This Selected Issues paper presents a proposal for the creation of savings funds (SF) for rehabilitation and reconstruction after natural disasters (ND) in Dominica. A Monte Carlo experiment is used to calibrate the size of the SF, based on the distribution of ND fiscal shocks estimated from an empirical fiscal model. ND shocks are identified by controlling for other major sources of shock affecting the cyclical fluctuations of output, and government revenue and expenditure, and by calibrating the probability of ND consistent with their historical frequency. It is concluded that under the parameter calibrations proposed, the SF would be financially sustainable with a low probability of depletion.

Abstract

This Selected Issues paper presents a proposal for the creation of savings funds (SF) for rehabilitation and reconstruction after natural disasters (ND) in Dominica. A Monte Carlo experiment is used to calibrate the size of the SF, based on the distribution of ND fiscal shocks estimated from an empirical fiscal model. ND shocks are identified by controlling for other major sources of shock affecting the cyclical fluctuations of output, and government revenue and expenditure, and by calibrating the probability of ND consistent with their historical frequency. It is concluded that under the parameter calibrations proposed, the SF would be financially sustainable with a low probability of depletion.

A Savings Fund for Natural Disasters: An Application to Dominica1

This paper presents a proposal for the creation of savings funds (SF) for rehabilitation and reconstruction after natural disasters (ND) in Dominica. A Monte-Carlo experiment is used to calibrate the size of the SF, based on the distribution of ND fiscal shocks estimated from an empirical fiscal model. ND shocks are identified by controlling for other major sources of shock affecting the cyclical fluctuations of output, and government revenue and expenditure, and by calibrating the probability of ND consistent with their historical frequency. The simulations provide estimates of the amount of savings needed to ensure the financial sustainability of the SF with a low probability of depletion. The simulation framework also allows for the specification of fiscal consolidation measures, and generates probabilistic public debt projections after accounting for the financing flows of the SF vis-à-vis the government budget.

A. Introduction

1. Recurrent tropical storms and other forms of natural disasters (ND) have affected Dominica in the past years, resulting in human loss, destruction of infrastructure, and fiscal costs. Natural disasters have put pressure to government’s finances both in the near and long term. In the near term, as a result of the unanticipated needs for immediate social protection and rehabilitation expenditures, at a time when revenues in general tend to decline. In the long term, the costs of reconstruction have contributed to the ratcheting up of public debt. Barro (2006, 2009) shows that the occurrence of large economic disasters in advanced economies (wars, economic depressions, financial crises) implies large welfare costs equivalent to about 20 percent of annual GDP, which he estimates to be much larger than the costs of economic fluctuations of less amplitude of about 1.5 percent of GDP. For developing small states such as Dominica, which are subject to larger and more frequent disasters than advanced economies, these events should have an even greater effect on the welfare of the average citizen.

2. This paper proposes the creation of a SF with revenues from the Economic Citizenship Program (ECP). In recent years, there was a substantial surge in budget revenues from the Economic Citizenship Program (ECP). These revenues have shown an increasing trajectory in recent years, and have become relevant also from a macroeconomic perspective. If spent without regard to the general macroeconomic conditions, they can pose challenges to macroeconomic management and affect sector activities, including on financial stability, fiscal discipline, external competitiveness and growth (see Rasmussen 2004; Noy 2009; Cavallo and Noy 2011; Cavallo, Galiani Noy and Pantano 2013; and Xin Xu, El-Ashram and Gold 2015). The possible instability of ECP revenues adds uncertainty and makes macroeconomic management more challenging. ECP revenues are difficult to predict and could be subject to a sudden stop, given the increasing scrutiny from advanced economies and growing competition, especially within the ECCU. On the latter, it should be noticed that there is a possible externality in the provision of passports and citizenship that affects the stability of the revenues if there are reputational spillovers to the ECP of other countries in the region.2

A01ufig01

2015: Citizenship by Investment Program

(Percent of GDP)

Citation: IMF Staff Country Reports 2016, 245; 10.5089/9781498377423.002.A001

Sources: National Authorities; and IMF Staff Estimates.

3. In addition, Dominica is affected by high public debt and fiscal sustainability challenges. Public debt is high at over 85 percent of GDP, imposing a constraint on the ability to borrow in the face of NDs. This fact reinforces the case for the use of ECP financing for the start-up and the subsequent funding of the SFs. If so, the saved ECP flows would be allocated to reconstruction after NDs, effectively preventing the need to issue additional debt in the face of a shock. Also, this allocation would reduce the scope of increasing recurrent expenditures from this unreliable source of revenue, further reinforcing fiscal sustainability.

