At a Crossroads: Prospects for Government Health Financing Amidst Declining Aid Government Resources and Projections for Health (GRPH) Series Anurag Kumar, Jacopo Gabani, Alberto Marino, Julio Cesar Mieses Ramirez, and Patrick Hoang-Vu Eozenou November 2025 2 © 2025 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. 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Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522- 2625; e-mail: pubrights@worldbank.org. Cover design: Carlos Javier Billordo Peres and Gaston Ariel Cleiman Graphics: Loredana Horezeanu Suggested citation: Kumar, Anurag, Jacopo Gabani, Alberto Marino, Julio Cesar Mieses Ramirez, and Patrick Hoang-Vu Eozenou. At a Crossroads: Prospects for Government Health Financing Amidst Declining Aid. Government Resources and Projections for Health (GRPH) Series. Washington, DC: World Bank, November 2025. https://hdl.handle.net/10986/43745. 3 Acknowledgments The team expresses sincere gratitude to Monique Vledder and Agnès Couffinhal for their overall guidance and support throughout the preparation of this report. We are grateful to the peer reviewers – Joana Silva, Owen Smith, and Sarah Alkenbrack (World Bank), and Mauricio Soto (IMF) – for their constructive and thoughtful feedback. We thank colleagues who provided early comments and technical input, including Ajay Tandon, Bill Savedoff, and Colin Andrews. Their insights helped refine the framing and analytical approach. We also acknowledge the authors of the Double Shock, Double Recovery (DSDR) Series as this report builds on their work. We owe a special thanks to Loredana Horezeanu for producing all graphs and figures in this report. We thank Jung-Hwan Choi and Katherine West Slevin from the Learning and Knowledge Program for facilitating the publication process, Carlos Javier Billordo Peres and Gaston Ariel Cleiman for designing the report, and Katherine West Slevin and Karin Silitonga for editorial assistance. We appreciate the support from Alexandra Humme on external communications. We acknowledge the valuable technical inputs from Victoria Fan, Moritz Piatti, Roberto Iunes, Kent Ranson, Edson Araujo, Lombe Kasonde, and Mirja Sjoblom. We also appreciate the close collaboration with colleagues in the Health, Nutrition, and Population (HNP) Global Practice who made the extensive data collection possible. We thank Jewelwayne Salcedo Cain for managing the health financing database. The authors gratefully acknowledge the generous financial support of Gavi, the Vaccine Alliance which made this work possible. 4 Contents Acknowledgments .......................................................................................................................4 List of Figures, Maps, and Tables...................................................................................................6 Acronyms and Abbreviations ........................................................................................................7 Executive Summary....................................................................................................................10 Introduction ...............................................................................................................................14 Stuck at the Starting Line: Progress during the Sustainable Development Goal Period (through 2024) ...........................................................................................................................20 Financing Required for UHC ....................................................................................................20 Financing Committed for UHC ................................................................................................23 Inadequacy of Government Financing .....................................................................................26 Stalled in Stride: Projections for the Remaining Sustainable Development Goal Period (2024–30) ..................................................................................................................................28 Macro-fiscal Outlook ..............................................................................................................29 Government Health Spending Outlook ....................................................................................32 Development Assistance for Health Outlook ...........................................................................34 Combined Government and Donor Health Spending Outlook ...................................................36 Steering through a Storm: Country Prospects and Options...........................................................42 Spend Better: Doubling Down on Efficiency during a Window of Opportunity ............................42 Spend More: Increasing Government Financing for Health within Fiscal Constraints ................. 44 References ................................................................................................................................49 Appendices ...............................................................................................................................55 Appendix 1. Methodology for the Costing Benchmark ..............................................................55 Appendix 2. Data Sources and Methodology for Nowcasting (2022–24) and Projection (2025–30) Analyses ................................................................................................................................60 Appendix 3. Sensitivity Analysis ..............................................................................................68 Appendix 4. Data Tables across Countries and Income Group Averages ....................................74 5 List of Figures, Maps, and Tables Figures Figure 1. Estimates of the Minimum Annual per Capita Cost for UHC, LIC, and LMIC Average (in Constant 2024 US$) ...................................................................................................................22 Figure 2. Annual per Capita Government and Donor Funding Compared to Minimum Benchmark Cost for UHC, 2018 and 2024, LIC and LMIC Median (in Constant 2024 US$) ................................24 Figure 3. UHC Financing Gap as Share of Minimum Benchmark Cost, 2018 and 2024, by Country, in LICs and LMICs (in %).................................................................................................................25 Figure 4. Projected per Capita General Government Expenditure Growth, 2024–30, LIC and LMIC Median (Index with 2024 = 100) ...................................................................................................30 Figure 5. Annual per Capita Government and Donor Funding Compared to Minimum Benchmark Cost for UHC, 2024 and 2030 (Projected), LIC and LMIC Median (in constant 2024 US$) ............... 33 Figure 6. Recipient Country Tagged DAH, 2023 and Projected from 2025–26 Onwards, by Donor (in US$ billions) ..............................................................................................................................36 Figure 7. Projected Change (2024–30) in GHE, and in Off-budget DAH, as Share of 2024 GHE, by Country, by LICs and LMICs (in %)...............................................................................................37 Figure 8. UHC Financing Gap as Share of Minimum Benchmark Cost, 2024 and 2030 (Projected), by Country, in LICs and LMICs (in %) ...............................................................................................38 Figure 9. Annual per Capita Government and Donor Funding Compared to Minimum Benchmark Cost for UHC, 2018, 2024, and 2030 (projected), LIC and LMIC Median (in Constant 2024 US$) .... 40 Figure 10. Health Share of Government Spending in 2024 and Projected Growth in Post-interest Government Spending between 2024 and 2030, by Country, by LICs and LMICs (in %) .................. 47 Maps Map 1. Countries Covered by Income Group ...............................................................................15 Map 2. Classification of Countries’ Fiscal Space Based on Projected Growth in Post-interest Government Spending, 2024–30 .................................................................................................31 Boxes Box 1. About the Series and the World Bank’s Health Sector Target .………………………………….……16 Box 2. Crowding out Government Health Spending from Interest Payments on Public Debt? ……..32 Box 3. The Win-Win Case for Health Taxes ………….………………………………………………………………45 6 Acronyms and Abbreviations CBI cost benchmark index CG central government CGHS central government health spending COVID-19 Coronavirus Disease 2019 CRS creditor reporting system DAH development assistance for health DCP3 Disease Control Priorities, 3rd edition DSDR Double Shock, Double Recovery EUHC essential universal health coverage FY fiscal year GDP gross domestic product GGE general government expenditure GHE government health expenditure GHED Global Health Expenditure Database GHER Global Health Expenditure Reports GRPH Government Resources and Projections for Health HIC high-income countries HIV/AIDS human immunodeficiency virus/acquired immunodeficiency syndrome HNP Health, Nutrition and Population HPP highest-priority package IHME Institute for Health Metrics and Evaluation IMF International Monetary Fund LICs low-Income countries LMICs lower-middle income countries NCDs non-communicable diseases ODA official development assistance OECD Organisation for Economic Co-operation and Development OOP out-of-pocket PHE public health expenditure PHC primary health care RMNCH&N reproductive, maternal, neonatal, and child health plus nutrition 7 SCI service coverage index SDGs Sustainable Development Goals SHA system of health accounts SHI social health insurance SNG subnational government SNGS subnational government health spending UHC universal health coverage UMICs upper-middle income countries UN DESA United Nations Department of Economic and Social Affairs UNCTAD United Nations Conference on Trade and Development USAID United States Agency for International Development WEO World Economic Outlook WHO World Health Organization 8 9 Executive Summary Investing in health is one of the most powerful drivers of human capital formation, economic growth, and job creation. By protecting households from financial hardship and ensuring access to essential health services, investing in universal health coverage (UHC) boosts productivity, increases labor force participation, and builds resilience. It also fosters social inclusion. Achieving UHC—and unlocking its long-term growth potential from healthier, more productive populations— requires public financing for health. Public funding enables effective risk pooling and reduces reliance on out-of-pocket (OOP) spending that often pushes families into poverty. Low- and lower-middle income countries (LIC and LMICs) are at a crossroads in their UHC journey. Governments’ ability to invest in health, and other development priorities, largely depends on their macro-fiscal conditions; it is supplemented by external aid in poorer countries. The growing economic uncertainty poses challenges. It puts further pressure on governments already struggling to finance and deliver on UHC, owing to weak economic growth, high public debt burden, stagnant revenue collection efforts, and a declining priority for health in government spending. Further, sharp cuts to external aid will drastically alter the health financing landscape, especially in LICs. This report—part of an annual series—monitors the latest trends in and provides an outlook on government and donor spending on health. Stuck at the Starting Line: Progress during the SDG Period (through 2024) LICs and LMICs continue to face large UHC gaps, with progress stalled since 2015. Today, around half the world’s population lacks access to essential health services, the majority of whom live in LICs and LMICs. Almost one-third of the LIC and LMIC population suffer from financial hardship due to OOP spending on health. Further, progress on UHC has slowed since 2015 after rapid gains between 2000 and 2015, particularly in service coverage. Government and donor health spending has remained far below the minimum benchmark cost for UHC in LICs and LMICs. UHC requires a minimum of US$60 per capita in LICs and ~US$90 in LMICs from government and donor sources. In 2024, combined government and donor health spending was US$17.2 per capita in the median LIC, less than one-third of the US$60 minimum benchmark cost for UHC. The median LMIC spent US$46.6 per capita in 2024, half the minimum UHC benchmark of ~US$90. Crucially, countries remain stuck at the starting line. Spending has not grown. In 2024, it remains at 2018 levels in real per capita terms. All LICs and 90 percent of LMICs continued to have a UHC financing gap in 2024. 10 Stalled in Stride: Projections for the Remaining SDG Period (2024–2030) Government health expenditure (GHE) is projected to increase moderately over the next few years. While government investments in health are buoyed by economic growth and effectiveness in collecting public revenues, their choices in allocating them also matter. The priority for health in public spending declined from 7.1 percent in 2018 to 6.3 percent in 2024 in the median LMIC and stagnated at 6 percent in the median LIC. Further, interest payment on public debt is projected to continue crowding out other public spending. Assuming governments dedicate their historical 2024 share of post-interest government spending to health, GHE is projected to grow by 13 percent in the median LIC and 17 percent in the median LMIC between 2024 and 2030 in real per capita terms. While the resulting growth in GHE will be insufficient to significantly dent the large UHC financing gap, it will push two-thirds of LICs and LMICs in the right direction. However, declining donor support is projected to widen the UHC financing gap, with spending expected to fall in over 80 percent of LICs and 40 percent of LMICs by 2030. Unless the cuts already announced by donor governments are reverted, development assistance for health (DAH) will decline by around 20 percent, mostly impacting off-budget aid. The drop in aid will offset the modest projected increase in GHE in many countries. The impact will be sharper in LICs—which rely more heavily on aid—with combined government and donor health spending projected to fall in over 80 percent of LICs between 2024 and 2030. Under the same assumptions, more than half of LMICs will make modest progress, though spending will still decline in 40 percent of them. Steering through a Storm: Country Prospects and Options Countries have policy options to alter their trajectories. A business-as-usual approach to public financing for health would reduce access to essential services and worsen financial hardship, especially for the poorest and most vulnerable. However, the health financing outlook presented in this report, and its implication for UHC, is not a foregone conclusion. Countries have demonstrated, and are continuing to demonstrate, that rapid progress is possible, even under constraints. Doubling down on efficiency will accelerate progress towards UHC and aid dependent countries have a reform window. As funding tightens, all countries can spend better by improving how they allocate their remaining resources, increasing budget execution, and aligning DAH with domestic priorities. For instance, countries can allocate a higher share of GHE to more cost- effective primary healthcare (PHC) services, which remain underfunded relative to secondary and tertiary care. Countries can also ensure allocated budgets for health are fully spent. Most importantly, recent declines in DAH have opened a reform window for aid dependent countries to restructure their health system and align remaining resources—government and donor—with domestically set priorities. 11 Progress also requires spending more and a third of LICs and LMICs have the fiscal conditions to raise the priority of health in government spending. Increasing efficiency could never be enough at current levels of government health spending: less than US$10 per capita in the median LIC and US$40 in the median LMIC. Progress towards UHC requires faster growth in government health spending. This is feasible. A third of LICs and LMICs are projected to expand post-interest government spending between 2024 and 2030 and currently give a much lower priority to health than their peers. They could make the political choice to increase the share going to health. Ultimately, all LICs and LMICs require broader macro-fiscal reforms to spur growth and increase government revenue. While these reforms are beyond the health sector’s purview, the sector can play a leading role in implementing health taxes. Raising excise taxes on unhealthy products increases government revenues but, more importantly, slows rising healthcare needs from non- communicable diseases and aging. Bold reforms will deliver rapid gains by saving lives, creating jobs, and driving economic growth. While the challenge may appear daunting, each additional dollar, well-spent, will save lives and deliver outsized returns. More and better health spending will facilitate the expansion of core health services and bring countries closer to reaching their UHC goals. Reforms will also deliver broader economic dividends by fostering human capital that enhances workforce productivity, fuels long-term growth, and stimulates job creation across the economy. 