## 2. Benchmarking WAEMU Countries Against Their Potential

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---

### I. Introduction
- Focus: examine where the West African Economic and Monetary Union (WAEMU) stands relative to selected Sub-Saharan Africa frontier economies labeled High Growth Non-Oil Exporters (HGNOEs), and identify factors explaining differences in financial depth.
- Novel approaches:
  - Compare a group with shared monetary and financial policies (WAEMU) to a group of high-performing SSA countries (HGNOEs).
  - Use two empirical approaches: (i) regression analysis to identify factors explaining differences in financial depth; (ii) case study benchmarking Côte d’Ivoire against Mozambique using the World Bank FinStat methodology.
- Data and scope:
  - Panel of 16 countries (eight WAEMU: Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo; eight HGNOEs: Botswana, Cape Verde, Ethiopia, Mauritius, Mozambique, Rwanda, Tanzania, and Uganda) over 1997–2009.
  - FSAPs performed on both Côte d’Ivoire and Mozambique during the period.
- Key overall result:
  - Stronger rule of law, infrastructure, and credit information collection and dissemination have a strong impact on financial depth.

### II. Stylized Facts and Empirical Literature
- Measures:
  - Financial depth measured by ratio of liquid liabilities to GDP or ratio of private credit to GDP; empirical work focuses on credit to the private sector as a share of GDP.
- Comparative facts (1997–2009):
  - WAEMU: private sector credit to GDP increased from 12 percent to 17 percent.
  - HGNOEs: private sector credit to GDP increased from 17 percent to 37 percent.
  - Broad money (monetization):
    - HGNOEs: grew from 31 percent to 52 percent of GDP.
    - WAEMU: rose from 20 percent to 29 percent of GDP.
  - Banking profitability:
    - Weighted return on assets in WAEMU remained around 1.3 percent, about half the level in HGNOEs.
- Literature highlights:
  - Structural factors: per capita income, population size and density, age dependency affect financial depth.
  - Macroeconomic policies: inflation, fiscal balance, and debt matter; inflation often negatively associated with financial depth.
  - Institutional/policy factors: contract enforcement, credit infrastructure, market liberalization, governance (government effectiveness, control of corruption, rule of law) influence financial development.
  - Regional findings: Ghura, Kpodar, and Singh (2009) link low financial depth in CFA franc zone to weaker legal/contractual/institutional environment.

### III. Regression Analysis (Panel 1997–2009)
- Model setup:
  - Dependent variable: credit to the private sector as share of GDP.
  - Main regressors: log of GDP per capita, log of inflation, rule of law, property rights, political stability, credit coverage, internet coverage (proxy for infrastructure), contract enforcement (days to enforce a contract).
  - WAEMU dummy = 1 for WAEMU countries, 0 otherwise.
  - Estimation method: two-step Feasible Generalized Least Squares (FGLS).
- Descriptive correlations (selected entries from correlation matrix):
  - Credit to private/GDP correlated with:
    - GDP per capita: 0.76
    - Rule of law: 0.72
    - Internet per hundred: 0.86
    - Credit coverage: 0.61
    - Property: 0.65
  - WAEMU_dummy correlations:
    - With Credit to private/GDP: -0.26
    - With Rule of law: -0.45
    - With Internet per hundred: -0.26
- Core regression findings (selected coefficients from Table 4; coefficient then standard error as reported):
  - Waemu_dummy:
    - Column (1): -0.028; 0.005 ***
    - Column (2): -0.003; 0.007
    - Column (3): -0.029; 0.007 ***
    - Column (4): -0.010; 0.006
    - Column (5): 0.013; 0.014
    - Column (6): -0.023; 0.006 ***
    - Column (7): 0.012; 0.012
  - Inflation:
    - Column (1): -0.171; 0.067 ***
    - Column (2): -0.180; 0.075 ***
    - Column (3): -0.273; 0.076 ***
    - Column (4): -0.126; 0.072 *
    - Column (5): -0.063; 0.132
    - Column (6): -0.164; 0.068 ***
    - Column (7): -0.156; 0.097
  - GDP per capita:
    - Column (1): 0.114; 0.003 ***
    - Column (2): 0.083; 0.003 ***
    - Column (3): 0.089; 0.004 ***
    - Column (4): 0.056; 0.006 ***
    - Column (5): 0.074; 0.011 ***
    - Column (6): 0.099; 0.004 ***
    - Column (7): 0.044; 0.008 ***
  - Rule of law:
    - Coefficient(s): 0.078; 0.045 with reported standard errors 0.006 ***; 0.020 ***
  - Political Stability:
    - Coefficient(s): 0.033; 0.009 with reported standard errors 0.003 ***; 0.009
  - Internet per hundred:
    - Coefficients: 0.020; 0.018 with reported standard errors 0.002 ***; 0.002 ***
  - Credit coverage:
    - Coefficient: 0.010; Standard error 0.004 ***
  - Property:
    - Coefficient: 0.001; Standard error 0.000 ***
    - Additional listed: 0.001; Standard error 0.001
- Observations and sample:
  - Observations by column: 167, 132, 132, 167, 52, 138, 122
  - Number of countries (all columns): 17, 17, 17, 15, 17, 17, 17
- Interpretation:
  - WAEMU dummy negative and significant in specifications with only macro variables, indicating weaker financial deepening in WAEMU versus HGNOEs.
  - Inflation: negative and significant in most specifications.
  - GDP per capita: positively associated with financial depth in all specifications.
  - Rule of law: significantly positively associated; its inclusion weakens the WAEMU dummy to insignificance in some specifications.
  - Internet coverage and credit coverage: positively associated and can explain away the WAEMU–HGNOE difference when included.
  - Political stability and property rights: positively associated; when rule of law, property rights, and political stability included jointly, only rule of law remains significant indicating overlap across institutional variables.
- Conclusion from regressions:
  - Differences in legal environment (rule of law and property rights), infrastructure (internet coverage), and credit information coverage largely explain the WAEMU–HGNOE gap in financial depth.

