity needs and the on-budget
            interest bill. But it is the fiscal policy geared toward
            balanced budgets that ultimately would permit Mauritania to
            improve the solvency indicators, and thus the public debt sustainability.
geo_reg_key: 1927320,517191
repnme: repnme: Assessing public debt sustainability in
            Mauritania with a stochastic framework
display_title: Assessing public debt sustainability
            in Mauritania with a stochastic framework
listing_relative_url: /research/2014/11/20346371/assessing-public-debt-sustainability-mauritania-stochastic-framework
topicv3: Fiscal Risks m1326223 449,Economic Growth m1326241 471,Public Finance m1326315 713,Debt and Macroeconomic Stability m1326503 450,Fiscal Policy m1327144 445
publishtoextweb_dt: 2014-11-01T00:00:00Z
historic_topic: Governance,Macroeconomics and Economic Growth,Public Sector Development
no_of_pages: 42
author_keys: 0: author_key: 956701
1: author_key: 1141463
2: author_key: 1081625
model_extractions: WPS7088 Policy Research Working Paper 7088 Assessing Public Debt Sustainability in Mauritania with a Stochastic Framework _William Baghdassarian Gianluca Mele Juan Pradelli_ Macroeconomics and Fiscal Management Global Practice Group November 2014
Policy Research Working Paper 7088

# **Abstract**

This work presents a stochastic framework for assessing public debt sustainability and applies it to the case of Mauritania. The sustainability assessment projects solvency and liquidity indicators—public debt stock and gross financing needs relative to GDP—for 2014–23. The analysis uses deterministic scenarios and stochastic simulations to analyze policy options and fiscal risks. The study relies on simple econometric models to generate forecasts of key macroeconomic variables driving the public debt dynamics and to compute debt-distress probabilities and debt thresholds. The study builds on basic techniques to determine optimal portfolios suitable as benchmarks for public debt management. A main result is that, if Mauritania maintains a strong growth performance and pursues sound policies to balance the budget and take advantage of concessional financing opportunities, it could reduce the public debt from 74 percent of GDP in 2013 to 30 percent by 2023, and the gross financing needs from 12 percent of GDP to 4 percent. Further scaling up capital spending is likely to deteriorate public debt sustainability because the estimated (marginal) growth-dividend is small. A more promising avenue would be to improve the quality of public investment and institutions, as opposed to the volume of capital expenditure. Different debt strategies can significantly affect the liquidity needs and the on-budget interest bill. But it is the fiscal policy geared toward balanced budgets that ultimately would permit Mauritania to improve the solvency indicators, and thus the public debt sustainability.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gmele@worldbank.org and jpradelli@worldbank.org .

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Assessing Public Debt Sustainability in Mauritania with a Stochastic Framework

## _William Baghdassarian_

## _Gianluca Mele_

_Juan Pradelli_

**Keywords:** Debt Sustainability, Stochastic, Modeling, Fiscal, Debt Strategies, Institutions, Growth, Policies, CPIA **JEL classification codes:** H63, H68, E62, O43, C54
# **Contents**

|Ov|erview ................................................................................................................................................... ii|
|---|---|
|A.|Country Context ................................................................................................................................ 4|
||Main recent fiscal and debt developments .......................................................................................... 5|
|B.|Macro-Fiscal Framework for 2014-2023 and Debt Sustainability Analysis ...................................... 7|
|F|iscal risks and debt sustainability ........................................................................................................ 9|
|A|detour: stochastic DSA and scenario analysis.................................................................................. 11|
|C.|Public Investment: Growth Dividend and Debt Sustainability ........................................................ 13|
||Quality of Public Investment: Growth Dividend and Fiscal Multipliers .............................................. 17|
|D.|Public Debt Strategies: Cost and Risk of Debt Portfolios ............................................................... 20|
|E.|Conclusions ..................................................................................................................................... 26|
|F.|References ...................................................................................................................................... 27|
|G.|Annexes ........................................................................................................................................... 28|
|A|nnex I: A DSA model for Mauritania. ................................................................................................ 28|
||Debt Dynamics, Financing, and Borrowings ....................................................................................... 29|
||Macroeconomic Dynamics .................................................................................................................. 29|
|A|nnex II. Optimal composition of public debt in Mauritania. ............................................................ 33|
|A|nnex III.Thresholds on Public Debt for Mauritania.......................................................................... 38|

The authors would like to extend their sincere and unreserved thanks to Fernando Blanco, Carlos Cavalcanti, Philip English, Faya Hayati, Mark Thomas – on the World Bank side – and Tarak Jardak and Mercedes Vera-Martin from the International Monetary Fund, for providing invaluable feedback and peer-reviewing this document.

i
# **<u><mark>Overview</mark></u>**

- **Mauritania continues to enjoy macroeconomic stability and growth, sustained by its sizeable natural resources** . Economic performance has been strong over recent years, with real GDP growing at roughly 5 percent in the last decade and accelerating to 7 percent in 2012-13. Inflation was 4.5 percent in 2013, a relatively low level for historical standards. Investment projects are opening new opportunities for economic and social development. Despite this remarkable macroeconomic performance, Mauritania remains exposed to external shocks and heavily dependent on mining exports. Improved fiscal policies have accompanied economic growth, strengthening revenues and supporting public investment.

- **This paper utilizes a stochastic Debt Sustainability Analysis (DSA) to assess Mauritania’s public debt prospects and vulnerabilities over the next ten years, consistently with a macroeconomic outlook and a number of alternative fiscal and debt policies** . This framework is neither substitutive nor antithetical to the Debt Sustainability Framework for Low-Income Countries (LIC DSF) jointly developed by the World Bank and the IMF; on the contrary, it is offered as a complementary and distinct analytical perspective to policy makers. The methodology involves stochastic simulations suitable to address macroeconomic uncertainties and fiscal risks, thus improving upon the traditional scenarioanalysis approach of the LIC DSF. New insights are then obtained to inform the formulation of fiscal and debt policies.

- **Under this framework, Mauritania’s public debt appears to be sustainable in the long term and poses limited fiscal vulnerability provided that robust growth and rigorous fiscal policy are achieved.** Fiscal policies targeting a balanced budget will largely compress the net borrowing needs and thus slow down the accumulation of financial liabilities. A fastgrowing nominal GDP, in addition, will boost fiscal revenues and strengthen the capacity to repay the government debt. Mauritania’s public debt is then expected to decline from 74 percent of GDP in 2013 to 30 percent by 2023, whereas its gross financing needs fall from 12 percent of GDP to 4 percent. A virtuous combination of strong economic performance and fiscal prudence is required for Mauritania to mitigate solvency risk, reduce the probability of experiencing debt distress, and ensure public debt sustainability.

- **Fiscal risks stemming from unforeseen macroeconomic shocks are relevant to assess the sustainability of the public debt.** Thus, the stochastic DSA aims at providing quantitative estimates of the range of possible debt outcomes resulting in the wake of macroeconomic shocks, as well as their associated probabilities of occurrence. This type of analysis is meant to support policy makers in strengthening fiscal and debt policies on sustainability grounds.

- **Our results clearly indicate that the sound fiscal and debt policies assumed in the baseline scenario have a direct and positive effect on debt sustainability, even in the presence of macroeconomic shocks** . In 2023, there is a 50 percent probability that the debtto-GDP ratio could be as low as 27 percent and as high as 33 percent, i.e., deviations of +/-3 percentage points around the baseline path. Similarly, there is a 90 percent probability that the debt-to-GDP ratio could lie in the range 24-40 percent, i.e., with wider deviations relative to the baseline path. Thus, the sound policies seeking balanced budgets—which Mauritania is assumed to follow in the baseline scenario—do deliver a large reduction of public debt in 2013-23, even in the presence of macroeconomic shocks. Arguably, even the public debt of 40 percent of GDP by 2023 that would result from very unfavorable shocks represents, for practical purposes, a notable improvement against the current ratio of 74 percent. In other words, fiscal prudence always proves to be effective to strengthen debt sustainability in Mauritania.

ii
- **Public investment policy is a key determinant of Mauritania’s fiscal and debt performance going forward.** Alternative public investment policy options are assessed in this DSA, giving due consideration to estimates of growth-dividends. Public investment implies borrowings to fund spending and higher financial obligations, and, on the other hand, it may (or not) increase the government’s revenues and repayment capacity. While poorquality investment projects are likely to generate additional financial obligations and ultimately impair debt sustainability, good-quality investment projects would create additional financial obligations as well as resources to _partly_ repay them. Our analysis shows that further scaling up public investment—over and above the already robust level of current capital spending—is likely to deteriorate Mauritania’s debt sustainability because the estimated (marginal) growth-dividend is small. Increasing the capital expenditures by 2 percentage points of GDP relative to the baseline scenario is estimated to raise the long-term growth of real GDP by 1.2 percentage points. Given this estimated growth-dividend and the additional borrowings funding higher capital expenditures, the public debt would decrease from 74 percent of GDP in 2013 to 40 percent of GDP in 2023. Thus, the growth-dividend is not sufficiently large to offset the new financial obligations funding the additional public investment. In addition, an insufficient growth-dividend augments the exposure to fiscal risks, even for good-quality investment projects.

- **One of the main findings of this analysis is that Mauritania can further yield positive economic spillovers by improving the quality of its public financial management, especially the public investment management system** . Fiscal multipliers of capital spending can be used as summary indicators of the quality of public investment. On such basis, we show that Mauritania could attain larger (average) growth-dividends and improve its debt sustainability just by raising the quality of public investment.

- **This paper also quantitatively shows that debt management policies can influence Mauritania’s prospective fiscal and debt performance, although the effects are smaller than those produced by fiscal policies.** A Medium-Term Debt Management Strategy (MTDS) Report was prepared in 2012 and characterized Mauritania’s public debt portfolio in terms of its composition and cost-risk profile. The MTDS identified four debt management strategies for the period 2012-15. This paper utilizes these four strategies together with two optimal debt strategies as alternative options. The optimal strategies are useful benchmarks to assess the performance of debt outcomes. Our simulations show that debt policies have a lesser effect in improving debt sustainability, which instead depends by and large on fiscal policies. Debt policies can indeed attenuate the exposure of the on-budget interest bill to macroeconomic shocks affecting the exchange rate and the domestic interest rates. Thus, debt strategies should be geared towards macroeconomic risk management and cannot substitute rigorous fiscal policies as a mean to improve Mauritania’s debt sustainability.

iii
# **A. Country Context**

**Mauritania is enjoying macroeconomic stability and growth, sustained by sizeable natural resources** . Economic performance has been strong over recent years, with real GDP growing at roughly 5 percent in the last decade and accelerating to 7 percent in 2012-13 (Figure A1). Inflation is now at relatively low levels for historical standards (Figure A2). Mauritania has largely benefited from a recent expansion of mining output (e.g., iron ore production grew by 30 percent in 2013) and high international commodity prices in the mining cluster, conditions that are anticipated to continue into the medium term. Trade, livestock and iron would drive growth in the next few years, while copper, gold and manufacturing are expected to be the fastest growing sectors.

**Investment projects are opening new opportunities for economic and social development** . Mauritania has scaled-up public investment in the agriculture sector with the objective of expanding arable land by 2015-16. Iron ore production has recently registered a significant expansion, with SNIM (the national iron ore producer) increasing its production by roughly 25 percent, passing from a historical average of 11 million tons to 13 million tons in 2013. Further expansions are planned and the potential output could reach up to 40 million tons by 2025. Copper production alone is expected to almost double in 2014 as new discoveries were recently made. Expansion projects in the gold sector by Kinross (the leading extractive company active in the country), which would triple gold production within 3 to 4 years, have temporarily been put on hold due to the volatility in world prices. A large energy investment plan is under development, including projects to builds power plants using gas, wind, and solar. The gas-topower project, in particular, could more than double the power generation in Mauritania, generate exports to Senegal and Mali, and substantially reduce costs. Electricity connectivity is expected to improve for more than half the urban population living in informal settlements. Business opportunities would open in the Free Trade Area in the northern region of Nouadhibou, which is already attracting international investors and financial institutions.

**Despite this remarkable macroeconomic performance, Mauritania remains exposed to external shocks and heavily dependent on mining exports** . Mining exports (e.g., iron ore, copper and gold) account for four-fifths of all export receipts and so, even though their contribution to the economic boom is notable, their concentration stands very high in terms of product base and trade partners. On the other hand, Mauritania largely imports mining-related capital goods and around three-quarters of its food requirements. As a consequence, current account imbalances are persistent and particularly sensitive to the volatility of terms of trade between key exports and imports, as well as to Mauritania’s ability to attract FDI flows. Climate shocks (particularly droughts) represent another significant source of external vulnerability as they often translate into higher food import requirements, affecting _in primis_ the poorest clusters of population.

4
**Main recent fiscal and debt developments**

**Improved fiscal performance has accompanied economic growth, with government policies strengthening revenues and supporting public investment** . Budget deficits used to exceed 5 percent of GDP back in 2007-09, after the _surplus_ recorded in 2006 when Mauritania benefited from debt relief. Deficits have hovered around 1 percent since 2010—excluding the _surplus_ attained in 2012 when substantial foreign aid in grants helped the government cope with a severe drought (Figure A4). Measures to increase tax bases and the introduction of some new taxes, together with a better coordination among fiscal administrations, were conducive to increase revenues—excluding external grants—from 25 percent of GDP in 2007-11, on average, to 34 percent of GDP in 2012-13. With booming revenues available to spend and an explicit objective to support the growth spurt, the government decisively scaled up capital expenditure—which jumped from 7 percent of GDP in 2007-11 to 13 percent of GDP in 2012-13. Thus, total public expenditure reached 36 percent of GDP in 2012-13, on average, vis-à-vis 31 percent of GDP in 2007-11. The fiscal bonanza not only helped fund additional investment spending, but also contained budget imbalances and reduced the net borrowing needs.