12 13 Introduction Investing in health is a core driver of human capital, productivity, and job creation. Human capital— the health, knowledge, skills, and resilience accumulated over a lifetime—is the foundation of wealth embodied in people. Good health is not only intrinsically valuable, but a productive asset that raises lifetime earnings, extends labor force participation, and enhances the returns to education (Becker 2007). Healthier individuals are more likely to work, earn higher wages, and sustain productivity across their careers. Conversely, poor health—beginning in utero or through low birthweight—has on average, lifelong negative consequences for income and employability. At the macroeconomic level, investments in health generate jobs directly in the health sector. These jobs have a multiplier effect with each health job creating an additional 3.4 jobs in the wider economy (Buchan et al. 2017). Ultimately, investing in health leads to higher economic growth by raising the productivity of workers across all sectors of the economy. Low- and lower-middle income countries (LICs and LMICs) are at a crossroads in sustaining their investments in health. Countries’ ability to invest in health, and other development priorities, depends on their macro-fiscal conditions; it is supplemented by external aid in poorer countries. Growing economic uncertainty from trade tensions and rapid geopolitical shifts, along with sharp reductions in external aid pose challenges. This volatility unfolded in 2024 just as the global economic outlook was finally stabilizing after successive shocks at start of the decade from COVID-19 and Russia’s invasion of Ukraine. These pressures, particularly the unprecedented decline in development assistance, will drastically alter the health financing landscape in LICs and LMICs. They will further constrain available public resources at a time when governments are already struggling to adequately finance and deliver on health targets due to weak economic growth, high burden of public debt, stagnant revenue collection efforts, and declining priority for health in government spending (World Bank forthcoming (b)). Purpose and Scope This report—the first of a revamped annual series—discusses the implications of the above macro- level shifts on government and donor financing for health in LICs and LMICs (see Box 1 for details and previous reports on this theme). It focuses on government (i.e., public) financing because public resources are necessary for rapid progress on health, most importantly for reducing financial hardship from the high out-of-pocket (OOP) health spending that often pushes families into poverty. No country has achieved high levels of health coverage without majority of funding coming from 14 public sources.1, 2 The report also includes donor financing because development assistance for health (DAH) has been important for these countries in recent decades, despite the inefficiencies created by existing aid modalities. The report limits its scope to LICs and LMICs since inadequate government financing for health—driven by their macro-fiscal situation— is a binding constraint in these countries (Map 1).3 Map 1. Countries Covered by Income Group 1 Countries with higher universal health coverage (UHC) service coverage index scores—a composite measure of 14 tracer health service indicators—have a larger share of their health spending from government sources (Wagstaff and Neelsen 2020). 2 While there is a strong global push to mobilize private capital to finance development, healthcare is different. Experience from Organisation for Economic Co-operation and Development (OECD) countries demonstrates this clearly. On average, around three-quarters of health spending is publicly financed, even in market-oriented systems (WHO 2024). Only government financing can effectively pool financial risks from ill health to prevent financial hardship and promote equity; objectives that private capital, driven by profit incentives cannot deliver at scale. Health also involves public goods, such as disease prevention and epidemic preparedness, that markets systematically underprovide. This does not mean the private sector has no role. Private providers, innovators, and supply chains are essential for delivery and account for the majority of health provision in many LMICs. However, the fundamental responsibility for financing health must rest with governments, with private capital engaged in complementary ways. 3 While inadequate government financing is a concern in some upper-middle income countries (UMICs) and high-income countries (HICs), the primary financing challenges is often the efficiency, equity, and/or sustainability (from spending outpacing economic growth) of public health spending. 15 Source: World Bank staff Notes: 1. Data available for 22 of 26 LICs (87% of the LIC population) and 47 of 51 LMICs (99% of the LMIC population). The remaining 8 countries or economies are excluded due to missing or incomplete macro-fiscal series from the IMF’s October 2025 World Economic Outlook (WEO): Afghanistan, Bolivia, Eritrea, Democratic People’s Republic of Korea, Lebanon, Sri Lanka, Syria, West Bank and Gaza. 2. Country income groups are based on the 2025 fiscal year (FY) classifications from the World Bank. The FY26 update, released on July 1, 2026, would impact three countries. Cabo Verde and Samoa— included in the report—would move out as they transition from LMICs in FY25 to upper-middle income countries (UMICs) in FY26. Conversely, Namibia—excluded from the report—would move in as it slides back from a UMIC to a LMIC. Disclaimer: This map was produced by the Cartography Unit of the World Bank Group. The boundaries, colors, denominations and any other information shown on this map do not imply, on the part of the World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries. Box 1. About the Series and the World Bank’s Health Sector Target The Government Resources and Projections for Health (GRPH) Series will be an annual check on the ambition of universal health coverage (UHC) compared to the reality of government health spending in low- and lower-middle income countries (LICs and LMICs). The series will also support efforts to track progress toward the World Bank’s target of providing affordable, quality health services to 1.5 billion people by 2030 (World Bank 2024b) and will complement the Global Monitoring Reports on UHC which track progress on health service coverage and financial protection (WHO and World Bank forthcoming). The GRPH series builds on the World Bank’s Double Shock Double Recovery (DSDR) series. The DSDR series was started during the COVID-19 crisis to assess the implications of the macro-fiscal outlook on government financing for health (Kurowski et al. 2021). DSDR continued to monitor government health spending trends as the world came out of the pandemic (Kurowski et al. 2023). The GRPH series will retain a set of core analytics from the DSDR series to provide the latest trends and outlook on government health financing. Each edition of the GRPH series will also feature a specific theme or focus area. The first edition (this report) focuses on the impact of the sharp decline in aid on the health sector. The GRPH series supplements existing data and reports. Specifically, the series supplements the World Health Organization’s (WHO’s) Global Health Expenditure Reports (GHERs), based on their Global Health Expenditure Database (GHED). The GHERs have more detailed analyses on the level, trend, and composition of health financing, but have a lag of at least two years, and data are often modelled. For instance, their latest data (as of September 2025) are from 2022. In contrast, the GRPH series has up- to-date data on government health spending (until 2024) and makes projections for the next five years (through 2030). The latest data and projections facilitate a more real-time dialogue with policymakers at the global and regional levels. 16 Structure The remainder of the report is structured as follows: • The next (second) section describes progress on mobilizing government and donor financing for essential health services until 2024. It also discusses the adequacy of health spending, comparing the financing committed to minimum levels required. • The third section projects financing until the end of the SDG period (2030) based on countries’ fiscal outlook and official announcements on aid commitments from key donors. It projects a business-as-usual approach to health financing as a reference point for policy discussions. • The final section concludes by outlining the policy options available to countries based on their financing prospects. Notes on Methods and Key Assumptions The following points highlight key methods and assumptions to guide the reader. Additional details on data sources, methods, and results are available in the appendices. 1. Government health expenditure (GHE) data from 2018 to 2024 in LICs and LMICs are compiled from over 5,000 budget documents. All figures reflect executed budgets, i.e., spending, not initial or revised allocation. 2. GHE is projected (2025 to 2030) by assuming a constant share of health in post-interest government spending. Accordingly, projected GHE growth equals the IMF’s October 2025 World Economic Outlook (WEO) forecast for post-interest government spending growth. 3. DAH is projected from 2025 to 2030 by assuming a one-time decline in 2025 to the historical baseline from OECD’s Creditor Reporting System (CRS) based on official announcements from top 15 donors. 4. “Government health expenditure (GHE)” and “public health expenditure (PHE)” are used interchangeably throughout this report. They include government budget transfers, compulsory social health insurance contributions, and on-budget DAH in line with the 2011 system of health accounts definition. 5. DAH or external aid for health includes both on- and off-budget DAH. Off-budget DAH refers to aid that is not channeled via government ministries, departments or agencies. Since on- budget DAH is already included in GHE (see point 4 above), all figures are disaggregated by off-budget DAH and GHE. 17 Additional Technical Notes for the Reader 1. All numbers are in constant 2024 US$ (indicated by ‘$’ in the following text) per capita terms unless otherwise stated. 2. Medians are generally used to present income group summary measures because outliers skew averages, particularly in LMICs. When used, income-group averages are simple. They are not weighted by income or population to avoid large country bias (e.g., India is nearly half the LMIC population). 3. The country income group is based on the FY25 classification from the World Bank. The income category for all countries is held constant for all years (historical and projected), based on the FY25 classification. 4. All figures and maps are followed by notes to ensure they are independently interpretable. The notes elaborate on the relevant methods and key assumptions. 18 19 Stuck at the Starting Line: Progress during the Sustainable Development Goal Period (through 2024) The 2030 Agenda for Sustainable Development laid out ambitious goals for the 2016–2030 period. The health-related Sustainable Development Goals (SDGs), in particular SDG3 on good health and well-being, called for sharp declines in mortality and morbidity and rapid progress towards universal health coverage (UHC) (UN DESA 2025).4 However, there has been little progress on both dimensions of UHC—the core of SDG3—since 2015 (WHO and World Bank 2023). Today, around half the world’s population— the majority of whom live in low- and lower-middle- income countries (LICs and LMICs)—lacks access to essential health services, the first dimension of UHC (SDG 3.8.1). And almost one-third of the LIC and LMIC population suffer from financial hardship due to out-of-pocket (OOP) spending on health, which the second dimension of UHC (SDG 3.8.2) aims to prevent (WHO and World Bank 2023). Progress on UHC has slowed since 2015 after rapid gains between 2000 and 2015, particularly in health service coverage. 5 The ambitious goals under SDG3, including UHC, acknowledged large financing gaps at the outset but expected substantial increases in financing (UNCTAD 2023). Financing Required for UHC Achieving the 2030 SDG3 goals by providing UHC requires an estimated annual per capita health spending of $75–$105 in LICs and $115–$140 in LMICs (Figure 1). This level of spending would cover at least 80 percent of the population with a set of around 200 health services or interventions. The increase in health service coverage could avert over 6 million deaths per year, and cut under-five, neonatal, and maternal mortality rates by half (Stenberg et al. 2017). However, health spending in 2022 at $40 per capita in the median LIC was around half the minimum estimated cost for UHC. In contrast, the median LMIC’s health spending, at $104 per capita, was closer to the minimum cost for UHC. 6 4 The targets include reduction in global maternal, under-five, neonatal, and premature mortality from non- communicable diseases. 5 UHC service coverage index—a composite measure of 14 tracer health service indicators used for monitoring progress—has increased by only three points since 2015, compared to 20 points between 2000 and 2015. In LICs and LMICs, UHC service coverage index has remained almost flat since 2017 after substantial gains over the previous decade and half. 6 Health spending refers to current health expenditure. Latest data available in the WHO Global Health Expenditure Database are from 2022. 20 UHC also requires around 80 percent of the funding to come from government and donor resources (Jowett and Kutzin 2015). In LICs and LMICs, total financing for health comes principally from three channels: domestic government, external, and private OOP spending. When public and external financing is too low, the demand for health is mostly financed from OOP expenditure, pushing families into poverty or forcing them to forego care.7 As a result, it compromises financial protection, the second dimension of UHC. Though sustainable financing for health can only come from government funding in the long term, donor resources can help bridge important gaps in the short- to medium-term as countries develop their fiscal capacity.8 To achieve UHC, LICs require a minimum of around $60 per capita, while LMICs require at least $90 from government and donor sources (Figure 1). 9 However, meeting the minimum benchmark is a necessary, but not sufficient condition. UHC also requires efficient and equitable allocation of these resources, which hinges on deeper reforms to strengthen governance and institutional capacity. Ultimately, progress towards UHC needs strong, country-led leadership to effectively steward the health system, but these efforts will fall short without a minimum level of financing. 7 Out-of-pocket expenditure on health is already high at around 45 percent of current health expenditure on average, in both LICs and LMICs (WHO 2024). 8 However, donor financing creates inefficiencies when it is not aligned with government priorities and systems (see sub-section on Inadequacy of Government Financing). 9 Estimates are conservative. They do not account for additional needs which have emerged over the last few years, particularly from ageing, health impacts of climate change, and further investments in pandemic preparedness and response. They also assume high efficiency of spending, which does not align with real world experience. WHO (Stenberg et al. 2017) assumes full efficiency of spending while Disease Control Priorities, 3rd edition (DCP3) assumes constant efficiency (at current levels), which is unlikely due to scale-up constraints in organization and infrastructure. 21 Figure 1. Estimates of the Minimum Annual per Capita Cost for UHC, LIC, and LMIC Average (in Constant 2024 US$) A. Total health spending B. Government and donor health spending Source: World Bank staff calculations based on Stenberg et al. 2017 (WHO) and Watkins et al. 2020 (DCP3). Notes: 1. The original cost estimates in 2014 US$ and 2016 US$ are standardized to constant 2024 US$ by adjusting for price inflation and currency fluctuations using gross domestic product (GDP) deflators and local currency units to US$ exchange rates. Per person costs are further updated using the latest population projections to 2030 from UN World Population Prospects 2024. The benchmarks therefore refer to total health system costs per person in 2030 (see Appendix 1 for details). 2. The cost estimates use micro-costing approaches based on unit prices to deliver a set of essential services to at least 80 percent of the population (coverage and interventions vary by scenario). The most recent edition of Disease Control Priorities’ (DCP3’s) essential UHC (EUHC costs reflect a harmonized, de-duplicated list of 218 unique health interventions organized into 21 care packages (e.g., child health, tuberculosis) to reach 80 percent population coverage. WHO costs cover 187 health interventions. They reflect 95 percent population coverage in the ambitious scenario and 67–90 percent of the coverage gap in the progress scenario. Estimates from DCP3’s EUHC and WHO’s ambitious scenarios show a potential 97 million deaths (cumulative from 2016 to 2030) averted from the additional coverage (i.e., 6.5 million incremental deaths averted per year). 