## _wp10136

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---

### Banking sector role and study objectives
- Financial development is crucial to economic growth.
- In sub-Saharan Africa (SSA), banks are the most important element of the financial system; other financial structures are underdeveloped or almost nonexistent.
- Two principal questions addressed:
  - How efficient are banks in SSA and what determines their degree of efficiency?
  - What other factors may explain the low level of financial development in SSA?
- Methods used:
  - Stochastic frontier analysis (SFA) to assess bank cost-efficiency and its determinants.
  - Generalized method of moments (GMM) system (Arellano and Bover, 1995) to investigate determinants of financial development.

### Key findings on bank behavior and strategy
- Product mix and intermediation:
  - SSA banks emphasize deposit-taking and display difficulty transforming deposits into loans to the private sector.
  - Intermediation ratio (claims on the private sector relative to total deposits):
    - SSA overall: 57 percent
    - Asia: 91 percent
    - Latin America: 75 percent
- Regional intermediation differences within SSA:
  - Non-WAEMU west African countries: 61 percent
  - WAEMU countries: 70 percent
  - Central Africa: 65 percent
  - Southern Africa including South Africa: 59 percent
  - East Africa: 39 percent
- Client and branch strategy:
  - Loans mainly extended to large foreign companies or domestic public enterprises; local small and medium enterprises often underserved.
  - Branches concentrated in more economically developed (urban) regions, reducing rural financial access.

### Execution, technology, and environment
- Banking soundness indicators post-reform:
  - Total problem loans as percent of assets: decreased by 0.9 percent after restructuring; they equaled 8.3 percent in 2003.
  - Capital as share of assets: 18.9 percent in 2003 vs. 14.5 percent during the 1990s.
  - Ratio of liquid assets to total assets: 28.8 percent (2003).
- Technology adoption:
  - Computerization, ATMs, mobile banking units (Kenya), chip/fingerprint technology (Malawi).
  - Anglophone countries (Kenya, Nigeria, South Africa) tend to have more ATMs than Francophone countries.
- Regulatory environment:
  - Post-1980s crises led to tightened supervision; single supervisory institutions in CFA subzones (Commission Bancaire de l’UMOA and Commission Bancaire de la CEMAC).
  - By late 1990s most countries strengthened banking supervision and aligned prudential regulations with Basel core principles, though implementation remains weak.

### Evidence from literature on competition and foreign banks
- Competition increases efficiency (example: Uganda, Hauner and Peiris, 2005).
- Foreign banks:
  - Tend to be more efficient, import expertise, collect more deposits and make more loans.
  - Foreign bank penetration inversely related to X-inefficiency in Anglophone SSA (Kirkpatrick, Murinde and Tefula, 2008).
  - Post-privatization and foreign entry studies (Boubakri et al., Bonin et al.) find benefits from foreign ownership, including cautious risk-taking.

### Financial development indicators and trends (M2/GDP and credit to private sector)
- M2/GDP ratio evolution (1980–2002):
  - SSA: flat at about 25 percent in 1980–2002
  - Latin America: increased from 32 percent to 52 percent
  - Asia: increased from 30 percent to 64 percent
- Credit to the private sector to GDP:
  - Declines over the period of reforms in SSA.
  - Average levels over the period:
    - SSA: 14.6 percent
    - Latin America: 31.8 percent
    - Asia: 29 percent
- Caveats on indicators:
  - M2/GDP can overlook openness to capital flows, public borrowing from domestic system, nonbank intermediation, and banking sector competitiveness.
  - Credit-to-private-sector/GDP can be affected by financial innovations, emergence of nonbank credit, and bank lending to other financial intermediaries.

### Determinants of financial development (literature and empirical pointers)
- Gelbard and Leite (1999) propose six additional dimensions beyond M2/GDP:
  - market structure and competitiveness;
  - availability of financial products;
  - degree of financial liberalization;
  - institutional environment;
  - degree of integration with foreign financial markets (financial openness);
  - degree of sophistication of monetary policy instruments.
- Detragiache, Gupta, and Tressel (2005) identify:
  - Negative impact on financial development: corruption, inflation, foreign bank penetration.
  - Positive association with private sector credit: better contract enforcement and better information about borrowers.

