Objectives

The main objective of the study is to provide updated estimates of the gaps in social protection and health care coverage and financing for achieving SDG targets 1.3 and 3.8 of the 2030 Agenda, taking into account the effects of COVID-19 pandemic.

Specific objectives include:

1. To identify the coverage gaps in non-contributory systems for a selected number of social protection policy areas that provide income security benefits for children, maternity, disability and old age.

2. To measure coverage gaps in the provision of health care services.

3. To measure the cost of providing an SPF comprised of the selected package of benefits mentioned above, including health care.

4. To assess the current allocation of resources to finance social protection programmes.

5. To measure the total global and regional costs of a selected SPF package and estimate the magnitude of the additional resources needed to close the social protection financing gaps by 2030.

6. To measure the financing gaps of contributory systems.

7. To analyse and discuss the potential fiscal space that could be created using domestic and external resources.

Methodology

Assessing the financing gap for achieving the SPF, including health protection, raises a number of conceptual, methodological and practical challenges.

A practical exercise like the one attempted in this study requires moving from a theoretical definition to an operational one of what types of benefits should be included as a part of the SPF. In addition, the exercise implies decisions on the benefit levels of the different policy areas in order to make it possible to work with a base that is comparable across the countries and territories included in the study. The third type of challenge relates to the availability of information on the coverage, financing and expenditures of social protection programmes. Despite significant progress in building national capacity to generate data on social protection, many countries lack the necessary data. Such a lack of data is particularly severe in low-income countries. Finally, the COVID-19 pandemic is still prevalent and consequently its expected impact on the level of spending on social protection and health care initiatives is still based on data that are subject to continuous change.

Given those circumstances, this estimation of the financing gap of a social protection floor comprises several steps, which are discussed below.

Methodological considerations

The methodological considerations require a number of assumptions and decisions to be made to overcome the challenges mentioned above.

A key initial decision involves defining the potential beneficiary population and specifying the type and size of benefits that would be granted to the different beneficiary groups. Another key issue to resolve is how to move from a conceptual definition to an operational one that can be captured in a quantitative model, which is explained as follows.

1. Four categories of social protection benefits are selected for the social protection floor package: benefits for children, maternity and disability benefits and old-age benefits. In addition, the analysis includes health care.

2. For children, the analysis considers children aged between 0 and 5 years. The maternity benefit is considered for women aged 15–49 with newborns and the number of beneficiaries is calculated based on the observed country-specific fertility rates. For disability benefits, the study only considers persons with a severe disability, on the assumption that participation in employment may be challenging and may require specific support such as transportation allowances; the size of the eligible population is obtained from country-specific disability estimates from the WHO’s Estimated Years Living with Disability database. For old age, the potential beneficiary population includes persons aged 65 years and over. Finally, for health care, coverage would be for the entire population.

3. Benefit rates are defined as equivalent to national poverty lines or a proportion of them. For children, the benefit is defined as 25 per cent of the national poverty line – a lower percentage applied to children compared with adult household members in order to reflect differences in consumption levels (Ortiz et al., 2017b; OECD, 2011). For maternity, the cash benefit is set at 100 per cent of the national poverty line during four months around childbirth to protect the critical period when mothers and newborns are most vulnerable. For disability and old-age pensions, the amount of the benefit is 100 per cent of the national poverty line.4

4. Health care services differ in terms of the definition of the benefit. According to Stenberg et al. (2017),5 health care corresponds to the provision of four service delivery platforms:

   • policy and population wide interventions;

   • periodic schedulable and outreach services;

   • first-level clinical services; and

   • specialized services.

• The assumptions and estimations of this paper are also based on the work of Stenberg et al. (2017), who estimate per capita costs and health care financing needs for 67 countries and four platforms of services (policy and population wide interventions; periodic schedulable and outreach services; first-level clinical services; and specialized care). The authors consider two scenarios – the progress and the ambitious scenario – where the difference between the two is the level of achievement of the SDG health care targets by 2030. This paper uses the results of the ambitious case. A U-shaped curve is observed in the path of total and per capita costs in Stenberg et al. (2017). This behaviour is due to the assumption that core investments in human resources and infrastructure may take time to consolidate and to be effectively implemented. In the case of human resources, for instance, it may take five years to graduate trained medical doctors and nurses, while infrastructure projects require some initial design and planning before construction can commence. Stenberg et al. (2017) assume that the most important investments would be done in the middle of the 2016–2030 period covered in the analysis. The available data on per capita incremental financing needs to close the basic health care coverage by 2030 come from Stenberg et al. (2017), who use the databases of WHO. The cost of the public health package, consequently, refers to the sum of the public per capita spending in 2020 plus the incremental needs as previously mentioned.

5. The estimations cover the period 2020–2030, on the assumption that, by 2030, the five policy areas included in this study will achieve universal coverage for the respective population groups. There is an additional scenario that estimates the financing needs of achieving universal coverage in social protection and health care in 2020.

6. Financing effects of the COVID-19 pandemic on social protection systems, including health care, are incorporated for the years 2020 and, to a lesser degree, in 2021. Although the impact of the crisis is still ongoing and subject to continuous change, the exercise provides initial estimates based on reported figures from a set of countries and their policies in terms of increases in benefits, increments in health care budgets and the number of potential beneficiaries.

7. Only developing countries and territories are included in the study. For the purpose of this study’s analysis, countries are classified by geographic subregion and by country-income level. From a geographic point of view, each country was categorized using the ILO regional classification (see ILO, 2017); of the 12 regions defined, the study utilizes 11 regions since 1 of the regions (Northern America) only includes high-income countries. From the point of view of income, each country or territory was classified under the country-income classification of the World Bank, which categorizes countries by gross national income (GNI) per capita as follows: low-income, US$1,025 or less; lower-middle-income, US$1,026–3,995; upper-middle-income, US$3,996–12,375; and high-income, US$12,376 or more.

Projections and estimations of parameters

For projecting costs and financing gaps, five variables are critical, as set out below.

First, coverage rates were assumed to be 100 per cent in 2030 for each country. Therefore, the annual path to universal coverage in social protection and health care was assumed to be the difference between that eventual 100 per cent and the existing effective coverage rate, divided by 9 (the number of years between 2022 and 2030, both inclusive). The covered population for each year is the result of multiplying the coverage rate of each year by the potential population to be covered in the year. Due to the COVID-19 effect, the coverage rates for years 2020 and 2021 are considered to remain at the levels of 2019 but with the addition of 15 and 7.5 points, respectively. The rationale for the assumption of a 15-point increase in the coverage rate in 2020 is based on the observations of the ILO Monitor on Social Protection Response to the COVID-19 Crisis, which note an increased effort by countries in different regions. However, the study projects that the heightened effort due to COVID-19 would be reduced by half in 2021 and therefore assumes a 7.5-point increase relative to the 2019 coverage rate.

The second variable of importance was the amount of the benefit, as proxied by adjusted national poverty lines in the case of social protection interventions. As noted above, poverty lines in United States dollars are assumed to maintain their real value during the period of analysis. With respect to health care, the exercise makes use of the results of Stenberg et al. (2017), as already mentioned. The incremental per capita financing needs of 2030 (when universal coverage is achieved) is brought to 2020 in present value terms.

Third, to calculate the estimated cost in GDP terms at the beginning of the projection period, the data on GDP for the year 2020 was taken from the IMF. For the remaining years, the nominal GDP was projected by applying the average real GDP growth rate observed in the last 8–10 years, depending on country data availability. In some specific cases, that average rate was calculated considering the specific country’s conditions observed in the past decade. For example, some countries have experienced periods of negative rates punctuated by a few years of positive rates; in such cases, the average rate was calculated taking into account only the positive growth rates. The use of real rates instead of nominal rates follows the same principle of the poverty line to avoid introducing inflationary effects in the projections.

