## _cr1698

## Source details

**Canonical URL:** [_cr1698](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr1698.pdf)

## Other formats

- [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr1698.pdf.md)
- [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr1698.pdf.json)

---

### Introduction and infrastructure investment outlook
- WAEMU countries projected public capital expenditures: around 9.5 percent of GDP in 2015 to 2019 (up from an average below 8 percent in 2011–14).
- Infrastructure gaps and quality:
  - WAEMU lags sub-Saharan African benchmark countries in electricity supply, paved road density and telecommunication infrastructure.
  - World Economic Forum Global Competitiveness Indicators rank WAEMU countries 110 out of 148 countries (average 2013–2015).

### Assessing public investment efficiency (DEA results, period 2006–12)
- DEA specification:
  - Inputs: real public capital stock per capita; real GDP per capita.
  - Outputs: World Economic Forum Global Competitiveness Indicator; telecommunication (fixed and cellular phones per capita); access to water (percent of population); power generation per capita; airline capacity (km per sq. km).
- Key DEA efficiency scores (2012):
  - WAEMU 0.84
  - SSA, all countries 0.82
  - SSA, benchmark countries 0.93
  - Emerging Asia 0.94
- Interpretation:
  - WAEMU’s investment efficiency broadly in line with SSA average but below peer averages.
  - Reported gap of 10 percent between WAEMU’s efficiency and sub-Saharan African benchmark.

### Explaining public investment efficiency (panel regressions of DEA scores)
- Regression (Dependent variable: Output oriented DEA score (0 to 1); Observations: 388; Number of Countries: 73; R-squared: 0.390; Year dummies: Yes):
  - WGI: Regulatory Quality (-2.5 to 2.5): 0.039*** (0.012)
  - Resource rich dummy variable (1 = rich): -0.093*** (0.023)
  - Public investment volatility (2005-2012): -0.738 (1.123)
  - Percentage of urban population (0 to 1): 0.058 (0.062)
  - Log of real GDP per capita (000's of US$): 0.016 (0.013)
  - Official development assistance (% GDP, 0 to 1): 0.002 (0.005)
  - Constant: 0.916*** (0.033)
- Key interpretation:
  - Institutional quality (WGI regulatory quality): one point improvement → increase in efficiency score by 4 percent (0.039 coefficient).
  - Resource dependency negatively associated with efficiency (-0.093).
  - Other covariates not robustly significant.

### Improving Public Investment Management (PIM) — PIMA framework
- Policy message: Strengthening PIM can reduce the efficiency gap and raise the growth "bang" for the investment "buck."
- PIMA evaluates 15 PIM institutions across three stages:
  - Planning:
    1. Fiscal Rules
    2. National & Sectoral Planning
    3. Central-Local Coordination
    4. Management of PPPs
    5. Company Regulation
  - Allocating:
    6. Multi-year Budgeting
    7. Budget Comprehensiveness
    8. Budget Unity
    9. Project Appraisal
    10. Project Selection
  - Implementing:
    11. Protection of Investment
    12. Availability of Funding
    13. Transparency of Execution
    14. Project Management
    15. Monitoring of Assets
- Practical use: PIMA provides comparator charts and recommendations; IMF and World Bank piloting PIMA in WAEMU countries.

### Monitoring of assets and infrastructure indicators (2012)
- Qualitative index: 1 = worst, 7 = best (Air, Road, Rail, Port, Electricity, overall pillar).
- Quantitative indicators vs. stock of public investment (US$ per capita):
  - Annual electricity generation (Kwh per capita)
  - Percent of population with access to water
  - Fixed and mobile telephones per capita
  - Broadband internet subscriptions per capita
  - Weekly airline capacity (available seat miles per capita)
  - Rail lines (average 2007-12, meters per capita)

### DEA country coverage and benchmark groups
- Inclusion criterion: emerging economies with less than 30,000 US$ stock of (public and private) investment per capita.
- WAEMU countries in DEA: Benin, Burkina Faso, Cote d'Ivoire, Mali, Senegal.
- SSA Benchmark group (under 4,000 US$ per capita investment, higher efficiency): Ghana, Kenya, Malawi, Rwanda.
- Note: Guinea-Bissau, Togo, and Niger excluded from DEA due to Global Competitiveness Report coverage.

### Fiscal space: magnitudes and channels
- Two major channels to create fiscal space: increasing tax revenue; increasing spending efficiency.
- Estimated room to improve domestic tax collection: up to 0.8 to 2 percent of GDP (panel regression and stochastic frontier analysis).
- Potential fiscal savings from improved spending efficiency in education and health: on average 1 to 3 percent of GDP annually if all WAEMU countries achieve the spending efficiency of the top performer.
- WAEMU convergence criterion for tax-to-GDP ratio: 20 percent.

### Need for scaling up public investment — growth impacts
- Infrastructure contribution to per-capita growth in West Africa (2001–05): about 1 percentage point.
- Country examples:
  - Benin: infrastructure contributed 1.6 percent points to per capita growth.
  - Senegal: infrastructure contributed about 1 percent point to per capita growth.
  - Raising Benin to Africa middle-income infrastructure endowment → boost annual growth by 3.2 percentage points.
  - Raising Senegal to Africa middle-income infrastructure endowment → boost annual growth by 2.7 percentage points.
- External financing constraints: reduced availability of attractive external financing; caution warranted where total government debt increased since debt relief.

### Raising tax revenue: performance, composition, and targets
- WAEMU tax-to-GDP ratio trend:
  - 2000: 11.7 percent of GDP
  - 2011: 14.7 percent of GDP
- Country examples (change 2000 to 2011):
  - Benin: +2.1 percentage points
  - Togo: +6.6 percentage points
- Tax category highlights:
  - Personal income taxes: rose from about 3 to close to 4 percent of GDP.
  - Goods and services taxes: WAEMU higher by around 0.6 to 0.8 percent of GDP relative to comparators; most pronounced improvement 2000–2011.
  - Trade taxes: tariff rates dropped only marginally; trade-tax-to-GDP ratio broadly stable.
- Cross-country variation examples:
  - Togo: income tax declined from 2.9 percent to 2.5 percent of GDP; goods and services tax rose from 2 percent to 9.2 percent of GDP.
  - Benin: revenue gains driven by higher trade tax revenue; goods and services tax revenue declined.
  - Côte d'Ivoire: decline mainly due to falling trade tax revenues.

### Tax potential and tax gap analysis (2011 benchmarks)
- Total tax collection in WAEMU exceeded potential by:
  - around 6 percent compared with low-income countries
  - around 12 percent compared with sub-Saharan African countries
- Income tax performance deterioration vs. SSA benchmark:
  - From around -1½ percent to around -5½ percent (income tax gap)
- Policy implication: need to improve income tax collection; analytical finding: room for improvement in income tax revenues by around 0.8 to 2 percent of GDP.