### IV. Case Study: Côte d’Ivoire versus Mozambique
- Rationale:
  - Mozambique chosen as most structurally similar to Côte d’Ivoire (population, dependency ratios, GDP per capita) among the control group; differences likely attributable to policy and institutional factors.
- A. Relative positions (FinStat evidence):
  - Up to 2007: Côte d’Ivoire and Mozambique had private sector credit to GDP around 15 percent.
  - After 2007: Mozambique outperformed Côte d’Ivoire.
  - Côte d’Ivoire systematically underperformed relative to its expected 75th percentile benchmark.
- B. Policy and institutional contrasts (selected reforms and outcomes)
  - Mozambique reforms and outcomes:
    - Contract enforcement:
      - Established a specialized commercial court.
      - Increased number of new judges by 10 percent.
      - Introduced performance measures.
      - Time to resolve a commercial dispute fell by 72 percent.
      - Doing Business 2008: Mozambique cited as top performer in Africa on contract enforcement.
    - Credit information:
      - New legal framework for credit registries enacted; depth of credit information index improved from 3 to 4 (out of 6) per Doing Business 2009.
    - Financial sector and supervisory reforms (FSAP, 2009):
      - Shifted from overall-compliance to risk-based supervision.
      - Improved national payments system.
      - New microfinance legislation.
      - Limited dollarization of loans by raising provisioning on foreign exchange loans to non-exporters.
    - Outcomes:
      - Non-performing loans fell from 17 percent of gross loans in 2003 to 2.8 percent in September 2008.
      - Number of microfinance institutions rose from 19 in 2003 to 87 in 2008.
      - Private sector credit to GDP rose from less than 15 percent to almost 25 percent between 2007–2010 (a 10 percentage point gain).
      - Increased branches and ATM machines, particularly in rural and semi-urban areas, though credit remained concentrated and limited for SMEs and non-salaried individuals.
  - Côte d’Ivoire: limited progress during the covered period (Regional FSAP 2007–08 and FSAP 2009):
    - No major improvements in legal and judicial framework; contract enforcement described as costly and slow.
    - Credit information infrastructure deficient: Doing Business 2010 rated depth of credit information 1 out of 6.
    - Microfinance sector: largest networks virtually bankrupt; institutional weaknesses discouraged lending.
    - Result: Côte d’Ivoire’s increase in private sector credit to GDP over 2007–2010 was less than one fourth of Mozambique’s 10 percentage point gain.
  - Post-sample steps mentioned (without quantified impact):
    - Ivoirian authorities took steps in 2011–12: creation of commercial courts (January 2012) and adoption of decree on enforcement of arbitration decisions (February 2012); efforts to simplify business activities noted.
  - Remaining challenges in Mozambique:
    - Enforcement of collateral still costly and slow.
    - Credit information infrastructure and national payments system require further improvement.
    - Excess liquidity remained high; lending to agriculture remained below 10 percent of total loans for most of the period.
    - Continued rural financing constraints due to weak infrastructure, property rights, and short-maturity liabilities.

