Abstract

The primary aim of the study is to provide updated estimates of the financing gap to attain universal coverage for social protection floors. This estimation encompasses 133 low- and middle-income countries, and includes five income security guarantees (for children, persons with severe disabilities, mothers of newborns, older persons and the unemployed), together with essential health care. Estimates show that, for low- and middle-income countries, the financing gap to achieve universal coverage of social protection floors is 3.3 per cent of GDP annually.¹ However, for low-income countries, the financing gap is an overwhelming 52.3 per cent of their GDP annually. In 2024, for low- and middle-income countries, the additional government spending needed to achieve universal social protection represents 10.6 per cent of their annual government expenditure, or 31.6 per cent of their social protection expenditure. However, for low-income countries, building social protection floors requires the mobilization of four times their annual government expenditure or nearly 28 times their social protection expenditure.

1  Of the 3.3 per cent of GDP, 2.0 per cent is required for essential health care and 1.3 per cent for the five key social protection cash benefits (child/family, disability, maternity, unemployment and old age).

About the authors

Umberto Cattaneo is a Public Finance Economist in the Universal Social Protection Department of the International Labour Organization (ILO) in Geneva. Umberto is currently overseeing the global, regional, and country-level ILO work on financing and fiscal space for social protection. Recently, he was among the main authors of the new major ILO Care at work report and the ground-breaking ILO-Gallup report on violence and harassment at work. Prior joining the ILO, Umberto worked for the Office of the Chief Economist for the Africa Region at the World Bank. He studied in several universities, including the School of Oriental and African Studies of the University of London, Universidad Carlos III de Madrid, Université Libre de Bruxelles and obtained his PhD in Economics from the University of Genoa in Italy.

Helmut Schwarzer is the Head of the Public Finance, Actuarial and Statistical Services Unit (PFACTS) of the ILO Universal Social Protection Department in Geneva since December 2021. He studied in Economics and Economic Development at the Universidade Federal do Paraná (Curitiba/Brazil) and obtained a Dr. rer. pol. in Economics from the Freie Universität Berlin (Germany). Previous positions were Researcher at the IPEA (Applied Economic Research Institute, Brasília), Secretary for Social Security (2003-2010) and for the Bolsa Familia programme (2015-16) both in Brazil, as well as Senior Specialist for Social Security at the ILO Social Protection Department in Geneva and at the ILO Country Office for Mexico.

Shahra Razavi is the Director of the Universal Social Protection Department of the International Labour Organization (ILO). She is a development economist, with more than 25 years of experience working on social policy, social protection, gender and development, and the political economy of care. She obtained her Bsc. from the London School of Economics and Political Science (LSE), and her MSc. and D.Phil./PhD from Oxford University. Before joining the ILO in 2020, she was Chief of the Research and Data Section at UN Women, and prior to that Senior Researcher at the United Nations Research Institute for Social Development (UNRISD).

Andrea Visentin recently completed an internship in public finance within the Universal Social Protection Department of the International Labour Organization (ILO) in Geneva. He obtained his Master of Science in Public Policy and Human Development with a specialization in social protection at the United Nations University in Maastricht, where he also served as a research assistant carrying out data collection and analysis on social protection issues.

Executive summary

This study provides global, regional and country-level estimates of the financing gap to achieve universal social protection, including universal access to five key social protection guarantees (that is, for children, persons with severe disabilities, mothers of newborns, older persons and the unemployed) and essential health care. The estimations cover 133 low- and-middle-income countries. It is important to underline that the methodology used to calculate the financing gap provides a broad global overview and cannot replace the fine-grained country-level costing and fiscal planning exercises that are urgently needed to guide national action in building social protection systems and floors.

In low- and middle-income countries, the financing gap to achieve universal social protection accounts for 3.3 per cent of GDP annually, with 2.0 per cent of GDP required for essential health care and 1.3 per cent for the five key social protection cash benefits. Of this, 0.6 per cent of GDP is for child benefits, 0.3 per cent for old-age pensions, 0.2 per cent for disability benefits, 0.2 per cent for unemployment benefits and 0.05 per cent for maternity benefits. In absolute terms, bridging this gap across all low- and middle-income countries requires an additional US$1.4 trillion per year. Most of these funds (60.1 per cent) are required for essential health care. The remaining portion is distributed among social protection cash benefits as follows: 17.8 per cent for child benefits; 8.3 per cent for old-age pensions; 7.1 per cent for disability benefits; 5.2 per cent for unemployment benefits; and 1.5 per cent for maternity benefits.

The global averages mask significant disparities among country income groups and regions. Among the former, low-income countries face the largest financing gap as a proportion of their GDP, amounting to 52.3 per cent, due to large social protection coverage gaps and low GDP in these countries. Despite the substantial absolute amounts involved, totalling US$308.5 billion, low-income countries represent just 22.3 per cent of the overall financing gap. The regional analysis shows that Africa faces the most substantial challenge in achieving universal coverage for social protection, with a financing gap of 17.6 per cent of the region's GDP per year, followed by low-and-middle-income countries in the Arab States (11.4 per cent), Latin America and the Caribbean (2.7 per cent), Asia and the Pacific (2.0 per cent) and Europe and Central Asia (1.9 per cent).

Across all regions and national income groups, two thirds of the additional government expenditure associated with universalizing old-age benefits is needed for women. In many countries, the combination of low female participation in employment and informal employment have resulted in the contributory capacity of women being lower compared to that of men. Despite the fact that some countries have partially offset such inequalities by introducing compensatory mechanisms, such as pension care credits for both women and men, or an extension of social protection floors guarantees, large gender gaps in pension coverage persist. The proportion of the financing gap in old-age pensions allocated to women rises with the level of economic development, with 58.9 per cent of the financing gap in old-age pensions attributed to women's pension coverage gaps in low-income countries, reaching up to 71.0 per cent in upper-middle-income countries.

The study presents the financing gap relative to general government expenditure and social protection expenditure, which is pivotal to assess countries’ capacity to close the financing gap. Globally, governments of low- and middle-income countries should progressively increase their social protection spending by 10.6 per cent of the current government expenditure. The expenditure increase would correspond to 31.6 per cent of their present social protection expenditure. In low-income countries, the financing gap is considerable, amounting to more than four times their government expenditure and a striking 28 times their actual social protection expenditure.

Despite widespread austerity, including cuts to social protection expenditure in many low- and-middle income countries, there is a spectrum of strategies for these countries to actively expand the fiscal space for social protection to close the financing gap. Domestic resource mobilization, including progressive taxation and social security contributions, are key sources for social protection, considering that building social protection systems and floors imply long-term commitments. It is critical that countries develop programmes to formalize employment, enterprises and economic transactions, as well as the institutions required to collect taxes and social contributions.

At the domestic level, considerable fiscal space to close the financing gap could be generated by progressively removing explicit fuel subsidies as well as implicit fuel subsidies that is, by introducing or increasing carbon pricing schemes in a way that charges for the environmental costs of carbon. Across all low- and middle-income countries, explicit fuel subsidies represent, on average, 1.2 per cent of GDP, while implicit fuel subsidies represent 9.8 per cent of GDP. These subsidies compare with a social protection floor financing gap of 3.3 per cent of GDP. While in lower- and upper-middle-income countries investing revenues from the progressive removal of explicit and implicit fuel subsidies would more than cover existing the financing gap, the potential for closing the financing gap through fuel subsidies finance in low-income countries is almost non-existent.

Sovereign debt cannot be conceived as permanent source of finance for closing the financing gap. However, its management could unlock resources to expand fiscal space for social protection. In many low-and-middle income countries, if a portion of government debt could be renegotiated with lower interest rates, this could free a sizable share of financial resources to devote to social protection.

Official development assistance becomes necessary, especially in the case of low-income countries, where the financing gap accounts for 52.3 per cent of GDP. However, to close such a gap, the current official development assistance directed to low-income countries would need to be more than tripled and fully allocated to social protection. It should be noticed that official development assistance has remained at levels below the internationally agreed goal of 0.7 per cent of gross national income of the high-income countries (in 2023, the average rate was 0.37 per cent). According to data collected by the OECD Development Assistance Committee, in 2023, only five countries met this goal – Denmark, Germany, Luxembourg, Norway, and Sweden. Despite some increase in the share of official development assistance allocated to employment and social protection due to the pandemic, official development assistance allocations to social protection still remain very low. Collaboration between the ILO, international financial institutions and other United Nations entities is of utmost importance to expand the fiscal space for social protection in low-income countries.

Introduction

In September 2015, global leaders adopted the 2030 Agenda for Sustainable Development, setting ambitious goals for advancing sustainable development and ensuring social, economic and environmental justice by 2030. At the heart of this Agenda is the pivotal role of social protection, captured under Sustainable Development Goal (SDG) target 1.3 on ending poverty, and SDG target 3.8 on achieving universal health coverage. Social protection also contributes to ensuring gender equality (SDG 5), creating decent work and driving economic growth (SDG 8), reducing multiple and intersecting inequalities (SDG 10), and promoting peaceful and inclusive societies and effective, accountable and inclusive institutions at all levels (SDG 16). As a key enabler of social protection, SDG target 1.a calls for and tracks the mobilization of resources from a variety of sources to end poverty and specifically considers government spending on education, health and social protection (SDG indicator 1.a.2). SDG target 10.b highlights the need for enhanced official development assistance in countries where domestic resources are not sufficient to ensure adequate investments in social protection.

The ILO’s two-dimensional strategy for the extension of social security provides practical guidance for countries to achieve universal social protection (ILO 2012). The Social Protection Floors Recommendation, 2012 (No. 202) affirms that all countries should give priority to establishing national social protection floors as a fundamental element of their social security systems. To do so, countries are encouraged to pursue a two-dimensional strategy to guarantee at least basic levels of income security and access to essential healthcare for all (the “horizontal dimension”). They are also encouraged to incrementally secure higher levels of protection, guided by the Social Security (Minimum Standards) Convention, 1952 (No. 102) and the more advanced standards, for as many persons as possible and as soon as possible (the “vertical dimension”).

Responding to the growing involvement of multiple international actors in this field, including United Nations bodies, international financial institutions and other international organizations, various initiatives have been established with the aim of streamlining policy guidance and development cooperation in the area of social protection, so as to improve policy coherence, ensure coordination and avoid duplication. The Social Protection Inter-Agency Cooperation Board, created in 2012, and the Global Partnership for Universal Social Protection to Achieve the Sustainable Development Goals (USP2030), created in 2016, which are both co-chaired by the ILO and the World Bank, have contributed to greater policy coherence in the field of social protection. USP2030 promotes five core principles to accelerate action towards universal social protection: protection throughout the life cycle; universal coverage; national ownership; sustainable and equitable financing; and participation and social dialogue.

However, despite these commitments, more than half of the world’s population remains without any form of social protection coverage. The situation is worse in low-income countries, where, for example, in 2020 only 23 per cent of the elderly were covered by pensions (global average: 77 per cent), less than 10 per cent of children aged under 15 years were covered by statutory social protection programmes (global average: 26 per cent), and less than 7.8 per cent of vulnerable persons received cash benefits (global average: 28.9 per cent) (ILO 2021a).

