The Spending Challenge for Reaching the SDGs in Sub-Saharan Africa: Lessons Learned from Benin and Rwanda
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund

This paper documents the additional spending that is required for sub-Saharan Africa (SSA) to achieve meaningful progress in SDGs by 2030. Benin and Rwanda are presented in detail through case studies. The main lessons are: i) average additional spending across SSA is significant, at 19 percent of GDP in 2030; ii) countries must prioritize their development objectives according to their capacity to deliver satisfactory outcomes, iii) financing strategies should articulate multiple sources given the scale of additional spending, and iv) strong national ownership of SDGs is key and should be reflected in long-term development plans and medium-term policy commitments.

Abstract

This paper documents the additional spending that is required for sub-Saharan Africa (SSA) to achieve meaningful progress in SDGs by 2030. Benin and Rwanda are presented in detail through case studies. The main lessons are: i) average additional spending across SSA is significant, at 19 percent of GDP in 2030; ii) countries must prioritize their development objectives according to their capacity to deliver satisfactory outcomes, iii) financing strategies should articulate multiple sources given the scale of additional spending, and iv) strong national ownership of SDGs is key and should be reflected in long-term development plans and medium-term policy commitments.

I. Introduction

Sub-Saharan Africa (SSA) faces considerable development challenges. Rapid population growth, high-persistence of inequality (Odusola et al., 2017)2 and prevalence of fragility (Calderon et al., 2019)3 in certain countries explain a very slow decline in the level of extreme poverty4 from 54 percent in 1990 to 41 percent in 2015 while global extreme poverty was reduced from 36 to 10 percent (Figure 1a). Other regions have experienced much larger reductions of their extreme poverty rates: over the same period, extreme poverty has decreased from 62 to 2 percent in the Asia and Pacific region—driven by the reduction of extreme poverty in China and India—and from 14 to 4 percent in the region of Latin America and Caribbean. Some progress has been achieved in maternal, infant and child mortality. But life-expectancy at birth in SSA countries is lagging that in other regions of the world with an average of 60 years against 74 across the rest of the world (Figure 1b). More needs to be done to provide universal access to education, health, electricity and potable water, and improve the quality of infrastructure.

Figure 1.
Figure 1.

Extreme Poverty and Life Expectancy at Birth in Sub-Saharan Africa

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculation on IMF FAD Expenditure Assessment Tool (EAT) and World Bank data.

In 2015, SSA has embraced, at the highest political level, the Addis Ababa Action Agenda to finance development and the Sustainable Development Goals (SDGs). Building on progress achieved under the Millennium Development Goals, the SDGs offer a wide-range of development targets to build a world free of poverty and deprivation, conscious of the planet’s limits. This sustainable development agenda is consistent with the Africa Union’s Agenda 2063 that seeks to accelerate the implementation of past and existing continental initiatives for growth and sustainable development. SSA countries are in the process of aligning their own national development plans to the SDGs.

This paper argues that a first and key step in operationalizing a strategy to achieve the SDGs is the costing of spending required to achieve high development outcomes. Building on a methodology developed by the IMF to quantify such additional spending in five sectors— education, health, water and sanitation, roads and electricity—this paper presents global estimates for SSA. Spending estimates for Benin and Rwanda are discussed in detail, as well as their sectoral costing.

Additional spending needs to achieve the SDGs are considerable in many SSA countries. Relative to the region’s GDP, the median sub-Saharan African country must spend an extra 18.8 percent in 2030 in education, health, water and sanitation, roads and electricity to achieve high development outcomes. These needs are shared equally between human and social investment and physical capital investment. Additional spending estimates cover both public and private spending in each sector.5 Therefore, SSA countries will need to mobilize all sources of financing— public and private, internal and domestic—to achieve the SDGs. Beyond financing challenges, strengthening national ownership of development goals and improving governance and the business environment are also critical.

Benin and Rwanda are two low-income SSA countries with spending estimates comparable to the SSA average; they demonstrate the multifaceted challenges faced by SSA countries. Benin and Rwanda spending estimates are respectively 21 and 19 percent of GDP in 2030. Despite facing challenges of comparable size, Benin and Rwanda have different current development outcomes reflecting differences in economic endowment, economic policies, and resilience to various shocks, and calling for different investment priorities in the future. Indeed, Benin has been a stable country since the 1990s while Rwanda went through some periods of fragility in early 1990s which translated into Benin having a GDP per capita 1.7 times higher than Rwanda’s during the same period. However, since the mid-1990s, Rwanda has managed to record a sustained growth stronger than Benin’s, reducing the GDP per capita gap between the two countries to a factor of 1.1. In addition, while Benin is currently on par with SSA average along key state capacity and development indicators, Rwanda overperforms relative to other SSA peers (Table 1).6 Beyond these differences, the two countries share some characteristics: i) they show strong ownership of the UN goals with national development plans well-aligned with the SDGs, and ii) they are experiencing important development progress even if large gaps remain.