22 Financing Committed for UHC Towards the start of the SDG period, combined government and donor health spending was inadequate compared to the most conservative cost estimate. The median LIC spending at $16.8 per capita in 2018 was less than one-third of the minimum benchmark for UHC (Figure 2). Further, over half of this spending came from off-budget donor funds which do not go through government systems, often creating inefficiencies (Piatti-Funfkirchen et al. 2021) (see sub-section Inadequacy of Government Financing). The median LMIC was relatively closer to the target, spending $42.9 per capita in 2018, though still below half the minimum cost for UHC. In contrast to LICs, off-budget donor funds were less important in LMICs and accounted for less than 15 percent of government and donor spending in the median LMIC. In 2024, more than halfway through the SDG period, countries are stuck at the starting line; their financing gap has not declined. Government and donor health spending in 2024 remains close to its 2018 level at $17.2 per capita in the median LIC and $46.6 in the median LMIC (Figure 2). While there was a sharp increase in both government and donor health spending in 2020 and 2021 in response to the COVID-19 crisis, the rise was temporary. Increased health spending in those years was targeted for COVID-19 response, including for testing, treatment, and vaccination. Government and donor health spending subsequently declined, falling back to pre-pandemic levels in 2024 (Kurowski et al. 2024) (Appendix 2). In 2024, the median LIC still needed to mobilize over three times its current health spending from government and donor resources to achieve UHC. The median LMIC needed to double its spending. Crucially, the priority for health in government spending—an important lever for growing government health spending—remained flat in LICs and declined in LMICs between 2018 to 2024. 10 In the median LIC, it stagnated at 6.0 percent of government spending, while in the median LMIC, it fell by 0.8 percentage points to 6.3 percent in 2024. 11, 12 Persistently low public expenditure has real consequences for investments in the health sector, delivery of services, and ultimately, health outcomes. The health workforce has large shortfalls in most LICs and LMICs (Boniol et al. 2022; Haakenstad et al. 2022). The public sector faces a chronic shortage of essential medicines, and when they are available, they remain unaffordable in several LICs and LMICs (Oldfield et al. 2025; Wirtz et al. 2017). These shortfalls—combined with underinvestment in primary health care (PHC) infrastructure, laboratory and diagnostic capacity, 10 The two other relevant levers for increasing government health spending are in the domain of macro-fiscal policy: economic growth and increase in the government expenditure to gross domestic product (GDP) ratio, driven by stronger revenue collection efforts (Tandon and Cashin 2010). 11 Using averages reveal a similar trend: Health’s share of government spending fell from 6.3 percent in 2018 to 6.1 percent in 2024 in LICs, and from 7.6 percent to 7.2 percent in LMICs over the same period. 12 As a share of GDP, government health spending was 1.0 percent in the median LIC and 1.4 percent in the median LMIC in 2024. 23 and supply chain—leads to poor and uneven population coverage with publicly financed affordable and quality health services. Absence of these foundational investments has stalled progress towards UHC since 2015 and constrained human capital formation (WHO and World Bank 2023). Low public spending also reinforces inequities. For example, coverage gaps are higher in rural areas and among underserved populations and the reliance on OOP payments disproportionately affects the poor, pushing millions into poverty and deterring them from seeking care. Moreover, underinvestment undermines the resilience of health systems, leaving them ill-prepared to respond to health emergencies such as pandemics or climate-related shocks. Figure 2. Annual per Capita Government and Donor Funding Compared to Minimum Benchmark Cost for UHC, 2018 and 2024, LIC and LMIC Median (in Constant 2024 US$) Financing gap: US$ 45.4 Requires ~2x current spending Financing gap: to cover gaps US$ 43 (or 72%) Requires ~3.5x current spending to cover gaps Source: World Bank staff calculations using World Bank country health budgets’ repository for public health expenditure (PHE), Organisation for Economic Co-operation and Development’s Creditor Reporting System for off-budget development assistance for health (DAH), IMF’s October 2025 World Economic Outlook. Universal health coverage (UHC) cost benchmarks from Stenberg et al. 2017 (WHO) and Watkins et al. 2020 (DCP3). Notes: 1. Government (public) health expenditure (government health expenditure (GHE) or PHE) reflects actual spending based on published budget documents. In country-years where spending data are unavailable due to publishing lag, initial or revised budget allocations are used to estimate spending based on their historical relationship (Appendix 2). 2. Health spending totals are presented as the sum of median components (GHE and off-budget DAH). 24 3. The GHE to general government expenditure ratio shows the share of health in government spending in the median country by year and income-group. All LICs and nearly 90 percent of LMICs had a UHC financing gap in 2024, mirroring their situation towards the start of the SDG period (Figure 3). While some countries bridged part of their financing gap, the situation worsened in others between 2018 and 2024. However, there are substantial differences in the magnitude of the gap within LICs and LMICs. For example, while Rwanda (a LIC) is close to the minimum benchmark for UHC with a financing gap of around 20 percent, Madagascar (another LIC) has a financing gap of over 80 percent. The heterogeneity is larger in LMICs. In 2024, while four LMICs crossed the most conservative UHC benchmark, and countries like Morocco and the Philippines had financing gaps below 20 percent, several others (e.g., Bangladesh and Cameroon) faced gaps of around 80 percent (Figure 3). Figure 3. UHC Financing Gap as Share of Minimum Benchmark Cost, 2018 and 2024, by Country, in LICs and LMICs (in %) Source: World Bank staff calculations using World Bank country health budgets’ repository, Organisation for Economic Co-operation and Development’s Creditor Reporting System, IMF’s October 2025 World Economic Outlook. Universal health coverage (UHC) cost benchmarks from Stenberg et al. 2017 (WHO) progress scenario. Notes: 1. Financing gap refers to the difference between the country’s per capita government and donor health financing for the year and the minimum cost benchmark for UHC. The share is calculated by dividing 25 the financing gap by the country’s minimum cost benchmark for UHC. Minimum UHC cost benchmarks are based on 2014 income-group level estimates which are converted to 2024 US$ per capita using country- specific prices, exchange rates, and 2030 population numbers. 2. Individual countries might have their own national estimates for the minimum cost for UHC. The cost estimates used in this report serve as benchmarks; they are not universal targets and should not replace national estimates (where available). 3. A red arrow indicates a decline in government and donor spending on health between 2018 and 2024 leading to an increase in the financing gap; a blue arrow indicates the opposite. 4. The figure excludes 12 small states with a population of less than 1.5 million: Bhutan, Cabo Verde, Comoros, Djibouti, Eswatini, Kiribati, Micronesia (Federated States), Samoa, São Tomé and Príncipe, Solomon Islands, Timor-Leste, and Vanuatu. The minimum cost for UHC in smaller states is generally higher than the benchmark used in this report as they lack economies of scale due to their remote location and/or bargaining power. Inadequacy of Government Financing Government funding is critical to sustainably finance and deliver a core package of health services. Countries should not rely on volatile donor resources channeled outside the government system. Such resources, called off-budget development assistance for health (DAH), create inefficiencies and undermine the sustainability of countries’ health systems (Piatti-Funfkirchen et al. 2021). Off- budget DAH is generally delivered at much higher costs through disease-specific vertical programs (e.g., for HIV and malaria). The parallel systems created by such arrangements often do not strengthen the government health system and create inefficiencies by duplicating horizontal functions (e.g., information systems and supply chain). Further, they undermine sustainability by partially substituting government financing and blunting government incentives to strengthen their health system capacities (US Department of State 2025; World Bank, forthcoming (b); Barr et al. 2019). Since off-budget DAH is often not aligned with long-term UHC goals, government financing is critical. 13 However, government health expenditure (GHE) is clearly inadequate to finance UHC. In 2024, the median LIC’s GHE was less than 15 percent of the minimum benchmark cost for UHC, while GHE in the median LMIC was under half (Figure 2). Given the UHC financing gap, more modest spending benchmarks that reflect a core package of health services, especially for LICs and some LMICs are more realistic. Such benchmarks might be better aligned with their macro- fiscal realities. Countries can gradually increase their investments in health as their economies grow, and revenue collection improves. 13 This does not imply off-budget development assistance for health (DAH) is not important in the short-to- medium term. It fills important gaps but could be better channeled—for example, through government systems—to align with recipient country’s priorities, reducing inefficiencies and contributing to ensuring sustainability. 26 However, government financing remains inadequate to even finance a modest package of services. The highest priority package (HPP)—a set of 115 interventions providing the highest value for money substantial financial protection, and priority to the worst off (Watkins et al. 2020)—would require LICs to reach spending levels of $42 per capita and LMICs to reach $60 on average. However, government financing was inadequate in 2024 to finance the HPP in all LICs and almost two-thirds of LMICs. In fact, in all LICs, and over half of all LMICs, public financing for health was insufficient to finance an even more selective package focused on reproductive, maternal, neonatal, and child health plus nutrition (RMNCH&N) and infectious diseases The 96 interventions in the RMNCH&N and infectious disease package would cost $32 per capita in LICs and $37 in LMICs. These reflect an even more cost-effective set of interventions that are the foundation of any health system and can be almost entirely delivered through PHC facilities. 14 14 Improving reproductive, maternal, neonatal, and child health plus nutrition services and tackling infectious diseases constitute seven of 19 “best buy” SDG investments. They offer the highest rate of return among all potential SDG investments yielding over $15 per $1 spent (Copenhagen Consensus Center 2021). 27 28 Stalled in Stride: Projections for the Remaining Sustainable Development Goal Period (2024–30) Countries’ ability to make rapid progress on their universal health coverage (UHC) journey by closing their financing gap hinges on their macro-fiscal outlook, inter-sectoral allocation decisions, and the anticipated changes in donor financing. The macro-fiscal outlook—specifically, economic growth, effectiveness in collecting public revenues, and public debt dynamics—will determine the overall pot of money available for governments to allocate. Political decisions then influence how this money is allocated across sectors. Finally, donors’ future appetite for aid, and its sectoral composition, will matter, particularly for LICs. Macro-fiscal Outlook After emerging from consecutive shocks at the start of the decade, including COVID-19 and Russia’s invasion of Ukraine, the global economic outlook was stabilizing in 2024. While trade tensions and geopolitical developments, starting in early 2025, led to downward revisions of the global economic outlook, their impact on growth prospects, especially in low- and lower-middle income countries (LICs and LMICs) has been relatively muted. 15 However, large uncertainties remain, particularly for trade-reliant economies (IMF 2025c; IMF 2025b).16 Further, fiscal support in response to the COVID-19 pandemic 17 and to alleviate the food and energy price spikes from Russia’s invasion of Ukraine had diminished fiscal space in many countries. Public debt as a share of gross domestic product (GDP) remains at or above its pandemic level high and nearly half of all LICs and LMICs are in debt distress or at high risk of it (World Bank 2025a, World Bank 2025d). In 2024, public debt to GDP stood at 57 percent in the median LIC and 50 percent in the median LMIC (IMF 2025a). 18 Despite these constraints, and the anticipated decline in external on-budget grants from sharp aid cuts, the total fiscal envelope is projected to increase (IMF 2025a). General government 15 This is partially due to trade deals over the last six months which have tempered some of extreme scenarios around higher tariffs in the April 2025 economic outlook. However, while growth projections have been revised upwards in the latest October 2025 economic outlook (compared to April 2025), they remain below the October 2024 projections i.e., forecasts prior to tariff hikes and other policy shifts (IMF 2025c). 16 The exports to GDP ratio are over 60 percent in several Southeast Asian countries, for example Cambodia and Viet Nam (World Bank 2025b). 17 This included additional government spending on health measures such as COVID-19 vaccination and social protection measures, such as cash transfers. 18 Using averages reveal a similar trend: In LICs, public debt to GDP was 68 percent in 2024, close to its 2020 peak of 69 percent. In LMICs, it was 56 percent in 2024 in LICs, near its 2020 peak of 59 percent. 29 expenditure (GGE) is forecasted to grow by 12 percent in the median LIC and 14 percent in the median LMIC between 2024 and 2030 in real per capita terms (IMF 2025c) (Figure 4). While public debt levels and government spending on interest payments will remain elevated, they will not create a further drag on fiscal space (Box 2). Public debt is projected to decline between 2024 and 2030—by 8 percentage points of GDP in the median LIC and 7 percentage points in the median LMIC—although higher borrowing costs will limit their effect on lowering interest payments. As a result, GGE excluding interest payments on public debt (henceforth, post-interest GGE) is projected to grow at similar rates as GGE; 13 percent in the median LIC and 17 percent in the median LMIC (Figure 4) (see also Box 2). However, there are sharp differences in the fiscal outlook across countries. In both LICs and LMICs, by 2030, the post-interest GGE is projected to contract in around 10 percent of the countries, stagnate in 25 percent, and expand in the remaining (Map 2).19 Figure 4. Projected per Capita General Government Expenditure Growth, 2024–30, LIC and LMIC Median (Index with 2024 = 100) Source: World Bank staff calculations using IMF’s October 2025 World Economic Outlook 19 In the 22 LICs in our sample, post-interest general government expenditure (GGE) will contract in three countries, stagnate in six, and expand in the remaining 13. In the 47 LMICs in our sample, post-interest GGE will contract in four countries, stagnate in 11, and expand in 31. One LMIC, Samoa, does not have data on post-interest GGE. 30 Note: The index represents the cumulative growth in per capita general government expenditure (GGE) (total and post-interest) with the 2024 value set at 100. The index median should be interpreted as the median of the 2024–2030 GGE growth for all countries in the income-group. Map 2. Classification of Countries’ Fiscal Space Based on Projected Growth in Post-interest Government Spending, 2024–30 Source: World Bank staff calculations using IMF’s October 2025 World Economic Outlook Note: Cumulative post-interest per capita general government expenditure (GGE) growth between 2024 and 2030 is negative in contraction countries, between zero and 10 percent in stagnation countries, and greater than 10 percent in expansion countries. The stagnation threshold (i.e., any growth up to 1.6 percent per year) reflects the historical average growth of post-interest GGE per capita in the median country between 2010 and 2024. Disclaimer: This map was produced by the Cartography Unit of the World Bank Group. The boundaries, colors, denominations and any other information shown on this map do not imply, on the part of the World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries. 31 Box 2. Crowding