## sdn1903

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### Executive summary and introduction
- SDGs endorsed in September 2015 as a road map to more inclusive growth and development that respects the limits of nature.
- Focus: investment in human, social, and physical capital—education, health, roads, electricity, and water and sanitation.
- Progress and indicators:
  - Since 1990, over a billion people have been lifted out of extreme poverty.
  - Infant mortality decreased from 65 to 31 deaths per 1,000 births.
  - Share of primary-school-age children out of school fell from 18 to 9 percent.
  - Median composite SDG index score: advanced economies 78 percent; low-income developing countries 53 percent.
- Sample and scope:
  - Sample of 155 countries: 49 low-income developing countries (LIDCs), 72 emerging market economies (EMEs), and 34 advanced economies (AEs).
  - Five country case studies: Benin, Guatemala, Indonesia, Rwanda, and Vietnam.
- Fiscal policy role:
  - Public intervention critical for SDG areas with uncertain or long-term private returns (SDG1, SDG2, SDG3, SDG4, SDG5, SDG6, SDG7, SDG9, SDG10, SDG11).
  - Public expenditure and tax revenue tend to rise with per capita income (Wagner’s Law).

### Main spending estimates and variation
- Headline additional spending needs in 2030 (relative to current sector spending as a share of GDP):
  - US$0.5 trillion for low-income developing countries.
  - US$2.1 trillion for emerging market economies.
- Average additional spending as a share of GDP:
  - Emerging market economies: about 4 percentage points of GDP.
  - Low-income developing countries: about 15 percentage points of GDP.
- Cross-country variation:
  - Some LIDCs (e.g., Vietnam) have needs similar to EMEs; others (e.g., Rwanda and Benin) require more than 15 percentage points of GDP.
- Methodology summary:
  - Estimates quantify annual cost of achieving high performance across five SDG areas using input-outcome models.
  - Inputs benchmarked to median input levels of high-performing countries, controlling for demographics and projected GDP per capita in 2030.
  - Assumes improvements in spending efficiency; absent efficiency gains required spending would be larger.
  - Additional spending reported for 2030 in percentage points of GDP and in constant 2016 dollars.

### Current spending patterns (2016, percent of GDP) — GDP-weighted averages by income group
- Education, health, infrastructure total:
  - LIDCs: 4.9
  - EMEs: 7.3
  - AEs: 15.5
- By function (percent of GDP):
  - Education: LIDCs 2.3; EMEs 3.2; AEs 5.2
  - Health: LIDCs 0.9; EMEs 2.3; AEs 7.8
  - Transport: LIDCs 0.9; EMEs 1.5; AEs 1.9
  - Fuel and energy: LIDCs 0.4; EMEs 0.2; AEs 0.3
  - Water: LIDCs 0.4; EMEs 0.1; AEs 0.3
  - Social protection: LIDCs 1.6; EMEs 6.7; AEs 13.0
  - Defense, order, and safety: LIDCs 2.1; EMEs 2.7; AEs 3.9
  - Other primary spending: LIDCs 7.9; EMEs 10.6; AEs 6.1
  - Interest: LIDCs 1.4; EMEs 2.1; AEs 2.0
  - Total primary spending: LIDCs 17.9; EMEs 29.4; AEs 40.5

### Global total and regional breakdown (2030)
- Additional spending in 2030 amounts to 2.6 trillion US dollars (2.5 percent of the 2030 world GDP) in 121 emerging market economies and low-income developing countries.
- Regional shares (as percent of 2030 world GDP):
  - Asia and Pacific: 1.5 percent
  - Sub-Saharan Africa: 0.4 percent
- Regional labels used: AP = Asia and Pacific; CIS = Commonwealth of Independent States; EUR = Europe; LAC = Latin America and the Caribbean; MENAP = Middle East, North Africa, Afghanistan, and Pakistan; SSA = sub-Saharan Africa; EMEs = emerging market economies; LIDCs = low-income developing countries.

### Variation across EMEs and LIDCs
- EMEs:
  - Average additional spending: 4 percentage points of GDP (GDP-weighted average).
  - Countries above 8 percentage points of GDP (75th percentile): average GDP per capita spending US$4,200 in 2016; main driver education.
  - Median additional spending: 4.2 percentage points of GDP; often resource-rich and driven by health care.
  - Countries below 25th percentile: higher-income EMEs with average GDP per capita US$9,100 in 2016.
- LIDCs:
  - Average additional spending: 15.4 percentage points of GDP in 2030 (GDP-weighted average).
  - Split in LIDCs between education and health care: 8.3 percentage points of GDP; infrastructure: 7.1 percentage points of GDP.
  - Range across 49 LIDCs: about 50 percentage points to about 0.5 percentage point of GDP in 2030.
  - Countries above 22 percentage points of GDP (75th percentile): average GDP per capita US$580 in 2016; two-thirds are fragile states; large health care needs.
  - Median LIDC (17 percentage points of GDP): average GDP per capita about US$1,200 in 2016; drivers education and roads.
  - Countries below 12 percentage points of GDP (25th percentile): average GDP per capita about US$1,600 in 2016; major need for roads.
  - Global perspective: LIDCs additional spending in 2030 amounts to US$528 billion, equivalent to 0.5 percentage point of 2030 world GDP and equivalent to 0.9 percent of the 2030 advanced economy GDP.

### Aggregate sectoral totals and uncertainties
- Aggregate sectoral estimates:
  - Roads, electricity, water and sanitation: US$1.4 trillion.
  - Education and health care: US$1.2 trillion.
- Economy-wide headline totals (as of 2030):
  - Additional spending in EMEs and LIDCs combined: US$2.6 trillion (2.5 percent of 2030 world GDP).
- Sources of uncertainty and limitations:
  - Estimates likely a lower bound because only 5 sectors out of 17 SDG sectors included.
  - Growth projection deviations would substantially affect spending estimates.
  - Methodological assumptions (benchmarks, unit costs, efficiency gains) materially affect results.
  - Estimates reported as annual flows in 2030; cumulative spending to 2030 would be significantly larger.