**The government is aware of the need to further consolidate fiscal performance, sustain economic growth, improve social spending, and spur the attainment of the Millennium Development Goals (MDGs)** . The 2014 Budget Law recognizes these goals as well as the importance of capital spending, and defines four guiding principles: (i) sustain productive sector to spur economic growth; (ii) promote employment and income-generating activities; (iii) strengthen access of population to basic services, most notably health, education, water and sanitation; and (iv) develop key infrastructure to continue attracting investment. A recent shift in expenditure priorities is noticeable, with public spending focusing more on investment and human development—especially infrastructure, energy, transportation, and education— and less on consumption—particularly food and fuel subsidies. The phasing-out of subsidies and the abandonment of reactionary approaches to crises, in favor of more systematic methods such as conditional cash transfers programs, are additional evidence of the same shift. Against this background it is critical to enhance public investment management, with a view to ensure that the scaling-up of investment spending is oriented to high-return projects that build macroeconomic resilience and foster productivity and diversification.

**Mauritania’s public debt is manageable and poses limited fiscal vulnerability, but efforts should be**

**made to bring it down to lower levels** . Since debt relief was granted in 2006, the government debt remained around 95 percent of GDP—an admittedly high level (Figure A3). This report considers the public debt to be 74 percent of GDP as of end-2013, assuming a resolution of the Kuwait bilateral debt dispute under HIPC terms. There is little difficulty in managing and servicing financial obligations,

5
15.0,—@_—MH S HH TY 10.0 5.0 0.0 -5.0 [oeARAEaRTHRHRSSESHSSSSSSSgAgAAart nt At Tt TDR TH TAooNN NNN NN NNN ooo)NNN <!-- Start of picture text --> 20.0.iii—WAATA<br>16.0<br>12.0<br>8.0<br>4.0<br>0.0<br>SBARADASZRASDADADAAARAAnt tat AH TRH TH TRH TH A GGNNNNSESSSSSESsaguagCGCeaeooacaeaeooocoltllONNN NNNN NN<br><!-- End of picture text --> <!-- Start of picture text --> 220.0<br>190.0<br>160.0<br>130.0<br>100.0<br>70.0<br>isa) t+ wo wo ~~ oO a o a N om<br>8 8 8 8 8 8 8 8 8 8 8<br>N N N N N N N N N N N<br><!-- End of picture text --> <!-- Start of picture text --> 30.0.—§_AAAA<br>20.0<br>10.0<br>0.0<br>-10.0<br>20.0<br>-30.0., ——<br>” t+ wn wo nd ioe} a o a N m<br>So& °8 So86 c=}86 °86 f=}8 f=}8 a8&8 a8&8 a8 a6<br>N N N N N N N N N N N<br><!-- End of picture text -->
# **B. Macro-Fiscal Framework for 2014-2023 and Debt Sustainability Analysis**

**A Debt Sustainability Analysis (DSA) assesses Mauritania’s public debt prospects and vulnerabilities in the next 10 years, consistently with a macroeconomic outlook and alternative fiscal and debt policies** . The analysis focuses on the gross public debt of the Central Government, including foreign and domestic liabilities.<sup>1</sup> Debt projections are built upon the macroeconomic and fiscal framework prepared by the World Bank that covers the period 2014-23 (Table B1). Information on public debt instruments and financing terms is drawn from the latest Medium-Term Debt Strategy Report (2012) as well as from some updates for 2012-13. Annex I describes the DSA model used in this section.

**The baseline outlook envisages the continuation of the strong growth performance observed in recent years** . The World Bank macro-fiscal framework is the foundation of the DSA baseline scenario. Real GDP growth rates around 7 percent are expected to be maintained during the period 2014-23, whereas the inflation rates measured by the GDP deflator would converge towards 5 percent per annum. The foreign exchange receipts generated by mining exports would permit to stabilize the nominal exchange rate, and so the local currency is expected to appreciate in real terms.

**The baseline outlook is predicated on the preservation of sound policies that seek to achieve balanced budgets and take full advantage of concessional borrowing opportunities** . Fiscal bonanza has brought budget deficits down to nearly 1 percent of GDP in recent years, and so now Mauritania is not far from achieving a balanced budget. The baseline scenario assumes that further efforts would be undertaken to consolidate public finances and target a zero-deficit budget in the next few years. Admittedly, these efforts are feasible in the short-term because of the favorable initial conditions whereby deficits are already small. Challenges may arise in the medium- and long-term, though. Mauritania’s steps up the income ladder and the inevitable exhaustibility of its non-renewable natural resources are likely to affect some relevant components of government revenue—most notably oil income and mineral royalty and tax receipts—which would decline relative to the size of the economy. If fiscal policies seek to preserve balanced budgets, the Government would have to adjust expenditures downwards _pari passu_ and make choices on what spending programs are to be rationalized or expanded at slow motion. If the pattern of policy priorities observed in 2013 persists, a sensible conjecture is that the government would prefer to maintain robust levels of public investment, and instead cut on subsidies and transfers. Against this backdrop, the baseline outlook projects revenues and expenditures to gradually decrease from the current levels of 35-36 percent of GDP to 33 percent of GDP by 2018. As far as debt policies are concerned, the government is expected to pursue a debt management strategy similar to that observed recently, whereby maturing liabilities are re-financed under similar terms. Thus, the composition of the public debt portfolio

> 1 In this work, the debt owed to the Kuwaiti Investment Authority is excluded.

7
would remain heavily biased towards concessional loans. In addition, the baseline scenario assumes that the government seeks to build up a reserve of liquid assets and accumulate cash balances (e.g., bank deposits) by 1 percent of GDP per annum, on average, over the projection horizon.

**Provided that growth prospects remain strong and public policies are sound, Mauritania’s public debt could decline as low as 30 percent of GDP by 2023, thus mitigating solvency risk and strengthening debt sustainability** . Fiscal policies targeting a balanced budget will largely compress the net borrowing needs—down to the minimum indebtedness necessary to pile up the desired stock of liquid assets—and thus slowdown the accumulation of financial liabilities. A fast-growing nominal GDP, in addition, will boost fiscal revenues and strengthen the capacity to repay the Government debt. A smooth decline in the public debt, from 74 percent of GDP in 2013 to 50 percent of GDP in 2018, and to 30 percent of GDP in 2023, is the noteworthy outcome of such a virtuous combination of strong economic performance and fiscal prudence (Figure B1). Mauritania would then mitigate solvency risk and strengthen debt sustainability. In addition, the country would alleviate liquidity pressures as the gross financing needs decrease from 12 percent of GDP in 2013 to 4 percent of GDP in 2023. It is critical to emphasize that achieving these results requires fiscal discipline and solid institutions supporting policies.

**With lower public debt and faster economic growth, Mauritania is much less likely to experience debt distress and difficulties to honor the government debt obligations** . The Debt Sustainability Framework for Low-Income Countries (LIC DSF) jointly developed by the World Bank and the IMF, presented in Annex III, is built upon an econometric cross-section model to estimate the probability of a country running into debt distress and facing difficulties to service the public debt. Not surprisingly, the empirical evidence offered by numerous developing countries in different historical circumstances suggests that such a probability decreases with lower public debt, higher economic growth, and sounder policies. This is precisely the mix of circumstances characterizing the baseline scenario for Mauritania. If the country continues to achieve a strong growth performance and the government commits and delivers on prudent fiscal and debt policies, Mauritania can expect to halve its probability of experiencing debt distress, from 15 percent in 2013 to 7 percent in 2023 (Figure B2). This achievement can eventually help reduce borrowing costs to the extent that lower debt-distress probabilities—which ultimately reflect a lower solvency risk—feed into lower credit spreads on government securities. As Mauritania develops its government securities market and transitions away from external concessional funding, the advantages of enjoying lower credit spreads will be apparent.

**Fiscal risks stemming from unforeseen macroeconomic shocks, as well as alternative policy options, are worth exploring in order to assess the robustness of the baseline DSA results and to inform decisions on what fiscal and debt policies Mauritania should pursue going forward** . Macroeconomic trends characterizing the baseline outlook presented in Table B1 may not materialize as expected if and

8
when unforeseen shocks hit the Mauritanian economy. To assess the robustness of the baseline DSA results to the occurrence of these shocks—particularly very adverse events—it is good practice to bring alternative projections of key macroeconomic variables into the DSA model. Similarly, the fiscal and debt policies assumed in the baseline outlook may eventually not be pursued going forward. Policy makers in Mauritania may fail to implement these policies or, more fundamentally, they may have strong preferences for other strategies better suited to their objectives, priorities, and institutional and market constraints. It is also good practice, therefore, to consider alternative policy options and confront their outcomes against those of the baseline policies. A comparison of relative performance in terms of debt sustainability should ideally inform any decision on what fiscal and debt policies Mauritania should adopt in the future. In the reminder of this section, a stochastic version of the DSA addresses the fiscal risks induced by macroeconomic uncertainties. The next two sections focus, instead, on alternative policy options.

# **Fiscal risks and debt sustainability**

# **Fiscal risks arise from the uncertainties surrounding the World Bank macro-fiscal framework**

**upon which the baseline outlook is predicated** . The set of consistent macroeconomic projections reported in Table B1 refer to future—and thus uncertain—trends and underpin the DSA results reported so far. _Fiscal risks_ arise as the macroeconomic uncertainty spreads to the performance of the government budget and debt. Arguably, Mauritania is subject to external and domestic shocks—e.g., a drop in prices of exported commodities, higher-than-expected oil reserves, droughts and other climate disasters. If and when these unforeseen shocks occur, the macroeconomic, fiscal, and debt outcomes would deviate from those obtained in the baseline scenario. These deviations are determined by three factors: the size of a shock, its probability of occurrence, and the exposure (sensitivity) of the country’s economy and public finances. Shocks inducing large deviations—especially if very likely to occur—should be a serious concern for policy makers because they could derail the government budget and debt path, and lead to an unsustainable debt dynamics. For instance, a significant and protracted drop in prices of exported commodities can slowdown economic growth, reduce tax revenues, widen the budget deficit and net borrowing needs, and ultimately increase the public debt up to imprudent levels—and well above the projected debt path in the baseline scenario. Assessing the robustness of the baseline DSA results to the occurrence of macroeconomic shocks is thus warranted.

**From the perspective of debt sustainability, the analysis of fiscal risks aims at quantifying the range of possible debt outcomes that may result as a consequence of macroeconomic shocks, as well as**

9
**their associated probabilities** . The methodology to assess sources of (and exposure to) fiscal risks undertaken in this study proceeds firstly simulating stochastic shocks to key macroeconomic variables; next, calculating the projection of any public-finance variable of interest for each and every simulation— e.g., budget balance, public debt stock, gross financing needs; and finally, building the probability distribution of that variable—i.e., the range of values and the associated probabilities of occurrence.<sup>2</sup> The analysis focuses on three macroeconomic variables whose importance for Mauritania’s public finances is uncontroversial and are subject to all sorts of shocks: economic growth (proxied by the real GDP growth), competitiveness (proxied by the real exchange rate), and the cost of domestic borrowing (proxied by the domestic real interest rate on government bills and bonds). Macroeconomic scenarios that bring into the picture the various unforeseen circumstances affecting Mauritania’s growth, competitiveness, and borrowing costs, are built analytically by running a large number of simulations with stochasticallygenerated shocks to these three variables.<sup>3</sup> For practical purposes, each simulation gives rise to an alternative debt projection that naturally departs from the baseline path. The full set of debt projections, therefore, describes all the possible (shock-driven) debt outcomes and permits assessing how robust the baseline DSA results are.<sup>4</sup> This assessment is easily undertaken through fan charts depicting the range of possible outcomes and their associated probabilities, e.g., the probability distribution of the public debtto-GDP ratio in a given year along the projection horizon.

**Fiscal risks are reflected in fan charts** . As explained above, projections of Mauritania’s public debt were obtained from numerous simulations where macroeconomic shocks hit the real GDP growth, the real exchange rate, and the domestic real interest rate. Results are summarized in the fan chart reported in Figure B3. The dotted line represents the baseline (expected) path: the public debt-to-GDP ratio decreases from 74 percent in 2013 to 30 percent in 2023. The colored bands, in turn, depict the probability distribution (density) of the debt outcomes resulting from macroeconomic shocks and stochastic simulations. The bands—which can be seen as defining confidence intervals—indicate that in 2023 there is a 50 percent probability that the debt-to-GDP ratio could be as low as 27 percent and as high as 33 percent, i.e., deviations of +/-3 percentage points around the baseline path. Similarly, there is a 90 percent probability that the debt-to-GDP ratio could lie in the range 24-40 percent, i.e., with wider deviations

> 2 For a similar approach, see Bella (2008), Tielens et al. (2010), Giovanni and Gardner (2008), and Pradelli and Baghdassarian (2013).

> 3 The econometric time-series model presented in Annex I is used to estimate parameters (e.g., a covariance matrix) that reflect the co-movements of shocks hitting the three macroeconomic variables observed in recent years. The stochastically-generated shocks rely on those parameters as well as on the admittedly arbitrary (albeit widely used) assumption that shocks are drawn from a multivariate Gaussian distribution. In doing so, the generation of random shocks intends to preserve as much as possible the salient patterns of the macroeconomic dynamics of Mauritania.