Under-investment in social protection continues to be one of the main reasons for these low coverage rates. Public expenditure on social protection (excluding healthcare) was, on average, 13.0 per cent of GDP worldwide (in 2023). This average, however, hides enormous inequalities. While high-income countries spend 16.4 per cent of GDP on social protection annually, upper-middle-income countries spend half of this amount (8.6 per cent), lower-middle-income countries only spend one quarter (4.1 per cent) and low-income countries less than one sixteenth (0.7 per cent of GDP). Despite this bleak backdrop, it is encouraging to see that at least 42 out of 133 low- and middle-income countries have achieved universal social protection coverage for at least one social protection benefit (among child, disability, maternity, old-age and essential health care benefits) (ILO 2020a).¹

To support national level deliberations, fine-grained calculations and policy action, this paper provides a bird’s-eye-view estimation of the financing gap to ensure national social protection floors. It provides estimations of how much more each country needs to spend to ensure that, over the life cycle, all persons in need have effective access to at least essential healthcare and basic income security. Despite the importance that is often attached to such global estimations, a comprehensive analysis of the financing gap disaggregated by social protection benefit and country is lacking. This working paper therefore aims to fill this knowledge gap by providing global, regional and country level estimates of the financing gap to achieve universal social protection, including universal access to essential health care, which is one of the four key guarantees of national social protection floors identified by Recommendation No. 202. The estimations cover 133 low- and middle-income countries.

It is important to underline that the methodology used in this paper, which provides a broad global overview cannot replace the fine-grained country level costing and fiscal planning exercises that are urgently needed to guide national action in building social protection systems and floors. National social dialogues led by governments and involving the participation of workers’ and employers’ organizations as well as other stakeholders, are critical in this regard. These national social dialogues should guide the formulation of social protection policies and strategies, including the costing and financing options, to provide a clear road map for extending social protection (both horizontally and vertically) and increase the coherence and comprehensiveness of national social protection systems.

Section 1 of this paper provides a description of the methodology used to calculate the financing gap, including the beneficiary populations and the level of benefits. Section 2 presents new estimates of the financing gap expressed as a percentage of GDP, general government expenditure and social protection expenditure. Lastly, section 3 discusses potential fiscal space options for closing the social protection financing gap and concrete actions to chart a way forward.

1  Universal coverage for essential health care is assumed when the universal health coverage (UHC) service coverage index (SDG indicator 3.8.1) has a value greater or equal to 80.

Objectives and methodology

1.1. Revised ILO methodology

The current study builds on the previous two editions of the financing gap estimations (ILO 2020b) and provides a revised methodology for computing the financing gap for the five social protection income security guarantees – that is, children, persons with severe disabilities, mothers of newborns, older persons and the unemployed. This methodology differs from the one used in the previous two editions and, therefore, the results are not comparable. To calculate the financing gap in essential health care, however, a similar methodology to ILO 2020b has been used.

The revised methodology for the five social protection income security guarantees offers detailed country level data, including the possibility of disaggregating the estimates by social protection benefit. The newly developed approach employs selected sub-indicators contained in SDG indicator 1.3.1, which correspond to the proportion of the population receiving cash benefits from contributory and non-contributory social protection programmes (UN 2022). It then estimates the number of persons who are in need but who are not receiving any benefit and calculates the cost of covering those persons with a basic income guarantee (floor level benefit).

As shown in figure 1, the methodology employs two distinct approaches, one for the five income security guarantees and another for essential health care. The rationale for the distinct approaches stems from the input data used for estimations. The methodology for the five income security guarantees utilizes SDG indicator 1.3.1, which measures the proportion of the population covered by each social protection floor guarantee. This approach allows us to estimate the number of individuals who remain uncovered by social protection by calculating the difference between those who are receiving the benefit and all those who are eligible and should be covered.

As mandated by Recommendation No. 202, national social protection floors should also include access to nationally defined sets of goods and services, constituting essential health care, including maternity care, that meet the criteria of availability, accessibility, acceptability and quality. While the series of sub-indicators under SDG indicator 1.3.1 do not monitor the coverage of essential healthcare, Stenberg et al. (2017), in collaboration with the World Health Organization, identified the sets of SDG indicators that contribute to the broader attainment of universal health coverage. In this paper, their estimations are used for calculating the financing gap in essential health care.

Figure 1. Structure of the methodology used for calculating the financing for achieving universal social protection coverage

Source: Authors’ elaboration.

Note: Some targets under SDG 2 (zero hunger), 6 (clean water and sanitation) and 7 (affordable clean energy) are considered in the financing gap for essential health care as per the methodology presented in Stenberg et al. 2017.

1.2. Financing gap to achieve universal coverage of the five social protection income guarantees

The financing gap for the five social protection income guarantees (for children, persons with severe disabilities, mothers of newborns, older persons and the unemployed) refers to the monetary resources needed to provide a basic social security guarantee to those who should be eligible for a benefit but are not currently receiving it. In essence, it represents the additional expenditure needed to achieve 100 per cent social protection coverage, as indicated by SDG indicator 1.3.1 on effective social protection coverage (ILO 2020a). The formulation details appear below.

In equation 1, FGij corresponds to the financing gap in country i for the jth social protection guarantee corresponding to one of the following population groups: children, persons with severe disabilities, mothers of newborns, older persons and the unemployed. The financing gap in the year 2024 FGij is obtained by multiplying the beneficiary population group Popij in country i for guarantee j times PLi, the national poverty line in country i and the proportion of those who are not covered by such guarantee (1-COVij).

FGij=Popij∙PLi∙(1-COVij) [1]

The financing gap in country i for the five social security income guarantees is calculated in equation 2 and corresponds to the total monetary efforts that is needed to ensure universal coverage for the five areas.

FGi=∑j=15FGij [2]

A key initial decision involves defining the beneficiary population and specifying the type and size of benefits that would be granted to the five different beneficiary groups.

1.2.1. Beneficiary population

The beneficiary population group considered in the financing gap estimations reflects those included under SDG indicator 1.3.1. For child benefits, the financing gap is calculated for persons aged from 0 to 14 years, who are currently not receiving any child benefits. In the previous edition of the financing gap estimations, the analysis considered only children aged between 0 and 5 years. In the present study, a broader age group is chosen in line with the Minimum Age Convention, 1973 (No. 138), which establishes 15 years as the minimum age for admission to employment. It should be noted that, due to the continued vulnerability and dependency of children, especially in the context of access to education and transition into the workforce, and in order to align with the Convention on the Rights of the Child, a broader age range is recommended covering all those who are below 18 years (ILO and UNICEF 2023). However, to avoid double counting with the unemployed and with mothers of newborns, the defined age group for child benefits for this revision of the financing gap has been maintained at 0 to 14 years (ILO 2023b).

For disability benefits, the financing gap considers persons with severe disabilities currently not receiving any benefit for their condition. For old-age pensions, the financing gap is calculated based on the number of persons aged 65 years and over who are not receiving any type of old-age pensions. For maternity benefits, the financing gap is based on the number of mothers of newborns not receiving any maternity benefit, using the number of live births as a proxy for identifying mothers. Finally, for unemployment benefits, the financing gap is based on the number of persons who are unemployed but not covered by unemployment benefits. Data sources for each beneficiary population are indicated in table 1.

Table 1. Data sources for beneficiary population

Beneficiary population

Target population

Children

Number of children aged between 0 and 14 years in 2024, United Nations, Department of Economic and Social Affairs, World Population Prospects 2022 (United Nations, Department of Economic and Social Affairs, Population Division 2022)

Persons living with severe disabilities

Persons living with severe disabilities in 2024, World Health Organization, World Report on Disability, 2011 and United Nations, Department of Economic and Social Affairs, World Population Prospects (2022)

Persons in old-age

Persons aged 65 years and above in 2024, United Nations, Department of Economic and Social Affairs, World Population Prospects (2022)

Mothers of newborns

Number of live births in 2024, United Nations, Department of Economic and Social Affairs, World Population Prospects (2022)

Unemployment

Number of unemployed persons, ILO modelled estimates, ILOSTAT (2024)

Source: Authors’ elaboration.

1.3. Benefit level

As mandated by Recommendation No. 202, national social protection floors should comprise basic social security guarantees to all residents and all children, and should allow life in dignity. To identify minimum levels of income that correspond to such floors, the monetary value of a set of necessary goods and services, national poverty lines, income thresholds for social assistance or other comparable thresholds established by national law or practice should be taken into consideration. This approach ensures that the social protection floors established at the national level prevent persons from falling into poverty and empower those who are poor to escape poverty (ILO, UNDP and Global South-South Development Academy 2011).

In light of Recommendation No. 202, this study employs national poverty lines as the basis for benefit calculation used for estimating the financing gap for social protection floors. National poverty lines in local currency units were gathered from official sources in 133 developing countries. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographical areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets. Since social protection effective coverage cannot be systematically disaggregated to reflect such differences, when both lower and upper poverty lines were accessible, the upper poverty line was selected.

The majority of countries surveyed adopted absolute national poverty lines, which directly identify a basket of basic needs and determine the poverty threshold as the cost of that basket (Aprea and Raitano 2023). The basic needs considered in the majority of countries refer to food, adequate housing, and the affordability of decent clothing and main social activities. However, in a few countries, such as Bosnia and Herzegovina, Mauritius and Türkiye, relative poverty lines were employed. These relative lines define poverty thresholds based on the living standards of specific populations, such as median household income (Aprea and Raitano 2023). For certain countries, where poverty lines were not nationally defined, minimum wages (for example, Cameroon) or social assistance cash benefit levels (for example, China) were used.

National poverty lines must be adjusted for inflation to remain constant in real terms, thus allowing for meaningful estimates of the financing gap in 2024. In this study, the poverty lines were adjusted to 2024 prices using the IMF Consumer Price Index (IMF 2023a). National poverty lines were also transformed into current United States (US) dollars using the United Nations exchange rate for January 2024.¹

For each social protection income guarantee included in the financing gap calculations, a specific benefit level is defined (table 2). For children, the cash transfer benefit is defined as 25 per cent of the national poverty line – a lower percentage is applied to children compared with adult household members to reflect differences in consumption levels (Ortiz et al. 2017; OECD 2011; ILO 2020b). For mothers of newborns, the cash benefit is set at 100 per cent of the national poverty line, for a duration of four months around childbirth to give protection during the critical period when mothers and newborns are most vulnerable. For persons with severe disabilities and in old-age, the amount of the benefit is set at 100 per cent of the national poverty line (ILO 2020b). For the unemployed, the social protection benefit is also set at 100 per cent of the national poverty line for 26 weeks, as mandated by Article 14 of Convention No. 102. The financing gap in the five social protection guarantees includes a 5 per cent administrative cost, given that non-contributory schemes usually entail high initial set-up costs and the need to procure assets to support delivery (ILO 2020b).

Table 2. Level of benefit by social protection guarantee

Guarantee

Benefit level

Children (0-14 years)

25% of national poverty line for one year

Mothers of newborns

100% of national poverty line for four months

Unemployed

100% of national poverty line for 26 weeks

Older persons (65+ years)

100% of national poverty line for one year

Persons with a severe disability

100% of national poverty line for one year

Source: Authors’ elaboration.

1.4. Financing gap for essential health care

The financing gap for essential health care is based on WHO estimates (Stenberg et al. 2017), which present the per capita costs of reaching the SDG health targets for 67 countries, representing 95 per cent of the total population in low- and middle-income countries. For the remaining 66 countries, the per capita cost of achieving the SDG health targets is estimated using the population weighted averages by national income group based on the 67 countries with available data.

The WHO cost model accounts for necessary investments to meet basic requirements across the health system, including workforce, health facility availability and diagnostic equipment. The costing model considers the provision of 187 specific interventions, ranging from preventive care (such as iron and folic acid for pregnant women and outreach services to high-risk populations for HIV/AIDS prevention) to curative care (for example, treatment for breast cancer and medication to improve mental health). Recognizing the health sector’s interconnectedness with other sectors, Stenberg et al. (2017) included in the per capita cost of universalizing health care the cost of improving access to water, sanitation, hygiene and clean cooking fuels, which directly impact health but are not primarily funded by the health government budget. In addition, the cost of strengthening health system institutions is also factored in, including governance activities such as audits, licensing, provider inspections and service outsourcing. The methodology was shaped by a thorough consultation and review process carried out by the WHO, including inputs from country planning experts and international experts and academics.