4. The paper is organized in four sections. Section B presents some reasons why government of countries affected by large and recurrent ND should consider self-insurance, setting aside fiscal savings commensurate to the frequency, size, and anticipated fiscal costs of NDs. Section C presents the methodology used in the simulation exercise. Section D includes the calibration of the case of Dominica. Section E presents the results, and section F concludes.

B. Why a SF for Self-Insurance against ND?

5. Theoretically, countries could purchase insurance against ND, but in reality insurance markets and the existing regional schemes offer insufficient and costly options. The private sector is in general uninsured or underinsured for ND, especially in the most vulnerable segments of the population, which is also the vast majority. Also, ND could affect a significant share of the population and wealth in a single ND event, especially in a case of Dominica given its small size (low probability and high damage episodes), complicating the assessment of risk and the need for capital and liquidity by the insurers.3 In addition, the difficulties in calculating the probability of occurrence of a ND and the variety of possible types of ND (hurricanes with high wind; tropical storms with abnormally abundant rainfall; earthquakes) also complicates the actuarial assessment of expected losses and the specification of insurance contracts. These factors result in high cost of insuring against NDs. General equilibrium calibration analysis indicates that ensuring against ND by issuing catastrophe (CAT) bonds would be beneficial only if the cost of issuing these bonds was significantly smaller than in the data (Borensztein, Cavallo and Jeanne 2015)4. Moreover, CAT bonds’ triggers for payment are imperfectly correlated with the actual losses.5

6. Given insufficient market-based insurance, governments become the de-facto ultimate insurer. This means that governments are typically called to cover not only the costs of destruction of public infrastructure, but also a significant share of private losses and to provide social support. All ECCU members have access to the Caribbean Regional Insurance Fund (CRIF), but the costs are high and the coverage purchased is typically limited. Moreover, the CRIF also faces similar complications than those mentioned for the insufficient development of market insurance.

7. As a result, a SF could provide public self-insurance for immediate expenditure needs, rehabilitation and reconstruction, while supporting fiscal sustainability. In principle, if access to financing was granted and immediate, a saving Fund would not be necessary. A government could allocate the fiscal savings to debt reduction (of an amount commensurate to the expected cost of reconstruction) and save on interest expenditures, and then borrow when hit by a ND to cover the costs. However, there are several reasons why this strategy is difficult to implement in practice. First, access to financing is typically not sufficiently rapid, especially for a small country like Dominica with no access to international financial markets. Obtaining an increase in official loans, and changing the scope of exciting official loans towards reconstruction, would typically involve a lengthy process. Furthermore, the disbursement of grants from bilateral donor countries also requires lengthy application and approval processes, and can also take time to materialize. Access to rapid domestic financing could also be limited, especially if the ND shock affects financial institutions’ asset quality, and if deposits decline as the population copes with the shock. As the fiscal savings for reconstruction are saved in a dedicated SF, this would facilitate long-term fiscal sustainability by imposing a recurrent saving discipline of an amount that is commensurate to the expected reconstruction costs.

8. Creating a SF does not imply the crowding-out of other spending priorities nor higher debt servicing costs, as it substitutes for future debt issuance after NDs. Given the developmental and infrastructure needs in Dominica, governments typically face competing expenditure needs that also have high social returns. SF would therefore have high opportunity costs, either in the form of investments foregone or otherwise as higher interest expenditures if the resources in the SF were used for debt reduction. However, the fact that ND are recurrent implies that the resources saved would be fully used at some point, and therefore public debts would have a similar level over the long-term as without a SF. Specifically, public debt would not decline as much when savings are allocated into the Fund, but then countries would need less debt issuance after ND. In this way, the SFs could facilitate fiscal discipline by setting aside the savings to cover the expected costs of reconstruction.

