Methodological guidance to determine the "size" of premium and capital support (PCS) at macro level

Panwar, Vikrant; Ward, John; Weingärtner, Lena; Wilkinson, Emily Research Report Methodological guidance to determine the "size" of premium and capital support (PCS) at macro level Advisory report Provided in Cooperation with: ODI Global, London Suggested Citation: Panwar, Vikrant; Ward, John; Weingärtner, Lena; Wilkinson, Emily (2022) : Methodological guidance to determine the "size" of premium and capital support (PCS) at macro level, Advisory report, Overseas Development Institute (ODI), London This Version is available at: https://hdl.handle.net/10419/280298 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Methodological guidance to determine the ‘size’ of premium and capital support (PCS) at macro level Vikrant Panwar, John Ward, Lena Weingärtner and Emily Wilkinson December 2022 Advisory report Disclaimer: This advisory report has received financial support from Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH through the InsuResilience Global Partnership (IGP) secretariat. The views expressed do not necessarily reflect the official policies of GIZ or IGP. Readers are encouraged to reproduce material for their own publications, as long as they are not being sold commercially. ODI requests due acknowledgement and a copy of the publication. For online use, we ask readers to link to the original resource on the ODI website. The views presented in this paper are those of the authors and do not necessarily represent the views of ODI or our partners. This work is licensed under CC BY-NC-ND 4.0. How to cite: Panwar, V., Ward, J., Weingärtner, L. and Wilkinson, E. (2022) Methodological guidance to determine the ‘size’ of premium and capital support (PCS) at macro level. Advisory report. ODI and InsuResilience Global Partnership. London: ODI (www.odi.org) Acknowledgements The authors would like to thank members of the advisory working group, Annette Detken, Daniel Clarke, Nicola Ranger, Olivier Mahul and colleagues at the IGP secretariat particularly Daniel Stadtmüller, Janek Töpper and Kay Tuschen for their insights, inputs and guidance. We are grateful to the interviewees who participated in the key informant interviews (KIIs) on the political economy of premium subsidies and provided critical inputs for the development this advisory report. At ODI, we are grateful to Silvia Harvey. This study has received financial support from Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH through the InsuResilience Global Partnership (IGP) secretariat. The views and findings presented here are of the authors’ and do not represent the views of GIZ or IGP. About this publication This advisory report is an output of the Global Risks and Resilience Programme (GRR) at ODI. GRR provides rigorous analysis of multiple interconnected risks, interrogates narratives and risk perceptions, and uses this evidence to recommend tailored solutions for the management of systemic risks in development, humanitarian, climate adaptation and disaster risk management policies and actions. About the authors Vikrant Panwar Senior Researcher with ODI’s Global Risks and Resilience programme. An economist with specialisation of macro-fiscal disaster and climate risks and impacts. He conducts research around disaster and climate risk financing, macro-fiscal climate and disaster impacts at the sovereign and sub-sovereign levels. Email: v[email protected].uk ORCID: https://orcid.org/0000-0003-1259-9789 John Ward An economic and strategy expert working at the intersection between climate, disaster risk and development policy. He has more than 20 years’ experience in economic strategy and analysis. His work involves both supporting companies, governments, international financial institutions, philanthropies and donors in developing their strategic approaches to climate action and disaster risk management and finance. Lena Weingärtner Research Associate with ODI’s Global Risks and Resilience programme, working primarily on disaster risk management and financing. She has experience in researching and informing financial instruments and delivery mechanisms at micro, meso and sovereign levels. This includes approaches – such as parametric insurance, adaptive social protection, or anticipatory action. Emily Wilkinson Senior Research Fellow with ODI’s Global Risks and Resilience Programme, specialising in resilience in Small-Island Developing States. Emily has 25 years’ experience as a researcher, analyst, journalist, lecturer and adviser, identifying critical entry points and opportunities for governments and communities to mange risks, access finance and build resilience to external shocks. She is Chief Scientific Adviser to the Climate Resilience Agency for Dominica (CREAD); and is Director, Resilient and Sustainable Islands (RESI). Contents Acronyms / iii Executive Summary / 1 1 Background / 2 2 Where to use this guidance document / 4 3 Feasibility of the ‘fraction’ in the proposed SMART PCS sizing formula / 6 Limitations of the proposed fraction with possible remedies / 6 4 Defining the value of the scaling factor for macro-level PCS / 9 4.1 Existing evidence on provisioning for PCS at macro level / 9 4.2 Selection of factors and indicators / 13 4.3 Weighting indicators and calculating results / 17 4.4 Additional considerations / 19 References: / 21 Annex 1 Selected resource allocations mechanisms at global scale / 24 International Development Association (IDA) / 24 Global environment facility (GEF) – STAR allocation method / 25 Global Risk Financing Facility (GRiF) – Appraisal framework for grant support / 27 Official development assistance (ODA) / 30 Annex 2: Inclusion and treatment of qualitative criteria / 31 Annex 3: A potential alternative to determine the size of premium support / 33 Acronyms ADB Asian Development Bank ADF African Development Fund, AfDB ADRiFi African Disaster Risk Financing Programme AfDB African Development Bank ARC African Risk Capacity CATDDO Catastrophe Deferred Drawdown Option CCRIF Caribbean Catastrophe Risk Insurance Facility CDRFI Climate and Disaster Risk Finance and Insurance CEO Chief Executive Officer DRF disaster risk finance FCDO Foreign, Commonwealth and Development Office, UK IDA International Development Association, World Bank IGP InsuResilience Global Partnership KII key informant interview MoF Ministry of Finance M&E monitoring and evaluation PCRIC Pacific Catastrophe Risk Insurance Company PCS premium and capital support PEA political economy analysis PIC Pacific Island country TWG technical working group UK United Kingdom 1ODI Advisory report Executive Summary This report is based on the SMART Principles for premium and capital support (PCS), developed by the InsuResilience Global Partnership (IGP) for the purposes of scaling up climate and disaster risk and finance insurance (CDRFI) solutions. It proposes methodological guidance to define the ‘scaling factor’ to determine the size/amount of premium support allocations. This guidance aims to support actors who are part of the IGP (e.g. the Programme Alliance) and policymakers and practitioners who are responsible for such allocation decisions. The policy note for SMART PCS (Töpper and Stadtmüller, 2022) provides conceptual guidance to determine the size/amount of PCS. It suggests an indicative formula to calculate externally supported (donor) share of the premium for a government (see below). The formula is proposed as a fraction that reflects need-based considerations, along with a scaling factor that needs to be defined in an evidence-based fashion to suit