Fourth, for administrative costs, a rate of 5 per cent is applied to total cost of providing benefits for four of the five policy areas included in this study. That assumption is based on the experiences of a number of universal and targeted social protection programmes around the world. A previous ILO study assumed 3 per cent administrative costs for all universal benefits (for a detailed explanation, see Ortiz et al., 2017b, Annex I). This study assumes a slightly higher administrative cost of 5 per cent, given that non-contributory schemes usually entail high initial set-up costs and the need to procure assets to support delivery in developing countries. In addition, it is also assumed that in developing countries it takes time to gain from economies of scale and thereby reduce administrative costs.

Finally, the fifth variable of importance is the COVID-19 effect. The exercise reviews information on increases in social protection coverage, benefits and expenditure in response to the COVID-19 pandemic obtained from the ILO Monitor on Social Protection Responses to the COVID-19 Crisis. Based on observations for a set of 15 countries from different regions, it estimates the expected average growth of social protection benefits and beneficiaries in 2020. Overall, the study applies a 28.2 per cent increase in poverty lines and 15 additional points in coverage to the number of beneficiaries in 2020 relative to 2019. For 2021, the study assumes that this heightened effort due to COVID-19 would be reduced by half, that is, it assumes 7.5 additional points relative to the 2019 coverage. In the case of health protection, the additional resources were the result of applying a 14.6 per cent increment of the projected public health per capita spending in 2020, which is multiplied by each country population. The 14.6 per cent increment is a simple average of the expected health expenditure increases, based on an observation from a set of countries taken from the ILO Monitor on Social Protection Responses to the COVID-19 Crisis.

Cost estimates

Individual costs per benefit area are estimated using two indicators – the total monetary cost of the benefits package and the total cost as a percentage of GDP. The total cost is calculated by multiplying the desired benefit amount for the respective social protection policy areas by the potential covered population, according to the coverage rate of each year and country. The total cost of social protection benefits, for each region and income group, is calculated by adding up countries’ costs for each of the four benefits. That procedure applies to both monetary estimates and estimates as a proportion of GDP. For health care, the analysis applies a different methodology that consists of multiplying the expected beneficiary population by the incremental per capita financing needs. The COVID-19 factor is added to cost estimates for 2020 and 2021.

Financing gap analysis

The assessment of the financing gap considers the difference between two components: (a) the projected cost of the four social protection benefits per region and country-income group, expressed in monetary and GDP terms in the relevant year; and (b) the baseline expenditure or the social assistance expenditure for each region or country-income group in 2019. It is assumed that, in the absence of universal coverage, the baseline will maintain its per capita value during the period of analysis. The financing gap consists, therefore, of the difference between the cost of the four social protection benefits considered in the estimations and the baseline spending on social assistance. It is important to highlight that, due to the COVID-19 effect, the exercise assumes a 21.9 per cent increment in social assistance spending in 2020 (based on the observations made by the ILO Monitor), which subsequently declines by 50 per cent in 2021 (assuming that the COVID-19 effect dissipates). The study assumes that in 2022, countries will revert to the level of social assistance spending (in GDP terms) that prevailed in 2019. From 2022 to 2030, this initial value is adjusted to keep the same per capita value.

As noted above, the gap for health care is equivalent to the multiplication of the per capita incremental value by the beneficiary population, supplemented with a COVID-19 factor.

Fiscal space analysis

The last step takes the results of the previous stage and evaluates the possibilities for regions/country-income groups to finance the gap from different sources. It records the financing efforts taken by individual countries and international financial institutions in response to the COVID-19 crisis. It also presents the two alternative options that are considered in Durán-Valverde et al. (2019) – taxation and ODA, before the pandemic. The first option shows how domestic resources can be mobilized, which is a fundamental element of the strategy to create comprehensive and sustainable social protection systems, including social protection floors. The second option takes into account situations in which domestic fiscal capacity is insufficient and international aid is needed. The study also presents the exercise on social security contributions provided by Durán-Valverde et al. (2019), which assumes that countries with coverage rates and contribution rates below their expected average will experience an increase in coverage and contribution rates over the medium term until they reach the averages of countries with the same level of per capita income. However, decisions in that regard should be taken only after consultations have taken place between governments and social partners, given that a participatory approach is the most promising way to obtain necessary support in the implementation and roll-out of new policy measures that affect employers and workers to a significant extent.

The model

The construction of the model for estimations is carried out in three stages. First, the Cost of a Universal Social Protection Benefit for the four income protection benefits is calculated. This represents the optimal situation of universal coverage at the desired level of benefits. The Financing Gap is then calculated, defined as the difference between the total cost of a universal SPF benefit and the current total expenditure on social assistance in 2020. Finally, the Incremental Financing Needs are measured. This represents the amounts associated with progressively increasing coverage to meet the goals to be achieved between 2020 and 2030. The formulation is detailed below.

The Cost of a Universal Social Protection Benefit for the four benefit areas (excluding health care) is: 6

${CUC}_{i,j.t}={PCP}_{i,j,t}* \stackrel{-}{{BA}_{i,j,t}}+{ADM}_{i,j.t}$

Where,

${CUC}_{i,j.t}$ stands for the cost in monetary terms of the universal benefit

${PCP}_{i,j,t}$ is the Potential Covered Population (100 per cent for universal coverage)

$\stackrel{-}{{BA}_{i,j,t}}$ is the desired average benefit amount, and

${ADM}_{i,j.t}$represents the administrative costs of running the programme.

The aggregated Financing Gap for the four social protection policy areas considered in this study corresponds to the difference between the cost of achieving universal coverage and the baseline level of social assistance expenditure in each period.

${FG}_{ j,t}= \underset{Universal coverage}{\underbrace{\sum _i{\mathrm{C}\mathrm{U}\mathrm{C}}_{i,j,t}}}-\underset{Baseline expenditure}{\underbrace{{CEXPsocial assistance}_{j,t}}}$

Where,

${CEXPsocial assistance}_{j,t}$ is the baseline of expenditure on social assistance in the period $t$. The baseline is adjusted every year in relation to each year’s population growth rate (pgr) of the period in order to keep constant its value in per capita terms:

${CEXPsocial assistance}_{j,t}={CEXPsocial assistance}_{j,t_0}*(1+{pgr}_{j,t})$

The Incremental Financing Needs of a social protection benefit or programme in order for it to move from its current level of coverage to that needed for achieving universal coverage in 2030 is calculated by subtracting the baseline expenditure from the projection of the incremental expenditure (${IE}_{i, j,t}$) associated with the desired target coverage rate in each year. The target coverage rate is assumed to evolve linearly to reach 100 per cent by 2030.

${IE}_{i, j,t}={PCP}_{i,j,t}* {tcov}_{i,j,t}* \stackrel{-}{{BA}_{i,j,t }}$ $+ {ADM}_{i,j.t}$

Where,

${IE}_{i, j,t}$ is the incremental expenditure associated with the target coverage rate

${tcov}_{i,j,t}$ is the target coverage rate every year.

To close the coverage gap so that the region achieves universal protection by 2030, the coverage rate would need to be annually adjusted by

${\theta }_{i,j}=\frac{100\%-{cov}_{i,j,t_a}}{(2030-t_a)}$

Where,

${\theta }_{i,j}$ is the level of annual adjustment (in percentage points) of the coverage rate necessary to achieve universal coverage by 2030

$t_a$ refers to the year for which latest data on effective coverage is available, which is considered as the start year for the projections for universal coverage

$\stackrel{-}{{BA}_{i,j,t}}$ as mentioned above is the desired average benefit amount, which may also be understood as a desired level ($\stackrel{-}{{\beta }_i}$) of replacement rate with respect to the national poverty line ${PL}_{j,t}$.