out Government Health Spending from Interest Payments on Public Debt? The impact of interest payments depends on the timeframe of the analyses and varies substantially across countries. First, interest payments on public debt remain at historically high levels, crowding out other public spending, including for health. In 2024, the median low-income country (LIC) spent 7.5 percent of their general government expenditure (GGE) on interest payments—their highest level in recent years and more than the 6.0 percent share they spent on health. Similarly, the median lower-middle income country’s (LMIC’s) interest payment was also close to its highest share in recent years at 7.3 percent in 2024, and more than the 6.3 percent they spent on health. Second, the crowding out effect varies substantially across countries. In 2024, the share of GGE spent on interest payments among LICs varied from 0.7 percent in Somalia to 22.3 percent in Malawi. Similarly, among LMICs it ranged from and from 0.2 percent in Kiribati to 39.9 percent in Pakistan. However, nearly half of all LICs and LMICs are in or at risk of debt distress, impacting their ability to make productive public investments, including in health (World Bank 2025a). Third, and most importantly for fiscal space discussions, interest payments on public debt are not forecasted to increase systematically—their impact varies across countries. While the share of GGE spent on interest payments will increase in nearly half of LICs and LMICs, it will fall in the other half. For example, Malawi and Senegal are forecasted to see a sharp increase in interest payments, by 17.3 and 6.0 percentage points respectively between 2024 and 2030. On the other end, Pakistan and Zambia will see a sharp decline in their share of interest payments, by 14.0 and 9.1 percentage points respectively during the same period (IMF 2025c). In summary, while interest payments will continue to crowd out other public spending in many countries, they are not projected to create a further drag on fiscal space. Government Health Spending Outlook The resulting growth in government (i.e., public) health expenditure (GHE)—assuming the historical budgetary priority for health 20—will bring two-thirds of all countries closer to bridging their financing gap and progressing towards UHC. However, the gains will be relatively modest by 2030 (Figure 5). The median LIC—projected to increase its GHE by $1.7 per capita between 2024 and 2030—will bridge less than five percent of its remaining UHC financing gap of $43. Similarly, the projected GHE 20 Government health expenditure (GHE) projections hold the health share of post-interest GGE constant at the 2024 level for all countries. Therefore, GHE per capita grows at the same rate as post-interest GGE per capita. The assumption of stagnant budgetary priority for health is in line with historical trend. 32 per capita increase of $4.8 in the median LMIC will cover a little over 10 percent of their remaining $46 financing gap. Figure 5. Annual per Capita Government and Donor Funding Compared to Minimum Benchmark Cost for UHC, 2024 and 2030 (Projected), LIC and LMIC Median (in constant 2024 US$) Source: World Bank staff projections using World Bank country health budgets’ repository for public health expenditure, Organisation for Economic Co-operation and Development’s Creditor Reporting System for off- budget development assistance for health (DAH), official donor announcements on aid budgets, IMF’s October 2025 World Economic Outlook. Universal health coverage cost benchmarks from Stenberg et al. 2017 (WHO) and Watkins et al. 2020 (DCP3). Notes: 1. Government health expenditure (GHE) projections hold health share of post-interest general government expenditure constant at the 2024 level for all countries. 2. Projected off-budget development assistance for health (DAH) per capita assumes the same percentage decline in donor funding for each country (by donor). However, the country-level decline in projected off- budget DAH varies depending on donor mix. See Appendix 2 for details. 3. Health spending totals are presented as the sum of median components (GHE and off-budget DAH). 33 Several countries—including Ethiopia, Niger, Bangladesh, and Viet Nam—will see a rapid increase in their GHE over the next five years; they can make rapid progress on bridging their UHC financing gap. At the same time, GHE is expected to contract in a few countries, including Malawi, Angola, and Senegal (Figure 7 and Map 2). The UHC financing gap will widen in these countries without an increase in budgetary priority for health. Development Assistance for Health Outlook Recent announcements and actions by major donors point to a prolonged period of declining development assistance as spending priorities shift in many donor countries (OECD 2025a). The sharp cuts to official development assistance (ODA) disproportionately impact the health sector. Health has been the second largest recipient of ODA after humanitarian assistance, accounting for nearly one-seventh of all ODA (OECD 2025b). 21 Further, and unlike many other sectors, the health sector in several countries—particularly LICs and countries affected by fragility, conflict, or violence—remains highly dependent on development assistance for health (DAH). In LICs, off- budget DAH is equal to government spending on health in 2024 (Figure 5). Rapid declines in DAH have, and will continue to, severely impact health service provision – particularly for HIV/AIDS; reproductive, neonatal, maternal, adolescent and child health; and malaria. The declines also impact critical health system capacities and functions, including supply chain for essential commodities (e.g., antiretroviral drugs), human resources for health, and information monitoring systems. 22 While the affected DAH resources created some inefficiencies (see sub-section Inadequacy of Government Financing), their rapid and sharp decline has created large financing and service delivery gaps in the health sector, at least in the short-term. 23 Total DAH to recipient countries is expected to decline by at least 20 percent from 2025 onwards based on official announcements and policy actions by major donors, most importantly the United States government (Figure 6; see Appendix 2 for details). 24, 25 Over 90 percent of the projected DAH 21 Health is classified as official development assistance (ODA) to sector codes 120 to 130 pertaining to health and population. The calculation is based on country-tagged ODA loans, grants, and private development finance (flow codes 11, 13, and 30); it excludes regional and bilateral unspecified recipients. 22 These include routine administrative data systems, commonly called health management information systems, as well as sample surveys, most notably the demographic and household survey. 23 Based on World Bank staff discussion with ministry of health officials in countries with high development assistance for health (DAH) reliance. 24 Official announcements and policy actions refer to government sources, e.g., public finance bills, executive budget proposals, and official policy documents. 25 The estimated decline in DAH aligns with the projections from OECD and the Institute for Health Metrics and Evaluation (IHME) (OECD 2025a; IHME 2025). 34 decline will be through the off-budget channel, currently accounting for two-thirds of all DAH. 26, 27 In the median LIC, off-budget DAH is expected to decline by $3.9 per capita (or ~45 percent) between 2024 and 2030. In the median LMIC—where it is a relatively less important source of financing— off-budget DAH is expected to decline by $3.0 per capita (or ~45 percent) during the same period (Figure 5). 28 However, there is large variation across countries. The impact on each recipient country is a function of their overall level of donor dependence and the donor composition for DAH. 29 On one extreme, countries like Democratic Republic of Congo, Mozambique, and Liberia—with high donor dependence and sharper declines in DAH—will be highly impacted. On the other end, countries such as India, Morocco, and Viet Nam will not be impacted; their DAH is negligible relative to government spending on health. Others, for example, Burkina Faso, Lao People’s Democratic Republic, and Côte d’Ivoire will sit between these extremes with a moderate impact (Figure 7). 26 The decline in the on-budget component of DAH is already reflected in the GHE outlook. The GHE outlook mirrors the growth in GGE (specifically, post-interest GGE), and GGE projections in the IMF’s World Economic Outlook already factor in a decline in grant revenue (external aid). 27 In 2023, two-thirds of total DAH was channeled off-budget. The off-budget share is projected to decline to 60 percent from 2025–26 onwards because the DAH decrease is primarily driven by donors who channel their funds off-budget (assuming the country-donor channel share remains constant). 28 The median per capita off-budget DAH decline is larger than the 2023–25 DAH decline of 20 percent for two main reasons. First, it is expressed in per capita terms over a longer period (up to 2030). Second, off-budget DAH declined more sharply than overall DAH (also see footnotes 26 and 27). 29 Pockets of donor dependence can arise even when DAH is relatively low but concentrated in specific programs or functions. For example, governments with low overall DAH dependence might still be heavily reliant on donors for specific disease areas and programs (e.g., HIV, family planning) and/or critical functions (e.g., commodities, digital systems, and specific technical assistance). 35 Figure 6. Recipient Country Tagged DAH, 2023 and Projected from 2025–26 Onwards, by Donor (in US$ billions) US$ 23.7B Estimated US$ 19.4B Source: World Bank staff projections using Organisation for Economic Co-operation and Development’s Creditor Reporting System and official donor announcements on aid budgets (for development assistance or development assistance for health (DAH), where available). Notes: 1. Numbers for 2023 refer to DAH traceable to individual recipient countries (by donor). They exclude DAH for global and regional programs. The data include all ODA loans, grants, and private development finance (flow codes 11, 13, and 30) for the health sector (sector codes 120 to 130). 2. Orange hues are used for bilateral donors, blue hues for multilateral organizations, and grey hues are used for private foundations. Countries with recipient-country tagged DAH less than $250 million in 2023 are aggregated under others. 3. Projected decline in bilateral aid from Germany (10%), France (25%), and the UK (40%) is not visible due to their relatively small share of total DAH. See Appendix 2 for details on assumptions. Combined Government and Donor Health Spending Outlook The projected sharp decline in DAH will dampen the modest gains from growth in GHE. In the median LIC, the government and donor funding for health is projected to decline to $15 per capita by 2030 under the status quo, a decline of 13 percent between 2024 and 2030 (Figure 5). The sharp decline in off-budget DAH more than offsets the marginal increase in GHE during this period. In 36 contrast, the median LMIC is forecasted to see a slight increase of five percent during this period with government and donor funding for health projected to reach $48.8 per capita by 2030. By 2030, eight in ten LICs and four in ten LMICs are projected to have lower per capita government and donor funding for health than in 2024 (Figure 7). The large financing gap in these countries— concentrated in Sub-Saharan Africa—will widen further (Figure 8). However, the majority of LMICs and two in ten LICs will make modest gains towards the minimum benchmark cost for UHC. A few countries, for example, Viet Nam and Philippines will make rapid progress in closing their UHC financing gap by 2030. Figure 7. Projected Change (2024–30) in GHE, and in Off-budget DAH, as Share of 2024 GHE, by Country, by LICs and LMICs (in %) Source: World Bank staff projections using World Bank country health budgets’ repository for public health expenditure, Organisation for Economic Co-operation and Development’s Creditor Reporting System for off- budget development assistance for health (DAH), official donor announcements on aid budgets, IMF’s October 2025 World Economic Outlook Notes: 1. Countries above the dotted line had a net increase in government and donor spending on health between 2024 and 2030 while countries below the line had a net decrease. The perpendicular distance from the line indicates the magnitude of the decline; further away implies a larger magnitude. 2. The different yellow bands (horizontal) indicate the magnitude of the development assistance for health (DAH) decline (2024–30) relative to the baseline government health expenditure (GHE) (in 2024). Darker 37 yellow indicates a sharper decline. They are a composite measure of the decline in DAH and its relevance compared to GHE. 3. The vertical grey band indicates stagnation countries, i.e., where post-interest GGE growth (2024–30) is between zero and 10 percent. Countries with a contracting post-interest general government expenditure are to the left of the grey band and expansion countries are to the band’s right (see Map 2 notes for details). 4. Seven outliers excluded for better representation, of which three had GHE (2024–30) growth larger than 90% (South Sudan, Sudan, Yemen Rep.—all LICs) and four where DAH decline (2024–30) as share of 2024 GHE was over 100% (Haiti among LMICs; Central African Republic, Malawi, and Somalia among LMICs). Figure 8. UHC Financing Gap as Share of Minimum Benchmark Cost, 2024 and 2030 (Projected), by Country, in LICs and LMICs (in %) Source: World Bank staff projections using World Bank country health budgets’ repository, Organisation for Economic Co-operation and Development’s Creditor Reporting System, official donor announcements on aid budgets, IMF’s October 2025 World Economic Outlook, and universal health coverage (UHC) cost benchmarks from Stenberg et al 2017 (WHO) and Watkins et al 2020 (DCP3). Notes: 1. Financing gap refers to the difference between the country’s per capita government and donor health financing for the year and the minimum cost benchmark for UHC. The share is calculated by dividing the financing gap by the country’s minimum cost benchmark for UHC. Minimum UHC cost benchmarks are based on 2014 income-group level estimates which are converted to 2024 US$ per capita using country- specific prices, exchange rates, and 2030 population numbers. 38 2. Individual countries might have their own national estimates for the minimum cost for UHC. The cost estimates used in this report serve as benchmarks; they are not universal targets and should not replace national estimates (where available). 3. A red arrow indicates a projected decline in government and donor spending on health between 2024 and 2030, leading to an increase in the financing gap; a blue arrow indicates the opposite. 4. The figure excludes 11 small states with a population less than 1.5 million: Bhutan, Cabo Verde, Comoros, Djibouti, Eswatini, Kiribati, Micronesia (Federated States), Samoa, São Tomé and Príncipe, Solomon Islands, and Vanuatu. The minimum cost for UHC in smaller states is generally higher than the benchmark used in this report as they lack economies of scale due to their remote location and/or bargaining power. In summary, the minimum level of financing required for UHC had not materialized until 2024, more than halfway through the Sustainable Development Goal (SDG) period. Most countries remained far off the minimum UHC benchmark cost, and projections indicate that sufficient government and donor financing will not materialize by 2030, the end of the SDG period. In the median LIC, projected government and donor spending on health in 2030 will be 10 percent lower than it was in 2018, driven by sharp declines in external aid. The median LMIC is forecasted to make some progress, increasing its health spending by 14 percent during this period, though its spending will remain around half the minimum benchmark (Figure 9). Continued financing shortfalls translate into persistent service coverage gaps, and higher levels of preventable mortality and morbidity. They weaken human capital accumulation at all stages of the life cycle, from child survival and early learning to the health and productivity of workers. Ultimately, chronic underinvestment in health—and its impact on human capital—constrains labor force participation, workforce productivity, and economic growth (World Bank, forthcoming (c)). 39 Figure 9. Annual per Capita Government and Donor Funding Compared to Minimum Benchmark Cost for UHC, 2018, 2024, and 2030 (projected), LIC and LMIC Median (in Constant 2024 US$) Source: World Bank staff projections using World Bank country health budgets’ repository for public health expenditure, Organisation for Economic Co-operation and Development’s Creditor Reporting System for off- budget development assistance for health (DAH), official donor announcements on aid budgets, IMF’s October 2025 World Economic Outlook. Universal health coverage (UHC) cost benchmarks from Stenberg et al. 2017 (WHO) and Watkins et al. 2020 (DCP3). Notes: 1. Health spending totals are presented as the sum of median components (government health expenditure and off-budget DAH). 