A N A LYS I S Matthew Martin October 2025 Inequality Reduction and Debt Sustainability Analysis A practical approach to integration

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Matthew Martin October 2025 Inequality Reduction and Debt Sustainability Analysis A practical approach to integration

Content Section 1: Why bring inequality reduction policies into Debt Sustainability Analysis?.............................  3 BWI integration of SDGs vs. inequality ..............................  3 Debt service vs. social spending crisis ...............................  3 Investment returns from inequality reduction spending ................  3 Previous successful integration: the MDG experience ..................  4 Section 2: Which countries should have inequality reduction policies in their DSAs?....................................................  4 IMF inequality threshold framework ................................  4 Implementation approach ........................................  4 Section 3: Key inequality-reduction sectors and their impact on inequality ...  5 IMF findings confirm the impact ...................................  5 Section 4: Constructing spending cost estimates for key anti-inequality sectors ..............................................  5 Adapting scenarios to national priorities .............................  7 Expanding beyond the core three sectors ............................  7 Section 5: Multiplier effects on growth of inequality-reduction spending....  7 Direct Effects ...................................................  7 Indirect Effects ..................................................  7 Section 6: The impact of non-spending policies on inequality and growth ...  8 Progressive tax policies..........................................  8 Labour rights, unionization and minimum wages.....................  8 Land policies...................................................  9 More analysis needed on non-fiscal policies.........................  9 Section 7: Incorporating the spending and multipliers in the LIC-DSF and SRDSF templates ..............................................  9 LIC-DSF integration approach .....................................  9 SRDSF module design ...........................................  9 Implementation requirements and pilot testing ......................  10 Section 8: Conclusions and recommendations.........................  10 References ......................................................  11 Annex 1: Latest country net GINI coefficients.........................  13 Annex 2: Integrating inequality shocks into DSAs ......................  15

Section 1: Why bring inequality reduction policies into Debt Sustainability Analysis? 1 UN Member States adopted Agenda 2030 and the Sustainable Development Goals in 2015. These are now the foundation of national development plans in countries of the global South. Both Bretton Woods institutions have integrated SDG 10(reducing inequality) into their work programs. The World Bank adopted SDGs 1(end poverty) and 10(reduce inequality) as core operational goals and recently added an inequality-reduction goal to its Corporate Scorecard(World Bank 2025c). Since 2015, the IMF has focused on inequality-related SDGs, particularly gender equality and income inequality reduction, due to their negative impact on growth and enhancement of financial stability(IMF 2023). BWI integration of SDGs vs. inequality Many experts view extreme inequality as having major negative effects on economic growth, political stability, crime, and progress on education and health indicators – impacts as significant as those from the climate crisis(Martin 2025). In addition, analysts of the climate crisis including the IPCC have emphasized that the climate crisis cannot be tackled successfully without also sharply reducing inequality and poverty through a“Just” Green Transition. However, in contrast to their efforts on climate, the IMF and World Bank have not modified their debt sustainability methodologies(SRDSF or LIC-DSF) to address inequality. In fact, the only mention of the“social sector SDGs” in the debt sustainability frameworks is the inclusion of a module in the SRDSF, tracking the potential negative demographic risks for debt sustainability(in countries with a growing proportion of elderly citizens) of growing costs for social security, pensions and public healthcare(see IMF 2022b). While the IMF and World Bank have developed tools for costing SDG spending, they have not integrated these with DSAs to show at country level how such spending needs could be financed without compromising debt sustainability, or to help countries to mobilize funding to support inequality reduction policies. As a result, DSAs are limited to being used as“negative” tools – analyzing risks of default rather than identifying how to mobilize more finance for sustainable development. This is an unsustainable outcome: IMF and World Bank research has repeatedly shown how extreme poverty and income inequality undermine growth and debt sustainability, with effects extending beyond individual countries, impacting migration and security in countries across the world. Debt service vs. social spending crisis In addition, the failure to ensure that SDG spending was adequately funded, or that debt service was being kept at reasonable levels to allow countries to invest more in the SDGs, has meant that since 2020, global South countries have spent more on debt service than on anti-inequality sectors. According to my own organization’s“Debt Service Watch” database, in 2025 countries will spend 1.1 more times on debt service than on core anti-inequality sectors(education, health and social protection) combined. Debt service averages three times education spending, 4.5 times health spending, and 11.4 times social protection spending(DFI 2025). 2 Investment returns from inequality reduction spending Conversely, as will be shown in this paper, investments to combat inequality – particularly when combined with green investments for a Just Green Transition – offer higher returns and faster growth than traditional infrastructure spending. To the degree that these effects are not being demonstrated in debt sustainability analysis, they are being ignored, and governments are not being encouraged to mobilize funding to support transformative spending while building resilience against future pandemics and crises. 1 Some experts interviewed for this paper indicated its recommendations might be more readily taken up if pitched as focusing DSAs on SDG 1(ending extreme poverty) rather than SDG 10, given that the key sectors requiring spending would be similar. However, since it is the IMF and World Bank’s position that ending extreme poverty requires dramatic reductions in inequality, the focus in this paper is on SDG 10. 2 This database tracks debt service and spending on core social and environmental SDGs, across all countries that borrow from the World Bank. It differs from other debt service data, as it covers both external and domestic debt and is compiled in real time as soon as budget documents or debt management reports are released by developing countries. Current data are for 2025. Development Finance International(2023a) presents the methodology used for the overall summary database, and Development Finance International(2025) the latest data for 2025 and forecasts through to 2030. Inequality Reduction and Debt Sustainability Analysis 3