$\stackrel{-}{{BA}_{i,j,t}}= {\stackrel{-}{{\beta }_i}*PL}_{j,t}$

Therefore, the Incremental Financing Needs results from the following expression:

${IFN}_{ j,t}= \underset{Coverage }{\underbrace{\sum _i{IE}_{i,j,t}}}-\underset{Baseline expenditure}{\underbrace{{CEXPsocial assistance}_{j,t}}}$

Given the particular parameters and data availability that this paper utilizes for health care estimations, the basic equation would differ from the ones described above. Therefore, to estimate the health care incremental financing needs of the 2020 universal coverage scenario, the mathematical expression was synthetized as follows:

$∆{Cost}_{2020}^{universal}= \left({ IncrementalPVcapita}_j^{2030}+{PHEcapita}_j^{2020}*cov2020\right)*Population$

Where Incremental PV refers to the present value of the 2030 per capita incremental financing need estimates, as per the calculations of Stenberg et al. (2017) based on WHO methodologies and databases (2017). The value for the COVID-19 financing demands parameter, cov2020, was estimated at 14.6 per cent of the projected 2020 public per capita health care spending $\left({PHEcapita}_j^{2020}\right)$. To obtain this last figure, the exercise linearly applied the average 2013–2017 public per capita health care expenditure growth rate to the 2017 level.

Programme/benefit-specific considerations

Given the heterogeneity of the programmes involved in the exercise, it is important to adjust the equations mentioned above to generate social protection benefit-specific calculations. Such adjustments should include the specific beneficiary populations to be covered and the dynamics of the path to universal coverage (scenarios based on hypotheses of how to close the coverage and financing gaps gradually over time), depending on the starting point.

In the case of protection for children, the specific desired benefit level $\stackrel{-}{{\beta }_i}$ is usually lower than 1 as it reflects age-adjusted needs that in many cases vary according to age group, such as lower calorie consumption needed for children aged 0-5.

For maternity, the specific desired benefit level $\stackrel{-}{{\beta }_i}$ is usually lower than 1 because the benefit is paid for only a part of the year, that is to say, 14 weeks (3.5 months) in line with Article 4 of the Maternity Protection Convention, 2000 (No. 183).

Data and sources of information

The data on a set of variables have been collected to produce estimates using the model and perform additional calculations. The following list specifies the data collected:

• National poverty line by country. The data were obtained from the World Bank’s World Development Indicators and national sources such as central banks and national statistical offices. Each of the lines was adjusted to 2020 terms using inflation rates from the year of the definition of the line and converted into United States dollars using the corresponding exchange rate. During the period of projection, poverty lines are assumed to maintain their values in real terms.

• Coverage rates by country. The source of these data is the ILO World Social Protection Database update as of September 2019 and comprises information on the proportion of the population groups that receive in-cash social protection benefits. For contributory systems, pension effective coverage rates as a proportion of the labour force were considered as a proxy of all social insurance programmes. The estimates for coverage rates are weighted by the number of people in the relevant population group. With respect to health care coverage, the information is taken from WHO and World Bank (2017), in which the figures represent the average coverage rate for the sample of countries considered in that report. The report uses a “universal service index” that includes information on effective coverage derived from 14 indicators covering prevention (health promotion and illness prevention) and treatment (curative, rehabilitation and palliation) services.

• Government expenditure by function and by expense category, as a percentage of GDP. This information comes from the following sources: (a) the ILO Social Protection Database as of 2019; (b) the IMF Government Finance Statistics database; (c) the Asian Development Bank (ADB) (2019); (d) the Economic Commission for Latin America and the Caribbean (ECLAC) database on non-contributory social protection programmes; (e) the African Union and the United Nations Development Programme (UNDP)(2019); and World Bank’s Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE) database. The latest available country information was utilized. Estimates for regional and income groups are weighted by nominal GDP.

• Actual and projected population by country. The source of this information is the United Nations World Population Prospects 2019 and covers 2020–2030 by age group.

• GDP in nominal terms, per capita, and in terms of purchasing power parity (PPP) in the last ten years. Real GDP growth rates in the last ten years by country were used, based on the World Bank’s World Development Indicators.

• Inflation rates and official exchange rates. This information was also obtained from the World Development Indicators of the World Bank.

• Health care incremental financing needs. Data on per capita financing needs to achieve universal health coverage in 2030 are taken from the work of the authors of Stenberg et al. (2017), a WHO team. They estimate the incremental financing needs of 67 low- and middle-income countries between 2016 and 2030.

• COVID-19 expenditure and coverage parameters. The calculation basis to estimate the growth in the level of social protection benefits and the additional number of beneficiaries is taken from the ILO’s monitor on “Social Protection Responses to COVID-19 Crisis around the World”.7 Increments in public health expenditures were estimated after collecting information online for country-specific cases.

For each analytical category, the latest available country information is utilized. For missing information or when the available data are obsolete (i.e. from well before 2019), data imputations are carried out based on regressions between the GDP per capita (PPP terms) and the variable of interest. The resulting equation is then applied to estimate missing data. Imputation analyses are conducted for coverage and spending variables at the country level. In some other cases, such as in social security contributions, imputations are developed to calculate the expected coverage rate with social security (proxied by the contributory coverage of the labour force with pensions) and estimate the contributory rate based on national old-age dependency ratios.

Table 1 presents the variables for which data have been collected, including their sources.

Table 1. Required variables/data and sources of information

Note: Most of the data from different sources listed above are part of the ILO World Social Protection Database 2019.

Social protection coverage patterns

According to the latest available data and the projections carried out, the four benefits considered in the SPF package calculated in the exercise cover 1.55 billion8 people in low- and middle-income countries in 2020 and health care covers 6.59 billion people, giving a combined coverage of 8.14 billion people. Significant coverage differences exist across the social protection benefits. The estimated average coverage rates show that about two out of every three older persons are covered by some type of pension benefit, although the rate is well below that average in low-income and lower-middle-income countries (see figure 1). By contrast, persons with severe disabilities have the lowest social protection coverage: only 18.5 per cent of persons with a severe disability receive a benefit in low- and middle-income countries (see table 2). Coverage rates for children and mothers are 29.7 and 34.8 per cent, respectively. Health care, as is the case for old-age protection, reaches more than 60 per cent of the potential beneficiaries. In sum, old-age protection has the highest levels of coverage and disability protection the lowest.

Table 2.Potential population covered by social protection policy areas in low-and middle-income countries (latest available data)

Source: ILO estimates based on World Social Protection Database 2019 and WHO (2017)

Several facts emerge from cross-tabulating geographic areas by types of benefit. Table 3 is coloured using the stoplight approach: coverage rates between 0.0 and 33.3 per cent are marked with one star, rates between 33.4 and 66.7 per cent with two stars, and rates above 66.7 per cent with three stars.9

The colours yellow and red predominate in the overall map of social protection benefits. Of the 55 cells in table 3 (11 regions x 5 benefit areas), 27 cells are yellow (33.4–66.7 per cent) and 19 cells are red (< 33.3 per cent), while only 9 cells are green (> 66.7 per cent), 4 of which refer to old-age benefits and 2 to health care.

Disability predominates in terms of low coverage, with 9 regions coloured red for that benefit area. Maternity, children and health coverage rates are mainly coloured yellow, with most regions showing moderate coverage for those two benefit areas. Old-age coverage rates, as previously mentioned, are the highest, with the rates of 4 regions coloured green, 3 yellow and 4 red. Health care has the second highest coverage and is the only policy area that has no coverage rates coloured red (<33.3 per cent).

