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

Annex 1. Country Sample

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Sources: WEO (2019)Note: IMF Guidance Note (IMF 2018) classifies SDS in four income groups: (i) high-income SDS, as countries with GDP per capita above US$12476, (ii) upper middle-income, as countries with GDP per capita between US$4036 and US$12475; (iii) lower middle-income SDS as countries with GDP per capita between US$1026 US$4035; and (iv) low-income SDS with GDP per capita below US$1025. Our sample does not include low-income SDS.

Annex 2. An indication of National Hazard Categories and Intensities in SDS

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Source: ThinkHazard, GFDRR. .Notes: ThinkHazard indicates the intensity of potential natural hazards and disasters by calculating the probability of frequency and severity. Red shows a climate-related disaster with high severity and frequency; Orange– a potentially damaging disaster that is expected to occur in a human lifetime; Yellow—a low or very low potentially damaging event less likely to occur in a human lifetime. The ThinkHazard exposure profiles have been used as a comparable example of the possible set of hazards across all SDS included in this study, however, the ThinkHazard assessment is not always complete due to a lack of comparable data. For example, some Caribbean islands such an Antigua and Barbuda, Dominica, Grenada, St Kitts and Nevis, St Lucia and St Vincent and the Grenadines are vulnerable to the impacts caused by hurricanes. Detailed individual country and regional assessments are needed to fully understand the costs of resilience in each country.

Annex 3. Selected Data Sources

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Source: Gaspar and others (2019); Authors.

Annex 4. Summary Statistics

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Sources: Global Climate Risk, IMF , NDCs, ND-GAIN, UNICEF, World Bank (WDI, HDI), WHO.

Annex 5. Tax Capacity Estimates using Stochastic Frontier Analysis (SFA)

This annex discusses briefly the methodology used to estimated tax capacity in 19 SDS using an SFA (Martinez-Vazquez and others 2013) and provides the main results. The methodological approach follows Langford and Ohlenberg (2016) in the sense that we use a time varying true random effects model which takes into account random shocks, accounts for heterogeneity within the panel, and distinguishes between invariable or persistent structural factors and time-varying factors affecting countries’ tax effort. However, we use a different specification to the one used by Langford and Ohlenberg (2016). Our specification is tailored, and the sample is limited to SDS to capture their unique characteristics compared to larger economies. This may lead to a different frontier compared with larger economies.42 Furthermore, to better capture SDS features, we adjust and augment the standard set of explanatory variables used in the literature. The chosen specification attempts to mimic the most important tax bases from which SDS collect revenue. Another advantage of this selective approach is that targeted estimates –that also account for SDS data limitation–allow us to estimate tax potential for SDS usually omitted in the empirical work in this area.

Methodology

An SFA models a production function (Equation 1) in which inputs—Xit—are transformed into tax revenues (TRit) for country i in year t. In this approach, countries potentially collect less than it would be possible due to a level of inefficiency (Εit), this is random normally distributed and independent of the inefficiency shocks (Vit).

TRit=f(Xit, β).Eit.expVit;(1).

A set of inputs Xi includes Gross National Income, the size of agriculture and tourism sectors, the government wage bill and a measure of geographic dispersion. If E equals 1, the country collects the maximum tax revenues possible, using the inputs.

The natural logarithm form of Equation (1) provides the basis for the basic econometric model as proposed by Aigner and others (1977):

ln(TRit)=ln[f(Xit,β)]+ln(Eit)+Vit;(2).

Assuming the tax revenue input function [f(Xit, β)] is linear in logarithms and defining the inefficiency as uit = -ln(Eit):

ln(TRit)=α+Σβln(Xit)+vituit;(3).

Following Langford and Ohlenburg (2016) and using the specification (3), we use a time-varying inefficiency model for panel data that accounts for observable heterogeneity (Battese and Coelli 1995). The parameters of the stochastic frontier and the inefficiency model are estimated simultaneously to avoid bias. The unobserved time-invariant heterogeneity is captured in a “true random effects” model.43 As in Langford and Ohlenburg (2016), we interpret unobserved heterogeneity as a lack of tax effort, suggesting that the influence of the unobserved factors could be overcome with tax policy and administration measures.