9. Some countries in the region already have similar SFs, but none is specifically targeting the financing of ND fiscal costs. The Sugar Industry Diversification Fund in St. Kitts and Nevis is a national development fund that is also financed with ECP inflows, set up as a public fund. It was established in 2006 with the objective to support the financing of economic diversification away from the sugar industry through training and research. In 2011, its focus was expanded to maintain stability and the financing of industries. It provides budgetary support, undertakes direct social spending, and supports subsidized credit by banks. In 2014 Grenada launched a National Transformation Fund funded by ECP revenues. Set up as a Sovereign Wealth Fund (SWF), it is owned by the government but governed by an independent Board of Directors including both public and private representatives. It is regulated to make transfers to the government for the repayment of arrears and investment projects. Trinidad and Tobago has a SWF dedicated to the savings of oil revenues, which serves the purposes of cyclical stabilization and inter-generational equity. Turks and Caicos also has separate funds that serve different objectives, including a Development Fund, a Sinking Fund, and a Contingencies Fund. The experience with these funds in the region, however, has been mixed, in part due to political influence and capture affecting the allocation of resources. This underscores the importance of a strong institutional design and oversight.

C. Methodology

10. The starting point is to estimate an empirical model of the Dominica economy that captures the dynamics of output and the main fiscal variables in response to ND. To this end, a Vector Auto-regression Model (VAR) is estimated. The vector of endogenous variables in the VAR includes the cyclical components of GDP; government revenues excluding grants; grants; current primary expenditures; and capital expenditures.6

11. ND shocks are identified by including control variables that account for other major sources of shocks. The historical data includes information about the impact of ND as these affect output and fiscal indicators. However, the variability in the historical data would typically also reflect other shocks. Because of this possibility, control variables are necessary to account for the variability of the estimated residuals that could be the result of the other type of shocks. This is important because the estimated distribution of the residuals is later used to draw random ND shocks, as needed to generate the simulated time series. The vector of control variables includes the U.S. real effective exchange (to capture competitiveness pressures given that the EC dollar is pegged to the U.S. dollar); the oil price (all countries are highly dependent on oil imports); the cyclical component of the U.S. output (the main source of tourist revenues); and a dummy for the September 2001 shock that significantly disrupted tourism exports. The underlying assumption is that the control variables “remove” the main alternative sources of fiscal shocks from the estimated vector residuals, resulting in a streamlined distribution that includes natural disasters as the most significant shock remaining.7

12. The second step is to generate a large number of simulations using the estimated model for 2016-2030. Each simulation is a projection consisting of a sequence of the five endogenous variables in the model. 1000 simulations are run, each affected by a sequence of simulated random shocks. The shock simulations are drawn from the normally-distributed probability density function estimated from the model residuals. The simulations generate data that mimic historical patterns in terms of the volatility, persistence, and co-movement of the endogenous series in each simulation in response to shocks that are orthogonal to the controls (and therefore include NDs as the main shock and other smaller shocks).

13. The results are then used to compute probability density functions for each of the five endogenous variables for each year projected. Values for each projected variables in percent of GDP are obtained after assuming a deterministic trend for each, which are assumed to grow at the same constant rate –starting from the end point of the estimated trend in the sample period. The calculation of the overall balance and the stock of public debt require also a projection of interest expenditures. To this end, the debt stock at the end of the previous year is multiplied by an implicit interest rate path (the ratio of interest expenditures to public debt stock), which is treated as a parameter for calibration. The calculation of interest expenditures is then added to revenues and primary expenditures to compute the public debt stock dynamics using the debt accumulation identity, which is expanded to also include the budget financing flows vis-à-vis the SF.

14. The third step is to identify the occurrence of natural disasters in each simulation, as needed to inform the triggering of financing flows vis-à-vis the SFs. To this end, the simulations include an algorithm that identifies as a ND the largest X percent fiscal deteriorations –and therefore the remaining 1-X percent is interpreted as other smaller shocks that are different from ND. The assumption is therefore that all other “large” sources of shocks have been accounted for by the control variables. The fiscal deteriorations are computed as the sum of the year-on-year changes of (i) non-grant revenue (with a negative sign as tax revenues would tend to decline along with output during ND); (ii) grant revenues (which would presumably increase after ND as donor partners increase their supports) (iii) current primary expenditure (as more social assistance and goods and services are needed); and (iv) capital expenditure (on account of additional expenditures for rehabilitation and reconstruction). The algorithm then looks at the distribution of this sum, and identifies as a ND all the random realizations that fall in the highest X percent tail of the of the probability density function of the distribution of this sum. In this way, if (statistically) in a given simulation non-grant revenues decline significantly, and grant revenues, current primary expenditures, and capital expenditures increase significantly (a typical pattern after a ND), then the random simulation is identified as a ND.