A horizontal analysis, by region, allows for disaggregating locations by coverage performance. The top group with relatively higher performance includes Eastern Europe, Latin America and the Caribbean, and Central and Western Asia. Eastern Europe has the highest coverage rates, with high coverage rates (> 66.7 per cent) in the 4 social protection areas and a moderate coverage rate in health care (33.4–66.7 per cent). Latin America and the Caribbean ranks second, with 2 rates coloured green and 3 yellow. Central and Western Asia ranks third, with 1 rate green, 3 yellow and 1 red.

The second group of moderate performers can be split into two subgroups. The upper-moderate performers (Northern Africa; Northern, Southern and Western Europe; and South-Eastern Asia) all have 4 coverage rates coloured yellow, 1 red and 0 green. The lower-moderate performers (Arab States and Eastern Asia) have 2 coverage rates coloured red, although the latter has 2 rates coloured green (old age).

Finally, Southern Asia, Oceania, and sub-Saharan Africa comprise the group of low performers because their coverage rates are coloured red for the majority of the benefit areas, except for the case of maternity in Oceania and health care in all the three regional groupings.

Table 3. Coverage rates by type of social protection benefit (low-and middle-income countries/territories only, in
percentages)

¹ Developing countries/territories in Northern, Southern and Western Europe refer to Albania, Bosnia and Herzegovina, Montenegro, Kosovo, North Macedonia and Serbia, all classified as upper-middle-income countries/territories.

Source: ILO estimates based on World Social Protection Database 2019 and WHO (2017)

By country-income group, upper-middle-income countries show a quasi-universal coverage for persons aged 65 years and over (figure 1) and almost 75 per cent of the total population have health coverage. However, that rate is influenced by the weight of China and in those countries, only one third of the children have access to child benefits (34.5 per cent) and half of the mothers enjoy maternity benefits (53.6 per cent). In lower-middle-income countries, coverage is much lower for pensions (28 per cent) and the best-performing benefit area (health care) covers a little more than 5 out of 10 people. Disability is the least developed benefit area, covering only 8.6 per cent of persons with severe disability conditions. Finally, low-income countries present very low coverage across the different social protection areas, with disability having the lowest coverage for all regions and types of benefits. Only about 15 per cent of the elderly receive a pension in low-income countries, while only 4 out of 10 people have access to health care.

Figure 1. Coverage rates by social protection benefit area and country-income group
(low-and middle-income countries), 2020

Source: ILO estimates based on World Social Protection Database 2019 and WHO (2017)

Trends in social protection expenditures

According to the latest available information, the estimated global expenditure on social protection benefits (excluding health care) for developing countries amounted to US$2,086.6 billion or 6.6 per cent of GDP (134 countries).

The share of social protection expenditure in developing countries differs considerably across regions. For example, while social protection represents 1.9 per cent of GDP in Oceania, in Eastern Europe and Northern, Southern and Western Europe it exceeds 11.0 per cent of GDP (figure 2).

Figure 2 allows three separate groups of regions to be identified according to their levels of investment in social protection. The first group comprises Oceania, South-Eastern Asia, sub-Saharan Africa and Southern Asia, all with spending-to-GDP ratios below 3 per cent. The second group comprises the Arab States, Northern Africa, Latin America and the Caribbean, Central and Western Asia, and Eastern Asia, with spending-to-GDP ratios between 3.7 and 8.9 per cent. The third group comprises Eastern Europe and Northern, Southern and Western Europe, with spending-to-GDP ratios of 11 per cent of GDP or above.

Figure 2. Total social protection expenditures as a share of GDP, by region
(low- and middle-income countries)

Source: ILO estimates based on World Social Protection Database 2019.

There is a close connection between GDP per capita and the level of social protection spending. Figure 2 shows that upper-middle-income countries allocate, on average, about 6 times more than low-income countries and 3.3 times more than lower-middle-income countries, in percentage terms.

Information on the different components of social protection spending is available for a small selection of 38 developing countries that have full or partial data on how total social protection spending is disaggregated, which is summarized in figure 3. Old-age benefits account for 54.9 per cent of the total social protection expenditures of those countries, followed by social protection not elsewhere classified (n.e.c.) (20.7 per cent) and family and children (9.1 per cent). The four social protection policy areas included in this study represent 68.6 per cent of their total social protection spending. 10

Figure 3. Share of total social protection spending by function, selected developing countries, in percentages

Source: ILO estimates based on World Social Protection Database 2019.

Costing the package of social protection benefits, including health care, under universal coverage in 2020

The definitions of benefits and beneficiary groups are explained in section 2 above.

The cost estimation findings are summarized in the following two tables and figure 4. These figures contain the cost estimations for the four social protection benefits included in the analysis (children, maternity, disability and old-age protection). The results are presented both in monetary terms and as a percentage of GDP, based on the methodology presented in section 2.

In 2020, according to the estimations, the cost of providing the universal package is US$1,040.8 billion. From this total, US$49.6 billion corresponds to the administrative costs of providing the benefits. Four regions stand out in terms of total costs (Latin America and the Caribbean, Eastern Asia, Eastern Europe and Southern Asia) and together they account for 66.8 per cent of the total cost estimated for the 11 regions (US$649.9 billion). One third of the total cost corresponds to Latin America and the Caribbean. By benefit area, old age is the benefit making up the highest proportion of total cost (55.2 per cent), followed by disability (18.9 per cent) and children (17.0 per cent). At the bottom, maternity accounts for 4.1 per cent of the total cost.

When the analysis is broken down by income category, it shows that upper-middle-income countries account for 72.8 per cent of the total cost of the package, while lower-middle-income countries account for 23.2 per cent and low-income countries just 4.0 per cent. By benefit, a similar trend may be observed of an increased share in the total cost with increased income. The old-age benefit is highly concentrated in upper-middle-income countries, which account for almost 80 per cent of the total cost of that benefit, while low-income countries account for just 1.9 per cent. The remaining three benefits show a similar pattern of distribution among income groups, with upper-middle-income countries on average accounting for 61.7 per cent of the total cost, lower-middle-income countries for 30.5 per cent and lower-income countries for 7.7 per cent.

Table 4. Cost of a universal package of four social protection benefits in 2020 (low- and
middle-income countries, in US$ billion)

Source: ILO estimates based on World Social Protection Database, including IMF Government Finance Statistics (GFS), World Development Indicators (WDIs), UNDP, ADB, ECLAC and several national sources of information on poverty lines.

The total cost, including the administrative cost, is estimated at 3.3 per cent of the GDP of the set of developing countries included in the study (table 5). Three regions stand out with the highest percentages, namely Northern, Southern and Western Europe (10.8 per cent of GDP), Central and Western Asia (10.1 per cent) and Latin America and the Caribbean (7.5 per cent). In contrast, Eastern Asia is the region with the lowest percentage, which corresponds to 0.9 per cent of GDP; this can be explained mainly by the presence of China in that region.

With respect to health care, Stenberg et al. (2017) estimate the cost of providing universal health services in 2020 at US$1,436.6 billion, with Eastern Asia accounting for US$601.2 billion (41.8 per cent of the total cost) and Latin America and the Caribbean for US$276.8 (19.3 per cent of the total cost). In contrast, Oceania and Northern, Southern and Western Europe are the two regions with the lowest total cost in health care (US$8.1 million combined). By income category, upper-middle-income countries account for more than 75 per cent of the total cost, while lower-middle-income countries account for 20 per cent and lower-income countries for just 3.3 per cent.