Previous successful integration: the MDG experience The BWIs have recognized this problem and begun to adapt their DSAs to the SDGs, but mostly around climate issues. Including inequality-reduction spending in DSAs is feasible, as was done in support to multiple African and Latin American countries provided by the HIPC Capacity Building Programme and by DFI/GIZ during 2000–2010. This was achieved by helping them to conduct their own DSAs, using as a“high case scenario” the spending needs and financing sources for the then Millennium Development Goals, which in most countries were falling way behind schedule and required accelerated implementation. Such scenarios helped countries such as Bolivia, Burkina Faso, Rwanda and Tanzania to mobilize budget revenue or donor financing and to use their debt relief proceeds to accelerate progress on many of the core MDGs. Notably, in the current global context of polycrisis, slow growth and reduced ODA flows, it will be essential for countries to prioritize which inequality reduction spending they wish to include. However, given the likely major effects of reducing inequality on accelerating growth, this context makes an even stronger case for governments and the Bretton Woods Institutions to prioritize inequality reduction in order to improve debt sustainability. The paper proceeds by identifying target countries(Section 2), defining key sectors and their inequality impacts (Section 3), estimating costs(Section 4), calculating multiplier effects(Section 5), examining non-spending policies(Section 6), and outlining implementation(Sections 7–8). Section 2: Which countries should have inequality reduction policies in their DSAs? Most IMF member countries should include inequality reduction in their DSAs, since IMF research shows current inequality levels undermine growth. IMF inequality threshold framework The IMF identifies a post-tax and transfer Gini coefficient of 0.27 as the threshold where an“inequality overhang” begins to undermine growth. World Bank data shows 157 of 172 countries in its Poverty and Inequality Platform(PIP) database exceed this threshold, suggesting widespread need for including inequality reduction policies in DSAs(World Bank 2025b). 3 Poverty reduction provides another basis for this decision. The IMF already requires climate change modules for all RSF loan recipients and debt restructuring countries. Following this precedent, the IMF could mandate anti-inequality modules for all PRGF loan recipients—covering roughly 70 PRGTeligible countries. However, using loan type as the sole criterion seems inadequate, especially since reducing inequality effectively combats both poverty and climate change. Logic suggests that RSF loan countries and debt restructuring cases should also prioritize inequality reduction, given that debt restructuring aims to free up funds for social spending. The most targeted approach would set a“macro-critical” inequality threshold, following the climate module precedent. The World Bank focuses on countries with Gini coefficients above 0.4—approximately 52 countries—based on definitions from UN agencies(Statistics Division, UNICEF, UNRISD, WHO). IMF staff interviews confirm this 0.4 threshold as “macro-critical” for growth and macroeconomic stability. Given BWI capacity constraints and the stronger inequality effects in high-inequality countries, a combined approach would be more practical: automatic modules for PRGT recipients, debt restructuring countries, and all countries with high inequality, with optional modules available upon request. Implementation approach It is worth noting that there are countries that do not fall into the high-inequality category whose governments nevertheless see reducing inequality as a key part of their development strategy. Examples of these include Ethiopia, Kenya, Mongolia, Senegal, Sierra Leone and Vietnam. As with climate change, use of an inequality-reduction module could also be made an option. In addition, country governments or stakeholder groups should be allowed(with independent support where necessary) to conduct inequality reducing DSAs for other countries. In the case of the current SRDSF, a distinction is made between a standardized sub-module for countries meeting ­certain criteria and a ccustomized sub-module for countries where the government has detailed national data on costs of spending that could be applied in integrating inequality ­reduction policies. 3 According to the World Bank data, the 15 countries which would not require an anti-inequality DSA module on this basis would be the Slovak Republic, Slovenia, Belarus, India, Ukraine, the Netherlands, Moldova, Czechia, the Kyrgyz Republic, Belgium, the UAE, Azerbaijan, Iceland, Syria and Norway. Other data sources indicate India would also fall into the high inequality group(Gini above 0.4) if income rather than consumption data were used(WIDER 2024). 4 Friedrich-Ebert-Stiftung e.V.