### Financing the SDGs — creating conditions for growth
- Structural reforms that boost level and durability of growth can lower additional spending needs.
- Doubling projected GDP per capita in 2030 would reduce additional spending by 4.5 percentage points.
- Vietnam case:
  - Additional spending: 6.4 percentage points in 2030 (one of the lowest in LIDCs).
  - Extreme poverty rate: 5 percent.
  - Tax revenue increased from 16 to 19 percent of GDP during 1998–2017 (Doi Moi example).

### Financing the SDGs — boosting tax revenue and enhancing spending efficiency
- Tax revenue trends and targets:
  - LIDCs: tax revenue increased from about 12 percent of GDP in the early 2000s to nearly 15 percent of GDP today.
  - Median tax-to-GDP ratios in 2016: LIDCs 15 percent; EMEs 18 percent; AEs 26 percent.
  - About one-third of EMEs and one-half of LIDCs have tax-to-GDP ratios lower than 13 percent.
  - Raising tax-to-GDP ratio by 5 percentage points of GDP in the next decade described as an ambitious but reasonable target in many countries.
  - If countries below the 75th percentile for their income group raised revenue to the 75th percentile, the increase would average 5 percentage points of GDP (75th percentiles: LIDCs 19 percent; EMEs 22 percent; AEs 29 percent).
- Historical examples:
  - China’s 1994 tax reform: revenue rose from 10 to 15 percent of GDP between 1995 and 2002.
  - Georgia’s reforms: raised revenue by more than 12 percentage points of GDP between 2004 and 2009.
- Policy guidance — medium-term revenue strategy recommended, framed around:
  1. Building broad-based consensus for medium-term revenue goals.
  2. Designing comprehensive tax reform (policy, administration, legal framework).
  3. Committing to sustained political support over multiple years.
  4. Securing adequate resources to support coordinated implementation.
- Application: medium-term revenue strategy approach applied in dialogue with 15 countries (Egypt, Ethiopia, Indonesia, Georgia, Ghana, Guatemala, Lao P.D.R., Liberia, Mongolia, Myanmar, Papua New Guinea, Rwanda, Senegal, Thailand, Uganda).
- Spending efficiency:
  - Addressing inefficiencies is critical; countries could save about as much through efficiency efforts as through tax reforms.
- External financing:
  - Many LIDCs will require support from private sector, donors, philanthropists, and international financial institutions.
  - Delivering on official development assistance targets can help close development gaps in many LIDCs.

### Box: Indonesia’s Medium-Term Revenue Strategy — key numeric targets
- Tax-revenue-to-GDP ratio: close to 10 percent.
- Medium-term revenue increase targeted: about 5 percentage points of GDP.
- Tax policy reforms potential: up to 3.5 percentage points of GDP.
- Tax administration measures potential: 1.5 percentage points of GDP.
- Government reforms started in: 2016.

### Methodology and key modeling assumptions (Annex Table 1.1)
- Outcomes proxied with SDG indices: SDG4 (education), SDG3 (health), SDG9.1.1 (Rural Access Index) for roads, SDG7.1.1 for electricity, SDG6.1 and 6.2 for water and sanitation.
- Outcome indicators range: 0 to 100.
- Education inputs and assumptions:
  - Key inputs: Number of teachers; other current and capital spending.
  - Unit cost driver: Teacher wage.
  - Enrollment targets for 2030: 50 percent preprimary and tertiary; 100 percent primary and secondary.
  - Number of teachers computed as TSR * enrollment rates * school-age population.
- Health inputs and assumptions:
  - Key inputs: Number of health care workers (doctors/others); doctor wages; other current and capital spending.
  - Unit cost drivers: DAWAGE and ratio of non-doctor wages to doctor wages assumed α = 0.5.
- Roads:
  - Key input: Kilometer of all-weather road.
  - Unit cost minimum: US$500,000 per kilometer.
  - Depreciation adder: increase total cost of additional kilometers by 5 percent.
- Electricity:
  - Unit cost per kilowatt: US$2,250.
  - Method accounts for on/off-grid mix and increasing per capita consumption in line with real GDP per capita.
- Water and sanitation:
  - Costs derived using WASH World Bank methodology (Hutton and Varughese 2016) with unit costs calibrated at country level.
- Benchmarks by income group for education and health:
  - GDP per capita groups: less than US$3,000; between US$3,000 and US$6,000; between US$6,000 and US$18,000.
  - High-performing thresholds (education SDG4): above 80 (low-income), above 82 (middle-income), above 84 (high-income).
  - High-performing thresholds (health SDG3): above 70 (lowest income), above 78 (median income), above 84 (highest income).
- Infrastructure costing:
  - Annualized to close 2019–2030 gap; after 2030 infrastructure spending assumed to decline to about 60 percent to cover depreciation.
- Efficiency and synergies:
  - Costing assumes better-than-average efficiency based on high-performing benchmarks; estimates partially account for intersectoral synergies.

### Summary of estimated additional spending by sector (as of 2030)
- Health and Education (Billion 2016 US$):
  - LIDCs: 284
  - EMEs: 244
  - Total: 528
- Health and Education (Percentage points of GDP):
  - LIDCs: 8.3
  - EMEs: 7.1
  - Total: 15.4
- Roads, Electricity, and Water and Sanitation (Billion 2016 US$):
  - LIDCs: 1,011
  - EMEs: 1,048
  - Total: 2,059
- Roads, Electricity, and Water and Sanitation (Percentage points of GDP):
  - LIDCs: 2.0
  - EMEs: 2.1
  - Total: 4.1