2. The minimum public spending UHC benchmark refers to the WHO progress scenario in Figure 1. 3. See Figure 2 and Figure 5 notes for details. 40 41 Steering through a Storm: Country Prospects and Options Countries have policy options to alter their trajectories. The health financing outlook presented in this report assumes the status quo; it is not a foregone conclusion. Some countries have, and are demonstrating that rapid progress is possible, even under constraints. For example, Burkina Faso launched the Gratuite scheme, providing free (i.e., no user fees) primary care services, focusing on maternal, newborn, and child health services, and nearly doubled its domestic government financing for health between 2013 and 2017 – a period when external aid for health halved and the fiscal envelope was stagnant (World Bank, forthcoming (b)). More recently, Nigeria’s lawmakers proposed an additional $200 million, a 25 percent increase in the federal government’s health budget in response to aid cuts (Shibayan 2025). LICs and LMICs are at a crossroads in their universal health coverage (UHC) journey. While the rapidly changing health financing landscape poses challenges, it also provides opportunities for bold reforms to fundamentally reshape their health systems. Governments have two broad levers: to spend better and to spend more. This section briefly discusses policy options for both levers, along with their constraints and limitations. It also provides indicative country examples. However, the specific mix of reforms and their intricacies are highly dependent on a country’s context.30 They cannot be addressed in a global report and lie outside the scope of this report. Finally, the two broad levers should not be seen as alternates. In many countries spending better requires spending more. Spend Better: Doubling Down on Efficiency during a Window of Opportunity Efficiency will be even more important as funding for health declines in most LICs and several LMICs. All countries have scope to improve their efficiency by spending on the right things and delivering care in the right settings.31 For example, government spending on primary healthcare (PHC), including preventive services, remains insufficient while substantial money goes towards less cost-effective secondary and tertiary care (Hanson et al. 2022).32 In the Gambia, Nigeria, and Sierra Leone, PHC receives less than 20 percent of government health spending; the large majority is spent on secondary, tertiary care, and administrative functions (World Bank, forthcoming (a)). 30 World Bank diagnostics, most importantly public expenditure reviews often facilitate such reforms. 31 The Disease Control Priorities program—which started at the World Bank in 1993 and is now at the University of Bergen—provides economic evidence on resource allocation within the health sector along with tools and approaches to support policymakers in setting sectoral priorities. 32 In general, these measures enhance spending equity by improving access to care for the entire population. 42 Some countries have prioritized cost-effective health interventions and financing mechanisms to deliver them. For instance, India’s Ayushman Bharat Health and Wellness Centers program expanded the number of services available in PHC facilities, crowding in government financing to frontline facilities. The funding enables upgrades to PHC facilities and covers operational costs to deliver comprehensive PHC services, including six new packages to address unmet needs related to non-communicable diseases (NCDs) and other chronic conditions (Alwan et al. 2025).33 Several African countries, including Burkina Faso and Sierra Leone, prioritized the delivery of PHC services to vulnerable populations by removing user fees and increasing government financing. Other countries have used conditional grants for health to incentivize spending on explicit benefit packages with highly cost-effective PHC services, for example Argentina’s ‘Sumar’ and Brazil’s ‘Saude da Familia’ programs (Hanson et al. 2022). Countries have also found ways to get better value for their money by reducing costs of key inputs. For example, the Indonesian Ministry of Health, with coordinated support from the World Bank and other development banks, adopted public procurement innovations for medical equipment, consumables, and operations and maintenance services. It implemented bulk procurement and a total cost of ownership approach, which considers not just the purchase price but also costs like installation, maintenance, repairs, and usage over time. These innovations led to cost savings of up to 50 percent for the Indonesian Ministry of Health, compared to historical pricing for high-value and complex medical equipment. DAH dependent countries have a reform window to restructure their health systems in response to aid cuts. The decline in off-budget DAH—mostly funding vertical programs which fragment the health system—can break the current, inefficient equilibrium (Piatti-Funfkirchen et al. 2021). It provides space for countries to define their priorities, and to improve efficiency by aligning the remaining donor and government resources, in line with the Lusaka Agenda. Some countries have already taken initial steps in this direction. For example, Ethiopia—a longstanding exemplar of national ownership and government-led coordination 34—recently initiated compacts for reproductive, maternal, newborn, and child health commodities and NCDs where donor and private resources complement public financing (Teshome and Hoebink 2018). Additionally, all countries can improve budget execution in the health sector. 35 On average, 13 percent of the health budget in LICs and LMICs remained unspent between 2010 and 2020, and the execution rate declined over this period (World Bank and WHO 2025). Improving health budget execution rates will be particularly important in countries where the health sector seeks substantial increase in their budget allocation from ministries of finance. While the underlying reasons vary across countries and usually entail action from both health and finance ministries, diagnosing 33 Ayushman Bharat Health and Wellness Centers expand PHC services from reproductive, maternal, newborn, child, and adolescent health and infectious disease focus (organized in six packages) to a set of 12 packages including screening and management of NCDs, mental health, geriatric, and palliative care. 34 The government led roadmap for donor alignment is known as the “One Plan, One Budget, One Report”. 35 Budget execution is defined as “the degree to which money has been spent in line with agreed priorities and in support of effective health service delivery”. 43 them and removing the bottlenecks unlocks more domestic financing and ensures continued access to quality health services. For example, Pakistan improved its budget execution by contracting out PHC services to the private sector in three provinces and by integrating payroll and personnel records (Hadi et al. 2025). Ethiopia has achieved high overall budget execution rate for health—95 percent on average between 2016 and 2021—by keeping arrears low, ensuring timely and accurate payroll expenditures, and using effective communication in its budgeting processes (Cros et al. 2025). Spend More: Increasing Government Financing for Health within Fiscal Constraints However, efficiency will not be enough given the large financing gap in most countries. No amount of efficiency will enable rapid progress towards UHC when government spending remains below $10 per capita in the median LIC and $40 per capita in the median LMIC (Figure 5). Raising government financing for health remains critical, particularly in countries with large financing gaps and those facing the sharpest DAH cuts. However, countries will have to work within their overall fiscal constraints. Raising government financing will first and foremost require faster economic growth and a stronger fiscal effort in all countries to increase their government revenue. LICs and many LMICs can raise their tax-to-gross domestic product by 9 percentage points—nearly two-thirds of their existing level-by improving the design of core taxes (e.g., broadening the tax base) and by strengthening their tax administration capacity (Benitez et al. 2023). While these reforms are beyond the mandate of the health sector, health ministries can play a supportive role by highlighting how low government revenues hinder progress toward UHC. They can also clearly position health spending as an economic investment that delivers economic growth by building human capital and creating jobs (World Bank 2025c). 36 For instance, they can highlight the multiplier effect of health sector jobs (3.4 additional jobs in wider economy for each health sector job) and its contribution to raising female labor force participation (women represent 70 percent of the global health workforce). The health sector can play a leading role in one aspect of tax policy: raising health taxes. These refer to excise taxes on products such as tobacco, alcohol, and sugar-sweetened beverages, whose consumption leads to harmful health effects. 37 Countries can broaden the set of products covered 36 The Joint Learning Network’s Making the Case for Health: A Messaging Guide for Domestic Resource Mobilization and the World Bank’s Human Capital Project can help countries position health as an economic investment. 37 Harmful health effects could be borne by the user (negative internalities) or others (negative externalities). The latter includes healthcare costs borne by the government for treating illness related to consumption of these products, road traffic accidents from excessive alcohol consumption, and second-hand smoke. 44 by these taxes and increase the tax rates. Recent estimates suggest that raising health taxes could generate between $260–$420 billion per year in low- and middle-income countries, raising government health spending by up to 40 percent in LMICs if they were invested in the health sector (The Task Force on Fiscal Policy for Health 2024).38, 39 While all countries would benefit from higher health taxes due to their positive impact on health (see Box 3), they are particularly important from a revenue perspective for countries with a stagnating or contracting fiscal envelope (WHO 2025). Box 3. The Win-Win Case for Health Taxes Health taxes are a "win-win" because they improve health outcomes and generate additional government revenue. Raising excise taxes on unhealthy products prevents premature mortality and lowers morbidity by reducing their consumption while raising additional government revenue, at least in the short-term (The Task Force on Fiscal Policy for Health 2024). Health taxes are a policy “win” even if the incremental government revenue potential is limited because they reduce the future burden of disease and associated medical costs, including those borne by the government. For example, in Indonesia, smoking-related illnesses account for almost 5 percent of the hospitalization costs borne by their social health insurance agency, Jaminan Kesehatan Nasional (JKN). Countries, such as Philippines, have demonstrated that substantial and sustained action is feasible. Consistent increases in excise taxes on tobacco raised its real price by 300 percent between 2012 to 2023, leading to a decline in smoking prevalence by 10 percentage points and a quadrupling of government revenue from tobacco taxes (The Task Force on Fiscal Policy for Health 2024). More recently, in 2023, Ghana introduced a 20 percent tax on sugar-sweetened beverages and increased taxes on tobacco products from 23 percent of the retail price in 2020 to 38 percent in 2024 (Vital Strategies 2025). Finally, and most importantly, many countries could also increase their budgetary priority for health. The share of health in government spending declined in the majority of LMICs between 2018 and 2024 (Figure 2). While all countries face competing sectoral demands, they can create fiscal space by reallocating spending away from inefficient and poorly targeted subsidies, such as broad- based fuel and food subsidies (World Bank 2024a, World Bank 2025a, World Bank 2025e). And other countries, particularly those facing temporary liquidity pressures—but with sustainable debt 38 The lower-bound estimate assumes excise taxes on tobacco, alcohol and sugary beverages generate a 20 percent price increase while the upper-bound estimate corresponds to a 50 percent price increase. 39 However, government revenue from health taxes does not need to be earmarked for the health sector. 45 and adequate macroeconomic policy frameworks—can create fiscal space through debt-for-health swaps which free up resources for investments in health. 40 Prospects for increasing the share of health in government budgets are already favorable in approximately one-third of all LICs and LMICs. These countries are projected to have fiscal space from growing post-interest government spending over the next five years, and their existing priority for health remains relatively low (red box in Figure 10). 41 All LICs in this group, for example Ethiopia and Madagascar, also face sharp DAH cuts. Increasing their budgetary priority for health is projected to raise their government health spending by an additional 45 percent in the median LIC and 40 percent in the median LMIC in 2030. 42 Though the additional resources—$2.2 per capita in the median LIC and $10 in the median LMIC—will not cover the UHC financing gap in these countries by 2030, they will facilitate expansion of core health services and bring countries closer to their UHC goals. While the challenges may appear daunting, countries have options to steer through the storm. Bold reforms—broadly outlined above—will deliver rapid gains by saving lives, creating jobs, and driving economic growth. Every additional efficiently spent dollar will save lives and deliver outsized returns. Better and more government health spending will facilitate the expansion of core health services and bring countries closer to their UHC goals. Reforms will also deliver broader economic dividends by fostering human capital that enhances workforce productivity and fuels long-term growth, while stimulating job creation across the economy. 40 Debt-for-health (or development more generally) swaps enable a country to exchange its expensive debt with cheaper one, and then redirect the savings for development programs, such as for health. However, they are not appropriate for all countries (e.g., countries with solvency problems that need debt restructuring) and must be carefully designed to ensure savings remain in government coffers and directed towards development spending where results can be transparently monitored and verified. 41 These refer to countries where the post-interest GGE is expanding (Map 2) and where the health share of government spending was below the median for their income-group in 2024. 42 The projected increase is based on raising the health share of government spending in 2030 to the median for the income-group in the one-third of LICs and LMICs which have fiscal space to do so (red box in Figure 10). The projected increase is incremental over the baseline 2030 GHE projection for these countries. 46 Figure 10. Health Share of Government Spending in 2024 and Projected Growth in Post-interest Government Spending between 2024 and 2030, by Country, by LICs and LMICs (in %) Post interest GGE growth (2024 – 2030) Source: World Bank staff projections using World Bank country health budgets’ repository and IMF’s October 2025 World Economic Outlook. Notes: 1. The red box (bottom right) indicates the focus countries for increasing budgetary priority for health. The LMICs (17 countries) are Bangladesh, Benin, Cambodia, Cameroon, Comoros, Cote d'Ivoire, Egypt Arab Rep., Guinea, Haiti, India, Kenya, Lao PDR, Mauritania, Morocco, Nepal, Pakistan, and Tanzania. The LICs (6 countries) are Congo Dem. Rep., Ethiopia, Gambia, Madagascar, South Sudan, and Uganda. 2. Outliers (three countries) with post-interest general government expenditure growth larger than 90 percent (South Sudan, Sudan, Yemen Rep., all LICs) excluded for better representation. Samoa was excluded because of missing post interest GGE data. 3. The vertical grey band indicates stagnation countries, i.e., where post-interest general government expenditure growth (2024–30) is between zero and 10 percent (see Map 2 notes for details). 4. The dotted horizontal line indicates the median health share of government spending for the income group. 47 48 References Alwan, A., Mirutse, M. K., Twea, P. D., and Norheim, O. F. 2025. Disease Control Priorities, Fourth Edition: Volume 1. Country-Led Priority Setting for Health. https://doi.org/10.1596/978-1-4648- 2105-9. Barr, A., Garrett, L., Marten, R., and Kadandale, S. 2019. “Health sector fragmentation: Three examples from Sierra Leone 16 Studies in Human Society 1605 Policy and Administration.” Globalization and Health, 15 (1): 1–8. https://doi.org/10.1186/S12992-018-0447-5/METRICS. Becker, G. 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Budget Execution in Health: From Bottlenecks to Solutions. Washington, DC: World Bank. https://hdl.handle.net/10986/42930. 