> 4 Specifically, the baseline DSA results are robust if the range of possible debt paths narrowly concentrates around the baseline path—meaning that macroeconomic shocks hitting the Mauritanian economy would have a limited impact on the country’s fiscal and debt performance—or if any debt path that largely deviates from the baseline path turns out to have a very small probability of occurrence—meaning that macroeconomic shocks that do have a large impact are nevertheless rather unlikely to happen.

10
relative to the baseline path. For illustrative purposes, Figure B4 depicts the fan chart corresponding to the gross financing needs.

**Sound fiscal and debt policies envisaged in the baseline scenario do strengthen debt sustainability even in the presence of macroeconomic shocks** . The stochastic DSA suggests two points relevant for policy makers in Mauritania. Firstly, macroeconomic volatility and uncertainty should be taken seriously because the fiscal and debt performance are exposed to adverse shocks. In particular, if unfavorable circumstances unfold, such as lower growth and higher interest rates, the government debt can eventually reach levels significantly above those anticipated otherwise. For instance, the public debt is expected to be 30 percent of GDP by 2023 but in a poor macroeconomic environment it can be as high as 40 percent of GDP. Secondly, it is noteworthy that the sound policies seeking balanced budgets and inexpensive concessional funding sources—which Mauritania is assumed to follow so far—do deliver a large reduction of public debt in 2013-23, not only in the baseline scenario but also in all the simulations undertaken. Arguably, even the public debt of 40 percent of GDP by 2023 that would result from very unfavorable shocks represents, for practical purposes, a notable improvement against the current ratio of 74 percent. In other words, regardless of the adverse macroeconomic shocks that may slowdown the pace of debt reduction, the fiscal prudence always proves to be effective to strengthen debt sustainability in Mauritania. This result, already highlighted in the baseline scenario, turns out to be robust to alternative macroeconomic outlooks that do bring into the picture the shocks the country is exposed to.

# **A detour: stochastic DSA and scenario analysis**

**A stochastic DSA has technical advantages vis-à-vis the use of scenario analysis to address macroeconomic uncertainties and fiscal risks** . Scenario analysis is another popular methodology to investigate uncertainties and fiscal risks, and it is actually the approach adopted in the LIC DSF to formulate stress tests. Scenario analysis proceeds by introducing a few standardized shocks one-by-one into the DSA model and gauging their individual impact on the fiscal and debt performance. The modelling of shocks is extremely simple: their size is often chosen arbitrarily and not looking at countryspecific circumstances; little attention is paid to co-movements (covariances) between disturbances hitting key macroeconomic variables; and there is no consideration whatsoever regarding their likelihood of occurrence. A stochastic DSA, instead, takes all these modelling issues seriously and hence improves upon scenario analysis on purely technical grounds. Both approaches, therefore, should be seen as complements.

**A stochastic DSA also has advantages vis-à-vis scenario analysis for the formulation of fiscal and debt policies** . The stochastic DSA offers advantages on practical, policy-making grounds that derive from

11
the explicit recognition of probabilities of events and associated risks. Useful insights for policy makers provided by a stochastic DSA include, _inter alia_ , the calculation of the probability that the public debt breach a threshold level that is prudent not to exceed. This is especially relevant when debt levels are high and breaching thresholds may impair a country’s access to certain sources of funding (e.g., IDA lending) or trigger pre-payment clauses in loan contracts. Besides, the explicit quantification of the probabilities of shocks help avoiding policy over-reactions that may arise when policy makers are _unduly_ focused on preventing events that are perceived to have deleterious effects on public finances because they fail to recognize that those events have a low likelihood of occurrence.<sup>5</sup> The stochastic DSA is also a useful tool for assessing the performance of policies aimed at mitigating fiscal risks and build resilience, such as fiscal consolidation reducing net borrowing needs and debt stocks.

**Table B1. Macro-fiscal framework 2014-2023.**

|**Variables**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|**2021**|**2022**|**2023**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP at current prices (billion MRO)|1,252|1,299|1,418|1,566|1,756|1,973|2,218|2,493|2,803|3,151|3,543|
|GDP at constant prices, annual growth rate (%)|6.7|6.8|6.8|6.7|7.0|7.0|7.0|7.0|7.0|7.0|7.0|
|GDP deflator, annual growth rate (%)|-0.1|-2.9|2.3|3.5|4.8|5.0|5.1|5.1|5.1|5.1|5.1|
|Exchange rate MRO/USD (end of period)|302.9|276.4|273.3|273.4|275.6|278.1|280.7|283.4|286.1|288.8|290.1|
|Exchange rate MRO/USD (period average)|298.8|278.8|274.0|272.5|274.3|276.8|279.4|282.0|284.7|287.4|290.1|
|Exchange rate MRO/EUR (end of period)|417.7|381.2|376.8|377.1|380.0|383.5|387.1|390.8|394.5|398.2|400.1|
|Exchange rate MRO/EUR (period average)|412.0|384.5|377.9|375.8|378.3|381.7|385.3|388.9|392.6|396.3|400.1|
|Exchange rate USD/EUR (period average)|1.4|1.4|1.4|1.4|1.4|1.4|1.4|1.4|1.4|1.4|1.4|
|Real exchange rate (MRO/USD), index 2008=10|115.6|113.3|111.1|108.8|106.7|104.5|102.4|100.4|98.4|96.4|94.5|
|Total revenue (% GDP)|34.6|36.4|36.1|35.9|34.2|33.3|33.2|33.2|33.2|33.2|33.2|
|Domestic revenue|32.0|32.4|32.8|33.3|31.9|31.0|31.0|31.0|31.0|31.0|31.0|
|External grants|0.9|2.1|1.4|0.8|0.7|0.6|0.5|0.5|0.5|0.5|0.5|
|Net revenues from Oil|1.7|2.0|1.9|1.8|1.7|1.7|1.7|1.7|1.7|1.7|1.7|
|Total expenditure (% GDP)|35.7|36.0|35.5|35.3|33.9|33.3|33.2|33.2|33.1|33.0|32.9|
|Interest expenditure|1.3|1.4|1.2|1.2|1.1|1.1|1.1|1.1|1.0|0.9|0.8|
|Wages and salaries (emoluments)|8.5|8.4|9.1|9.1|8.6|8.6|8.6|8.6|8.6|8.6|8.6|
|Transfers and subsidies|5.7|5.7|3.9|3.6|3.0|2.5|2.0|2.0|2.0|2.0|2.0|
|Other current expenditure|1.4|1.5|1.3|1.0|0.8|0.7|0.7|0.7|0.7|0.7|0.7|
|goods and services|4.9|5.1|5.6|5.6|5.8|5.8|6.2|6.2|6.2|6.2|6.2|
|Capital expenditure|13.9|13.8|14.5|14.8|14.6|14.6|14.6|14.6|14.6|14.6|14.6|
|Primary balance (% GDP)|0.2|1.8|1.8|1.8|1.4|1.1|1.1|1.1|1.1|1.1|1.1|
|Overall budget balance (% GDP)|-1.1|0.4|0.5|0.6|0.3|0.0|0.0|0.0|0.1|0.2|0.3|

Notes: (*) Real exchange rate defined as the exchange rate MRO/USD (per.av.) deflated by GDP deflator and an assumption of a fixed 2% US inflation. Source: World Bank projections

> 5 The LIC DSF, for instance, conducts various stress tests to assess fiscal risks. For some countries, the shocks used in the stress tests appear too extreme and severe, but indeed they are very unlikely to happen. The LIC DSF could unintendedly over-exaggerate the risks emerging from these shocks because it looks at their size and not at their likelihood—in fact, there is no explicit probability measure associated with the stress-test scenarios. Failure to appreciate both size and probability of occurrence of shocks may bias policy decisions towards very conservative strategies weighting more the risk-mitigation objective, which are usually more expensive that others weighting that objective less.

12
<!-- Start of picture text --> 80.0 12.0<br>70.060.0 IN 9.010.0<br>50.0<br>200 ee ee ee 7-5 .0<br>30.0 >aoSs i=}t+a8s ©ao& i=}bebt©a& NRao& i=}&@a& i=}DBDa& aNi=}&O0@& GENi=}iolH& i=}QaWw& onQao8B 3.0<br><!-- End of picture text --> <!-- Start of picture text --> 80.0 16.0<br>70.060.0 14.012.0<br>45 0.0 i=S eee= 8.010.0<br>30.0 6.0<br>OosN OogsN i=}2N 2oON 3OoN 8oN f=}3N i=}8&8N 8&8oN 8OoN &i=}N<br>—pebt — Probability of Debt Distress<br><!-- End of picture text --> <!-- Start of picture text --> ==<br>= —— =<br>= > | =<br>= =<br><!-- End of picture text -->
revenues, or as a consequence of a decline in some revenue sources not matched with a contraction in capital expenditures (or any other spending program, for that matter).

**Alternative public investment policy options are assessed in the DSA** . The baseline scenario assumes that the recent public investment boom will be maintained—with capital expenditure averaging 14.5 percent of GDP in 2014-23—while fiscal policy will seek to attain balanced budgets. As argued above, policy makers in Mauritania may fail to implement these policies or may legitimately prefer others. Assessing the implications on debt sustainability of alternative investment strategies is therefore warranted, especially to assess their relative performance and thus strengthen the case for adopting certain options and disregarding others. In this study, an alternative investment policy is considered whereby projects are further scaled up and capital spending increases up to 16.5 percent of GDP in 2014-23, on average, i.e., an additional 2 percentage points of GDP relative to the baseline scenario. The _additional_ public investment expenditure is not offset by contracting current expenditures, so _ceteris paribus_ fiscal revenues, budgets would turn into deficit.

**Public investment implies borrowings to fund spending and higher financial obligations, and it may (or not) increase the government’s revenues and repayment capacity** . From the perspective of debt sustainability, public investment policies in Mauritania are to be assessed in two dimensions: i) the borrowings incurred to fund capital expenditures, which increase the debt stock and debt service obligations; and ii) the growth-dividend expected from expanding productive capacity in the country, which would translate into debt repayment capacity provided that the projects are worthwhile and the government can appropriate more revenues in the future, say through taxes. Along these lines, the higherinvestment policy described above is evaluated in two different contexts: one scenario in which the _additional_ capital expenditure eventually has no growth-dividend whatsoever and, more broadly, no impact on the anticipated macroeconomic trends in Mauritania; and another scenario where the _additional_ capital expenditure does deliver a growth-dividend and affects the prospective performance in terms of growth, competitiveness, and borrowings costs—i.e., the three key variables discusses in the previous section.<sup>7</sup> It should be bear in mind that the analysis conducted here refers to the _additional_ ( _marginal_ ) new investment projects and their potential benefits (if any), as opposed to the overall level of public capital expenditure and related projects assumed in the baseline outlook—whose benefits are probably worthy but not quantified here.

> 7 The econometric time-series model presented in Annex I is used to estimate parameters of dynamic equations that formalize the co-movements of the three macroeconomic variables and the public investment—treated as a policydriven, exogenous variable—observed in recent years. The scenario with growth-dividend is built on those parameters and thus intends to capture the salient interactions and feedbacks between the macroeconomy and the public capital projects in Mauritania.

14
**Poor-quality investment projects are likely to only create additional financial obligations and ultimately impair debt sustainability** . The higher-investment policy situated in the scenario without growth-dividend can be seen as supporting _additional_ poor-quality projects with little (if any) contribution to the country’s production capacity and the government’s revenue-generation capacity, e.g., _white elephant projects_ and _bridges to nowhere_ . Thus, having implications only on the government’s borrowing and financial obligations, the policy will nothing but impair public debt sustainability (relative to the baseline scenario). Indeed, with a scenario-analysis approach and assuming no growth-dividend, Mauritania’s public debt would decrease from 74 percent of GDP in 2013 to 46 percent of GDP in 2023 (Figure C1). The poor-quality investment policy induces budget deficits—which are slightly above 2 percent of GDP per annum in 2014-23 because of both higher capital expenditure and interest payments on the new loans financing projects—and thus it creates new _net_ borrowing needs but fails to concomitantly accelerate growth of nominal GDP and fiscal revenues. Thus, the debt ratio declines much less than in the baseline scenario—where it reaches 30 percent of GDP in 2023. In addition, the _gross_ financing needs—and associated liquidity risks—are much higher than in the baseline scenario, because of both higher budget deficits and principal amortization payments on the new loans financing projects (Figure C2).

**Good-quality investment projects, instead, would create additional financial obligations as well as resources to** **_partly_ repay them** . The higher-investment policy adopted in the scenario with growthdividend, on the other hand, represents undertaking _additional_ good-quality projects which do boost the potential for income- and fiscal revenue-generation in Mauritania. Increasing the capital expenditures by 2 percentage points of GDP relative to the baseline scenario is estimated to raise the long-term growth of real GDP by 1.2 percentage points—i.e., to an average annual growth of 8.2 percent in 2014-23, compared against the 7 percent anticipated in the baseline outlook.<sup>8</sup> Faster growth of nominal GDP and fiscal revenues, in turn, strengthens the government’s capacity to repay the new loans financing projects. Given the estimated growth-dividend, the public debt would decrease from 74 percent of GDP in 2013 to 40 percent of GDP in 2023 (Figure C1). Thus, the impairment of debt sustainability observed in the scenario without growth-dividend is partly mitigated. Budget deficits and new _net_ borrowing needs still arise, but the sustainability outcomes are less severe thanks to the better performance of nominal GDP and fiscal revenues. It should be emphasized, however, that the higher-investment policy delivers paths of debt and gross financing needs above the baseline paths (Figure C1 and C2). Hence, the growthdividend—while certainly welcomed—is not sufficiently large to offset the new financial obligations created to fund the _additional_ public capital projects.