different country contexts. Pe = tn * expected contingent government liabilities from disasters total government budget Where Pe + Pp = Pa and Pa = 1 Where Pe is the externally supported premium share, Pp is the remaining premium share payable by the policyholder (country), and Pa is the full, actuarially priced premium charged by the risk carrier. tn is a scaling factor. Based on the suggested formula, this report provides methodological guidance to define the scaling factor (tn). The proposed approach is based on a multi-criteria decision model (MCDM), involving the selection and prioritisation of multiple factors/objectives. It primarily builds on the performance-based allocation (PBA) systems used by multilateral development institutions and funds to allocate financial resources. The proposed approach is predominantly quantitative and considers factors that are readily quantifiable and widely available for a large set of countries. It includes discussion of (i) the selection of critical factors (along with appropriate indicators) that could be used to determine the size of premium support; (ii) the preliminary guidance on weighting the selected factors; (iii) the calculation of a composite or final score/value; and (iv) the duration of premium support. However, several simulations (trial and error) would need to be performed to obtain suitable weights (and get robust values) to factor in PCS priorities/principles in the suggested indicators. With necessary adjustments, the approach depicted in this document could also be applied to directly derive (i.e. without the fraction) the ‘allocation share’ by country, in cases where decisions need to be made regarding the allocation of a fixed donor fund among recipients. In addition, the feasibility of the overall formula, in terms of its practical use, is also reviewed to identify limitations and suggest appropriate remedies. 2ODI Advisory report 1 Background This document proposes methodological guidance to determine the ‘size’ of premium and capital support (PCS) at macro level. It is based on the SMART PCS Principles developed by the InsuResilience Global Partnership (IGP) for the purposes of scaling up Climate and Disaster Risk and Finance Insurance (CDRFI) solutions (see Box 1). Conceptual guidance on what considerations need to be taken to determine the size/amount of PCS is provided across all five SMART PCS Principles. Principle A (accessibility) in the SMART PCS concept note (hereafter ‘the policy note’) argues ‘transparent, uniform and consistent criteria for needs-based PCS levels should be formulated’ to guide donors in determining an ‘uptake-enabling’ size of PCS intervention (Töpper and Stadtmüller, 2022). Box 1 The SMART premium and capital support principles S – Sustainable impact for the most vulnerable: To enable tangible, lasting change in the lives of those most vulnerable to disasters, PCS should be used to fund risk transfer mechanisms coupled with effective, development-oriented delivery systems. M – Value for money: To maximise poor and vulnerable countries’ and people’s resilience for each dollar of premium or capital support, PCS initiatives should support needs-based CDRFI products that add value and entail a clear assessment framework that makes improvements in resilience verifiable and comparable. Smart PCS proactively and effectively crowds-in private capital rather than undermining private sector potentials. A – Accessibility: Smart PCS is needs-based, (climate) risk-adjusted, and aligned with appropriate measures for enabling access, while empowering beneficiaries and promoting client ownership of the solutions employed. R – Resilience-building incentives: To build financial, physical and social resilience, only risks that are too costly to reduce further should be absorbed by risk financing instruments, and only risks stemming from low-frequency and high-severity events should be transferred via insurance. Reducing premiums through PCS should not alter this; rather, it should keep incentives to reduce risks in place. T – Transparency and Consistency: To empower recipients and maximise synergies, PCS should be provided and employed in a manner that promotes transparency and accountability towards recipients and at-risk communities as well as consistency and coordination among support offers and providers. Source: Töpper and Stadtmüller (2022) 9ODI Advisory report 4 Defining the value of the scaling factor for macro-level PCS 17 Despite being a critical factor in in determining the size of PCS, a country’s income level only reflects an annual status, and is therefore not a forward-looking metric that would account for (for example) increased climate risks to a country. 18 Panda et al. (2021c: 8) provide a detailed discussion on the eligibility of countries to receive premium support based on their ability and willingness to pay for insurance. 19 With a fixed availability of PCS, further prioritisation might be required among the IDA eligible countries as well. There might also be situations where premium support to non-IDA (IBRD countries) would be justified (see Principle S in Töpper and Stadtmüller, 2022). As highlighted earlier, this guidance document uses a multi-criteria decision model (MCDM) to define the scaling factor and it builds on the performance-based allocation (PBA) systems used to allocate financial resources by multilateral development institutions and funds. This approach could also be used (with necessary modifications) to directly determine the ‘allocation share’ out of a fixed donor fund among recipient countries. In this section, conceptual guidance and evidence on PCS allocation is revisited. Since the suggested approach builds on the PBA systems operational at the global level, a review of such allocation mechanisms is also presented to contextualise the choices of factors and indicators as well as the calculation method suggested later in this section. 4.1 Existing evidence on provisioning for PCS at macro level 4.1.1 Considerations for ‘sizing’ macrolevel PCS interventions The SMART PCS Principles suggest that both needs-based and performance-based considerations should inform PCS sizing decisions. Subsidies should not be provided universally to all countries, and income should not be the only criterion in deciding their size;17 rather, the eligibility of countries (and the size of premium support) should be evaluated based on a country’s (climate and disaster) risk profile and its government’s ‘ability to pay’ and ‘willingness to pay’ for insurance (Vivid Economics et al., 2016; Panda et al., 2021c).18 For instance, IDA eligibility could serve as proxy for countries with lack of ability to pay, and specific risk metrics that account for both physical and social vulnerability could be used to approximate the climate and disaster risk of a country.19 Therefore, higher premium support should be provided to countries that are poor (with weak fiscal position) and have the most vulnerable (at-risk) populations (Principle S). These considerations are consistent with the conceptual guidance provided by Panda et al. (2021a; 2021b; 2021c) and Vivid Economics et al. (2016). For instance, Panda et al. (2021c) provide insights into three main considerations for appropriately sizing PCS: (i) needs-based considerations for target countries, (ii) optimal level of insurance protection, and (iii) sustainability of the supported scheme. The needs-based considerations include higher allocation of premium support for low-income 10 ODI Advisory report countries, as they typically have limited fiscal space (and debt accessibility constraints) to