### SFA methodology for bank cost-efficiency (specification)
- Outputs (intermediation and value-added approach): deposits, loans, securities.
- Inputs: labor, physical capital, financial capital.
- Functional form: translogarithmic cost function using panel data and Battese and Coelli (1992, 1995) one-step maximum likelihood estimation with truncated-normal inefficiency term.
- Cost function as presented:
  - Ln CT = α0 + ∑ αi lnp_i + ∑ ßj lny_j + 1/2∑∑ αik lnp_i lnp_k + 1/2∑∑ ßhj lny_h lny_j + ∑∑ δij lnp_i lny_j + z_it + v_it - u_it
  - Error decomposition: v_it ~ N(0, σ_v^2); u_it positive inefficiency term.
- Determinants of bank efficiency integrated into frontier: capitalization (book equity/total assets), bank size (bank deposits/system deposits), bank ownership (Foreign_dummy), GDP per capita (GDPc), NPLs (ratio of NPLs to total loans), rural population share.

### Data used for SFA and sample
- Sample for cost-efficiency estimate:
  - 137 banks in 29 African countries (on average five banks per country; more in Ghana, Kenya, Nigeria, South Africa).
- Data sources:
  - Bankscope, International Financial Statistics, World Development Indicators, IMF African Department country profile dataset, IMF African Department country economists.
- Selected average logarithmic values used for cost frontier arguments (Table 1):
  - Total costs/Total assets: Number of observations 455, Mean 0.9768, Standard Deviation 0.8063, Minimum -1.7317, Maximum 8.5436
  - Deposits/Total assets: Number of observations 455, Mean -0.2768, Standard Deviation 0.3696, Minimum -5.4764, Maximum 1.8553
  - Equity/Total assets: Number of observations 454, Mean -2.5487, Standard Deviation 1.6556, Minimum -11.5418, Maximum -0.1369
  - Loans/Total assets: Number of observations 454, Mean -0.8917, Standard Deviation 0.5346, Minimum -4.3870, Maximum -0.1139
  - PK (Capital cost): Number of observations 454, Mean 1.6280, Standard Deviation 1.0332, Minimum -1.4350, Maximum 6.6369
  - PL (Labor cost): Number of observations 454, Mean -0.4761, Standard Deviation 1.0026, Minimum -6.2604, Maximum 7.8868

### SFA estimation results (translog cost-frontier, 2000–2004)
- Number of observations: 351
- Gamma: γ = σu2/(σu2 + σv2) = 0,9989 (0.0002)*** — significant, indicating nonzero inefficiency variance.
- Log likelihood: 340,38
- Selected coefficient estimates (Coefficient (Standard error)):
  - Constant 1 beta0: -0,0153 (0.0015)***
  - Deposits beta1: 6,0270 (0.7938)***
  - Equity beta2: -0,1470 (0.1025)
  - Loans beta3: -0,2709 (0.1585)*
  - PK (Capital cost) beta4: 0,4303 (0.2091)**
  - PL (Labor cost) beta5: -0,2254 (0.2879)
  - Deposits*deposits beta6: 6,1279 (0.6438)***
  - Deposits*loans beta8: 3,1331 (0.7317)***
  - Loans*loans beta11: -0,4590 (0.1803)**
  - PK*PK beta12: -0,1493 (0.0222)***
  - PK*PL beta13: 0,2281 (0.0770)***
  - PL*PL beta14: 0,1359 (0.0353)***
  - Loans*PK beta19: -0,1601 (0.0831)*
- Determinants in inefficiency (delta coefficients):
  - Constant 2 delta0: -2,3398 (0.1110)***
  - ROE delta1: 1,0477 (0.2388)***
  - Bank_size delta2: -0,0946 (0.2091)
  - NPLs delta3: 0,0062 (0.0031)** — NPLs worsen cost-efficiency
  - GDPc delta4: 0,0004 (0.0000)*** — small, unexpected sign reported
  - Rural_pop delta5: 0,0419 (0.0017)*** — larger rural share associated with lower efficiency
  - Foreign_dummy delta6: -0,1139 (0.0917)
  - Concentration delta7: -0,1372 (0.1302)
- Significance notation: ***, **, * significant at the 1, 5 and 10 percent level respectively.