15. The calibration of the probability threshold is important, as it determines the annual frequency of NDs in the simulations. For example, if recent episodes indicate that a ND occurs every 5 years, then a parameter of 0.2 would be appropriate, or Probability[x(t) < X]=0.2, where x(t) is a random realization of the fiscal deterioration sum in year t, as explained above. In this way, on average 200 out of the 1000 simulations in each year through 2016-2035 would be identified as a ND –and the rest are identified as other smaller shocks that are different from ND and the controls. The distribution of the intensity or size of each ND is captured by the simulated fluctuations of government revenues and expenditures: if the negative impact on revenues and expenditures is severe, then a large ND has occurred.

16. The use of the SFs is modeled by specifying financing flows vis-à-vis the budget. The simulations assume that in years with no ND, the budget generates an additional overall balance surplus as a percent of GDP that is deposited in the SF8. These budget contributions to the SF are modeled as a fixed parameter as a percent of the previous’ year GDP. The amount of this annual saving is calibrated to achieve the financial sustainability of the Fund with a sufficiently low probability of depletion, and ensuring the SF stock is stable in expected terms9. In the event a ND occurs, as identified by the algorithm, a financing inflow to the budget from the SF takes place. This budget financing is computed as the sum of four components:

  • Gap of non-grant revenues below trend. Captures the decline in tax and non-tax revenues that typically take place after natural disasters as a result of a decline in economic activity and tax compliance.

  • Gap of grant revenues above trend. Grants tend to be higher after natural disasters as a result of an increase in donor support, reducing the need for financing flows from the Fund.

  • Gap of current primary expenditure above trend. Captures higher expenditures in social support and rehabilitation of infrastructure after natural disasters. An additional fixed amount as a percent of GDP is added that captures below-trend reprioritization of spending.

  • Gap of capital expenditure above trend. Captures the higher public investment that typically follows NDs. An additional fixed amount as a percent of GDP is added that captures below-trend reprioritization of spending.

The contributions to the budget continue until the year in which each indicator returns to a level that is below the value in the year prior to the natural disaster –the SF therefore finances the “hump”.

17. The simulation strategy also accounts for expenditure re-prioritization, resulting in a realistic assessment of the need for fiscal savings. On first impression, the simulation assumption that the SF finances only the increase of fiscal needs above trends may appear insufficient when considering the large amount of the estimated fiscal cost. For example, in an extreme case in which a ND results in a destruction of public infrastructure of 50 percent of GDP, one could expect an increase in public investment of 5 percent of GDP over a ten-year period. However, this is not what is observed in practice: a significant share of the fiscal resources used for social support and reconstruction are obtained by way of reallocation and re-prioritization: some pre-ND allocations are postponed or cancelled. As a result, the reconstruction expenditures do not require an equivalent increase in public investment. This is the reason for the additional savings explained above relative to the estimated trends allowed for the current primary and capital expenditures.

18. The modeling of the SF also includes an assumption for the initial stock value, the start-up cost. This initial amount of assets affects the probability of depletion over a time horizon. For example, if the initial size of the Fund is set too low, then the probability of depletion (say, within the next 10 years) would be high for a given set of inflow and outflow financing assumptions vis-à-vis the budget, undermining the sustainability prospects of the Fund. In the opposite case, if the initial stock size is set too high, the opportunity cost as measured in terms of interest costs (i.e. if the funds were used for debt repayment) or the returns of public investment would outweigh the welfare benefits of the precautionary reserve in the SF. As the proposal assumes that the start-up cost of establishing a SF is funded with existing ECP assets, it has not been added to the debt stock at the beginning of the projection horizon (end-2015).

19. The simulations are then used to compute probabilistic public debt projections, taking into account the government budget financing flows vis-à-vis the SF. The simulated series of revenues and primary expenditures allow the calculation of primary balances and public debt dynamics using the debt accumulation identity. In years with no ND, the budget contributes the specified savings to the Fund –as opposed to reducing debt in that amount. If a ND occurs, the Fund is used to finance the additional fiscal needs as specified in the SF disbursement rules –as opposed to issuing public debt.

D. Calibration

20. The simulation parameters are calibrated consistent with staff’s macroeconomic framework. Potential GDP growth is set at 1.7 percent of GDP. The potential output growth rate calibrated assumption is also applied to the trend growth of the remaining endogenous fiscal indicators in the simulations. In this way, the simulated projections are stable in the long-term as a percent of GDP. The implicit interest rate (interest payments / debt stock) is set consistent with measured implicit interest rates in recent years. The calibration also includes fiscal consolidation amounts in percent of GDP per year, allocated across the four simulated fiscal variables, also consistent with the macroeconomic framework. Table 1 shows the specific parametric calibrations used in the simulations.