Table 5. Cost of a universal package of four social protection benefits in 2020, by region and country-income group (low- and middle-income countries, in percentage of GDP)

Source: ILO estimates based on World Social Protection Database 2019, including IMF/GFS, WDIs, UNDP, ADB, ECLAC and several national sources of information on poverty lines.

The total cost of providing the four social protection benefits (maternity, children, disability and old age) to 100 per cent of potential beneficiaries in 2020 amounts up to 8.5 per cent of GDP for low-income countries, 3.4 per cent for lower-middle-income countries, and 3.2 per cent for upper-middle-income countries. According to figure 4, among the four benefits, benefits for children in low-income countries make up the highest cost in terms of GDP (3.1 per cent). By region, developing countries in Northern, Southern and Western Europe account for the highest cost in GDP terms (10.8 per cent). In contrast, benefits for children in Eastern Asia have a total cost of 0.9 per cent of GDP. Old age is the benefit with the highest cost across regions, except for sub-Saharan Africa, where the highest cost is registered for children. Variations in costs across regions can be partly explained by demographic trends within these regions and differences in the benefits (as determined by the poverty line), with relatively higher income countries paying higher level of benefits.

With respect to health care, total cost represents 9.7 per cent of GDP in low-income countries, 4.1 per cent in lower-middle-income countries and 4.6 per cent in upper-middle-income countries. The top two regions in terms of total cost to GDP are the Arab States and developing countries in Northern, Southern and Western Europe, at 6.5 per cent and 6.4 per cent of GDP, respectively. Conversely, three regions are at the bottom, all at 3.8 per cent of GDP (Eastern Europe, South-Eastern Asia and Southern Asia).

Figure 4. Cost of a universal package of four social protection benefits in 2020, by country-income group (low- and
middle-income countries, in percentage of GDP)

Source: ILO estimates based on World Social Protection Database 2019.

Estimating the financing gaps for achieving universal coverage of SPFs in 2020

This section presents the annual resources needed to close the financing gap in 2020. The objective is to estimate the global effort needed to close the coverage gap in 2020. To do so, the financial gap is calculated by subtracting the social assistance expenditure in 2020 from the total cost for the universal provision of the benefits for the five policy areas included in the analysis.

The total financing gap is estimated at US$1,191.6 billion (see table 6). The two regions with the highest proportion of the total gap are Latin America and the Caribbean (28.0 per cent of total) and Southern Asia (15.9 per cent of total). By income group, upper-middle-income and lower-middle income countries account for 63 per cent and 30 per cent of the total financing gap, respectively. In contrast, of the total financial gap, low-income countries account for just 6.5 per cent. The differences are partly explained by the composition of the set of countries considered in the study, in which low-income countries represent a smaller share of the total number of 134 developing countries represented. Differences in the amount of benefits in different country-income groups are an additional explanatory factor.11 When the size of the financial gap is considered vis-à-vis the regional level of GDP, the highest ratios are found in Central and Western Asia (9.3 per cent), Northern Africa (8.3 per cent), and sub-Saharan Africa (8.3 per cent).

Separately, the gap in the four social protection areas (children, maternity, disability and old age) reaches US$707.4 billion, which represents 2.2 per cent of GDP of the developing countries included in the study. In relative terms, the gap is higher in low-income countries, where 7.4 per cent of GDP should be devoted to close the gap by 2020. In upper-middle-income countries, the gap represents 2.1 per cent of GDP. For health care, the effort needed to close the gap by 2020 reaches US$484.2 billion or 1.5 per cent of GDP. By income level, this gap varies from 1.1 per cent of GDP in upper-middle-income countries to 8.5 per cent of GDP in low-income countries.

Table 6. Financing gap for achieving universal social protection coverage in 2020, in US$ billions and as a percentage of GDP (low- and middle-income countries only)

Source: ILO estimates based on World Social Protection Database 2020 and Stenberg et al. (2017) using WHO methodologies and databases (2017).

Incremental financing needs under progressive universal coverage from 2020 to 2030

This section aims to present a scenario in which universal coverage of the SPF is gradually achieved. The estimations cover the period 2020–2030 and at the end of the period the figures show the 100 per cent coverage for the five policy areas of the SPF (children, maternity, disability, old age and health care) included in the study. The coverage of the SPF is estimated to increase progressively from the levels observed in 2020 to reach 100 per cent in 2030, following a linear progression in the targeted coverage rate for each year.

Two components need to be estimated to obtain incremental financing needs. The first component is an estimation of the SPF cost year by year (following the progressive increase in coverage explained in the previous paragraph). To reflect the effect of COVID-19, the coverage rate is increased by 15 per cent in 2020 and by 7.5 per cent in 2021, the rationale for which has been provided above. In line with the previous assumption, the level of benefits will also increase by 28.2 per cent in the period 2020–2021, after which it will remain constant. The second component is the baseline social assistance expenditure, which remains constant in its real per capita value during the projection period. Due to the COVID-19 effect, as explained in section 2.2 above, social assistance expenditure is projected to increase by 21.9 per cent in 2020 and by 10.95 per cent in 2021. The estimation of incremental financing needs corresponds to the difference each year, in monetary or GDP terms, between the total cost and the baseline social assistance expenditure. Table 5 and figure 7 summarizes the results of incremental financing needs.

Figure 5. Incremental financing needs for progressively closing the social protection coverage gap, in US$ billions per year and as a percentage of GDP (low- and middle-income countries), 2020–2030

Source: ILO estimates based on World Social Protection Database 2020 and Stenberg et al. (2017) using WHO methodologies and databases (see WHO 2017).

At the beginning of the period, the incremental financing needs for the four social protection areas (children, maternity, disability and old age) represent 1.56 per cent of GDP in developing countries. The figures for 2020 and 2021 are higher than the rest of the series, due to the increases in the coverage and the level of benefits as a result of the COVID-19 effect, many of which were announced as temporary measures. After this period, these financing needs grow progressively until 2030 to reach 1.32 per cent of GDP. In the case of health care, these needs vary from 0.88 per cent of GDP in 2020 to 0.87 per cent in 2030. These needs follow an inverted “U” shape pattern, based on the assumptions of Stenberg et al. (2017) and an increase of 14.6% in the health care expenditure in 2020 due to the COVID-19 effect.

Table 7 summarizes the amounts of incremental financing needs required to achieve the social protection floor by 2030 in the developing countries included in the study. The total incremental financing needs are set at US$769.0 billion in 2020 and at US$781.0 billion in 2021, influenced by the COVID-19 effect. After 2022, the incremental need grows progressively until 2030 to reach US$1,200.7 billion. To close the gap by 2030, low-income countries require a higher effort in terms of their GDP (11.49 per cent) compared to lower-middle income countries (3.21 per cent) and upper-middle income countries (1.67 per cent).

Table 7. Annual incremental financing needs for progressive universal coverage, by income level, in US$ billions and percentage of GDP (low- and middle-income countries), 2020–2030

Source: ILO estimates based on World Social Protection Database 2019 and Stenberg et al. (2017) based on WHO methodologies and databases (2017).

Fiscal space creation is feasible even in low-income countries

Concerning financing options, SDG target 1.a calls on countries to “[e]nsure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries …” Recommendation No. 202 also underscores the need for “ … using a variety of different methods to mobilize the necessary resources … ”. Indeed, there exist several approaches, even in low-income countries, to create fiscal space for financing social protection. International experience shows that countries can draw on eight different strategies for creating fiscal space, which should be examined in the context of a national social dialogue, namely: (i) increasing tax revenues; (ii) expanding social security coverage and contributory revenues; (iii) eliminating illicit financial flows; (iv) reallocating public expenditures; (v) using fiscal and central bank foreign exchange reserves; (vi) managing debt: borrowing and restructuring existing debt; (vii) adopting a more accommodating macroeconomic framework; and (viii) increasing ODA aid and transfers (see ILO, 2017; Ortiz et al., 2019a).