> 8 Besides, the _additional_ capital spending would slightly increase the domestic real interest rate on government bills and bonds, and accelerate the long-term appreciation of the real exchange rate. These two effects, nevertheless, are of small magnitude and so exert little influence on the public-finance outcomes.

15
**Good-quality investment projects with insufficient growth-dividend augment the exposure to fiscal risks** . Higher-investment policy not accompanied with an offsetting rationalization in current expenditures tilts budgets towards deficit and deteriorates the performance of public finances (relative to the baseline outlook). The estimated growth-dividend appears insufficient to prevent such deterioration. Furthermore, the policy widens the exposure to fiscal risks precisely because weaker public finances are more sensitive to unforeseen macroeconomic shocks whenever they occur. Fan charts obtained using the stochastic DSA and the scenario with higher investment and growth-dividend are reported in Figure C3 and C4. The dotted line represents the path in the absence of shocks, in which the public debt-to-GDP ratio reaches 40 percent in 2023. The higher-investment policy coupled with macroeconomic shocks may deliver a debt ratio in the range 33-50 percent in 2023 with a 90 percent probability. Thus, if public capital projects are scaled up and yield an insufficient growth-dividend—as this study estimates—there are fewer chances for Mauritania to achieve a large reduction in the public debt in the next 10 years.

**Further scaling up the public investment—over and above the current level of capital spending, which is already high—is likely to deteriorate Mauritania’s debt sustainability despite slightly accelerating economic growth** . Two recommendations for the policy makers in Mauritania can be drawn from the analysis of alternative public investment options. Firstly, whereas keeping the current level of capital expenditure and seeking for balanced budgets—i.e., the baseline policies—are anticipated to support a strong macroeconomic performance and permit a large reduction in the public debt relative to GDP, an attempt to further scale up capital projects—i.e., the higher-investment policy—would bring the budgets back to deficit and slowdown the pace of debt reduction. Even if capital projects are of goodquality, selected strategically, and implemented efficiently, the estimated (marginal) growth-dividend associated to the higher-investment policy is not strong enough so as to avoid a deterioration of debt sustainability (relative to the baseline policies). In other words, Mauritania should not take for granted that any _additional_ investment expenditure is good for the economy and automatically creates the repayment capacity necessary to service the public debt incurred to fund it. Secondly, the stochastic DSA suggests macroeconomic volatility and uncertainty would be more worrisome if higher public investment leads to budget deficits and thus widens exposure to adverse shocks. The government debt may reach levels significantly above those anticipated in the baseline scenario—it can be as high as 50 percent of GDP if a very poor macroeconomic performance unfolds. The window of opportunity offered by a prudent fiscal policy to reduce the government debt and strengthen sustainability will probably narrow if the country’s public spending—even in capital projects—goes beyond the revenue potential, abandons the target of balanced budgets, and requires further borrowings.

16
**Quality of Public Investment: Growth Dividend and Fiscal Multipliers**

**Mauritania could boost economic growth by improving the quality of public investment—as opposed to its level** . Public investment policies not only determine the level but also the quality of capital projects. For Mauritania, improving the public investment management system governing, _inter alia_ , the activities of project appraisal, selection, implementation, and evaluation, is probably more promising than further increasing the level of capital spending—which is currently already high. A better quality of investment permits to attain a growth-dividend—boosting the potential for income- and fiscal revenuegeneration—without creating pressures on the public expenditure—and ultimately worsening the outcomes regarding the government budget, borrowings, and debt sustainability, as discussed above. The macroeconomic benefits of public investment, therefore, come without the _financial_ costs of _additional_ capital spending. But there is another type of costs that should not be overlooked: the institutional reforms to the administrative apparatus running the public investment management system will affect stakeholders—civil servants, suppliers, contractors, etc.—and some of them will have to make sacrifices (and even take losses) for the benefit of the system as a whole. The political economy of institutional reform is always complex and has to be handled.

**Fiscal multipliers of capital spending can be used as summary indicators of the quality of public investment** . A simple methodology to measure the quality of public investment is to use fiscal multipliers of capital spending. Analytically, multipliers measure the impact of the fiscal policy on output, and are computed as the ratio of a change in output to a change in revenues or expenditures (relative to a reference scenario). The policy relevance of fiscal multipliers cannot be exaggerated. But in practice, there are serious difficulties to estimate them and bring them into a fiscal policy framework.<sup>9</sup>

**Any evaluation of fiscal policies informed by multipliers should be undertaken with caution because the size of a multiplier varies with country-specific circumstances and several factors identified in the specialized literature** .<sup>10</sup> Most of the empirical literature about fiscal multipliers focuses on advanced economies, and there is limited research on emerging markets and low-income countries. In advanced economies, the short-term (i.e., first-year) multipliers generally lie between zero and one in _normal_ times, and can exceed one in _abnormal_ circumstances such as a severe economic downturn with monetarypolicy transmission problems. Besides, spending multipliers seem to be greater than revenue multipliers; overall, a multiplier of 0.6 results from averaging effects of spending and revenues under _normal_ times. In

> 9 Estimating fiscal multipliers is a complicated endeavor for reasons such as the lack of reliable and sufficiently-long data series; the statistical problems to isolate the direct effect of the fiscal policy on economic activity as there is actually a two-way relationship between them; and the availability of theoretical models whose implications on fiscal policy and its effects—which would guide the formulation and evaluation of statistical models—are very different.

> 10 See Batini et al (2014).

17
emerging markets and low-income countries, the short-term multipliers are smaller than in advanced economies, with spending multipliers in the range 0.1-0.3 and revenue multipliers in the range 0.2-0.4. Interestingly, these countries may have long-term multipliers with negative values—i.e., expansionary fiscal policies eventually reduce long-term growth—because of a weak institutional environment. The size and persistence of the fiscal multipliers depend on a variety of structural and cyclical factors, anyway.<sup>11</sup> Empirical evidence suggests the efficiency of fiscal policy depends on a country’s institutional framework, which includes the public investment management system.

# **Mauritania could attain a significant growth-dividend and improve debt sustainability by raising**

**the quality of public investment** . A multiplier of capital spending is the ratio between the _change_ in output and the _change_ in public investment expenditure. Unfortunately, there is no well-established estimate of such a measure for Mauritania. This study then formulates an alternative indicator whereby the _level_ of public investment expenditure is used as denominator, rather than its _change_ . The observed relationship between the growth-dividend _per unit_ of investment expenditure and the quality of capital spending is thus preserved. Specifically, the better the quality, the higher the indicator—and hence the larger the growth-dividend attained for any given quantity of public investment. For Mauritania, after controlling for other relevant influences on economic growth, the estimated indicator is 0.325 and falls in line with the spending multipliers for developing countries reported above.<sup>12</sup> Scenario analysis is now used to investigate the implications on growth and debt sustainability of improving the quality of capital spending. Starting with the indicator currently at 0.325, a better quality is modelled with two alternative values, 0.35 and 0.375. These figures are still well below the spending multipliers found in advanced economies, and thus they reflect a public investment environment that Mauritania may reasonably be in a position to achieve. The baseline investment policy—i.e., capital expenditure averaging 14.5 percent of GDP in 2014-23—is introduced in the two alternative, better-quality-of-investment scenarios. Enhancing the country’s institutional framework—and notably the public investment management system—may increase the average annual real GDP growth in 2014-23 to 7.8 percent if the indicator raises to 0.35, and even further up to 8.3 percent if the indicator reaches 0.375 (Figure C5). In the latter case, the growth performance is as good as that obtained with the higher-investment policy with growth-dividend, but with the advantage that no _additional_ capital spending is incurred at all. Faster growth of nominal GDP and fiscal revenues, with no _additional_ capital spending, can nothing but improve the public debt

> 11 Structural factors include the degree of trade openness and labor market rigidity, the exchange rate regime, the size of automatic stabilizers, the level of debt, and the quality of public expenditure management and revenue administration. Cyclical factors refer to the phase of the business cycle and the degree of monetary accommodation to changes in fiscal policy. Persistence of fiscal multipliers is also affected by the persistence of changes in fiscal policy and whether expenditures or revenues are used as policy instrument.

> 12 The econometric time-series model presented in Annex I estimates the marginal effect on the real GDP growth rate of an additional unit of public investment relative to GDP, controlling for other relevant factors. The estimated value is 0.325.

18
<!-- Start of picture text --> 80.0<br>70.0 ~<br>60.0 Oo—<br>50.0<br>40.0<br>SS<br>30.0 ” + my © ~ © a ° 4 nN ~ ~9<br>5N 8N 8x 8N 8x 8x 8N 8N 8N 8N 8N<br>— — Baseline —-— Higher Inv. without Growth Dividend ——— Higher Inv. with Growth Dividend<br><!-- End of picture text --> <!-- Start of picture text --> 15.0<br>- \ N<br>N<br>9.0<br>x se ~ 7 a 7 eA xs<br>-— - 7 sO<br>6.0 .<br>a<br>~s. .<br>3.0<br>aeN 3sN 2N 2N 8N $8N 38N 8N 8N gsN 8N<br>— = Baseline ——— Higher Inv. without Growth Dividend ——— Higher Inv. with Growth Dividend<br><!-- End of picture text --> <!-- Start of picture text --> 80.0<br>95% - 99%<br>70.0 lm 90% - 95%<br>mm 75% - 90%<br>60.0 MEE 67% - 75%<br>Mm 50% - 67%<br>mm 33% - 50%<br>50.0<br>ME 25% - 33%<br>40.0 mmm 10% - 25%<br>lm 5% - 10%<br>1%-5%<br>30.0<br>aoas6RS t+3&& ©38&8A ©$$8&8NR HR3&88&8R ©S$&6RN BD38&8AN 08&88A aHfg8A NAN&&8KN 88A — — ~ Expected Debt<br><!-- End of picture text --> <!-- Start of picture text --> 16.0<br>95% - 99%<br>14.0 lm 90% - 95%<br>mm 75% - 90%<br>120 MEE 67% - 75%<br>Mm 50% - 67%<br>mm 33% - 50%<br>10.0<br>ME 25% - 33%<br>8.0 mmm10% - 25%<br>lm 5% - 10%<br>1%-5%<br>6.0 aoas6SRS t+3&& ©428&8A ©38&8A &R36&6A &@8S$&KR BD2&8QR FS8&88NR 4Hgs8&8NR A§€8A om88A — — ~ Expected GFN<br><!-- End of picture text --> <!-- Start of picture text --> 85<br>ra eo oo<br>J pein<br>8.0 AZsev7<br>4“AdsC4 //aeT)<br>75 fa oa¢<br>/ oe<br>ei/ -eeve<br>7<br>Q‘<br>65 2 x 4 2 5 2 Q ° gq a 2<br>8 8 g 8 gs 8 8 8 8 8 8<br>—— FM 0.325 and Baseline Investment Policy (BIP) =~ FM0.350 and BIP<br>— -FM0.375 and BIP — — FM0.325 and Higher Inv. with Growth Dividend<br><!-- End of picture text --> <!-- Start of picture text --> 80.0<br>70.0 =<br>NeensA< SK .<br>: ~e mek 7s.<br>50.0 7S =<br>7s.<br>40.0 = -S - ~ i<br>“SN oT<br>30.0 ~ Ss se<br>“Nos~N ~ ss<br>~<br>20.0 a x pe s a g 2 8 g g g<br>g g g g g g g 8 g g 8<br>—— FM 0.325 and Baseline Investment Policy (BIP) ----FM 0.350 and BIP<br>— -FM0.375 and BIP — — FM0.325 and Higher Inv. with Growth Dividend<br><!-- End of picture text -->
# **D. Public Debt Strategies:  Cost and Risk of Debt Portfolios**

**Public debt management policies can influence Mauritania’s prospective fiscal and debt performance** . After Mauritania benefited from debt relief and largely reduced the burden of public foreign liabilities, the debt management strategy has been seeking to exploit the external, concessional financing sources available, and to expand gradually the domestic market for short-term government securities. Currently, foreign liabilities represent 90 percent of the total public debt, most of it instrumented through multilateral and bilateral loans, often contracted on concessional terms. Domestic liabilities, on the other hand, are chiefly short-term T-bills. Because of the large share of concessional debt denominated in foreign currencies, the interest bill is fairly light—in the order of 1.5 percent of GDP and just 5 percent of the total public expenditure—but the _exposure_ to currency fluctuations is significant. Besides, despite of the domestic debt being small, the need to roll it over at short maturities induces a large _exposure_ to refinancing risk and interest rate risk. Weighting so heavily in the built-up of exposure to macroeconomic shocks—especially to exchange and interest rates, the debt management policies can influence Mauritania’s fiscal and debt outcomes in the future. Changes in the borrowing strategies cannot be ruled out—e.g., non-concessional borrowings to finance infrastructure projects seem to be on the rise and initiatives to develop the domestic market for government securities are being considered—and thus assessing alternative options for public debt management is justified.

**A Medium-Term Debt Management Strategy (MTDS) Report prepared in 2012 characterized Mauritania’s public debt portfolio in terms of its composition and cost-risk profile.** The MTDS Report analyzed the composition and cost-risk profile of Mauritania’s public debt as of end-2011, with the main findings summarized in Box 1. The MTDS constructed stylized, representative debt instruments to adequately describe the financing terms and cost-risk characteristics of the various loans and securities involving a financial liability of the Mauritanian government. These instruments are listed in Table D1, which also reports the outstanding stocks as of end-2011—obtained from the MTDS Report—and as of end-2013—estimated using recent debt data.<sup>13</sup> The characterization of the public debt portfolio made in the MTDS also applies to the government debt as of end-2013 because the new borrowings in 2012 and 2013 did not involve large issuances of financial instruments very different from those existing as of end2011.