Section 3: Key inequality-reduction sectors and their impact on inequality Multiple studies have identified government spending sectors that can significantly reduce inequality. While education, health, and social protection receive the most attention, predominates, the research also covers public housing, water and sanitation, smallholder agriculture, and rural infrastructure. The best method for measuring anti-inequality spending impact is fiscal incidence analysis, which tracks how fiscal policies affect different income groups(Lustig 2022). These studies now cover nearly all EU and OECD countries plus 43 developing countries, with disaggregated data on the impact of education, health and social protection spending. → In EU/OECD countries covered by existing analysis, 4 each one per cent of GDP spent reduces the Gini by 0.41 points for education/health and 0.8 points for social protection (OECD 2025). → In developing countries covered by CEQ analysis, 5 education spending achieves 0.78 points reduction per GDP per cent, health achieves 0.62 points, and social protection achieves 0.44 points(CEQ 2025). Table 1: Impact of anti-inequality spending on inequality (Gini coefficient reduction per percentage point of GDP spent) Sector Education Health Social ­Protection EU/OECD Countries Gini ­reduction Reduction per% of GDP spent 5.9 points combined 0.41 points 12.6 points 0.8 points Developing Countries(CEQ) Gini ­reduction Reduction per% of GDP spent 3.6 points 0.78 points 1.4 points 0.62 points 3.0 points 0.44 points Source: DFI 2025a; draws on data from OECD/EU and CEQ The data reveals that in wealthy countries, social protection delivers the biggest inequality reduction impact, with 0.8 Gini points per GDP percent, while in developing countries, education leads(0.78 points). IMF findings confirm the impact A 2021 IMF study reinforced these conclusions, finding that social protection transfers reduced Gini coefficients by 10 percentage points(16 per cent) in OECD countries and 1.5 percentage points(3.3 per cent) in developing countries(IMF 2021c). Education and health spending combined achieved 2.2 percentage points(3.3 per cent) reductions in OECD countries and 2.4 points(1.6 per cent) in developing countries. The study also revealed important nuances: contributory pension schemes outperformed targeted social safety nets du to broader coverage, while early education proved more effective than tertiary spending, which often benefits wealthier households more. The World Bank and CEQ have moved beyond historical analysis to create simulation tools that let countries model the inequality impact of proposed spending or other fiscal policy changes. With fiscal incidence analyses available for 69 countries(and 3 more in production), policymakers can now predict outcomes with unprecedented precision. Why focus on education, health and social protection? Studies quantifying other sectors’ inequality effects are few and far between. Public housing research covers only OECD countries, while nutrition, water, and energy studies offer country-specific insights that do not translate broadly across developing countries. This evidence gap explains why this paper concentrates on the three sectors with robust, crosscountry data: education, health, and social protection. Section 4: Constructing spending cost estimates for key anti-inequality sectors In the climate changes modules already used in the SRDSF and LIC-DSF, the IMF foresees using two types of scenario for spending estimates: → A“standard scenario” which uses default average spending levels as a percentage of GDP to estimate climate adaptation and mitigation spending needs, broken down by different income group classifications. → A“customized scenario” which uses nationally calculated estimates of spending needs, based on national adaptation and mitigation plans prepared for the COP process. Both methods should also be possible to use to cost spending for the core anti-inequality sectors of education, health and social protection. For the“standard scenario”, the IMF has already calculated costing estimates for the education and health sectors for a 4 Based on analysis of Australia, Austria, Belgium, Bulgaria, Canada, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Latvia, Lithuania, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye, UK and USA(OECD 2025). 5 The countries covered: Argentina, Benin, Bolivia, Burkina Faso, Cambodia, Chile, China, Comoros, Costa Rica, Djibouti, Egypt, El Salvador, Eswatini, the Gambia, Georgia, Guinea, India, Iran, Iraq, Kenya, Lao, Lesotho, Malaysia, Mali, Mauritius, Moldova, Mongolia, Morocco, Namibia, Niger, Panama, Romania, Senegal, Serbia, South Africa, Tajikistan, Togo, Türkiye, Ukraine, Vietnam and Zambia(CEQ 2025). Inequality Reduction and Debt Sustainability Analysis 5

number of countries. These were based largely on a wideranging process of consultation with sectoral experts, coordinated by the Sustainable Development Solutions Network (SDSN) and IMF, which eventually led the IMF to cost some of the“core” SDGs for a limited number of countries, and to develop a methodology to replicate this exercise across all countries. There have been three rounds of work on detailed country costings for some of the SDGs(education, health, electricity, water and sanitation, and rural roads). The first was released in January 2019, looking at SDG costings for countries with different income levels(IMF 2019). The second major multi-country study was released in April 2021; it updated calculations to account for the COVID pandemic’s negative impact on SDG progress and prospects, concluding that more financing is needed(IMF 2021b). The third round was released in 2023(IMF 2023b). These are equivalent to the“current best estimates” already used by the IMF in the SRDSF climate change module. The cost levels derived for different groups of countries from the first study for combined education and health costs of additional spending needed to reach the SDGs were 8.3 per cent of GDP a year for LICs, with a range of between 0.3 per cent and 30 per cent of GDP(the higher figures are for fragile states where little is currently spent in either sector). For emerging markets, the extra spending needed for education and health combined averaged 2 per cent of GDP. 6 Since then, allowing for spending delays and reductions due to COVID, and refinement of the methodology, the latest edition of the IMF costing tool suggests that low-income developing countries would need to spend around 9 per cent of GDP a year more on education and health, while emerging markets would need to spend around 2.4 per cent of GDP. Country-specific estimates for all countries have been published in the Appendix of the IMF’s“How To” guide to using the costing tool(IMF 2023b). The IMF emphasizes the need to adapt desk-based costings to specific country circumstances and unit costs, agreeing on these with national authorities before using in country analysis and policy advice. It has included 15 such country-specific costings in its latest list of costings, based on case study analysis which was conducted for the earlier global studies or as part of Article IV or country program discussions with countries. 7 The IMF costings work has not so far included social protection – even though this is shown to be a highly effective form of spending to reduce inequality and poverty and promote growth. The IMF has believed that universal social protection in lower-income countries(as opposed to a targeted safety net) would be unaffordable, but this position should be dropped, given the high impact social protection has on reducing inequality, and the essential role social protection played in protecting overall progress on all the SDGs during the COVID pandemic. In addition, the International Labour Organization(ILO) provides desk-based estimates for 133 low- and middle-income countries, showing an average 1.3 per cent of GDP gap between current spending and SDG-necessary levels for five key social protection benefits(child, maternity, disability, unemployment and old age pensions(ILO 2024). By income group, the gaps average is 19.8 per cent of GDP for LICs, 2.3 per cent of GDP for LMICs and 0.7 per cent of GDP for UMICs. The LIC figure is inflated by conflict-affected states with minimal existing social protection(Afghanistan, Eritrea, Somalia, Sudan, and Yemen). These estimates provide a ready starting point for costing social protection spending needs(ILO 2024). Development Pathways, a UK-based consultancy that designs social protection systems, offers alternative calculations for countries finding ILO estimates too high. Using international rather than national poverty lines for benefit calculations, they estimate universal social protection could cost as little as four per cent of GDP annually in low-income countries. They also propose gradual scaling up over 10-20 years – the approach most countries have used – with initial costs between 0.1 per cent and 0.3 per cent of GDP annually (Development Pathways 2023). It is preferable to use customized scenarios based on fully costed national sectoral plans designed to achieve the sectoral SDGs. Virtually every developing country has plans for education and health sectors that last 10-15 years and are compatible with the SDGs. However, in some cases there may be problems with using the education and health plans: 1. They may be out of date – the initial wave of plans was designed in 2016-17 immediately after the SDGs were agreed – and therefore their unit costs need updating to account for high post-COVID inflation in many countries. Countries should be able to prepare national costings if they have updated unit costings. 2. Spending is likely to have fallen short of original plans given the pandemic’s interruption and post-COVID austerity measures. Therefore, countries must choose whether to simulate higher spending levels to“catch up“on progress with the education and health SDGs, or delay the 6 For the costing methodologies used in 2019 see Annex 1 of IMF 2019. For the latest methodology see IMF 2023b. 7 The countries for which such“customized” calculations exist are Angola, Benin, Cambodia, Chad, Guatemala, India, Indonesia, Mexico, Myanmar, Nigeria, Pakistan, Rwanda, Turkmenistan, Uzbekistan and Vietnam. 6 Friedrich-Ebert-Stiftung e.V.