### Interpretation of SFA results and implications
- Cost elasticity with respect to deposits is positive: deposit increases raise costs.
- Negative elasticities for equity and loans indicate these outputs are complementary products.
- Higher capital price (PK) increases costs; banks reduce capital usage as average capital price rises.
- Significant negative interaction coefficients (e.g., loans*loans beta11) suggest shared costs/economies.
- Explanatory variables behavior:
  - ROE positive in inefficiency equation (risk-reduction/moral hazard interpretation).
  - NPLs increase costs and reduce cost-efficiency.
  - Higher rural population share raises costs and reduces efficiency (scale/delivery costs).
  - GDP per capita coefficient small with unexpected sign.
  - Foreign ownership shows a negative inefficiency coefficient but not statistically significant in reported results.

### Banking cost-efficiency: levels and regional differences
- Overall estimated efficiency: 0.76 over the period.
- Comparison: close to Kirkpatrick, Murinde, and Tefula (2008) for Anglophone African banks (0.80).
- Regional average cost-efficiency scores (Translogarithmic Cost Function, Table 3):
  - Eastern Africa: 0.7402
  - Southern Africa: 0.7660
  - Non-WAEMU West Africa: 0.8171
  - WAEMU: 0.7141
  - Western Africa (aggregate): 0.7529
  - Sub-Saharan Africa (aggregate): 0.7648
- Exchange rate regime and language effects:
  - Fixed Exchange Rate: 0.7341
  - Flexible Exchange Rate: 0.7800
  - Anglophone countries: 0.7737
  - Non-Anglophone countries: 0.7524
- Caution: interpretation complicated because most fixed-exchange-rate countries are Francophone.

### GMM methodology for financial development
- Model: dynamic panel GMM (system GMM of Arellano and Bover, 1995) combining first-difference and level equations.
- Estimated equation:
  - Ln Y_it = a + b Ln Y_it-1 + c foreign + X_it + d_i + u_it
  - Y_it: financial development indicator (private loans to GDP)
  - Foreign: share of foreign bank deposits in the system
  - X_it: vector of macroeconomic, political, and geographical controls
  - d_i: country-specific effect
- Variables rationale:
  - Market structure: foreign ownership, state-owned banks, concentration (share of deposits in three largest banks)
  - Macroeconomic: inflation (expected negative), GDPc (expected positive)
  - Geography/legal: rural population density, English legal system
  - Political: ethnic fractionalization, political instability, corruption, internal conflict
- Data and sample:
  - Data sources: IFS, World Development Indicators, Country Risk Guide, Alesina et al. (2003), Bankscope, Micco, Panizza, and Yanez (2004).
  - Sample period: 1998–2002; panel of 46 SSA countries, unbalanced.
  - Inflation measure: ln(1+ inflation).

### GMM estimation results (selected)
- Dynamic persistence (examples of lncredit_pr (-1) coefficients across specifications): 1,0098; 0,9194; 0,9768; 0,9818; 1,1308; 1,0555; 1,0279; 1,0685; 0,9393 — frequently significant.
- Foreign banks (Foreign) coefficients (examples): -1,5323; -1,8774; -1,5281; -1,4531; -1,2207; -1,6126; -1,4367; -1,6626; -1,3498 — generally significant at ** or * levels.
- Inflation (representative coefficients): -0,1785; -0,2427; -0,2091; -0,2396; -0,1883; -0,0829; -0,2227 — inflation significant in almost every regression; negative impact.
- GDPc: reported as 0,0000 with standard error (0,0000); GDP per capita not significant and dropped in early tests.
- State ownership (State): examples -0,5333; -1,0708; -1,0935 (not significant in reported specifications).
- Concentration: significant in only one regression (example -0,8218 (0,4048)*), suggesting a negative role of concentration in some specifications.
- Diagnostic tests (examples across specifications):
  - AR(2): 0,16; 0,18; 0,25; 0,24; 0,18; 0,13; 0,16; 0,55; 0,11
  - Hansen Test: 0,26; 0,48; 0,38; 0,8; 0,48; 0,45; 0,52; 0,45; 0,99
- Number of observations across columns: 98; 98; 88; 88; 88; 84; 88; 73; 73
- Significance notation: ***, **, * significant at the 1, 5 and 10 percent level.