Stenberg et al. (2017) report the per capita costs in 2014 US dollars. In order to express the estimations in 2024 prices, the GDP deflator from the IMF World Economic Outlook (IMF 2023a) was used. This is in line with the guidance from Turner et al. (2019) and WHO (2003). The GDP implicit price deflator is the most appropriate measure of inflation, as it closely reflects the general price level of the resources used by healthcare interventions. ²

1  See Annex, table A3, for country-level data.

2  Countries exhibiting a GDP deflator factor from 2014 to 2024 exceeding the 90th percentile had their value adjusted to meet the 90th percentile threshold.

Financing gap estimates

This section presents the results of the financing gap analysis based on the methodology presented in section 1. The financing gap is measured in absolute terms and expressed in billions of US dollars at 2024 prices, as well as in relative terms as percentage of GDP. In addition, the financing gap is compared to the actual level of government expenditure and to expenditure on social protection.

In low- and middle-income countries, the financing gap to achieve universal social protection equals 3.3 per cent of GDP annually (figure 2 and table 3), with 2.0 per cent of GDP required for essential health care and 1.3 per cent for the five key social protection cash benefits. Of this, 0.6 per cent of GDP is for child benefits, 0.3 per cent for old-age pensions, 0.2 per cent for disability benefits, 0.2 per cent for unemployment benefits and 0.05 per cent for maternity benefits. In absolute terms, bridging this gap across all low- and middle-income countries requires an additional US$1.4 trillion per year (table 4). Most of these funds (60.1 per cent) are required for essential health care. The remaining portion is distributed among social protection cash benefits as follows: 17.8 per cent for child benefits; 8.3 per cent for old-age pensions; 7.1 per cent for disability benefits; 5.2 per cent for unemployment benefits; and 1.5 per cent for maternity benefits.

Figure 2. Financing gap for achieving universal social protection coverage per year, as a percentage of GDP, by social protection benefit, by national income group, and by region, 2024

Source: Authors’ elaboration.

Note: See Annex, table A1 for country-level data.

Table 3. Financing gap for achieving universal social protection coverage per year, as a percentage of GDP, by social protection benefit, by national income group, and by region, 2024

Social protection (including essential health care)

Essential health care

Five social protection cash benefits

Social protection cash benefits

Children

Disability

Maternity

Old-age

Unemployment

Low- and middle-income

3.3

2.0

1.3

0.6

0.2

0.05

0.3

0.2

Low-income

52.3

32.5

19.8

10.1

3.0

1.1

3.3

2.3

Lower-middle-income

6.9

4.6

2.3

1.0

0.5

0.1

0.5

0.2

Upper-middle- income

1.4

0.7

0.7

0.3

0.1

0.02

0.2

0.1

Africa

17.6

11.4

6.2

3.2

1.1

0.3

0.9

0.7

Arab States

11.4

6.9

4.5

1.7

0.4

0.3

1.2

0.9

Asia and the Pacific

2.0

1.2

0.8

0.3

0.2

0.02

0.2

0.1

Europe and Central Asia

1.9

1.2

0.6

0.3

0.1

0.02

0.1

0.2

Latin America and the Caribbean

2.7

1.0

1.7

0.7

0.3

0.1

0.3

0.3

Source: Authors’ elaboration.

Note: See Annex, table A1 for country-level data.

Table 4. Financing gap for achieving universal social protection coverage per year, in US$ billion, by social protection benefit, by national income group, and by region, 2024

Social protection (including essential health care)

Essential health care

Five social protection cash benefits

Social protection cash benefits

Children

Disability

Maternity

Old-age

Unemployment

Low- and middle-income

1 385.7

833.4

552.3

246.4

98.1

20.5

115.0

72.2

Low-income

308.5

192.0

116.6

59.6

17.8

6.3

19.6

13.3

Lower-middle-income

616.6

410.4

206.2

88.8

41.6

7.5

46.6

21.8

Upper-middle-income

460.6

231.1

229.5

98.1

38.7

6.7

48.8

37.2

Africa

525.6

339.7

186.0

95.3

32.4

8.6

28.4

21.3

Arab States

45.2

27.5

17.8

6.7

1.6

1.1

4.8

3.5

Asia and the Pacific

554.3

345.1

209.2

86.5

43.2

5.5

54.3

19.7

Europe and Central Asia

83.4

54.6

28.8

11.6

2.7

0.7

6.5

7.2

Latin America and the Caribbean

177.1

66.6

110.6

46.3

18.2

4.6

21.1

20.4

Source: Authors’ elaboration

2.1. National income groups and regions

The global averages described in the previous section mask significant disparities among country income groups. Among these groups, low-income countries face the largest financing gap as a proportion of their GDP, amounting to 52.3 per cent, due to large social protection coverage gaps and low GDP in these countries. Of this gap, 19.8 per cent of GDP is needed for social protection cash benefits, with 10.1 per cent for child benefits, 3.3 per cent for old-age pensions, 3.0 per cent for disability benefits, 2.3 per cent for unemployment benefits and 1.1 per cent for maternity benefits. The remaining 32.5 per cent of GDP is needed for ensuring universal coverage of essential health care. Despite the substantial absolute amounts involved, totalling US$308.5 billion, low-income countries represent just 22.3 per cent of the overall financing gap (figure 3). To close such a gap, the current official development assistance directed to low-income countries would need to be more than tripled and fully allocated to social protection (OECD 2024b).

By contrast, lower-middle income countries account for the largest portion of the financing gap, representing 44.5 per cent of the total or US$616.6 billion. Relative to their GDP, however, the financing gap represents 6.9 per cent. Of this, 4.6 per cent of GDP is for essential health care, and 2.3 per cent for the five social protection income guarantees, with children absorbing 1.0 per cent. Upper-middle-income countries are responsible for the remaining 33.2 per cent or US$460.6 billion of the financing gap, where it accounts for only 1.4 per cent of their GDP, distributed as follows: 0.7 per cent for essential health care and 0.7 per cent for social protection cash benefits, with children, like in other income groups, needing the largest share of GDP among the five cash benefits.

Across regions, Africa faces the most substantial challenge in achieving universal coverage for social protection, with a financing gap of 17.6 per cent of the region’s GDP per year. This breaks down to 6.2 per cent of GDP for the five social protection income guarantees, including 3.2 per cent for child benefits, 1.1 per cent for disability benefits and 0.9 per cent for old-age pensions, as well as 0.7 per cent for unemployment, and 0.3 per cent for maternity benefits, along with 11.4 per cent for essential health care. In the region, the financing gap ranges between 1.8 per cent of GDP in Mauritius to 380.0 per cent of GDP in Sudan. This discrepancy stems partly from Mauritius’s robust economic recovery post-pandemic and subdued inflationary pressures in 2023 (IMF 2024). In stark contrast, Sudan faces the largest financing gap across all low- and middle-income countries, which is attributed to continuous conflicts, disease outbreaks, economic and political turmoil and climate crises (USA for UNHCR 2024; Famine Early Warning Systems Network 2023). Between 2016 and 2024, Sudan’s GDP plummeted by more than half, compounded with significantly above-average prices for essential food and non-food items (Famine Early Warning Systems Network 2023). Consequently, the Sudanese national poverty line in 2024, in US dollars, is the second highest in the African region at US$5,599 per year (Bacil and Silva 2020), compared with a regional average of US$982.7. These challenges, though to a lesser extent, are common in many African countries and result in Africa bearing 37.9 per cent (figure 3) or US$525.6 billion of the total financing gap to achieve universal social protection coverage in low- and middle-income countries.

The low- and middle-income countries in the Arab States have a financing gap as a proportion of GDP of 11.4 per cent, which is slightly lower than that of Africa. However, it should be noted that this figure only represents 3.3 per cent of the global gap, or, in absolute terms, US$45.2 billion. The majority of the resources are required to address the financing gap in essential health care, amounting to 6.9 per cent of GDP. Nevertheless, 4.5 per cent of the GDP of the low- and middle-income countries in the Arab States region is necessary for the five key social protection cash benefits, with more than one third of this allocation directed towards child benefits (1.7 per cent of GDP), followed by old-age pensions (1.2 per cent), unemployment benefits (0.9 per cent), disability benefits (0.4 per cent) and maternity benefits (0.3 per cent). Looking at individual countries, the financing gap as a percentage of GDP in the Arab States region varies significantly, ranging from as low as 4.7 per cent in Iraq to as high as 104.0 per cent of GDP in Yemen. The latter alarming figure is attributable to a combination of factors. Despite cautious optimism about the ongoing peace process the economic conditions in Yemen have remained precarious and call for urgent external support (IMF 2023b).

In Latin America and the Caribbean, the financing gap constitutes a notably lower proportion of GDP than in the Arab States or Africa, amounting to 2.7 per cent of GDP, with 1.0 per cent specifically designated for essential health care. This result is partly driven by Brazil, which has the largest GDP in the region,¹ and the fifth lowest financing gap (1.4 per cent of GDP) across all low- and-middle income countries.² However, Brazil still needs to allocate 0.8 per cent of its GDP to social protection cash benefits to achieve a universal social protection floor. At the other end of the spectrum, Haiti, the poorest country of the continent, faces a substantial financing gap, amounting to 45.5 per cent of its GDP per year, with 34.0 per cent attributed to the five key social protection cash benefits and 11.5 per cent to essential health care.

Asia and the Pacific follow with a financing gap of 2.0 per cent of the regional GDP. Nonetheless, it is crucial to note that this region bears the largest part of the global financing gap for low- and middle-income countries, accounting for 40.0 per cent or US$554.3 billion. Of this value, US$135.3 billion (almost one fourth) is required to ensure universal social protection in India, US$111.7 billion (almost one fifth) in China, and US$76.2 billion (almost one seventh) in Pakistan. Although China has achieved the universalization of old-age benefits, the primary driver for the financing gap remains the coverage gaps in child benefits, necessitating 0.1 per cent of Chinese GDP to achieve universality for all children below 15 years.

Low- and middle-income countries in Europe and Central Asia exhibit the lowest financing gap as a proportion of GDP across regions at 1.9 per cent of their GDP, with 1.2 per cent for essential health care and 0.6 per cent for the five social protection cash benefits.³ Despite population aging being one of the region’s main challenges, child benefits still require the largest portion of the regional GDP, accounting for 0.3 per cent, surpassing other cash benefits. Following child benefits, unemployment benefits require 0.2 per cent of the regional GDP, old age pensions and disability benefits 0.1 per cent each, while maternity benefits require only 0.02 per cent. At the country level, the analysis reveals that the financing gap ranges from 0.8 per cent of GDP in the Russian Federation to 10.1 per cent in Tajikistan. In the latter, 7.3 per cent of GDP is required to universalize essential health care, while the remaining portion needs to be dedicated to child benefits (2.0 per cent), as well as disability benefits (0.4 per cent), unemployment benefits (0.2 per cent) and maternity benefits (0.08 per cent).

Figure 3. Distribution of the annual financing gap for achieving universal social protection coverage, by national income group, and by region, 2024

Source: Authors’ elaboration.

2.2. The financing gap in old-age, by sex

Stereotypes about gender roles have resulted in women spending a disproportionate amount of time doing unpaid care work at the expense of time spent in paid work, and women being overrepresented in low-paid jobs, with poor working conditions, involuntary part-time arrangements, and limited prospects for career advancement (Cattaneo, Beghini and Pozzan 2022). All these factors are exacerbated across low- and middle-income countries and compound with a female informal employment rate ranging between 50.6 per cent in upper-middle income countries and 86.7 per cent in low-income countries (ILO 2023a). In many countries, the combination of low female participation in employment and informal employment have resulted in women’s contributory capacity being lower compared to that of men (Cattaneo, Beghini and Pozzan 2022). As a result, some countries have partially offset such inequalities by introducing compensatory mechanisms, such as pension care credits for both women and men, or an extension of social protection floors guarantees (ILO 2021b).