Data

We begin with 25 SDS listed in Annex 1 over the period from 1995 to 2019. Data are taken from the WoRLD, World Economic Outlook, World Development Indicators, International Financial Statistics and World Tourism Organization databases. Data on public sector employment and wage bills come from Gupta and others (2016). Data for selected indicators restrict our sample to 19 SDS: Antigua and Barbuda, the Bahamas, Belize, Cabo Verde, Comoros, Dominica, Grenada, Guyana, Maldives, Mauritius, Micronesia, Samoa, Seychelles, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sao Tome and Principe, Timor-Leste and Vanuatu.

Appendix Table 1.

Summary Statistics

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Empirical Results

Table 2 indicates the coefficients in the models used to estimate the maximum level (capacity) of tax revenue that could be theoretically mobilized given an SDS’ economic structure and prevailing economic conditions.44 The larger is the gap between the actual and theoretical tax revenue, the larger is scope for tax policy and revenue administration to reach the potential tax revenue. The gap for SDS is reported in Figure 7 of the main text.

The sign and statistical significance of the coefficients in the models are consistent with the literature, but also capture a specific economic and institutional structure of SDS.

Appendix Table 2.

Stochastic Frontier Analysis Coefficients

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Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
  • Economic development. Tax revenues increase with the country’s income level and economic development as higher-income countries (society) has a higher ability to pay taxes (Bahl 1971). We use Gross National Income per capita—accounting for both GDP and remittances from the diaspora particularly significant in SDS—to capture the impact of these variables on tax revenue potential in SDS.

  • Sectoral development. Tax revenues are unusually lower in countries with a larger share of agriculture in GDP. This sector is characterized by a higher number of tax exemptions, small producers, and a higher level of informality (Fenochietto and Pessino, 2013). In contrast, tax revenues are higher in countries with larger tourism receipts (as a share of GDP) as this sector is comprised of larger hotels and transportation companies (Glenday and others 2019).

  • Size of the public sector. The impact of the public sector on tax revenue is ambiguous. On one side, in many developing countries, the public sector contributes to the bulk of personal income tax revenue. Hence, a larger public sector would imply higher taxes. On the other hand, a large public sector can indicate a less diversified economy and a narrowing of the tax base (e.g., the public sector does not pay corporate income tax), contributing to lower tax revenues. Our estimates indicate a positive coefficient associated with the size of the public sector—as a key economic actor—in SDS.

  • Geographic characteristics. The main text stresses the impact of geographic location and dispersion on sustainable development and climate resilience goals. These characteristics are difficult to approximate by existing indicators. We use a distance in kilometers between the extreme north-south and east-west borders of each SDS to capture the dispersion and multiply it by import deflators to capture the impact of some SDS’ distant location. The role of geographic characteristics on tax revenue is ambiguous. On the one hand, geographic dispersion may reflect a country’s size, which is positively correlated with tax revenue. On the other hand, tax collection may be weaker in more ‘disperse’ countries with lower collection capacities. Our estimates indicate a positive relationship.

ANNEX 6. EXAMPLES OF ‘CLIMATE FUNDS’

Global Environment Facility (GEF). Countries are eligible for GEF funding if they have ratified UNFCCC. The GEF is used for a range of climate and environment-related projects and operates through 18 partner agencies, which are selected to deploy funds. These are the only institutions that can access GEF funding directly. Special funds include the Special Climate Change Fund , which supports adaptation and technology transfer and the Least Developed Countries Fund , which is accessible specifically to LDCs that are vulnerable to adverse impacts of climate change.

Green Climate Fund (GCF). A range of instruments, including grants, concessional loans, etc., that support the delivery of the NDCs. Developing country parties to UNFCCC are eligible. The GCF requires both a nationally designated authority and an Accredited Entity . Organizations seen to have specialized capacities in climate action may apply to be an Accredited Entity and can be private, public, non-governmental, sub-national, national, regional or international. The fund supports eight impact areas across two broad categories: low-emission sustainable development and increasing climate-resilience.

Climate Investment Funds (CIF). Concessional finance is provided to accelerate climate action by empowering transformation in clean technology and renewable energy sources, making them cost-competitive with fossil fuels (e.g., Climate Investment Fund 2019). Recipient countries must meet the Official Development Assistance eligibility criteria and have an active MDB program. Specific funds include the Clean Technology Fund , the Pilot Program for Climate Resilience and the Scaling Up Renewable Energy in Low Income Countries Program .