53 54 Appendices Appendix 1. Methodology for the Costing Benchmark This appendix details the methodology used for the universal health coverage (UHC) costing estimates provided in the main text. The goal of this analysis was to establish a reasonable, data- informed benchmark for low- and lower-middle income countries (LICs and LMICs) in the pursuit of UHC by 2030. Data After a first identification round, three studies were selected as the primary sources for costing estimates: Stenberg et al. 2017, henceforth ‘WHO;’ Watkins et al. 2020, henceforth ‘DCP’; and the IMF Sustainable Development Goals (SDG) Costing Tool 2023, henceforth ‘IMF’. These studies were selected based on recency (within the past 10 years), country availability (maximizing LIC and LMIC coverage), and outcome comparability (SDG3 additional costs attributable to health systems to reach the UHC goal). The three studies employ different approaches to costing, both in terms of how the UHC portion of the SDG3 goal is defined and in terms of the methodology employed to obtain the estimates. WHO uses a bottom-up, ingredients-based costing approach (quantities x prices) to quantify the additional resources needed to strengthen health service delivery across four service delivery platforms. These include policy and population wide interventions, periodic schedulable and outreach services, first level clinical services, and specialized care. As such, it provides a comprehensive outlook for the UHC goal spanning a wide range of policies and resource use in the health sector. DCP employs a counterfactual annual costs approach (with harmonized, de-duplicated prices) to derive long-run average costs using published intervention unit costs for 218 unique interventions, part of 21 care packages. For DCP, we used the essential UHC costing (EUHC) rather than the highest-priority package (HPP) in the main benchmark comparison, as EUHC better reflects the definition of UHC. We also obtained HPP measures, as well as constructing a smaller package of reproductive, maternal, newborn, child, and adolescent health and infectious diseases (96 interventions). IMF uses a benchmarking macro-economic approach to estimate the gap in human and physical capital between peers: as bottom-up approaches are more robust than top-down macro-economic approaches, IMF costing has not been used in this report. As such, the UHC costings resulting from this exercise should be compared with caution and serve mostly to provide an evidence-based range of cost estimates across country groups. Notwithstanding these methodological differences, all three studies provide estimates of total or additional health expenditure requirements by 2030 to reach the UHC goal for health at the income group level. The primary challenge in comparing these studies lies in their use of different base years, currencies, and population denominators. WHO provides estimates in 2014 US$ per capita using 2030 population; DCP uses 2016 US$ per capita using 2015 population; and IMF expresses requirements as additional percentage of gross domestic product (GDP) needed by 2030. Both 55 WHO and DCP present results as population-weighted income group means. To enable meaningful comparison, all estimates were harmonized to 2024 current US$ per capita values. Analysis The first step is the construction of price and exchange rate adjustment factors for each study. These factors account for both inflation and exchange rate movements between the respective base years and 2024. IMF GDP deflators and local currency units to US dollar exchange rates were used to capture price and currency changes over time. For each study, the total adjustment factor was calculated as the product of the price deflator (2024 relative to study-specific base year) and the exchange rate growth for the same period in each country. For example, the WHO costing adjustment is as follows, for each country: 2024 2024/14 = 2014 2024 2024/14 = 2014 2024/2014 = 2024/14 ∙ 2024/14 This approach ensures that both domestic inflation and currency fluctuations are properly reflected in the final estimates of UHC costs in 2024 US$. Income group classifications presented an additional methodological consideration: Since the studies were conducted at different points in time, the income group costing estimates presented in each paper reflect different classifications. For each study, the relevant base year (2014 for WHO, 2016 for DCP, and 2023 for IMF) was used to assign income groups to individual countries. The WHO estimates include two scenarios, ‘Progress’ and ‘Ambitious,’ representing different trajectories toward achieving UHC. From these total estimates, health-specific shares were extracted from the study, ranging from 76–79 percent for LICs, 75–77 percent for LMICs, and 69–71 percent for upper-middle income countries, depending on the scenario. These percentages reflect the proportion of total SDG3 costs directly attributable to health systems improvements to access and scope of interventions. The remainder of these costs are attributable to SDG3 achievements outside of the health system (e.g., sanitation, water, and others). DCP also presents similar scenarios, called ‘highest priority package’ (HPP) and (EUHC). They both focus on a range of core interventions to achieve UHC, with HPP focusing on a selected sub- package of highly efficient and cost-effective interventions and EUHC on comprehensive coverage of basic services. In addition to those, other sub-packages based on specific intervention types can be estimated through weighting the EUHC package by the cost share of each intervention type. In this report, we construct a sub-package that includes reproductive, maternal, neonatal, and child health plus nutrition and infectious diseases (RMNCH&N+). 56 For each income group, we use simple arithmetic means to reaggregate country-specific estimates. Given that both WHO and DCP estimates are population weighted before price and exchange rate adjustments are made at the country level, the resulting reaggregated values still reflect population size at the income group level. The final master dataset contains one observation per country for 2030, including the original estimates from each study, all adjustment factors applied, the harmonized estimates in 2024 US$ per capita, and both weighted and unweighted income group averages. Supporting variables such as population projections, GDP deflators, and exchange rates are retained to ensure full transparency and replicability of the analysis. A repository with the final master dataset, budget documents collected as part of the study, and the raw data collected from budget documents, is available at this link. Some limitations should be noted when interpreting these harmonized estimates. First, country coverage varies across studies, potentially affecting the representativeness of income group averages. Second, the fundamental differences in how each study defines UHC achievement means that the estimates capture different dimensions of the resource challenge. For example, WHO defines UHC as the achievement of normative levels of health workforce and facility density, as well as high coverage (e.g., 95 percent in the ambitious scenario) in 187 interventions; DCP defines it as 80 percent coverage of 218 interventions in a model health benefits package. Third, the studies make different assumptions about implementation trajectories, efficiency gains, and health system absorptive capacity over the 2024–30 period. These factors suggest that the harmonized estimates are best interpreted as a range rather than point predictions, with the variation across studies reflecting both methodological differences and genuine uncertainty about the resources required to achieve UHC. Finally, the costing benchmarks we estimate at the income group level should be applied with caution at the country level. The micro-costing studies we use for the benchmarks do not provide country-specific costing numbers and apply population weights when constructing income group aggregates. As such, population size outliers (especially small countries) are likely to exceed these benchmarks simply due to variable construction. Given these limitations, we identified the income group level midpoint estimates across all studies and scenarios in the WHO progress scenario. As already noted, the WHO progress scenario is also a bottom-up costing exercise, which further supports this choice. Figure A1.1 below shows the final range of UHC cost benchmark totals before adjustment. As the estimate refers to total health system costs, we further adjust the estimates by assuming that 80 percent of the expansion of services should be covered by pre-paid sources, i.e., public health expenditure and development assistance for health off-budget. This assumption is commonly used in the literature, as the goal of UHC is to ensure fair and equitable access to health services. As such, most additional costs should come from government sources (Jowett and Kutzin 2015). For the WHO Progress cost benchmark, these amount to $60 for low-income countries and $92 for lower-middle income countries. 57 Figure A1.1 UHC Costing Benchmarks for 2030, all Scenarios, by Income Group Source: WB staff calculations based on Stenberg et al. 2017 (WHO) and Watkins et al. 2020 (DCP3). Note: All values are in 2024 US$ per capita (using 2030 population). Lastly, we use the midpoint WHO Progress cost benchmark to estimate a costing gap at the country level, defined as the difference in any given year between the cost benchmark and a country’s total public health expenditure (PHE) and off-budget development assistance for health (DAH). We produce this costing gap both using the income-group level numbers ($60 and $92), and using the country-specific benchmark costs estimated earlier in the analysis, which use prices, exchange rates, and 2030 populations at the country level. Comparing UHC Measures As an additional sensitivity check, we compare the performance of our UHC cost benchmark gap measure to the World Bank’s UHC Service Coverage Index (SCI). The SCI combines 14 tracer indicators of service coverage for reproductive, maternal, newborn, and child health, infectious diseases, noncommunicable diseases, and service capacity and access. As such, the SCI measures UHC performance in a way that is complementary to our estimates: SCI measures current access and coverage of essential services, while our cost benchmark gap measures the monetary effort required to reach the minimum UHC goal. First, we look at the relationship between each of the two measures and an indicator of income and government capacity to invest in health (general government expenditure (GGE) per capita in 2030). The SCI is expressed as a 0–100 index, while the cost gap is expressed as the additional country- specific per capita expenditure required in 2030 to reach the minimum benchmark for UHC. We perform this test as a sense-check of the constructed measure and its performance on income variables, rather than health-specific expenditures, due to the endogeneity that our benchmark measure would yield with government health expenditure (GHE). GGE is used because as our cost benchmark gap is constructed by subtracting GHE from UHC costing, it would display high levels of correlations with GHE by definition. 58 Figure A1.2 below shows the relation between GGE per capita and the two measures. Both measures show a similar relationship with GGE, with our cost benchmark gap performing slightly better at 61 percent R2. Figure A1.2 Relationship between GGE per Capita and UHC Measures Source: Authors’ elaboration of IMF and World Bank data. Note: Countries with small population or missing in either UHC measure are excluded from the chart (Bhutan, Kiribati, Micronesia, Samoa, South Sudan, Vanuatu). All monetary variables are in 2024 US$. We also test the relationship between the two UHC measures. To do so, we transform the UHC cost gap (WHO Progress) into a comparable 0–100 cost benchmark index (CBI) by assigning a score of 100 for countries with a negative gap (i.e., they satisfy the minimum benchmark) and a simple weighted score based on the comparative gap as a percentage of the benchmark (e.g., the relative distance of the gap from the total benchmark) in each country c against the worst performer M, as such: �1 − � ℎ = 100 ⋅ � � ℎ Figure A1.3 below shows the comparison between the SCI and the CBI. As expected, we see a somewhat linear relationship between the two measures but less pronounced compared to common determinants like income (GGE per capita). This is likely due to differences in what aspects of UHC are being measured by each index, as well as the CBI being agnostic to the efficiency and allocation of current PHE. 59 Figure A1.3 Relationship between UHC Service Coverage Index and Cost Benchmark Index Source: Authors’ elaboration of IMF, World Bank, and WHO data. Note: Countries with small population or missing in either UHC measure are excluded from the chart (Bhutan, Kiribati, Micronesia, Samoa, South Sudan, Vanuatu). Appendix 2. Data Sources and Methodology for Nowcasting (2022–24) and Projection (2025–30) Analyses This report provides an analysis of government health expenditure and donor health expenditure estimates from 2018 to 2030 across LICs and LMICs. This appendix is organized into several sections to discuss the data and methodologies employed. Initially, we define the key terms used in the report and compare them with standard frameworks, such as the OECD System of Health Accounts. Following this, we delve into the data collection process and the nowcasting techniques applied to government and donor health expenditures up to 2024. Lastly, we outline the data collection and methodologies used for projecting government and donor health expenditures from 2025 to 2030. 60 Definitions We define PHE as the aggregate of several components: central government health spending (CGHS) and subnational government health spending (SNGS) as referenced in the equations below), and contributory compulsory health insurance, such as social health insurance (SHI). PHE and its components include on-budget development assistance for health (DAH), i.e., DAH that flows through governments’ public financial management systems. This is aligned with the previous Double Shock Double Recovery (DSDR 2024) report (Kurowski et al 2024). Additionally, in this report we will also examine off-budget DAH, which refers to DAH not processed through governments’ public financial management systems. To further elucidate the definition of PHE and its constituent elements, we will compare it to the framework provided by the System of Health Accounts (SHA) 2011 Revised Edition, with a particular focus on Chapter 8, "Revenues of Health Care Financing Schemes (ICHA-FS)." Figure A2.1 Correspondence between GRPH 2025 and SHA 2011 Definitions As illustrated in Figure A2.1, the combined total of central government, subnational government, and SHI health expenditures, including any on-budget DAH, corresponds in the SHA 2011 framework to transfers from government domestic revenues (FS1), contributions from compulsory contributory health insurance (FS3), and transfers distributed by the government from foreign sources (FS2). Off-budget DAH, on the other hand, aligns with direct foreign transfers in the SHA 2011 framework (FS7). It is important to note that GRPH 2025 concentrates on current health expenditures, excluding capital expenditures wherever feasible. A more detailed figure related to PHE is also present in the DSDR 2024 report, Appendix 1 (Kurowski et al 2024). The definition of low and lower-middle income is taken from the World Bank FY25 country grouping. With these fundamental definitions clarified, we will proceed to the section on data collection and nowcasting for the period from 2018 to 2024. 61 Nowcasting of Government Health Expenditures and DAH Off-budget until 2024 Data We have collected data on government health expenditures up to 2024 by reviewing over 3,000 government-issued financial documents. These documents were gathered until April 30, 2025. The data on government health expenditures includes both domestic government health spending and on-budget DAH, as recorded in these financial documents (i.e., budget documents, health insurance agencies' financial statements, and similar). Recipient country-specific off-budget DAH data is sourced from the OECD CRS. It is calculated using sector codes 120–130, which pertain to health and population, and flow codes 11, 13, and 30, which represent official development assistance (ODA) loans and grants, as well as private development finance from private foundations. Off-budget DAH refers to all DAH that is reported in the OECD CRS dataset as not being channeled through "recipient government" or "public sector institutions." In total, data are available for 69 countries: 22 out of 26 LICs, covering 87 percent of LICs population, and 47 out of 51 LMICs, covering 99 percent of LMICs population. Eight countries (four LICs and four LMICs43) were excluded due to data challenges. In some cases, budget documents had not been published, particularly in conflict-affected settings. In others, inconsistencies in data series could not be reconciled, often due to hyperinflation and other forms of macroeconomic volatility. By April 2025, several countries had not released their expenditure data for health-related spending in 2024 due to various reasons, including delays, standard timing for release of budget documents being later than April 30 of the following year, fiscal year not matching the calendar year. In some cases, even government health expenditure data for 2023 is unavailable. Consequently, we 'nowcast' (i.e., estimate) PHE for 16 out of 69 countries in 2023 and for 38 out of 69 countries in 2024. Off-budget DAH is excluded from the nowcasting model for two reasons: first, DAH data is fully available up to 2023 in OECD CRS; second, official budget documents do not encompass DAH off-budget data for 2024 from which to estimate off-budget DAH spending. As such, and because ODA declines from 2023 to 2024 were expected to be concentrated in two non- DAH categories (i.e., in-donor country refugee costs, humanitarian support and Ukraine aid (OECD 2025a)), we assume constant DAH levels in 2024. 