### V. Conclusions and Policy Implications
- Main conclusions:
  - Stronger rule of law, better infrastructure, and improved credit information collection and dissemination are associated with higher bank lending to the private sector.
  - Institutional and infrastructural factors played a key role in advancing financial depth in HGNOEs and help explain relatively poor performance in WAEMU.
  - Côte d’Ivoire–Mozambique comparison underscores the role of banking supervision reform, commercial courts, and credit information in driving financial deepening.
  - Empirical magnitude: Mozambique’s private sector credit to GDP rose by 10 percentage points between 2007–2010 (from less than 15 percent to almost 25 percent); Côte d’Ivoire’s increase was less than one fourth of that.
- Policy implications:
  - Strengthen rule of law and property rights to reduce lending risk and increase banks’ willingness to extend credit.
  - Improve credit information systems (credit bureaus/registries) to enhance borrower screening and expand access to finance.
  - Enhance infrastructure (telecommunications/internet coverage and payments systems) to lower cost of bank penetration and improve geographical outreach.
  - Reform banking supervision toward risk-based approaches and pursue complementary reforms (microfinance legislation, limits on foreign-exchange lending) to support financial sector stability and outreach.

*Source: IMF staff analysis as presented in "2. Benchmarking WAEMU Countries Against Their Potential" (chapter/section content provided).*

### References .............................................................................................................

### _wp13161 - References

### References
- References ................................................................................................................................15

### Tables
- 1. Descriptive Statistics, 1997–2009 ..............................................................................17
- 2. Correlation Matrix ......................................................................................................17
- 3. Variables Definitions ..................................................................................................18
- 4. Financial Development in WAEMU and HGNOE in SSA, 1997–2009 ....................19
- 5. Structural Variables in 2010 .......................................................................................19
- 6. Main Financial Reforms in 2003–10 ..........................................................................20

### Figures
- 1. Credit to the Private Sector ............................................................................................5
- 2. Broad Money .................................................................................................................6
- 3. Return on Assets and Return on Equity .........................................................................6
- 4. Private Credit/GDP (%) ...............................................................................................12

### Annex
- 1. Financial Possibility Frontier: Benchmarking WAEMU Against HGNOES ..............21

*Source: _wp13161 - References, https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13161.pdf*

### 2. Benchmarking WAEMU Countries Against Their Potential .......................................23

### 2. Benchmarking WAEMU Countries Against Their Potential

### I. Introduction
- Focus: examine where the West African Economic and Monetary Union (WAEMU) stands relative to selected Sub-Saharan Africa (SSA) frontier economies labeled High Growth Non-Oil Exporters (HGNOEs), and identify factors explaining differences in financial depth.
- Novelty:
  - Focus on a group of African countries with shared monetary and financial policies (WAEMU) versus a group of high-performing SSA countries (HGNOEs).
  - Uses two empirical approaches: (i) regression analysis to identify factors explaining differences in financial depth; (ii) a case study benchmarking Côte d’Ivoire against Mozambique using the World Bank financial benchmark methodology (FinStat).
- Data and scope:
  - Panel of 16 countries (eight WAEMU countries: Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo; and eight HGNOEs: Botswana, Cape Verde, Ethiopia, Mauritius, Mozambique, Rwanda, Tanzania, and Uganda) over the period 1997–2009.
  - FSAPs were performed on both Côte d’Ivoire and Mozambique during the period.
- Key overall result stated up front:
  - Stronger rule of law, infrastructure, and credit information collection and dissemination have a strong impact on financial depth.