Table 1.

State Capacity and Development in Benin and Rwanda

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Source: Authors’ calculations on World Bank Worldwide Governance Indicators and World Development Indicators.

The rest of the paper is organized as follows: Section II provides an overview of the performance of SSA countries with respect to the SDGs and the findings of the additional spending required to close the gap. Sections III deepens on the cases of Benin and Rwanda. Section IV concludes with key lessons on how to operationalize costing estimates at the regional and country levels.

II. Development Outcomes and Spending Estimates in Sub-Saharan African Countries

Overview of Development Outcomes

Current development performance of SSA countries, as measured by the SDG global index and the Human Development Index, is lagging that of all other regions in the world. There is more variation across SSA countries in current development achievements than in any other region in the world (Figure 2a). But the majority of SSA countries have an SDG global index score lower than the median score in all other regions, with the median SDG index score across SSA countries on average 25 percent lower than the median in other region (Figure 2a). With US$1,574 of income per capita (US$3,906 in PPP terms) in 2018, sub-Saharan Africa remains the poorest region in the world. This income gap largely explains SSA overall development gap with an average index of Human Development averaging 0.52 compared to 0.76 in the rest of the world (Figure 2b).

Figure 2.
Figure 2.

SDG Composite Index and Human Development Index, by Regions

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations on data from the SDG Index and Dashboards Report 2019 and UNDP Human Development Index.Note: WHD = Western Hemisphere; MCD = Middle East and Central Asia; EUR = Europe; APD = Asia and Pacific; SSA = sub-Saharan Africa.

In all five sectors at the core of sustainable and inclusive growth—health, education, roads, electricity and water and sanitation—SSA median outcomes are behind medians across EMEs. In the selected five sectors, there is large variation in development outcomes across sub-Saharan African countries, except in water and sanitation where most SSA countries concentrate around an average SDG score of 49 out of 100 (Figure 3).

Figure 3.
Figure 3.

Sub-Saharan Africa Performance in Selected SDGs

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations on data from the SDG Index and Dashboards Report 2019.1/ Indicator: Access to electricity (percentage of population).2/ Indicator: Quality of Infrastructure.Note: These indicators range between 0 (lowest outcome) and 100 (highest outcome). For infrastructure, the index takes values between 0 (lowest outcome) and 7 (highest outcome).

Additional progress will require boosting investment in human, social and physical capital, beyond current expenditure levels. In education and health, public expenditure averaged 4.6 and 2.8 percent of GDP respectively in 2015 across SSA countries and these levels have only slightly increased since 2000. While in health SSA average is lagging other emerging economies’ average of 4.2 percent of GDP, the education average across SSA countries is slightly above that in other EMEs—reaching 4.1 in 2015 (Figure 4a and b)—also reflecting differences in demographics with a much younger SSA population. In infrastructure, the average real capital stock per capita remained almost flat between 1990 and 2015 while it has increased on average by 5.4 percent and 3.4 percent per year in other LIDCs and emerging markets, respectively over the same period (Figure 5).

Figure 4.
Figure 4.

2015 Public Expenditure in Health and Education, by Income Group

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations on IMF FAD Expenditure Assessment Tool (EAT).
Figure 5.
Figure 5.

Public Real Capital Stock per capita between 1990 and 2015, by Income Group

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations on IMF Making Public Investment More Efficient (2015).Note: Public real capital stock is presented as a per capita weighted average.

While overall performance is lagging in SSA, a few indicators show that important progress has been achieved in the past three decades. Between 1990 and 2017, net primary enrollment rate has increased by almost 30 percentage points and infant mortality has been halved (Table 2). Decent progress in primary healthcare and in the containment of communicable diseases has contributed to a 22 percent increase in life expectancy at birth. Access to electricity has improved. However, half of SSA population still lacks access to reliable electricity which slows down considerably the development of the region.

Table 2.

SSA: Performance in Selected SDGs Indicators

article image
Source: World Development Indicators, World Bank.

Large outcome dispersion suggests that high development performance is reachable by SSA countries. Rwanda, for instance, ranks relatively high with respect to the quality of its infrastructure, with a score of 4.7 compared to a median score of 2.9 across SSA countries. However, high-performing SSA countries reach only median levels achieved by emerging economies.