In the absence of other forms of pension coverage, non-contributory pensions, particularly in low- and lower-middle income countries, help to bridge gender gaps in coverage. While these pensions can go a long way towards securing women’s (and men’s) access to basic protection, benefit levels are often too low to allow beneficiaries to fully meet their needs. Hence, the provision of non-contributory pensions should be accompanied by efforts to support women’s increased participation in contributory schemes (ILO 2021b). This study assesses the cost of achieving universal old-age pension coverage by extending a social protection floor to all older persons currently without old-age pensions. As seen in the previous section, this corresponds to 0.3 per cent of the GDP of 133 low- and middle-income countries or US$115.0 billion. For 72 countries, the financing gap for old-age pensions can be disaggregated by sex, based on the availability of sex-disaggregated coverage rates.

The findings from figure 4 reveal that women are currently bearing the brunt of the financing gap for old-age benefits. Two-thirds of the total additional government expenditure required to achieve universal coverage for old-age pensions would be allocated to paying benefits to women. Across all regions and national income groups, a significant portion of the expenses associated with universalizing old-age benefits is needed for women. This proportion tends to rise with the level of economic development, with 58.9 per cent of the financing gap in old-age pensions attributed to women’s coverage gaps in low-income countries, reaching up to 71.0 per cent in upper-middle-income countries. Regionally, despite Europe and Central Asia exhibiting the lowest financing gap for old-age pensions, an astonishing 97.2 per cent of this amount is required for ensuring benefits to women in old age not currently receiving any pension. Latin America and the Caribbean follow with 77.6 per cent, Africa with 64.7 per cent, and Asia and the Pacific with 57.6 per cent.

Figure 4. Distribution of the annual financing gap in old-age, by sex, by national income group, and by region, 2024

Source: Authors’ elaboration.

Note: 72 countries representing 86.1 per cent of the GDP of low- and middle-income countries.

2.3. The financing gap as a proportion of government and social protection expenditure

To evaluate the capacity of countries to address existing funding shortfalls to ensure universal social protection coverage, it is useful to consider the financing gap relative to domestic general government expenditure and social protection expenditure. Globally, governments of low- and middle-income countries should progressively increase their social protection spending by 10.6 per cent of the current government expenditure (table 5). The expenditure increase would correspond to 31.6 per cent of their present social protection expenditure.

In low-income countries, the financing gap is considerable, amounting to 310.0 per cent of government expenditure and a striking 2,737.3 per cent of their actual social protection expenditure. Lower-middle-income countries also confront significant gaps, where achieving universal social protection necessitates an additional 26.9 per cent of government spending and more than double the present levels of social protection expenditure. Given the higher levels of existing social protection coverage and public expenditure in upper-middle-income countries, the financing gap constitutes 4.3 per cent of their government expenditure and 11.9 per cent in terms of their social protection expenditure. Regionally, Africa needs to quadruple its social protection spending, whereas the Arab States must increase theirs approximately 2.5 times to close the financing gap. When measured as a proportion of government spending, the financing gap constitutes 70.1 per cent in Africa and 28.1 per cent in the Arab States.

Figure 5 further complements these findings by showing the desired level of government and social protection expenditure that would be needed to ensure universal coverage for social protection floors. Across all low-and-middle income countries, universalizing social protection floors would require expenditure on social protection cash benefits to increase from 7.5 per cent to 8.8 per cent of GDP and health expenditure from 2.9 per cent to 4.9 per cent of GDP. This would mean increasing total government expenditure from the current 31.3 per cent of GDP to a new desired level of 34.6 per cent.

Table 5. Financing gap as a percentage of GDP, general government expenditure and social protection expenditure, per year, by national income group, and by region, 2024

% of GDP

% of general government expenditure

% of social protection expenditure

Low- and middle-income

3.3

10.6

31.6

Low-income

52.3

310.0

2 737.3

Lower-middle-income

6.9

26.9

120.7

Upper-middle-income

1.4

4.3

11.9

Africa

17.6

70.1

306.2

Arab States

11.4

28.1

158.1

Asia and the Pacific

2.0

6.6

22.4

Europe and Central Asia

1.9

5.4

11.5

Latin America and the Caribbean

2.7

7.7

18.0

Source: Authors’ elaboration.

Note: See Annex, table A1 for country-level data on the financing gap as percentage of GDP and table A2 for country-level data on the financing gap as percentage of general government expenditure.

Figure 5. Government expenditure and financing gap for achieving universal social protection coverage, as a percentage of GDP, per year, by type of expenditure, by national income group, and by region, 2024

Source: Authors’ elaboration.

1  US$2.2 trillion in 2024.

2  The lowest financing gap as a percentage of GDP is registered in Palau (0.6 per cent), followed by China (0.6 per cent), the Russian Federation (0.8 per cent) and Kazakhstan (1.4 per cent).

3  Due to rounding, the financing gap for essential health care and the financing gap for the five social protection income guarantees do not sum to 1.9 per cent of GDP.

Strategies to close the financing gap

Numerous countries are grappling with bridging the financing gap necessary for achieving universal social protection. Estimating the magnitude of the financing gap, as this ILO working-paper has done, is undoubtedly only an initial step for conducting a comprehensive cost analysis of social protection floor guarantees based on national dialogue, with government, employers and workers, as well as other stakeholders. The next important step is for countries to carefully examine their specific economic and financial landscape and all the fiscal space strategies that are available and suited to their circumstances, to be able to bridge the financing gap in the current context.

With signs of an inflation pick-up in the United States in the early months of 2021 and rising inflation rates in the European Union in 2022, central banks of major economies took bold and frequent actions, particularly by hiking interest rates (ILO 2024). This has led to higher borrowing costs, which, combined with the substantial sovereign debt accumulated during the COVID-19 pandemic, have increased the debt servicing costs of many developing countries that were already facing unsustainable debt burdens even on the eve of the pandemic (ILO 2024; UNCTAD 2024). As a result, numerous countries now find their interest payments exceeding their expenditure on social protection. In Latin America and the Caribbean, interest payment on public debt represents 54 per cent of the total expenditure on social protection, 64 per cent of the expenditure on health and 63 per cent of the expenditure on education (ECLAC 2023). To avoid accumulating further debt, 134 governments began to cut public spending already in 2021, a trend expected to continue until at least 2025. A significant aspect of this budget tightening has involved targeting and rationalizing spending on social protection to curtail overall government expenditure, considered by 120 governments (Ortiz and Cummings 2022). Despite this challenging backdrop, there is a spectrum of strategies for countries to actively create and extend fiscal space for social protection (Ortiz et al. 2019).

However, there is no one-size-fits-all approach for extending fiscal space for social protection. The key principles of Recommendation No. 202 provide a useful basis for defining financing approaches and mechanisms: universality of protection based on broad risk-pooling; solidarity in financing to achieve an optimal balance between the responsibilities and interests among those who finance and benefit from social security schemes; overall and primary responsibility of the State for social protection; social inclusion, including of workers in the informal economy; respect for the rights and dignity of people covered by the social security guarantees; non-discrimination, gender equality and responsiveness to special needs; the right to adequate and predictable benefits; and the progressive realization of universal coverage, including by setting targets and time frames. The key sources of financing are domestic regular sources such as progressive taxes and social security contributions, given that the commitments of social protection systems and floors are long-term rather than one-off.

3.1. Increasing tax revenues

Taxation is one of the key channels for mobilizing the resources to establish universal social protection systems, including floors with a view to provide adequate protection for all persons in need across the life cycle. Countries have a wide range of options to raise tax revenue. The most common in low- and middle-income countries are the rather regressive consumption/sales and value-added taxes (VAT), while the less frequently used are progressive taxation such as on income, wealth and corporate profits –including taxes on the financial sector– as well as property and inheritance taxes.

International competition to attract foreign direct investment has led many governments to reduce the respective tax rates and to otherwise increase tax concessions to corporations. However, there is no consensus on the advantages of granting such concessions in the matter of direct taxes, since the diversion of international investment or the attraction of such flows is determined by many factors, including the availability and quality of infrastructure, an educated and productive workforce, and the quality of institutions. Therefore, countries should examine the cost and benefits of such tax concessions as they may constitute an unnecessary loss of revenue, which could be used to ensure universal social protection coverage. Declining corporate tax rates have resulted in lower proceedings from corporate taxes as a share of GDP. In 8 out of 13 G20 countries with available data, government revenues from corporate taxes have declined, driven by lower tax rates, but also by non-compliance with tax obligations (ILO, ISSA and OECD 2023). To level the playing field in terms of minimum corporate taxation rates, the United Nations General Assembly approved a resolution in 2023 to create a global tax convention. The proposal, which was drafted by Nigeria, is more centred on the needs of low- and middle-income countries (UN 2023).

Governments face challenges in ensuring compliance with taxes at all levels, ranging from large corporations, MSMEs, own-account workers, to wage and salaried workers. To promote tax compliance for small contributors, voluntary tax compliance programmes have been adopted in many countries (for example, Argentina, Indonesia and Spain). Countries may introduce these programmes for a variety of reasons, including raising tax revenues, regularizing the payment of social security contributions, and/or facilitating asset repatriation for the purpose of economic policies, especially when the country is in economic crisis (Council of Europe 2014).

Limited intergenerational social mobility has led to renewed interest in introducing wealth taxes to finance social protection programmes. A remarkable example is Argentina, which, in 2022, introduced a wealth tax to be paid by the 12,000 richest individuals residing in the country. It is expected to generate US$3.5 billion in revenues, which will be used to finance health care and social protection, but which will also foster SMEs, education and natural gas exploitation (Malax-Echevarria 2021; Razavi, Cattaneo and Schwarzer, forthcoming).

3.1.1. Revenues from explicit and implicit fossil fuel subsidies

To mitigate climate change and its unequal impact on populations within countries, there is an urgent need to promote progressive taxation, including by taxing those who consume and produce more CO2. One way of doing this is by removing regressive fossil fuel subsidies (explicit fossil fuel subsidies) or increasing the price of carbon intensive goods and services through a carbon tax in a way that captures the environmental costs of carbon consumption (implicit fossil fuel subsidies).

Evidence shows that, at the domestic level, considerable fiscal space could be generated by progressively removing explicit fuel subsidies as well as implicit fuel subsidies, that is, by introducing/increasing carbon pricing schemes in a way that charges for the environmental costs of carbon (Black et al. 2023). Across all low- and middle-income countries, explicit fuel subsidies represent, on average, 1.2 per cent of GDP, while implicit fuel subsidies represent 9.7 per cent of GDP. These subsidies compare with a social protection floor financing gap of 3.3 per cent of GDP (figure 6). Investing revenues from the progressive removal of explicit and implicit fuel subsidies would more than cover the existing financing gap with substantial revenues remaining to be allocated to other public investments.

However, significant variations exist across countries and regions as shown in figure 6. Generally, the potential for closing social protection floor financing gaps using funds currently allocated to fossil fuel subsidies is highest in upper-middle-income countries and very weak in low-income countries. This is because richer countries tend to spend more money on explicit fuel subsidies and, as they consume and produce more carbon emissions, the revenue potential from removing implicit subsidies is much higher. In low-income countries, existing spending on explicit subsidies, carbon emissions and social protection coverage is generally low, which explains why explicit and implicit fuel subsidies only represent 1.0 per cent and 2.6 per cent of GDP, respectively. This finding is in line with the conclusions from previous social protection financing gap analyses which show that low-income countries will not be able to close their financing gaps using domestic resources alone and will require international assistance to do so (Durán-Valverde et al. 2020; ILO 2020b).