Table 1.

Parameter Calibration for Dominica

article image

21. The parameters affecting the SF are calibrated to achieve its long-term financial sustainability with a low probability of depletion. A key parameter is the ND probability threshold. This parameter was set at 0.2, broadly consistent with the historical frequency of ND occurring every 5 years on average. The initial size of the Fund stock is set at 10 percent of GDP, as needed to obtain a probability of depletion within the next ten years of 0.08. 10 The budget saving flows into the SF in years without a ND were set at 1.5 percent of previous-year’s GDP. For consistency, if in a given simulation the Fund is depleted it is assumed that the deficit is covered with debt issuance.

22. The remaining parameters for calibration specify the amount of SF financing to the budget after a ND, including for spending re-prioritization. To this end, “base” levels of capital expenditures and current primary expenditures are calibrated, with “base” defined as the level of spending that would prevail in a year in which there is no spending associated with the occurrence of a ND. The “base” capital expenditure is set at 6.4 percent of GDP by specifying a gap from the estimated capital expenditure trend in 2015 of 2 percent of GDP. The “base” current primary expenditure is set at 19 percent of GDP, obtained after specifying 4 percent of trend current primary expenditures associated with ND. As explained above, the SF is assumed to disburse financing to the budget after a ND of an amount equivalent to the gap between the simulated amounts of current primary expenditures and capital expenditures and the calibrated base levels, respectively (net of the simulated increase in grants). These financing flows to the budget continue during the years after a ND for as long as the simulated level of spending is higher than the level registered before the ND.

23. The fiscal consolidation is also calibrated in line with the policies in the macroeconomic framework. It includes cumulative fiscal consolidation measures of 5.5 percent of GDP, largely from revenue measures that are introduced smoothly through 2016-2021. Capital expenditures are also calibrated to map the expected increase in capital expenditures in the near term out of the tropical storm Erika in 2015, which the subsequent unwinding towards 2021.

E. Results

A01ufig02

Off-sample Simulated Dynamics of a Fund for Natural Disasters

(In percent of GDP)

Citation: IMF Staff Country Reports 2016, 245; 10.5089/9781498377423.002.A001

24. Under the parameter calibrations proposed, the SF would be financially sustainable with a low probability of depletion. The text figure shows one random simulation out of the 1000 draws to illustrate how the SF would operate in practice. The SF stock of assets would fluctuate around the start-up level depending on the random realization and size of the simulated NDs through 2016-2030. In the particular simulation used in the figure, three ND take place between 2016 and 2030. The SF stock of assets increases up to 2020, as 1.5 percent of GDP in assets are saved every year, to more than 15 percent of GDP. In 2021 a ND hits Dominica, and SF disbursements to the budget of about 5 percent of GDP take place. The figure also shows two additional simulated ND in 2025 and 2028.

25. A Fund stock of at least 10 percent of GDP and annual savings of 1.5 percent of GDP are needed to achieve the Fund’s financial sustainability with a low probability of depletion.

A01ufig03

Annual Budget Contributions to the Fund for Natural Disasters (ND)

(percent of GDP)

Citation: IMF Staff Country Reports 2016, 245; 10.5089/9781498377423.002.A001

Probability thresholds used to calibrate the frequency of simulated natural disasters The blue oval indicates the calibration value in the baseline simulation
A01ufig04

Probability of Depletion of the Saving Fund

(Average probability through 2016-25)

Citation: IMF Staff Country Reports 2016, 245; 10.5089/9781498377423.002.A001

Size of saving fund (percent of GDP) The red oval indicate the size of SF recommended for its financial

26. The text charts illustrate alternative assumptions and the rationale for the size and amount of annual saving proposed. The left chart shows the sensitivity of the results to changes in the calibrated frequency of natural disasters, as determined by the probability threshold (Probability[x(t) < X]). As explained, with a probability of ND in any given year set at 0.2, and given the specified parameters for the rules for budget financing in the case of a ND, annual budget savings of 1.5 percent of GDP are needed to achieve financial sustainability of the SF over time (no gradual depletion and no unnecessary perpetual accumulation of savings). However, if the probability of ND is set at 0.25 (a ND occurring every 4 years on average), budget savings of 2 percent of GDP per year would be needed for the SF financial sustainability. Other calibrations are also displayed in the left chart. The right chart shows the probability of depletion of the SF when the ND probability is set at 0.2 for different initial sizes of SF stock of assets. For example, if the SF starts up with assets of 2 percent of GDP, the SF would be depleted at some year during 2016-2030 with a probability of more than 30 percent, implying that there would be no sufficient savings to cover the ND costs. In order to reduce this probability to less than 10 percent, a more prudent level, a SF of at least 8 percent of GDP is required.