Additional Spending Estimates

Estimates of the spending required to reach the SDGs for 37 SSA countries are derived following the methodology developed by Gaspar and others (2019). The methodology quantifies the annual cost of achieving high SDG performance in five sectors—education, health, water and sanitation, electricity and roads—(Box 1). Estimates correspond to the additional total expenditure required in the year 2030 compared to what countries spend in 2016; they are reported as percentage points of 2030 GDP and refer to total expenditure, i.e., public and private.7 Since high performing countries with comparable levels of GDP per capita are used as benchmarks for each country, costing estimates assume high spending efficiency and can be interpreted as a lower spending floor needed to achieve the benchmarked progress in SDGs.

Costing Methodology in Five SDG Sectors

The methodology developed by Gaspar and others (2019) follows three steps: (i) identifying the main cost parameters, including inputs and their associated unit costs; (ii) benchmarking the cost parameters to their levels in countries with comparable GDP per capita and reaching high development outcomes today; and (iii) estimating the spending levels associated with these benchmarks, given individual country’s GDP per capital and population growth projections until 2030.

Education. Total spending for education can be expressed as a function of the number of teachers, teacher salaries, share of non-compensatory current expenses and capital spending. All these main inputs are benchmarked against median values observed today in countries with comparable per-capita income and high education outcomes.1 Education spending in 2030 is then estimated using the corresponding benchmarked main inputs and unit costs and countries’ projections for economic growth and school-age demographics, assuming full enrollment for at least 2 years of preprimary and tertiary education, and 12 years of primary and secondary education.2

Health. Likewise, total spending in health is calculated as a function of doctor salaries, number of doctors and other medical personnel, the ratio of non-doctor to doctor wages, the share of non-compensatory current expenses and capital spending.3 All these main inputs are benchmarked against median values observed today in countries with comparable per-capita income and high healthcare outcomes.4 Health spending in 2030 is then estimated based on the benchmarked parameters using countries’ projections for growth and demographics.

Roads. Road density—proxy for road network—is regressed on variables capturing the size and structure of the economy, including GDP per capita, population density, agriculture and manufacturing sector shares in the economy, urbanization rate, and the World Bank’s Rural Access Index (RAI), for a cross section of low-income countries and emerging economies. The RAI is used as the proxy for adequate access to the transport system. Using the regression results, additional kilometers of roads needed to ensure road access for all are estimated, proxied by raising the RAI to at least 75 percent in LIDCs, while accounting for projected changes in population and GDP per capita through 2030. The total cost of the additional road network is derived by multiplying the estimated additional kilometers by the unit cost of constructing one kilometer—set at a minimum of USD 500,000, following Imi and others (2016) unless country specific estimates suggest otherwise— and accounts for depreciation.

Electricity. Additional electricity network corresponds to a 100 percent access of projected population in 2030, while accounting for an increase in per-capita consumption in line with GDP per capita. The total cost of the additional electricity network is estimated using the unit cost per kilowatt of generation capacity set by the World Bank (2013) at USD 2,250.

Water. The cost of providing basic access to improved water and sanitation are derived using the WASH World Bank methodology described in Hutton and Varughese (2016). The model estimates the cost of meeting the water, sanitation, and hygiene (WASH)-related targets of SDG 6, using unit costs calibrated at the country level, including costs for capital investment, operations, and major capital maintenance to sustain the life span of the infrastructure created.

1 Countries are mapped for the benchmarking according to three 2016 GDP per capita buckets: i) between USD 0 and 3,000, ii) between USD 3,000 and 6,000 and iii) between USD 6,0000 and 15,000. These buckets are used to derive benchmarked inputs. High-performing low-income developing countries are those with an SDG4 education index above 80.2 The assumed enrollment rates are consistent with target rates of 50 percent for preprimary and tertiary education, and 100 percent for primary and secondary education.3 The ratio of non-doctor to doctor wage is assumed to be 0.5; shares of capital and other current spending to total spending are imputed using the World Bank income group averages.4 High-performing low-income developing countries are those with an SDG3 health index above 70.

The median SSA country faces additional spending of about 19 percent of GDP, which is almost four times larger than other typical low-income and developing or emerging country.8 The median additional cost required to achieve high performance in the selected five SDGs is estimated at 19 percent of SSA GDP in 2030 larger than the median additional spending estimated at 12 percent of GDP and 4 percent of GDP for non-SSA LIDCs and EMEs respectively (Figure 6).

Figure 6.
Figure 6.

Additional Spending Estimates in 2030, by Income Group

(Percentage Points of GDP)

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations based on Gaspar and others (2019).