Adaptation Fund. Grants are provided to developing country members of the UNFCC list of parties, and financing flows through accredited implementing entities. Investments predominately support food security, agriculture, water management and disaster risk reduction projects for the promotion of community resilience.

Climate Funds for preparedness and response. Activities that address the impacts of hazards are also available to SDS. This support is provided both through MDBs and climate funds. The Global Facility for Disaster Risk Reduction and Recovery (GFDRR), for example, targets the most disaster-prone countries and provides grants in support of building resilience and enabling recovery. Through its Small Island States Resilience Initiative (SISRI), technical assistance supports small island states to build pipelines of resilient investments to withstand climate change impacts.

References

  • African Development Bank, Asian Development Bank, Asian Infrastructure Investment Bank, the European Bank for Reconstruction and Development, the European Investment Bank, the Inter-American Development Bank Group, the Islamic Development Bank, and the World Bank Group, 2020, Joint Report on Multilateral Development Banks’ Climate Finance, August 2020

    • Search Google Scholar
    • Export Citation
  • Aigner, D., C.A.K. Lovell and P. Schmidt, 1977, “Formulation and estimation of stochastic frontier production function models”, Journal of Econometrics 6: 2137.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, R., M. El Rayess, L. Doherty, and P. Goel, 2020, “Review of the Public Financial Management Reform Strategy for Pacific Island Countries, 2010-2020,” IMF Working paper 20/183, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Babii, A., S. Cevik, S. Kaendera, D. Muir, S. Nadeem, and G. Salinas (2021), “Tourism in the Post Pandemic Wold Economic Challenges and Opportunities for Asia-Pacific and the Western Hemisphere,” IMF ADP-WHD Departmental paper forthcoming, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Bahl, R. W., 1971, “A regression approach to tax effort and tax ratio analysis,” Staff Papers, 18(3), pp.570612.

  • Battese, G.E. and T.J. Coelli. 1995, “A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data,” Empirical Economics 20: 325332.

    • Search Google Scholar
    • Export Citation
  • Benedek, D., E. Gemayel, A. Senhadji, and A. F. Tieman, 2021, “A Post-Pandemic Assessment of the Sustainable Development Goals,” IMF Staff Discussion Note forthcoming, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Bonato, L., A. Cheasty, M. Pigato, K. Antoine, A. De Kleine Feige, A. Guerson, S. Lakhtakia, I. Parry, G. Salinas, and D. Stephan, 2018, St. Lucia. “Climate Change Policy Assessment,” IMF Country Report 18/181, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Burdescu, R, C. van den Berg, N. Janson, and O. Alvarado, 2020, “A Benchmark for the Performance of State-Owned Water Utilities in the Caribbean,” World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Caldeira, E., A. Compaoré, A. Adessé Dama, M. Mansour, G. Rota-Graziosi, 2020, “Tax effort in Sub-Saharan African countries: evidence from a new dataset,” Working Papers hal-02543162, HAL.

    • Search Google Scholar
    • Export Citation
  • Cheasty, A., B. Garnaud, T. Konuki, I. Parry, and W. Samuel, 2017, “Seychelles. Climate Change Policy Assessment,” IMF Country Report 17/162, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Cheasty, A., D. Leigh, I. Parry, D. Vasilyev, M. Boyer, R. Gunasekera, and R. Alfaro-Pelico, 2018. Belize. “Climate Change Policy Assessment,” IMF Country Report 18/329, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Climate Central, 2019, Source Data for Coastal DEM,Flooded Future: Global vulnerability to sea level rise worse than previously understood.” https://www.climatecentral.org/news/report-flooded-future-global-vulnerability-to-sea-level-rise-worse-than-previously-understood.

    • Search Google Scholar
    • Export Citation
  • Climate Investment Funds, 2019, Preparing Outer Island Sustainable Electricity Development Project,

  • Commonwealth Parliamentary Association, 2019, “Climate Change and Small States Parliamentarian’s Toolkit: A guide for effective climate action,” CPA Small Branches Network, London, UK.

    • Search Google Scholar
    • Export Citation
  • Cyan, M., J. Martinez-Vazquez and V. Vulovic. 2013, “Measuring tax effort: Does the estimation approach matter and should effort be linked to expenditure goals?,” International Center for Public Policy Working Paper 13–08, Atlanta, GA.