### II. Stylized Facts and Empirical Literature
- Definitions and measures:
  - Financial depth measured by either ratio of liquid liabilities to GDP or ratio of private credit to GDP.
  - Paper focuses on credit to the private sector as a share of GDP for empirical work.
- Comparative stylized facts (1997–2009):
  - WAEMU: private sector credit to GDP increased from 12 percent to 17 percent.
  - HGNOEs: private sector credit to GDP increased from 17 percent to 37 percent.
  - Broad money (monetization):
    - Benchmark (HGNOEs): grew from 31 percent to 52 percent of GDP.
    - WAEMU: rose from 20 percent to 29 percent of GDP.
  - Banking profitability:
    - Weighted return on assets in WAEMU remained around 1.3 percent, about half the level in HGNOEs.
- Literature summary (selected findings cited in text):
  - Structural factors: per capita income, population size and density, age dependency affect financial depth.
  - Macroeconomic policies: inflation, fiscal balance, and debt matter; inflation often negatively associated with financial depth.
  - Institutional/policy factors: contract enforcement, credit infrastructure, market liberalization, governance (government effectiveness, control of corruption, rule of law) influence financial development.
  - Specific regional findings: Ghura, Kpodar, and Singh (2009) link low financial depth in CFA franc zone to weaker legal/contractual/institutional environment; McDonald and Schumacher (2007) financial liberalization index related to greater depth.

### III. Regression Analysis (Panel 1997–2009)
- Model and estimation:
  - Dependent variable: credit to the private sector as share of GDP.
  - Main regressors: log of GDP per capita, log of inflation, rule of law, property rights, political stability, credit coverage, internet coverage (proxy for infrastructure), contract enforcement (days to enforce a contract).
  - WAEMU dummy variable equals 1 for WAEMU countries, 0 otherwise.
  - Estimation method: two-step Feasible Generalized Least Squares (FGLS).
- Descriptive correlations:
  - Credit to private sector (% of GDP) most correlated with infrastructure, rule of law, and GDP per capita.
  - Rule of law, property rights, and political stability are highly correlated with each other.
- Core empirical findings (summarized from regression results):
  - The WAEMU dummy is negative and significant when controlling only for macroeconomic variables, indicating weaker financial deepening in WAEMU versus HGNOEs.
  - Macroeconomic variables:
    - Inflation: negative and significant in most specifications.
    - Per capita GDP: positively associated with financial depth in all specifications.
  - Institutional/policy variables:
    - Rule of law: significantly positively associated with financial depth; when included, it weakens the WAEMU dummy to insignificance (rule of law explains much of the WAEMU–HGNOE gap).
    - Property rights: positively associated; its inclusion slightly weakens the WAEMU dummy, which may remain significant in some specifications.
    - Political stability: positively associated with financial depth; WAEMU dummy remains significant when political stability is added alone.
    - Internet coverage (infrastructure proxy): positively affects financial depth and can explain away the WAEMU–HGNOE difference when included.
    - Credit coverage (credit information): positively associated with financial depth; its inclusion also explains away the WAEMU–HGNOE difference, although sample size drops substantially due to limited data on credit coverage.
  - Joint inclusion:
    - When rule of law, property rights, and political stability are included simultaneously, only rule of law remains significant, indicating overlap across institutional variables.
- Interpretation:
  - The difference in financial depth between WAEMU and HGNOEs can largely be explained by weaker legal environment (rule of law and property rights), poorer infrastructure, and limited credit information coverage in WAEMU countries.