Additional spending among SSA countries is equally driven by required investment in social and human capital and investment in physical capital. Total needs across the five selected sectors are equally distributed between the human capital sectors—with health and education needs representing about 9 percent of GDP—and the physical capital sectors—with needs in water, electricity and roads summing up to 10 percent (Figure 7). Additional physical infrastructure investment are 40 percent higher than average infrastructure needs across LIDCs and four times more than the EMEs’ average.

Figure 7.
Figure 7.

Additional Sectoral Spending Estimates in 2030, by SSA and Income Group

(Percentage Points of GDP)

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations based on Gaspar and others (2019).Note: “RR” stands for “Resource Rich” countries and “Non-RR” for “Non-Resource Rich” countries.

There is substantial variation of spending estimates across SSA countries. Variation across SSA countries reflects heterogeneity in levels of development and other country’s circumstances. For example, with 24 percent of GDP in 2030, fragile countries have a 6 percentage points higher additional spending requirement than non-fragile SSA countries due to higher political, economic and social pressures. However, there is no major difference between resource rich and non-resource rich countries.

Population dynamics and the initial level of development explain most of the differences in additional spending between SSA countries and other countries:

  • In education, additional spending needs in SSA countries average 4.2 percent of 2030 GDP, against 0.3 percent in other LIDCs and EMEs. SSA countries’ demographics explain 91 percent of the difference of additional spending needs with other LIDCs and EMEs. Indeed, SSA countries are expected to have a much larger share of student age population in 2030 (almost half the population with 47 percent) compared to other LIDCs and EMEs (30 percent).

  • In health, additional spending needs in SSA countries average 4.7 percent of 2030 GDP, against 1.5 percent in other LIDCs and EMEs. Almost half the difference in spending needs between SSA countries and other LIDCs and EMEs is driven by their lower GDP per capita, which requires a costlier combination of health staff density and salaries in proportion to GDP per capita than for other LIDCs and EMES. The rest is driven by other factors, including the differences in the current share of non-compensation spending in total spending.

  • In roads, additional spending needs in SSA countries average 6.9 percent of 2030 GDP, against 0.9 percent in other LIDCs and EMEs. SSA countries’ lower 2030 GDP per capita and initial lower RAI explain 84 percent of the difference in additional spending needs between now and 2030 compared to other LIDCs and EMEs.

III. Benin and Rwanda Case-Studies

Benin

Benin’s main development plans are all anchored in the achievement of the SDGs. Two key reports formalize the government’s commitment to SDGs: the Programme d’Actions du Gouvernement (PAG, 2016–21) and the Plan National de Développement (PND, 2018–25).9 The PAG identified three big levers that could help Benin meet the objectives of the 2030 Agenda for Development: priority projects, 45 flagship projects (projets phares), and structural reforms.10 The PND integrates strategic guidelines that support ministries in formulating their sectoral development programs consistent with the SDGs, thus becoming the reference for all development initiatives in the next eight years. In addition, the definition of all cross-cutting themes in the PND is based on the SDGs.11 An analysis based on the Rapid Integrated Assessment—a tool developed by the UNDP to facilitate mainstreaming of SDGs into national and local plans—shows that the Benin’s strategic documents are at least aligned at 70 percent with the SDGs (République du Benin, 2017a and 2017b). In addition, since 2017, the government of Benin have started publishing voluntary national reviews of progress on the SDGs.

Benin has made progress on key SDGs. On education, Benin has among the highest net primary enrollment rate in SSA, at about 96 percent in 2018. On health, steady implementation of measures providing social assistance and support for vulnerable groups (free health measures for caesarean, cases of malaria among pregnant women, care for children aged 0-5, etc.) enabled Benin to achieve performance in terms of accessibility and quality of health services. Access to improved water source in rural areas has increased from 57.2 percent in 2010 to 67.7 percent in 2015. In urban areas, access has reached 84.8 percent in 2015 from 58.7 percent in 2010 (République du Benin, 2018a). Finally, Benin has recently made progress on the construction of paved roads.

Despite this progress, Benin is far from reaching the SDGs (Figure 8). For example, on education, various indicators that measures the efficiency and the quality of the education system are weak: access rates from primary to secondary school is only 66 percent; completion rates in the secondary and tertiary are 45 percent and 28 percent, respectively (République du Benin, 2017c). In addition, because of repetition and drop-out, a high proportion of children— 27 percent of 9 to17 years-old—are out of school. Benin also performs poorly with respect to the expected years of schooling and literacy rate of 15–24-years-olds, suggesting a low quality of the education system. In 2016, only 19.7 percent of the population has access to improved sanitation (SDSN, 2017) even though the length of gutters has doubled in one year to reach 34.8 km in 2015 and only 41.4 percent of Benin’s population has access to electricity.