cover premium costs compared to higher income countries. Further, countries that are exposed to risk of low-frequency but high-impact events, though it may not be strongly reflected in their AAL, will still have a larger share of output/capital or population at risk than countries whose risk profile is dominated by higher-frequency/lowerimpact events, and should get higher premium support. The optimality consideration requires identifying the optimal level of insurance for a country, and argues for higher support from donors to under-insured countries to help them achieve their optimal level of insurance protection.20 In addition, the sustainability of the supported insurance scheme is an important consideration as the premium support should make the scheme viable and not disincentivise other risk reduction measures (ibid.). As suggested in the SMART PCS policy mote, the policy performance of the government in proactive disaster risk management (and risk financing) should be considered as an important criterion, in addition to the needsbased consideration, in deciding the size of PCS interventions. Principle A (accessibility) suggests that higher premium support should be provided to countries that show strong political commitment and create an enabling policy environment for greater CDRFI uptake. As highlighted in Panda et al. (2021c), performance indicators that might be used for defining the 20 Note, though, that it is difficult to estimate the optimal level of insurance for a country, as it requires information on various benchmarks; for example, suitability and adequacy of insurance, and government preferences over debt and growth outcomes, among others. See Cebotari and Youssef (2020) for a detailed discussion on optimality of insurance for sovereigns. 21 Although the suggested performance indicators are relevant for this purpose, some of them may already be accounted for in the fraction (and might be pulling scaling factor and/or external premium share in different directions). For example, investment in adaptation measures and disaster preparedness should reduce the expected government contingent liabilities (the numerator of the fraction). scaling factor could include (i) improvements in the financial protection status of the country, and (ii) investment in adaptation measures and improvement in disaster preparedness and resilience. Novel indices constructed for measuring performance could be used for this purpose (ibid.). According to the policy note, the needs-based considerations are ‘reasonably’ accounted for in the suggested fraction (Töpper and Stadtmüller, 2022: 16) while performance indicators could be used to define the scaling factor.21 However, several important needs-based factors that could influence the demand of PCS (e.g. per capita income, debt stress, vulnerable population, among others) are not accounted for in the suggested fraction. Therefore, even for defining the scaling factor, it is important to explore such factors in addition to the performance indicators. This approach is consistent with multiple global resource allocation mechanisms (discussed in the next section), where needs-based and performance-based criteria are collectively used to allocate resources. 4.1.2 PCS allocations and resource allocation methodologies at global scale Historically, ad hoc provisions have been made for targeting and allocating premium subsidies; for example, they have been based on countries’ perceived needs, and/or on the political and 11 ODI Advisory report historical ties between donors and recipients (Vivid Economics et al., 2016; World Bank, 2017; Panda et al., 2021c).22 Although the policy note provides conceptual guidance on allocation of PCS (in Principles A and S), there is limited evidence on empirical methods of appropriately allocating premium support to recipient countries. This could be partly because using PCS for CDRFI is a relatively new and evolving field that requires the development and refinement of operational guidelines based on increasing evidence (Panda et al., 2021a; 2021b). However, appropriately allocating ‘fixed’ financial resources among recipient countries to achieve maximum impact has always been a complex optimisation problem for donors and multilateral financial institutions (Kharas and Noe, 2018). 22 For example, in the Africa Disaster Risk Financing Programme (ADRiFi), a country will receive up to 50% of its annual premium as subsidy until the fourth year of a country’s participation. Similarly, a direct capital support of $98 million as a 20-year non-interest-bearing loan was provided to ARC Limited by the UK Department of International Development and KfW (Panda et al., 2021c). The extent to which these ad hoc provisions have aligned with the SMART principles has been reviewed in evaluations of individual schemes, for example, recent evaluation of ADRiFi (not published). Aid allocation mechanisms, mostly used by multilateral development banks, could serve as a benchmark for developing an appropriate method to define the size of PCS (and the scaling factor). Performance-based allocation (PBA) systems are widely used to allocate development funds. The World Bank has been using PBA since 1977 to allocate IDA resources, and almost all major multilateral development institutions have adopted a PBA system over the past two decades (GEF, 2017). Annex 1 summarises the key allocation mechanisms relevant for the purpose of identifying and weighting indicators to define the scaling factor. 12 ODI Advisory report Table 1 Formulae in major performance-based allocation systems Note: GBI = GEF’s Benefits Index; CEPIA = Country Environmental Policy and Institutional Assessment; CPIA = Country Policy and Institutional Assessment; AIDI = African Infrastructure Development Index; CIPE = Country Institutional and Policy Evaluation Source: GEF (2017) 23 Table 1 is adopted from GEF’s evaluation of STAR (GEF, 2017). PBA systems typically involve multi-criteria decision models (MCDM). Table 1 presents the formulas used in major PBA systems at a global scale.23 Allocations in a PBA system are generally determined by two components: (i) country needs; and (ii) policy performance and institutional capacity. The needs-based component generally includes indicators like income (e.g. GNI per capita) and population in order to assess the socio-economic conditions that prevail in a country. The second component measures the policy performance and institutional capacities in the country to make best use of allocated resources. Income and population, as key determinants of country needs, dominate most of the PBA systems. However, multi-dimensional vulnerability metrics are increasingly finding a place in such allocation systems – especially after 13 ODI Advisory report the COVID-19 pandemic, from which many highincome countries (e.g. SIDS) found it difficult to recover without external support (see UN-DESA, 2022).24 The focus of most of the allocation methods has been on including factors that are readily quantifiable and available at global scale. As in Table 1, all PBA systems use a multiplicative formula where all the factors that constitute the formula are critical and cannot have zero value (to avoid zero sum). On the contrary, in an additive formula, zero value for one factor will not result in a zero sum. Such additive formulae are seldom used in multilateral development aid allocations (GEF, 2017). One potential reason for this is that additive methods are more sensitive to decisions on weights. It is important to note that PBA systems also suffer from a limitation of allocating ‘appropriate’ funding to a large set of countries, i.e. at a global scale. It is typically the case that some countries receive a much lower-than-expected allocation inter alia due to choices of indicators, weights and calculation method. Therefore, to increase their robustness, PBA systems are often operationalised for a group of countries and/or selected after setting some minimum eligibility criteria. The GEF’s STAR allocation and IDA, among others, have minimum eligibility criteria for countries to receive funding (see Annex 1 for details). 