**The MTDS Report assessed four debt management strategies for 2012-15.** A _debt strategy_ is the combination ( _mix_ ) of financial instruments issued by the government to cover its gross financing needs—

> 13 The MTDS Report maps all domestic government securities, whose maturities are less than one year, to a oneyear T-bond, thus realistically assuming that instruments are rolled over _within_ the year in volumes that preserve the structure of domestic debt at the beginning of that year. The mapping of external debt instruments is more detailed, as seven different, stylized categories are used to represent the universe of foreign loans.

20
i.e., most notably, the budget deficit, the principal amortization payments, and the accumulation of financial assets. The debt strategy affects the fiscal and debt performance of a country to the extent that those instruments have different financing terms and cost-risk characteristics. Analytically, the strategy outlines the _composition_ of the new borrowings in terms of representative, stylized debt instruments, and over a certain projection horizon. The MTDS Report explored four options for the period 2012-15, described below, and concluded that the current debt strategy should be maintained. The salient features of these strategies are as follows: Strategy S1 (baseline): external borrowing using mostly concessional, fixed-rate loans from multilateral creditors; and zero _net_ domestic borrowing, i.e., new T-bills are issued just to roll over maturing domestic government securities.

Strategy S2: external borrowing using mostly concessional, fixed-rate loans from multilateral creditors; and zero _gross_ domestic borrowing, i.e., no new T-bills are issued.

Strategy S3: external borrowing using mostly concessional, fixed-rate loans from multilateral creditors; and non-zero _net_ domestic borrowing at short maturities, i.e., new T-bills and a small amount of two-year T-bonds are issued to roll over maturing domestic government securities and raise additional funding.

Strategy S4: external borrowing using mostly concessional, fixed-rate loans from multilateral creditors; and non-zero _net_ domestic borrowing at slightly longer maturities, i.e., new T-bills, two-year T-bonds, and three-year T-bonds are issued in volumes higher than those in strategy S3, with the view of further developing the domestic government securities market.

**Alternative debt strategies are assessed in the DSA** . This study uses the instruments already identified in the MTDS Report—reported in Table D1—and extends the projection horizon to 10 years. A continuation of the current debt strategy—i.e., S1—is the assumption underpinning all the scenarios— including the baseline outlook—presented in Section B and C. Other options, however, are worth exploring in the DSA in order to inform decisions on which policies are preferable to pursue. The options S2, S3, and S4 proposed in the MTDS Report are re-assessed, together with two _optimal_ strategies— denoted S5 and S6—whose nature is explained below.

**Optimal debt strategies are benchmarks useful to assess the performance of debt outcomes** . The debt strategies envisaged in the MTDS Report incorporate a specific approach in terms of objectives and cost-risk preferences. Nevertheless, by construction, these policies are neither _efficient_ nor _optimal_ . A portfolio is _efficient_ if it has the lowest possible cost for a given level of risk. In turn, an _optimal_ portfolio is an _efficient_ portfolio that best matches the policy makers’ attitudes towards risk. Optimal portfolios

21
represent the best trade-off in terms of costs and risks—given the subjective risk tolerance of policy makers—and so they constitute a natural benchmark to confront against other portfolios—and their underlying debt policies. This work considers two optimal strategies whose derivation is explained in Annex II: Strategy S5, denoted _unconstrained optimal debt strategy_ : it assumes that Mauritania faces no constraints on the volume of concessional financing it may choose to borrow from official lenders. Analytically, S5 results from optimizing the composition of the borrowings in 2014-23 using the 10 debt instruments listed in Table D1. It turns out that S5 involves heavy borrowing from concessional, fixed-rate loans extended by multilateral creditors, through the instrument I1. The policy is useful as a benchmark because of its optimality, but is admittedly un-realistic since multilateral creditors often have a resource envelope allocated to any country and therefore the funds supplied may not be as large as the notional demand for funds.

Strategy S6, denoted _constrained optimal debt strategy_ : in sharp contrast to S5, it assumes that Mauritania cannot access any concessional financing at all and is therefore constrained to borrow only from foreign bilateral and commercial lenders and domestic government securities. While the undisbursed funds of concessional loans contracted in the past are assumed to be received in the next few years, all the remaining (residual) gross financing needs are to be covered with the available sources of funding represented by instruments I5 to I10. Analytically, S6 results from optimizing the composition of the residual borrowings in 2014-23 using only those six debt instruments. The strategy involves heavy borrowing from semi-concessional, fixed-rate loans extended by bilateral creditors, through the instrument I6. This policy is also useful as a benchmark because of its (constrained) optimality, and could be seen as reflecting circumstances in which the development of Mauritania makes it less entitled to foreign aid and concessional lending.

**Debt policies can do little to improve debt sustainability, which instead largely depends on fiscal policies** . With a scenario analysis approach, the implications on debt sustainability of adopting alternative borrowing strategies are explored. It is noteworthy that all the six policies deliver similar debt paths: Mauritania’s public debt would decrease from 74 percent of GDP in 2013 to a figure in the range 27-32 percent of GDP in 2023 (Figure D1). Therefore, it is the fiscal policy, as opposed to the debt strategy, that really matters for Mauritania to improve sustainability. Large differences arise, however, concerning liquidity outcomes. Strategies relying more on concessional financing at low interest rates and long maturities—most notably S5, where all financing comes from IDA loans, as well as S2 and S6—imply relatively low annual borrowing needs and debt service obligations (Figure D2 and D3).

22
**Debt policies can attenuate the exposure of the on-budget interest bill to macroeconomic shocks affecting the exchange rate and the domestic interest rates** . Interest payments in the government budget are currently low but could increase whenever unforeseen, unfavorable events raise the exchange rate and the domestic interest rates. Debt policies can help mitigating the sensitivity to these financial disturbances and the associated fiscal risks. Introducing the six debt strategies into the stochastic DSA permits an assessment of such opportunities for risk mitigation. Fan charts for each debt strategy are easily computed but it would be burdensome to report all of them. Instead, Figure D4 shows a single indicator summarizing the un-reported fan charts: the percentile 95 of the probability distribution of the interest-to-GDP ratio obtained in each debt strategy. This indicator is a standard cost-at-risk measure and gives the order of magnitude of how high the interest bill could jump if adverse shocks hit the Mauritanian economy. It is noteworthy that, again, the strategies seeking for more concessional financing—S5, S2, and S6—perform relatively better. They prove to significantly reduce the burden of interest payments that might have to be effectuated if adverse shocks occur. In addition, in the underlying fan chart of interest-to-GDP ratio for these three policies, the dispersion of outcomes around the expected path is quite small.

**Debt strategies should be geared towards macroeconomic risk management and cannot substitute fiscal policies as means to improve Mauritania’s debt sustainability.** Three relevant insights for country authorities result from the analysis of debt policies. Firstly, the differences across alternative debt strategies—even considering the optimal portfolios—are less important to render Mauritania’s public debt sustainable than the effect of a sound fiscal policy. Secondly, the way policy makers assess risks has relevant implications on debt management. The traditional DSA and MTDS methodologies in often cases apply arbitrary and large shocks (e.g., to exchange rates, interest rates, or both) in order to assess risks. The likelihood of these shocks, however, is very small and thus they are very extreme events that eventually may not discourage the adoption of debt strategies that do increase exposure to them. In Mauritania, for instance, historical data suggest the variance of the exchange rate against the US dollar is not too large—and the exchange rate paths in the stochastic simulations do preserve such a pattern. As a consequence, the strategies with heavy borrowing from concessional sources—which admittedly fuels exposure to currency risk—eventually perform better than others—and S5 is actually the optimal (unconstrained) policy. Thirdly, optimal debt strategies are useful guides for debt management, but it should be recognized that financial cost, risk, and risk tolerance are not the only variables of concern. Public debt managers need to consider other objectives as well. For Mauritania, there is a need to develop the domestic government securities market for both deficit financing and monetary policy implementation. Policies seeking to boost domestic borrowing will help in that regard, despite bringing extra financial cost and risk.

23
<!-- Start of picture text --> 75.0<br>65.0<br>55.0<br>45.07 < <9<br>Ss.Sn.<br>35.0 ma es~~,<br>Bex<br>><br>25.0 ba” <x ”2 °g 55 Pss 2g ° 8 -g a 8 ” 8<br>8 g 8 8 8 g 8 4 & 8<br>—s1 seeeees $2 - + -s3 ----54 —s5 -- 56<br><!-- End of picture text --> <!-- Start of picture text --> 14.0<br>12.0<br>10.0 4<br>4S perree -.<br>8.0 Y . . ied et’7. 7 ~~ ~ ~lLUN,~~Ss,.<br>\ </% ~.y<br>~—--- ~<br>6.0<br>40<br>StL<br>2.0 <s.>2-5 > +<br>wae<br>00 2a =x 24 2g 55 %g ga °g as ag °g<br>& & & ES 8 g a 5 R 5 5<br>—s1 ----52 — + -s3 -- 34 —ss —- $6<br><!-- End of picture text -->
<!-- Start of picture text --> VA. 25<br>12.0 = soccer.<br>10.0 — sve eooecccteePa _- ~ee~~a ~. --. 2.0 eo ern?’=. ceerent =.teepte~s.,2<br>a ra “Ss ~ “~~ -.<br>= ¢ SSS<br>8.0 son> 15<br>x<br>6.0 10 No. ~ - =~. ===<br>4.0 a aininietetatet ee<br>Sse k 05 ——=ae<br>2.0 —_—<br>0.0 0.0<br>3 &=3& a3R 23g a3R a3& a3& 8 g 585 8 g 8 R =3& a33 =33 a3& a3g a3g 8 5 a84 a8s BY8g<br>—1 ----52 - +33 -- 4 —s5 -- 56 —s1 =---52 =. =s3 == s4 —ss == 6<br><!-- End of picture text -->
# **E. Conclusions**

**This work shows that, provided the country continues to register a robust growth as well as persists on the current path of rigorous fiscal consolidation, the ratio of debt-to-GDP will decrease dramatically over the next decade** . A deterministic DSA shows that the debt-to-GDP ratio is expected to fall from 74 percent of GDP in 2013 to 30 percent by 2023. Gross financing needs also will fall from 12 percent of GDP to 4 percent in the same period, which shows that Mauritania’s public debt can be sustainable in the long term.

**This result is robust also in the presence of fiscal shocks that may stem from unforeseen macroeconomic changes threatening debt sustainability.** Our stochastic DSA indicates that in 2023 there is a 50 percent probability that the debt-to-GDP ratio could be as low as 27 percent and as high as 33 percent, i.e., deviations of +/-3 percentage points around the baseline path. Similarly, there is a 90 percent probability that the debt-to-GDP ratio could lie in the range 24-40 percent, i.e., with wider deviations relative to the baseline path. Therefore, sound fiscal and debt policies envisaged in the baseline scenario do strengthen debt sustainability even in the presence of macroeconomic shocks.

**The quality of public investment policies appears to be a key determinant of Mauritania’s fiscal and debt performance going forward.** This paper presents quantitative evidence that Mauritania could boost economic growth by improving the quality of public investment. Such result would be achieved by enhancing the quality of its policies and institutions. By doing this, Mauritania could attain a significant growth-dividend and improve debt sustainability.

**With respect to public debt management, this analysis shows that debt strategies can influence Mauritania’s prospective fiscal and debt performance, although their effect is smaller than the one deriving from fiscal policy.** The simulations presented in this paper show that debt policies have a lesser impact on debt sustainability, which instead depends by and large on fiscal policies. Debt policies can attenuate the exposure of the on-budget interest bill to macroeconomic shocks affecting the exchange rate and the domestic interest rates. Thus, debt strategies should be geared towards macroeconomic risk management and cannot substitute fiscal policies as a means to improve Mauritania’s debt sustainability.

26
# **F. References**

Batini, N., Eyraud, L., and Weber, A. (2014). “A Simple Method to Compute Fiscal Multipliers”. IMF Working Paper No. WP/14/93.

Bolder, D. J. (2002). “Towards a more complete debt strategy simulation framework”. Bank of Canada Working Paper No. 2002-13.

Bolder, D. J. (2003). “A stochastic simulation framework for the government of Canada’s debt strategy”. Bank of Canada Working Paper No. 2003-10.

Bolder, D. J. (2005). “Measuring and managing foreign currency risk in a sovereign liability portfolio”. Manuscript. Financial Markets Department, Bank of Canada.

Bolder, D. J. (2008). “The Canadian debt-strategy model”. Bank of Canada Review, pp. 3-16.

Briceño-Garmendia, C. M., and D. A. Benitez (2011). “Cape Verde’s Infrastructure. A Continental Perspective”. World Bank Policy Research Working Paper No. 5687.

Cabral, R., and M. Lopes (2005). “Benchmark for public debt: Two alternative approaches”. Manuscript. Annual Meeting of the Brazilian Society of Finance.

Cabral, R., Lopes, M., Baghdassarian, W., Alves, L. F., Souza Junior, P., and A. Santos (2008). “A benchmark for public debt: The Brazilian case”. Manuscript. Latina America and the Caribbean Debt Group, IDB.

Di Bella, G. (2008). “A Stochastic Framework for Public Debt Sustainability Analysis”. IMF Working Paper No. WP/08/58.