attainment of the SDGs and stick to the speed of progress envisaged when the plans were drafted. On the other hand, national social protection strategies(especially those with detailed costings) have been prepared much more gradually over time, and many are therefore relatively recent. In many cases, this will reduce the time period for which unit costings need to be updated or adjusted to take account of COVID. Adapting scenarios to national priorities Customized national scenarios must align with each country’s specific priorities and constraints. Countries may want to prioritize certain inequality reduction sectors over others, scale up the spending gradually, or adjust amounts based on funding availability through iterative modeling of costs, multipliers, and financing options. However, any customization must preserve the core objective: establishing clear pathways to universal coverage in education, health, and social protection, consistent with the SDGs and national development strategies. Expanding beyond the core three sectors The IMF has developed desk-based estimates for water and sanitation, electricity access, and rural roads(IMF 2023b). Like the education and health estimates, these could anchor discussions with national officials and stakeholders to develop refined, country specific costings. Most countries include infrastructure investments in their national development strategies – rural markets are common, while some feature public housing and transport plans. Lower income countries rarely prioritize major spending in these areas, and crucially, we lack comprehensive studies of their anti-inequality and growth multiplier impacts. Section 5: Multiplier effects on growth of inequality-reduction spending There are two approaches for calculating growth and budgetrevenue multiplier effects of inequality-reduction spending: via direct and indirect effects. Direct Effects Ideally multiplier effects would be calculated using direct coefficients for education and health investments on growth. Extensive literature demonstrates that additional education and health spending accelerate growth, while social protection spending typically has neutral direct growth effects (Bank of Greece 2022). Multi-country studies containing these calculations are few in number. The Bank of Greece study finds that for OECD countries, a 1 per cent increase in education spending will raise GDP growth by 0.91 per cent in year 1 rising to 3.25 per cent cumulatively after five years; the corresponding figures for health are 0.94 per cent rising to 2 per cent. This study finds a neutral effect from social protection spending increases. The Inter-American Development Bank finds that for 107 countries, including many developing countries, a 1 per cent increase in education spending can increase real per capita GDP by 0.031 per cent(IADB 2025). McKinsey and Brookings find a much larger potential growth effect from health spending, with a multiplier of 200-400 per cent for every dollar of extra health spending(McKinsey 2020). Nevertheless, it is clear that – since these studies use different data sets, time periods and methods – a comprehensive study identifying sectoral spending multipliers would be desirable before using such methods with confidence. For over a decade, the IMF has incorporated second-round investment effects on growth through case studies and model analysis in Article IV documents and other publications. A good example is the Debt, Investment and Growth(DIG) model developed in 2012, which the IMF is still recommending as the simplest way to model the impact of investments on accelerating growth(IMF 2012). Most of the analytical work conducted with this model relates to the growth impact of large infrastructure project investments, but according to the authors, there is no reason it cannot also be used to simulate the impact of human capital investments and social transfers, with their results then incorporated into DSAs. There are ample existing studies showing that the social returns on human capital spending for growth are much higher than those of infrastructure(Psacharopoulos and Patrinos 2018). Indirect Effects This approach calculates the indirect multiplier effects of the reduction in inequality on enhancing growth rates through increased purchasing power and consumption from higher disposable income. The IMF’s“Inequality Overhang” study provides the analytical foundation, finding that countries with Gini coefficients above 0.27 can accelerate growth by reducing inequality(IMF 2017). The study quantifies substantial growth potential: reductions in net Gini(post tax and transfer) can boost real per capita GDP growth by up to 0.43 per cent for every 1 percentage point of net Gini reduction, with the effect varying by initial inequality level. The World Bank complements this GDP growth with revenue-growth findings, which are vital to show that countries will be able to afford to pay more debt service if they invest in reducing inequality. It shows that moving from 7% to 15% of GDP in tax collection generates 10 percentage points of additional cumulative growth over ten years, with each Inequality Reduction and Debt Sustainability Analysis 7