24 See, for example, UNDP’s multi-dimensional vulnerability index for SIDS at https://sdgs.un.org/topics/smallisland-developing-states/mvi. 25 Respondents were asked to pick their three most preferred choices, with justification, out of a list of key factors (identified based on AWG discussion and literature review) that must be used to determine the size of PCS allocations. See Scott et al. (2022) for more information on the political economy analysis of premium and capital support. 4.2 Selection of factors and indicators Building on the discussion presented in the foregoing sections, the following set of factors are suggested along with relevant indicators to define the value of the scaling factor (and/or allocation share). Following the guidance in the SMART PCS policy note and PBA systems of resource allocation, these factors are placed under two main components: the needs-based component and the performance-based component. The selection of factors and indicators is also guided by the consultation with key stakeholders and AWG members. Table 2 presents a summary of stakeholder responses (during KIIs for the political economy analysis), recorded when asked about their most preferred choices among the factors that could influence PCS allocations and that should be part of the analysis determining the size of premium support.25 Note: The indicators suggested in this guidance document are quantifiable and readily available for most countries. The list of factors in Table 2 is not exhaustive; there could be additional indicators suitable for consideration under either of the two components. This means that indicators based on qualitative criteria, with no readily available value, could also be included along with (or potentially instead of) the suggested quantitative indicators. However, the inclusion of such indicators would have implications for the underlying 14 ODI Advisory report method suggested in this guidance document. Considerations for the inclusion and treatment of such qualitative criteria are discussed in Annex 2. 26 The GNI per capita indicator suggested here is in current US dollars. During consultations, some experts suggested using GNI per capita in PPP terms to account for differences in living standards across countries. 27 This could be because GDP is a measure of the economic activity taking place in a country but not the income received by residents. For example, if a large multinational corporation has lots of extractive activity in a country in the global South but most of its dividends and salaries go to people living in the global North, then the GDP value would be higher than the GNI numbers. Table 2 Stakeholders’ preferred choices (during KIIs for political economy analysis) of factors to determine size of PCS allocation Rank* Factors determining PCS allocation size factor choice by % of respondents 1Proportion of vulnerable population in total population 73% 1 Climate and disaster risk profile 73% 2Country income level 60% 3Prior risk reduction actions/policy of a country 53% 4Country debt accessibility constraints 27% 5Level of insurance penetration 13% 6 Others – country size, economy size, etc. 7% *Ranked by proportion of choices by respondents. Respondents were asked to pick their three most preferred choices. There was a total of 15 KIIs. 4.2.1 Needs-based component Country income level In line with Principle S (sustainable impact), allocation of premium support should differentiate between countries’ ability to pay; as such, PCS should be provided to countries with ‘weak fiscal positions’ (criteria A1 in GRiF, 2019; World Bank, 2017; Panda et al., 2021c). Therefore, a higher allocation should be given to low-income countries as they have limited ‘scope of tradeoff between economic growth and the impact of insurance-related expenses on fiscal positions’ (see discussion on ‘needs-based consideration’ in Panda et al. (2021c)). As with several global allocation mechanisms, ‘GNI per capita’ can be used as a measure of countries’ financial need (and by extension, its demand for PCS). This measure is also the basis for the World Bank’s income-based country classification. Some evidence (e.g. ARC, 2021) also suggests that ‘GDP per capita’ can be used as a measure of the financial needs of a country. However, in comparison with GNI per capita (which is a more comprehensive measure of the income received by residents of a country),26 GDP per capita is rarely preferred by multilateral development institutions in allocating resources (see Table 1 in section 4.1).27 15 ODI Advisory report Debt accessibility constraints/Debt status In addition to the economic criteria captured under the ‘income level of a country’ indicator, a country’s ability to diversify risks across time through issuing debt (to meet the initial costs of a disaster and be repaid over time) should be considered a key factor for determining the level of premium support (see Principle S; World Bank, 2017; Panda et al., 2021c). Therefore, debt accessibility constraints and/or debt stress levels would help in determining a country’s lack of ability to pay for insurance and its need for higher levels of PCS. The policy note and Panda et al. (2021c) suggest using the World Bank–IMF Debt Sustainability Framework for Low-Income Countries (LIC DSF) list to determine countries’ debt status and risks of debt stress.28 The framework’s Highly Indebted Poor Countries (HIPC) status could also be utilised to approximate debt stress levels. Poor (vulnerable) population PCS allocations should be prioritised for countries with a higher number of poor and vulnerable people (see Principle S, and IGP’s pro-poor principles (IGP, 2019)). Poor people are disproportionately affected by climate change and disasters (Hallegatte, 2020). Donors, in general, would want to focus on utility-maximising allocation to countries with a larger proportion of poor and at-risk people, where an extra unit of 28 See IMF (2018) for more details on the LIC DSF. 29 According to the World Bank (https://data.worldbank.org/topic/poverty), poverty headcount ratio at $1.90 a day is the percentage of population living on less than $1.90 a day (in 2011 PPP). 30 Poverty is considered as unidimensional here, i.e. based on income only. 31 Climate change attribution science could provide a potential alternative for deciding the size of premium support. See Annex 3 for more details. 