### IV. Case Study: Côte d’Ivoire versus Mozambique
- Rationale:
  - Mozambique chosen as most structurally similar to Côte d’Ivoire (population and dependency ratios, GDP per capita) among the control group; therefore differences likely attributable to policy and institutional factors.
- A. Where Côte d’Ivoire stands relative to Mozambique and to its potential
  - Evidence from FinStat:
    - For much of the period up to 2007, Côte d’Ivoire and Mozambique had private sector credit to GDP around 15 percent.
    - After 2007, Mozambique outperformed Côte d’Ivoire.
    - Côte d’Ivoire systematically underperformed relative to its expected 75th percentile benchmark (the value under which 75 percent of structurally similar countries are expected to be).
  - Implication: policy and institutional gaps in Côte d’Ivoire may explain underperformance.
- B. Explaining the financial depth gap (policy and institutional contrasts)
  - Mozambique reforms and outcomes highlighted:
    - Strengthened contract enforcement:
      - Established a specialized commercial court.
      - Increased number of new judges by 10 percent.
      - Introduced performance measures.
      - Time to resolve a commercial dispute fell by 72 percent.
      - Doing Business 2008 cited Mozambique as top performer in contract enforcement in Africa.
    - Credit information:
      - New legal framework for credit registries enacted, expanding scope and accessibility of credit information.
      - Doing Business 2009: depth of credit information index improved from 3 to 4 (out of 6).
    - Financial sector and supervisory reforms (FSAP, 2009):
      - Shifted from overall-compliance to risk-based supervision.
      - Improved national payments system.
      - New microfinance legislation.
      - Limited dollarization of loans by raising provisioning on foreign exchange loans to non-exporters.
    - Outcomes in Mozambique:
      - Non-performing loans (NPLs) fell from 17 percent of gross loans in 2003 to 2.8 percent in September 2008.
      - Increased banks’ outreach: rise in branches and ATM machines, particularly in rural and semi-urban areas (though credit concentrated and limited for SMEs and non-salaried individuals).
      - Number of microfinance institutions rose from 19 in 2003 to 87 in 2008.
      - Private sector credit to GDP rose in Mozambique from less than 15 percent to almost 25 percent between 2007–2010 (a 10 percentage point gain).
  - Côte d’Ivoire: limited progress during the covered period (Regional FSAP 2007–08 and FSAP 2009):
    - No major improvements in legal and judicial framework; contract enforcement described as costly and slow.
    - Credit information infrastructure deficient: Doing Business 2010 rated depth of credit information 1 out of 6.
    - Microfinance sector: largest networks virtually bankrupt; institutional weaknesses discouraged lending.
    - Result: Côte d’Ivoire’s increase in private sector credit to GDP over 2007–2010 was less than one fourth of Mozambique’s 10 percentage point gain.
  - Note on later steps (post-sample, mentioned in text without quantified impacts):
    - Some steps taken by Ivoirian authorities in 2011–12: creation of commercial courts (January 2012) and adoption of decree on enforcement of arbitration decisions (February 2012); efforts to simplify business activities; regional financial sector policy constraints limit some national actions.
  - Limitations and remaining challenges in Mozambique:
    - Enforcement of collateral still costly and slow despite reforms.
    - Credit information infrastructure and national payments system require further improvement.
    - Excess liquidity remained high; lending to agriculture remained below 10 percent of total loans for most of the period.
    - Continued challenges in boosting rural financing due to weak infrastructure, property rights, and short-maturity liabilities.

### V. Conclusions
- Main conclusions:
  - Stronger rule of law, better infrastructure, and improved credit information collection and dissemination are associated with higher bank lending to the private sector.
  - These institutional and infrastructural factors played a key role in advancing financial depth in HGNOEs and help explain the relatively poor performance in the WAEMU region.
  - The Côte d’Ivoire–Mozambique comparison underscores the role of policies on banking supervision, commercial courts, and credit information in driving financial deepening.
  - Empirical magnitude highlighted: Mozambique’s private sector credit to GDP rose by 10 percentage points between 2007–2010 (from less than 15 percent to almost 25 percent), while Côte d’Ivoire’s increase was less than one fourth of that.
- Policy implications implied by analysis:
  - Strengthen rule of law and property rights to reduce lending risk and increase banks’ willingness to extend credit.
  - Improve credit information systems (credit bureaus/registries) to enhance borrower screening and expand access to finance.
  - Enhance infrastructure (telecommunications/internet coverage and payments systems) to lower cost of bank penetration and improve geographical outreach.
  - Reform banking supervision toward risk-based approaches and pursue complementary reforms (microfinance legislation, limits on foreign-exchange lending) to support financial sector stability and outreach.

*Source: IMF staff analysis as presented in "2. Benchmarking WAEMU Countries Against Their Potential" (chapter/section content provided).*