### Country-level tax deviations and methods
- Six of eight WAEMU members increased tax collection efforts relative to benchmarks.
- Income tax revenue increases relative to benchmarks only in Mali, Niger and Senegal.
- Goods and services tax gap widened for Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali and Senegal.
- Trade tax gap closed or turned positive for Benin, Burkina Faso, Niger and Senegal.
- Methods: panel regressions and stochastic frontier analysis produce broadly consistent findings; divergence on VAT performance (positive in panel regression; negative in stochastic frontier analysis).

### Spending efficiency: education and health (DEA analysis, 2003−12 baseline; WAEMU data 2008−12)
- Education (WAEMU averages):
  - Education spending: 2003-07 4.0% of GDP; 2008-12 4.4% of GDP.
  - Primary enrollment rates: 2003-07 74.9%; 2008-12 88.9%.
  - Adult literacy rate: 2003-07 35.4%; 2008-12 46.0%.
- Peer country means:
  - Education spending: 2003-07 4.9%; 2008-12 4.8%.
  - Primary enrollment rates: 2003-07 90.7%; 2008-12 101.5%.
  - Adult literacy rate: 2003-07 41.5%; 2008-12 59.2%.
- Education efficiency findings:
  - All WAEMU countries below the efficiency frontier in both periods.
  - Potential fiscal savings from improved education spending efficiency: on average, WAEMU could save between 1 and 2 percent of GDP by reaching the Union's best performer.
- Health (WAEMU averages):
  - Public health spending: about 2.3% to 2.7% of GDP (2003−07 to 2008−10).
  - Private health spending: 3.3% to 3.5% of GDP.
  - Life expectancy: 53.8 to 55.6 years.
  - Child survival rates (per 1,000): 859.3 to 887.6.
- Peer country means (health):
  - Public health spending: 3.0% to 3.2% of GDP.
  - Life expectancy: 53.4 to 57.6 years.
  - Child survival rates: 890.2 to 923.2 per 1,000.
- Health efficiency findings:
  - WAEMU efficiency score range: 43 percent (Guinea Bissau) to 100 percent (Senegal).
  - Estimated potential savings from improved health spending efficiency: about 0.4 to 0.8 percent of GDP on average.
- Aggregate implication: achieving the Union's top efficiency could yield estimated fiscal savings of about 1 to 3 percent of GDP.

### Financial development, volatility of growth, and banking sector indicators
- Financial development regressions (General Index FD specification):
  - Financial Development Index coefficient: -4.65***; Financial Development Index 2: 3.35*
  - GDP per Capita PPP 5-year Lag: 0.00***
  - Energy Exports (% GDP) 5-year Lag: 0.09*
  - Volatility of foreign growth: 0.43***
  - Terms of Trade Changes: 0.04***
  - Government Balance: -0.11***
  - Observations and Number of Countries entries reported as concatenated digits in source.
- Decomposition highlights:
  - Financial Markets: -4.39***; Financial Markets 2: 4.73***
  - Financial Institutions Access: -2.14**; Financial Institutions Access 2: 1.51*
  - Financial Institutions Efficiency: -2.81*; Financial Institutions Efficiency 2: 3.96**
- Contribution magnitude:
  - Lower financial development could account for about 10 percent of the volatility differential with Asian and African benchmark groups.
- Banking sector metrics:
  - Credit to the economy growing at around 15 percent year on year.
  - Sovereign exposures rose to more than 26 percent of total assets in 2015.
  - Capital adequacy rose to around 12 percent in June 2015 (from 9.6 percent in June 2014); SSA averages of 18 percent.
  - Banks’ gross NPLs reached 15.8 percent of total loans in 2014.
  - Net NPL amounted to 33.5 percent of banks’ capital (SSA averages: 8 percent gross NPLs and 10.7 percent net NPLs).
  - Capital adequacy still based on Basel I rules on average.
- Microfinance sector:
  - Around 724 MFIs at end June 2015.
  - MFI deposits increased at an average annual rate of 12.2%; credit activities at 9.9%.
  - Policy actions: liquidation/license revocation of some MFIs; regrouping small MFIs into networks; temporary administration for 13 MFIs as of June 2015; BCEAO initiatives for regional financial inclusion strategy and credit information reporting for MFIs.

### Gender inequality: magnitudes, growth effects, and policy recommendations
- Gender gaps:
  - Labor force participation gap (male minus female) fell from >30 percentage points in 1990 to around 20 percentage points in 2013.
  - Asian Benchmark average gap: <17 percentage points.
  - African Benchmark gap: ~6 percentage points.
- Education gender indicators:
  - In Benin, Cote d’Ivoire, Mali, Niger: fewer than 9 girls enrolled in primary education per 10 boys; only 7 girls in secondary education per 10 boys; women <50 percent as likely to be enrolled in tertiary education.
- Health and legal constraints:
  - Lifetime risk of maternal death much higher in WAEMU (especially Mali and Niger).
  - In at least half of WAEMU countries women cannot be head of household the same way as men.
  - In none of the WAEMU countries does legislation prohibit discrimination in access to credit based on gender.
  - In at least half the countries, women are not legally protected from domestic violence.
- Growth impacts:
  - Income and gender inequality can explain around 0.5 percentage points of WAEMU’s real GDP per capita shortfall vs. Asian Benchmark.
  - Reducing gender and legal inequality to ASEAN-5 levels could boost real GDP per capita growth by 1 percentage point.
  - Country examples:
    - Mali: potential boost of 1¼ percentage points in real GDP per capita growth from such reforms.
- Policy recommendations:
  - Remove legal inequalities between men and women.
  - Foster education (increase schooling for girls).
  - Boost infrastructure (access to water, electrification) to free women’s time.
  - Reduce regressivity of fiscal spending and taxes; target social transfers.
  - Foster financial inclusion, including for women.

### Risks to the outlook and scenario impacts
- Risks skewed to the downside; baseline assumes timely domestic reforms (spending efficiency, PIM, fiscal space, financial access, competitiveness).
- Domestic risk (delay in reforms) scenario impacts relative to baseline:
  - WAEMU growth lower by 1–1.5 percentage points.
  - Domestic investment growth lower by about 2.5–3 percentage points.
  - Consumption growth lower by about 1.2 percentage points.
  - Current account balance (excluding grants) improves by about 0.5 percentage point of GDP on average.
  - Overall fiscal balance (excluding grants) improves by about 0.6 and 0.2 percentage point of GDP in 2016 and 2017 respectively, then deteriorates by about 0.1 percentage point of GDP over the medium term.
  - Small cumulative reduction in WAEMU official reserves of about US$ 0.2 billion.
- External risk (global financial volatility) scenario impacts relative to baseline:
  - Availability of external financing and capital inflows reduced by about one percentage point.
  - Private investment growth slows by about 0.5 percentage point.
  - Public investment growth lower by about 0.8 percentage point over the medium term.
  - WAEMU growth reduced by about 0.6 percentage points on average.
  - Country-partner shock elasticities:
    - 1-percentage point lower growth in advanced economies → WAEMU real GDP lower by about 0.8 percent after two years and about 1 percent at peak after five years.
    - 1-percentage point lower growth in China → WAEMU real GDP lower by about 0.5 percent in the short term.
    - 1-percentage point lower growth in Nigeria → WAEMU real GDP lower by 0.2 percent after 3–4 years.
  - Heterogeneity: effects larger in trade-open countries and those with larger Chinese investment (e.g., Niger, Togo); example peak impact of China slowdown: 1.2 percent in more trade-open countries vs. 0.4 percent in less open economies.