### Main empirical findings from GMM and robustness checks
- Presence of foreign banks in SSA is significantly and negatively associated with financial development (credit to private sector/GDP) — consistent with cream-skimming by foreign banks.
- Inflation has a robust and negative impact on financial development across specifications.
- Banking system concentration shows some evidence of a negative impact on financial development (significant in one regression), implying potential benefits from greater competition.
- Other controls (state ownership, GDP per capita, geography, political variables) lacked consistent significance.
- Introducing cost-efficiency scores into financial development regressions did not change primary results—cost-efficiency coefficients reported but not significant:
  - Cost_efficiency coefficients in Appendix I:
    - i: -0,1315 (0,3901)
    - ii: -0,5826 (0,550)
    - iii: -0,2096 (0,4944)
  - Number of observations in these columns: i: 40; ii: 36; iii: 34
  - Hansen Test: i: 0,5; ii: 0,92; iii: 1
  - Cost-efficiency scores available from 2000 to 2002.

### Policy-relevant conclusions and recommendations
- Banks in SSA are, on average, cost-efficient in producing deposits and short-term loans; overall estimated efficiency: 0.76.
- Determinants and implications:
  - Capitalization and NPLs negatively impact efficiency (moral hazard concerns).
  - Density of rural population negatively affects cost-efficiency (geographic delivery barriers).
  - Per capita GDP shows a very small but significant negative impact in the estimation (contrary to expectations).
  - Inflation and, to some extent, concentration adversely affect financial development.
  - Presence of foreign banks tends to reduce credit to the private sector in SSA (cream-skimming effect).
- Policy recommendations implied by findings:
  - Improve the credit environment through better functioning judicial and legal processes.
  - Improve accessibility of information on borrowers (credit information systems).
  - Strengthen macroeconomic stabilization to keep inflation under control.
  - Promote a more competitive banking system to reduce concentration-related drawbacks.
  - Strengthen banking regulation and supervision to mitigate moral hazard and improve efficiency.
  - Consider geographic-targeted reforms to address rural population density challenges and improve financial inclusion.

*Content derived from the IMF working paper chapter titled "4. Evolution of Credit to the Private Sector in Sub-Saharan Africa, 1980–2002" contained in the provided PDF.*

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

### _wp10136 - References

### Tables
- 1. Average Logarithmic Values Used for the Arguments of the Cost Frontier Function ........14
- 2. Results of the Translogarithmic Cost-frontier Function Using the One-Step Method ........15
- 3. Average Cost-efficiency Score Measured by the Translogarithmic Cost Function ............16
- 4. GMM Estimation for Financial Development Determinants...............................................19

### Figures
- 1. Evolution of the M2/GDP Ratio for Sub-Saharan Africa, Latin America, and Asia, 1980–2002...............................................................................................................................7
- 2. Evolution of the M2/GDP Ratio by SSA Regions, 1980–2002 .............................................8
- 3. Evolution of Credit to the Private Sector in Sub-Saharan Africa, Latin America, and Asia, 1980–2002 .....................................................................................................................9

*Source: _wp10136 - References*

### 4. Evolution of Credit to the Private Sector in Sub-Saharan Africa, 1980–2002 ......................9

### 4. Evolution of Credit to the Private Sector in Sub-Saharan Africa, 1980–2002 ......................9

### Banking sector role and study objectives
- Financial development is crucial to economic growth.
- In sub-Saharan Africa (SSA), banks are the most important element of the financial system; other financial structures are underdeveloped or almost nonexistent.
- Two principal questions addressed:
  - How efficient are banks in SSA and what determines their degree of efficiency?
  - What other factors may explain the low level of financial development in SSA?
- Methods used:
  - Stochastic frontier analysis (SFA) to assess bank cost-efficiency and its determinants.
  - Generalized method of moments (GMM) system (Arellano and Bover, 1995) to investigate determinants of financial development.

### Key findings on bank behavior and strategy
- SSA banks emphasize deposit-taking in their product mix; they display difficulty transforming deposits into loans to the private sector.
- Intermediation ratio (claims on the private sector relative to total deposits):
  - SSA overall: 57 percent
  - Asia: 91 percent
  - Latin America: 75 percent
- Regional intermediation differences within SSA:
  - Non-WAEMU west African countries: 61 percent
  - WAEMU countries: 70 percent
  - Central Africa: 65 percent
  - Southern Africa including South Africa: 59 percent
  - East Africa: 39 percent
- Client mix: loans are mainly extended to large foreign companies or domestic public enterprises; local small and medium enterprises often underserved.
- Branch/network strategy: banks concentrate branches in more economically developed (urban) regions, reducing rural financial access.