Figure 6. Comparison between the resources allocated to explicit and implicit fuel subsidies and the financing gap for a social protection floor, as a percentage of GDP, per year, by national income group, and by region, 2024

Source: Authors’ elaboration.

3.1.2. International climate financing mechanisms to build social protection floors

As was shown in the previous section, low-income countries may not be able to generate sufficient resources from carbon taxes and the removal of fuel subsidies to close the financing gap for social protection floors. At the same time, many low-income countries are among the most vulnerable to climate change (IPCC 2023). Social protection is increasingly recognized as a key adaptation strategy, as it is a way to strengthen coping and adaptive capacities, reduce vulnerability and increase resilience (Costella et al. 2023; Sengupta and Costella 2023). In these countries, leveraging international climate financing, including loss and damage funding arrangements, can help reinforce and adapt their social protection systems to ensure that they are better prepared to tackle the impacts of the climate crisis. In addition, social protection is also a central enabling factor for more inclusive and equitable climate action, thus facilitating a just transition.

The first concrete step to move in this direction is to introduce clear commitments regarding the role of social protection for climate action in the context of countries’ Nationally Determined Contributions, National Adaptation Plans and pledges for net zero and sovereign green bonds. Yet, maximizing the reduction of emissions and boosting government revenues through such mechanisms requires international cooperation. An effective method to amplify global climate change mitigation efforts and to meet the temperature goals of the Paris Agreement is the introduction of an international carbon price floor (Roaf, Black and Parry 2021). This approach underscores the importance of setting global minimum carbon prices, adjusted according to a country's level of development, to ensure substantial global emissions reductions.

Government revenues collected through such international financing arrangements, such as the carbon price floor arrangement, could be directed towards multilateral funds that low- and middle-income countries can access, such as the newly created Loss and Damage Fund. The aim of the Fund, operationalized at 2023 United Nations Climate Change Conference (UNFCCC COP 28), is to assist countries in responding to loss and damage associated with the adverse effects of climate change (UNFCCC 2023).

Exploring the integration of initiatives to strengthen global financing for social protection with existing climate financing mechanisms, such as the Loss and Damage Fund, would facilitate fairer international burden-sharing (Kaltenborn 2023). Cross-country financial transfers, especially in the area of social protection, can alleviate the burden on middle- and low-income countries, addressing income and health losses caused by the already observed and projected unavoidable impacts of climate change, while addressing their adaptation and developmental needs (Schwerhoff, Chateau and Jaumotte 2022). In this way, international climate financing can contribute towards building social protection systems that are adapted to the challenges of climate change and can support just transition, especially in low-income countries, which are among the most vulnerable to the climate crisis and where it will not be possible to close social protection financing gaps based on domestic resources alone.

3.2. Extending social security finance by increasing contributory revenues

Social security contributions play a critical role in financing social protection. Social security contributions are linked to legal entitlements and should be considered a deferred wage and a social and economic investment. According to Convention No. 102, social security contributions shall be collected in a manner which avoids hardship to persons of small means and takes into account the economic situation of the Member and of the classes of persons protected. Furthermore, the total of the insurance contributions borne by the employees protected shall not exceed 50 per cent of the total of the financial resources allocated to the protection of employees and their families including children. Evidence has shown that there are no significant employment or formalization gains in reducing contribution rates that have remained stable throughout the twenty-first century. The sole clear outcome that is achieved through a reduction of social security contributions is a wider financing gap for social protection (Calligaro and Cetrangolo 2023).

The ILO Transition from the Informal to the Formal Economy Recommendation, 2015 (No. 204) provides a useful framework to ensure that an integrated policy framework to facilitate the transition to the formal economy is included at all levels of government with a view to progressively extending coverage of social protection for all, including for difficult-to-cover groups in the informal economy. According to Recommendation No. 204, reducing compliance costs by introducing simplified tax and contributions assessment and payment regimes is one concrete way of moving in this direction and extending social security contribution financing.

Several countries have adopted strategies in line with this provision by, for example, simplifying administrative procedures to enable difficult-to-cover workers to enrol and pay contributions and carry out other formalities, such as tax payments through single portals and mobile applications. Argentina, Brazil and other Latin American countries, as well as Indonesia and other Asian countries, have implemented schemes and service delivery tools to cover these groups. In addition, very low-income self-employed persons can benefit from government subsidies for their contributions, for example through the Monotributo/Monotax schemes in Argentina and Brazil (ILO, ISSA and OECD 2023).

Regular and high-quality financial forecasts of the social insurance system are an essential step for extending the fiscal space for social protection through contributory revenues and close the financing gap. Actuarial valuations are at the core of these and are indispensable to assess the sustainability of social security programmes, but are also required to assess system adequacy, financing and funding considerations. Actuarial valuations and financial forecasts are based on a range of assumptions including life expectancy, labour market participation, scheme coverage, real wage increases and economic growth, and are inherently complex. Findings of actuarial valuations also have an impact on investment decisions, benefit calculations and communication.

3.3. Eliminating illicit financial flows

Illicit financial flows generally involve money laundering, bribery by international companies, tax evasion and trade mispricing. Stemming illicit financial flows and fighting corruption can expand the fiscal space and help close the financing gap for social protection floors.

Domestic tax base erosion and profit shifting (BEPS) due to multinational enterprises exploiting gaps and mismatches between different countries' tax systems affect all countries. The higher reliance of developing countries on corporate income tax means that they suffer from base erosion and profit shifting disproportionately. Working together in the OECD/G20 Inclusive Framework on BEPS, over 135 countries and jurisdictions are implementing 15 actions to tackle tax avoidance, improve the coherence of international tax rules, ensure a more transparent tax environment and address the tax challenges arising from the digitalization of the economy (OECD 2023).

Success through this framework alone would free up more than ten times the annual total of official development assistance disbursed and received across the globe. Such is the extent of resources that illegally escape developing countries every year. There are increased efforts to crack down on money laundering, bribery, tax evasion, trade mispricing and other financial crimes that are not only illegal but also reduce much-needed revenues for social protection and countries’ efforts towards attaining the SDGs. For Egypt, it is estimated that combating illicit financial outflows could on average generate an annual amount equivalent to 3 per cent of GDP; illicit financial outflows from the country in 2014 were estimated at between 1.9 per cent and 4.7 per cent of GDP (Ortiz et al. 2019).

3.4. Sovereign debt, foreign aid and international financing mechanisms

Sovereign debt should not be viewed as a permanent source of government finance for closing the financing gap, however, managing it effectively can free up resources to increase fiscal space for social protection. Effective sovereign debt management provides important options to expand fiscal space for socio-economic investments with positive impacts on women, children and other population groups. Debt service-related development distress is apparent in many countries. For instance, in Latin America and the Caribbean, the rise of interest payments between 2012 and 2021 curtailed spending on key public services and contributed to a decline in public investment (ECLAC 2023). If a portion of government debt could be renegotiated with lower interest rates, this could free a sizable share of financial resources to devote to these three key areas.

While domestic resource mobilization must remain the cornerstone of national social protection systems, in the case of low-income countries, the financing gap of 52.3 per cent of their GDP is such that international solidarity is absolutely necessary. However, to close such a gap, the current ODA directed to low-income countries would need to be more than tripled and fully allocated to social protection. It should be noticed that ODA has remained at levels below the internationally agreed goal of 0.7 per cent of gross national income of the high-income countries (in 2023 the average rate was 0.37 per cent). According to data collected by the OECD/DAC, only five countries met this goal – Denmark, Germany, Norway, Luxembourg, and Sweden. Despite some increase in the share of ODA allocated to employment and social protection as because of the pandemic, ODA allocations to social protection still remain very low. (OECD 2024a).

To unlock social protection financing opportunities to close the financing gap, especially for low-income countries, in 2021, ILO constituents proposed the creation of a new international financing mechanism, such as a global fund for social protection (ILO 2021b). ILO-commissioned research on the diverse experiences of global health, agriculture and climate funds suggests that a putative global fund for social protection could give priority to low-income countries to gradually build their national social protection floors, complementing and supporting domestic resource mobilization efforts (Yeates et al. 2023). However, there are also risks associated with creating new vertical funds which should be carefully managed, including the risk of such funds being under-resourced and creating fragmentation in the area of financing social protection. Ensuring effective coordination with on-going climate financing efforts is also key. While private sources of finance can make substantial new funds available, great care is needed to ensure that ethical and vested interest concerns and due diligence are soundly anchored in governance structures and processes to avoid this. The full involvement of diverse representatives – government (different ministries), social partners and civil society groups (such as users and beneficiaries) – from countries of the global South in global-level deliberations is also crucial for a fund’s legitimacy (Yeates et al. 2023).

Conclusion

Based on a revised methodology, this study has shown that the financing gap to ensure universal social protection coverage is still a reality across low- and middle-income countries, where there is a need to spend an additional US$1.4 trillion or 3.3 per cent of GDP (2024) to extend a social protection floor to all. This means that the governments of low-and middle-income countries should progressively increase their social protection expenditure by 10.6 per cent of their existing government expenditure. Such an investment will ensure the universal coverage of basic benefits to all children, mothers of newborns, persons with severe disabilities, all persons in old-age, and the unemployed, as well as universal essential health care.

There is no one-size-fits-all approach for closing the financing gap and extending fiscal space for social protection. Domestic resource mobilization, including progressive taxation and social security contributions, are key sources for social protection, considering that building social protection systems and floors imply long-term commitments. Managing sovereign debt is another important option to expand fiscal space for social protection. Higher borrowing costs, combined with the substantial sovereign debt accumulated during the COVID-19 pandemic, have increased the debt-servicing costs of many developing countries that were already facing unsustainable debt burdens even on the eve of the COVID-19 pandemic.

While domestic resource mobilization remains the cornerstone of national social protection systems, international solidarity becomes necessary in the case of low-income countries, where the financing gap accounts for 52.3 per cent of GDP. To bridge this gap, the current official development assistance directed at these countries must be more than tripled and be exclusively used for social protection. Collaboration between the ILO, international financial institutions and other United Nations entities is also of utmost importance to close the financing gap in low-income countries.

Every strategy aimed at expanding the fiscal space for social protection must consider that the attainment of universal social protection is a pivotal policy lever to prevent and address the adverse consequences of the ongoing climate crisis and enable a just transition towards environmentally sustainable economies and societies for all. Leveraging international climate financing, including loss and damage funding arrangements, can help reinforce and adapt their social protection systems in low-and-middle income countries to ensure that they are better prepared to tackle the impacts of the climate crisis.

Annex

Financing gap for universal social protection as percentage of GDP

Table A1. Financing gap for universal social protection, as percentage of GDP, per year, by country, 2024

Territory

Social protection (including essential health care)

(% of GDP)

Essential health care

(% of GDP)

Five social protection cash benefits

(% of GDP)

Social protection cash benefits

Children

(% of GDP)

Disability

(% of GDP)

Maternity

(% of GDP)

Old-age

(% of GDP)

Unemployment

(% of GDP)

Low- and middle-income countries

3.3

2.0

1.3

0.6

0.2

0.05

0.3

0.2

Africa

17.6

11.4

6.2

3.2

1.1

0.3

0.9

0.7

Algeria

4.1

1.6

2.5

0.7

0.9

0.2

0.2

0.5

Angola

27.5

24.4

3.1

2.2

...

...

0.4

0.6

Benin

10.7

5.6

5.1

3.0

1.0

0.2

0.8

0.1

Botswana

6.9

1.0

5.9

2.8

1.1

0.2

0

1.9

Burkina Faso

16.8

9.4

7.4

4.2

1.3

0.5

1.0

0.4

Burundi

87.8

62.1

25.7

15.1

5.0

1.8

3.5

0.3

Côte d'Ivoire

6.2

3.3

2.9

1.8

0.6

...