### Conclusions and policy priorities (synthesis)
- Substantial room to improve public investment efficiency in WAEMU, particularly via better institutional quality.
- Strengthening institutions (regulatory quality) and PIM reforms (via PIMA) can reduce the efficiency gap and amplify the growth impact of public investment.
- Creating fiscal space requires a mix of tax revenue mobilization (notably income tax improvements of around 0.8 to 2 percent of GDP) and spending-efficiency gains (education and health savings of about 1 to 3 percent of GDP).
- Complementary priorities: improve financial development and inclusion, strengthen banking and MFI regulation/supervision, remove legal gender inequalities, boost human capital and infrastructure to support inclusive growth.

*March 3, 2016 — Prepared by Karim Barhoumi, Fan Yang, Monique Newiak, and Ha Vu*

### References _________________________________________________________________________________ 10

### _cr1698 - References _________________________________________________________________________________ 10

### Introduction
- WAEMU countries are projected to increase public investment volumes significantly to close the region’s infrastructure gap.  
- Staff projects that public capital expenditures will increase to around 9.5 percent of GDP in 2015 to 2019, up from an average below 8 percent in 2011–14.  
- The WAEMU’s infrastructure needs are substantial and the region lags sub-Saharan African benchmark countries in electricity supply, paved road density and telecommunication infrastructure.  
- The region’s infrastructure is perceived as relatively low quality; the World Economic Forum’s Global Competitiveness Indicators ranks WAEMU countries 110 out of 148 countries (average 2013–2015), behind the sub-Saharan African average and sub-Saharan benchmark countries.

### Assessing Public Investment Efficiency
- Objective: measure the relative efficiency in translating monetary inputs into infrastructure outputs using an efficiency frontier approach (Data Envelopment Analysis, DEA).  
- DEA specification (period 2006–12):  
  - Inputs:
    - real public capital stock per capita
    - real GDP per capita (proxy for private sector contribution to infrastructure provision)
  - Outputs:
    - World Economic Forum Global Competitiveness Indicator (qualitative infrastructure indicator)
    - telecommunication (fixed and cellular phones per capita)
    - access to water (percent of population)
    - power generation per capita
    - airline capacity (km per sq. km)

Key empirical results from DEA:
- DEA Efficiency Scores Relative to Selected Economies (2012):
  - WAEMU 0.84
  - SSA, all countries 0.82
  - SSA, benchmark countries 0.93
  - Emerging Asia 0.94
- Interpretation: WAEMU’s investment efficiency is broadly in line with the SSA average, but lags peer groups’ averages, with a reported gap of 10 percent between WAEMU’s efficiency and sub-Saharan African benchmark.

### Explaining Public Investment Efficiency
- Empirical strategy: panel regressions of DEA efficiency scores on institutional quality and other covariates for a sample of sub-Saharan African countries (see Annex II for variable descriptions).
- Main regression estimates (Dependent variable: Output oriented DEA score (0 to 1); standard errors in parentheses; significance: *** p<0.01):
  - WGI: Regulatory Quality (-2.5 to 2.5): 0.039*** (0.012)
  - Resource rich dummy variable (1 = rich): -0.093*** (0.023)
  - Public investment volatility (2005-2012): -0.738 (1.123)
  - Percentage of urban population (0 to 1): 0.058 (0.062)
  - Log of real GDP per capita (000's of US$): 0.016 (0.013)
  - Official development assistance (% GDP, 0 to 1): 0.002 (0.005)
  - Constant: 0.916*** (0.033)
  - Year dummies: Yes
  - Observations: 388
  - R-squared: 0.390
  - Number of Countries: 73
- Key interpretation: quality of institutions (as measured by WGI regulatory quality rating) is positively correlated with public investment efficiency; a one point improvement in Regulatory Quality will lead to an increase in the efficiency score by 4 percent. Dependency on natural resources is negatively associated with efficiency. Other variables are not robustly significant.

### Improving Public Investment Management (PIM) to Reduce the Efficiency Gap
- Policy message: Improvements in public investment management can reduce the efficiency gap and deliver substantial growth dividends—the most efficient investors achieve greater growth “bang” for the investment “buck.”  
- Diagnostic tool: IMF’s Public Investment Management Assessment (PIMA) framework evaluates 15 PIM institutions across the three stages of the investment cycle:
  - Planning:
    1. Fiscal Rules
    2. National & Sectoral Planning
    3. Central-Local Coordination
    4. Management of PPPs
    5. Company Regulation
  - Allocating:
    6. Multi-year Budgeting
    7. Budget Comprehensiveness
    8. Budget Unity
    9. Project Appraisal
    10. Project Selection
  - Implementing:
    11. Protection of Investment
    12. Availability of Funding
    13. Transparency of Execution
    14. Project Management
    15. Monitoring of Assets
- The PIMA provides summary charts with comparator countries and practical recommendations to strengthen institutional practices. The IMF and World Bank are piloting PIMA in a number of WAEMU countries.

### Conclusions and Main Findings
- There is substantial room to improve public investment efficiency in WAEMU, particularly by improving the quality of institutions.  
- WAEMU’s public investment efficiency is weak relative to the best performers in SSA based on efficiency frontier analysis.  
- Regression analysis suggests stronger institutions could reduce the public investment efficiency gap in WAEMU; resource dependency is a negative correlate of efficiency.  
- Recommendation: WAEMU countries should evaluate PIM practices using the PIMA framework, identify country-specific institutional priorities for reform, and implement targeted PIM reforms to boost the growth impact of public investment.

*March 3, 2016 — Prepared by Karim Barhoumi, Fan Yang, Monique Newiak, and Ha Vu*

### 15. Monitoring of Assets

### 15. Monitoring of Assets

### Qualitative and Quantitative Infrastructure Indicators (2012)
- Infrastructure qualitative index scale: Index value 1 = worst 7 = best.
- Charts compare qualitative ratings (Air, Road, Rail, Port, Electricity) and an overall Global Competitiveness Index pillar against "Stock of public investment (US$ per capita)".
- Quantitative indicators plotted against "Stock of public investment (US$ per capita)" include:
  - Annual electricity generation (Kwh per capita).
  - Percent of population with access to water (percent of population).
  - Fixed and mobile telephones per capita (landline and cellular phone subscriptions per capita).
  - Broadband internet subscriptions (subscriptions per capita).
  - Weekly airline capacity (available seat miles per capita).
  - Rail lines (average 2007-12, meters per capita).