### Country case-study refinements and selected findings
- Five countries refined for country-specific circumstances: Benin, Guatemala, Indonesia, Rwanda, Vietnam.
- Benin:
  - Country-specific additional annual spending: 21.3 percentage points of GDP (standard methodology 19.8).
  - GDP per capita: US$791.
  - Road unit cost revised to US$610,000 per kilometer.
  - Financing considerations: curb tax exemptions (2019 Budget Law), PPP framework (2017), participation in G20 Compact with Africa since October 2017.
- Rwanda:
  - Country-specific additional annual spending: 18.7 percentage points of GDP (standard methodology 13.8).
  - GDP per capita: US$734.
  - Country-specific road unit cost: US$1.1 million per kilometer.
  - Government expects two-thirds of additional spending to be public; local annual water estimates about 4 percent of GDP.
- Vietnam:
  - Country-specific additional annual spending: 6.4 percentage points of GDP (standard methodology 1.5).
  - GDP per capita: US$2,172.
  - Country-specific road unit cost: US$1.1 million per kilometer.
  - Public debt approaching statutory limit of 65 percent; revenue ratio high for income group; ODA declined after 2010 graduation.
- Indonesia:
  - Country-specific additional annual spending: 5.6 percentage points of GDP (standard methodology 8.3).
  - GDP per capita: US$3,604.
  - Tax-revenue-to-GDP ratio close to 10 percent.
  - Medium-term revenue strategy aims to raise revenue by about 5 percentage points of GDP; tax policy reforms potential up to 3.5 percentage points; tax administration potential 1.5 percentage points of GDP.
- Guatemala:
  - Country-specific additional annual spending: 8.7 percentage points of GDP (standard methodology 5.2).
  - GDP per capita: US$4,141.
  - Country-specific road cost: US$800,000 per kilometer.
  - Fiscal context: tax-to-GDP ratio about 10 percent; financing requires integrated fiscal package (tax administration, tax policy, spending flexibility).
- Interpretation:
  - Low GDP per capita countries (Rwanda, Benin below US$800) have very large additional spending needs (about 19–21 percentage points of GDP).
  - Country-specific estimates generally within 3 percentage points of standard methodology except Rwanda (higher road costs) and Guatemala (extensive road gap).

### Service delivery, sectoral gaps, and financing (selected country notes)
- Rwanda service delivery (since 1995):
  - Stunting: 38 percent of children.
  - Electricity access: 46 percent of households.
  - Development financing requirement: additional 19 percentage points of GDP.
  - Government expects two-thirds public financing; remainder via private borrowing.
- Guatemala:
  - Poverty: 60 percent; extreme poverty: 23 percent.
  - Stunting among highest in world; more than 40 percent lack access to safe water.
  - Secondary enrollment and preprimary low; rural and indigenous populations worse off.

### Comparisons to other studies and broader context (Annex comparisons)
- Literature range of headline estimates: US$1.4–US$3.9 trillion.
- IMF vs other studies:
  - IMF infrastructure additional annual spending ~US$1.4 trillion (roads, power, water) vs UNCTAD (2014) ~US$1.8 trillion (US$2.0 trillion inc. telecommunications).
  - IMF education and health additional spending US$1.2 trillion for EMEs and LIDCs; Schmidt-Traub (2015) reports lower figures for comparable country groups due to different definitions (average over 2016–30 vs end-2030 level).
  - Climate mitigation/adaptation costs not included in IMF analysis; other studies estimate mitigation/adaptation at about 20–40 percent of infrastructure spending.

*Source: IMF Staff Discussion Note (sdn1903).*

### EXECUTIVE SUMMARY ___________________________________________________________________________ 5

### EXECUTIVE SUMMARY

### Introduction
- The Sustainable Development Goals (SDGs) were endorsed in September 2015 as a road map to more inclusive growth and development that respects the limits of nature.
- This Staff Discussion Note focuses on investment in human, social, and physical capital—education, health, roads, electricity, and water and sanitation.
- Progress highlights and development indicators:
  - Since 1990, over a billion people have been lifted out of extreme poverty.
  - Infant mortality decreased from 65 to 31 deaths per 1,000 births.
  - The share of primary-school-age children out of school has fallen from 18 to 9 percent.
  - The median composite SDG index score for advanced economies is 78 percent.
  - The median composite SDG index score for low-income developing countries is 53 percent.
- Sample and scope:
  - Spending estimates apply to a sample of 155 countries: 49 low-income developing countries, 72 emerging market economies, and 34 advanced economies.
  - Five country case studies refine the analysis: Benin, Guatemala, Indonesia, Rwanda, and Vietnam.
- Fiscal policy role:
  - Public intervention is critical for SDG areas where private returns are uncertain or long-term (SDG1, SDG2, SDG3, SDG4, SDG5, SDG6, SDG7, SDG9, SDG10, SDG11).
  - Public expenditure and tax revenue tend to rise with per capita income (Wagner’s Law).

### Main spending estimates and variation
- Headline additional spending needs in 2030 (relative to current sector spending as a share of GDP):
  - US$0.5 trillion for low-income developing countries.
  - US$2.1 trillion for emerging market economies.
- Average additional spending as a share of GDP:
  - Emerging market economies: about 4 percentage points of GDP.
  - Low-income developing countries: about 15 percentage points of GDP.
  - Variation across countries is large: some LIDCs (e.g., Vietnam) have needs similar to EMEs, while others (e.g., Rwanda and Benin) require more than 15 percentage points of GDP.
- Methodology summary:
  - Estimates quantify the annual cost of achieving high performance across five SDG areas: education, health, roads, electricity, water and sanitation.
  - For each sector, performance is modeled as a function of input variables; median input levels of countries that perform well today (measured by SDG index scores) are assigned to each country, controlling for demographics and projected GDP per capita in 2030.
  - Estimates assume improvements in spending efficiency (assigning input levels observed in high-performing countries implies both more and better spending). If efficiency gains do not materialize, required spending would be larger.
  - Additional spending is reported for 2030 in percentage points of GDP and in constant 2016 dollars (by multiplying percentage-point estimates by projected 2030 GDP in constant 2016 dollars).
  - The variation in additional spending across countries reflects current spending levels, demographics (e.g., share of school-age children), and current infrastructure/access gaps.