32 Important to note here is that the fraction in the suggested formula for PCS sizing already accounts for countries’ (financial) vulnerability to climate risks through the level of contingent liability (or AAL). However, in light of Principle S, some experts argued that it is necessary to consider a ‘physical vulnerability’ measure as a key determinant of PCS size. allocation would make the biggest difference to their well-being (see Ward et al. (2022) for a ‘value for money’ assessment of PCS). The World Bank’s ‘poverty headcount ratio’29 can be a readily available proxy for poor and vulnerable population in a country, and is typically measured as a proportion of total population.30 Alternatively, IGP’s ‘vulnerable populations’ indicator can be used, where ‘people vulnerable to slipping into poverty as a result of climate risks are defined as those who earn less than $15 PPP/day’ (see IGP, 2021: 9). The IGP indicator includes ‘at-risk’ population, in addition to the poor population as defined by the World Bank’s headcount ratio. Climate (and disaster) risk profile As with Principles S and A, the levels of PCS should be climate (and disaster) risk-adjusted, i.e. higher premium support should be provided to countries at higher risks of climate stress. This would recognise that current and future insurance premiums might be higher in such countries due to the increasing frequency and intensity of climate-related fast-onset disasters, and therefore they would require higher premium support (Panda et al., 2021c).31 Suitable global indices on climate and disaster risk can be used to approximate a country’s risks (hazard exposure and vulnerability).32 The ND-GAIN Country Index can be suitable 16 ODI Advisory report for this purpose as it summarises a country’s exposure and sensitivity to climate risks (and geophysical disasters) using a comprehensive set of criteria.33 Other global indices can also be considered, such as the Global Climate Risk Index,34 the INFORM Risk Index,35 the Verisk Climate Change Vulnerability Index36 and the Climate Vulnerability Monitor.37 4.2.2 Performance-based component Country’s resilience to disaster and climate risks Along the lines of baseline resilience/past policy action signalling readiness for further improvements (spurred by PCS) in the future, a country’s resilience to climate (and disaster) risks, typically measured in terms of its ability to cope with climate risks, should be considered as an important determinant of PCS size (see Principle A; World Bank, 2017). This consideration will help promote the resilience-building incentives of PCS (see Principle R). Further, it could be useful in the periodic monitoring and evaluation of PCS allocations to observe progress in furthering the disaster risk financing and management actions of a country.38 Performance indices that reflect a country’s resilience to climate risks could be used to 33 ND-GAIN Country Index: https://gain.nd.edu/our-work/country-index/rankings/ 34 The Global Risk Index, GermanWatch: www.germanwatch.org/en/cri 35 The INFORM Risk Index, DRMKC: https://drmkc.jrc.ec.europa.eu/inform-index 36 Verisk Climate Change Vulnerability Index:www.maplecroft.com/risk-indices/climate-change-vulnerability-index/ 37 The Climate Vulnerability Monitor: https://daraint.org/climate-vulnerability-monitor/climate-vulnerabilitymonitor-2012/monitor/ 38 This factor could also be (partly) captured by the fraction in the suggested formula if the DRM-related component of the government budget is used as denominator instead of the total government budget. In line with the discussion in section 3 (bullet c), an increase in the DRM-related budget over time would reflect (in financial terms) a country’s progress in prioritising disaster risk management. 39 ND-GAIN’s readiness index ‘measures a country’s ability to leverage investments and convert them to adaptation actions. ND-GAIN measures overall readiness by considering three components – economic readiness, governance readiness and social readiness’. See ND-GAIN’s Technical Document for more details. approximate this factor. Since a (climate) risk index is already suggested as part of the needsbased criteria above, use of the same index for this criterion would help promote consistency and comparability – ensuring, for instance, that data is available for the same countries and is likely to refer to country performance at the same point in time. As before, the ‘readiness index’ part of the ND-GAIN country risk index could be a suitable choice.39 Other similar indices, such as, among others, the INFORM risk index or the Climate Risk Index, may also be considered. While they do not provide a specific measure/index for resilience, related indices such as ‘coping capacity’ may be considered. Country’s policy performance and institutional effectiveness Principle A (accessibility) suggests that ‘higher premium support should be provided to countries that show strong political commitment and create an enabling policy environment for greater CDRFI uptake’. While this is partly captured by the country resilience indicator discussed above (which captures policy commitment specifically to CDRFI), it is suggested that a country’s overall policy performance and institutional effectiveness also be included, in order to account for (a) the effectiveness of its economic management and structural policies, and of its human 17 ODI Advisory report development and social inclusion policies; and (b) its institutional capacity to carry out macro-level policy changes. The World Bank’s Country Policy and Institutional Assessment (CPIA) index could be used to assess the quality of each country’s political and institutional framework.40 There are 16 criteria defined for the CPIA, grouped into four clusters of equal weights (see Annex 1 for details). The index was developed to aid IDA allocations and is currently being used by several multilateral development institutions for this purpose. Some institutions have also used a harmonised/modified version of the CPIA (see, for example, GEF’s STAR in Annex 1) to make it specific for their context, but, for this context, there is not a version of the CPIA that focuses specifically on issues related to disaster risk management or disaster risk finance. 4.3 Weighting indicators and calculating results Assigning appropriate weights to different indicators is a critical next step to account for allocation priorities outlined in the SMART PCS Principles. However, it is a difficult task for the donors/practitioners to quantitatively reflect such priorities in the calculations. Therefore, to factor in PCS priorities/principles in the suggested indicators, several simulations would have to be performed to obtain suitable weights.41 In this regard, the weights used in PBA systems operational at global level could 40 As per the CPIA criteria, ‘quality’ refers to how conducive a given policy and institutional framework is to fostering poverty reduction, sustainable growth and the effective use of development assistance (see the CPIA criteria in World Bank 2018). 41 For example, some small island developing states (SIDS) might not get an appropriate allocation share inter alia due to their higher (per capita) income status. However, the guidance note includes multiple vulnerability and performance indicators that could compensate for the income dimension in the case of SIDS. This would require a careful calibration of weights for the suggested indicators. guide practitioners. Table 3 presents a summary of existing guidance (range) on weighting the suggested indicators. 