### REFERENCES

### _wp13161 - REFERENCES

### References
- “Enhancing Financial Sector Surveillance In Low Income Countries—Background Paper”, 2012, International Monetary Fund.
- “Enhancing Financial Sector Surveillance In Low Income Countries—Financial Deepening and Macro-Stability”, 2012, International Monetary Fund.
- Adarov, A., and R, Tchaidze, 2011, “Development of Financial Markets in Central Europe: The Case of the CE4 Countries”, IMF Working Paper WP/11/101 (Washington: International Monetary Fund).
- Amadou N. R. Sy, 2006, “Financial Integration in the West African Economic and Monetary Union”, IMF Working Paper WP/06/214 (Washington: International Monetary Fund).
- Andianova, S., Baltagi, B., Demetriades, P., and D. Fielding, 2011, “Why Do African Banks Lend So Little”, University of Leicester, UK.
- Babatounde, L, A., 2010, “Efficiency of Financial Micro Intermediation in the WAEMU Countries: A Stochastic Production Function Analysis”, Munich Personal RePec Archive.
- Beck, T., Feyen, E., Ize, Alain and Moizeszowicz, F., 2008, “Benchmarking Financial Development”, Policy Research Working Paper, The World Bank.
- Boyd J. H., R. Levine, B. D. Smith, 2001: “The Impact of Inflation on Financial Sector Performance”, Journal of Monetary Economics 2001; 47 (2):221–248.
- Claessens, Stijn & Feijen, Erik, 2006. "Finance and Hunger: Empirical Evidence of the Agricultural Productivity Channel," Policy Research Working Paper Series 4080, The World Bank.
- Demetriades, P., and D, Fielding, 2011, “Information, Institutions and Banking Sector Development in West Africa” Western Economic Association International (ISSN-0095-2583).
- Demirguc-Kunt, Asli, 2006. "Finance and Economic Development: Policy Choices for Developing Countries, "Policy Research Working Paper Series 3955, The World Bank.
- Detragiache, E., Gupta, P., and T, Tressel, 2005, “Finance in Lower Income Countries: An Empirical Exploration”, IMF Working Paper WP/05/167 (Washington: International Monetary Fund).
- Flamini, V., McDonald, C., and L. Schumacher, 2009, “The Determinants of Commercial Bank Profitability in Sub-Saharan Africa”, Working Paper, WP/09/15. (Washington: International Monetary Fund).
- Gaertner, M., and S. Sanya, 2012, “Assessing Bank Competition Within the East African Community.” IMF Working Paper, WP/12/32. (Washington: International Monetary Fund).
- Ghura, D., Kpodar, K., and R.J, Singh, 2009, “Financial Deepening in the CFA Franc Zone: The Role of Institutions”, IMF Working Paper WP/09/113 (Washington: International Monetary Fund).
- Honohan, P., and Beck T., 2007, “Making Finance Work for Africa, The World Bank.
- International Monetary Fund (IMF), 2010, “Sub-Saharan Africa: Resilience and Risks”, Regional Economic Outlook, October 2010.
- Kablan., S., “Banking Efficiency and Financial Development in Sub-Saharan Africa” IMF Working Paper WP/09/1 (Washington: International Monetary Fund).
- Kablan., S., “Measuring Bank Efficiency in Developing Countries: The case of WAEMU”, 2007, African Economic Research Consortium.
- La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny, 1998, “Law and Finance,” Journal of Political Economy, Vol. 106, No. 6, pp. 1113–55.
- Leon, F., 2012, “Financial Sector Competition in WAEMU”, CERDI, Etudes et Documents 2012.14
- Levine, R., and S. Zervos, 1998, “Stock Markets, Banks, and Economic Growth,” American Economic Review, 88, 537–558.
- Levine, R., 2003, “More on Finance and Growth: More Finance, More Growth?” Federal Reserve Bank of St. Louis Review, 85(4), 2003, 31–46
- McDonald, C., and L. Schumacher, 2007, “Financial Deepening in Sub-Saharan Africa: Empirical Evidence on the Role of Creditor Rights Protection and Information Sharing,” IMF Working Paper, WP/07/203.
- Maechler, A., Mitra, S., and D, Worrell, 2007, “Decomposing Financial Risks and Vulnerabilities in Eastern Europe”, IMF Working Paper WP/07/248 (Washington: International Monetary Fund).
- Moussa, B., 2010, “On the Development of Western African Accounting System”, Economics and Management School, Wuhan University.
- Mueller, J., Yackovlev, I., and H, Weisfeld, 2009, “The Global Financial Crisis: Impact on WAEMU Member Countries and Policy Options”, IMF Staff Position Note SPN/09/16 (Washington: International Monetary Fund).
- Sacerdoti, E., 2005, “Access to Bank Credit in Sub-Saharan Africa”, IMF Working Paper WP/05/166 (Washington: International Monetary Fund).