Di Giovanni, J., and Gardner, E. (2008). “A Simple Stochastic Approach to Debt Sustainability Applied to Lebannon”. IMF Working Paper No. WP/08/97.

Fatás, A. (2001). “The Effects of Business Cycles on Growth”, working paper prepared for the 5<sup>th</sup> annual conference of the Central Bank of Chile, November, 2001.

Government of Cape Verde (2011). “Medium-Term Debt Management Strategy MTDS Report”.

International Monetary Fund (2012). “Cape Verde. Second Review Under the Policy Support Instrument and Request for Waivers of Nonobservance of assessment Criteria”. EBS/12/7.

International Monetary Fund and International Development Association (2012). “Revisiting the Debt Sustainability Framework for Low-Income Countries”. SM/12/10. (Washington).

International Monetary Fund and International Development Association (2004). “Debt-Sustainability in LowIncome Countries-Proposal for an Operational Framework and Policy Implications”. SM/04/27. (Washington).

Pradelli, J., and Baghdassarian, W. (2013). “Cape Verde: Building Resiliense in a Small Island State”. CEM – Cape Verde. World Bank.

Tielens, J. Van Aarle, B. and Van Hove, J. (2014). “Effects of Eurobonds: A stochastic Sovereign Debt Sustainability Analysis for Portugal, Ireland and Greece”. Discussion Paper Series DPS 14.10, Ku Leuven Center for Economic Studies.

27
# **G. Annexes**

## **Annex I: A DSA model for Mauritania.**

The stock of public debt measured as a share of GDP, which is widely-used indicator of solvency and debt repayment capacity, depends on four key variables: (i) the initial public debt stock, which results from past borrowing choices; (ii) the primary balance that reflects the current fiscal policies and institutions concerning revenues and spending; (iii) the cost of borrowing, represented by the average interest rate charged on the inherited public debt; and (iv) the growth rate of nominal GDP. Formally, a DSA model postulates a debt dynamics equation to determine the public debt-to-GDP ratio: where _Dt_ denotes the public debt-to-GDP ratio at end of year _t_ , _PDt_ is the primary deficit as a share of GDP, _it_ is the average interest rate paid on the inherited public debt, and _Ŷt_ is the growth rate of nominal GDP that determines the growth effect on the debt ratio.

The macroeconomic and fiscal variables involved in the public debt dynamics are neither isolated nor determined independently. On the contrary, they depend on each other through several interactions and feedbacks, which underlie their co-movements observed in the historical series of every country. For instance, in Mauritania, the output growth depends on the public investment, and the fiscal revenues depend on the economic activity. A realistic DSA model for Mauritania would then enrich the public debt dynamics modelled in equation (1) by introducing a set of relevant behavioral hypotheses—formalized through functional forms—that capture the interactions and feedbacks characterizing the economy. Some of these specificities are brought into the following debt dynamics equation: where relevant features are introduced: (i) the public debt ratio _Dt_ is disaggregated into domestic debt _Dd,t_ and foreign debt _Df,t_ (converted into local currency using the nominal exchange rate _Et_ ); (ii) the primary deficit ratio _PDt_ is broken down into primary current expenditure _CEt_ , capital expenditure _KEt_ (i.e., public investment), and total revenues _Tt_ (all variables scaled by GDP); (iii) the primary current expenditure _CEt_ , and total revenues _Tt_ explicitly depend on the nominal GDP _Yt_ , which used as a proxy for the scale of expenditure programs and the relevant tax bases; (iv) the interest expenditure reflects the cost of carrying domestic and foreign liabilities, with the respective interest rates denoted _id,t_ and _if,t_ ; and (v) the valuation effect of currency depreciation _Êt_ on the foreign liabilities is explicitly added to their interest cost.

28
(1+Y¥)=(1+9 Ut d= (14+r )Q+p
interest rate _rd,t_ and the domestic inflation rate; and (iii) � where the nominal currency depreciation rate _Êt_ against the US dollar is decomposed into the relative change in tt tt tt dd,tt the real exchange rate _ȇt_ , the domestic inflation rate, and the foreign inflation rate. �1 + EE<sup>�</sup> �= (1 + eê )(1 + pp̂ )/�1 + pp̂ We specify an econometric time-series model—a Vector Auto-regressive (VAR) model—to formalize the dynamic interactions and feedbacks between the real GDP growth rate _ŷt_ , the real currency depreciation rate _ȇt_ , the domestic real interest rate _rd,t_ , and the public investment-to-GDP ratio _KEt_ . The first three variables are treated as endogenous in the VAR specification, and proxy, respectively, Mauritania’s growth, competitiveness, and borrowing costs for the government. Public investment, instead, is deemed an exogenous, policy-driven variable. The VAR model intends to captures the pattern of co-movements between these four variables, e.g., _KEt_ affects _ŷt_ (through the accumulation of physical capital) as well as _ȇt_ (through the productivity improvements, the foreign-exchange inflows associated to external financing of public capital projects, etc.).

For the purposes of this study, the VAR model is seen as a useful device to represent the stylized behavior of key macroeconomic variables driving the public debt dynamics and to simulate shocks and scenarios. Admittedly, the performance of the VAR model for Mauritania is severely affected by data availability constraints and structural breaks in the development experience of recent years. Hence, a strict scrutiny of the statistical properties of the VAR model is likely to reveal weaknesses and drawbacks, e.g., failures to pass basic specification and forecast tests. Nevertheless, lacking a better macroeconomic modelling device and acknowledging that the Mauritanian economic phenomena being modelled are themselves quite complex, the formulation and use of VAR model is justified in our view.

The VAR is estimated blending the historical data in 2003-13 and the projections in 2014-18 embedded in the baseline macro-fiscal framework. As times series are not long, we consider only one lag of the endogenous variables, i.e., a VAR(1) model. A casual observation to the historical series of the three endogenous variables suggests macroeconomic volatility was excessive in the past and would probably be largely attenuated in the next few years (Figure AI.1 and AI.2). In addition, the real exchange rate appreciated extremely fast in 2003-08 and arguably the prospective pace of real currency appreciation will be much slower. Against this backdrop, in order to smooth the volatility of the VAR forecasts and stochastic simulations, we proceed by estimating the VAR model using data points in 2014-18—drawn from the baseline macro-fiscal framework—and adjusting a few estimated covariances and intercepts. The estimated VAR coefficients are reported in Table AI.1 and the (adjusted) estimates are used in Section C to generate the scenario with growth-dividend consistent with the higher-investment policy. The estimated matrix of covariances of residuals is reported in Table AI.2 and the (adjusted) estimates are used in Section B to generate shocks to the macroeconomic trends characterizing the baseline outlook.

In Section B, the analysis of fiscal risks relies on stochastic simulations of macroeconomic scenarios. Starting with the baseline outlook’s trends reported in Table B1, randomly-generated shocks are added to the real GDP growth rate _ŷt_ , the real currency depreciation rate _ȇt_ , and the domestic real interest rate _rd,t_ . Following standard practices, in any given year, the shocks are generated with a multivariate Gaussian distribution centered in zero and with the VAR residuals’ covariance matrix. Shocks are correlated in any given year, but independent through time. The various (shock-driven) paths of the three macroeconomic variables feed into the DSA model and thus underpin the fan charts of public debt and gross financing needs reported in Figure B3 and B4.

For illustrative purposes, the fan charts in Figure AI.3 and AI.4 correspond to the real GDP growth rate and the nominal exchange rate—derived from the real currency depreciation rate and the domestic and foreign inflation rates. The dotted lines represent the baseline (expected) paths: the real GDP growth stabilizes at 7 percent in the next few years; while the nominal exchange rate smoothly moves within the range of 275-300 MRO per unit of US dollar. The colored bands, in turn, depict the probability distribution (density) of the growth and exchange rate outcomes resulting from macroeconomic shocks and stochastic simulations. The bands indicate that there is a 90 percent probability that Mauritania’s real 30
GDP will grow as slow as 5 percent per annum, and as fast as 9 percent, in the next ten years. Similarly, with identical probability, the exchange parity can lie in the range of 225-375 MRO per unit of US dollar by 2023.

**Table AI.1. VAR model: estimated coefficients, t-statistics in [ ].**

|Growth rate of real<br>GDP (%)|Relative change of<br>real exchange rate (%)|Domestic real interest<br>rate (%)|
|---|---|---|
|Growth rate of real GDP (%) - lagged<br>0.306<br>[ 0.64211]|-2.876<br>[-1.81657]|-0.236<br>[-0.15491]|
|Relative change of RER (%) - lagged<br>0.003|-0.329|-0.198|
|[ 0.02408]|[-0.86196]|[-0.53842]|
|Domestic RIR (%) - lagged<br>0.172|-0.758|-0.351|
|[ 1.21590]|[-1.60890]|[-0.77467]|
|Intercept<br>-0.759|-5.747 (*)|8.267|
|[-0.20346]|[-0.46428]|[ 0.69346]|
|Public Investment (% of GDP)<br>0.325|2.020 (**)|0.117|
|[ 0.98032]|[ 1.83960]|[ 0.11042]|
|R-squared<br>0.316|0.411|0.227|
|Adj. R-squared<br>0.042|0.176|-0.082|
|Sum sq. resids<br>82.715|911.335|845.353|
|S.E. equation<br>2.876|9.546|9.194|
|F-statistic<br>1.155|1.748|0.733|
|Log likelihood<br>-34.089|-52.086|-51.522|
|Akaike AIC<br>5.212|7.611|7.536|
|Schwarz SC<br>5.448|7.847|7.772|
|Mean of dependent variable<br>5.441|-3.570|6.612|
|S.D. of dependent variable<br>2.939|10.517|8.837|
|Determinant resid covariance (dof adj.)|12713.8||
|Determinant resid covariance|3767.0||
|Log likelihood|-125.6||
|Akaike information criterion|18.7||
|Schwarz criterion|19.5||

Notes: Adjusted values are (*) = 8.5 and (**) = 1.

Source: World Bank calculations

# **Table AI.2. VAR model: estimated matrix of covariances of residuals.**

||Growth rate of real<br>GDP (%)|Relative change of<br>real exchange rate (%)|Domestic real interest<br>rate (%)|
|---|---|---|---|
|Growth rate of real GDP (%)|8.3|-16.4|-21.6|
|Relative change of RER (%)|-16.4|91.1|53.9|
|Domestic RIR (%)|-21.6|53.9|84.5|

Notes: Adjusted values are one-fifth of all covariances.

Source: World Bank calculations 31
<!-- Start of picture text --> 200.0 18.0<br>180.0 - 15.0<br>A peaeneae<br>160.0 —| oeeeepepw<br>a i 7 Terre<br>at maNAbrearahs . = 8 |<br>soo EE LE See ee Ee Eee Ly<br>coaleee»eee»» o882888888ad882888888adad oO a o a N m st ”8288o8288oo S cs)88<br>lm Real GDP growth GDP growth growth (%, right) mmm Public Investm. (% of GDP, Public Investm. (% of GDP, Investm. (% of GDP, (% of GDP, of GDP, GDP, right)<br>—RER (index 2008=100, left)<br><!-- End of picture text -->

<!-- Start of picture text --> 25.0<br>20.0 ii is<br>15.0 - i i<br>100 ———— i a<br>o BREET SE EEE EEEE<br>5.0 iF<br>15.0<br>gsSssseessseess2828Sssseessseess28282828 358 828H2aR F445eses 8BB<br>@ Real GDP growth Real GDP growth GDP growth growth (%) —® RIR (%) (%)<br><!-- End of picture text --> <!-- Start of picture text --> 200.0 18.0 25.0<br>180.0 - 15.0 20.0 iii sis<br>A peaeneae 15.0 - i i<br>160.0 —| oeeeepepw 100 ———— i a<br>a i 7 Terre o BREET SE EEE EEEE<br>at maNAbrearahs . = 8 | 5.0 iF<br>soo EE LE See ee Ee Eee Ly 15.0<br>coaleee»eee»» o882888888ad882888888adad oO a o a N m st ”8288o8288oo S cs)88 gsSssseessseess2828Sssseessseess28282828 358 828H2aR F445eses 8BB<br>lm Real GDP growth GDP growth growth (%, right) mmm Public Investm. (% of GDP, Public Investm. (% of GDP, Investm. (% of GDP, (% of GDP, of GDP, GDP, right) @ Real GDP growth Real GDP growth GDP growth growth (%) —® RIR (%) (%)<br>—RER (index 2008=100, left)<br>10.0 400<br>50.. mmm 95%90%90% --- 99%95%95% 350 mmm95%90% -- 99%95%95%90% -- 99%95%90% -- 99%95% -- 99%95%- 99%95% 99%95%95%<br>8.0 mmm 75% - 90% mmm 75% - 90%<br>mmm 67% - 75% Mm67%67% - 75% 75%<br>7.0 mm 50% - 67% 67% 300 mm5.0% - 67%5.0% - 67% - 67% 67%<br>60 mmm 33% - 50% mmm33% - 50%33% - 50% - 50% 50%<br>mmm 25% - 33% mmm25%25% - 33% 33%<br>5.0 mmm 10% - 25% 25% 250 mmm 10% - 25% 25%<br>4.0 mmm 5% - 10% mmm 5% - 10%<br>1% - 5% 1%-5%-5%5%<br>3.0 o28S28S8SS t+$88$88888 ©38S&38S&8S&S& ©$8&$8&8&& RK&8B&B&8B&B8B&BB&B 8&€&6&&€&6&&6&& D9&@2@8FR&@2@8FR8FRFR OCgs8KRgs8KR8KRKR +H&8BR&8BR8BRBR Af&8KKf&8KK8KKKK om88A88A8AA ---ExpectedGrowth 200 os8&s8&8&& tt=88=88888 838&38&8&& ©8&SR8&SR&SRSR HR538R538R8RR &@88&8RB88&8RB8&8RBRB D928KR28KR8KRKR 088R88R8RR HH&8RK&8RK8RKRK KN&8KK&8KK8KKKK om&88AR&88AR8ARAR — -— — Expected Parity<br><!-- End of picture text -->