Increasing tax revenues. This is a key channel for generating government revenue that is achieved by altering the rates for different types of taxes – for example taxes on corporate profits, financial activities, property, inheritance, import/exports and natural resources – or by strengthening the efficiency of tax collection methods and overall compliance. Many countries are increasing taxes for social protection or raising revenues through innovative taxes. For example, the Plurinational State of Bolivia, Mongolia and Zambia are financing universal pensions, child benefits and other schemes from mining and gas taxes; Ghana, Liberia and the Maldives have introduced taxes on tourism to support social programmes; Gabon has used revenues from value-added-tax on mobile communications to finance its universal health care system; Algeria, Mauritius and Panama, among others, have supplemented social security revenues with a high tax (sin tax) on tobacco; and Brazil has introduced a temporary tax on financial transactions to expand social protection coverage.

Initiatives to levy taxes on big tech companies and to enact a Google tax or diverted profit tax to deal with profit-shifting to low tax jurisdictions have gained momentum in recent years. The OECD has an initiative to design a strategy that addresses the challenges of taxing the digital economy. It involves 134 countries and proposes that the large corporations, including digital companies, pay taxes where their users are, regardless of the tax domicile of the companies. OECD and G20 have an inclusive framework on base erosion and profit-shifting (BEPS). According to them, BEPS practices cost countries about US$100 to 240 billion in lost revenue annually, which is equivalent to 4 to 10 per cent of global corporate income tax revenue.15 Other examples of innovative taxes include taxes on international billionaires that could generate about US$ 40 to 50 billion per year additional to existing ODA (United Nations 2012).

Extending social security coverage and increasing contributory revenues. Increasing coverage and thereby raising contributions is a reliable way to finance social protection, freeing fiscal space for other social expenditure. Social protection benefits linked to employment-based contributions also encourage the formalization of the informal economy: Uruguay’s Monotax provides a remarkable example. Argentina, Brazil, Tunisia and many other countries have demonstrated the possibility of broadening both coverage and contributions.

Eliminating illicit financial flows. Estimated at more than ten times the size of all ODA received, a colossal amount of resources illegally escapes developing countries each year. There is a growing effort, particularly within the United Nations and other international agencies, to devote more attention to cracking down on money-laundering, bribery, tax evasion, trade mispricing and other financial crimes that are both illegal and deprive governments of revenues needed for social protection and the SDGs.

Reallocating public expenditures. This orthodox approach includes assessing ongoing budget allocations through public expenditure reviews, social budgeting and other types of budget analysis; replacing high-cost, low-impact investments with investments that result in more substantial socio-economic impacts; eliminating spending inefficiencies; and tackling corruption. For example, Costa Rica and Thailand have reallocated military expenditures to universal health care, while Ghana, Indonesia and many other developing countries have reduced or eliminated fuel subsidies and used the proceeds to extend social protection programmes.

Using fiscal and central bank foreign exchange reserves. This option includes drawing down fiscal savings and other state revenues stored in special funds, such as sovereign wealth funds, and/or using excess foreign exchange reserves in the central bank for domestic and regional development. Chile, Norway and the Bolivarian Republic of Venezuela, among others, are tapping into fiscal reserves for social investments, while Norway’s Government Pension Fund Global is perhaps the best-known example of this option.

Managing debt: borrowing and restructuring existing debt. This strategy involves an active exploration of domestic and foreign borrowing options at low cost, including concessional, following careful assessment of debt sustainability. For example, in 2017, Colombia launched the first social impact bond in developing countries and South Africa issued municipal bonds to finance basic services and urban infrastructure. Moreover, in the past more than 60 countries have successfully renegotiated debt and more than 20 (for example, Ecuador and Iceland) have defaulted on or repudiated public debt, directing debt-servicing savings to social protection. In the context of the COVID-19 crisis, the argument in favour of debt moratorium – delaying the payment of debts or obligations – and debt cancellation is gaining strength. A moratorium on public external debt service could release up to US$50.4 billion over the next two years (Eurodad 2020).

Adopting a more accommodating macroeconomic framework. This entails permitting higher budget deficit paths and/or higher levels of inflation without jeopardizing macroeconomic stability. A significant number of developing countries used deficit spending and a more accommodating macroeconomic framework during the global recession to attend to pressing demands at a time of low growth and support socio-economic recovery. In the current COVID-19 crisis, the fear of triggering high inflation has halted the new issuance of Special Drawing Rights (SDRs) – reserve assets created by the IMF. A large amount of new SDRs, at least 1–2 trillion, could inject additional liquidity into the global financial system and provide developing countries with much-needed foreign exchange reserves to combat the crisis (see Ghosh 2020).

Increasing aid and transfers. The extension of fiscal space by drawing on domestic sources is a fundamental element of strategies for creating comprehensive social protection systems. However, there are considerable gaps, especially in some developing countries, between domestically generated resources and the resources required for universal social protection systems. Fiscal deficits and the inadequacy of resources translate in many cases into gaps in coverage and loss of well-being. ILO Recommendation No. 202 accordingly suggests that countries “…whose economic and fiscal capacities are insufficient to implement the guarantees may seek international cooperation and support that complement their own efforts.” (para. 12). The Governments of countries such as Pakistan, Madagascar, Namibia, Tajikistan and Zimbabwe report that they have received support from international partners to finance their social protection systems in the past. Moreover, the Government of Burkina Faso counts on international cooperation for its national social protection floors strategy, while the implementation of national plans in Czechia has been based on resources from the state budget and the European Social Fund (ILO 2017).

The table below presents a selection of innovative financing instruments, grouped under three different mechanisms – taxes, contributions and other obligatory charges; debt-based borrowing mechanisms; and voluntary and solidarity contributions – with a list of eight criteria to assess each instrument. The instruments in each group are arranged according to the degree of innovation, from the most to the least innovative approach. The list does not include traditional instruments such as corporate taxes, international trade taxes and social security contributions, which evidently play an important role in generating revenue. The assessment has been prepared using a Harvey-Ball approach, in which no shade implies no effect and a full shaded ball implies a high impact. Although the methodology gives a quick assessment, each variable has multiple analytical dimensions that require specific considerations. There are two ways to read the table. A “vertical reading” allows a comparison of each criterion across different financing instruments. For example, in terms of “correction of misbehaviour”, some options (such as sin taxes on tobacco and fast food) are better alternatives than others. The “horizontal reading” allows each instrument to be assessed by different criteria. For instance, it may happen that taxes on internet purchases have the potential to generate a large share of resources but the institutional capacity to effectively collect those funds is limited

Assessing taxation and ODA for closing the financing gap17

Taxation

Member States acknowledged in the Addis Ababa Action Agenda that additional domestic public resources are required in order to achieve the SDGs (UN, 2015). Taxation is usually considered the first source of additional financing to finance non-contributory programmes. Based on the information on tax revenues in the World Bank World Development Indicators, the global tax burden in 2018 is estimated at 11.1 per cent of GDP

In order to understand the magnitude of the gap in financing SDG target 1.3 in terms of overall tax collection, a costing exercise was undertaken to calculate and analyse the corresponding indicator. On average, the 2019 SPF financing gap represents 13.5 per cent of the total tax revenue.