1 percentage point GDP increase in taxes correlating with 3.6 percentage points of additional future growth(World Bank 2024a). Nevertheless, significant gaps remain. The IMF study covers 36 emerging market economies but only 11 low-income developing countries, limiting broad application. Additionally, while studies demonstrate strong positive relationships between per capita GDP and revenue/GDP ratios(coefficients of 1-2.5), more work is needed to derive country-specific marginal coefficients for reliable forecasting(IMF 2023c; Fenochietto and Pessino 2013). 8 The IMF has existing frameworks that could incorporate these effects. The same analytical approach used in Article IV documents for infrastructure multipliers could model inequality-growth linkages, with results integrated into DSAs. This is particularly relevant since studies consistently show that enhanced budget revenue reduces volatility and enables pro-poor infrastructure investment, generating additional second-round growth effects beyond direct human capital returns(IMF 2021a; IMF 2023c; World Bank 2024b). Section 6: The impact of non-spending policies on inequality and growth Progressive tax policies These policies focus on higher levels of direct taxes(income and wealth) rather than indirect(sales and consumption), through rates, thresholds and exemptions that favor the poor and ensure that corporations and high net worth individuals pay their fair share. The impact of tax policies on reducing inequality is substantial, although it is lower than that of spending(Table 2). For OECD countries, 9 tax systems reduce inequality by a substantial amount because they collect large amounts of personal income tax. The average reduction in inequality due to taxation is 3.9 Gini points(which is under one fifth of the impact of spending on education, health and social protection combined). However, the impact of each 1 per cent of GDP collected as taxes is still significant, at 0.44 Gini points. Global South countries, with weaker direct tax collection, achieve smaller inequality reductions – averaging only 1.2 Gini points. However, results vary significantly: progressive tax countries such as Colombia, Eswatini, Kenya, and South Africa achieve 4-point reductions, while countries with flat taxes like Moldova achieve only 0.1 points. In addition, because personal income tax collection levels are low in most countries of the South(averaging only 2.6 per cent of GDP), the reduction for every per cent of GDP collected is slightly higher than the impact of spending(and than the impact in OECD countries), at 0.46 Gini points. Like spending coefficients, country-specific tax coefficients can forecast inequality reduction from progressive tax policies. Alternatively, detailed CEQ and World Bank simulation tools can model the effects of tax policy changes. Table 2: Impact of progressive tax policies on inequality (Gini coefficient reduction per percentage point of GDP collected) Sector EU/OECD (­overall tax ­system) CEQ(personal income tax) Personal ­Income Tax Gini ­reduction 3.9 points Reduction per% of GDP ­collected 0.44 points Gini ­reduction 1.2 points Reduction per% of GDP ­collected 0.46 points Source: DFI 2025a; draws on data from OECD/EU and CEQ Progressive tax policies accelerate growth. According to the World Bank, two main channels explain this effect:“mitigating under-investment in human capital because of credit market imperfections and lowering high fertility rates among the poor(World Bank 2024b).” 10 Labour rights, unionization and minimum wages These address market income inequality through stronger worker protections and wage standards. Inequality of“market income”(i.e. before government fiscal interventions) is consistently higher than inequality after government interventions because of the redistributive impact of government spending and tax policies(CEQ 2025). A key component of this inequality is high wage and broader labor-income inequality. IMF, OECD and ILO research indicates a strong relationship between unionization rates and wage inequality as well as between declines in the minimum wage compared to median wages and inequality(Jaumotte and Osorio 2015; ILO 2025b; OECD 2011). IMF has found for OECD countries a 10 percentage point decline in the ratio of the minimum wage to the median wage 8 In recent years, many countries have agreed with the IMF on major revenue increases through the Platform for Collaboration on Tax’s Medium-Term Revenue Strategy(MTRS), although MTRS cover only 5 years, and generally do not assume increased GDP per capita will increase revenue collection(PCT 2025). 9 As with spending calculations, Chile and Mexico have been removed from the OECD category and categorized with countries of the global South, because more detailed CEQ analysis exists for both countries. 10 The World Bank shows the same results using a“prosperity gap” indicator rather than a Gini, and therefore its results would not be feasible to fit into a calculation of multipliers based around Gini(World Bank 2024a). 8 Friedrich-Ebert-Stiftung e.V.