### Data Envelopment Analysis (DEA): Country Coverage and Benchmarks
- Inclusion criterion: emerging economies with less than 30,000 US$ stock of (public and private) investment per capita; not all countries included for every year due to data availability or breach of threshold.
- Note: Guinea-Bissau, Togo, and Niger excluded from DEA because not covered by the Global Competitiveness Report.
- Sub-Saharan Africa countries listed: Angola, Burundi, Benin, Burkina Faso, Cabo Verde, Cameroon, Chad, Cote d'Ivoire, Ethiopia, Gambia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Nigeria, Rwanda, Senegal, Swaziland, Tanzania, Uganda, Zambia, Zimbabwe.
- WAEMU countries in DEA: Benin, Burkina Faso, Cote d'Ivoire, Mali, Senegal.
- Other Emerging and Developing Economies listed include: Albania, Armenia, Azerbaijan, Bangladesh, Barbados, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Cambodia, China, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Georgia, Guatemala, Haiti, Honduras, India, Indonesia, Iran, Islamic Rep., Jordan, Mauritania, Moldova, Mongolia, Montenegro, Morocco, Nepal, Pakistan, Panama, Paraguay, Peru, Philippines, Serbia, South Africa, Sri Lanka, Tajikistan, Thailand, Tunisia, Turkey, Ukraine, Uruguay, Vietnam.
- SSA Benchmark group: Ghana, Kenya, Malawi, Rwanda (countries with under 4,000 US$ per capita of investment and comparatively higher infrastructure efficiency than the WAEMU average).
- Emerging and Developing Asia group: Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Fiji, India, Indonesia, Kiribati, Lao PDR, Malaysia, Maldives, Marshall Islands, Micronesia, Fed. Sts., Mongolia, Myanmar, Nepal, Palau, Papua New Guinea, Philippines, Samoa, Solomon Islands, Sri Lanka, Thailand, Timor-Leste, Tonga, Tuvalu, Vanuatu, Vietnam.
- Resource Rich Economies used in regression analysis: Bolivia, Chile, Ecuador, Guatemala, Mexico, Peru, Venezuela, RB, Trinidad and Tobago, Iran, Islamic Rep., Indonesia, Vietnam, Angola, Botswana, Cameroon, Chad, Ghana, Cote d'Ivoire, Madagascar, Mali, Mauritania, Mozambique, Nigeria, Tanzania, Uganda, Zambia, Azerbaijan, Albania, Mongolia.

### Fiscal Space in WAEMU: Overview and Magnitudes
- Core challenge: creating sufficient fiscal space to finance ambitious growth, development, and poverty-reduction programs while preserving macroeconomic stability.
- Two major channels to create fiscal space: increasing tax revenue and increasing the efficiency of spending.
- Estimated room to improve domestic tax collection: up to 0.8 to 2 percent of GDP (panel regression and stochastic frontier analysis).
- Potential fiscal savings from improved spending efficiency in education and health: on average 1 to 3 percent of GDP annually if all WAEMU countries achieve the spending efficiency of the top performer among them.
- WAEMU convergence criterion for tax-to-GDP ratio: 20 percent.

### The Need for Scaling Up Public Investment
- Infrastructure is identified as a growth bottleneck; infrastructure development estimated contribution to per-capita growth in West Africa in 2001–05: about 1 percentage point.
- Country-specific estimates:
  - Benin: infrastructure contributed 1.6 percent points to per capita growth.
  - Senegal: infrastructure contributed about 1 percent point to per capita growth.
  - Raising these two countries’ infrastructure endowment to that of Africa’s middle-income countries could boost annual growth by 3.2 and 2.7 percentage points respectively.
- External financing constraints:
  - External debt levels declined after HIPC multilateral debt relief, but availability of financing at attractive terms is limited.
  - Some countries’ total government debt has increased considerably since debt relief, warranting caution with additional borrowing.

### Raising Tax Revenue: Performance, Composition, and Targets
- Improving tax collection remains the main channel for enlarging fiscal space; WAEMU has a convergence criterion of 20 percent for the tax-to-GDP ratio.
- WAEMU tax-to-GDP ratio trend:
  - Improved from 11.7 percent of GDP in 2000 to 14.7 percent of GDP in 2011.
  - Improvement driven by broad trends in all member countries except Côte d'Ivoire (affected by internal conflicts).
  - Country examples of change in total tax revenue (2000 to 2011):
    - Benin: increased by 2.1 percent percentage points.
    - Togo: increased by 6.6 percent percentage points.
- Tax category performance:
  - Trade taxes:
    - WAEMU tariff rates dropped only marginally; trade-tax-to-GDP ratio broadly stable over time.
    - Comparator groups (sub-Saharan Africa and low-income countries) experienced tariff declines; some low-income countries offset rate declines via efficiency measures.
  - Personal income taxes:
    - WAEMU income tax revenue rose from about 3 to close to 4 percent of GDP, but remained below low-income countries and sub-Saharan Africa ratios.
  - Goods and services taxes:
    - WAEMU shows relatively higher rates due to francophone tradition relying more on indirect than direct taxation.
    - This translates into a higher level of tax revenues than in comparator countries by around 0.6 to 0.8 percent of GDP.
    - Improvement over 2000–2011 most pronounced in this tax category.
- Cross-country variation in tax drivers:
  - Togo: income tax declined from 2.9 percent to 2.5 percent of GDP while goods and services tax revenue rose from 2 percent to 9.2 percent of GDP.
  - Benin: revenue gains driven by higher trade tax revenue while goods and services tax revenue declined.
  - Côte d'Ivoire: decline in tax revenue mainly due to falling trade tax revenues.

### Tax Potential and Tax Gap Analysis: Regression-Based Findings
- Tax potential defined as the maximum level of tax revenue achievable given macroeconomic fundamentals; tax gap is the percentage deviation of actual revenue from potential revenue.
- Determinants considered in tax potential regressions include: GDP per capita, consumption, gross fixed capital formation, inflation, import and export as a share of GDP, share of agriculture in GDP, share of urban population, natural resource rents, broad money as a share of GDP.
- Findings for 2011 relative to benchmarks:
  - Total tax collection in WAEMU exceeded potential revenue by around 6 percent compared with low-income countries and by around 12 percent compared with sub-Saharan African countries (Figure 2a).
  - Improvement from 2000 when WAEMU total tax collection was below potential by around 4 percent compared with both low-income and sub-Saharan African countries.
  - Goods and services taxes: relative improvement between 2000 and 2011 mainly driven by a more positive goods and services tax gap; higher tax rates in WAEMU explain at least part of positive tax potential (Figure 2b).
  - Trade taxes: tax potential analysis indicates only a moderately positive trade tax gap in 2011; improvements compared to 2000 range from below to slightly above potential (Figure 2c).
  - Income taxes: revenue performance deteriorated relative to sub-Saharan African benchmark from around -1½ percent to around -5½ percent (Figure 2d), and improved only slightly relative to low-income benchmark from around -3½ percent to around -2 percent.
- Policy implication: WAEMU needs to improve income tax collection compared with peer groups; efforts should be country-specific focusing on underperforming tax categories.