    • Search Google Scholar
    • Export Citation
  • Daniel, J., A. Banerji, A. Blackman, S. Ester, T. Moeaki, R. Neves, N. Palu, V. Piatkov, D. Prihardini, C. Sandoz, and A. Zdzienicka, 2020, “Climate Change Policy Assessment for Tonga,” April 2020, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Daniel, J., L. Doherty, G. Preston, T. Schneider (2021) “Barriers Preventing Pacific Island Countries Access to Climate Financing,” IMF Working Paper forthcoming, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Davies, M., B. Lissovolik, I. Parry, A. Guerson, G. Huang, T. Komatsuzaki, W. Mitchell and M. Boyer, 2019 (a), “Grenada. Climate Change Policy Assessment,” IMF Country Report 19/193, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Davies, M., M. Nozaki, R. Singh, K. Abdelkader, A. Le, D. Prihardini, G. Huang, and S. Esler, 2019 (b), “Federated States of Micronesia. Climate Change Policy Assessment,” IMF Country Report 19/292, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Fenochietto, R. and C. Pessino. 2013, “Understanding Countries’ Tax Effort,” IMF Working Paper 13/244, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaspar, V., D. Amaglobeli, M. Garcia-Escribano, D. Prady, and M. Soto, 2019, “Fiscal Policy and Development: Human, Social, and Physical Investment for the SDGs,” Staff Discussion Note 19/03, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Georgieva, K., 2020, “Beyond the Crisis,” Finance & Development, June 2020, Vol. 57/2.

  • Greene, W. H., 2005. “Fixed and Random Effects in Stochastic Frontier Models,” Journal of Productivity Analysis, 23: 732. https://centerforpolicyimpact.org/wp-content/uploads/sites/18/2019/04/CPIGH-Report_Tax-report_Enhancing-Domestic-Revenues__April-2019_FINAL.pdf

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glenday, G., I. Bharali, and Z. Wang. 2019, “A Cross-Country Comparative Study of Tax Capacity, Effort and Gaps.” 2019, 171.

  • Griffith-Jones, S; Attridge, S; and Gouett, M., 2020, “Securing Climate Finance Through National Development Banks,” ODI Report January 2020

    • Search Google Scholar
    • Export Citation
  • Gupta, S.; D. Coady; M., Fouad; R., Hughes; M., Garcia-Escribano; T., Curristine; C., Abdallah; K., Dybczak; Y., Endegnanew; M., Francese; T., Hansen; L.F., Jirasavetakul; M., Nozaki; B., Shang; M., Simmonds; and M. Soto, 2016, “Managing Government Compensation and Employment – Institutions, Policies, and Reform Challenges,” Policy Papers 47 (2016), International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Hallegatte, S., M. Bangalore, L. Bonzanigo, M. Fay, T. Kane, U. Narloch, J. Rozenberg, D. Treguer, A. Vogt-Schilb, 2016, “Shock Waves: Managing the Impacts of Climate Change on Poverty,” Climate Change and Development Series, World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Hallegatte, S., A. Vogt-Schilb, M. Bangalore, and J. Rosenberg, 2017, “Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters,” Climate Change and Development Series, World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Hallegatte, S., J. Rentschler, and J. Rosenberg, 2019 (a), “Lifelines. The Resilient Infrastructure Opportunity,” World Bank, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hallegatte, S., J. Rentschler, J. Rosenberg, C. Nicolas, C, Fox, 2019 (b), “Strengthening New Infrastructure Assets. A Cost-Benefit Analysis,” Policy Research Working Paper 8896, World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Hutton, G. and M. Varughese, 2016, “The Cost of Meeting the 2030 Sustainable Development Goal Targets on Drinking Water, Sanitation, and Hygiene,” World Bank, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IFC, 2016, “Climate Investment Opportunities in Emerging Markets, International Finance Corporation,” Washington, DC.

  • International Monetary Fund, 2016, “Small States’ Resilience to Natural Disasters and Climate Change—Role of the IMF,” IMF Policy Paper, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2018, “2017 Staff Guidance Note on the Fund’s Engagement with Small Developing States,” Washington, DC.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2019, “Building Resilience in Developing Countries Vulnerable to Large Natural Disasters,” IMF Policy Paper 19/020, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2019, 2020 (a), World Economic Outlook, “The Great Lockdown,” Washington, DC. April 2020.