18 ODI Advisory report Table 3 Suggested weighting range for further calibration (based on performance-based systems used by multilateral development institutions) Factor Suggested indicator/proxy Suggested range of weights as exponent (for simulations) Rationale/priorities Needs-based component Income level of a country GNI per capita -0.08 to -0.25 Level of income is inversely linked to allocation size to provide for higher allocation to lower-income countries Debt accessibility/debt stress levels Debt stress risk/ranking (World Bank–IMF’s DSF) or other suitable metric No guidance available* Suggested: 0.1–1 Higher allocation for countries with high debt stress/accessibility constraints Poor (vulnerable) population World Bank’s poverty headcount ratio, or IGP’s vulnerable population criteria Guidance used for population/rural population in PBA systems (see Table 1) Suggested: 0.1–1 Higher allocation for countries with larger proportion of poor (and vulnerable) population Climate and disaster risk (hazard exposure) ND-GAIN index, or hazard exposure score from other similar indices 0.1–2 Higher allocation to countries that have higher vulnerability to climate risks Performance-based component Climate and disaster resilience ND-GAIN readiness index, or resilience score from other similar indices No guidance available Suggested: 0.1–2 Higher allocation to countries that show progress in resiliencebuilding Policy performance and institutional effectiveness World Bank’s CPIA For combined† CPIA score: 2–4 Higher allocation to countries that have effective policy performance and institutional capacity * No guidance on weighting range is available from PBA systems reviewed under section 4.1.2. The suggested range is based on expert judgement considering the rationale/priorities relevant for an indicator. This also accounts for the nature of underlying data. For example, to increase the value of an indicator which has a value more than 0 and less than 1 (for example, the poverty headcount ratio), an exponent weight between 0.1 and 0.99 should be tried, as the value of that indicator will increase when weight moves downwards from 0.99 to 0.1, and vice versa. † In some cases, CPIAA-C and CPIAD are used separately with different weights (see Table 1). Although guidance from the existing PBA systems could help, weights for the indicators should ideally be assigned using a participatory approach. For this, consultative processes such as workshops, focus group discussions and key informant interviews could be helpful. 25 ODI Advisory report with written justifications. Details of the ratings criteria are provided in the CPIA questionnaire (see CPIA criteria in World Bank 2018). A. Economic management 1. Monetary and exchange rate policies 2. Fiscal policy 3. Debt policy and management B. Structural policies 4. Trade 5. Financial sector 6. Business regulatory environment C. Policies for social inclusion/equity 7. Gender equality 8. Equity of public resource use 9. Building human resources 10. Social protection and labour 11. Policies and institutions for environmental sustainability D. Public sector management and institutions 12. Property rights and rule-based governance 13. Quality of budgetary and financial managemen 14. Efficiency of revenue mobilisation 15. Quality of public administration 16. Transparency, accountability, and corruption in the public sector Portfolio Performance Rating This rating refers to the financial health of the IDA portfolio, which is measured by the percentage of problem projects in each of the IDA countries. Therefore, it captures the quality of management of IDA’s projects and programmes. Using the CPIA and PPR, the IDA Country Performance Rating (CPR) is developed. The CPR of IDA are determined annually. Country Performance Rating (CPR) = (0.24*CPIAA-C + 0.68*CPIAD + 0.08*PPR) Here, CPIAA-C represents the average rating for clusters A to C from the CPIA criteria, and CPIAD represents the rating for cluster D. The performance-based allocation formula for IDA is presented below. In the formula, CPR has an exponent of 3 and it is the main determinant of the allocation. Population size has an exponent of 1 (as it affects allocations positively). GNI per capita is negatively related to allocations and has an exponent of -0.125. IDA country allocation = f (CPR3, population, GNI per capita-0.125) IDA also provides additional resources to countries through some dedicated windows, which are described in detail in the Annexes of the IDA19 Replenishment Report (IDA, 2020). Further, there are specific exemptions to the performancebased allocation method discussed above; for example, the small island exemption, which allows small island economies with population less than 1.5 million to receive IDA, even if they are a high-income country. Such exemptions are also discussed in detail in the IDA19 Replenishment Report (IDA, 2020: Annex 2). Global environment facility (GEF) – STAR allocation method The GEF funds country-specific initiatives for biodiversity protection, climate change response, pollution reduction and nature restoration in developing countries. It works closely with environmental financiers and connects 184 member countries with a network of civil society, indigenous people, and the private sector. Since its inception in 1991, it has provided more than 26 ODI Advisory report $22 billion of funding through grants and blended finance, and mobilised more than $120 billion for national and regional projects and programmes across the globe. The GEF uses the System for Transparent Allocation of Resources (STAR) to allocate resources to its eligible countries. STAR replaced the Resource Allocation Framework (RAF), the former resource allocation system of the GEF, during the fifth replenishment period of the GEF (GEF-5). STAR is a performance-based allocation system that aims ‘to allocate resources to countries in a transparent and consistent manner based on global environmental priorities and country capacity, policies and practices relevant to successful implementation of GEF projects and programs’ (GEF, 2018). STAR allocation method The STAR allocation method is applicable to countries which satisfy the eligibility conditions to receive funding from the GEF trust fund.46 STAR consists of the following three indices and subindices: Global Benefits Index (GBI) GBI is a measure of GEF’s investment benefits in a country, pertaining to a specific focal area. There are three focal areas in STAR: (i) biodiversity (GBIBD); (ii) climate change (GBICC); and (iii) land degradation (GBILD). For a specific focal area, GBI represents a country’s relative share of GEF potential benefits that can be generated with a fixed resource input in that focal area (a higher GBI means higher potential benefits generated). 46 To be eligible for GEF funding, a country should (i) be a Party to the relevant Convention and meet the eligibility criteria decided by the Conference of the Parties to that Convention; (ii) not be member of the European Union; and (iii) have had at least one national project in the past five years, excluding projects that involve reporting to the Conventions (GEF, 2018: point 5). GBIBD is a weighted score of a country’s terrestrial (0.75) and marine (0.25) biodiversity. GBICC is a weighted score of two sub-indices – GHG emissions (0.95) and forest cover and change in forest cover (0.05). GBILD constitutes global share of land area affected (0.2), proportion of dryland area (0.6) and proportion of rural population (0.2). Country performance index (CPI) The GEF CPI (or GPI) measures a country’s relative performance and capacity to deliver on potential global environmental benefits. It is considered the same for all focal areas in a country, and calculated based on the country’s current and past performance in project development and implementation, along with the effectiveness of its policy and institutional frameworks. CPI works as a counterbalance measure for GBI. CPI is calculated using two main sources – the CPIA index developed by the World Bank, and the GEF portfolio performance index. GDP index This is designed to benefit countries with low per capita income, as it is used to decrease the allocation to countries with high per capita income. A floor (minimum allocation) is also set for the respective focal areas, differentiating between least-developed countries (LDCs) and non-LDCs. A ceiling (maximum allocation) is set at 10% of the total focal area allocations for each of the focal areas (for GEF-7). Details on the floor and ceiling limits are provided in GEF (2018: 7). 