Figure 10 allows the identification of three categories of regions. The first category (Eastern Europe and Eastern Asia) refers to places where the financing gap represents less than 10.5 per cent of total revenues from taxes. These regions may explore the possibility of implementing reallocation strategies to reduce the financing gap, which would require strong political will to prioritize social protection in terms of public financing.

In the second category (Southern Asia, South-Eastern Asia, Oceania and sub-Saharan Africa), the financing gap represents 10 to 20 per cent of the total revenues from taxes. Even if expenditure reallocation is an option, the level of effort needed to reduce the financing gap would require structural, long-term measures to generate more savings for allocation to social protection; however, such measures usually take a significant amount of time.

Finally, in the third category (Arab States, Northern Africa, Northern, Southern and Western Europe, Latin America and the Caribbean, and Central and Western Asia), the financing gap represents more than 20 per cent of total revenues from taxes. The reduction of such a large financing gap would require either the implementation of new taxes or a search for alternative and innovative sources of funds.

In low-income-countries, the SPF financing gap is very high – at 45 per cent of current tax revenues. Therefore, reducing the financing gap in low-income-countries would require a significant reallocation of public resources to finance social protection at the expense of other social spending priorities.

Figure 10. Total SPF financing gap as a percentage of the tax burden in 2019, by region (low- and middle-income countries)

Source: ILO estimates based on World Social Protection Database 2019.

Even if regions are able to finance SPFs in the short term by reallocating expenditure, in the medium and long terms their financing strategy should include structural changes and span multiple sources in order to achieve the objective of a universal social protection floor. Possible options include a mix of financing sources, such as increasing taxation and social security contributions, additional ODA for social protection and other alternatives, such as increasing corporate taxes, taxing the digital economy and creating special taxes on financial transactions as discussed above.

Role of ODA

As it was done for taxation above, a similar exercise was conducted for ODA. Based on analysis of information on ODA flows to developing countries in 2017, table 10 compares the SPF financing gap with ODA as a percentage of GDP. Overall, ODA does not seem to be a viable source if it remains at current levels: the estimated global gap in SPF financing is five times the level of ODA currently allocated to developing countries.

Of the 11 regions and 3 income groups considered in the exercise, only 3 categories (Arab States, Oceania and low-income countries) have an SPF financing gap that is smaller than their total ODA flows and even in those instances the gap already represents a significant share of existing ODA. The SPF financing gap is equivalent to between 65 and 85 per cent of the total ODA allocated by the OECD to developing countries. In Latin America and the Caribbean, the gap would represent 36 times existing ODA, while in upper-middle-income countries the multiplier would be 13.5.

Some specific regions may deserve attention. In sub-Saharan Africa, the SPF financing gap in 2019 is equivalent to the total ODA allocation to that region. In other words, to fill the gap in financing the SPF in that region with ODA flows only, total development assistance would have to be doubled. Table 10 gives ODA flows to Eastern Asia at 0 per cent of GDP owing to the significant influence of China in both size of GDP and ODA outflows. If China were removed from the calculation, the level of ODA flows would jump to 6.8 per cent of GDP and would thus greatly exceed the existing SPF financing gap.

Even if there is room for action in some regions, in general terms the use of ODA for social protection financing seems to be limited. For example, in order to fully close the SPF financing gap with ODA, overall assistance for development would have to double between 2019 and 2030 and in some cases such as Latin America and the Caribbean, regional ODA would have to be multiplied by 13. At the same time, to achieve the desired result, the estimated increment would have to be fully allocated to social protection, which seems unrealistic given the long list of other priority development areas. Moreover, the idea of reassigning existing ODA to social protection without altering the level that OECD/DAC countries allocate is a very complex one given the history of past and future commitments.

This situation becomes even more complex when analysis focuses on ODA for social protection rather than on total ODA allocated to all development areas. Between 2010 and 2015, the disbursed ODA to social protection under OECD/DAC CRS code 16010 18 averaged US$2,346.7 million, while the committed level of social protection ODA totalized US$ 2,647.7 million. One of the critical characteristics of disbursed ODA flows is its highly unstable growth rate. Over the same period, social protection ODA grew at -1.0 per cent, so that in three of the five assessed years the rate was negative. The disbursed flows represented 0.0037 per cent of GNI of the donor countries; since 2011, that contribution has never returned to its 2010 levels (Ortiz et al., 2017b). 19

Table 10. Comparison of SPF financing gap in 2019 and ODA allocation in 2017, by region (low- and middle-income countries, in percentage of GDP)

Note: These ODA allocations comprise all categories of development assistance and not only social protection.

Source: ILO estimates based on World Social Protection Database 2019.

Main figures and findings

According to ILO estimates, only 45 per cent of the world's population are covered by at least one social protection benefit. Given the specific situation of developing countries, the extent of the coverage gap is even more worrying: coverage in those countries barely reaches 30 per cent of children, 18 per cent of people with severe disabilities and 35 per cent of mothers with newborns.

This study shows that coverage gaps affect virtually all regions of the world and all developing countries, including upper-middle-income countries. As might be expected, the gaps in coverage – measured as the percentage of the population who are potential beneficiaries of social protection programmes – are significantly larger in low-income countries than in middle- or upper-middle-income countries. For example, in low-income countries only 8.7 per cent of children and 15.3 per cent of older persons are covered by social protection programmes, whereas in upper-middle-income countries 35 per cent of children and 90 per cent of older persons are covered. In terms of absolute population size, total coverage gaps are much more significant in middle- and upper-middle-income countries. More people are excluded from social protection in a few large middle- and upper-middle-income countries than in all low-income countries worldwide. This is a significant finding that should be taken into account when analysing the regional and income distribution of absolute gaps in social protection financing and considering strategies to fill the global gaps.

The total cost of the SPF with five policy areas in order to reach universal coverage in 2020 is about US$2,476 billion. Concerning the cost of achieving universal coverage of a basic set of SPF benefits – covering children between 0 and 5 years, women with newborn children, persons with severe disabilities and older persons – the findings of this study indicate that costs vary both by region and by country-income level. Globally, for the 134 developing countries considered in the study, the total estimated cost for 2020 is about US$1,040 billion or 3.3 per cent of their GDP, including administrative expenditures. However, that cost is considerably higher in low-income countries, estimated at 8.5 per cent of GDP. These findings are consistent with those of previous ILO studies (such as Ortiz et al. 2017). The cost of ensuring universal health coverage increases the total cost by US$1,436.6 billion or 4.6 per cent of the GDP of developing countries. As is the case for the four social protection benefits (children, maternity, disability and old age), the cost for health protection is higher for low-income countries (9.7 per cent of GDP).

The findings presented in this study show that closing the global SPF financing gap in social protection, including health care, would require an additional US$1,191.6 billion per year or 3.8 per cent of the GDP of developing countries. This SPF financing gap varies across regions and country-income groups. It ranges from 1.3 per cent of GDP in Eastern Asia to 9.3 per cent in Central and Western Asia and from 3.1 per cent in upper-middle-income countries to 15.9 per cent in low-income countries. In monetary terms, however, the gap may be as low as US$2.4 billion per year in Oceania (due to the region’s small population) or as high as US$332.2 billion per year in Latin America and the Caribbean. The financing gap in low-income countries is estimated at US$77.9 billion per year.

In terms of the incremental financing needs to progressively achieve universal coverage by 2030, the required amount is about US$491.1 billion in 2020 (excluding health care), equivalent to approximately 1.56 per cent of the GDP of the developing countries considered in the study. That required amount will rise gradually in subsequent years to reach US$725.5 billion in 2030, equivalent to 1.32 per cent of GDP. If health protection is included, the required amount by 2030 increases to US$1,200.7 billion or 2.19 per cent of GDP. For lower-middle income countries, the incremental financing needs (including health protection) represent 11.49 per cent of GDP by 2030, while for lower-middle-income countries it represents 3.2 per cent and for upper-middle-income countries 1.67 per cent.