  • International Monetary Fund, 2020 (b), World Economic Outlook, “A Long and Difficult Ascent,” Washington, DC. October 2020.

  • IPCC, 2019, “Special Report on the Ocean and the Cryosphere in a Changing Climate,” https://www.ipcc.ch/srocc/

  • Langford, B. and T. Ohlenburg. 2016, “Tax revenue potential and effort – an empirical investigation,” S-43202-UGA-1. International Growth Centre, London, UK.

    • Search Google Scholar
    • Export Citation
  • Mitchell, A., M. Wickham, and M. Torres, 2020, “Strengthening Public Investment Management in the Eastern Caribbean Currency Union: Getting more bang for the dollar!,” IMF Working Paper, 20/177, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miyamoto International, 2019 a, “Overview of Engineering Options for Increasing Infrastructure Resilience,” Background Paper for Lifeline Report, World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Miyamoto International, 2019 b, “Increasing Infrastructure Resilience -- Technical Annex,” Technical annex to background paper above, World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Nishizawa, H., S. Roger, and H. Zang. 2019, “Fiscal Buffers for Natural Disasters in Pacific Island Countries,” IMF Working Paper 19/152, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • OECD and World Bank, 2016, “Climate and Disaster Resilience Financing in Small Island Developing States,” joint report by OECD, SISRI and the Climate Change Group of the World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • OECD, 2018, “Climate-resilient Infrastructure. Policy Perspective,” OECD Environmental Policy Paper. 14, Paris, France.

  • OECD, 2020, “Climate Finance Provided and Mobilized by Developed Countries in 2013–2018,” OECD, Paris, France.

  • Parry, I., B. Shang, P. Wingender, N. Vernon, and T. Narasimhan, 2016, “Climate Mitigation in China: Which Policies Are Most Effective?,” IMF Working Paper 16/148, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parry, I., V. Mylonas, and N. Vernon, 2017, “Reforming Energy Policy in India: Assessing the Options,” IMF Working Paper 17/103, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rozenberg, J. and M. Fay, 2019, “Beyond the Gap: How Countries Can Afford the Infrastructure They Need while Protecting the Planet. Sustainable Infrastructure,” World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Tanner, T., Bisht, H., Quevedo, A., Malik, M., Nadiruzzaman, M., Biswas, S., 2019, “Enabling Access to the Green Climate Fund: Sharing Country Lessons From South Asia,” Action On Climate Today Learning Paper, Oxford Policy Management and ODI, UK.

    • Search Google Scholar
    • Export Citation
  • UNICEF and WHO, 2019, “Progress on household drinking water, sanitation and hygiene 2000–2017. Special focus on inequalities,” New York, NY.

    • Search Google Scholar
    • Export Citation
  • World Bank, 2016, “World Bank Group Engagement in Small States the Cases of the OECS, Pacific Island Countries, Cabo Verde, Djibouti, Mauritius, and the Seychelles — Clustered Country Program Evaluation,” Washington, DC.

    • Search Google Scholar
    • Export Citation
  • World Bank, 2020, “Understanding Poverty – Topics – Water – Overview,” World Bank Group Water Global Practice, Washington, DC. https://www.worldbank.org/en/topic/water/overview#2 (accessed 15 October 2020).

    • Search Google Scholar
    • Export Citation
  • World Travel & Tourism Council, 2019, Travel & Tourism Economic Impact Reports. https://wttc.org/Research/Economic-Impact.

1

This work benefited greatly from discussions with experts and economists from various institutions and academia acknowledged in Section VII. Johanna Tiedemann conducted this work during the IMF Fund Internship Program.

2

Climate resilience refers to the capacity of social, economic and environmental systems to deal with or respond to a hazardous event or trend related to disasters and effects of climate change (World Bank, 2019, Climate Change Knowledge Portal, Glossary).

3

Small Developing States (SDS) are developing countries that are Fund members with a population below 1.5 million as of 2011 and income per capita below International Development Assistance-related level as identified in the IMF Guidance Note (IMF 2018).

4

This paper reviewed NDCs up to August 2020. Countries are revising their NDCs for COP26 for end-2021. NDCs at https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement/nationally-determined-contributions-ndcs.