*Source: _cr1698 - 15. Monitoring of Assets (PDF chapter/section)*

### 9.      The analysis on the country level shows deviations from the overall WAEMU trend for

### _cr1698 - 9.      The analysis on the country level shows deviations from the overall WAEMU trend for

### Tax revenue gaps: country-level deviations and drivers
- Six of the eight WAEMU members increased their tax collection efforts compared with what was done in sub-Saharan African and low income countries during the observation period.
- Income tax revenue collection efforts increased relative to benchmarks only in Mali, Niger and Senegal.
- The goods and services tax gap widened for Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali and Senegal compared with at least one benchmark group.
- Trade tax revenue gap closed significantly or turned positive for Benin, Burkina Faso, Niger and Senegal, indicating that overall improvement for these countries was mostly driven by improved trade tax collection relative to the benchmarks.
- Conclusion: Varied developments within the WAEMU group suggest the need for country-specific policies to improve revenue collection in the identified tax categories.

### Methods: panel regressions and stochastic frontier analysis
- Stochastic frontier analysis (following Pessino and Fenochietto (2010)) applies a time-varying parameter of technical inefficiency in tax collection to the same tax categories as the panel regression analysis.
- Results from stochastic frontier analysis broadly confirm panel regression findings, with some modifications:
  - Regardless of the reference group (sub-Saharan African and low-income countries), WAEMU performed relatively well in terms of tax collection, a result largely driven by trade taxes.
  - For goods and services and income taxes, WAEMU countries appear less efficient compared with sub-Saharan African countries and all low-income countries.
  - Main methodological divergence: Value Added Tax performance was positive in the panel regression approach and negative in the stochastic frontier analysis.
  - For income tax and trade taxes, results across methods were broadly consistent, although stochastic frontier analysis gave a more positive assessment of trade tax performance than did the panel regression model.

### Policy implications on tax side
- WAEMU countries have substantial room to improve domestic tax collection.
- WAEMU initiated trade liberalization reforms, notably the introduction of a common external tariff for all Economic Community of West African States countries in January 2015.
- Expected: trade revenues will decline (implementation will be gradual).
- Policy recommendation: enhance domestic tax revenue base, in particular income tax revenues.
- Analytical finding: both approaches indicate room for improvement in income tax revenues by around 0.8 to 2 percent of GDP.

### Spending efficiency: scope for fiscal space through improved education and health outcomes
- Analysis focused on technical efficiency of public spending using nonparametric data envelopment analysis (DEA) comparing WAEMU to other sub-Saharan African countries with similar development levels and to fast-growing non-resource rich sub-Saharan African countries.
- DEA uses data for 46 low-income and lower-middle income sub-Saharan countries in 2003−12 to define the efficiency frontier; potential savings estimated using WAEMU data in 2008−12 to strengthen comparability.

Education sector findings and metrics
- Education spending and results (WAEMU averages):
  - Education spending: increased from 4 percent to 4.4 percent of GDP between 2003−07 and 2008−12.
  - Primary enrollment rates: increased by about 14 percentage points.
  - Adult literacy: increased by about 11 percentage points.
- Peer country (fast-growing sub-Saharan African) metrics:
  - Education spending: 4.9 to 4.8 percent of GDP (2003−07 to 2008−12).
  - Primary enrollment rates: increase of 11 percentage points.
  - Adult literacy: increase of 18 percentage points.
- Table values (WAEMU mean vs. Peer country mean):
  - WAEMU mean — Education spending in % of GDP: 2003-07 4.0; 2008-12 4.4.
  - WAEMU mean — Primary enrollment rates (%): 2003-07 74.9; 2008-12 88.9.
  - WAEMU mean — Adult literacy rate (%): 2003-07 35.4; 2008-12 46.0.
  - Peer country mean — Education spending in % of GDP: 2003-07 4.9; 2008-12 4.8.
  - Peer country mean — Primary enrollment rates (%): 2003-07 90.7; 2008-12 101.5.
  - Peer country mean — Adult literacy rate (%): 2003-07 41.5; 2008-12 59.2.
- DEA results and savings potential for education:
  - All WAEMU countries are below the efficiency frontier in both 2003−07 and 2008−12.
  - Example comparisons: Benin at average public education spending close to 5 percent of GDP lags behind Liberia, Rwanda, and Togo in achieving higher adult literacy at lower per-capita spending.
  - Estimated potential fiscal savings from improved education spending efficiency: on average, WAEMU countries could save between 1 and 2 percent of GDP by improving their spending efficiency to the highest level in the Union.

Health sector findings and metrics
- Health spending and results (WAEMU averages):
  - Public health spending: increased from about 2.3 to 2.7 percent of GDP between 2003−07 and 2008−10.
  - Private health spending: 3.3 to 3.5 percent of GDP (2003−07 to 2008−10).
  - Life expectancy: increased from 53.8 to 55.6 years.
  - Child survival rates (per 1,000): 859.3 to 887.6 (2003−07 to 2008−10).
- Peer country mean metrics:
  - Public health spending (%GDP): 3.0 to 3.2.
  - Private health spending (%GDP): 3.3 to 4.1.
  - Life expectancy (years): 53.4 to 57.6.
  - Child survival rates (per 1,000): 890.2 to 923.2.
- DEA results and savings potential for health:
  - WAEMU efficiency score ranges from 43 percent (Guinea Bissau) to 100 percent (Senegal) in the baseline estimate.
  - Estimated potential savings from increased health spending efficiency: about 0.4 to 0.8 percent of GDP on average.

### Aggregate fiscal space implications and development linkages
- If all WAEMU countries could achieve the highest efficiency already reached by the top country in the Union, estimated fiscal savings are about 1 to 3 percent of GDP.
- Complementary to improved tax efforts, enhancing spending efficiency could significantly contribute to WAEMU countries’ fiscal space to support scaling up infrastructure investments.
- Improving efficiency in education and health would also enhance wellbeing, human capital, and a more productive labor force, supporting more inclusive growth.

*Italic: Sources: IMF staff estimates; analysis and data as presented in the content unit.*

### Annex Table 3. WAEMU: Determinants Of Income Tax

### Annex Table 3. WAEMU: Determinants Of Income Tax

### Regression results — Income tax revenue (% of GDP)
- Sample columns: LICs | SSA
- GDP per capita: 1.964*** | 2.370***  
  - Robust standard errors: [0.592] | [0.626]
- Agriculture (% of GDP): -0.015 | -0.035*  
  - Robust standard errors: [0.020] | [0.020]
- Consumption (% of GDP): 0.027*** | 0.025*  
  - Robust standard errors: [0.007] | [0.013]
- Gross fixed capital formation (% of GDP): 0.024** | 0.010  
  - Robust standard errors: [0.010] | [0.013]
- Urban population (% of total): 0.045 | 0.091**  
  - Robust standard errors: [0.026] | [0.035]
- Total natural resources rents (% of GDP): 0.023** | 0.006  
  - Robust standard errors: [0.010] | [0.014]
- M2 (% of GDP): 0.019*** | 0.023**  
  - Robust standard errors: [0.007] | [0.009]
- Public wage bill (% of GDP): 0.196*** | -0.000***  
  - Robust standard errors: [0.042] | [0.000]
- Constant: -15.962*** | -19.051***  
  - Robust standard errors: [4.419] | [5.101]
- Observations: 461 | 629
- Number of id: 25 | 35
- R-squared: 0.462 | 0.201
- R2: 0.452 | 0.191
- Note: Robust standard errors in brackets. Significance levels: *** p<0.01, ** p<0.05, * p<0.1