### Current spending patterns (2016, percent of GDP) — GDP-weighted averages by income group
- Education, health, infrastructure:
  - LIDCs: 4.9
  - EMEs: 7.3
  - AEs: 15.5
- By function:
  - Education: LIDCs 2.3, EMEs 3.2, AEs 5.2
  - Health: LIDCs 0.9, EMEs 2.3, AEs 7.8
  - Transport: LIDCs 0.9, EMEs 1.5, AEs 1.9
  - Fuel and energy: LIDCs 0.4, EMEs 0.2, AEs 0.3
  - Water: LIDCs 0.4, EMEs 0.1, AEs 0.3
  - Social protection: LIDCs 1.6, EMEs 6.7, AEs 13.0
  - Defense, order, and safety: LIDCs 2.1, EMEs 2.7, AEs 3.9
  - Other primary spending: LIDCs 7.9, EMEs 10.6, AEs 6.1
  - Interest: LIDCs 1.4, EMEs 2.1, AEs 2.0
  - Total primary spending: LIDCs 17.9, EMEs 29.4, AEs 40.5

### Financing the SDGs: policy priorities and options
- Countries’ primary responsibility:
  - Countries “own” the SDG agenda and should pursue reforms that foster sustainable and inclusive growth to generate domestic tax revenue.
  - Policy priorities include strengthening macroeconomic management, combating corruption and improving governance, strengthening transparency and accountability, and fostering enabling business environments.
- Domestic revenue mobilization:
  - Raising domestic tax revenue is essential; increasing the tax-to-GDP ratio by 5 percentage points of GDP in the next decade is described as an ambitious but reasonable target in many countries.
- Spending efficiency:
  - Addressing spending inefficiencies is critical—countries need to spend not only more, but better.
  - The paper estimates that countries could save about as much through efficiency efforts as through tax reforms.
- External financing and partnerships:
  - For many LIDCs, the scale of additional spending needs requires support from the private sector, donors, philanthropists, and international financial institutions.
  - Delivering on official development assistance targets can help close development gaps in many LIDCs.
  - A national reform agenda should map SDGs to national circumstances and articulate the complementary role of development partners.

### Methodological caveats and scope limitations
- The analysis covers five SDG areas (education, health, roads, electricity, water and sanitation) and includes private and public spending; other SDG areas might involve substantial additional costs not captured here.
- Estimates are annual flows in 2030 rather than cumulative stocks up to 2030; cumulative spending up to 2030 would be significantly larger because spending would need to rise before 2030.
- For infrastructure, additional spending in percentage points of GDP corresponds to the annualized spending required to close infrastructure gaps between 2019 and 2030.
- The estimates incorporate synergies across the analyzed sectors but should be viewed with caution due to potential omitted SDG costs.

*Source: IMF Staff Discussion Note (EXECUTIVE SUMMARY).*

### 7.      Additional spending in 2030 amounts to 2.6 trillion US dollars (2.5 percent of the

### 7.      Additional spending in 2030 amounts to 2.6 trillion US dollars (2.5 percent of the

### Global total and regional breakdown
- Additional spending in 2030 amounts to 2.6 trillion US dollars (2.5 percent of the 2030 world GDP) in 121 emerging market economies and low-income developing countries.
- The Asia and Pacific region has the largest global additional spending requirement, estimated at 1.5 percent of 2030 world GDP.
- Sub-Saharan Africa has the second largest additional spending requirement, estimated at 0.4 percent of 2030 world GDP.
- Regional labels used: AP = Asia and Pacific; CIS = Commonwealth of Independent States; EUR = Europe; LAC = Latin America and the Caribbean; MENAP = Middle East, North Africa, Afghanistan, and Pakistan; SSA = sub-Saharan Africa; EMEs = emerging market economies; LIDCs = low-income developing countries.

### Variation across emerging market economies (EMEs)
- On average, emerging market economies face additional spending of 4 percentage points of their GDP in 2030 (GDP-weighted average of emerging market economies).
- Countries with estimated additional spending above 8 percentage points of GDP (75th percentile of income group) had average GDP per capita spending of US$4,200 in 2016; their additional spending is driven mostly by the education sector.
- Countries around the median for additional spending (4.2 percentage points of GDP) are resource-rich and have estimates driven largely by the health care sector.
- Countries below the 25th percentile for additional spending are higher-income emerging market economies, with average GDP per capita of US$9,100 in 2016.

### Country cases: Guatemala and Indonesia
- Both Guatemala and Indonesia have relatively low tax-to-GDP ratios—close to 10 percent of GDP—and large outcome gaps in key indicators.
- Guatemala: additional spending of 8.7 percentage points of GDP in 2030 (more than twice the average EME additional spending). Main drivers: relatively low enrollment in education and a deficient road network.
- Indonesia: additional spending of 5.6 percentage points of GDP in 2030 (slightly above the EME average). Main driver: necessary investment in health care.

### Low-income developing countries (LIDCs)
- Average additional spending in low-income developing countries is estimated at 15.4 percentage points of GDP in 2030 (GDP-weighted average of low-income developing countries).
- In LIDCs, additional spending in 2030 is split between education and health care (8.3 percentage points of GDP) and infrastructure (7.1 percentage points of GDP).
- Across the 49 LIDCs included in the analysis, additional spending ranges from about 50 percentage points to about half a percentage point of countries’ GDP in 2030.
- Countries with additional spending above 22 percentage points of GDP (75th percentile of LIDCs) had average GDP per capita of US$580 in 2016 and a large need for health care investment; two-thirds of these countries are fragile states.
- Countries near the LIDC median (17 percentage points of GDP) had GDP per capita of about US$1,200 in 2016 on average; spending pressure driven by education and roads.
- Countries with additional spending below 12 percentage points of GDP (25th percentile of LIDCs) had GDP per capita of about US$1,600 in 2016 on average and must invest heavily in roads.
- From a global perspective, additional spending in LIDCs in 2030 amounts to US$528 billion, equivalent to half a percentage point of 2030 world GDP.
- The estimated additional spending for LIDCs is equivalent to 0.9 percent of the 2030 advanced economy GDP.

### Comparisons and uncertainties
- Aggregate sectoral estimates: additional spending of US$1.4 trillion on roads, electricity, and water and sanitation and US$1.2 trillion on education and health care.
- The estimates are comparable to those in the literature after adjusting for differences in country groupings, sectoral coverage, spending definitions, and reporting years.
- Sources of uncertainty:
  - Estimates could be a lower bound because only 5 sectors out of 17 are included.
  - Growth projections could deviate from baseline projections; alternative growth paths would significantly affect spending estimates.
  - Changes in underlying methodological assumptions could affect the estimates.