5

Health refers to SDG 3; education, to SDG 4; WASH to SDG 6.1 and 6.2 under SDG 6 Clean Water and Sanitation; energy to SDG 7.1.1. under SDG 7 Clean and Affordable Energy; and roads to SDG 9.1.1 under SDG 9 Industry, Innovation, and Infrastructure.

6

These five of 17 SDGs were selected given the critical role of public financing in these areas as well as the spillover effects that advancing these SDGs could have for inclusive growth and sustainable development (see Gaspar and others 2019 and the references included there). This paper focuses on the climate-development nexus and thus how climate change resilience is integrated into core infrastructure and social SDGs. It does not consider a standalone SDG13 on climate action that also includes objectives on strengthening resilience and adaptive capacity to climate-related disasters.

7

Gaspar and others (2019) consider six SDS: Belize, Bhutan, Djibouti, Guyana, Mauritius, and Timor-Leste.

8

A complete list of sources is available upon request.

9

See Acknowledgments (Section VI).

10

ND-GAIN is developed by Notre-Dame University (https://gain.nd.edu/our-work/country-index/)

11

The estimates for Tonga are available in its Climate Change Policy Assessment (Daniel and others 2020).

12

Under the UNFCCC, advanced economies have committed to mobilizing US$100 billion per year by 2020 to help developing countries tackle and adapt to climate change. Only a small part of this financing has been mobilized so far (https://climateactiontracker.org/).

13

This is in line with the Sendai Framework for Disaster Risk Reduction 2015–2030: Enhancing disaster preparedness for effective response and to Build Back Better in recovery, rehabilitation and reconstruction.

14

SDGs’ targets and indicators are universal and apply to all countries. When adopting SDG targets and indicators, countries nationalize implementation and monitoring and reflect their own development plans with their own levels of ambition.

15

IMF Guidance Note (IMF 2018) classifies SDS in four income groups: (i) high-income SDS, as countries with GDP per capita above US$12476, (ii) upper middle-income, as countries with GDP per capita between US$4036 and US$12475; (iii) lower middle-income SDS as countries with GDP per capita between US$1026 US$4035; and (iv) low-income SDS with GDP per capita below US$1025. Our sample (Annex 1) does not include low-income SDS.

16

In SDG 9, indicator 9.1.1 is defined as “the proportion of the rural population who live within 2 km of an all-season road”. The Rural Access Index is used in this instance to quantify this measure. In SDG 7, indicator 7.1.1 is defined as “the proportion of the population with access to electricity” and is measured as the share of people with electricity access at the household level, comprising electricity sold commercially both on- and off-grid. In SDG 6, indicator 6.1.1 is defined as the “proportion of the population using safely managed drinking water services”, defined as one located on-premises, available when needed and free from contamination.

17

A 6-percent depreciation rate is calibrated based on discussions with sectoral experts. Refining the assessment by desk economists in collaboration with SDS’ authorities is needed to further reflect country-specific circumstances.

18

SDS’ infrastructure vulnerabilities reflect the effects of climate change and disasters stemming from natural hazards, including geophysical and meteorological impacts (e.g., earthquakes, flooding, strong winds, earth erosion), and weak adaptation of initial construction coupled with a lack of regular maintenance. Following sectoral reports and projects, we do not estimate the cost of reprofiling existing infrastructure against all—even extreme and very rare—climate events.

19

See Annex 4 for Summary Statistics, Annex 3, for more details on data sources.

20

CRI is developed by Germanwatch (https://germanwatch.org/en/cri).

21

We assume, as in Gaspar and others (2019), that infrastructure spending would decline to about 60 percent to cover the depreciation of the capital stock build through 2030.

22

To identify the peers, we group our 25 SDS countries and their peers into three groups: (i) Group 1 includes countries with GDP per capita below US$3500 at end-2019; (ii) Group 2, countries with GDP per capita between US$3500 and 7000; and Group 3, countries with GDP per capita between US$7000–19000. Group 3 includes the Bahamas. To reflect our sample income composition, we use higher income brackets than in Gaspar and others (2019) that use income bins of US$0–3000, US$3000–6000, US$6000–18000.

23

Computed as an average of Health Conditions and Access to Health Services. Health Conditions include indicators of Malaria and Tuberculosis incidence, HIV infections, child mortality, percentage of children who are underweight, the number of people requiring interventions against neglected tropical diseases. Access to Health Services includes indicators such as immunization coverage, physician (medical doctors) density and maternal mortality ratio.

