Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund

Appendix: Sovereign Wealth Funds in PICs

article image
article image

References

  • Abrigo, M. R., and I. Love (2015). Estimation of panel vector autoregression in Stata: A package of programs. manuscript, Febr 2015 available on paneldataconference2015.ceu.hu/Program/Michael-Abrigo.pdf.

    • Search Google Scholar
    • Export Citation
  • Arellano, M., and O. Bover (1995). Another look at the instrumental variable estimation of error-components models. Journal of econometrics, 68(1), 2951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Becker, T., and P. Mauro (2006), “Output Drops and the Shocks that MatterIMF Working Paper 06/172 (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Bova, E., M. Ruiz-Arranz, F. Toscani, and H. E. Ture (2016), “The Fiscal Costs of Contingent Liabilities: A New Dataset” IMF Working Paper 16/14 (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Cabezon. E., P. Tumbarello, and Y. Wu (2015), “Strengthening Fiscal Frameworks and Improving the Spending Mix in Small StatesIMF Working Paper 15/124 (Washington, DC: International Monetary Fund).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavallo, E., S. Galiani, I. Noy, and J. Pantano (2013), “Catastrophic natural disasters and economic growth.” Review of Economics and Statistics 95, no. 5 (2013): 15491561.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Felbermayr, G., and J. Gröschl (2014), “Naturally negative: The growth effects of natural disasters.” Journal of Development Economics 111 (2014): 92106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fomby, T., Y. Ikeda, and N. Loayza (2013), “The Growth aftermath of Natural DisastersJournal of Applied Econometrics, 28(3), 412434.

  • Government of Vanuatu, (2015), “Post-Disaster Needs Assessment, Tropical Cyclone Pam, March 2015.”

  • Guerson, A. (2016), “Assessing Government Self-Insurance Needs against Natural Disasters—An Application to the ECCU”, ECCU Article IV Staff Report, Appendix, 2016.

    • Search Google Scholar
    • Export Citation
  • Guo. S., and F. Narita, (2018), “Self-insurance Against Natural Disasters: The use of Pension Funds in Pacific Island CountriesIMF Working Paper 18/155 (Washington, DC: International Monetary Fund).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, (2014), “Staff Guidance Note on the Fund’s Engagement with Small States>” (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, (2015), “Macroeconomic Developments and Selected Issues in Small Developing States” (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, (2016a), “Analyzing and Managing Fiscal Risks—Best Practices” (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, (2016b), “Assessing fiscal space: An Initial Consistent Set of Consideration” (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, (2016c), “Small States’ Resilience to Natural Disasters and Climate Change—Role for the IMF” (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, (2018), “How to Manage the Fiscal Costs of Natural Disasters” (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, (Forthcoming), “Building Resilience in Developing Countries Vulnerable to Large Natural Disasters” (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Klomp, J., and K. Valckx (2014). Natural disasters and economic growth: A meta-analysis. Global Environmental Change, 26, 183195.

  • Le Borgne, E. and P. Medas (2007), “Sovereign Wealth Funds in the Pacific Island Countries: Macro-Fiscal LinkagesIMF Working Paper 07/297 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Lee. D., H. Zhang, and C. Nguyen (2018) “The Economic Impact of Natural Disaster in Pacific Island Countries: Adaptation and PreparednessIMF Working Paper 18/108 (Washington, DC: International Monetary Fund).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Love, I., and L. Zicchino (2006). Financial development and dynamic investment behavior: Evidence from panel VAR. The Quarterly Review of Economics and Finance, 46(2), 190-210.

    • Search Google Scholar
    • Export Citation
  • Loayza, N., E. Olaberria, J. Rigolini, and L. Christiansen (2012), “Natural disasters and growth: Going beyond the averages.” World Development 40, no. 7 (2012): 1317-1336.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noy, I., and A. Nualsri (2011), “Fiscal storms: public spending and revenues in the aftermath of natural disasters.” Environment and Development Economics 16, no. 1 (2011):113128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • World Bank, (2015a), “Country Note, Fiji, Disaster Risk Financing and Insurance” (Washington, DC: World Bank).

  • World Bank, (2015b), “Country Note, Marshall Islands, Disaster Risk Financing and Insurance” (Washington, DC: World Bank).

  • World Bank, (2015c), “Country Note, Solomon Islands, Disaster Risk Financing and Insurance” (Washington, DC: World Bank).

  • Yang, D. (2008), “Coping with disaster: The impact of hurricanes on international financial flows, 1970-2002.” The BE Journal of Economic Analysis & Policy 8, no. 1 (2008). (Nickell, 1981; Baltagi, 2013)

    • Search Google Scholar
    • Export Citation
1

The authors are also thankful to Alison Stuart, Dongyeol Lee, Sandile Hlatshwayo, and colleagues in the IMF’s Statistics, Strategy and Policy Review, and Western Hemisphere Departments for their helpful comments, together with thoughtful feedback from Executive Directors offices .

2

Fiji, Kiribati, Marshall Islands, Micronesia, Palau, Papua New Guinea, Samoa, Solomon Islands, Timor-Leste, Tonga, Tuvalu, and Vanuatu.

3

The paper defines the severe disasters that could be economically destructive as disasters that are above 75th percentile in the damage-to-GDP ratio and in the population affected-to-total population ratio. This corresponds to 7.0 percent of GDP and 7.5 percent of total population, respectively.

4

In this case, it would be calculated as the number of years in the sample (30) divided by the number of disasters during that period.

5

SWFs in the PICs were set up with revenue from non-renewable sources (Kiribati, Timor-Leste, Papua New Guinea, Nauru), revenue windfall (Tonga, Tuvalu), or donor contributions (Tuvalu, Marshall Islands, Micronesia, Palau) (Le Borgne, 2007).

6

For both Samoa and Tonga, total assistance is US$ 6 million ($3.1 million for loans and $2.9 million for grants). For Tuvalu, only grant financing worth US$ 3 million is provided.

7

EM-DAT database was established by the Center for Research on the Epidemiology of Disasters (CRED), and provides more than 22,000 mass disasters observations worldwide since 1900.

8

The sample period used begins in 1980, mainly owing to lack of sufficient fiscal data prior to 1980, but also reflecting concern over the comparability of earlier data with later data on disasters.

9

Only annual fiscal data are available for the sample countries.

10

From Table 3, results are based on model (1) (2). At 5 percent significance level, the sum of natural disaster dummy coefficients is 13.8, and at 10 percent significance level, the sum of natural disaster dummy coefficients is 20.6.

11

The fiscal cost numbers are taken from the regression coefficients in Table 3. The lower bound is at 5 percent significance level and the higher bound is at 10 percent significance level.

12

The analysis could, of course, be conducted using a lower threshold for disaster severity. This would increase the estimated frequency of disasters and likely lower the estimated cost per disaster. The overall cost would be likely to rise.

Fiscal Buffers for Natural Disasters in Pacific Island Countries
Author: Hidetaka Nishizawa, Mr. Scott Roger, and Huan Zhang