0.4

0.1

Cameroon

13.5

6.2

7.3

4.2

1.3

0.5

0.9

0.3

Cabo Verde

3.9

1.5

2.4

0.9

0.4

...

0.4

0.6

Central African Republic

41.6

22.4

19.2

12.0

3.3

1.5

2.4

...

Chad

25.9

12.6

13.4

8.6

2.3

1.0

1.3

0.1

Comoros

18.2

10.1

8.2

5.5

...

...

2.2

0.5

Congo

4.0

2.6

1.3

...

...

0.2

0.4

0.8

Congo (Democratic Republic of the)

66.3

41.4

24.9

15.8

4.6

...

3.4

1.2

Djibouti

5.9

1.8

4.1

2.0

...

0.2

1.1

0.8

Egypt

11.1

8.6

2.4

1.4

0.5

0

0.4

0.2

Equatorial Guinea

4.6

0.8

3.8

2.1

0.7

...

0.7

0.3

Eritrea

31.5

13.7

17.7

11.7

4.1

...

...

1.9

Eswatini

5.2

2.1

3.1

1.6

0.6

0.1

0

0.8

Ethiopia

33.4

26.2

7.2

3.9

1.3

0.4

1.2

0.3

Gabon

3.4

0.7

2.7

1.3

...

0.2

0.5

0.7

Gambia

18.5

10.3

8.2

5.9

...

0.6

1.2

0.5

Ghana

17.6

14.4

3.2

1.5

0.7

0.1

0.7

0.2

Guinea

19.2

9.2

10.1

5.5

1.8

0.6

1.8

0.5

Guinea-Bissau

16.5

10.3

6.3

...

3.2

...

2.5

0.6

Kenya

13.7

7.6

6.1

3.4

1.2

0.2

0.8

0.5

Lesotho

13.1

9.1

4.0

1.6

...

0.4

0

2.0

Liberia

29.1

11.5

17.6

11.7

...

1.2

3.9

0.8

Libya

2.5

1.9

0.6

0.2

0.04

0.03

0.1

0.2

Madagascar

32.4

26.7

5.6

3.7

...

0.5

1.3

0.2

Malawi

50.0

44.4

5.6

2.8

1.1

0.4

0.9

0.4

Mali

17.3

11.0

6.3

4.0

1.2

...

0.9

0.2

Mauritania

62.4

3.7

58.7

34.3

10.8

...

9.1

4.6

Mauritius

1.8

0.6

1.1

0.9

0

0.1

0

0.2

Morocco

5.8

3.5

2.3

0.8

0.4

0.1

0.8

0.2

Mozambique

33.9

26.2

7.7

4.9

1.2

0.5

0.6

0.4

Namibia

2.7

1.7

1.1

0.5

0.1

0.1

0

0.4

Niger

22.1

14.2

7.9

6.7

...

...

1.2

0.1

Nigeria

15.4

11.9

3.5

2.0

0.6

0.3

0.5

0.1

Rwanda

16.2

12.0

4.2

2.0

0.7

0.2

0.7

0.6

Sao Tome and Principe

13.8

4.2

9.5

4.8

1.7

0.5

1.0

1.4

Senegal

9.0

3.7

5.3

2.8

1.1

0.4

0.8

0.2

Sierra Leone

94.9

74.1

20.8

15.4

...

...

4.6

0.9

Somalia

44.7

18.5

26.2

15.1

4.0

1.8

3.1

2.2

South Africa

3.8

2.1

1.8

0.4

0.2

0.1

0.2

0.9

South Sudan

58.9

54.5

4.5

2.4

0.7

0.2

0.6

0.5

Sudan

380.0

163.2

216.8

104.3

34.6

11.4

33.4

33.1

Tanzania (United Republic of)

12.5

10.6

1.9

...

0.8

0.3

0.7

0.1

Togo

13.3

7.9

5.4

1.4

1.7

0.5

1.4

0.4

Tunisia

4.5

1.6

2.9

1.2

0.7

0.1

0

0.8

Uganda

11.8

9.8

2.1

...

...

0.8

0.9

0.4

Zambia

14.3

11.5

2.8

1.7

0.5

0.2

0.3

0.1

Zimbabwe

13.8

12.6

1.2

0.7

0.2

0.1

0.2

0.1

Arab States

11.4

6.9

4.5

1.7

0.4

0.3

1.2

0.9

Iraq

4.7

1.8

2.9

1.6

0.4

...

0.4

0.5

Jordan

6.3

1.4

4.9

2.5

0.5

0.2

0.8

0.9

Lebanon

9.4

6.2

3.2

0.9

...

...

1.9

0.4

Occupied Palestinian Territory

9.3

1.7

7.6

4.7

1.0

0.5

1.4

...

Syrian Arab Republic

29.9

23.0

6.9

...

...

...

6.9

...

Yemen

104.0

82.7

21.3

...

...

4.2

9.3

7.8

Asia and the Pacific

2.0

1.2

0.8

0.3

0.2

0.02

0.2

0.1

Afghanistan

46.8

21.7

25.1

15.1

3.6

1.6

2.4

2.4

Bangladesh

4.8

3.0

1.8

0.7

0.3

0.1

0.5

0.2

Bhutan

6.7

2.4

4.3

1.4

0.8

0.1

1.4

0.5

Cambodia

14.0

4.9

9.1

3.9

1.7

0.3

3.1

0.03

China

0.6

0.4

0.2

0.1

0.04

0.002

0

0.03

Fiji

2.6

0.9

1.7

1.1

0.3

0.1

0

0.2

India

3.3

2.3

1.0

0.4

0.2

0.03

0.3

0.1

Indonesia

2.8

1.8

1.0

0.3

0.2

0.03

0.4

0.1

Iran (Islamic Republic of)

13.1

8.1

4.9

0.6

2.2

0.3

0.8

1.1

Kiribati

12.3

4.4

7.9

6.7

...

...

0.2

1.1

Lao People's Democratic Republic

6.8

5.4

1.4

...

0.5

0.1

0.7

0.1

Malaysia

8.5

0.5

8.0

3.0

1.1

0.1

3.3

0.5

Maldives

2.6

0.4

2.2

1.5

0.3

0.1

0

0.3

Marshall Islands

4.6

0.9

3.6

2.6

...

...

0.5

0.5

Micronesia, Federated States of

7.1

1.9

5.2

3.3

1.2

...

0.1

0.7

Mongolia

2.5

2.3

0.2

0

0

0

0

0.2

Myanmar

11.1

7.7

3.4

1.3

0.6

0.1

1.3

0.1

Nepal

9.2

6.5

2.6

1.3

0.5

0.1

0.3

0.4

Pakistan

22.4

18.3

4.1

2.2

0.7

...

0.9

0.3

Palau

0.6

0.4

0.2

...

0.1

...

0

0.1

Papua New Guinea

16.8

2.9

13.8

8.0

2.6

...

2.9

0.4

Philippines

3.1

1.1

1.9

1.0

0.4

0.1

0.3

0.1

Samoa

6.3

1.7

4.6

3.1

0.6

0.2

0.1

0.5

Solomon Islands

7.9

3.2

4.7

...

1.9

0.5

1.9

0.3

Sri Lanka

4.8

2.0

2.8

0.6

0.4

0.1

1.5

0.2

Thailand

1.5

0.7

0.8

0.4

0.01

0.02

0.4

0.01

Timor-Leste

8.9

5.0

3.9

2.6

1.1

...

0

0.2

Tonga

7.9

1.5

6.5

5.4

0.4

0.4

0

0.3

Tuvalu

2.4

1.4

0.9

0.7

0.1

...

0

0.04

Vanuatu

8.3

2.5

5.8

3.0

1.0

0.3

1.2

0.4

Viet Nam

3.4

1.9

1.5

0.7

0.1

0.03

0.7

0.02

Europe and Central Asia

1.9

1.2

0.6

0.3

0.1

0.02

0.1

0.2

Albania

2.5

0.7

1.7

0.8

0

...

0.2

0.7

Armenia

2.5

0.7

1.7

0.7

0

0.03

0.5

0.4

Azerbaijan

2.8

1.0

1.8

1.3

0

0.1

0.1

0.4

Belarus

2.1

2.0

0.1

...

0

0

0

0.1

Bosnia and Herzegovina

4.7

0.8

3.9

1.2

0

0

1.8

0.9

Bulgaria

1.4

0.5

0.9

0.3

0

0

0.4

0.1

Georgia

2.1

1.1

1.0

0.5

0

0.04

0

0.4

Kazakhstan

1.4

0.9

0.5

0.3

0

0.04

0.03

0.1

Kyrgyzstan

9.8

6.5

3.3

2.7

0

0.2

0

0.3

Moldova, Republic of

4.4

2.0

2.3

1.8

0

0

0.4

0.1

Montenegro

3.0

0.6

2.4

0.7

0

0

1.0

0.7

North Macedonia

4.6

0.9

3.7

1.0

0.04

0

1.7

0.9

Russian Federation

0.8

0.7

0.1

0

0.02

0.02

0

0.05

Serbia

5.1

0.7

4.4

0.8

0

...

3.1

0.5

Tajikistan

10.1

7.3

2.8

2.1

0.4

0.1

0

0.2

Türkiye

2.2

1.6

0.7

0.3

0.1

...

0.1

0.2

Turkmenistan

3.3

0.7

2.6

2.1

0.1

...

0.4

...

Ukraine

3.6

2.3

1.3

0

0

0

0.3

1.0

Uzbekistan

6.9

5.4

1.5

0.8

0.4

0.1

0

0.2

Latin America and the Caribbean

2.7

1.0

1.7

0.7

0.3

0.1

0.3

0.3

Argentina

2.7

1.9

0.8

0.2

0

0.1

0.3

0.3

Belize

4.7

0.8

3.9

1.8

0.6

0.1

0.9

0.5

Bolivia (Plurinational State of)

4.8

1.7

3.1

1.3

1.1

0.1

0

0.5

Brazil

1.4

0.6

0.8

0.3

0

0.03

0.2

0.3

Colombia

4.0

1.4

2.6

0.7

0.5

...

0.9

0.5

Costa Rica

1.9

0.4

1.5

0.5

0.1

0.1

0.8

...

Cuba

4.1

2.7

1.4

0.5

...

0.02

0.8

0.03

Dominica

3.0

0.7

2.3

1.4

0.7

...

0.2

...

Dominican Republic

2.5

0.8

1.6

0.3

0.3

0.1

0.9

0.2

Ecuador

2.8

1.1

1.7

0.8

0.3

0.1

0.4

0.1

El Salvador

3.4

1.2

2.2

0.9

0.3

0.1

0.9

0.1

Grenada

2.9

0.6

2.3

...

0.5

...

1.2

0.6

Guatemala

6.4

1.2

5.3

2.7

0.9

0.2

1.3

0.2

Haiti

45.5

11.5

34.0

16.1

...

1.5

9.4

7.0

Honduras

12.8

2.7

10.1

4.3

1.8

0.4

2.5

1.1

Jamaica

2.6

1.3

1.3

0.5

0.3

0.1

0.2

0.3

Mexico

1.9

0.4

1.5

1.0

0.3

0.1

0

0.1

Nicaragua

9.9

3.7

6.2

3.1

0.9

0.2

1.4

0.5

Paraguay

3.7

1.1

2.6

1.3

0.5

0.1

0.3

0.3

Peru

4.4

1.6

2.8

1.1

0.4

0.1

1.0

0.2

Saint Lucia

3.7

0.5

3.2

0.8

0.5

0.04

1.0

0.9

Saint Vincent and the Grenadines

5.1

0.5

4.6

1.3

0.6

0.1

1.3

1.2

Suriname

5.8

4.1

1.7

0.8

0.3

0.2

0

0.5

Venezuela (Bolivarian Republic of)

17.7

7.8

9.9

...