The study also assesses the capacity of contributory systems to reduce their financing gap by increasing the coverage and contribution levels of existing contributory schemes. Assuming that all countries below the expected level of coverage and contribution rates move up to that level, this would generate additional social security contributions equivalent to 1.2 per cent of the GDP of developing countries. In particular, low-income countries would double their current levels of collection of social security contributions (from 0.4 to 0.8 per cent of GDP).

Domestic efforts to respond to COVID-19 amount to approximately US$10.6 trillion. However, most of these fiscal resources were mobilized in high-income countries – a mere 0.06 per cent of that amount was mobilized in low-income countries. International efforts are also under way to raise resources to support countries in responding to the pandemic. As of 3 September 2020, the amount of up to US$126.6 billion has been effectively approved and allocated to support countries in the area of social protection and health care. Although this allocated amount has contributed to combating the crisis, it is insufficient to cover the total financing gap of US$1,191.6 billion in social protection and health care for developing countries in 2020.

Given the goals of the 2030 Agenda and the commitment to achieve the specific SDG targets1.3 and SDG 3.8, these findings call attention to the need for a global effort that involves most countries and does not focus exclusively on the poorest countries. However, strategies for achieving the goals of universal coverage of the SPF may vary according to the specific level of development of countries.

As documented in this study, the social protection financing gap represents on average about 13.5 per cent of the tax burden of developing countries in the pre-COVID-19 period. Many countries have the potential to fill their gaps from domestic sources and that should undoubtedly be a policy priority. For example, for upper-middle-income countries, the gap is equivalent to 13 per cent of the tax burden. Experience shows that policy decisions on social protection reforms usually have a long-lasting effect on the country’s national budget as well as on employers’ and workers’ contributions to the system. In many countries, therefore, governments do not take such decisions in isolation; rather, they seek support and consult with a full range of stakeholders, in particular workers and employers’ organizations, in order to ensure that decisions are politically sustainable, understood and accepted. As workers and employers are the most directly affected by such decisions – in particular by the levels of contributions and benefits in contributory systems – the success rate will increase considerably if they understand the reasons for reforms and they can and should be involved in the smooth implementation of such reforms. Genuine social dialogue is therefore an absolute condition of reaching these policy priorities.

On the other hand, many countries are far from being in a position to fill social protection financing gaps through their own efforts; for example, low-income countries would require an equivalent of 45 per cent of their current tax revenues to do so (based on an analysis during normal times). Therefore, the challenge is much greater for low-income countries, both during and outside periods of crisis as well as in terms of the relative cost and their relative fiscal, administrative and institutional capacity. That distinction must be considered as a critical factor in the formulation of a specific development assistance policy. Massive financial assistance for starting up and temporarily financing benefits could be a feasible option for addressing the SPF gap in low-income countries.

According to the estimates produced by this study, at least an annual investment of about US$77.9 billion, equivalent to 15.9 per cent of GDP of low-income countries, would be required to fill the social protection (including health care) financing gaps in these countries. When that figure is considered as a percentage of the GNI of donor countries, the amount becomes negligible.

However, the current level of ODA for social protection is insufficient to meet the financing needs identified in this study. In terms of the Addis Ababa Action Agenda, many countries still fall short of their ODA commitments. Moreover, in terms of ODA for social protection as opposed to total ODA, the shortfall is much greater: the disbursed ODA for social protection represented 0.0047 per cent of the GNI of donor countries in 2017.

Moving from general strategies to specific policies and actions

The possibilities for the development of a universal social protection system are closely linked to the strategy and level of countries’ overall development. Social protection and social and economic development go hand in hand and support each other. The positive effects on development of investing in social protection are well documented and widely accepted. A new development model should place investment in social protection and social investment in general at the heart of development policies.

The development of both contributory and non-contributory social protection systems can have a significant positive impact in the short, medium and long terms. SPF development, for example, can lead to an immediate reduction of poverty by improving the opportunities for better employment of young people entering the labour market and supporting a more productive business environment that leverages economic development. On the other hand, the extension of contributory systems linked to formalization policies can also have immediate effects on the formalization of employment and enterprises, and poverty reduction, as well as covering people with higher levels of protection. In terms of action, both strategies must complement each other and both are indispensable.

Opportunities to extend fiscal space exist in virtually every country, as international experience shows. Countries and policymakers worldwide face the difficult task of thinking about and implementing innovative ways of creating fiscal space beyond traditional recipes to offset the growing economic inequality. This and other ILO and United Nations studies explore and discuss several strategies for creating fiscal space, including the actions proposed below.

Concrete actions for discussion at the level of national governments and with social partners

1. Maximize the domestic fiscal space, including through taxes and social security contributions. The link with tax, labour market, employment and enterprise formalization policies plays a critical role in this strategy.

2. Strengthen ODA. Developed countries should make an effort to comply with the minimum commitments established in the Addis Ababa Action Agenda, which are far from being met. Given the financing requirements for achieving the whole set of SDGs (several trillion US$), it is clear that current ODA levels have limited capacity to fill the gap. In the area of social protection, ODA should focus primarily on two objectives:

   • First, ODA should contribute to the development of national capacities to improve social protection systems, including the proper design, management and financial sustainability of those systems.

   • Second, ODA can play an important role in the implementation of nationally defined social protection floors in low-income countries that guarantee universal protection, including by financing social protection benefits where national resources are insufficient.

3. Foster transitions from the informal to the formal economy. These are critical for promoting coverage and financing based on taxes and social security contributions. Social security contributions must continue to play a fundamental role in financing social protection. Formalization, decent work and the extension of contributory coverage are indispensable policies and are directly linked to an integrated, fairer and more inclusive development model.

4. Commit the IFIs to play a bigger part in protecting social expenditure. The IMF, in particular, could play a critical role. In that regard, it is crucial to refer to the IMF's recent commitments to developing a strategic framework that will provide broad guidance for future IMF engagement on social protection issues, including the use of social spending floors.

5. Work at multiple levels (global, regional, national) with diverse actors, including the IFIs, to increase financing mechanisms for social protection through national and global solidarity and promote the application of ILO standards in financing social protection, prioritizing low-income countries. This action could facilitate the identification of sources to temporarily and partially finance social protection benefits in low-income countries (through matching and other conditions), as well as the protection of countries in need against the shocks linked to climate change, humanitarian crises and health emergencies. The estimates presented in this study provide a sound basis for initiating discussions on designing such mechanisms.

International statistics and data sources

ADB. 2019. “Social Protection Index”. Available at: https://spi.adb.org/spidmz/

ECLAC. 2019. “Non-contributory Social Protection Programmes Database”. Available at: https://dds.cepal.org/bpsnc/about?bd=cct

ILO. 2019. ILOSTAT. Available at: https://ilostat.ilo.org/

–––. 2019. “Social Security Statistical Databases”. Available at: https://www.ilo.org/sesame/IFPSES.SSDBMenu

IMF. 2019. “Government Finance Statistics”. Available at: https://data.imf.org/?sk=a0867067-d23c-4ebc-ad23-d3b015045405

World Bank. 2019. “ASPIRE: The Atlas of Social Protection Indicators of Resilience and Equity”. Available at: http://datatopics.worldbank.org/aspire/.

–––. 2019. “World Development Indicators”. Available at: https://databank.worldbank.org/source/world-development-indicators

WHO. 2017. Seguimiento de la Cobertura Sanitaria Universal: Informe de Monitoreo Global 2017.