43 Four excluded low-income economies: Afghanistan, Eritrea, Democratic People’s Republic of Korea, , Syrian Arab Republic. Four excluded lower-middle income economies: Bolivia, Lebanon, Sri Lanka, West Bank and Gaza. Please note: the term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics. 62 Tables A2.1 and A2.2 below show the number of data points by year for initial, final, and spending allocations from the official budgets, and the list of countries included in the final sample. A full dataset of the financial budgets collection is available at this link. Table A2.1 Data Availability for Central Government Spending Allocations Central government allocation of spending for health (n) Year Initial allocation Final allocation Spending 2018 65 42 63 2019 66 50 66 2020 66 58 66 2021 65 57 65 2022 65 53 61 2023 64 42 48 2024 64 45 25 Source: Authors’ elaboration. Note: The total number of countries with initial budget data for central government is not 69 because there are some countries without any data, or have data for some, but not all, years. These are Djibouti, Somalia, South Sudan, Sudan, Yemen, and Zimbabwe, and the approach to estimate their expenditures is noted below in this section. Table A2.2 Countries Included in the Sample Low income Lower-middle income Burkina Faso Mozambique Angola Eswatini Mauritania Senegal Burundi Niger Bangladesh Ghana Micronesia, Fed. Sts. Solomon Islands Central African Republic Rwanda Benin Guinea Morocco Tajikistan Chad Sierra Leone Bhutan Haiti Myanmar Tanzania Congo, Dem. Rep. Somalia* Cabo Verde Honduras Nepal Timor-Leste Ethiopia South Sudan* Cambodia India Nicaragua Tunisia Gambia, The Sudan* Cameroon Jordan Nigeria Uzbekistan Guinea-Bissau Togo Comoros Kenya Pakistan Vanuatu Liberia Uganda Congo, Rep. Kiribati Papua New Guinea Viet Nam Madagascar Yemen, Rep.* Cote d'Ivoire Kyrgyz Republic Philippines Zambia Malawi Djibouti* Lao PDR Samoa Zimbabwe* Mali Egypt, Arab Rep. Lesotho São Tomé and Príncipe n = 22 n = 47 Source: Authors’ elaboration. Note: An asterisk (*) denotes missing or incomplete official budgets data. For these countries, WHO GHED series are used instead. Methodology for Nowcasting We largely follow the methodology used in previous reports from the DSDR series (Kurowski et al 2024). For countries where expenditure data is available from budget documents, PHE is calculated as follows, for country and year : = + + [1] 63 When actual PHE data is available, no regression or nowcasting is employed, and the data collected is simply added up as shown in Eq. [1], except for translating nominal US$ into constant 2024 US$ as described in the Costing Appendix. As noted previously, for some countries expenditure data might not be available. For those country years (16 countries in 2023 and 38 countries in 2024), we estimate expenditures from budgets (i.e., planned expenditures) for each component of Eq. [1]. = + + + [2] = + + + [3] = + + + [4] Central government health budget refers to CGHS budget (i.e., allocated, planned expenditures) country in year . Similarly, SNGB and SHIB refer to subnational government expenditures and social health insurance (SHI) budgets, respectively. Budgets are sometimes revised during the year: when available, revised budgets are used. Finally, are country fixed effects, and are year fixed effects. All variables are in 2024 US$ per capita. Predicted values from these three equations, appropriately exponentiated, are then used as the expenditure values in Eq. [1]. For example, for CGHS: � ) � = exp ( [55] For countries where we have budget data for some, but not all, years in which spending data is not available, we fill in the missing years by using the average of the closest three years. For SNG expenditures, this approach is used in Micronesia, Nigeria, Tajikistan, and Tanzania. For SHI, this approach is used in most countries. Despite this approach, Djibouti, Somalia, South Sudan, Sudan, Yemen, and Zimbabwe still lack data for 2018–24 due to too many missing years or other data issues like hyperinflation which did not allow for reasonable translation of local currency data in 2024 US$. In these cases, we estimate PHE using WHO Global Health Expenditure Database (GHED) data in US$. Finally, for countries where we know from budget documents or other sources that there are SNG or SHI expenditures, but they are not captured in budget documents, we adjust our estimates upward using SNG and/or SHI expenditure as a percentage of CGHS from GHED or other available documents. For SNG expenditures, in 12 countries the necessary adjustments to our estimates exceeded 10 percent. In each of these countries, we adjusted 10 separate data points (observations) to account for SNG spending not captured in the budget documents. The countries affected are Cambodia, Central African Republic, Chad, Democratic Republic of Congo (DRC), Eswatini, Guinea-Bissau, Jordan, Lesotho, Malawi, Philippines, Rwanda, and Solomon Islands. 64 For SHI expenditures, there are eight countries where adjustments greater than 10 percent were required. In Jordan and Egypt, we adjusted six and seven observations, respectively. In the remaining six countries—Burundi, Côte d’Ivoire, Haiti, Kenya, Nigeria, and Laos People’s Democratic Republic—either one or two observations per country were adjusted to reflect SHI expenditures not included in the original budget data. As previously mentioned, DAH off-budget data is collected from OECD CRS data. This data is available until 2023. For 2024, we assume the total off-budget DAH remains the same as in 2023, in real terms (i.e., in 2024 US$). However, there is a slight decline in per capita terms due to population growth, as we assume donor countries did not increase off-budget DAH based on recipient countries' population growth. Projecting Public Health Expenditures from 2025 to 2030 Data To project PHE from 2025 to 2030 we make use of WHO GHED data, the result from the ‘nowcasting’ exercises noted in the previous section, and the IMF World Economic Outlook (WEO). The data used from IMF WEO is general government expenditure, and interest rate payments. The latter is calculated as primary deficit as a percentage of GDP, minus deficit as a percentage of GDP. For off-budget DAH, OECD CRS data is used as the baseline. We use official announcements and policy actions, specifically donor government sources, e.g., public finance bills, executive budget proposals, and official policy documents to make assumptions about DAH declines, as shown in Table A2.3 below. Table A2.3 DAH Decline Assumptions for Major DAH Donor Entities DAH Decline Links Assumption Donor for CY25 (%) Detailed Notes The Executive’s FY 26 discretionary funding request https://www.whitehouse.gov/o suggests a cut of $6.2B for "Global Health mb/information- Programs/Family Planning" programs. While the resources/budget/the- document does not specify the base, the equivalent presidents-fy-2026- 60% FY24 estimate and the FY25 request was US$ 10B per discretionary-budget-request/ the FY25 international affairs budget, implying a 60% cut. The estimate aligns broadly with the text in the https://www.state.gov/fy-2025- United FY26 request which sustains funding for PEPFAR international-affairs-budget/ States while reducing most USAID health programs. The draft 2025 budget (August 2024) proposes a 10% cut by the Federal Ministry for Economic Cooperation and Development (BMZ), to €10.28 billion in 2025 https://www.bundestag.de/pre 10% from the previous €11.22 billion. This means that the sse/hib/kurzmeldungen- development budget will shrink by €936.97 million, or 1015388?utm_source=chatgpt Germany approximately 10%. .com 65 ODA, measured as Crédits de paiement in the public 25% finance bill, declines from 5.9B Euros in 2024 to 4.4B https://www.budget.gouv.fr/bu France Euros in 2025, implying a cut of 25%. dget-etat/mission The UK Government announced an ODA reduction https://researchbriefings.files.pa from 0.5% to 0.3% of GNI, equivalent to a 40% cut. rliament.uk/documents/CBP- Official statements mention strong commitment to 10243/CBP-10243.pdf 40% health, so the DAH cut might be lower. However, for United conservativeness, 40% has been assumed as the UK Kingdom DAH cut. Source: Official donor announcements on aid budgets (for DAH, where available); refer to links. Notes: 1. We looked for announcements related to DAH decline from the top 15 DAH donor countries and institutions globally. The donors listed here are the ones that had DAH decline announcements. For other donors (e.g., Japan, Canada, European Union) for which there were no announcements of DAH and/or ODA cuts, we have assumed a 0% decline in total DAH real US$. 2. The DAH declines in the table refer only to bilateral DAH. Methodology for Projections from 2025 to 2030 As outlined in the main document, the method for calculating projected government health expenditure is relatively straightforward. For 2024, the PHE per capita, derived from the nowcasting exercise and WHO GHED, is divided by the GGE per capita from the IMF WEO 2025. This calculation yields the proportion of GGE allocated to health in 2024. This proportion is multiplied by the GGE for each subsequent year (e.g., 2025, 2026, 2027) to determine the PHE for those years. In other words, the proportion of PHE to GGE in 2024 is maintained fixed from 2025 to 2030. Since PHE as a percentage of GGE remains constant for each country, any growth in GGE will directly result in growth in PHE at both country and income group level when using averages. The formula to project PHE from 2025 to 2023 is shown below: � = %2024 ∗ [6] Where is country, is year, and 2025 ≤ ≤ 2030 The projection method is identical when post-interest GGE is used: the only difference being that the PHE share is calculated as a percentage of post-interest GGE (i.e., GGE minus interest payments). In this case, growth in post-interest GGE will directly lead to growth in PHE. We follow a two-step process for projecting off-budget DAH. First, total off-budget DAH for 2025 is calculated by adjusting off-budget DAH starting from 2024 based on the DAH declines discussed in Table A2.1. The decline in DAH assumed in this report would result in an approximate decrease in global DAH of 20 percent in 2025, which is within the range of the OECD projection for DAH in 2024– 25 (decline between 19 percent and 33 percent) (OECD 2025a), and very close to the projection of the Institute for Health Metrics and Evaluation of a 21 percent DAH decline in 2025 (Apeagyei et al 2025). Our estimate is toward the lower end of the OECD projected DAH decline because, differently from OECD, we assume no decline of GAVI and Global Fund in real terms. 66 Second, we keep the total off-budget DAH constant in real terms until 2030. Since we use total DAH rather than per capita DAH, the result is that real DAH per capita decreases over time due to population growth in recipient countries. The underlying assumption is that DAH budgets do not increase in response to population growth in recipient countries. Again, in doing so we align with projections made by the OECD (OECD 2025a). Results While the main results are already presented in the main report, in this section we will provide additional details. The coefficients from the regressions used in the nowcasting section are shown in the table below for central government spending, subnational government spending and SHI spending. In all cases the budget coefficients are statistically significant at the 1 percent level, with the only exception being the SHI initial budget. However, SHI final budget data is available more frequently than SHI initial budget, therefore the use of the imprecise coefficient is limited to six countries in 2024, of which one had SHI as a percentage of GHE below 10 percent. In all cases, the adjusted R-squared is above 0.97, suggesting a large part of the variation in spending is explained by the variation in budgets. Table A2.4 Initial and Final Budget Regression Coefficients for Nowcasting Government Spending Initial budget Final budget Dep variable → CG SHI SNG CG SHI SNG spending spending spending spending spending spending Indep. Variable ↓ (1) (2) (3) (4) (5) (6) Central government budget (log) 0.465*** 0.756*** (0.0927) (0.0528) Social health insurance budget (log) 0.131 1.263*** (0.285) (0.238) Subnational government budget (log) 0.641*** 0.768*** (0.0864) (0.128) Constant 10.93*** 17.90*** 8.361*** 4.865*** -5.464 5.378* (1.968) (5.915) (2.022) (1.108) (4.857) (2.995) Observations 562 70 52 423 101 36 R-squared 0.984 0.985 0.999 0.993 0.981 1.000 Country FE YES YES YES YES YES YES Year FE YES YES YES YES YES YES Adjusted R2 0.981 0.979 0.999 0.992 0.974 0.999 Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. 67 Appendix 3. Sensitivity Analysis The projection methodology detailed in Appendix 2 is corroborated by a range of sensitivity checks to validate both data sources and results. In this Appendix, we present a selection of sensitivity analyses performed, divided into three sections: data, spending and outlook analysis, and limitations. First, we compare historical series (2018–22) used as our primary data sources (PHE estimated through budgets and DAH from OECD CRS) with WHO GHED; second, we briefly discuss performance and alternative specifications for the spending and outlook projections; third, we discuss limitations of the analysis. Data Collection Checks One of the key contributions of this work is the use of official budgets data to estimate PHE ahead of scheduled SHA data releases, which provide expenditure data only up to − 2 in WHO GHED. In this section, we explore the differences between official budgets and GHED arising from methodological differences between the two data collections. As detailed in Appendix 2, PHE is calculated from official budgets as the sum of central government (CG), subnational government (SNG), and social health insurance (SHI) contributions to spending. This number should ideally match the GHED sum of domestic government transfers (FS.1), government-distributed foreign transfers, or on-budget DAH (FS.2), and health insurance contributions (FS.3). As budgets data captures the entirety of expenditure flows from government sources, the sum of CG and SNG spending is equivalent to the sum of government transfers from domestic and foreign origin in GHED. In addition to the public portion of spending, we estimate the magnitude of direct foreign transfers, or off-budget DAH (FS.7 in GHED). Modelling the impact of announced DAH cuts for the projection period requires detailed information on funding levels by donors for each recipient country, as well as marker codes to attribute each item to on- or off-budget sources. GHED estimates FS.7 largely through health accounts, supplementing with data from OECD CRS when health accounts data is unavailable. However, GHED does not provide detailed breakdowns by donors and recipients. Therefore, we use OECD CRS data to estimate the equivalent of FS.7 for our analysis. Table A3.1 Mean DAH Breakdown by Income Group and Year, WHO GHED vs OECD CRS, 2024 US$ WHO GHED OECD CRS Income Year FS2 (on) FS7 (off) Total EXT On-budget Off-budget Total DAH DAH DAH LICs 2018 37% 63% 14.4 29% 71% 16.3 LICs 2019 39% 61% 11.8 32% 68% 16.3 LICs 2020 43% 57% 13.1 34% 66% 17.0 LICs 2021 45% 55% 13.1 32% 68% 17.1 LICs 2022 41% 59% 13.5 32% 68% 18.5 LMICs 2018 65% 35% 21.6 31% 69% 15.8 68 LMICs 2019 60% 40% 24.4 35% 65% 15.2 LMICs 2020 69% 31% 24.2 54% 46% 28.3 LMICs 2021 71% 29% 28.8 42% 58% 23.3 LMICs 2022 63% 37% 26.5 42% 58% 21.2 Source: Authors’ elaboration using WHO GHED and OECD CRS data. Notes: Total external aid (EXT) and Total DAH are simple means in 2024 US$ per capita terms. Shares represent the percentage of each item over Total EXT or Total DAH. Years with missing values are excluded from the calculation (countries excluded in at least one year include Bangladesh, Gambia, Mali, Morocco, Niger, Philippines, Uzbekistan). Table A3.1 shows the income group breakdown of on-budget and off-budget DAH from CRS compared to FS.2 and FS.7 breakdowns from GHED. Notably, while the total levels of DAH are broadly aligned, the attribution between on-budget and off-budget items differs significantly between the two databases. This attribution issue is more pronounced across LICs, as shown in Figures A3.1 and A3.2, at least in terms of the proportion of on-budget DAH. Fourteen countries out of 20 shown in Figure A3.1 have higher on-budget DAH shares in GHED, compared to 26 out of 43 LMICs (60 percent) shown in Figure A3.2. However, expenditure shares for LICs are more sensitive to relatively small differences in levels. As such, attribution issues in LMICs may prove larger in absolute terms even if shares appear more similar. Figure A3.1 Breakdown of On-budget and Off-budget DAH in OECD CRS and GHED, Low-income Countries, 2022 Source: Authors’ elaboration using WHO GHED and OECD CRS data. 69 Figure A3.2 Breakdown of On-budget and Off-budget DAH in OECD CRS and GHED, Lower-middle Income Countries, 2022 Source: Authors’ elaboration using WHO GHED and OECD data. Comparing budget estimates of PHE with GHED sheds more light on the differences between the two databases. Since our estimates of PHE cannot disaggregate the share of on-budget DAH spending, we cannot directly assess the relationship between the three data sources. However, we can observe what happens when we compare PHE to a similar construction from GHED (the sum of FS.1, FS.2 and FS.3) and compare that to the same measure with the addition of off-budget DAH for PHE and FS.7 for GHED. Table A3.2 below shows that our estimated PHE accounts for 70–78 percent of GHED expenditure from the sum of FS.1, FS.2, and FS.3 across LICs, and 80–88 percent for LMICs. Once we add off- budget DAH to both sides, this difference completely disappears for LICs, with an almost perfect match between PHE and GHED for all years except for 2018. In LMICs, the gap between PHE and GHED also closes significantly, with PHE plus off-budget DAH accounting for 85–94 percent of the total in GHED. These numbers suggest that in LICs, WHO GHED may over-attribute DAH transfers to on-budget expenditure. For LMICs the picture is less clear, as there is still a difference between GHED and our estimates when off-budget DAH is included. However, while non-negligible, the difference is moderate in size (10 percent average). 