### Regression results — Goods and services tax revenue (% of GDP) (Annex Table 4)
- Sample columns: LICs | SSA
- GDP per capita: 3.956*** | 4.415***  
  - Robust standard errors: [1.370] | [1.270]
- Inflation, consumer prices (annual %): 0.006 | 0.006  
  - Robust standard errors: [0.008] | [0.006]
- Agriculture (% of GDP): 0.004 | -0.026  
  - Robust standard errors: [0.037] | [0.036]
- Gov. consumption (% of GDP): 0.014 | 0.023  
  - Robust standard errors: [0.069] | [0.046]
- Household consumption (% of GDP): -0.038 | 0.042  
  - Robust standard errors: [0.054] | [0.037]
- Gross fixed capital formation (% of GDP): -0.042 | 0.003  
  - Robust standard errors: [0.054] | [0.034]
- Urban population (% of total): 0.157** | 0.129*  
  - Robust standard errors: [0.072] | [0.064]
- M2 (% of GDP): 0.013 | 0.023  
  - Robust standard errors: [0.016] | [0.019]
- Imports (% of GDP): 0.081 | 0.021  
  - Robust standard errors: [0.060] | [0.036]
- Exports (% of GDP): -0.043 | 0.021  
  - Robust standard errors: [0.063] | [0.035]
- Constant: -26.563** | -37.550***  
  - Robust standard errors: [11.500] | [9.178]
- Observations: 571 | 698
- Number of id: 33 | 38
- R-squared: 0.349 | 0.397
- R2: 0.338 | 0.388
- Note: Robust standard errors in brackets. Significance levels: *** p<0.01, ** p<0.05, * p<0.1

### Key empirical findings (from tables and adjoining text)
- Higher GDP per capita is strongly and positively associated with both income tax revenue and goods and services tax revenue (coefficients significant at p<0.01 in all columns).
- Money supply (M2 as % of GDP) is positively associated with income tax revenue (0.019*** for LICs; 0.023** for SSA).
- Public wage bill (% of GDP) shows a large positive association with income tax revenue in LICs (0.196***), and a statistically significant negative coefficient reported for SSA (-0.000*** with [0.000] standard error).
- Urban population share is positively associated with goods and services tax revenue (0.157** for LICs; 0.129* for SSA) and with income tax revenue in SSA (0.091**).
- Consumption (% of GDP) is positively associated with income tax revenue (0.027*** for LICs; 0.025* for SSA).
- Total natural resources rents (% of GDP) positively associated with income tax revenue in LICs (0.023**).

### Policy-relevant analysis and institutional implications (extracted from surrounding text)
- Financial development: A composite index of financial development (Svirydzenka (2016); Sahay and others (2015)) is used to benchmark WAEMU against African and Asian groups. Differences in financial development account for 10 percent of the difference in volatility of growth between WAEMU and benchmark groups over recent decades.
- Banking sector strengths and weaknesses:
  - Credit to the economy growing at around 15 percent year on year; credit to private sector remains a main banking activity.
  - Sovereign exposures rose to more than 26 percent of total assets in 2015.
  - Capital adequacy rose to around 12 percent in June 2015 (from 9.6 percent in June 2014), but remains below SSA averages of 18 percent.
  - Banks’ gross NPLs reached 15.8 percent of total loans in 2014; net NPL amounted to 33.5 percent of banks’ capital, compared with SSA averages of 8 percent and 10.7 percent respectively.
  - Capital adequacy in WAEMU is still based on Basel I rules; some comparator SSA countries have moved to Basel II/III.
- Regulatory and supervisory recommendations:
  - Regulatory reforms by BCEAO to align with Basel II and III are positive, but enforcement of prudential limits needs strengthening. On average, 25 percent of banks are in violation of prudential requirements.
  - Regulatory framework improvements should include consolidated supervision and stronger enforcement.
- Financial inclusion and microfinance:
  - Access to financial services (share of population with a bank account) increased in most WAEMU countries from 2011 to 2014 but remains below African and Asian benchmark groups.
  - Financial access gaps persist across gender, age, education, and income; penetration of debit/credit cards remains much lower than benchmark groups.
  - Microfinance sector: around 724 MFIs at end June 2015; MFI deposits increased at an average annual rate of 12.2% and credit activities at 9.9%; overall MFI activity remains small relative to the banking sector.
  - Challenges in the MFI sector: weak credit management, poor governance, lacking information systems and internal controls, weak application of prudential rules, and presence of unlicensed MFIs.
  - Policy actions taken: liquidation and license revocation of some MFIs, regrouping small MFIs into networks, closer monitoring, temporary administration for 13 MFIs as of June 2015, BCEAO initiatives for a regional financial inclusion strategy, and creation of a credit information reporting system for MFIs.
- Composite index dimensions: Financial institutions and markets are assessed by access (branches, ATMs per 100,000 adults; percent market capitalization outside top 10; number of issuers), depth (private sector credit to GDP; pension fund assets to GDP; mutual fund assets to GDP; insurance premium to GDP; stock market capitalization to GDP; stocks traded to GDP), and efficiency (net interest margin; lending-deposit spread; non-interest income to total income; overhead costs to total assets; return on equity; stock market turnover ratio).

*Source: Annex Table 3 (and adjoining annex tables and text) from the WAEMU chapter provided in the supplied content.*

### 10.      The composite measure of financial inclusion suggests that the WAEMU lags behind

### _cr1698 - 10.      The composite measure of financial inclusion suggests that the WAEMU lags behind

### Benchmarking financial development
- WAEMU’s level of financial development was similar to that of other regions in the 1980s and 1990s but has been outperformed, especially by the Asian benchmark group in the 2000s and today.
- The difference versus the Asian benchmark is:
  - Significant for the financial institutions index.
  - Even larger for the financial markets index.
- Financial market development is described as insignificant in most WAEMU countries, except for Cote d’Ivoire.
- Low levels of financial development in the WAEMU are comparable to the group of African benchmark countries.
- Index scale reported: Financial Development Index (0-low; 1-high), Financial Institutions Index (0-low; 1-high), Financial Markets Index (0-low; 1-high).
- Sources: Svirydzenka (forthcoming).

### Financial development and volatility of growth — approach
- Rolling standard deviations of growth (five-year period ending in current year) are regressed on:
  - financial development index,
  - controls: initial GDP per capita (PPP), trade and financial openness, energy exports in percent of GDP, volatility of foreign growth, gross capital inflows to the region excluding the country in question, terms of trade changes, the polity index, transition and offshore center dummies, growth of GDP per capita, and the government balance.
- Three regression specifications reported:
  1. Including the composite measure of financial development.
  2. Decomposing into financial institutions and financial markets.
  3. Further decomposing into depth, access and efficiency for both institutions and markets.
- Main interpretation: higher financial development is associated with lower volatility of growth, with the relationship weakening at higher levels of financial development.