correlates to a 5 per cent increase in Gini of gross income (IMF 2015). The is particularly the case in OECD countries where most employment is formalized through contracts. It is weaker in countries where a high proportion of the workforce is in the informal sector with no contracts. However, no comprehensive studies exist across developing countries to clarify how changes in union density and minimum wages can reduce inequality. The IMF and ILO have used models in the past to assess minimum wage impacts; these would need to be applied to assess effects on inequality and growth. Access to finance This reduces wealth inequality by expanding financial inclusion, particularly for women and marginalized groups. One key structural factor causing inequality(especially wealth and income resulting from wealth) is access to finance. IMF finds that increasing financial inclusion, especially for women, reduces inequality. Increasing financial inclusion to levels that match the 75th percentile(top quarter) of countries could reduce the Gini by 9 percentage points, while lack of ­financial regulation in access to credit can lead to financial market instability or excesses that will increase inequality (Cicak and Sahay 2020). Work by the World Bank reaches similar conclusions, also emphasizing the benefits of access to payments systems, savings accounts and insurance for the poor(Demirgüç-Kunt, Klapper, Singer 2017). Both BWIs have emphasized that financial deepening(an increase in financial assets/GDP) does not necessarily reduce inequality. The IMF finds that increasing financial deepening from the levels of low-income countries to those of OECD economies could increase the Gini by 10 percentage points. Land policies These address unequal land distribution and earnings. The literature identifies these as major inequality drivers but lacks quantitative studies from global South countries(International Land Coalition 2020). However, studies that quantify the potential benefits of land redistribution or land/property taxation for reducing inequality across countries of the global South are scarce, making it impossible at present to quantify this impact for DSFs. More analysis needed on non-fiscal policies These can also reduce inequality substantially but fall short of providing marginal coefficients of their inequality impacts on global South countries. This makes it hard to quantify the likely impact of these policies on reducing inequality. There is a dearth of studies at national level from lower-income countries showing the impact of labor income and labor policies, or financial income and financial policies, on inequality. More work is needed in the areas of labor income and policies, as well as financial income and policies, to quantify their impact on inequality reduction, making it possible to use the inequality reduction multipliers discussed in Section 6 to estimate their impact on growth and revenue. Section 7: Incorporating the spending and multipliers in the LIC-DSF and SRDSF templates Calculated spending needs and their inequality/growth impacts should be incorporated into LIC-DSF and SRDSF templates. LIC-DSF and SRDSF templates differ, requiring different approaches: → For the LIC-DSF, an alternative scenario needs to be designed that includes higher spending levels and financing needs as well as higher growth and revenue projections. → For the SRDSF, an alternative long-term scenario anti-­ inequality module needs to be designed, on the same basis as a strengthened climate module, with forecasts starting from year 1 rather than year 5(Martin 2025). LIC-DSF integration approach However, since the LIC-DSF is under review, a revised LICDSF could include both climate and anti-inequality sub-modules. The anti-inequality module would track increases in the key types of spending needed to reduce inequality, their impact on financing needs, and their positive impacts on growth and revenue mobilization, and thereby on debt sustainability. As with the SRDSF climate module, such a module could provide projections over a 30-year horizon under two scenarios: an“extended standardized baseline” scenario based on default costs in a template, and a“customized scenario”, where users adjust costs to country-specific characteristics. The customized scenario would also allow users to adjust the financing terms of the inequality-related investments, providing scope to show the difference between financing with concessional debt, tax or grants. SRDSF module design To facilitate the task of integrating anti-inequality spending into the DSF, an anti-inequality module could be pre-populated with estimates of total likely spending as a proportion of GDP for different countries, based on the IMF and ILO desk estimates, to ensure comparability of goals(i.e. the SDGs for universal education, health and social protection). It could also be populated with multipliers based on the national relationships between additional spending, inequality, growth and budget revenue. The user could then replace these in“customized scenarios” with more precise national calculations when these are possible. Inequality Reduction and Debt Sustainability Analysis 9

For countries with existing cost estimates(available for virtually all LICs and MICs) and a fiscal incidence analysis(completed for 73 countries), the required work involves only i) inserting the costed amount of extra spending in the templates; ii) simulating the likely impact of spending and tax policies on inequality using the World Bank/CEQ tool; iii) inserting the higher growth and revenue forecasts derived from direct and indirect multipliers of spending; and iv) experimenting with different combinations of financing sources to see how extra spending can be financed sustainably. Implementation requirements and pilot testing Based on experience, this represents a surmountable amount of work for both BWIs and governments. However, to ensure full ownership by different parts of government, it would be ideal to“pilot” this work in a small number of countries, via workshops bringing together national staff with experts(both BWI and independent) to conduct these tasks, produce national reports, and assess the workload for building a“high case” scenario. The funding of such pilots would be in addition to current IMF and country budgets. Section 8: Conclusions and recommendations This paper has examined how IMF and World Bank Debt Sustainability Analyses can incorporate national inequality reduction efforts to achieve SDG 10. The post-COVID context of multiple crises and limited concessional funding makes this adaptation increasingly urgent for member states prioritizing spending while maintaining debt sustainability. In particular, it emphasizes the vulnerability of almost all countries to the emergency of extreme inequality and poverty, a vulnerability worsened by the COVID pandemic effects that continue to undermine growth and stability. The paper has mentioned previous efforts to include anti-poverty spending in debt sustainability analysis, which was done successfully by many countries during the period of the Millennium Development Goals. The paper has also presented building blocks to make such inclusion practical and straightforward and has highlighted optional areas in which more work is needed: 1. Country selection criteria: Benchmarks should be based on macro-critical inequality levels, country demand, and IMF loan types. 2. Global cost templates: DSF scenarios should be pre-populated using agreed estimates for education, health, and social protection. 3. National strategies: These should include regularly up­ dated, country-specific costings for customized national scenarios. 4. Methods to adopt: 1) Inequality impact methods should calculate how spending and policy measures can reduce Gini coefficients; 2) Growth multiplier methods should measure inequality reduction’s growth effects and education/health spending returns, plus include macro-level multipliers; 3) Revenue impact methods should trace the impact of growth on tax revenues. 5. Policy analysis gaps: Additional work is needed before fully integrating into DSAs models for how labor rights, access to finance, and land/property policies reduce inequality. With these building blocks, it would be straightforward for the BWIs to improve on the significant progress already made to integrate climate spending for both the LIC-DSF and the SRDSF by inserting an“anti-inequality” module. Implementation would then parallel existing climate modules while highlighting that climate and inequality should be addressed together for maximum impact. Given current challenges for international cooperation at the global level, practical approaches such as those presented by this paper are a key way forward to continue making progress on reducing inequalities. 10 Friedrich-Ebert-Stiftung e.V.