Figure 8.
Figure 8.

Benin: 2018 Performance in Selected SDG Sectors

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations using Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G., Fuller, G. in SDG Index and Dashboards Report 2019.1/ Indicator: Access to electricity (percentage of population).2/ Indicator: Quality of infrastructure (RHS).

Given these challenges, the additional spending to achieve the SDGs in the five selected sectors is considerable—about 21 percent of GDP in 2030. Appendix 1 provides detailed sectoral estimates and results are summarized below (Figure 9):

Figure 9.
Figure 9.

Benin: Estimates for the Additional Spending in 2030, by Sector

(Percent of 2030 GDP)

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations based on Gaspar and others (2019).
  • In education, Benin would need to spend 8.7 percent of its GDP on education (US $395 per student) by 2030 compared to a current spending on education equivalent to 5.5 percent of GDP (US$135 per student). This reflects: (i) a decline in the ratio of student to teacher to 15.1 from 22.4 today and; (ii) a decline in the share of teacher compensation in total spending from 60 percent to 45 percent to make room for an increase in other current and capital expenditure. Given the already high enrollment rate, the additional spending reflects an allocation of spending geared toward improving the quality of the primary school notably through recruiting more teachers. There is also a need to improve the enrollment rate and the quality of the secondary and tertiary education.

  • In health, Benin would need to spend about 9.3 percent of its GDP on health (US$119 per capita) by 2030 compared to a current total spending on health equivalent to 4.2 percent of GDP (US$33 per capita). This reflects recruiting: (i) 8 times more doctors and (ii) 4 times more support staff than today. This reflects current weak performance in health. For this purpose, the Assurance pour le renforcement du capital humain (ARCH) project is launched with the objective of providing universal health insurance scheme (Box 2).

    Benin: The Universal Health Insurance System

    In May 2017, the government of Benin adopted the legal framework establishing a new social protection system (Assurance pour le Renforcement du Capital Humain, ARCH) with the aim of ensuring effective and affordable social insurance to the Beninese population, especially the poor (40 percent of the total population). ARCH contains four services: universal health insurance, training, credit provision, and pension insurance for people in the informal sector.

    The health insurance is the main component of ARCH and its implementation is the most advanced. In 2019, the government started a pilot phase in three regions by identifying and testing the system on the poorest populations. The insurance is expected to be progressively expanded to the rest of the population and become fully operational by 2022.

    Studies about financing and implementation are being conducted with the assistance of USAID and the World Bank. The system is expected to be self-financed, except for poor populations who will benefit from a public subsidy to cover their insurance premium.

    Source: IMF (2019a).

  • In roads, there is room to improve access particularly for those in rural areas. We estimate that Benin would need to build 12,276 km of road, thus requiring an additional spending equivalent to 8.1 percent of the country GDP (of which 1.9 percent of GDP for maintenance cost) in 2030. Road transportation is, by far, the most used transport mode in Benin: 93 percent of transport of people and 73 percent of transport of goods (AfDB, 2017). However, despite the preponderance of road as the main mean of transport, paved roads account only for 45 percent of total roads and Benin lags against key comparators (Figure 8). Therefore, the authorities consider the development of infrastructure as their priority. In fact, transport-related infrastructure is going to account for 25 percent of all investment under the PAG.

  • In electricity, providing universal access is feasible despite many challenges. Given current low access to electricity in Benin, important investment is needed to ensure universal access to affordable, reliable, clean and modern energy services by 2030. Therefore, electricity-related infrastructure is going to account for 10 percent of all investment under the PAG. The annual cost to reach universal access in Benin is estimated at 2.4 percent of GDP. Currently, domestic production of electricity meets only 12 percent of the domestic consumption and Benin relies mostly on imports from neighboring countries (Côte d’Ivoire, Ghana, and Nigeria) to fill the gap. Several factors explain this situation, among them: (i) the cost of importing electricity is much lower than the cost of generating electricity domestically and; (ii) local production is hampered by the low tariffs that only partially cover the cost of production and low price of imported electricity.

  • In water, the total cost of reaching universal access to safe and affordable drinking water and adequate sanitation is estimated at 2.5 percent of GDP per year—0.8 percent in water and 1.7 percent in sanitation—through 2030. Sanitation accounts for 70 percent of the total estimation because only about 20 percent of the population has access to improved sanitation. Benin aims to reach universal access water—both in rural and urban areas—by 2021 under the PAG. To this end, 3 out of the 45 flagship projects of the PAG are relate to this goal.