### Execution, technology, and environment
- Post-reform changes in SSA banking soundness indicators:
  - Total problem loans as percent of assets: decreased by 0.9 percent after restructuring; they equaled 8.3 percent in 2003.
  - Capital as share of assets: 18.9 percent in 2003 vs. 14.5 percent during the 1990s.
  - Ratio of liquid assets to total assets: 28.8 percent (2003).
- Technology adoption:
  - Computerization, ATMs, mobile banking units (Kenya), chip/fingerprint technology (Malawi).
  - Anglophone countries (Kenya, Nigeria, South Africa) tend to have more ATMs than Francophone countries.
- Regulatory environment:
  - Post-1980s crises led to tightened supervision; single supervisory institutions in CFA subzones (Commission Bancaire de l’UMOA and Commission Bancaire de la CEMAC).
  - By late 1990s most countries strengthened banking supervision and aligned prudential regulations with Basel core principles, though implementation remains weak.

### Evidence from literature on competition and foreign banks
- Studies find competition increases efficiency (Uganda: Hauner and Peiris, 2005).
- Foreign banks in SSA:
  - Tend to be more efficient, import expertise, collect more deposits and make more loans.
  - Foreign bank penetration is inversely related to X-inefficiency in Anglophone SSA (Kirkpatrick, Murinde and Tefula, 2008).
  - Post-privatization and foreign entry studies (Boubakri et al., Bonin et al.) find benefits from foreign ownership, including cautious risk-taking.

### Financial development indicators and trends (M2/GDP and credit to private sector)
- M2/GDP ratio evolution (1980–2002):
  - SSA: flat at about 25 percent in 1980–2002
  - Latin America: increased from 32 percent to 52 percent
  - Asia: increased from 30 percent to 64 percent
- Large regional differences in M2/GDP within SSA.
- Credit to the private sector to GDP:
  - Declines over the period of reforms in SSA.
  - Average levels over the period:
    - SSA: 14.6 percent
    - Latin America: 31.8 percent
    - Asia: 29 percent
- Caveats on indicators:
  - M2/GDP can overlook openness to capital flows, public borrowing from domestic system, nonbank intermediation, and banking sector competitiveness.
  - Credit-to-private-sector/GDP can be affected by financial innovations, emergence of nonbank credit, and bank lending to other financial intermediaries.

### Determinants of financial development (literature and empirical pointers)
- Gelbard and Leite (1999) suggest six indicators beyond M2/GDP:
  - market structure and competitiveness;
  - availability of financial products;
  - degree of financial liberalization;
  - institutional environment;
  - degree of integration with foreign financial markets (financial openness);
  - degree of sophistication of monetary policy instruments.
- Determinants identified in other studies (Detragiache, Gupta, and Tressel, 2005):
  - Negative impact on financial development: corruption, inflation, foreign bank penetration.
  - Positive association with private sector credit: better contract enforcement and better information about borrowers.

### SFA methodology for bank cost-efficiency (specification)
- Outputs: deposits, loans, securities (intermediation approach and value-added principle).
- Inputs: labor, physical capital, financial capital.
- Functional form: translogarithmic cost function using panel data and Battese and Coelli (1992, 1995) one-step maximum likelihood estimation with truncated-normal inefficiency term.
- Cost function form (as presented in the source):
  - Ln CT = α0 + ∑ αi lnp_i + ∑ ßj lny_j + 1/2∑∑ αik lnp_i lnp_k + 1/2∑∑ ßhj lny_h lny_j + ∑∑ δij lnp_i lny_j + z_it + v_it - u_it
  - Error decomposition: v_it ~ N(0, σ_v^2); u_it positive inefficiency term.
- Determinants of bank efficiency integrated into frontier: capitalization (book equity/total assets), bank size (bank deposits/system deposits), bank ownership (Foreign_dummy), GDP per capita (GDPc), NPLs (ratio of NPLs to total loans), rural population share.