### Financing the SDGs — A. Creating conditions for economic growth
- Structural reforms that boost the level and durability of growth can lower estimates of additional spending.
- Doubling projected GDP per capita in 2030 would reduce additional spending by 4.5 percentage points.
- Country case: Vietnam faces additional spending of 6.4 percentage points in 2030, among the lowest in LIDCs; extreme poverty rate is 5 percent. Vietnam’s experience reflects sustained growth, fiscal efforts (tax revenue increased from 16 to 19 percent of GDP during 1998–2017), and inclusive reforms (Doi Moi).

### Financing the SDGs — B. Boosting tax revenue and enhancing spending efficiency
- Tax revenue progress:
  - In LIDCs, tax revenue increased on average from about 12 percent of GDP in the early 2000s to nearly 15 percent of GDP today.
  - Median tax-to-GDP ratios in 2016: 15 percent in LIDCs, 18 percent in EMEs, and 26 percent in advanced economies.
- About a third of EMEs and half of LIDCs have tax-to-GDP ratios lower than 13 percent, a threshold documented as a tipping point for development.
- Raising tax-to-GDP ratios:
  - Increasing the tax-to-GDP ratio by 5 percentage points of GDP in the next decade is presented as an ambitious but reasonable aspiration in many countries.
  - If countries with tax-to-GDP ratios below the 75th percentile for their income group (19 percent in LIDCs; 22 percent in EMEs; 29 percent in advanced economies) raised their tax-to-GDP ratio to the 75th percentile, such an increase would amount to 5 percentage points of GDP on average.
  - Historical examples: China’s 1994 tax reform contributed to raising revenue from 10 to 15 percent of GDP between 1995 and 2002; Georgia’s tax reforms contributed to raising revenue by more than 12 percentage points of GDP between 2004 and 2009.
- Policy guidance:
  - Well-designed tax policies that broaden the tax base and minimize distortions can support growth.
  - The combined effect of tax measures and the spending that additional revenues finance determines the net impact on growth.
  - Adopting a medium-term revenue strategy is recommended, framed around:
    1. Building broad-based consensus for medium-term revenue goals to finance needed public expenditures.
    2. Designing a comprehensive tax reform covering policy, administration, and the legal framework.
    3. Committing to sustained political support over multiple years.
    4. Securing adequate resources to support coordinated implementation of the medium-term revenue strategy.
- The medium-term revenue strategy approach has been applied in dialogue with 15 countries (Egypt, Ethiopia, Indonesia, Georgia, Ghana, Guatemala, Lao P.D.R., Liberia, Mongolia, Myanmar, Papua New Guinea, Rwanda, Senegal, Thailand, Uganda).

*Source: IMF staff calculations and analysis presented in the provided PDF content.*

### Box 1. Indonesia’s Medium-Term Revenue Strategy

### Box 1. Indonesia’s Medium-Term Revenue Strategy

### Background and objectives
- Indonesia has made impressive progress in the past 20 years, including:
  - cutting the poverty rate by half;
  - increasing life expectancy;
  - expanding access to clean water, sanitation, and electricity;
  - improving educational attainment.
- Current constraint: a tax-revenue-to-GDP ratio close to 10 percent, which limits financing for additional expenditures critical to development.
- A medium-term fiscal strategy, with a medium-term revenue strategy at its core, is under consideration as part of government reforms started in 2016.

### Revenue target and composition
- Thrust of the fiscal strategy: raise revenue by about 5 percentage points of GDP in the medium term to finance growth- and equity-enhancing expenditure priorities in infrastructure, health, education, and social assistance.
- Potential revenue contributions:
  - Tax policy reforms could potentially generate up to 3.5 percentage points of GDP, including through new excises and major revisions to value-added and income taxes.
  - Tax administration measures can add 1.5 percentage points of GDP in revenue, including through compliance improvement and institutional reforms.

### Implementation and governance
- The medium-term revenue strategy should include efforts to strengthen governance through multiyear commitments with appropriate mandates and monitoring to ensure effective implementation.
- The strategy is being considered within broader national development planning and the mainstreaming of the SDGs into national plans.

### Relationship to SDGs and broader policy context
- Anchoring development plans to a medium-term revenue strategy is highlighted as a promising way to support SDG implementation.
- The revenue increase is intended to finance additional spending on infrastructure, health, education, and social assistance that are central to unlocking development potential.

### Key statistics and dates (exact values from source)
- Tax-revenue-to-GDP ratio: close to 10 percent.
- Medium-term revenue increase targeted: about 5 percentage points of GDP.
- Tax policy reforms potential: up to 3.5 percentage points of GDP.
- Tax administration measures potential: 1.5 percentage points of GDP.
- Government reforms started in: 2016.
- Source citation within box: de Mooij, Nazara, and Toro (2018).

*Source: Box 1. Indonesia’s Medium-Term Revenue Strategy (sdn1903).*

### Annex Table 1.1. Input-Outcome Approach in Five Sectors

### Annex Table 1.1. Input-Outcome Approach in Five Sectors

### Overview of the input-outcome framework
- Outcomes proxied with:
  - SDG4 index for education and SDG3 index for health (2018 SDG Index and Dashboards Report).
  - SDG9.1.1 index (Rural Access Index) for roads.
  - SDG7.1.1 index for electricity access.
  - SDG6.1 and 6.2 indices for water and sanitation.
- Outcome indicators take values between 0 (lowest outcome) and 100 (highest outcome).
- SDG4 summarizes three education variables; SDG3 reflects fourteen health variables.
- Validation by regression:
  - Controlling for GDP per capita, teacher-to-student ratio and teacher salary to GDP are significant on SDG4.
  - Controlling for GDP per capita, doctor-to-population ratio and doctor’s wages to GDP per capita are significant on SDG3.
- On/off-grid mix for electricity corresponds to shares of connections directly to the power grid (on) and to “individual” power generators (off), e.g., solar panels.