### Key regression coefficients and controls (Table 1)
- General Index - FD specification:
  - Financial Development Index -4.65***
  - Financial Development Index 2 3.35*
  - GDP per Capita PPP 5-year Lag 0.00***
  - Trade Openness 5-year Lag 0.000
  - Financial Openess 5-year Lag 0.01
  - Energy Exports (% of GDP) 5-year Lag 0.09*
  - Volatility of foreign growth 0.43***
  - Gross Capital Inflows 0.01
  - Terms of Trade Changes 0.04***
  - Polity Index 0.01
  - GDP per Capital PPP Growth 0.00***
  - Government Balance -0.11***
  - Constant 1.98***
  - Observations 231223122312
  - Number of Countries 103103103
  - R2 0.120.120.12

- FD Sub-components specification highlights:
  - Financial Institutions -1.000
  - Financial Institutions 2 -0.44
  - Financial Markets -4.39***
  - Financial Markets 2 4.73***

- FI and FM Sub-components (depth/access/efficiency) specification highlights:
  - Financial Institutions Access -2.14**
  - Financial Institutions Access 2 1.51*
  - Financial Institutions Depth -2.22
  - Financial Institutions Depth 2 2.02
  - Financial Institutions Efficiency -2.81*
  - Financial Institutions Efficiency 2 3.96**
  - Financial Markets Access -1.87
  - Financial Markets Access 2 1.83
  - Financial Markets Depth -1.21
  - Financial Markets Depth 2 -0.04
  - Financial Markets Efficiency -1.31
  - Financial Markets Efficiency 2 1.72

(Note: significance stars are presented as in the source.)

### Decomposition: contribution of financial development to volatility differentials
- On average, lower financial development could account for about 10 percent of the volatility differential with the groups of Asian and African benchmark countries (Figure 9).
- The overall growth volatility differential, WAEMU versus benchmarks (1980–2014):
  - Asia Benchmark: 1.64
  - Africa Benchmark: 0.60

### Country heterogeneity in effects (Figure 10)
- Financial development levels vary markedly across WAEMU countries:
  - Highest levels observed in Cote d’Ivoire.
  - Lowest levels observed in Guinea-Bissau.
- Volatility of growth also varies across countries:
  - Benin experienced on average lower volatility of growth than the African Benchmark group.
  - Guinea-Bissau experienced the highest volatility differential to benchmark groups.
- Quantified country-level contributions:
  - Financial development can explain about one sixth of the growth volatility differential between Guinea-Bissau and the group of Asian benchmark countries.
  - Financial development can explain about one fourth of the growth volatility differential between Guinea-Bissau and the group of African benchmark countries.
  - Effects are smaller for other WAEMU countries.

### Additional contextual findings referenced in the chapter
- Figures and country charts show the decomposition of average growth volatility differential 1980–2014 into contributions from:
  - Financial Development,
  - PPP five year lag,
  - Foreign growth volatility,
  - Terms of trade changes,
  - Energy exports,
  - GDP per capita growth,
  - Government balance.
- Example country overall growth volatility differentials (1980–2014) shown in figures:
  - Benin: Asia Benchmark: 0.18; Africa Benchmark: -0.85
  - Burkina Faso: Asia Benchmark: 1.22; Africa Benchmark: 0.18
  - Togo: Asia Benchmark: 2.46; Africa Benchmark: 1.42
  - Senegal: Asia Benchmark: 0.44; Africa Benchmark: -0.60
  - Cote d’Ivoire: Asia Benchmark: 1.24; Africa Benchmark: 0.21
  - Guinea-Bissau: Asia Benchmark: 2.47; Africa Benchmark: 1.43
  - Mali: Asia Benchmark: 1.79; Africa Benchmark: 0.76
  - Niger: Asia Benchmark: 3.31; Africa Benchmark: 2.27

*Prepared from the IMF chapter: "The composite measure of financial inclusion suggests that the WAEMU lags behind" (content unit provided).*

### 6.      Gender inequality in the WAEMU stands out as one of the highest in the world and has

### _cr1698 - 6.      Gender inequality in the WAEMU stands out as one of the highest in the world and has

### Overview
- The United Nations gender inequality index measures outcomes (gap between female and male labor force participation rates and the share of women’s seats in parliament) and inequality of opportunity (gender gaps in education and indicators of female health, such as the maternal death ratio and adolescent fertility).
- The WAEMU performs worse than most countries worldwide on the UN gender inequality index and has seen a much slower decline in gender inequality than two groups of fast-growing benchmark countries.
- At the global level, countries with high gender inequality tend to have a less equal income distribution, are poorer, and grow more slowly.

### Labor force participation
- The gender gap in labor force participation (male minus female labor force participation rates) declined from more than 30 percentage points in 1990 to around 20 percentage point in 2013 in the WAEMU.
- This reduction was mostly driven by large reductions in Benin, Cote d’Ivoire and Niger, with Cote d’Ivoire and Niger having started from very high levels.
- Benchmark comparisons:
  - Asian Benchmark average gap is less than 17 percentage points.
  - African Benchmark gap is only around 6 percentage points.
- Female labor force participation is very low in some WAEMU countries even at very low levels of per capita incomes (Mali, Niger).
- In Mali and Cote d’Ivoire, women who participate in the labor market are more likely than men to work in the informal sector.

### Education and health indicators
- Adult literacy rates are generally lower in most WAEMU countries compared to benchmark groups, particularly for women.
- Gender gaps in primary education have shrunk but are not closed in the majority of WAEMU countries.
- Specific educational ratios:
  - In Benin, Cote d’Ivoire, Mali, and Niger less than 9 girls are enrolled in primary education for every 10 boys.
  - Only 7 girls are in secondary education for each 10 boys.
  - Women are less than 50 percent as likely to be enrolled in tertiary education.
- Health indicators:
  - The lifetime risk of maternal death is much higher in WAEMU countries, especially in Mali and Niger, compared to benchmark groups.
  - Adolescent fertility rates have declined but remain relatively high in the region.

### Legal restrictions and institutional barriers
- Several WAEMU countries have laws that impose gender inequalities.
- Evidence and specific legal constraints:
  - Legal inequities have been shown to reduce women’s labor force participation and hence growth.
  - World Bank (2015) findings summarized:
    - In at least half of the WAEMU’s countries women cannot be the head of a household the same way as men.
    - In none of the WAEMU countries does there exist legislation which prohibits discrimination in access to credit based on gender.
    - In at least half of the countries, women are not protected legally from domestic violence.
  - Country examples:
    - Niger: restrictions on women getting a job the same way as men; female labor force participation is particularly low.
    - Cote d’Ivoire and Senegal: differences in property rights between men and women.