### Data used for SFA and sample
- Sample for cost-efficiency estimate:
  - 137 banks in 29 African countries (on average five banks per country; more in Ghana, Kenya, Nigeria, South Africa).
- Data sources:
  - Bankscope (bank-level variables),
  - International Financial Statistics (ratio of private loans to GDP),
  - World Development Indicators (GDP per capita, share of rural population),
  - IMF African Department country profile dataset (NPLs),
  - IMF African Department country economists (bank ownership).
- Table 1: Average logarithmic values used for cost frontier arguments (selected exact values):
  - Total costs/Total assets: Number of observations 455, Mean 0.9768, Standard Deviation 0.8063, Minimum -1.7317, Maximum 8.5436
  - Deposits/Total assets: Number of observations 455, Mean -0.2768, Standard Deviation 0.3696, Minimum -5.4764, Maximum 1.8553
  - Equity/Total assets: Number of observations 454, Mean -2.5487, Standard Deviation 1.6556, Minimum -11.5418, Maximum -0.1369
  - Loans/Total assets: Number of observations 454, Mean -0.8917, Standard Deviation 0.5346, Minimum -4.3870, Maximum -0.1139
  - PK (Capital cost): Number of observations 454, Mean 1.6280, Standard Deviation 1.0332, Minimum -1.4350, Maximum 6.6369
  - PL (Labor cost): Number of observations 454, Mean -0.4761, Standard Deviation 1.0026, Minimum -6.2604, Maximum 7.8868

### SFA estimation results (Table 2) — translog cost-frontier (2000–2004)
- Number of observations: 351
- Gamma: γ = σu2/(σu2 + σv2) = 0,9989 (0.0002)*** — significant, indicating the existence of the cost frontier function and nonzero inefficiency variance.
- Log likelihood: 340,38
- Selected coefficient estimates (Coefficient (Standard error)): significance noted in source
  - Constant 1 beta0: -0,0153 (0.0015)***
  - Deposits beta1: 6,0270 (0.7938)***
  - Equity beta2: -0,1470 (0.1025)
  - Loans beta3: -0,2709 (0.1585)*
  - PK (Capital cost) beta4: 0,4303 (0.2091)**
  - PL (Labor cost) beta5: -0,2254 (0.2879)
  - Deposits*deposits beta6: 6,1279 (0.6438)***
  - Deposits*loans beta8: 3,1331 (0.7317)***
  - Loans*loans beta11: -0,4590 (0.1803)**
  - PK*PK beta12: -0,1493 (0.0222)***
  - PK*PL beta13: 0,2281 (0.0770)***
  - PL*PL beta14: 0,1359 (0.0353)***
  - Loans*PK beta19: -0,1601 (0.0831)*
- Determinants in inefficiency (delta coefficients):
  - Constant 2 delta0: -2,3398 (0.1110)***
  - ROE delta1: 1,0477 (0.2388)***
  - Bank_size delta2: -0,0946 (0.2091)
  - NPLs delta3: 0,0062 (0.0031)** — NPLs worsen cost-efficiency
  - GDPc delta4: 0,0004 (0.0000)*** — small, unexpected sign reported in source
  - Rural_pop delta5: 0,0419 (0.0017)*** — larger rural share associated with lower efficiency
  - Foreign_dummy delta6: -0,1139 (0.0917)
  - Concentration delta7: -0,1372 (0.1302)
- Significance notation in source: ***, **, * significant at the 1, 5 and 10 percent level respectively.

### Interpretation of SFA results and implications
- Cost elasticity with respect to deposits is positive (deposit increases raise costs), consistent with high costs in African banks.
- Negative elasticities for equity and loans indicate these outputs are complementary products in bank activities.
- Capital price (PK) coefficient positive and significant: higher capital price increases costs; banks reduce capital usage as average capital price rises.
- Significant negative interaction coefficients (e.g., loans*loans beta11) suggest shared costs/economies when significant and negative.
- Explanatory variables mostly have expected signs:
  - ROE positive in inefficiency equation (interpreted in literature as risk-reduction/moral hazard implications).
  - NPLs increase costs and reduce cost-efficiency.
  - Higher rural population share raises costs and reduces efficiency due to lack of scale and higher service delivery costs.
  - GDP per capita coefficient small and not of expected sign in this estimation.
  - Foreign ownership (Foreign_dummy) has a negative coefficient in inefficiency (delta6 = -0,1139) but not significant at conventional levels in reported results.

### Policy-relevant conclusions and recommendations (as presented in the source)
- SSA banks are, on average, cost-efficient in producing deposits and short-term loans, but efficiency could be improved.
- Key policy actions to enhance banking efficiency and financial development:
  - Improve the credit environment through better functioning judicial and legal processes.
  - Improve accessibility of information on borrowers (credit information systems).
  - Strengthen macroeconomic stabilization to keep inflation under control (inflation hindered financial development in estimations).
  - Promote a more competitive banking system (concentration somewhat hindered financial development).
- Better enforcement of contract law and availability of borrower information should enable banks to transform deposits into private sector loans more effectively, thereby supporting investment and growth.