<!-- Start of picture text --> 10.0<br>50.. mmm 95%90%90% --- 99%95%95%<br>8.0 mmm 75% - 90%<br>mmm 67% - 75%<br>7.0 mm 50% - 67% 67%<br>60 mmm 33% - 50%<br>mmm 25% - 33%<br>5.0 mmm 10% - 25% 25%<br>4.0 mmm 5% - 10%<br>1% - 5%<br>3.0 ---ExpectedGrowth<br>o28S28S8SS t+$88$88888 ©38S&38S&8S&S& ©$8&$8&8&& RK&8B&B&8B&B8B&BB&B 8&€&6&&€&6&&6&& D9&@2@8FR&@2@8FR8FRFR OCgs8KRgs8KR8KRKR +H&8BR&8BR8BRBR Af&8KKf&8KK8KKKK om88A88A8AA<br><!-- End of picture text --> <!-- Start of picture text --> 400<br>350 mmm95%90% -- 99%95%95%90% -- 99%95%90% -- 99%95% -- 99%95%- 99%95% 99%95%95%<br>mmm 75% - 90%<br>Mm67%67% - 75% 75%<br>300 mm5.0% - 67%5.0% - 67% - 67% 67%<br>mmm33% - 50%33% - 50% - 50% 50%<br>mmm25%25% - 33% 33%<br>250 mmm 10% - 25% 25%<br>mmm 5% - 10%<br>1%-5%-5%5%<br>200 — -— — Expected Parity<br>os8&s8&8&& tt=88=88888 838&38&8&& ©8&SR8&SR&SRSR HR538R538R8RR &@88&8RB88&8RB8&8RBRB D928KR28KR8KRKR 088R88R8RR HH&8RK&8RK8RKRK KN&8KK&8KK8KKKK om&88AR&88AR8ARAR<br><!-- End of picture text -->
**Annex II. Optimal composition of public debt in Mauritania.**

This work analyses how optimal debt strategies contribute to enhance Mauritania’s debt sustainability. We define _debt strategy_ as the combination of different instruments that are issued by the government to cover its financing needs. Because of the differences in public debt instruments, we expect that alternative debt strategies will have different features in terms of costs and risks.

A specific portfolio is said to be _efficient_ if it has the lowest possible cost for a given level of risk. Alternatively, an _efficient portfolio_ can also be defined as the one with the lowest risk for a given level of cost. In turn, an _optimal portfolio_ is an _efficient portfolio_ that best matches public debt managers’ risk tolerance. To find an optimal portfolio we need to incorporate a behavioral assessment of public debt managers by using some sort of utility function. In this work, we consider that the optimal debt strategy is the one that minimizes the certainty equivalent cost of the debt which also depends on the macro-fiscal framework (i.e., a set of central projections, complemented with a stochastic distribution of shocks that might induce outcomes to deviate from the central projections) and the government’s risk tolerance (i.e., the subjective willingness of the authorities to bear risk).

Our model uses stochastic simulations to generate the public debt dynamics and finds optimal portfolio allocations by minimizing the certainty equivalent (CE) of the annual interest payment-to-GDP ratio. It allows evaluating the cost-risk trade-off of alternative debt strategies and the sensitivity of the optimal portfolio allocation to changes in the macroeconomic environment. The model has three components: a stochastic scenario generator based on the VAR model (described in Annex I), a debt issuance engine, and an optimization procedure to find optimal portfolio allocations.<sup>15</sup> The debt issuance engine replicates the way the Mauritanian Government refinances the public debt. In a given year, the government has to borrow to finance the overall budget deficit as well as to rollover maturing debts. There are different debt instruments that can be used for that purpose, with a diversity of maturities, currencies, and lenders. We consider a set of ten representative stylized debt instruments close to those described in the Mauritania’s Medium-Term Debt Management Strategy MTDS Report. Instruments numbered I1 to I10 are reported in Table D1. These instruments represent a broad spectrum in terms of maturities, exposure to different currencies, and allowance for concessional and market-based debt. Our model assumes that the government chooses an optimal debt portfolio with time-varying shares of the gross financing needs to be covered with each instrument over the projection horizon 2014-2023.

The optimization procedure focuses on the annual interest payments-to-GDP ratio (a proxy of the real carry cost of public debt): The optimization procedure involves a number of steps. First, we specify _N_ debt strategies, denoted _S1_ to _Sn_ , that are sufficient to adequately describe the cost-risk trade-off in the portfolio efficient frontier. Second, for each stochastic simulation and a given debt strategy, we compute the public debt dynamics over the period 2014-2023 and the corresponding average annual interest payments-to-GDP ratio. By averaging across simulations, we obtain the average annual interest payments-to-GDP ratio associated with that debt strategy. We repeat this procedure for all debt strategies and then construct a _Nx1_ vector of mean values. Third, for each stochastic simulation and a given pair of debt strategies, we compute the corresponding variance and covariance of annual interest payments-to-GDP ratios in 2014-2023. By averaging across simulations, we obtain the covariance matrix of annual interest payments-to-GDP ratios associated with those two strategies. We repeat this procedure for all pairs of debt strategies and then

> 15 For further references on applied models of optimal public debt portfolio, see Cabral and Lopes (2005), Cabral et al. (2008), and Bolder (2002, 2003, 2005, and 2008).

33
construct a _NxN_ covariance matrix.<sup>16</sup> Fourth, using the _Nx1_ mean vector and the _NxN_ covariance matrix, we construct the efficient frontier that depicts the debt portfolio with the minimum expected cost (measured by the average annual interest payments-to-GDP ratio corresponding to the debt strategy underlying that portfolio, denoted **μ** s) for any given level of risk (measured by the standard deviation of the annual interest payments-to-GDP ratio corresponding to the debt strategy underlying that portfolio, denoted **σ** s).<sup>17</sup> Fifth, we postulate the certainty equivalent (CE) of the annual interest payment-to-GDP ratio as the objective function: where λ<sup>+</sup> denotes the _coefficient of absolute risk tolerance_ that characterizes the government’s risk preferences. The CE is computed for all the portfolios in the efficient frontier. Finally, we find the optimal debt strategy _S*_ that underlies the efficient portfolio minimizing the CE for a given value of λ<sup>+</sup> .

In our model, the CE should be seen as the fixed amount of interest payment (as share of GDP) that would leave the government indifferent between paying that fixed amount and facing a random variable interest payment with chances of paying more or less. The first term of the CE in (9) is simply the expected value of the random variable interest payment (as share of GDP). The second term is the _additional_ interest payment (as share of GDP), over and above the expected interest payment, that the government is willing to make to be indifferent between paying a fixed amount of interests (as share of GDP) and paying a random variable amount. It is thus often interpreted as the _premium_ the government is willing to pay in order to eliminate the uncertainty in the interest payment. The lower the level of uncertainty (risk) the government accepts to face, the lower the _coefficient of absolute risk tolerance_ λ<sup>+</sup> and the higher the _additional_ interest payment (as share of GDP) the government is willing to borne. For simplicity, we report the _coefficient of relative risk tolerance_ λ, which is defined as the coefficient λ<sup>+</sup> scaled by the cost of the most expensive debt portfolio in the efficient frontier (measured by the average annual interest payments-to-GDP ratio corresponding to the most expensive debt strategy).<sup>18</sup> In this work, we consider two alternative issuance scenarios to test how optimal public debt management policies can contribute to enhance sustainability and resilience in Mauritania. The first scenario assumes that official lenders would provide as much concessional financing as the authorities find it optimal to borrow from them, so we call it _unconstrained optimal portfolio strategy_ . The second scenario assumes that the country will just use the already contracted loans from official lenders but will need to resort to other creditors (bilateral, commercial, and domestic debts) to cover the remaining financing needs. In terms of debt instruments, this scenario considers two types of bilateral semi-concessional debts (Paris Club – I5 and Non Paris Club – I6), a Commercial external debt – I7, and three domestic instruments

> 16 Note that the stochastic simulations enable us to characterize the probability distribution (density) of the average annual interest payments-to-GDP ratio associated with one strategy, as well as the probability distribution (density) of the covariance matrix associated with two strategies.

> 17 We generate a large collection of debt strategies by computing linear combinations of the initial _N_ strategies, and then we pinpoint 20 portfolios in the efficient frontier with their underlying strategies.

> 18 The concepts of CE, λ+, and λ are standard in the theory of choice under uncertainty. The CE of a gamble is the amount of money for which an individual is indifferent between receiving that amount of money directly and participating in such a gamble with chances of gaining or losing money, with both actions yielding identical level of utility. The _coefficient of absolute risk tolerance_ λ<sup>+</sup> , in turn, can be interpreted as follows: a risk-averse investor is offered to participate in a gamble in which she bets an amount of money _X_ and receives either 2* _X_ or 0.5* _X_ with equal chances, i.e., she faces a 50 percent probability of doubling the bet and another 50 percent probability of losing half of the bet; the maximum amount of money _X_ she would be willing to bet in such a gamble is the _absolute risk tolerance λ_<sup>_+_</sup> . This coefficient is the reciprocal of the absolute Arrow-Pratt risk aversion coefficient. When the amount of money _X_ is expressed as a proportion _x_ of the investor’s wealth _W_ , the maximum proportion _x_ she would be willing to bet in such a gamble is the _relative risk tolerance_ λ.

34
(one year T-bond – I8, two years T-bond – I9, and a three year T-bond – I10). Thus, the strategy optimizes the composition of the residual borrowing, i.e., the gross financing needs net of the available supply-constrained concessional resources, and we call it _constrained optimal portfolio strategy_ . The authorities choose the portfolio composition optimally using only the six instruments.

For the _unconstrained optimal portfolio strategies_ we obtain an unsurprising result: concessional debt instruments strongly dominate all the others and thus authorities would find it optimal to borrow _exclusively_ from official lenders (IDA) to meet their financing needs. In particular, the different optimal portfolios, corresponding to almost all risk tolerance levels, are those with heavy reliance on the USDdenominated concessional loans extended by multilateral banks (instrument I1, with a share above 92 percent in all optimal portfolios) (Figure AII.1).

This dominance is explained both by the low fixed interest rate charged on these long-term loans by multilateral banks and the small variability of the MRO/USD parity in our stochastic scenarios. This limited currency risk stems from the assumed stochastic distribution of shocks, in which the shocks to MRO/USD nominal exchange rate exhibit low variance and low correlation with GDP growth. A common concern when borrowing using foreign-currency denominated debt instruments is the currency risk and the possibility that large depreciations induce jumps in the debt-to-GDP ratio due to valuation effects. Furthermore, there cannot be large _unexpected_ valuation effects either, which would otherwise discourage using foreign currency-denominated debt because of concerns on risk. Thus, the unexpected random MRO/USD exchange rate fluctuations are small and do not induce significant jumps in the debtto-GDP ratio through valuation or growth effects.

The optimal unconstrained portfolio strategies associated with different levels of risk tolerance deliver projections of public debt and interest payments that are fairly similar to the projections in the baseline scenario with a simple non-optimal debt strategy, thus implying that even optimal debt policies can do little to improve Mauritania’s debt sustainability and any enhancement should be addressed by the fiscal policy.

When we move to a more challenging issuance scenario, with lower access to concessional loans, which was labeled _constrained optimal portfolio strategies_ we also found an unsurprising result: Bilateral (semiconcessional) debt instruments strongly dominate all the others and thus authorities would find it optimal to borrow almost _exclusively_ from bilateral lenders (Paris Club and No-Paris Club) to meet their financing needs. In particular, the different optimal portfolios, corresponding to all risk tolerance levels, are those with absolute reliance (share of 100 percent in both scenarios and for all levels of risk tolerance) on the USD-denominated Bilateral Non-Paris Club loans, since they are, in this exercise, slightly cheaper than Bilateral Paris Club loans (Figure AII.2).

As expected, because of the constraints that raise the share of less concessional instruments in all possible portfolio allocations, costs and risks of the constrained optimal portfolio strategy is higher than the unconstrained strategy’s.