27 ODI Advisory report Weights for three STAR indices The weights to STAR indices are provided as exponents. GBI has an exponent of 0.8, CPI is given an exponent of 1, and the GDP index has an exponent of -0.12 in the GEF-7 period.47 47 The GEF-8 review has recommended changing the weight for the GDP index to -0.16 (see revised recommendations at www.thegef.org/sites/default/files/documents/2022-04/GEF_R.08_32_Revised_Policy_ Recommendations.pdf). Figure 1 STAR indices and sub-indices (as in GEF-7) Source: GEF (2018) Based on the values of the abovementioned indices for each country, the following steps are followed to calculate country allocations as per the GEF-7 guidelines (see GEF, 2018): • Country score is calculated using the following formula: Country score = GBI0.8 * CPI1.0 * GDP index-0.12 • Based on country score, country share is calculated as follows: Country share = Country score/ Sum of country scores for all STAR recipient countries • For preliminary STAR country allocation, a focal area is calculated as: Preliminary allocation = Country share * STAR resources • Finally, preliminary STAR country allocations are adjusted for floors and ceilings for each focal area. A review of the GEF-7 STAR policy guidelines is currently underway as part of the GEF-8 replenishment review. More details can be accessed from www.thegef.org/who-we-are/gefcouncil/council-meetings#replenishments. Global Risk Financing Facility (GRiF) – Appraisal framework for grant support The GRiF functions as a multi-donor trust fund, established in 2018 with pledges of over $200 million from Germany and the United Kingdom to help vulnerable countries develop and implement disaster and climate risk financing solutions. The facility provides finance and technical expertise to countries to develop innovative financial instruments while supporting the growth of existing ones. Financial solutions are typically designed as part of World Bank projects across different sectors. 28 ODI Advisory report The GRiF uses a set of principles and an appraisal framework for the use of grant financing under the Multi-Donor Trust Fund (MDTF) (GRiF, 2019). The guidelines and appraisal framework help in making resource allocations at the portfolio level, and appraise proposals at product/project level. This helps in the appraisal of decisions related to (but not limited to) providing start-up and operating costs, the capitalisation of risk financing vehicles, the cost of financial instruments and the cost of linking ex ante funding with national delivery mechanisms. At portfolio level, donors are expected to agree on prioritised countries, mainly based on their level of economic development and vulnerability to disaster and climate shocks. The GRiF appraisal method recommends prioritising IDA countries over IBRD countries, assuming all other factors are equal. It also recommends prioritising high-risk countries. Project and product appraisal is conducted as per the criteria described in the final table in the guidance note (GRiF, 2019: 9). Evaluation and scoring for Part B (project appraisal) and Part C (product appraisal) are to be completed by the technical task team of the GRiF secretariat. A colour-coded framework of appraisal is used to review co-financing proposals. The objective is to achieve a ‘green’ rating for all the indicators. A summary of indicators described as part of the appraisal framework is presented in Table 4 below. 29 ODI Advisory report Table 4 Summary of indicators for GRiF appraisal framework S. No. Indicator Criteria Part A: Portfolio appraisal A1 Level of economic development and vulnerability IDA countries will be prioritised against IBRD countries, all other things being equal. Higher-risk countries will be prioritised. Part B: Project appraisal B1 Sustainability and exit strategy The country is willing and able to allocate sufficient resources toward financial protection. B2 Country ownership and readiness The country has the required documents in place demonstrating readiness and political support to work on DRF; e.g. DRF strategy, and adequate legal and regulatory framework. B3 Comprehensive financial protection Financial solutions should be part of an integrated and comprehensive financial protection strategy. B4 Participatory process Appropriate stakeholder engagement is undertaken with communities, civil society organisations and private sector. B5 Improvements in preparedness and resilience The project demonstrates how the GRiF contributions will enable improved preparedness and resilience, either directly (in the project) or indirectly (incentives). B6 Capability, plans and systems The project demonstrates that pre-agreed plans and/or distribution systems are in place or being developed to channel the funding to the targeted beneficiaries. B7 Accountability and clear decision-making processes The project demonstrates clear accountability rules and decision-making processes either in place or under development as part of the project. B8 Target beneficiaries The project explicitly targets benefits to vulnerable people and steps are taken to support targeting of funds, with a special consideration of gender issues. Part C: Product appraisal C1 High-quality, open data and models The project demonstrates how data and risk modelling will be subject to external review and made publicly available. C2 Value for money (VfM) and suitability of the product The project demonstrates the added value of the proposed product/ strategy in the country’s disaster risk financing strategy, as set against their objectives, and relative to the alternatives (qualitatively and quantitatively). C3 Communication of the product The project demonstrates clear understanding of the product by the client, or actions are taken to ensure the client understands the product and that it is fully transparent to the client. C4 Quality and reliability of the product The project demonstrates how the quality and reliability of the product will be monitored. C5 Procurement process and non-preferential treatment The project demonstrates the extent to which the placement of the financial product will follow a competitive and transparent process. Source: GRiF (2020) 30 ODI Advisory report Official development assistance (ODA) ODA is the assistance provided by donors to countries and territories that feature in the Development Assistance Committee (DAC) list of ODA recipients and to multilateral development institutions.48 It consists of grants and concessional loans. ODA transactions can be bilateral as well as multilateral, including transactions to national and international nongovernment development organisations. ODA can also be provided by non-DAC members. 48 The DAC list of ODA recipients is available at www.oecd.org/dac/financing-sustainable-development/ development-finance-standards/daclist.htm. There is no set method for allocating ODA. It is typically targeted towards the poorest countries, meaning that the income level of a country (measured by GNI per capita) remains a critical factor in allocating assistance. However, there are other factors that influence the selection of partners and allocation of ODA in bilateral transactions, including historical and cultural relations with partner countries, and national security concerns. There are a few examples of countries which have developed their own criteria for allocating aid. Luxembourg, for example, uses Human Development Index (HDI) ranking as a benchmark, and selects beneficiary countries from among those ranking lowest. Netherlands uses factors like GNI per capita, positive trends in democratisation and governance, volume of aid per capita, perceived value-addition to Dutch development cooperation, historical ties and the number of donors already represented in a country. 