Rwanda

The Rwandan development strategy, i.e., the National Strategy for Transformation (NST), has been structured to align with the SDGs. SDG targets have been brought into the NST (Republic of Rwanda, 2017), which runs from 2017-2024 and is part of a larger Vision 2050, with the help of development partners. Line ministries have already produced comprehensive sectoral strategies and preliminary estimates of sectoral spending needs for achieving the NST and are in close dialogue with development partners (UNICEF, U.K. Department for International Development (DFID), Enabel, the World Bank, WHO, Global Fund for Health) to refine these estimates and prioritize their action plan. However, sector-specific costing estimates have not been reconciled with top-down multiyear rough budget estimates of NST implementation and baseline data are still being collected to provide a more comprehensive diagnostic of the current situation in many SDG-related sectors.

Rwanda has delivered impressive development outcomes in the past 20 years, allowing the country to be broadly on par with—and in some instances, outperforming—other SSA countries with respect to the SDG performance indicators. Despite its low GDP per capita, life expectancy at birth reached 65 years in 2015, more than most SSA countries (Figure 10). This is due to important progress in reducing infant and maternal mortality rates, cut to 1/5th between 2000 and 2016, thanks to the use of home-grown approaches and new technology that have enabled efficient use of public resource to improve healthcare access and delivery.12 In education, progress has been achieved at the primary level where gross enrollment rate now reaches 100 percent and the rate of out-of-school children is low. The overall infrastructure quality ranks relatively well but it masks sectoral disparities and the challenge of reliable access to water and electricity.

Figure 10.
Figure 10.

Rwanda: 2018 Performance in Selected SDG Sectors

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations using Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G., Fuller, G. in SDG Index and Dashboards Report 2019.1/ Indicator: Access to electricity (percentage of population).2/ Indicator: Quality of infrastructure (RHS).

Despite good progress to date, meeting the SDGs in a meaningful way will be challenging because gaps remain between NST goals and current basic service delivery. For instance, as of 2016, 38 percent of children still suffered from stunting and access to health facilities with qualified staff remained low; secondary gross enrollment rate remain low at 40 percent and the quality of education is still a serious problem; and only 13 percent of the population has access to safely managed sanitation (which includes containment through safe collection, treatment, and end use/disposal) and 43 percent to electricity.13

These important challenges are reflected in the sizeable additional spending required to meet the SDGs in the five selected sectors that amounts to about 19 percent of GDP in 2030. Appendix 1 provides detailed sectoral estimates and results are summarized below (Figure 11):

Figure 11.
Figure 11.

Rwanda: Estimates for the Additional Spending in 2030, by Sector

(Percent of 2030 GDP)

Citation: IMF Working Papers 2019, 270; 10.5089/9781513519913.001.A001

Source: Authors’ calculations based on Gaspar and others (2019).
  • In education, required spending to meet the SDGs is the largest, estimated at 7 percent of 2030 GDP. Performance in education, as assessed by SDG index 3 (SDG Index and Dashboards Report 2019), reaches 61, just above the SSA median. Due to lack of data on test scores, this achievement mainly reflects access rates rather than quality of education. At 3.6 percent of GDP, current public spending is relatively low and translates into student-to-teacher ratios higher than that observed in peer countries with better performance in education—41 vs. 15 students per teacher. The NST for education is an important step towards filling this gap. By 2030, the Rwandan government plans nearly to double current spending in education, to reach 6.3 percent of GDP. The bulk of the extra spending is planned for hiring more teachers to reduce the student-to-teacher ratio, especially at the primary level, where repetition rates are higher. However, our analysis suggests that more would be needed to reach high performance in education in 2030.

  • In health, Rwanda currently achieves better outcomes than the median peer in the comparable income group, but we estimate that an additional 2 percent of GDP should be spent by 2030 to reach top-level performance. While its current mix of doctors vs. other health workers has allowed for this relatively good outcome, our analysis suggests that an overall increase in the number of health workers, mostly doctors, would be needed to bring Rwanda to the level of high-performing peer countries. The NST for health will be an important step to narrow the gap, but more would be needed to achieve the best outcomes. By 2024, the government plans to increase per capita public spending on health by 36 percent, from US$38 to US$52 per capita per year. More granular data on spending composition (in Rwanda and comparator countries, e.g. for compensation vs. other spending) would enable better analysis on the desirable allocation of such spending.