### Sectoral inputs, units, and other determinants
- Education
  - Key inputs: Number of teachers; other current and capital spending.
  - Unit cost driver: Teacher wage.
  - Demographics and other factors: Enrollment rates, GDP per capita, school-age population, teacher-to-student ratio (TSR), share of noncompensatory current expenses (y), share of capital expenses (z).
  - Benchmarking: TSR, AWAGE, y, z set at median values observed today in countries with high education outcomes, separately by income group.
  - Target enrollment rates assumed for 2030: 50 percent for preprimary and tertiary; 100 percent for primary and secondary.
  - Methodology note: Number of teachers derived as TSR * enrollment rates * school-age population.
- Health
  - Key inputs: Number of health care workers (doctors/others); doctor wages; other current and capital spending.
  - Unit cost drivers: Health care workers’ wage structure (DAWAGE) and ratio of non-doctor wages to doctor wages (assumed α = 0.5).
  - Demographics and other factors: GDP per capita, total population, doctor density (DPR), ratio of doctors to other health staff (ρ), shares y and z.
  - Benchmarking: DAWAGE, DPR, and ρ set at medians observed in high-performing countries in each income group.
- Roads
  - Key input: Kilometer of all-weather road.
  - Unit cost: Unit cost of all-weather road kilometer set at a minimum of US$500,000 (Iimi and others (2016) reference).
  - Method: Regression of road density on GDP per capita, population density, agriculture and manufacturing shares, urbanization, and RAI to estimate additional kilometers required to raise RAI to at least 75 percent by 2030.
  - Depreciation: Increase total cost of additional kilometers by 5 percent to account for depreciation.
- Electricity
  - Key inputs: Additional electricity network and on/off grid mix.
  - Unit cost: Unit cost per kilowatt set at US$2,250 (World Bank (2013a)).
  - Method: Estimate networks needed to provide electricity access to 100 percent of projected population by 2030 while accounting for increase in per capita consumption in line with real GDP per capita.
- Water and Sanitation
  - Method: Costs derived using WASH World Bank methodology (Hutton and Varughese 2016) with unit costs calibrated at the country level.
- Cross-sector determinants noted in table:
  - Demographics, topography (roads), population density, GDP per capita, enrollment rates, and other factors.

### Key methodological assumptions and calibration
- Benchmarks by income group (education and health): three groups by GDP per capita: less than US$3,000; between US$3,000 and US$6,000; and between US$6,000 and US$18,000.
- High-performing country thresholds:
  - Education (SDG4): above 80 in low-income group, above 82 in middle-income group, above 84 in high-income group.
  - Health (SDG3): above 70 in lowest income group, above 78 in median income group, above 84 in highest income group.
- For education and health, 2030 spending estimated using benchmarked inputs and country projections for economic growth and demographics.
- For physical capital (roads, electricity, water and sanitation), spending is annualized to close the infrastructure gap between 2019 and 2030; after 2030 infrastructure spending assumed to decline to about 60 percent to cover depreciation.
- Efficiency and interactions:
  - Costing assumes better-than-average spending efficiency based on high-performing benchmark countries.
  - Estimates partially account for intersectoral synergies because high performers in one sector often perform well in others.

### Specific numeric model inputs and assumptions
- Road unit cost minimum: US$500,000 per kilometer.
- Electricity unit cost per kilowatt: US$2,250.
- Depreciation adder for roads: increase total cost of additional kilometers by 5 percent.
- Ratio of all non-doctor wages to doctor wage assumed to be 0.5.
- Enrollment targets for 2030: 50 percent preprimary and tertiary; 100 percent primary and secondary.
- Current median electricity access: below 50 percent in low-income developing countries; close to 100 percent in emerging market economies.

### Summary of estimated additional spending (as of 2030)
- Presentation format: Estimates reported as of 2030 in percentage points of GDP and in 2016 US dollars. For education and health, figures reported are the difference between the share of 2030 GDP consistent with high performance and the current level of spending as a share of 2030 GDP. For physical capital, annualized spending to close the 2019–2030 infrastructure gap is expressed as percent of 2030 GDP.
- Health and Education (Billion 2016 US$):
  - LIDCs: 284
  - EMEs: 244
  - Total: 528
- Health and Education (Percentage points of GDP):
  - LIDCs: 8.3
  - EMEs: 7.1
  - Total: 15.4
- Roads, Electricity, and Water and Sanitation (Billion 2016 US$):
  - LIDCs: 1,011
  - EMEs: 1,048
  - Total: 2,059
- Roads, Electricity, and Water and Sanitation (Percentage points of GDP):
  - LIDCs: 2.0
  - EMEs: 2.1
  - Total: 4.1

### Country case-study refinements and selected findings
- General note:
  - Five countries (Benin, Guatemala, Indonesia, Rwanda, Vietnam) had estimates refined to account for country-specific circumstances (updated latest values, unit costs, and targets in line with national circumstances).
  - Refinements included changes in enrollment rates (Benin), unit costs per road kilometer (revised upward in all countries but Indonesia), electricity cost adjustments (Rwanda and Vietnam), and target adjustments (Guatemala, Indonesia, Vietnam).
  - No systematic bias found from standard methodology; country-specific estimates generally decline with GDP per capita.
- Benin
  - Country context: SDGs central to development plans: Programme d’Actions du Gouvernement (PAG, 2016–21), Plan National de Développement (PND, 2018–25), and Programme de Croissance pour le Développement Durable (PC2D, 2018–21). Authorities prioritized 49 SDG targets and established a general directorate for SDG coordination in August 2016.
  - Resource needs: Additional annual spending of more than 21 percentage points of GDP to approach the SDGs (country-specific estimate 21.3 percentage points of GDP versus standard methodology 19.8 percentage points of GDP).
  - Drivers of divergence from standard methodology: cost per kilometer of road increased from US$500,000 to US$610,000; education targets aligned with local plans.
  - Financing measures under consideration: mobilizing revenue by curbing tax exemptions (2019 Budget Law envisions a cut in tax exemptions); anticipating private sector involvement under PAG via PPP legal/regulatory framework established in 2017; participation in the G20 Compact with Africa since October 2017.
- Rwanda
  - National Strategy for Transformation (NST) links SDG targets to the budget (NST runs from 2018 to ... [text continues beyond provided excerpt]).
- Country-specific additional spending estimates (percentage points of GDP) and 2016 GDP per capita (from Annex Figure 2.1):
  - Rwanda: Country-specific 18.7; Standard methodology 13.8; GDP per capita US$734.
  - Benin: Country-specific 21.3; Standard methodology 19.8; GDP per capita US$791.
  - Vietnam: Country-specific 6.4; Standard methodology 1.5; GDP per capita US$2,172.
  - Indonesia: Country-specific 5.6; Standard methodology 8.3; GDP per capita US$3,604.
  - Guatemala: Country-specific 8.7; Standard methodology 5.2; GDP per capita US$4,141.
- Interpretation:
  - Low GDP per capita countries (e.g., Benin and Rwanda with GDP per capita less than US$800) have large estimated additional spending needs (19–21 percentage points of GDP in 2030), reflecting large development gaps.
  - Vietnam (classified as a low-income developing country by IMF classification in this sample), Indonesia, and Guatemala have additional spending in single digits (percentage points of GDP).
  - Country-specific estimates are within 3 percentage points of GDP of the standard methodology for all reviewed countries except Rwanda (largely due to higher road costs) and Guatemala (largely due to extensive road infrastructure gap).