6.4

...

0

3.6

Source: Source: Authors’ elaboration.

Note: ILO modelled estimates regarding the number of persons in unemployment were not available for all countries. In the following countries, the latest available labour force surveys estimates were used: Ukraine 2021; Grenada 2020; Kiribati 2020; Marshall Islands 2021; Federated States of Micronesia 2014; Palau 2020; and Tuvalu 2022.

Financing gap for universal social protection as percentage of government expenditure

Table A2. Financing gap for universal social protection, as percentage of government expenditure (GE), per year, by country, 2024

Territory

Social protection (including essential health care)

(% of GE)

Essential health care

(% of GE)

Five social protection cash benefits(% of GE)

Social protection cash benefits

Children

(% of GE)

Disability

(% of GE)

Maternity

(% of GE)

Old-age

(% of GE)

Unemployment

(% of GE)

Low- and middle-income countries

10.6

6.3

4.2

1.9

0.7

0.2

0.9

0.6

Africa

70.1

45.3

24.8

12.7

4.3

1.1

3.8

2.8

Algeria

9.8

3.8

6

1.6

2.3

0.4

0.5

1.2

Angola

133

117.9

15.2

10.5

...

...

2

2.8

Benin

56.5

29.5

27

15.7

5.2

1.2

4.5

0.4

Botswana

23.3

3.3

20

9.4

3.7

0.7

0

6.3

Burkina Faso

64.7

36.1

28.6

16.3

5.1

1.9

3.7

1.5

Burundi

245.7

173.7

72

42.3

14

5.1

9.7

0.9

Côte d'Ivoire

29.2

15.5

13.7

8.6

2.9

...

1.8

0.4

Cameroon

83.7

38.4

45.3

26.1

8.3

3

5.7

2.1

Cabo Verde

13.9

5.4

8.5

3.4

1.4

...

1.5

2.2

Central African Republic

244

131.6

112.4

70.4

19.3

8.7

14.1

...

Chad

145.9

70.7

75.2

48.6

12.8

5.7

7.4

0.6

Comoros

93.3

51.5

41.8

28

...

...

11.5

2.3

Congo

18.9

12.5

6.4

...

...

0.9

1.9

3.6

Congo (Democratic Republic of the)

377.5

235.6

141.8

89.9

25.9

...

19.5

6.6

Djibouti

26.5

8

18.5

9.1

...

0.9

4.8

3.7

Egypt

38.3

29.9

8.5

4.7

1.6

0

1.5

0.7

Equatorial Guinea

23.7

4.2

19.5

10.8

3.6

...

3.3

1.7

Eritrea

100.7

43.9

56.7

37.5

13.1

...

...

6.1

Eswatini

17.1

6.8

10.3

5.2

1.9

0.5

0

2.7

Ethiopia

330.9

259.6

71.3

39

12.8

4

12

3.4

Gabon

17.2

3.6

13.7

6.5

...

0.9

2.6

3.7

Gambia

84.1

46.8

37.3

26.9

...

2.9

5.5

2.1

Ghana

84.9

69.5

15.3

7.1

3.5

0.6

3.3

0.9

Guinea

118.5

56.4

62

33.6

10.9

3.8

10.9

2.9

Guinea-Bissau

84.3

52.5

31.9

...

16.3

...

12.7

2.9

Kenya

61.2

34

27.2

15

5.1

1.1

3.8

2.2

Lesotho

26.2

18.2

8

3.2

...

0.7

0

4.1

Liberia

112.3

44.3

68

45.2

...

4.7

14.9

3.2

Libya

4.1

3.1

1

0.4

0.1

0.1

0.1

0.4

Madagascar

187.9

155.2

32.7

21.4

...

2.7

7.3

1.3

Malawi

196.1

174.1

22

11

4.5

1.4

3.6

1.6

Mali

66.9

42.6

24.3

15.3

4.8

...

3.3

0.9

Mauritania

251.6

14.9

236.7

138.4

43.4

...

36.7

18.3

Mauritius

6.4

2.2

4.2

3.1

0

0.2

0

0.8

Morocco

18.5

11.2

7.3

2.5

1.3

0.2

2.6

0.7

Mozambique

118.5

91.6

26.9

17.3

4.2

1.9

2.3

1.3

Namibia

7.5

4.6

2.9

1.5

0.3

0.2

0

1

Niger

97.8

62.8

35.1

29.5

...

...

5.3

0.2

Nigeria

108.4

83.5

24.9

14.3

4.6

1.8

3.4

0.9

Rwanda

54.4

40.2

14.2

6.8

2.3

0.7

2.3

2.1

Sao Tome and Principe

55.2

17

38.1

19.5

6.7

2.2

4

5.8

Senegal

35.4

14.4

21

11.1

4.4

1.5

3.3

0.6

Sierra Leone

395.2

308.6

86.5

64

...

...

19

3.6

Somalia

693.2

287.4

405.8

233.7

61.8

27.7

48.2

34.5

South Africa

11.6

6.2

5.4

1.2

0.7

0.3

0.6

2.6

South Sudan

206.5

190.9

15.6

8.3

2.5

0.8

2.2

1.9

Sudan

2 834.7

1 217.3

1 617.4

778.4

257.9

84.8

249.1

247.2

Tanzania (United Republic of)

68.5

58.3

10.3

...

4.2

1.6

3.8

0.7

Togo

60.8

36.2

24.6

6.4

7.7

2.4

6.4

1.6

Tunisia

14.5

5.2

9.2

3.8

2.4

0.3

0

2.7

Uganda

62.5

51.5

11

...

...

4.1

4.9

2.1

Zambia

53.8

43.3

10.5

6.3

1.9

0.8

1

0.6

Zimbabwe

66.7

60.7

5.9

3.1

1

0.3

0.9

0.6

Arab States

28.1

17.0

11.0

4.2

1.0

0.7

3.0

2.2

Iraq

9.6

3.7

5.9

3.3

0.9

...

0.8

1.0

Jordan

19.1

4.4

14.8

7.4

1.6

0.7

2.3

2.7

Lebanon

83.5

54.8

28.7

7.9

...

...

17.2

3.5

Occupied Palestinian Territory

30.5

5.6

24.9

15.6

3.3

1.5

4.6

...

Syrian Arab Republic

104.4

80.2

24.1

...

...

...

24.1

...

Yemen

1 233.3

980.9

252.4

...

...

49.8

110.3

92.2

Asia and the Pacific

6.6

4.1

2.5

1.0

0.5

0.1

0.6

0.2

Afghanistan

262.8

121.7

141.1

84.9

20

8.8

13.7

13.7

Bangladesh

36.4

22.9

13.5

5.2

2.5

0.7

3.8

1.4

Bhutan

21

7.5

13.5

4.5

2.6

0.3

4.5

1.5

Cambodia

52.6

18.4

34.2

14.8

6.5

1

11.8

0.1

China

1.8

1.3

0.5

0.3

0.1

0

0

0.1

Fiji

8

2.9

5.1

3.5

0.8

0.3

0

0.5

India

11.8

8.1

3.7

1.4

0.7

0.1

1.2

0.3

Indonesia

16.3

10.4

5.9

1.7

1.4

0.2

2.3

0.4

Iran (Islamic Republic of)

92.5

57.7

34.9

4.3

15.2

2

5.5

7.8

Kiribati

10.4

3.7

6.7

5.6

...

...

0.1

0.9

Lao People's Democratic Republic

36.6

29

7.6

...

2.7

0.6

3.8

0.5

Malaysia

39.5

2.3

37.2

14

5

0.7

15.3

2.2

Maldives

6.8

1.1

5.7

3.8

0.8

0.2

0

0.8

Marshall Islands

6.6

1.3

5.2

3.7

...

...

0.8

0.8

Micronesia, Federated States of

11.5

3

8.5

5.3

1.9

...

0.1

1.2

Mongolia

6.8

6.3

0.5

0

0

0

0

0.5

Myanmar

59.3

41.1

18.2

7

3.1

0.6

6.8

0.7

Nepal

35.7

25.4

10.2

4.9

2

0.6

1.3

1.5

Pakistan

111.3

91.1

20.2

11

3.4

...

4.4

1.3

Palau

1.2

0.7

0.4

...

0.3

...

0

0.2

Papua New Guinea

73.7

12.9

60.8

35.1

11.3

...

12.6

1.7

Philippines

12.3

4.6

7.7

4.2

1.8

0.4

1.1

0.3

Samoa

18.1

4.9

13.2

8.9

1.8

0.6

0.3

1.5

Solomon Islands

24.9

10.1

14.8

...

6.1

1.7

6.1

1.0

Sri Lanka

26.1

10.8

15.3

3.5

2.4

0.3

7.9

1.3

Thailand

6.5

3

3.5

1.6

0

0.1

1.7

0.1

Timor-Leste

10.1

5.7

4.4

2.9

1.3

...

0

0.2

Tonga

16.4

3

13.4

11.3

0.8

0.7

0

0.6

Tuvalu

2

1.2

0.8

0.6

0.1

...

0

0

Vanuatu

19.3

5.7

13.6

7

2.2

0.7

2.8

0.9

Viet Nam

16.6

9.4

7.2

3.4

0.3

0.2

3.3

0.1

Europe and Central Asia

5.4

3.6

1.9

0.8

0.2

0.0

0.4

0.5

Albania

8.1

2.4

5.7

2.8

0

...

0.5

2.4

Armenia

9

2.6

6.3

2.7

0

0.1

1.9

1.6

Azerbaijan

9

3.2

5.7

4.1

0

0.3

0.2

1.2

Belarus

6

5.7

0.3

...

0

0

0

0.3

Bosnia and Herzegovina

11.1

1.9

9.2

2.9

0

0

4.2

2.1

Bulgaria

3.6

1.4

2.3

0.9

0

0

1

0.4

Georgia

6.9

3.6

3.4

1.8

0

0.1

0

1.4

Kazakhstan

6.2

4

2.2

1.5

0

0.2

0.2

0.4

Kyrgyzstan

27.7

18.4

9.2

7.7

0

0.6

0

0.9

Moldova, Republic of

12.1

5.6

6.4

5

0

0

1

0.4

Montenegro

6.8

1.4

5.4

1.6

0

0

2.3

1.5

North Macedonia

13.1

2.6

10.5

2.9

0.1

0

4.9

2.5

Russian Federation

2.3

2.1

0.2

0

0.1

0.1

0

0.1

Serbia

11.4

1.5

9.9

1.8

0

...

7

1.1

Tajikistan

33.3

24.2

9.1

6.8

1.3

0.2

0

0.8

Türkiye

6.6

4.7

2

0.8

0.4

...

0.3

0.5

Turkmenistan

33

6.6

26.3

21.2

1.5

...

3.7

...

Ukraine

6.1

3.9

2.2

0

0

0

0.6

1.7

Uzbekistan

20.9

16.3

4.6

2.5

1.1

0.4

0

0.5

Latin America and the Caribbean

7.7

2.9

4.8

2.0

0.8

0.2

0.9

0.9

Argentina

7.1

4.9

2.2

0.4

0

0.2

0.7

0.9

Belize

20.3

3.3

17

7.8

2.8

0.5

3.7

2.2

Bolivia (Plurinational State of)

14.7

5.2

9.5

4.1

3.5

0.4

0

1.5

Brazil

2.9

1.3

1.6

0.6

0

0.1

0.4

0.6

Colombia

11.5

4.1

7.5

2.1

1.4

...

2.6

1.3

Costa Rica

9.9

2

7.9

2.6

0.6

0.3

4.4

...