70 Table A3.2 Comparison of Mean Government and Donor Health Spending (PHE+DAH) over WHO GHED by Income Group and Year, 2024 US$ Income Year PHE GHED FS.1+2+3 PHE % GHED PHE+DAH GHED FS.1+2+3+7 PHE % GHED LICs 2018 9.6 13.9 69% 21.3 23.2 92% LICs 2019 10.1 13.4 76% 21.2 20.7 102% LICs 2020 12.4 16.0 78% 23.7 23.6 100% LICs 2021 11.2 16.2 69% 22.9 23.3 98% LICs 2022 11.0 15.7 70% 23.6 23.7 99% LMICs 2018 65.5 74.1 88% 76.4 81.5 94% LMICs 2019 66.3 78.2 85% 76.2 88.0 87% LMICs 2020 72.0 83.8 86% 85.0 91.7 93% LMICs 2021 73.6 88.5 83% 87.0 97.0 90% LMICs 2022 68.4 85.2 80% 80.6 95.1 85% Source: Authors’ elaboration. All values are simple means in 2024 US$ per capita. PHE % GHED represents the ratio of PHE over GHED FS.1+2+3, or PHE+DAH over GHED FS.1+2+3+7. Table A3.3 below provides the implied breakdown by financing scheme for our estimates of PHE plus off-budget DAH and GHED. For our estimates of PHE, the sum of CG and SNG expenditures from budgets are assumed to be equivalent to the sum of FS.1 and FS.2, SHI expenditures are equivalent to FS3, and off-budget DAH is equivalent to FS.7. Since FS.2 cannot be disaggregated from PHE, we take the CRS value for on-budget DAH and subtract it from the sum of CG and SNG expenditure to obtain relative implied shares of FS.1 and FS.2. Table A3.3 Financing Scheme Breakdown of PHE+DAH and WHO GHED by Income Group and Year, 2024 US$ Income Year FS1 FS2 FS3 FS7 GHED FS1 FS2 FS3 FS7 PHE+DAH LICs 2018 34% 23% 2% 41% 23.2 22% 21% 3% 54% 21.3 LICs 2019 40% 22% 3% 35% 20.7 22% 24% 4% 510 21.2 LICs 2020 41% 24% 2% 33% 23.6 29% 23% 3% 45% 23.7 LICs 2021 41% 26% 2% 31% 23.3 26% 22% 4% 48% 22.9 LICs 2022 39% 24% 3% 34% 23.7 21% 24% 4% 51% 23.6 LMICs 2018 64% 18% 9% 9% 81.5 68% 6% 12% 14% 76.4 LMICs 2019 62% 17% 9% 11% 88.0 68% 7% 12% 13% 76.2 LMICs 2020 64% 19% 9% 9% 91.7 57% 18% 11% 15% 85.0 LMICs 2021 61% 22% 9% 9% 97.0 64% 11% 10% 15% 87.0 LMICs 2022 62% 18% 10% 10% 95.1 61% 12% 11% 16% 80.6 Source: Authors’ elaboration. All values are simple means in 2024 US$ per capita. Shares represent the percentage of each item over GHED or PHE+DAH. Lastly, we test correlations between our estimates of PHE vs GHED, both with and without off- budget DAH. Simple correlations show coefficients of 94 percent for PHE vs GHED, improving to 95 71 percent with off-budget DAH across all years. Figure A3.3 below shows the relationship between PHE plus off-budget DAH and comparable GHED totals in 2022. Figure A3.3 Correlation between PHE+DAH and GHED, 2022 Source: Authors’ elaboration based on WHO GHED and World Bank country health budgets’ repository. Spending and Outlook Analysis The methodology used for nowcasting 2023 and 2024 PHE values from budgets data in this report was introduced in the 2023 DSDR report (Kurowski et al 2024). This year, we implemented changes to the nowcasting methodology, including the individual estimation of subnational and SHI portions of spending in the main report. While only CGHS was estimated in previous reports (Kurowski et al 2024), the model used for estimating CGHS remains identical to those implemented in this paper. Therefore, we can test the accuracy of our model by comparing the estimated predictions for CGHS for 2023 from the previous report to the actual CGHS for 2023, which is now available. 72 Figure A3.4. Correlation between Predicted 2023 CGHS and Actual 2023 CGHS 100 90 CGHS - Actual 2023 (US$) 80 70 60 50 40 30 20 10 0 0 20 40 60 80 100 CGHS - Predicted 2023 (US$) Source: Authors’ elaboration. Notes: The figure shows on the x-axis the predicted 2023 CGHS, and on the y-axis the actual 2023 CGHS. For convenience, both axes have been limited to 100. As shown in the figure above (Figure A3.4), the correlation between the predicted 2023 CGHS from the previous DSDR report (Kurowski et al 2024) and the actual 2023 CGHS is 0.97. Regressing actual 2023 CGHS on 2023 predicted CGHS also shows a coefficient of 0.94, p<0.01. The average predicted CGHS in 2023 is 43.2 US$ and the average actual CGHS for the same year was 41.4 US$, a difference of less than 5 percent. This result shows that predicted values at the income group level are more reliable than predicted values at the country level. Additionally, we perform additional checks for the outlook analysis using previous versions of the WEO macro-fiscal projections (2024). The results remain stable across specifications, with slight variation observed on a country-by-country basis but consistent messaging at the income group level. For a more detailed discussion on the estimation methods for the nowcasting, please refer to Annex 2 in Kurowski et al 2024. Limitations While our model is robust to a range of sensitivity checks and consistent with observed data from different databases, it faces two important limitations that have not been noted elsewhere, primarily related to data limitations. First, the budget data collected to construct PHE does not provide breakdowns for domestic and external on-budget government expenditure. In our model, we supplement PHE with CRS and GHED data on external expenditure. While this approach shows promising similarities between 73 sources, we observe significant variation at the country level that cannot be definitively attributed to a specific source. Second, and related to this, the nowcasting methodology implicitly assumes that shares in subnational government and social insurance expenditures do not change significantly when data are unavailable. While this is a general limitation of longer-term projection models, the nowcasting portion of the analysis might misallocate expenditure from these sources if a financing reform is implemented but not yet documented within the framework of the budgets. Appendix 4. Data Tables across Countries and Income Group Averages The following tables present key variables for years 2018, 2024, and 2030 across all countries, including income group means and medians. Table A4.1 Key Dataset Variables for Low-income Countries Country Year Population GGE PHE Off-budget DAH WHO Progress benchmark WHO government gap Burkina Faso 2018 20438 216 16.0 5.5 59 37 Burkina Faso 2024 23549 273 16.2 7.1 59 35 Burkina Faso 2030 26730 288 17.0 3.6 59 38 Burundi 2018 11859 93 5.2 13.7 90 71 Burundi 2024 14048 77 4.7 7.6 90 77 Burundi 2030 16182 90 5.5 3.3 90 81 Central African Republic 2018 4879 91 3.6 8.7 61 49 Central African Republic 2024 5331 104 3.3 19.8 61 38 Central African Republic 2030 6478 82 2.7 10.3 61 48 Chad 2018 16157 97 6.3 3.8 48 38 Chad 2024 20299 184 9.3 4.6 48 35 Chad 2030 24207 179 9.0 3.0 48 36 Congo, Dem. Rep. 2018 90048 72 4.1 6.4 69 58 Congo, Dem. Rep. 2024 109276 114 4.8 7.1 69 57 Congo, Dem. Rep. 2030 131532 128 5.4 4.3 69 59 Ethiopia 2018 112664 134 7.8 5.8 68 55 Ethiopia 2024 132060 103 5.6 5.1 68 58 Ethiopia 2030 152855 196 10.7 3.6 68 55 Gambia, The 2018 2400 142 7.6 15.0 68 46 Gambia, The 2024 2760 220 9.3 10.0 68 49 Gambia, The 2030 3130 235 9.9 5.9 68 52 Guinea-Bissau 2018 1922 171 17.4 11.8 68 39 Guinea-Bissau 2024 2201 202 29.1 17.2 68 22 Guinea-Bissau 2030 2489 223 32.2 11.9 68 23 Liberia 2018 4945 279 19.1 22.5 75 33 Liberia 2024 5613 207 12.5 14.8 75 47 Liberia 2030 6366 221 13.3 7.8 75 54 Madagascar 2018 27495 79 4.3 4.7 59 50 Madagascar 2024 31965 88 3.5 6.3 59 49 Madagascar 2030 36693 107 4.3 3.3 59 51 Malawi 2018 18528 106 10.4 17.7 58 30 Malawi 2024 21655 161 11.8 16.6 58 30 74 Malawi 2030 25160 158 11.6 3.8 58 45 Mali 2018 20442 187 13.2 9.5 59 36 Mali 2024 24479 230 14.2 9.8 59 35 Mali 2030 29003 280 17.3 5.5 59 37 Mozambique 2018 29019 221 12.6 27.1 51 11 Mozambique 2024 34632 218 13.2 17.3 51 20 Mozambique 2030 40847 219 13.2 7.6 51 30 Niger 2018 22188 135 9.5 5.7 59 44 Niger 2024 27032 99 7.4 5.7 59 46 Niger 2030 32518 132 9.9 3.0 59 45 Rwanda 2018 12488 201 23.3 10.1 50 16 Rwanda 2024 14257 288 26.9 14.3 50 9 Rwanda 2030 16155 335 31.3 10.6 50 9 Sierra Leone 2018 7555 110 4.9 13.5 48 29 Sierra Leone 2024 8642 141 6.1 13.4 48 28 Sierra Leone 2030 9695 142 6.2 7.9 48 33 Somalia 2018 15452 23 0.9 8.7 80 70 Somalia 2024 19009 47 1.7 7.3 80 71 Somalia 2030 22915 49 1.7 4.5 80 74 South Sudan 2018 10123 349 7.4 38.6 30 -16 South Sudan 2024 11943 70 1.5 22.2 30 6 South Sudan 2030 13457 137 2.9 16.2 30 11 Sudan 2018 44231 199 12.3 3.4 62 46 Sudan 2024 50449 37 3.1 2.8 62 56 Sudan 2030 58572 109 9.2 1.9 62 51 Togo 2018 8259 167 12.8 2.8 53 37 Togo 2024 9515 271 17.7 4.5 53 31 Togo 2030 10793 290 18.8 2.6 53 31 Uganda 2018 41566 165 6.7 18.2 56 31 Uganda 2024 50015 210 12.5 13.5 56 30 Uganda 2030 58313 326 19.4 5.6 56 32 Yemen, Rep. 2018 34085 88 6.6 3.2 61 51 Yemen, Rep. 2024 40583 42 3.8 8.0 61 50 Yemen, Rep. 2030 47668 106 9.6 5.9 61 45 Sources: UN WPP 2024 (population), IMF WEO 2025 (GGE), official budget documents (PHE), OECD CRS (off-budget DAH), Stenberg et al. 2014 (WHO Progress benchmark). Note: Authors’ elaboration. Population in thousands, all other variables in 2024 US$ per capita. 75 Table A4.2 Key Dataset Variables for Lower-middle Income Countries Country Year Population GGE PHE Off-budget DAH WHO Progress benchmark WHO government gap Angola 2018 31297 624 30.2 1.8 189 157 Angola 2024 37886 560 39.8 2.1 189 147 Angola 2030 45160 483 34.3 1.2 189 153 Bangladesh 2018 163523 253 9.8 1.9 92 81 Bangladesh 2024 173562 312 9.6 1.8 92 81 Bangladesh 2030 186072 490 15.0 1.2 92 76 Benin 2018 12383 198 10.2 5.7 53 37 Benin 2024 14463 268 15.0 6.1 53 32 Benin 2030 16619 384 21.5 3.6 53 27 Bhutan 2018 760 1140 101.3 1.8 87 -16 Bhutan 2024 792 1062 128.8 8.3 87 -50 Bhutan 2030 821 1812 219.7 6.6 87 -122 Cabo Verde 2018 513 1239 142.9 9.1 77 -75 Cabo Verde 2024 525 1355 180.5 10.2 77 -113 Cabo Verde 2030 539 1744 232.3 8.7 77 -174 Cambodia 2018 16275 362 27.2 5.0 68 36 Cambodia 2024 17639 451 27.5 5.0 68 36 Cambodia 2030 18827 574 35.0 3.2 68 30 Cameroon 2018 24806 332 6.2 5.0 83 71 Cameroon 2024 29124 312 9.5 7.0 83 66 Cameroon 2030 33777 342 10.4 3.1 83 69 Comoros 2018 772 317 16.0 7.3 55 32 Comoros 2024 867 319 17.0 3.6 55 34 Comoros 2030 964 361 19.3 2.6 55 33 Congo, Rep. 2018 5483 472 33.4 2.6 75 39 Congo, Rep. 2024 6333 505 29.8 7.3 75 38 Congo, Rep. 2030 7282 470 27.7 5.2 75 38 Côte d'Ivoire 2018 27464 403 22.2 9.1 77 45 Côte d'Ivoire 2024 31934 556 31.4 7.1 77 38 Côte d'Ivoire 2030 36699 788 44.6 3.1 77 27 Djibouti 2018 1072 774 43.3 10.1 88 35 Djibouti 2024 1169 757 49.9 18.9 88 18 Djibouti 2030 1263 862 56.8 14.6 88 15 Egypt, Arab Rep. 2018 105682 811 43.9 0.3 61 17 Egypt, Arab Rep. 2024 116538 754 42.0 0.5 61 18 Egypt, Arab Rep. 2030 127139 860 47.9 0.3 61 8 Eswatini 2018 1169 1189 145.1 47.4 72 -120 Eswatini 2024 1243 1325 120.5 54.8 72 -103 Eswatini 2030 1321 1424 129.6 24.0 72 -80 Ghana 2018 30638 444 32.2 5.1 77 39 Ghana 2024 34427 558 33.6 6.1 77 37 Ghana 2030 38222 563 33.9 3.2 77 40 Guinea 2018 12705 222 10.7 10.9 84 62 Guinea 2024 14755 338 7.0 6.8 84 70 Guinea 2030 16807 471 9.8 4.2 84 70 Haiti 2018 10962 301 13.5 38.1 99 48 Haiti 2024 11773 113 4.9 15.1 99 79 Haiti 2030 12552 173 7.6 7.5 99 84 Honduras 2018 9765 846 108.4 1.6 106 -4 Honduras 2024 10826 880 127.6 4.6 106 -26 76 Honduras 2030 11885 1002 145.4 3.0 106 -41 India 2018 1374659 557 19.8 0.4 84 64 India 2024 1450936 765 24.3 0.3 84 59 India 2030 1525139 1016 32.3 0.2 84 52 Jordan 2018 10462 1446 158.5 8.3 336 170 Jordan 2024 11553 1528 142.4 6.7 336 187 Jordan 2030 12449 1540 143.5 4.3 336 182 Kenya 2018 50207 444 19.9 13.4 87 53 Kenya 2024 56433 492 19.6 12.3 87 55 Kenya 2030 63102 576 23.0 7.0 87 57 Kiribati 2018 122 2195 186.9 20.6 75 -133 Kiribati 2024 135 2246 205.0 52.9 75 -183 Kiribati 2030 146 2487 227.1 43.3 75 -195 Kyrgyz Republic 2018 6342 687 58.4 9.1 117 50 Kyrgyz Republic 2024 7186 820 59.3 5.5 117 52 Kyrgyz Republic 2030 7804 1107 80.0 3.5 117 36 Lao PDR 2018 7128 393 19.2 4.0 61 38 Lao PDR 2024 7770 320 15.6 7.2 61 38 Lao PDR 2030 8357 341 16.5 4.7 61 38 Lesotho 2018 2184 548 59.9 38.6 72 -26 Lesotho 2024 2337 526 49.6 31.5 72 -9 Lesotho 2030 2494 557 52.6 11.5 72 9 Mauritania 2018 4338 384 27.4 2.9 87 57 Mauritania 2024 5169 501 23.1 4.6 87 59 Mauritania 2030 6063 650 29.9 3.2 87 54 Micronesia, Fed. Sts. 2018 110 2359 449.2 10.8 104 -356 Micronesia, Fed. Sts. 2024 113 2377 404.1 29.6 104 -329 Micronesia, Fed. Sts. 2030 116 2893 491.8 23.8 104 -412 Morocco 2018 35840 1100 67.8 0.3 82 14 Morocco 2024 38081 1394 72.9 0.3 82 9 Morocco 2030 39953 1597 83.5 0.2 82 -1 Myanmar 2018 52272 249 8.8 3.8 65 52 Myanmar 2024 54500 234 5.5 3.4 65 56 Myanmar 2030 56351 261 6.2 2.3 65 57 Nepal 2018 28080 351 10.2 4.5 71 56 Nepal 2024 29651 320 13.8 4.2 71 53 Nepal 2030 30510 488 21.0 2.5 71 47 Nicaragua 2018 6400 725 137.4 2.8 99 -41 Nicaragua 2024 6916 772 145.0 6.8 99 -52 Nicaragua 2030 7442 887 166.7 5.1 99 -71 Nigeria 2018 204939 103 3.6 1.7 22 16 Nigeria 2024 232679 134 3.6 4.6 22 13 Nigeria 2030 262381 152 4.1 2.0 22 16 Pakistan 2018 226929 266 9.0 1.9 76 65 Pakistan 2024 251269 288 10.0 1.5 76 64 Pakistan 2030 276883 318 11.1 0.9 76 61 Papua New Guinea 2018 9395 594 50.7 12.2 80 17 Papua New Guinea 2024 10577 607 47.0 11.4 80 21 Papua New Guinea 2030 11671 628 48.6 10.0 80 19 Philippines 2018 109465 724 45.7 0.9 77 30 Philippines 2024 115844 994 63.1 1.5 77 12 Philippines 2030 121409 1194 75.7 1.0 77 1 Samoa 2018 208 1435 176.9 35.0 86 -126 77 Samoa 2024 218 1422 222.1 36.0 86 -172 Samoa 2030 226 1941 303.2 29.4 86 São Tomé and 2018 Príncipe 210 1016 176.9 61.4 174 -64 São Tomé and 2024 Príncipe 236 868 91.3 22.5 174 60 São Tomé and 2030 Príncipe 265 701 73.7 12.7 174 88 Senegal 2018 15914 357 14.3 9.0 77 54 Senegal 2024 18502 595 15.0 8.3 77 53 Senegal 2030 21163 512 12.9 4.4 77 60 Solomon Islands 2018 709 803 79.2 8.9 84 -4 Solomon Islands 2024 819 777 74.8 13.8 84 -5 Solomon Islands 2030 938 806 77.6 10.0 84 -3 Tajikistan 2018 9307 303 21.4 3.4 59 34 Tajikistan 2024 10591 371 32.7 5.6 59 21 Tajikistan 2030 11733 492 43.4 3.1 59 12 Tanzania 2018 57437 175 11.4 13.3 50 26 Tanzania 2024 68560 221 12.1 9.8 50 28 Tanzania 2030 80913 295 16.2 3.8 50 30 Timor-Leste 2018 1276 1261 51.0 13.7 100 35 Timor-Leste 2024 1401 1306 53.2 15.9 100 30 Timor-Leste 2030 1520 1344 54.8 11.4 100 33 Tunisia 2018 11766 1275 136.8 0.4 278 141 Tunisia 2024 12277 1458 145.2 0.9 278 132 Tunisia 2030 12628 1631 162.4 0.7 278 120 Uzbekistan 2018 32373 560 50.1 1.3 63 12 Uzbekistan 2024 36362 872 68.7 0.8 63 -6 Uzbekistan 2030 40248 1144 90.0 0.3 63 -27 Vanuatu 2018 285 1214 112.5 22.9 91 -45 Vanuatu 2024 328 1366 116.5 24.5 91 -50 Vanuatu 2030 372 1166 99.5 18.6 91 -23 Viet Nam 2018 96237 707 56.7 0.9 85 27 Viet Nam 2024 100988 870 61.6 0.8 85 22 Viet Nam 2030 104255 1228 86.9 0.4 85 -2 Zambia 2018 17974 336 24.0 20.1 54 10 Zambia 2024 21315 315 27.1 21.0 54 6 Zambia 2030 25025 364 31.3 7.6 54 12 Zimbabwe 2018 15034 418 37.3 24.0 81 19 Zimbabwe 2024 16634 382 25.4 21.5 81 35 Zimbabwe 2030 18610 512 34.0 13.5 81 34 Sources: UN WPP 2024 (population), IMF WEO 2025 (GGE), official budget documents (PHE), OECD CRS (off-budget DAH), Stenberg et al. 2014 (WHO Progress benchmark). Note: Authors’ elaboration. Population in thousands, all other variables in 2024 US$ per capita. In addition, the full dataset including all data (i.e., collected, nowcasted, and projected) from 2018 to 2030 is available at this link. 78 79