*Source: IMF staff.*

### 2024. Line ministries have produced sectoral strategies and preliminary estimates of sectoral

### sdn1903 - 2024. Line ministries have produced sectoral strategies and preliminary estimates of sectoral

### Service delivery and sectoral gaps (Rwanda)
- Since 1995, Rwanda has experienced rapid and inclusive growth; service delivery has improved greatly in the past two decades, but gaps remain.
- Health
  - Primary care is extensive via rural clinics.
  - Stunting affects 38 percent of children.
  - Access to qualified staff is low.
- Education
  - Enrollment is nearly universal at the primary level.
  - Secondary enrollment is less than 40 percent.
- Water and sanitation
  - Access remains a challenge.
- Electricity
  - The sector has excess on-grid capacity.
  - Only 46 percent of households have access.
- Development financing requirement
  - Closing development gaps requires an additional 19 percentage points of GDP in spending.
  - Country-specific estimates are higher than the standard methodology, reflecting higher unit cost assumptions:
    - Roads: US$1.1 million a kilometer.
    - Water: local annual estimates of about 4 percent of GDP.
- Financing approach
  - The government expects that two-thirds of the additional spending will be public.
  - The current medium-term fiscal framework relies on buoyant revenue from growth and remittances, with the remainder covered by private sector borrowing.

### Country-specific SDG performance and costing comparisons (Vietnam)
- National planning and SDG integration
  - The 17 SDGs were mapped into 115 Vietnam SDG goals in the “National Action Plan for Implementation of the 2030 Agenda for Sustainable Development.”
  - SDGs integrated into the Socio-Economic Development Strategy 2011-20 and the Socio-Economic Development Plan 2016-20.
  - Efforts led by the Ministry of Planning and Investment.
- Outcomes and benchmarking
  - Country outcomes are superior to median low-income developing countries and rank well relative to emerging market economies across several dimensions.
  - Country-specific estimates use the higher-income group of countries as a benchmark.
  - Country-specific estimates use a higher unit cost for roads: US$1.1 million a kilometer.
- Fiscal constraints and financing
  - Financing for additional public spending is limited.
  - The revenue ratio is high for its income group.
  - Public debt is approaching the statutory limit of 65 percent.
  - Tax policy changes under discussion to offset declining oil revenue.
  - Graduation to lower-middle-income status in 2010 led to a dramatic decline in official development assistance.
  - The government targets efficiency gains and increased private sector participation.

### Country-specific SDG performance and costing comparisons (Indonesia)
- SDG mainstreaming
  - The 17 SDGs mapped into National Visions and the National Medium-Term Development Plan (RPJMN) 2015-2019.
  - Presidential decree in 2017; National Action Plan SDGs 2017-2019; SDGs Road Map 2017-2030; Regional Action Plan of SDGs 2017-2019.
- Progress and remaining gaps
  - Strong improvements in poverty, infant mortality, life expectancy, access to clean water, sanitation, electricity, and educational attainment over 20 years.
  - Remaining gaps:
    - Secondary enrollment: 74 percent.
    - Program for International Student Assessment performance lower than east Asian economies.
    - Maternal and under-five mortality remain well above SDG targets.
    - Transportation infrastructure identified as a bottleneck.
  - Country-specific estimates reflect local strategy: increase efficiency of education spending, raise health care expenditure, enhance road and electricity infrastructure.
- Fiscal space and revenue
  - Little space to expand spending; government deficit has inched toward the 3 percent ceiling.
  - Tax-revenue-to-GDP ratio is close to 10 percent (noted as well below emerging market economies).
  - Other financing options: Islamic community contributions, improving spending efficiency (smaller magnitude than revenue mobilization), and greater private sector role in infrastructure.

### Country-specific SDG performance and costing comparisons (Guatemala)
- SDG integration
  - Ministry of Planning mapped K’atun 2032 national development plan into the SDGs and 10 National Priorities.
  - Execution of policies remains challenging.
- Social and infrastructure outcomes
  - Poverty: 60 percent of the population.
  - Extreme poverty: 23 percent of the population.
  - Prevalence of stunting in children under five among the highest in the world.
  - Infant and maternal mortality rates well above Latin American and Caribbean averages.
  - More than 40 percent of the population does not have access to safe water.
  - Preprimary education and secondary enrollment rates are relatively low.
  - Social outcomes markedly worse in rural areas and for indigenous populations.
- Additional spending needs and cost assumptions
  - Additional spending is 8.7 percent of GDP (noted as relatively large for an emerging market economy).
  - Main country-specific differences from the standard methodology:
    - Roads: higher cost per kilometer of US$800,000.
    - Health care: updating the inputs.
- Fiscal strategy
  - Guatemala can use existing fiscal space to accommodate higher spending, but integral fiscal reform is needed.
  - Financing requires an integrated fiscal package: tax administration efforts, tax policy changes to raise revenue (currently at 10 percent of GDP), and greater spending flexibility and efficiency.
  - Emphasis that higher spending alone is unlikely to lead to better outcomes; spending should be scaled up along with improvements in the provision of public goods.