*Italicized source attribution: Content derived from the IMF working paper chapter titled "4. Evolution of Credit to the Private Sector in Sub-Saharan Africa, 1980–2002" contained in the provided PDF.*

### 0.76 over the period. This result is close to that found by Kirkpatrick, Murinde, and Tefula

### _wp10136 - 0.76 over the period. This result is close to that found by Kirkpatrick, Murinde, and Tefula

### Banking cost-efficiency: levels and regional differences
- Overall estimated efficiency: 0.76 over the period.
- Comparison: close to Kirkpatrick, Murinde, and Tefula (2008) for Anglophone African banks (0.80).
- Regional average cost-efficiency scores (Translogarithmic Cost Function, Table 3):
  - Eastern Africa: 0.7402
  - Southern Africa: 0.7660
  - Non-WAEMU West Africa: 0.8171
  - WAEMU: 0.7141
  - Western Africa (aggregate): 0.7529
  - Sub-Saharan Africa (aggregate): 0.7648
- Exchange rate regime and language effects:
  - Fixed Exchange Rate: 0.7341 (cost efficiency)
  - Flexible Exchange Rate: 0.7800 (cost efficiency)
  - Anglophone countries: 0.7737 (cost efficiency)
  - Non-Anglophone countries: 0.7524 (cost efficiency)
- Caution: interpretation complicated because most fixed-exchange-rate countries are Francophone.

### Methodology for analyzing financial development
- Model: dynamic panel GMM (system GMM of Arellano and Bover, 1995) combining first-difference and level equations to address simultaneity, inverse causality, and omitted variables.
- Estimated equation (4):
  - Ln Y_it = a + b Ln Y_it-1 + c foreign + X_it + d_i + u_it
  - Y_it: financial development indicator (private loans to GDP)
  - Foreign: share of foreign bank deposits in the entire system
  - X_it: vector of macroeconomic, political, and geographical controls
  - d_i: country-specific effect
- Rationale for variables:
  - Market structure: foreign ownership (cream-skimming vs. technology/competition gains), state-owned banks, concentration (ratio of deposits in three largest banks).
  - Macroeconomic: inflation (expected negative effect), GDPc (expected positive effect; tested).
  - Geography/legal: rural population density (proxy for delivery barriers), English legal system (associated with more active financial systems).
  - Political: ethnic fractionalization, political instability, corruption, internal conflict (expected negative effects).
  - Regulation/business environment: considered but excluded from regressions due to lack of data.

### Data, sample, and estimation details
- Data sources:
  - Macroeconomic variables: IFS and World Development Indicators
  - Political variables: Country Risk Guide
  - Ethnic fractionalization: Alesina and others (2003)
  - Concentration: Bankscope (share of three largest banks’ deposits)
  - Share of foreign bank and state-owned banks: Micco, Panizza, and Yanez (2004)
- Sample period: 1998–2002 (data limitations note: regressions limited to 1998–2002; a more rigorous approach would use 2000–04 but data unavailable).
- Panel: sample of 46 SSA countries observed over 5 years (1998–2002), unbalanced panel data.
- Inflation measure: ln(1+ inflation), following Detragiache, Gupta, and Tressel, 2005.

### GMM estimation results (Table 4) — key coefficients and significance
- Dynamic persistence:
  - lncredit_pr (-1) coefficients across specifications range (examples): 1,0098; 0,9194; 0,9768; 0,9818; 1,1308; 1,0555; 1,0279; 1,0685; 0,9393
  - These lag coefficients are frequently significant at ***, **, or * levels as reported.
- Foreign banks (Foreign):
  - Coefficients across specifications (examples): -1,5323; -1,8774; -1,5281; -1,4531; -1,2207; -1,6126; -1,4367; -1,6626; -1,3498
  - Significance: generally significant at ** or * levels as reported.
- Inflation:
  - Representative coefficients: -0,1785; -0,2427; -0,2091; -0,2396; -0,1883; -0,0829; -0,2227
  - Inflation is significant in almost every regression; negative impact on financial development.
- GDPc:
  - Reported as 0,0000 with standard error (0,0000); GDP per capita was not significant and was dropped in early tests.
- State ownership (State):
  - Example coefficients: -0,5333; -1,0708; -1,0935 (not significant in reported specifications).
- Other variables:
  - Concentration: significant in only one regression (coefficient example -0,8218 with (0,4048)* reported), suggesting a negative role of concentration in some specifications.
  - Rural-pop, Ethnic, English_legal, Corruption, Internal_conflict, Political_risk: included in some specifications; limited significance reported (e.g., English_legal reported -0,2862 (0,5690)* in one column; Ethnic -0,6161 (2,9558) in another; Internal_conflict -0,0829 (0,0784) in another).
- Number of observations across columns: 98; 98; 88; 88; 88; 84; 88; 73; 73.
- Diagnostic tests (examples across specifications):
  - AR(2): 0,16; 0,18; 0,25; 0,24; 0,18; 0,13; 0,16; 0,55; 0,11
  - Hansen Test: 0,26; 0,48; 0,38; 0,8; 0,48; 0,45; 0,52; 0,45; 0,99
- Significance notation: ***, **, * significant at the 1, 5 and 10 percent level.