### Descriptive statistics (Table 1, 1997–2009)
- Credit to private/GDP: Observations 168, Mean 0.16, Std. Dev. 0.15, Min 0.01, Max 0.85
- WAEMU_dummy: Observations 208, Mean 0.50, Std. Dev. 0.50, Min 0.00, Max 1.00
- Inflation: Observations 206, Mean 0.05, Std. Dev. 0.04, Min -0.09, Max 0.16
- GDP per capita: Observations 208, Mean 6.28, Std. Dev. 1.03, Min 4.73, Max 8.94
- Rule of law: Observations 160, Mean -0.44, Std. Dev. 0.66, Min -1.90, Max 1.05
- Political Stability: Observations 160, Mean -0.26, Std. Dev. 0.88, Min -2.28, Max 1.13
- Internet per hundred: Observations 207, Mean 2.55, Std. Dev. 4.36, Min 0.00, Max 25.00
- Credit coverage: Observations 67, Mean 2.76, Std. Dev. 4.84, Min 0.00, Max 21.80
- Property: Observations 160, Mean 38.41, Std. Dev. 16.51, Min 10.00, Max 75.00

### Correlation matrix (Table 2)
- (Matrix entries listed by row)
  - Credit to private/GDP: 1.00
  - Waemu_dummy: -0.26 1.00
  - Inflation: 0.08 -0.47 1.00
  - GDP per capita: 0.76 -0.26 0.12 1.00
  - Rule of law: 0.72 -0.45 0.09 0.74 1.00
  - Political Stability: 0.52 -0.10 -0.05 0.58 0.76 1.00
  - Internet per hundred: 0.86 -0.26 0.11 0.64 0.56 0.39 1.00
  - Credit coverage: 0.61 0.03 -0.34 0.44 0.33 0.37 0.65 1.00
  - Property: 0.65 -0.38 0.03 0.77 0.86 0.58 0.53 0.38 1.00

### Variable definitions (Table 3)
- Credit to private/GDP: Credit to the private sector as percentage of GDP — IFS
- Waemu_dummy: Take value 1 if the country belongs to the WAEMU
- Inflation: Inflation rate — IFS
- GDP per capita: Gross Domestic Product per capita — IFS
- Rule of law: Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Worldwide Governance Indicators (World Bank)
- Political Stability: Political Stability and Absence of Violence/Terrorism captures perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Worldwide Governance Indicators (World Bank)
- Internet per hundred: Internet users are people with access to the worldwide network (number per hundred people) — World Development Indicators (World Bank)
- Credit coverage: Public credit registry coverage reports the number of individuals and firms listed in a public credit registry with current information on repayment history, unpaid debts, or credit outstanding. The number is expressed as a percentage of the adult population — World Development Indicators (World Bank)
- Property: An index measuring the ability of individuals to accumulate private property, secured by clear laws that are fully enforced by the state — Heritage Foundation Database

### Regression results (Table 4: Financial Development in WAEMU and HGNOE in SSA, 1997-2009)
- Coefficients and reported standard errors (row order: coefficient then standard error)
- Waemu_dummy:
  - Column (1): -0.028; 0.005 ***
  - Column (2): -0.003; 0.007
  - Column (3): -0.029; 0.007 ***
  - Column (4): -0.010; 0.006
  - Column (5): 0.013; 0.014
  - Column (6): -0.023; 0.006 ***
  - Column (7): 0.012; 0.012
- Inflation:
  - Column (1): -0.171; 0.067 ***
  - Column (2): -0.180; 0.075 ***
  - Column (3): -0.273; 0.076 ***
  - Column (4): -0.126; 0.072 *
  - Column (5): -0.063; 0.132
  - Column (6): -0.164; 0.068 ***
  - Column (7): -0.156; 0.097
- GDP per capita:
  - Column (1): 0.114; 0.003 ***
  - Column (2): 0.083; 0.003 ***
  - Column (3): 0.089; 0.004 ***
  - Column (4): 0.056; 0.006 ***
  - Column (5): 0.074; 0.011 ***
  - Column (6): 0.099; 0.004 ***
  - Column (7): 0.044; 0.008 ***
- Rule of law:
  - Coefficients: 0.078; 0.045
  - Standard errors: 0.006 ***; 0.020 ***
- Political Stability:
  - Coefficients: 0.033; 0.009
  - Standard errors: 0.003 ***; 0.009
- Internet per hundred:
  - Coefficients: 0.020; 0.018
  - Standard errors: 0.002 ***; 0.002 ***
- Credit coverage:
  - Coefficient: 0.010; Standard error 0.004 ***
- Property:
  - Coefficient: 0.001; Standard error 0.000 ***
  - Additional listed: 0.001; Standard error 0.001
- Constant terms:
  - Column (1): -0.519; 0.021 ***
  - Column (2): -0.300; 0.024 ***
  - Column (3): -0.349; 0.027 ***
  - Column (4): -0.217; 0.030 ***
  - Column (5): -0.337; 0.067 ***
  - Column (6): -0.479; 0.023 ***
  - Column (7): -0.151; 0.054 ***
- Observations by column: 167, 132, 132, 167, 52, 138, 122
- Number of countries (all columns): 17, 17, 17, 15, 17, 17, 17
- Chi square (Wald Test) by column: 1231.91, 1638.05, 727.28, 489.96, 116.77, 267.99, 588.84