24

Following Gaspar and others (2019), we assume the spending on health (and education) will continue without making any specific assumptions on their levels.

25

These estimates do not capture the incremental capital, operational and maintenance costs for the most expensive technologies. For instance, the reverse osmosis of seawater for consumption in some countries that already face water scarcity.

26

The pace at which SDS reach the level of spending needed will depend on the trajectory of their fiscal space, financing opportunities, sectoral planning specificities and other country-specific conditions and choices until 2030.

27

Our estimates (available upon request) indicate, for instance, that the median unit cost in current US$ per kW of energy generation, transmission, maintenance and distribution is 14 percent higher in Pacific SDS than in the Caribbean SDS.

28

There is also a significant uncertainty related to the estimate of the effect of climate change and natural disasters, depending on the intensity and frequency of these events (Box 2).

29

For most lower- and middle-income SDS across all regions, there can also be high costs associated with accurate and relevant data to inform resilience spending. This includes, for example, adequate and accessible LiDAR survey data to inform infrastructure improvements and investment needs. This also includes investment in adequate Early Warning Systems ahead of a natural hazard.

30

The estimated costs may still include country-specific risk premiums not directly related to climate resilience and not captured in import prices.

31

Adequate public financial management systems are also required to ensure appropriate targeting of efforts toward the SDGs and climate resilience. Linking PFM processes with development and climate objectives is crucial toward tracking progress and includes monitoring of public investments at a whole-of-government level to ensure strong links to climate change mitigation and adaptation projects (e.g., Allen and others 2020, CCPAs)

32

Annex 5 discusses the approach applied to estimate the tax frontier for SDS. These estimates account for, for instance, the size of tourism and agriculture sectors, government wage bills, geographic dispersion of the country and the extent of remittances.

33

For many SDS, incorporating the externalities from GHG emissions into existing fuel excises would have a similar effect as a carbon tax, since the bulk of their GHG emissions are from the combustion of fuels.

34

These gains in domestic revenue from fuel taxation will erode over time, for example due to the switch towards renewable energy.

35

This includes financing for infrastructure, policy change and in some cases for mitigation, including transition costs toward a low-carbon economy.

36

In principle, SDS could benefit from financing committed under the UNFCCC. Advanced economies have committed to mobilizing US$100 billion per year by 2020 to help developing countries tackle and adapt to climate change. Although still far from the goal, efforts have been improving and in 2018 climate finance provided and mobilized by developed countries had totaled USD 78.9 billion. In that year, 70 percent went to climate change mitigation while only 21 percent went to adaptation (OECD 2020).

37

A broader assessment of access to climate finance options and barriers (Daniel and others 2021)

38

See, for instance, Climate Change Policy Assessments available on https://www.imf.org/en/Topics/climate-change.

39

One of the main differences between natural disaster shocks and the current pandemics is that following the former, all SDS usually benefit from some financial or in-kind support from the international community.

40

SDS authorities must contend with the management of fiscal risks stemming from climate-related disasters as well as more traditional risks from the public and private sectors. Careful fiscal management is required to ensure available fiscal space for SDG attainment.

41

Such analysis could be conducted at the country level within the Debt Sustainability Framework (see CCPAs, IMF 2016) and/or by developing a comprehensive Disaster Resilience Strategy (IMF 2019). For some SDS in our sample, policy priorities and cost-benefits analyses are already reflected in existing development plans and sectoral projects.

42

Estimating the frontier for a subset of countries with unique characteristics has been applied to countries with abundant natural resources (Fenochietto and Pessino 2013) and in Sub-Saharan Africa (Caldeira and others 2020).

43

Greene (2005). It is important to note that the choice of how to model unobserved time-invariant heterogeneity in SFA can have a substantive impact on the estimated size of inefficiency (tax effort).

44

Estimates of alternative specifications and robustness tests—available upon request—are broadly in line with the baseline.

Meeting the Sustainable Development Goals in Small Developing States with Climate Vulnerabilities: Cost and Financing
Author: Johanna Tiedemann, Veronica Piatkov, Dinar Prihardini, Juan Carlos Benitez, and Ms. Aleksandra Zdzienicka