Cuba

17.6

11.7

5.9

2.2

...

0.1

3.5

0.2

Dominica

6.7

1.5

5.2

3.2

1.5

...

0.5

...

Dominican Republic

13.6

4.5

9.1

1.5

1.6

0.3

4.7

0.9

Ecuador

7.6

3

4.5

2

0.7

0.2

1.2

0.4

El Salvador

12.2

4.2

8

3.1

1.2

0.2

3.2

0.3

Grenada

10.6

2.1

8.5

...

2

...

4.5

2

Guatemala

46.6

8.4

38.2

19.3

6.5

1.4

9.3

1.7

Haiti

512.9

129.8

383

181.6

...

16.7

105.6

79.2

Honduras

47.5

10.2

37.3

15.8

6.6

1.5

9.4

4

Jamaica

9

4.4

4.5

1.9

1

0.2

0.5

0.9

Mexico

6.6

1.5

5.1

3.4

0.9

0.3

0

0.5

Nicaragua

36.8

13.9

22.9

11.5

3.4

0.8

5.4

1.7

Paraguay

16.2

4.9

11.3

5.8

2.3

0.6

1.1

1.5

Peru

19.6

7.2

12.4

4.7

2

0.4

4.5

0.8

Saint Lucia

15.6

2

13.7

3.6

2

0.2

4.1

3.8

Saint Vincent and the Grenadines

14.1

1.5

12.7

3.7

1.7

0.2

3.7

3.3

Suriname

22.5

15.9

6.7

3.2

1.1

0.7

0

1.8

Venezuela (Bolivarian Republic of)

147.8

64.9

83

...

53.2

...

0

29.7

Source: Authors’ elaboration.

Note: See table A1 note.

National poverty lines

Table A3. National poverty lines in local currency units (LCU) and in 2024 USD, per year, by country, latest year

Country

Poverty line reference year

Annual poverty line, LCU 2024

Currency

Annual poverty line US$

Afghanistan

2020

32 534.0

AFS

467.1

Albania

2021

223 019.3

ALL

2 395.5

Algeria

2022

209 511.5

DZD

1 562.6

Angola

2019

377 370.7

AOA

455.4

Argentina

2023

2 597 388.2

ARS

3 185.4

Armenia

2021

810 047.8

AMD

2 015.0

Azerbaijan

2022

3 211.5

AZN

1 891.4

Bangladesh

2022

54 090.1

BDT

494.0

Belarus

2023

4 655.2

BYN

1 456.6

Belize

2009

4 252.6

BZD

2 126.3

Benin

2019

281 971.3

CFA

470.8

Bhutan

2022

81 827.9

BTN

985.5

Bolivia (Plurinational State of)

2019

12 171.1

BS

1 775.0

Bosnia and Herzegovina

2015

5 908.6

KM

3 308.3

Botswana

2022

2 772.2

USD

2 772.2

Brazil

2021

7 890.0

BRL

1 619.8

Bulgaria

2022

7 053.6

BGN

3 949.4

Burkina Faso

2018

237 540.5

CFA

396.6

Burundi

2020

1 035 852.5

FBU

365.4

Côte d'Ivoire

2015

330 820.8

CFA

552.4

Cambodia

2020

4 552 283.8

KHR

1 112.2

Cameroon

2014

458 631.0

CFA

765.8

Cabo Verde

2015

115 242.5

ECV

1 144.7

Central African Republic

2021

306 528.0

CFA

511.8

Chad

2019

294 188.3

CFA

491.2

China

2020

2 419.0

CNY

337.6

Colombia

2022

5 582 937.3

COP

1 422.0

Comoros

2014

416 257.8

KMF

926.7

Congo

2011

272 174.2

CFA

454.5

Congo (Democratic Republic of the)

2018

2 552 923.6

CDF

971.8

Costa Rica

2023

1 578 244.3

CRC

3 038.6

Cuba

2021

28 766.1

CUP

1 198.6

Djibouti

2017

174 779.4

DJF

987.5

Dominica

2009

7 935.6

XCD

2 939.1

Dominican Republic

2022

79 516.8

DOP

1 360.5

Ecuador

2023

1 090.5

USD

1 090.5

Egypt

2019

20 153.3

EGP

653.3

El Salvador

2023

782.8

USD

782.8

Equatorial Guinea

2006

1 271.3

USD

1 271.3

Eritrea

2003

10 159.6

ERN

677.3

Eswatini

2017

15 731.6

SZL

843.5

Ethiopia

2016

33 405.5

ETB

596.4

Fiji

2020

2 427.5

FJD

1 103.4

Gabon

2017

1 024 782.2

CFA

1 711.2

Gambia

2016

37 069.3

GMD

568.5

Georgia

2023

3 108.0

GEL

1 156.2

Ghana

2017

5 783.8

GHS

485.2

Grenada

2019

7 422.7

XCD

2 749.2

Guatemala

2014

15 837.4

GTM

2 026.5

Guinea

2019

8 052 518.8

GNF

946.2

Guinea-Bissau

2010

1 018.9

USD

1 018.9

Haiti

2012

4 801.5

USD

4 801.5

Honduras

2021

55 843.9

HNL

2 277.0

India

2021

19 744.6

RS

237.8

Indonesia

2023

6 770 497.0

IDR

435.5

Iran (Islamic Republic of)

2019

1 303 415 141.4

IRR

3 327.4

Iraq

2012

1 649 111.0

IQD

1 258.9

Jamaica

2012

184 755.1

JA$

1 201.5

Jordan

2010

1 146.0

JOD

1 618.7

Kazakhstan

2023

626 340.8

KZT

1 386.1

Kenya

2016

118 224.7

KSH

742.4

Kiribati

2020

2 067.8

AUD

1 380.4

Kyrgyzstan

2022

64 132.7

KGS

723.0

Lao People's Democratic Republic

2019

6 313 244.1

LAK

305.3

Lebanon

2012

70 185 406.3

LBP

820.9

Lesotho

2018

11 147.3

LSL

597.7

Liberia

2016

186 862.0

LD

993.9

Libya

2013

2 216.5

LYD

461.7

Madagascar

2013

1 155 685.0

AR

251.5

Malawi

2020

335 409.1

MWK

197.3

Malaysia

2022

32 809.7

RM

7 066.5

Maldives

2019

60 877.1

MVR

4 058.5

Mali

2021

220 572.8

CFA

368.3

Marshall Islands

2020

2 148.2

USD

2 148.2

Mauritania

2020

290 734.0

MRU

7 384.7

Mauritius

2017

126 775.6

INR

2 865.3

Mexico

2023

54 658.9

MXN

3 219.0

Micronesia, Federated States of

2014

1 920.4

USD

1 920.4

Moldova, Republic of

2022

42 008.5

LEI

2 384.8

Mongolia

2023

4 221 479.8

MNT

1 240.7

Montenegro

2022

2 910.1

EUR

3 187.4

Morocco

2014

5 559.8

MAD

558.8

Mozambique

2015

19 261.5

MZN

301.3

Myanmar

2017

1 047 193.6

MMK

306.2

Namibia

2016

8 960.6

NAD

480.5

Nepal

2011

45 082.8

NPR

339.4

Nicaragua

2019

39 009.7

NIO

1 074.3

Niger

2019

225 630.0

CFA

376.8

Nigeria

2019

332 794.2

Naira

349.9

North Macedonia

2020

148 921.0

MKD

2 663.2

Occupied Palestinian Territory

2017

6 633.6

NIS

1 812.5

Pakistan

2019

97 344.2

PKR

346.7

Palau

2014

4 914.4

USD

4 914.4

Papua New Guinea

2016

10 575.3

PGK

2 837.5

Paraguay

2022

10 793 006.5

PYG

1 484.2

Peru

2022

5 458.6

PEN

1 478.1

Philippines

2021

33 380.0

PHP

596.3

Russian Federation

2023

179 997.4

RUB

2 034.6

Rwanda

2016

260 404.1

RWF

207.5

Saint Lucia

2016

7 509.9

XCD

2 781.4

Saint Vincent and the Grenadines

2008

7 050.7

XCD

2 611.4

Samoa

2018

3 731.6

SAT

1 387.2

Sao Tome and Principe

2017

41 835.0

STD

1 865.6

Senegal

2019

419 937.7

CFA

701.2

Serbia

2022

376 481.8

RSD

3 518.2

Sierra Leone

2018

13 473 887.3

LE

622.5

Solomon Islands

2013

13 608.4

SBD

1 664.2

Somalia

2023

816.9

USD

816.9

South Africa

2023

19 589.4

ZAR

1 050.4

South Sudan

2008

152 651.2

SDG

141.0

Sri Lanka

2023

191 736.0

LKR

596.9

Sudan

2015

5 095 294.2

SDG

5 599.2

Suriname

2017

64 467.1

SRD

1 750.3

Syrian Arab Republic

2022

8 586 501.0

SL

686.9

Tajikistan

2018

3 577.3

TJS

327.9

Tanzania (United Republic of)

2018

740 602.3

TZS

294.5

Thailand

2020

36 689.0

THB

1 045.6

Timor-Leste

2014

694.1

USD

694.1

Togo

2019

338 514.5

CFA

565.2

Tonga

2021

7 662.9

TOP

3 286.0

Tunisia

2015

3 409.8

TND

1 102.1

Türkiye

2022

34 876.2

TRY

1 162.8

Turkmenistan

2022

14 743.9

TMT

4 212.5

Tuvalu

2016

785.3

AUD

524.3

Uganda

2020

783.3

USD

783.3

Ukraine

2020

80 647.5

UAH

2 122.3

Uzbekistan

2023

7 498 946.7

UZS

605.0

Vanuatu

2010

142 857.7

VT

1 246.1

Venezuela (Bolivarian Republic of)

2022

10 828.7

USD

10 828.7

Viet Nam

2020

13 857 328.4

VND

567.0

Yemen

2014

1 353 688.9

YER

2 631.1

Zambia

2022

7 533.8

ZMW

288.7

Zimbabwe

2023

1 278 401.5

ZWL

185.0

Source: Authors’ elaboration.

Note: National poverty lines were expressed in 2024 prices through the IMF Consumer Price Index and converted in US dollars with the United Nations official exchange rate of January 2024. For certain countries minimum wages were used in lieu of national poverty lines. Detailed information on the data sources used for the national poverty lines are available from the corresponding author upon request.

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Acknowledgements

The working paper was authored by Umberto Cattaneo, Helmut Schwarzer, Shahra Razavi and Andrea Visentin. The working paper benefitted from an ILO internal peer review by Christina Behrendt and an external peer review by Oscar Cetrangolo, who is affiliated with the University of Buenos Aires. Significant feedback, input and support throughout the drafting of the paper were also provided by Karin Stenberg (World Health Organization).

The authors also wish to acknowledge the following persons (listed in alphabetical order) for their comments, contributions and ideas: Andres Acuña-Ulate, Laura Addati, Pascal Annycke, Joana Borges Henriques, Belen Borja, Pablo Casalí, Fabio Durán Valverde, Hazel Elizondo-Barboza, Carlos Galian Barrueco, Ricardo Irra, Jie Yu Koh, Stefan Kühn, Kroum Markov, Siddhant Marur, Guillermo Montt, Valeria Nesterenko, Ian Orton, José F. Pacheco-Jiménez, Karuna Pal, Jasmina Papa, Luca Pellerano, Céline Peyron Bista, Valérie Schmitt, Fernando Sousa Jr., Maurice Starke, Lou Tessier, Luisa Valle, Rosalia Vazquez Alvarez, Veronika Wodsak and Hiroshi Yamabana.

Special thanks are also due to Meriem Allek for facilitating the production process, Sarah Lloyd for editing the working paper, as well as Sandrine Baronetti and Bénédicte Desvigne for handling the layout.