27. With this calibration, public debt would decline to near 60 percent of GDP by 2030 in expected terms, but with significant dispersion depending on the realization of shocks. This is obtained after accounting for the financing flows between the government budget and the SF depending on the occurrence of the ND in the simulations. Also, this result is broadly in line with the government regional commitments to reach a public debt ratio of 60 percent of GDP or lower by 2030.11 However, the results indicate that even with this significant fiscal consolidation effort there is still a significant probability that the target will not be met in under the more extreme conditions of ND hitting Dominica more frequently and/or harder than expected. This is illustrated in the text chart, which shows a fan chart with probabilistic ranges public debt projections depending on the distribution of frequency, intensity and severity of ND as identified in the simulations.

A01ufig05

Public Debt Dynamics with a Fund for Natural Disasters 1/

(In percent of GDP)

Citation: IMF Staff Country Reports 2016, 245; 10.5089/9781498377423.002.A001

1/ Assumes a probability threshold of natural disasters of 25 percent which requires annual budget contributions of 1.2 percent of GDP for the sustainability of the Fund.

F. Conclusion

28. A saving Fund for ND can be important to support immediate needs after ND and to provide financing for reconstruction within fiscally sustainable bounds. Probabilistic simulations in this paper indicate that a saving Fund stock of about 10 percent of GDP and annual budget savings of 1.5 percent of GDP in years with no ND are needed in order to have sufficient savings commensurate to the expected fiscal costs and observed frequency of NDs. A SF of this size would finance the increase in current primary expenditures and capital expenditures after a ND with a low probability of depletion (except in the most extreme events), and would therefore be consistent with its financial sustainability. These calculations take into account the expenditure reprioritization that typically takes place in the aftermath of ND. The results indicate that, conditional on a fiscal consolidation in line with the commitments in the RCF program approved in November 2015, a SF for ND would set public debt on a downward trajectory and would also be consistent with a decline of public debt towards the regional target of 60 percent of GDP by 2030, although with significant risk bands.

29. Supporting the SF with a strong institutional setup is critical to protect it from political pressures for spending or opportunistic appropriations. A strong institutional design should include unambiguous budget contribution and disbursement rules, with triggers based on verifiable criteria, a clearly-stated objective, and strict information disclosure requirements to ensure the transparency of its operations.

30. The start-up costs and subsequent saving flows could be financed with ECP resources. ECP revenues could be used as the funding source, and also for the subsequent annual savings with clear savings rules established in legislation. This would ultimately also support fiscal sustainability, for various reasons. First, it would avoid the need to issue public debt to finance the starting cost. Second, it would provide the discipline to save for future ND by explicitly treating ND as recurrent events, which could otherwise result in the ratcheting up of public debt. Third, it would reduce the scope of allocation of ECP revenues for recurrent spending, which could be problematic in a context of fiscal consolidation as these are typically more difficult to adjust. The later is especially important given the uncertain nature of ECP revenues as a result of increasing regional competition and scrutiny from advanced countries.12

References

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  • Barro, R. 2009. “Rare Disasters, Asset Prices and Welfare Costs.” American Economic Review, 99(1): 243-264.

  • Cavallo, E. A., and Noy, I., 2011. “Natural Disasters and the Economy: A Survey.” International Review of Environmental and Resource Economics, 5: 63102.

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  • Cavallo, E., Galiani, S., Niy, I., and Pantano, J., 2013. “Catastrophic Natural Disasters and Economic Growth.” Review of Economics and Statistics, 95(5): 1549{1561.

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  • Cummins, J.D. 2008. \CAT Bonds and Other Risk-Linked Securities: State of the Market and Recent developments.” Risk Management and Insurance Review, 11(1): 23-47.

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  • Cummins, J.D. 2012. “CAT Bonds and Other Risk-Linked Securities: Product Design and Evolution of the Market.” In Extreme Events and Insurance: 2011 Annus Horribilis, ed. Christophe Courbage and Walter R. Stahel, 39-61. The Geneva Association.