### Country structural variables (Table 5, 2010)
- Variables and values for Côte d'Ivoire and Mozambique:
  - GDP (constant 2000 US$): Côte d'Ivoire 11,603; Mozambique 8,972
  - GDP per capita (constant 2000 US$): Côte d'Ivoire 588; Mozambique 384
  - Population (millions): Côte d'Ivoire 22.0; Mozambique 21.6
  - Population density: Côte d'Ivoire 62.1; Mozambique 29.7
  - Urban population (in percent of total population): Côte d'Ivoire 50.1; Mozambique 38.4
  - Rural population (in percent of total population): Côte d'Ivoire 49.9; Mozambique 61.6
  - Dependency ratios: Côte d'Ivoire 80.1; Mozambique 89.5
- Sources: WDI, IFS

### Main financial reforms in 2003–10 (Table 6: selected entries)
- Banking supervision:
  - Mozambique: Risk-based supervision introduced.
  - Côte d’Ivoire: No reform. Overall-compliance supervisory.
- Accounting standards:
  - Mozambique: Implementation of IFRS (IAS).
  - Côte d’Ivoire: Uniform accounting standards for WAEMU members (SYSCO).
- Payment systems:
  - Mozambique: Payment and settlements strengthened.
  - Côte d’Ivoire: Payment and settlements systems strengthened.
- Credit information:
  - Mozambique: Expansion of the coverage and range of information collected by the credit registry. Legal framework to allow credit registries enacted.
  - Côte d’Ivoire: No reforms.
- Legal and judicial framework:
  - Mozambique: Commercial code has been reviewed; specialized commercial court and special division of Maputo city court to execute judgments have been established; the code of civil procedure and the Notary Office modernized; the legal framework for secured transactions updated.
  - Côte d’Ivoire: No reforms.
- Microfinance institutions:
  - Mozambique: Legislation on microfinance enacted.
  - Côte d’Ivoire: Legislation on microfinance enacted, but the staffing and supervision need to be strengthened. The financial position of much of the sector is critical.

### Annex 1 — Financial possibility frontier: benchmarking WAEMU against HGNOE
- Methodology notes:
  - Benchmarks estimated using FinStats, a tool developed by the World Bank implementing the Beck et al. 2009 methodology.
  - Financial benchmarking premise: structural factors determine the level of financial development a country can attain; benchmarking compares a country to peers with similar structural characteristics and stage of development.
  - Financial sector development is modeled as affected by three types of factors: economic development (Y), policy environment (P), and structural characteristics (Z).
- Model equations (as presented):
  - ttttt ZPYX εγβα +++= (equation 1)
  - ''1 ttttt ZPPY εγβα +++= − (equation 2)
  - tt P P υα += −1 '' (equation 3)
  - Reduced form (substitution to show policy innovations in the residual):
    - (complex reduced form given and labeled as equation 4)
  - FinStats estimates equation (4) via quantile regressions using data from 177 countries and compares a given country to its potential benchmark or to comparator countries.
- Comparator selection:
  - Comparator countries chosen based on similarity on two dimensions: GDP per capita and population.
  - Distance formula as given: |)()(|)1(|)()(| jigdppcjigdppcij POPPRPOPPRwgdppcPRgdpPRw −−+−=Δ
  - FinStats uses the expected 25th and 75th percentiles.
- Note: Quantile regressions are used to reduce the impact of outliers and produce different expected values to gauge the range of financial sector performance.

### Figures: Private Credit / GDP (%)
- Series shown in figures for Benin, Burkina Faso, Mali, Niger, Senegal, and Togo for 2001–2010.
- Each figure plots:
  - Value Observed
  - Expected
  - 75th percentile

*Source: _wp13161 - REFERENCES*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13161.pdf_