### Cross-country sample and descriptive statistics
- Sample composition
  - The sample includes 155 countries: 34 advanced economies, 72 emerging market economies, and 49 low-income developing countries.
  - These countries represent 99 percent of the population and 98.5 percent of aggregate GDP for the 193 IMF economies.
- GDP per capita (2016 US$) descriptive statistics presented by group (P5, P50, P95, Mean) in the source text for:
  - Low-income developing countries
  - Emerging market economies
  - Advanced economies
- Country lists: included countries enumerated across the three groupings in the source content.

### Comparison of SDG spending assessments (Annex 4)
- Overall comparability
  - Controlling for country grouping, sectoral scope, and definitions, IMF cost estimates are comparable to UNCTAD (2014), Schmidt-Traub (2015), and Manuel and others (2018).
  - Range of headline estimates is wide: US$1.4–US$3.9 trillion.
  - Variation reflects differences in:
    - Country groupings (IMF classification vs World Bank classification).
    - Sectoral coverage (infrastructure, health, education, social protection, climate change).
    - Definition of spending (total spending, additional spending, financing gaps).
    - Reference years.
- Sectoral comparisons
  - Infrastructure
    - IMF: additional annual spending of about US$1.4 trillion for roads, electricity, and water and sanitation in low-income countries and emerging market economies.
    - UNCTAD (2014): about US$1.8 trillion a year in the same sectors (US$2.0 trillion including telecommunications) in developing economies.
    - Main difference on water: UNCTAD estimates about US$300 billion higher.
    - For low-income and lower-middle-income countries, Schmidt-Traub (2015) finds US$660 billion versus IMF’s US$725 billion for the same grouping.
  - Education and health care
    - IMF estimates additional spending of US$1.2 trillion in 2030 in low-income developing countries and emerging market economies (of which US$600–US$700 billion in low-income and lower-middle-income countries).
    - Schmidt-Traub (2015) reports lower figures (US$200–US$300 billion) for low-income and lower-middle-income countries due to differing definitions of additional spending (average over 2016–30 vs end-2030 level).
    - If spending ramps gradually between 2019 and 2030 to reach IMF levels, IMF’s average annual additional spending would be less than US$400 billion.
    - UNCTAD reports capital spending in these sectors comparable to a 20 percent share of IMF’s total current and capital spending estimates.
    - Manuel and others (2018) findings are broadly comparable when accounting for differing definitions.
  - Climate mitigation and adaptation
    - Estimates range about 20–40 percent of infrastructure spending in other studies.
    - UNCTAD (2014) estimates about US$800 billion a year in developing economies (40 percent of their infrastructure spending estimate).
    - Schmidt-Traub (2015) estimates mitigation and adaptation costs of about US$130 billion for low-income countries and lower-middle-income countries (20 percent of the infrastructure spending estimate).
    - The IMF does not include the cost of climate mitigation and adaptation in its analysis.
- Annex Table 4.1 summary highlights (as presented)
  - IMF headline: Additional spending of US$528 billion for low-income developing countries and US$2.1 trillion for emerging market economies in 2030.
  - UNCTAD (2014) headline: Total annual investment of US$3.9 trillion in developing economies (US$120 billion in least developed countries); gap US$2.5 trillion in developing economies (US$80 billion in least developed countries). Extrapolated global investment US$5–$7 trillion a year.
  - Schmidt-Traub (2015) headline: Total incremental investment of US$1.4 trillion a year (US$400 billion in low-income countries and US$1 trillion in lower-middle-income countries).
  - ODI (Manuel and others 2018) headline: Annual financing gap about US$150 billion for 48 “under resourced” countries.
- Comparability notes from Annex Table 4.1
  - UNCTAD’s total investment for roads, power, and water (US$1.8 trillion) is higher than IMF additional spending in these sectors (US$1.2 trillion); main difference is water (UNCTAD about US$300 billion higher).
  - Differences in health and education across studies reflect definitions (e.g., average incremental investment vs end-2030 additional spending) and assumptions about per capita costs.
  - ODI’s US$150 billion gap in 2018 is roughly comparable to IMF’s US$340 billion difference between additional spending and potential additional revenue in 2030 (noted as 9.9 percent of low-income developing country GDP in 2030 in the source text).

*Prepared by IMF staff (content extracted from the provided chapter).*

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### sdn1903 - REFERENCES

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- Wagner, Adolph. 1958. “Three Extracts on Public Finance.” In Classics in the Theory of Public Finance—International Economic Association Series, edited by R. A. Musgrave and A. T. Peacock. London: Palgrave Macmillan. https://link.springer.com/chapter/10.1007/978-1-349-23426-4_1.

### Data, measurement, and cross-cutting empirical contributions
- Capgemini. 2018. World Wealth Report 2018. Paris. https://www.capgemini.com/wp-content/uploads/2018/06/Capgemini-World-Wealth-Report.pdf.  
- Fay, Marianne, and Tito Yepes. 2003. “Investing in Infrastructure: What Is Needed from 2000 to 2010?” World Bank Policy Research Working Paper 3102. http://documents.worldbank.org/curated/en/502041468739488372/pdf/multi0page.pdf.  
- Iimi, Atsushi, Ahmed Farhad, Edward Charles Anderson, Adam Stone Diehl, Laban Maiyo, Tatiana Peralta-Quirós, and Kulwinder Singh Rao. 2016. “New Rural Access Index: Main Determinants and Correlation to Poverty.” World Bank Policy Research Working Paper 7876, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/821621478094511356/pdf/WPS7876.pdf.  
- World Bank. 2013a. “Global Development Horizons Capital for the Future: Saving and Investment in an Interdependent World.” Technical Annexes, World Bank, Washington, DC. http://siteresources.worldbank.org/EXTDECPROSPECTS/Resources/476882-1368197310537/Annexes.pdf.  
- World Bank. 2013b. “Financing for Development Post-2015.” Washington, DC. http://documents.worldbank.org/curated/en/206701468158366611/pdf/828000WP0Finan0Box0379879B00PUBLIC0.pdf.  
- World Bank. 2018. “Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle.” Washington, DC. https://openknowledge.worldbank.org/bitstream/handle/10986/30418/9781464813306.pdf.

*References as listed in sdn1903 - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/sdn/2019/sdn1903.pdf_