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Annex 1: Latest country net GINI coefficients (Source – World Bank PIP Platform) Economy Year 1 South Africa 2014 2 Haiti 2012 3 Namibia 2015 4 Botswana 2015 5 Eswatini 2016 6 Colombia 2023 7 Brazil 2023 8 Zambia 2022 9 Angola 2018 10 St. Lucia 2015 11 Mozambique 2019 12 Zimbabwe 2019 13 Kosovo 2021 14 Congo, Rep. 2011 15 Panama 2023 16 Honduras 2023 17 Nicaragua 2014 18 Costa Rica 2024 19 Comoros 2014 20 Guatemala 2023 21 Guyana 1998 22 Lesotho 2017 23 Venezuela, RB 2006 24 Congo, Dem. Rep. 2020 25 Ecuador 2023 26 Turkiye 2022 27 Paraguay 2023 28 South Sudan 2016 29 Grenada 2018 30 Rwanda 2016 31 Ghana 2016 32 Mexico 2022 33 Chile 2022 34 Central African Republic 2021 35 Uganda 2019 36 Madagascar 2012 37 Argentina 2023 38 Cabo Verde 2015 39 Cameroon 2021 40 Bolivia 2023 41 Papua New Guinea 2009 Gini 63.03 60.45 59.07 54.91 54.58 53.88 51.64 51.48 51.26 51.23 50.26 50.26 49.38 48.94 48.88 46.83 46.16 45.83 45.33 45.21 45.05 44.88 44.7 44.67 44.58 44.47 44.38 44.05 43.82 43.71 43.51 43.45 43.04 43.03 42.7 42.53 42.39 42.38 42.18 42.05 41.85 Economy 42 United States 43 Djibouti 44 Uruguay 45 Turkmenistan 46 Sao Tome and Principe 47 Malaysia 48 Peru 49 Tanzania 50 Trinidad and Tobago 51 Micronesia 52 Belize 53 Jamaica 54 El Salvador 55 Morocco 56 Philippines 57 Suriname 58 Tuvalu 59 Lao PDR 60 Gambia, The 61 Samoa 62 Kenya 63 Equatorial Guinea 64 Malawi 65 Dominican Republic 66 Bulgaria 67 Gabon 68 Israel 69 Togo 70 Sri Lanka 71 Burundi 72 Burkina Faso 73 Chad 74 Solomon Islands 75 Mauritius 76 Yemen, Rep. 77 Lithuania 78 West Bank and Gaza 79 Portugal 80 Senegal 81 Viet Nam 82 Iran, Islamic Rep. Year 2023 2017 2023 1998 2017 2021 2023 2018 1992 2013 2018 2021 2023 2013 2023 2022 2010 2018 2020 2013 2021 2022 2019 2023 2022 2017 2021 2021 2019 2020 2021 2022 2012 2017 2014 2022 2023 2022 2021 2022 2023 Gini 41.82 41.59 40.87 40.81 40.73 40.67 40.66 40.49 40.19 40.05 39.95 39.87 39.8 39.55 39.34 39.18 39.14 38.8 38.76 38.74 38.7 38.54 38.54 38.4 38.2 38.02 37.89 37.86 37.66 37.46 37.39 37.36 37.05 36.76 36.71 36.56 36.38 36.25 36.16 36.09 35.88 Inequality Reduction and Debt Sustainability Analysis 13

Economy 83 China 84 Sierra Leone 85 Mali 86 Marshall Islands 87 Cote d‘Ivoire 88 Lebanon 89 Liberia 90 Russian Federation 91 Nigeria 92 Qatar 93 Indonesia 94 Georgia 95 Malta 96 Uzbekistan 97 Benin 98 Australia 99 Montenegro 100 Sudan 101 Luxembourg 102 Barbados 103 Tajikistan 104 Switzerland 105 Tunisia 106 Latvia 107 Jordan 108 Italy 109 Spain 110 North Macedonia 111 Thailand 112 Guinea-Bissau 113 Bangladesh 114 Greece 115 Bosnia and Herzegovina 116 Korea, Rep. 117 Niger 118 Serbia 119 Germany 120 United Kingdom 121 Nauru 122 Japan 123 Vanuatu 124 Romania 125 Estonia 126 Seychelles 127 Mauritania 128 Sweden 129 Taiwan, China 130 Cyprus Year 2021 2018 2021 2019 2021 2022 2016 2021 2018 2017 2024 2023 2022 2023 2021 2018 2021 2014 2022 2016 2015 2021 2021 2022 2010 2022 2022 2019 2023 2021 2022 2022 2011 2021 2021 2022 2020 2021 2012 2020 2019 2022 2022 2018 2019 2022 2021 2022 Gini 35.74 35.69 35.65 35.48 35.28 35.28 35.27 35.14 35.13 35.13 34.89 34.76 34.59 34.54 34.4 34.33 34.3 34.24 34.09 34.07 34 33.82 33.72 33.68 33.66 33.65 33.58 33.51 33.49 33.44 33.37 33.37 33.03 32.91 32.86 32.8 32.43 32.41 32.36 32.34 32.32 32.3 32.25 32.13 32 31.61 31.57 31.53 14 Friedrich-Ebert-Stiftung e.V. Economy 131 Mongolia 132 France 133 Ethiopia 134 Austria 135 Fiji 136 Myanmar 137 Hungary 138 Croatia 139 Nepal 140 Ireland 141 Canada 142 Iraq 143 Guinea 144 Pakistan 145 Albania 146 Maldives 147 Denmark 148 Kazakhstan 149 Poland 150 Timor-Leste 151 Egypt, Arab Rep. 152 Bhutan 153 Finland 154 Kiribati 155 Algeria 156 Armenia 157 Tonga 158 Norway 159 Syrian Arab Rep. 160 Iceland 161 Azerbaijan 162 United Arab Emirates 163 Belgium 164 Kyrgyz Republic 165 Czechia 166 Moldova 167 Netherlands 168 Ukraine 169 India 170 Belarus 171 Slovenia 172 Slovak Republic Year 2022 2022 2021 2022 2019 2017 2022 2022 2022 2022 2020 2023 2018 2018 2020 2019 2022 2021 2022 2014 2021 2022 2022 2019 2011 2023 2021 2022 2022 2018 2005 2018 2022 2022 2022 2022 2021 2020 2022 2020 2022 2022 Gini 31.42 31.24 31.14 30.89 30.71 30.7 30.22 30.02 30.02 29.91 29.85 29.77 29.59 29.59 29.42 29.29 29.26 29.19 28.86 28.65 28.48 28.46 27.93 27.83 27.62 27.15 27.1 26.95 26.63 26.57 26.55 26.4 26.38 26.35 25.87 25.86 25.74 25.63 25.51 24.38 24.31 24.08