  • In roads, additional required spending is estimated at about 4 percent of 2030 GDP. The rural access index for roads—RAI, used as a proxy for high sectoral performance—is relatively low in Rwanda, due largely to the poor quality of existing roads. Rwanda’s RAI is 52 out 100, as only 5 percent of the current road network is paved and only 10 percent reaches all-season riding quality. Given its population density, most of Rwanda’s investment will comprise upgrading and maintaining existing roads, especially in remote and high areas.

  • In electricity, universal access would require an additional spending of 2 percent of GDP. Rwandan electricity sector is in a relatively unique situation of current excess capacity and low access to electricity with 43 percent of households having electricity access. Assuming that 60 percent of all new connections will be on-grid while 40 percent will be off-grid and projecting electricity consumption will increase at the annual rate of 9 percent—reaching 730 kWh in 2030 from an average of 238 kWh in 2016—universal access cost is estimated at 2 percent of GDP.

  • In water, based on the World Bank WASH costing model, Rwanda would need to spend 4.5 percent of GDP per year to safely provide water to all households and provide fixed-point latrines to all. This estimate is broadly in line with the authorities’ own costing exercise which is based on current service coverage gaps, especially in rural areas, and with a large share of spending accounted for by projections for a growing urban population with increased water consumption.

IV. Going Forward: Operationalizing Costing Estimates

Tailoring costing estimates to individual countries

Variation of additional spending across SSA countries signals value in tailoring estimates at the country level. The costing methodology provides a solid framework that enables SSA countries to compare their development strategy to what SDG-high performing peers operationalize in health, education and key infrastructure areas. This benchmarking provides useful insights on sectoral input mix and potential spending efficiency gains that can help SSA countries design their own development strategies. However, countries may wish to tailor some cost-parameters to better mirror the country circumstances, while remaining consistent with their sustainable development commitments. These parameters include, for example, the unit cost to build a km of road or the unit cost to generate one kilowatt of electricity. Countries may also wish to tailor their “input-mix” such as the student-to-teacher ratio or the number of doctors per 1,000 population.

Tailoring the costing methodology to countries requires a good understanding of national development challenges and should be discussed at length with the authorities and other relevant stakeholders. Beyond quantitative estimates of additional spending, the costing exercise can help catalyze and coordinate ongoing different workstreams of development partners and ground a country’s development agenda on solid and shared knowledge of its development needs and priorities. For instance, in both Benin and Rwanda, the analyses were refined during two costing missions that visited Cotonou and Kigali in 2018. The missions met with officials from different ministries including line ministries and representatives of financial and development partners to discuss national development needs and priorities.

A key step in the costing at the country level is to establish clear baselines by which progress going forward can be measured and monitored. For example, in Rwanda, line ministries and development partners have been working together to gather the data necessary to assess where the country currently stands with respect to each SDG target. In education, for example, the authorities and UNICEF and UKDFID have established a common template to refine baseline data and goals to cost their strategy. In Benin, the authorities first prioritized the SDGs that best fit their context. They estimated development needs with the support of technical and financial partners. The next step should be to integrate identified and costed development needs into a Medium-Term Expenditure Framework (MTEF) and into a medium-term financial strategy (MTFS). However, more generally, 60 percent of all 17 SDG indicators are currently not documented in SSA countries and when data exists it is often outdated or does not allow comparability across countries (SDG Center for Africa, 2019). This lack of updated baselines complicates evidence-planning and threatens transparency on implementation and development progress.

Short-term implications of SDG costing in SSA: Prioritize, Raise Resources and Spend Efficiently

Many projects compete over limited resources: costing spending gaps can help rank existing projects according to countries’ priorities and their capacity to deliver. The SDG costing enables countries to have a transparent and rapid appreciation of additional spending required to meet their development goals in key sectors for human, social and physical capital. Spending gaps can be mapped to existing sectoral projects, that line ministries must then sort and rank according to an implementation timeline, commensurate to additional financing means and capacity to ramp-up service delivery. One of the key challenges going forward will also be to reduce access gaps to key infrastructure and education and health services, while ensuring quality services across rural and urban arears. For instance, prioritizing the provision of reliable access to potable water in urban areas should not come at the cost of not providing reliable water and sanitation services to rural or remote areas. Progressive national development strategies are thus key to leave no one behind and SSA governments must balance prioritization of development plans with the risk of deepening access inequalities.

The costing can be used to quantify current financing gaps and create the necessary impetus for governments to mobilize all sustainable financing sources available. Given the magnitude of additional spending estimates, SSA countries must mobilize sizeable financing beyond domestic sources. Ramping up domestic resources should be at the core of SSA countries’ strategy to finance the SDGs. With an average tax-to-GDP ratio of about 14 percent in 2017, SSA countries have room to increase tax revenues over the medium-term and reach the average of 18 percent for emerging economies. However, additional tax revenues will not be enough to cover additional investments required to reach the SDGs by 2030. Delivering on official development assistance targets, and prioritizing donors’ funds to SSA can help close development gaps in many countries. Finally, creating an enabling environment to attract private investment are necessary.