31 ODI Advisory report Annex 2: Inclusion and treatment of qualitative criteria 49 Notable here is that some of the proxies for the factors suggested in section 4.3 are already in the form of index scores, which have been developed using both qualitative and quantitative criteria (see, for example, the ND-GAIN Index and the CPIA). Qualitative criteria could also be used to quantify the suggested (see section 4.3) and additional factors for which quantities/data are not readily and/or widely available. However, the inclusion of such indicators would have implications for the underlying method suggested in this guidance document for calculating the score/value of the scaling factor. The multi-criteria decision model (MCDM) suggested in the guidance should be modified to define the qualitative criteria, along with the quantitative criteria.49 The modified approach would be similar to the one described in the guidance note developed for measuring the ‘value for money’ of PCS interventions (see Ward et al., 2022). Following is a summary of steps to be taken in the modified approach. As a first step, qualitative criteria for the suggested (and additional) factors should be determined. For example, an indicator for country’s prior policy performance in DRM (and DRF) could be judged by evaluating the qualitative criteria, such as whether the country has a DRF strategy/ policy/plan in place and whether there is adequate support in its legal and regulatory framework for the same (see criteria B2 in GRiF, 2019). In the next step, a scoring method should be designed that assigns scores against different qualitative and quantitative criteria on a standard metric. Typically, in such MCDMs, scoring is assigned in a range (e.g. 0–5, 0–10, 0–100), where a wider range provides more flexibility in scoring. Scoring the qualitative criteria requires expert judgment; based on this, ‘best’ (maximum) and ‘worst’ (minimum) scores can be defined. Similarly, for a quantitative criterion, the score for an expected quantity/value can be relative to predefined highs and lows. Other, more subjective, ways to score quantitative criteria may also be valid. Furthermore, there could be a scenario where the scoring scale for a (readily available) index (e.g. CPIA) is different from the designed scoring methodology. A unitary method may be used to convert scores to the same scale. For example, if the score for an indicator is 3.2 on a 6-point scale, it would be approximately 5.33 on a 10-point scale (i.e. (3.2/6) * 10). While this is a very straightforward approach, it may not be suitable in some cases (e.g. where the minimum values of the scales are different). Scoring should be done through a participatory and consultative process involving a wider group of stakeholders. Appropriate justification should be provided for the assigned scores to ensure transparency in allocation decisions. 32 ODI Advisory report As a next step, weighting criteria should be determined to account for SMART PCS allocation principles and priorities (see discussion in section 4.2.1 on considerations for PCS allocation). Weights could be determined once scoring has been completed, or after best and worst scores for a criterion are identified. Assigning weights requires expert judgement and consultations. The weighting process could follow a subjective, objective or integrated approach (see Odu, 2019 for discussion on weighting methods for MCDM). Weights and scores can be aggregated using either an additive method (viz., (s1 * w1) + (s2 * w2)… (sn * wn) ) or a multiplicative method (viz., (s1 w1) * (s2w2)… (sn wn) ), where the final score in the latter is less sensitive to selected weights. A similar method/procedure to aggregate weights as exponents is suggested in section 4.4, which is more suited to quantitative indicators. 33 ODI Advisory report Annex 3: A potential alternative to determine the size of premium support Climate change attribution science (hereafter: attribution science) could offer an alternative method for deciding allocation size for premium support. Simply put, attribution science can help in scientifically ascertaining the mechanisms that are responsible for climate change – i.e. whether and how much of recent climate change is caused by anthropogenic (human-induced) activities, and how much has been due to natural causes. For climate insurance purposes, climate modelling (e.g. global climate models, probabilistic event attribution) could be used to estimate changes in the risks of climate-related damages in a specific location and to what extent they can be attributed to climate change (Otto, 2020; James et al., 2019). A risk insurance premium share equivalent to the portion of risk attributed to climate change could be funded by the donors as premium support (ibid.). As highlighted by Otto (2020): …Rather than waiting until the total damage has been determined, which can take weeks, they (insurance providers) can pay out when droughts occur that exceed a specific extreme index – for example, a drought to be expected every twenty years or more. In this type of insurance, it is significant if an event that previously occurred every twenty years (i.e. exceeded the index every twenty years or so) is suddenly to be expected every five years – and can therefore cause much greater damage. If insurance companies want to profit from this model in the long term, they will need to keep raising premiums. At some point, many poorer countries will not be able to afford it – even today, some cannot or do not want to pay. The poorest of the poor will have very few options to escape their predicament. Attribution science may provide one solution. We could begin by calculating how the risk of climate damage has changed in a specific location and to what extent we can attribute this to climate change. This portion of the risk could be covered by an international fund paid into by industrialized countries. It would therefore be worthwhile for insurers to continue doing business in developing countries, who would continue paying their usual premiums but still receive full protection. Even now, insurers are only making a profit from many countries because of the millions contributed by countries like Germany and institutions like the World Bank In a more practical application of attribution science to risk insurance, New et al. (2020) used the case of drought-related agricultural losses in Malawi to estimate ‘climate change-implicated’ weather losses, in order to determine an equitable contribution to weather insurance premiums in Africa. Although considerable progress has been made in recent years in assessing the influence of climate change on an extreme event, attributing the influence of climate change on natural and social systems (among many confounding factors) is still a big challenge (New et al., 2020). Further, other considerations, such as a country’s ability and 34 ODI Advisory report willingness to pay, still have to be integrated into such assessments. Therefore, while attribution science could offer an objective way to estimate externally supported premium share, further research and evidence is warranted to make it practically usable for this purpose.