### Main empirical findings
- Financial development (credit to private sector / GDP) is significantly and negatively associated with the presence of foreign banks in SSA — evidence consistent with cream-skimming by foreign banks.
- Inflation has a robust and negative impact on financial development across specifications.
- Banking system concentration shows some evidence of a negative impact on financial development (significant in one regression), implying benefits from greater competition.
- Other tested control variables (state ownership, GDP per capita, geography, political variables) generally lacked consistent significance in the regressions.
- Introducing cost-efficiency scores into financial development regressions did not change results, possibly due to limited observations.

### Conclusions and policy implications
- Banks in SSA are generally cost-efficient: estimated at 76 percent given their transformation strategy of deposits into mainly short-term loans and long-term loans to big companies.
- Determinants of bank cost-efficiency in SSA:
  - Capitalization and NPLs have a negative impact on efficiency, highlighting moral hazard problems.
  - Density of rural population negatively affects cost-efficiency, implying geographic delivery barriers matter for policy.
  - Per capita GDP exhibits a very small but significant negative impact against conventional expectations.
- Determinants of financial development:
  - Inflation and, to some extent, concentration adversely affect financial development.
  - Presence of foreign banks leads to cream-skimming, reducing credit to the private sector.
  - Data limitations prevented testing the impact of regulation and the broader business environment.
- Policy recommendations implied by findings:
  - Pursue macroeconomic stabilization policies to reduce inflation and support financial development.
  - Strengthen banking regulation and the credit environment to mitigate moral hazard and improve efficiency.
  - Enhance competition in the banking sector to reduce concentration-related drawbacks.
  - Consider geographic-targeted reforms (address rural population density challenges) to improve bank cost-efficiency and financial inclusion.
  - Reinforce judicial and legal support to create a sounder credit environment that enables banks to better perform financial intermediation (transform deposits into productive loans).

*Source: _wp10136 - 0.76 over the period. This result is close to that found by Kirkpatrick, Murinde, and Tefula*

### REFERENCES

### _wp10136 - REFERENCES

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### Appendix I. GMM Regression Including the Cost Efficiency Scores Derived from the Stochastic Frontier Function
- Dependent variable: lncredit_pr
- Columns: i, ii, iii

- Constant
  - i: 0,3936 (0,5678)
  - ii: 1,1097 (0,5207)
  - iii: -1,4287 (2,0381)

- lncredit_pr (-1)
  - i: 0,8496 (0,3788)**
  - ii: 0,7239 (0,3695)*
  - iii: 0,8097 (0,4710)

- Foreign
  - i: -2,2523 (1,0652)**
  - ii: -1,6844 (0,6420)**
  - iii: -1,8256 (1,4651)

- GDPc
  - i: 0,0001 (0,0001)

- Inflation
  - ii: -0,4025 (0,1502)**
  - iii: -0,1217 (0,2148)

- Rural_pop
  - ii: -0,0066 (0,0065)
  - iii: 0,0607 (0,0436)

- Concentration
  - iii: -3,8004 (2,9403)

- Cost_efficiency
  - i: -0,1315 (0,3901)
  - ii: -0,5826 (0,550)
  - iii: -0,2096 (0,4944)

- Number of observations
  - i: 40
  - ii: 36
  - iii: 34

- AR(2): . (for all columns)
- Hansen Test
  - i: 0,5
  - ii: 0,92
  - iii: 1

- Significance note: **, * significant at 5 and 10 % level, cost-efficiency scores are available from 2000 to 2002.

*Source: _wp10136 - REFERENCES (PDF chapter/section).*

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