### Growth effects of gender and income inequality
- Benchmarks and growth differentials:
  - Comparison groups: African benchmark (Ghana, Kenya, Lesotho, Rwanda, Tanzania, Uganda, Zambia) and Asian benchmark (Bangladesh, Cambodia, India, Laos, Nepal, and Vietnam).
  - These benchmark groups experienced around 2½ and 3½ percentage points higher real GDP growth compared to the WAEMU in the last decade.
- Quantified impacts:
  - Income and gender inequality can explain around 0.5 percentage points of the WAEMU’s real GDP per capita shortfall compared to the Asian Benchmark group.
  - Reduction of gender inequality and legal inequality to levels observed in the five fast-growing ASEAN countries (Indonesia, Malaysia, Philippines, Thailand, and Vietnam) alone could boost real GDP per capita rates by 1 percentage point.
  - Country-level magnitudes vary:
    - In Mali, a reduction of gender inequality and an increase of legal equality between men and women to ASEAN-5 levels could have resulted in a boost of the real GDP per capita growth rate of 1¼ percentage points of GDP.
    - In Burkina Faso, income and gender inequality have been lower than in benchmark groups, positively affecting growth relative to benchmarks.
    - In Niger and Mali, income inequality is relatively low due to high poverty shares, but gender inequality is high in absolute and relative terms.

### Policy recommendations
- Main policy measures identified as effective to reduce gender and income inequality and to support growth:
  - Remove legal inequalities between men and women.
    - Example: Namibia equalized property rights for married women and granted women rights to sign a contract, head a household, pursue a profession, open a bank account, and initiate legal proceedings without the husband’s permission in 1996; in the decade that followed Namibia experienced a 10 percentage point increase in its female labor force participation rate.
  - Foster education, including increasing education for girls to raise total education levels.
  - Boost infrastructure, including improving access to water and increased electrification, to free women’s time for education and labor market participation.
  - Reduce the regressivity of fiscal spending and taxes; replace across-the-board subsidies with well-targeted social transfer schemes.
  - Foster financial inclusion, including for women (see SIP on financial inclusion and stability).

### Conclusions
- Decreasing income inequality and gender inequality could yield significant macroeconomic benefits for the WAEMU through higher real GDP per capita growth.
- Policy priorities reinforced by the analysis: boost infrastructure, increase schooling (including for girls), and improve the institutional environment.
- The paper advocates for a level playing field that allows all agents in the economy to explore their economic potential if they so choose, without making normative judgments on social and religious norms.

*Source: IMF — _cr1698 (chapter/section on gender inequality in the WAEMU) — extracted content.*

### 1.5 percentage points through different channels.

### _cr1698 - 1.5 percentage points through different channels.

### A. Risks to the Outlook
- Risks to the regional outlook are skewed to the downside.
- Baseline positive outlook assumes timely and effective implementation of several domestic reforms including:
  - enhancing the efficiency of spending and improving the quality of public investment;
  - creating additional fiscal space to meet development needs while safeguarding macroeconomic stability and debt sustainability;
  - increasing financial access and inclusion while preserving financial stability;
  - boosting competitiveness and diversification, improving the business environment and enhancing inclusion.
- External risks: growth slowdown in China or tighter/more volatile global conditions would affect WAEMU members through higher financing costs, higher regional risk premia, lower availability of external and regional financing, and cross-border spillovers from the Euro area and other emerging market partners.

### B. Impact of the Realization of Domestic Risks
- Scenario: delay in key reforms at individual country and regional level (fiscal consolidation, public finance management, infrastructure investment, energy, financial sector, business climate).
- Key quantitative impacts relative to the baseline:
  - WAEMU economic growth lower by 1–1.5 percentage points.
  - Domestic investment growth lower by about 2.5–3 percentage points, reflecting a significant reduction in private investment.
  - Consumption growth lower by about 1.2 percentage points.
  - Current account balance (excluding grants) projected to improve by about 0.5 percentage point of GDP, on average.
  - Overall fiscal balance (excluding grants) expected to improve by about 0.6 and 0.2 percentage point of GDP in 2016 and 2017, respectively, due to lower public investment.
  - Over the medium term, with falling fiscal revenue, the overall fiscal balance would deteriorate by about 0.1 percentage point of GDP.
  - Delays would lower current transfers and official loans by up to about one percentage point of GDP in favor of more short-term portfolio inflows.
  - Small cumulative reduction in WAEMU official reserves of about US$ 0.2 billion.
- Transmission summary: weaker business climate → lower private investment → lower domestic demand → immediate import reduction and more gradual export decline due to reduced competitiveness.

### C. Impact of the Realization of External Risks
- Global financial volatility scenario: investor reassessment and disorderly normalization of risk premia → higher corporate default rates, higher money market spreads, depressed consumer and investor demand.
- Key quantitative impacts relative to the baseline:
  - Availability of external financing and capital inflows to the region reduced by about one percentage point.
  - Private investment growth slows by about 0.5 percentage point, on average.
  - More costly regional financing leads to lower public investment growth by about 0.8 percentage point over the medium term.
  - This scenario would reduce WAEMU growth by about 0.6 percentage points, on average.
  - Initially, governments maintain investment and face higher debt service, contributing to an increase in the overall fiscal deficit.
- Country-partner growth shock simulations (impulse-response style estimates):
  - A 1-percentage point lower growth in key advanced economies reduces WAEMU real GDP by about 0.8 percent after two years and about 1 percent at the peak after five years.
  - A 1-percentage point lower growth in China is estimated to lower WAEMU real GDP by about 0.5 percent in the short term.
  - A 1-percentage point lower growth in Nigeria reduces WAEMU real GDP by 0.2 percent after 3-4 years.
- Heterogeneity and amplifiers:
  - Spillover effects larger in countries with higher trade openness, less diversified exports, and larger Chinese investment.
  - Example: a 1-percentage point lower growth in China reduces real GDP by 1.2 percent at the peak in more trade-open countries (Benin, Senegal, Burkina Faso) versus 0.4 percent for less open economies.
  - In WAEMU countries where Chinese investment has been above the regional average (e.g., Niger, Togo), a 1-percentage point lower growth in China reduces real GDP by 1.2 percent.
  - Countries with more diversified export structures (Côte d’Ivoire, Senegal) experience smaller spillovers.
  - Cross-border banking groups provide an additional shock propagation channel.

### Box 1 — Methodology and scope for China slowdown analysis
- Purpose: empirical assessment of the impact of lower growth episodes in China on growth in SSA and WAEMU over 1980–2014.
- Approach: identification of lower growth episodes in China as deviations from average growth; dynamic impacts on African countries estimated using local projection methods (Jordà, 2005).
- Outcomes reported as the impact of a 1-percentage point growth disappointment in partner economies on the percentage change in log level of real GDP in SSA or WAEMU countries, with horizon up to 8 years.
- Findings highlight increasing and more heterogeneous effects of China slowdown over time, and larger impacts for manufacturing-driven slowdowns.

*Prepared by Boriana Yontcheva, Stefan Klos, and Aleksandra Zdzienicka with inputs from WAEMU teams.*

---


_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr1698.pdf_