35
# **Figure AII.1. Unconstrained debt portfolio strategy**

- **a.  Efficient Frontier. Cost and Risk (% of GDP).**

<!-- Start of picture text -->
1.40%<br>1.20%<br>1.00%<br>0.80%<br>0.60%<br>0.40%<br>0.20%<br>0.00%<br>Risk<br>Cost<br>0.10% 0.47% 0.47% 0.48% 0.48% 0.49% 0.50% 0.50% 0.51% 0.53% 0.54%<br><!-- End of picture text -->

Source: World Bank calculations

- **b.  Efficient Portfolios. Cost and Risk (% of GDP) and Composition (% share of instruments).**

|**Risk**|**Cost**|**I1**|**I2**|**I3**|**I4**|**I5**|**I6**|**I7**|**I8**|**I9**|**I10**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|0.10%|1.26%|---|---|---|---|88.0%|---|0.0%|3.4%|2.9%|5.8%|
|0.47%|1.23%|1.6%|---|---|---|88.2%|---|---|3.1%|3.3%|3.7%|
|0.47%|1.21%|6.4%|---|---|---|84.2%|---|---|2.8%|2.8%|3.8%|
|0.47%|1.18%|12.5%|---|---|2.9%|75.9%|---|---|2.5%|2.3%|4.0%|
|0.47%|1.16%|26.9%|---|---|4.7%|58.5%|---|0.9%|2.2%|1.8%|4.8%|
|0.47%|1.13%|46.8%|---|---|---|41.0%|---|2.8%|1.9%|1.5%|6.0%|
|0.48%|1.10%|64.1%|---|---|---|22.0%|---|4.1%|1.7%|1.0%|7.1%|
|0.48%|1.08%|81.3%|---|---|---|3.1%|---|5.5%|1.4%|0.6%|8.1%|
|0.48%|1.05%|85.5%|---|---|---|---|---|5.5%|1.1%|0.4%|7.5%|
|0.48%|1.03%|87.2%|---|---|---|---|---|5.3%|0.7%|0.3%|6.5%|
|0.49%|1.00%|88.8%|---|---|---|---|---|5.1%|0.4%|0.2%|5.5%|
|0.49%|0.98%|90.5%|---|---|---|---|---|4.9%|0.0%|0.1%|4.5%|
|0.50%|0.95%|92.1%|---|---|---|---|---|4.7%|---|---|3.2%|
|0.50%|0.93%|93.6%|---|---|---|---|---|4.5%|---|---|1.8%|
|0.50%|0.90%|95.2%|---|---|---|---|---|4.4%|---|---|0.4%|
|0.51%|0.88%|96.2%|---|---|---|---|---|3.8%|---|---|---|
|0.51%|0.85%|97.0%|---|---|---|---|---|3.0%|---|---|---|
|0.52%|0.83%|97.7%|---|---|---|---|---|2.3%|---|---|---|
|0.53%|0.80%|98.5%|---|---|---|---|---|1.5%|---|---|---|
|0.53%|0.78%|99.2%|---|---|---|---|---|0.8%|---|---|---|
|0.54%|0.75%|100.0%|---|---|---|---|---|---|---|---|---|

Source: World Bank calculations

**c.  Optimal Portfolios for Different Levels of Risk Tolerance λ. Composition (% share of instruments).**

<!-- Start of picture text --> 100%<br>98% I10<br>I9<br>96%<br>I8<br>94% I7<br>I6<br>92% I5<br>I4<br>90%<br>I3<br>88% I2<br>I1<br>Risk Tolerance (λ)<br>Share in optimal portfolio<br>5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%<br><!-- End of picture text --> Source: World Bank calculations

**d.  Optimal Portfolios for Different Levels of Risk Tolerance λ. Composition (% share of instruments) and Certainty Equivalent CE (% of GDP).**

|λ|**CE**|**Risk**|**Cost**|**I1**|**I2**|**I3**|**I4**|**I5**|**I6**|**I7**|**I8**|**I9**|**I10**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|5.0%|1.2%|0.5%|1.1%|92.1%|---|---|---|---|---|4.7%|---|---|3.2%|
|10.0%<br>|1.0%<br>|0.5%<br>|0.9%<br>|98.5%<br>|---|---|---|---|---|1.5%|---|---|---|
|15.0%|0.9%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|20.0%|0.9%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|25.0%|0.9%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|30.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|35.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|40.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|45.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|50.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|55.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|60.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|65.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|70.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|75.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|80.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|85.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|90.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|95.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|
|100.0%|0.8%|0.5%|0.8%|100.0%|---|---|---|---|---|---|---|---|---|

Source: World Bank calculations 36
# **Figure AII.2. Constrained debt portfolio strategy.**

# **a.  Efficient Frontier. Cost and Risk (% of GDP).**

<!-- Start of picture text -->
1.40%<br>1.20%<br>1.00%<br>0.80%<br>0.60%<br>0.40%<br>0.20%<br>0.00%<br>Risk<br>Cost<br>0.10% 0.10% 0.10% 0.11% 0.11% 0.11% 0.12% 0.13% 0.13% 0.14% 0.15%<br><!-- End of picture text -->

Source: World Bank calculations

# **b.  Efficient Portfolios. Cost and Risk (% of GDP) and Composition (% share of instruments).**

|**Risk**|**Cost**|**I1**|**I2**|**I3**|**I4**|**I5**|**I6**|**I7**|**I8**|**I9**|**I10**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|0.10%|1.26%|---|---|---|---|87.96%|0.00%|0.04%|3.36%|0.04%|3.36%|
|0.10%|1.25%|---|---|---|---|89.0%|0.0%|0.0%|3.3%|0.0%|3.3%|
|0.10%|1.24%|---|---|---|---|87.4%|2.4%|0.0%|3.2%|0.0%|3.2%|
|0.10%|1.22%|---|---|---|---|81.3%|8.9%|0.0%|3.1%|0.0%|3.1%|
|0.10%|1.21%|---|---|---|---|75.2%|15.5%|0.0%|2.9%|0.0%|2.9%|
|0.10%|1.20%|---|---|---|---|69.1%|22.0%|0.0%|2.8%|0.0%|2.8%|
|0.11%|1.19%|---|---|---|---|62.9%|28.5%|0.0%|2.6%|0.0%|2.6%|
|0.11%|1.18%|---|---|---|---|56.8%|35.1%|0.0%|2.5%|0.0%|2.5%|
|0.11%|1.17%|---|---|---|---|50.7%|41.6%|0.0%|2.3%|0.0%|2.3%|
|0.11%|1.16%|---|---|---|---|44.6%|48.1%|0.0%|2.2%|0.0%|2.2%|
|0.11%|1.15%|---|---|---|---|38.5%|54.7%|0.0%|2.0%|0.0%|2.0%|
|0.12%|1.14%|---|---|---|---|15.7%|77.1%|0.5%|1.9%|0.5%|1.9%|
|0.12%|1.13%|---|---|---|---|0.0%|92.7%|0.8%|1.7%|0.8%|1.7%|
|0.12%|1.12%|---|---|---|---|0.0%|93.5%|0.6%|1.6%|0.6%|1.6%|
|0.13%|1.11%|---|---|---|---|0.0%|94.3%|0.5%|1.5%|0.5%|1.5%|
|0.13%|1.10%|---|---|---|---|0.0%|95.1%|0.4%|1.3%|0.4%|1.3%|
|0.13%|1.09%|---|---|---|---|0.0%|95.8%|0.2%|1.2%|0.2%|1.2%|
|0.14%|1.08%|---|---|---|---|0.0%|96.6%|0.1%|1.0%|0.1%|1.0%|
|0.14%|1.06%|---|---|---|---|0.0%|97.5%|0.0%|1.0%|0.0%|1.0%|
|0.14%|1.05%|---|---|---|---|0.0%|98.6%|0.0%|1.1%|0.0%|1.1%|
|0.15%|1.04%|---|---|---|---|0.0%|100.0%|0.0%|0.0%|0.0%|0.0%|

Source: World Bank calculations

**c.  Optimal Portfolios for Different Levels of Risk Tolerance λ. Composition (% share of instruments).**

# **d.  Optimal Portfolios for Different Levels of Risk Tolerance λ. Composition (% share of instruments) and Certainty Equivalent CE (% of GDP).**

<!-- Start of picture text --> λ CE Risk Cost I1 I2 I3 I4 I5 I6 I7 I8 I9 I10<br>100% 5.00% 1.22% 0.10% 1.25% --- --- --- --- 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%<br>10.00% 1.13% 0.10% 1.25% --- --- --- --- 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%<br>90% 15.00% 1.10% 0.10% 1.25% --- --- --- --- 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%<br>20.00% 1.09% 0.10% 1.25% --- --- --- --- 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%<br>80% 25.00% 1.08% 0.10% 1.25% --- --- --- --- 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%<br>30.00% 1.07% 0.10% 1.25% --- --- --- --- 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%<br>70% 40.00%35.00% 1.07%1.07% 0.10%0.10% 1.25%1.25% ------ ------ ------ ------ 0.0%0.0% 100.0%100.0% 0.0%0.0% 0.0%0.0% 0.0%0.0% 0.0%0.0%<br>60% I10 50.00%45.00% 1.06%1.06% 0.10%0.10% 1.25%1.25% ------ ------ ------ ------ 0.0%0.0% 100.0%100.0% 0.0%0.0% 0.0%0.0% 0.0%0.0% 0.0%0.0%<br>50% I9 60.00%55.00% 1.06%1.06% 0.10%0.10% 1.25%1.25% ------ ------ ------ ------ 0.0%0.0% 100.0%100.0% 0.0%0.0% 0.0%0.0% 0.0%0.0% 0.0%0.0%<br>40% I8 70.00%65.00% 1.06%1.06% 0.10%0.10% 1.25%1.25% ------ ------ ------ ------ 0.0%0.0% 100.0%100.0% 0.0%0.0% 0.0%0.0% 0.0%0.0% 0.0%0.0%<br>30% I7 80.00%75.00% 1.05%1.05% 0.10%0.10% 1.25%1.25% ------ ------ ------ ------ 0.0%0.0% 100.0%100.0% 0.0%0.0% 0.0%0.0% 0.0%0.0% 0.0%0.0%<br>20% I6 90.00%85.00% 1.05%1.05% 0.10%0.10% 1.25%1.25% ------ ------ ------ ------ 0.0%0.0% 100.0%100.0% 0.0%0.0% 0.0%0.0% 0.0%0.0% 0.0%0.0%<br>10% I5 100.00%95.00% 1.05%1.05% 0.10%0.10% 1.25%1.25% ------ ------ ------ ------ 0.0%0.0% 100.0%100.0% 0.0%0.0% 0.0%0.0% 0.0%0.0% 0.0%0.0%<br>0%<br>Risk Tolerance (λ)<br>Share in optimal portfolio<br>5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%<br><!-- End of picture text --> Source: World Bank calculations Source: World Bank calculations 37
# **Annex III. Thresholds on Public Debt for Mauritania**

We complement our assessment of Mauritania’s sustainability of public finances by estimating _thresholds_ applicable to the total public debt (as a share of GDP), using the econometric methodology laid out in IMF-IDA (2012). This methodology underpins the Debt Sustainability Framework for Low-Income Countries (LIC DSF) jointly developed by the World Bank and the IMF since 2005. Intuitively, the methodology seeks for a _norm_ or _threshold_ level of public debt that, if exceeded, would imply a probability (risk) of experiencing debt distress deemed _high_ or _excessive_ . The threshold gives due consideration to the specificities of Mauritania, most notably its policies, institutions, and growth prospects. Some background about the LIC DSF is worth discussing before estimating debt thresholds.

# _Debt sustainability and LIC DSF_

A forward-looking analysis of public debt sustainability gravitates around a debt indicator, e.g., a country’s stock of public liabilities or flow of financing needs, scaled by a measure of repayment capacity such as nominal GDP or fiscal revenues. The sustainability analysis basically confronts a norm on that indicator against projected debt paths over a medium- or long-term horizon. Whenever the projected debt paths breach the threshold, the public debt is deemed to be reaching an _unsustainable_ trajectory. A country breaching the norm is expected either to preemptively adopt policies aimed at correcting the budget imbalances, or to head towards facing debt servicing difficulties in the future—which in turn might trigger an abrupt fiscal adjustment or some form of debt default. Different approaches to public debt sustainability analysis derive debt thresholds from theoretical arguments or from empirical observations. There is consensus, however, in that the debt thresholds depend on country-specific financial, macroeconomic and institutional variables, as well as on the tolerance of risk of experiencing debt distress.

The LIC DSF provides a set of thresholds on _external public debt indicators_ , derived from econometric studies comparing countries that experienced difficulties to service public and publicly guaranteed foreign liabilities (i.e., an _external debt distress_ episode) against countries that did not [IMF-IDA (2004, 2012)]. These studies formulate probit models to investigate the determinants of debt distress episodes and to estimate their probability of occurrence conditional upon external public debt indicators and other explanatory variables. For instance, IMF-IDA (2004) estimate probability models of _external debt distress_ using a panel of LIC in the 1980s and 1990s. Distress episodes are identified when a country incurs into large arrears on public liabilities to foreign creditors. Parsimonious probit models are specified including one external public debt indicator, the World Bank’s Country Policy and Institutional Assessment (CPIA) score, and a few macroeconomic (control) variables. Debt indicators and policyrelated variables are found to be good predictors of sovereign defaults towards foreign creditors: the probability of debt distress increases with the debt indicator and decreases with the quality of policies and institutions measured by the CPIA score. With the estimated coefficients of the probit models, IMF-IDA (2004) calibrate the value of the external public debt indicator that matches an arbitrarily-chosen value of debt-distress probability and fixed values of other explanatory variables. The calibrated value of the debt indicator is precisely the debt threshold associated with the level of risk implicit in the arbitrarily-chosen debt-distress probability—which is set around 20 percent. The LIC DSF actually confronts debt thresholds thus calculated against long-term projections of external public debt indicators. Next, it classifies LIC as high-, medium-, or low-risk of external debt distress, depending on whether breaches are observed or not. Debt thresholds thus demarcate _danger zones_ that are useful to anchor policy discussions with country authorities and guide resource-allocation decisions by lenders and donors.

The latest revision of the LIC DSF in IMF-IDA (2012) introduces a number of methodological improvements and, in addition, provides a set of thresholds on _total public debt indicators_ . These norms are obtained with an approach along the lines of IMF-IDA (2004) but considering problems to repay public and publicly guaranteed liabilities owed to foreign and domestic creditors (i.e., episodes of _external_ and _domestic debt distress_ ). IMF-IDA (2012) explores probability models of _external_ and 38
_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