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  • Cummins, J.D., and O. Mahul. 2009. Catastrophe risk financing in developing countries: principles for public intervention. World Bank Publications.

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  • El-Ashram, A., Gold, J. Xu, X., 2015. “Too Much of a Good Thing? Prudent Management of Inflows under Economic Citizenship Programs.” IMF Working Paper 15/93.

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  • Froot, Kenneth A. 2001. “The Market for Catastrophe Risk: a Clinical Examination.” Journal of Financial Economics, 60(2): 529-571.

  • Lee, Jin-Ping, and Min-Teh Yu. 2007. “Valuation of Catastrophe Reinsurance with Catastrophe Bonds.” Insurance: Mathematics and Economics, 41(2): 264{278.

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  • Noy, Ilan. 2009. “The Macroeconomic Consequences of Disasters.” Journal of Development Economics, 88(2): 221-231.

  • Rasmussen, T. N. 2004. “Macroeconomic Implications of Natural Disasters in the Caribbean.” IMF Working Paper 04/224.

1

This paper was prepared by Alejandro Guerson.

2

This is because the benefits of a program are fully internalized by the country issuing a passport but the potential costs in the case of the granting of a passport to problematic beneficiaries could affect the reputation of the ECP programs of the region as a whole, therefore undermining the prospective revenues of other countries. This situation can distort the incentives towards reducing the efforts on due-diligence checks and therefore exacerbates the risks of revenue erosion or outright loss.

3

Although the largest insurance companies in the region have access to reliable re-insurance, typically from major European companies, but this is not the case for in the majority of cases of relatively smaller insurance companies operating in the region.

4

CAT bonds are inherently risky, typically pay coupons of Libor plus a spread in the range of 3-20 percent, and have maturities of less than 3 years. See also Froot 2001; Cummins 2008 and 2012; and Cummins and Mahul 2009.

5

CAT bonds are structured in four types of triggers for payment: (i) Indemnity (trigger by the actual losses in excess of a specific threshold0; (ii) modeled loss (based on catastrophe modeling run with the event parameters to measure if the modeled losses are above a specified threshold); (iii) indexed to industry loss (triggered when the insurance industry loss reached a specified threshold, as determined by a specified agency); (iv) parametric (trigger is indexed to the natural hazard caused by nature, such as wind speed in a specific location for a hurricane); and (v) parametric index (models used to compute an approximated loss, de-facto it is a hybrid parametric/modeled loss).

6

The cyclical components used in the empirical model are calculated as the ratio of the variable with respect to its estimated trend. The cyclical components of GDP are estimated using the Hodrick-Prescott filter on 1990-2015 annual data. All variables are transformed into real terms using the GDP deflator and expressed in logarithms. The identification of shocks is performed according to the Choleski decomposition, according to the ordering presented.

7

The sample data used in the estimation spans 1990-2015.

8

If the simulations result in a fiscal deficit, then there would be a need to issue public debt to finance the required contribution to the Fund.

9

In other words, if inflows into the Fund are too high (low), then the size of the Fund would tend to increase (decrease) in expected terms.

10

This probability of depletion is similar to insurance coverage, and it is to be chosen also consistent with risk tolerance of the authorities, although a sufficiently low probability of depletion is preferable to ensure the financial sustainability of the SF.

11

This result is below the level projected in the macroeconomic framework of 67 percent of GDP, but still the range with high probability. This is an important result confirming the realism of the macroeconomic framework, as the model equations from which the simulations are generated reflect historical patterns.

12

Other sources of revenues could be considered as funding sources in addition to the ECP, including from donor partners’ contributions for budget support and from international loans for investment projects to be financed with resources from the Fund.

Dominica: Selected Issues
Author: International Monetary Fund. Western Hemisphere Dept.
  • View in gallery

    2015: Citizenship by Investment Program

    (Percent of GDP)

  • View in gallery

    Off-sample Simulated Dynamics of a Fund for Natural Disasters

    (In percent of GDP)

  • View in gallery

    Annual Budget Contributions to the Fund for Natural Disasters (ND)

    (percent of GDP)

  • View in gallery

    Probability of Depletion of the Saving Fund

    (Average probability through 2016-25)

  • View in gallery

    Public Debt Dynamics with a Fund for Natural Disasters 1/

    (In percent of GDP)