A comprehensive and sustainable financing strategy must rely on efficient spending. SDG costing estimates assume not only more, but also more efficient spending. This should encourage countries to delve into the production function of development outcomes and optimize delivery processes. Because if countries fail to raise their spending efficiency, then additional spending required to reach the SDGs will be much larger. Focusing on the public delivery of the additional spending required, strengthening current public financial management systems is necessary in many SSA countries to ensure that additional spending is spent effectively and efficiently. Increasing spending in the medium term by an additional 19 percent of GDP would require substantial improvement in the capacity to deliver public services and to plan and execute the budget. For example, assuming the current public-private split in education and health continues, Benin and Rwanda are expected to more than double public spending in each of these sectors by 2030. For example, Benin has benefited of a Capacity Development mission from the IMF in 2017 and 2019 on how to improve the efficiency of its public capital investment.14

Medium-to-long term implications of SDG costing in SSA: Towards SDG budgeting?

The 2030 spending estimates can help governments anchor their medium-term budget strategy. The assessment of additional spending to reach the SDGs offers a frame to discuss and commit to spending and financing paths that are both sustainable and commensurate to development needs. In Rwanda, the medium-term expenditure framework includes a subset of the aggregated costs documented in sectoral National Strategy for Transformation (NST), chosen according to the authorities’ assessment of absorptive capacities, and current and projected additional revenues. This prioritization and medium-term strategy can help build the broad consensus that is required to anchor citizens’ expectations about the delivery of public services over the next decade.

In the short term, presenting part—or all—of a government’s budget according to a functional classification could help strengthen the accountability in delivering the SDGs. Indeed, a functional reporting would help ground development narratives into transparent and readable budgeting. Beyond budgeting, in 2019, the Inter-Agency Task Force on Financing for Development has identified the main building blocks for operationalizing broader financing frameworks called Integrated National Financing Strategy (INFF) (UN, 2019).15 Among them, improving political stability to reduce or eliminate pockets of fragilities, promoting good governance to favor ownership of the development objectives by citizens and improving the business environment are as important as anchoring development objectives into medium-term budgeting.

Another important step is the mapping of spending estimates with commensurate resources. Country authorities should lay out a credible financing strategy for their development plan articulated around a clear public/private investment split in each sector. In Rwanda, financing the government’s NST will be challenging, reflecting i) limited potential for additional tax revenues in the medium term—of about 2 to 3 percent of GDP, and ii) a declining trend in official development assistance (ODA) not compensated by increasing private investment.16 Rwanda’s MTEF reflects this financing challenge and includes “contingent” spending items that would materialize provided additional resources are being raised in the medium term, on top of resources already included in the MTEF. In any case, delivering the SDGs would require more than allocating occasional windfall to key development investments. In Benin, for instance, there is significant room to increase tax revenue by implementing tax policy reforms that exploit further the potential of consumption taxation, particularly VAT and excises.17 Benin authorities are also implementing many reforms to boost private investment.18 But despite these reforms, the participation of the private sector remains limited. Further analysis is required at the country level to understand how national development plans are aligned with i) well-assessed spending needs translated into operational sectoral objectives and ii) a comprehensive financing strategy detailing the respective private and public sectors’ roles.

The Spending Challenge for Reaching the SDGs in Sub-Saharan Africa: Lessons Learned from Benin and Rwanda
Author: Delphine Prady and Mouhamadou Sy
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    Extreme Poverty and Life Expectancy at Birth in Sub-Saharan Africa

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    SDG Composite Index and Human Development Index, by Regions

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    Sub-Saharan Africa Performance in Selected SDGs

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    2015 Public Expenditure in Health and Education, by Income Group

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    Public Real Capital Stock per capita between 1990 and 2015, by Income Group

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    Additional Spending Estimates in 2030, by Income Group

    (Percentage Points of GDP)

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    Additional Sectoral Spending Estimates in 2030, by SSA and Income Group

    (Percentage Points of GDP)

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    Benin: 2018 Performance in Selected SDG Sectors

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    Benin: Estimates for the Additional Spending in 2030, by Sector

    (Percent of 2030 GDP)

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    Rwanda: 2018 Performance in Selected SDG Sectors

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    Rwanda: Estimates for the Additional Spending in 2030, by Sector

    (Percent of 2030 GDP)