Annex 2: Integrating inequality shocks into DSAs This paper has not discussed in detail how to integrate“inequality shocks”(negative events that dramatically increase inequality) into DSAs. The COVID-19 pandemic exemplified such a shock and why a“pandemic stress test” – similar to the current SRDSF and LIC-DSF“natural disaster stress tests” – should be included in DSAs to assess future pandemic impacts on economic prospects. Given the pandemic’s massive negative impact on growth, budget revenue, exports and additional borrowing- plus its exacerbation of inequality and poverty) during 2020-21 – the case for including such stress tests needs no argument. The Independent Panel for Pandemic Preparedness and Response indicates we should take pandemic threats as seriously as climate change. Since most global analysts believe an“inequality and poverty shock” comparable to the COVID-19 pandemic is probable within the next decade, such shocks should be included in DSF templates, just as climate shocks appear in SRDSF(Martin 2025). Countries should simulate Covid-19 scale future shocks with larger spending responses from global South governments to provide greater social protection and support on education and health, thereby reducing long-term damage. Since the COVID impacted every country, this stress test should apply universally rather than limiting it to subgroups based on GDP/ budget revenue/exports impact. Based on the latest expert assessments of pandemic frequency – as well as existing examples that include comparable GDP and revenue impacts as stress tests in forecasting social sector financing prospects(see Hurley and Martin 2024) – stress tests could simulate comparable shocks occurring once per decade. Adaptation must extend beyond DSFs The IMF should integrate anti-inequality spending and policy measures into overall macroeconomic frameworks, ensuring that the“social spending floors” in IMF programs include social protection spending. The World Bank should base inequality and poverty assessments on anti-inequality and antipoverty spending plans, assisting countries to design strategies supported by policy-based loans. The message from revised DSAs with anti-inequality and climate modules should mirror the successful 2010-15 Millennium Development Goals approach: low- or no-cost financing plus multiplier effects can help achieve priority SDGs without triggering a debt crisis. However, including significant extra spending will initially increase debt ratios before positive multipliers materialize. To ensure SDGs are not sacrificed for debt sustainability, countries need minimal debt burdens to avoid increases that crowded out SDG spending over the past decade. This requires greater emphasis on the liquidity burden of debt service by making debt service/budget revenue the primary risk indicator, or by adding an indicator showing the ratio of debt service to climate and/or anti-inequality spending. The case for adapting the SRDSF and LIC-DSF to include anti-inequality spending and strategies is clear and feasible. The current LIC-DSF review should lead this reform, enhancing IMF and World Bank contributions to reducing poverty and inequality(SDGs 1 and 10) and to accelerating growth and enhancing macro stability. Inequality Reduction and Debt Sustainability Analysis 15

About the Author Matthew Martin is Executive Director of Development ­Finance International(DFI – www.development-finance.org), a non-profit consultancy helping countries of the global South to mobilize more top-quality financing(including debt relief) to spend on reducing inequality and poverty. Since 1994, DFI has helped more than 80 countries, as well as civil society organizations, parliaments, trade unions, inter­ national organizations and donor governments – through capa­city-building, analysis, research and advocacy support. Prior to establishing DFI, Mr. Martin worked at Oxford ­University, the World Bank and the Overseas Development Institute.

Inequality Reduction and Debt Sustainability Analysis This paper proposes a practical approach to incorporate national inequalityreduction efforts into the practice of debt sustainability analysis(DSA). It establishes why it would be beneficial to include inequality reduction into DSAs and how it would enhance IMF and World Bank contributions to reducing poverty and inequality(SDGs 1 and 10). It then demonstrates the feasibility of this proposal by identifying applicable countries, estimating spending needs and inequality impacts, and calculating multiplier effects on growth and tax revenues. With these building blocks, the case to the IMF and World Bank for adapting their sovereign debt frameworks(the SRDSF and LIC-DSF) to include anti-inequality spending and policies is clear. The current LIC-DSF review should lead this reform: enhancing contributions to reducing poverty and inequality, accelerating growth, and enhancing macro stability. Given current challenges for international cooperation at the global level, practical approaches such as these are a way to keep making progress on global goals. Further publications of the Friedrich-Ebert-Stiftung can be found here: ↗ fes.de