## _wp15235 - 1. Private credit to GDP  2. Private Credit to GDP

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### Benchmarking and observed patterns
- Benchmark financial development indicator: expected median of the private credit to GDP ratio obtained from yearly cross-country regressions.
- Financial gap definition: Gap = Benchmark / Observed private credit to GDP.
  - A financial gap at unity indicates financial development at its expected level.
  - A ratio above unity indicates underdevelopment relative to the benchmark.
- Empirical patterns:
  - Figures show most SSA countries deviate substantially from their benchmark; the average financial gap in SSA shrinks over time but remains above unity.
  - The financial development gap is larger in CEMAC countries; some (e.g., Equatorial Guinea and Gabon) had rapid GDP growth that did not translate into equally rapid financial sector growth.

### Data and methodology
- Data coverage:
  - Yearly observations during 1997 – 2012.
  - Country dimension: 42 SSA countries (Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cabo Verde, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Republic of Congo, Côte d'Ivoire, Equatorial Guinea, Eritrea, Gabon, The Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, São Tomé and Príncipe, Senegal, Seychelles, Sierra Leone, South Africa, Swaziland, Tanzania, Togo, Uganda, and Zambia).
  - All variables averaged over four-year non-overlapping intervals: 1997-2000; 2001-2004; 2005-2008; 2009-2012.
  - Averaging follows Levine (2005) to assess long-run relationships.
- Benchmarks estimated using quantile regressions; median estimate used as benchmark.

### Dependent variables
- Primary dependent variables:
  - Ratio of private credit to GDP.
  - Financial development gap = ratio between benchmark and observed private credit to GDP level.
- Data constraints prevented using bank branches in regressions; focus remains on credit-to-GDP as the primary measure.
- Access and depth measures are highly correlated; determinants of credit-to-GDP expected to map to other access measures.

### Explanatory variables (categories and expected signs)
- Macroeconomic:
  - Included: income per capita, inflation, debt-to-GDP ratio, natural resources GDP (non-renewable resources GDP / total GDP).
  - Expected: higher income → more financial development; higher inflation, debt-to-GDP, and resource GDP → negative for private credit to GDP and increase financial gap.
  - Macroeconomic variables used in lags to address endogeneity.
- Institutional:
  - Included: government effectiveness, rule of law, political stability (Worldwide Governance Indicators range: -2.5 to 2.5).
  - Expected: better institutions → higher private credit-to-GDP and lower financial gap.
- Banking infrastructure and governance:
  - Variables: deposits, interest rate spreads, registry coverage, depth of credit information, cost-to-income ratios, operational costs.
  - Expected: higher deposits and better information → higher private credit-to-GDP; higher cost-to-income and operational costs → negative for financial development.
- Geography and population:
  - Variables: country size, population density, share of urban population, poverty headcount, infant mortality.
  - Expected: population density and urban share → positive for access; high poverty and infant mortality → lower inclusion.
- Technological advances:
  - Variables: mobile phone subscriptions, internet utilization rates.
- CFA franc zone specific:
  - CFA franc zone dummy for CEMAC; interaction terms (e.g., CFA * Rule of Law, CFA * Government Effectiveness, CFA * Political Stability, CFA * Deposits, CFA * Operational costs) to detect differential effects.

### Empirical model
- Panel specification for private credit to GDP:
  - phi_it = alpha + X_it beta + country fixed effects + time fixed effects + error term.
  - X_it includes macroeconomic, structural, banking, institutional variables and CFA interaction terms.
  - Averaging over four-year intervals and lagging macroeconomic regressors used to mitigate endogeneity.
- Financial development gap regression:
  - Gap_it = Benchmark_it / Observed_it; Gap regressed on same set of controls with country and time fixed effects.
- Where limited observations exist (on average only two observations per country for some vars), random effects panel analysis used under assumption regressors are not correlated with country effects.
- Fixed effects regressions reported with robust standard errors; random effects used for variables with < 100 observations.

### Results — Macroeconomic determinants
- Main empirical findings:
  - Higher income per capita → more developed financial sector (private credit to GDP).
  - Inflation is negatively and significantly correlated with credit to the private sector.
  - Share of natural resources GDP is negatively associated with private credit to GDP and associated with a larger gap from the benchmark.
- Quantitative highlights cited:
  - In first set of regressions (Table 1), income per capita, inflation, and natural resources GDP are significant and robust across specifications.
  - In regressions of the financial gap, previous period inflation remains significant: the R2 of regression (3) indicates almost 30 percent of the financial gap is explained by lagged inflation.
  - Country income level is an insignificant determinant of the financial gap in Table 2; no linear relationship between financial gap and income per capita.

### Results — Institutional determinants
- Main findings:
  - Significant relationship between financial gap and rule of law for all SSA countries.
  - CFA franc zone countries with better government effectiveness and property rights tend to have a more developed financial system (based on CFA interaction terms reported in Table 3).
- Interpretation:
  - Better institutional quality (rule of law, government effectiveness, property rights) associated with closer performance to the benchmark and higher private credit to GDP.

### Results — Financial sector variables
- Main findings:
  - Financial sector infrastructure and governance variables affect financial development in SSA.
  - Higher deposits associated with higher private credit to GDP.
  - Banking-system specific factors (cost-to-income ratio, operational costs, interest spreads, registry coverage, credit information depth) influence both level of financial development and the financial gap.
- Exact quantitative note preserved from source:
  - "0.4 percent increase in private credit." (appears twice in source and indicates the translation of deposits into higher private credit-to-GDP in a reported specification).
- Additional results:
  - Better information availability (credit information depth and registry coverage) is positively associated with financial development and significantly narrows the underdevelopment gap.
  - Higher operational costs and cost-income ratio negatively affect financial development growth; higher operational costs associated with a wider gap.
  - Deposits-to-GDP ratio narrows the financial gap.

### Estimation approach and robustness
- Fixed effects regressions with robust standard errors for each dependent variable.
- Random effects panel regressions used when independent variables have less than 100 observations to maximize sample size; assumption checked for robustness.
- Inclusion of financial sector variables improves regression performance; highest R2 in Table 1 is around 66 percent when income per capita, inflation, natural resource GDP, deposits and banks’ cost-income ratio are jointly included (regression (7)).

### Key empirical findings (summary and attribution analysis)
- Population and poverty:
  - Total population (log) negatively related to the financial gap (consistent with Allen, et al. (2014)); large-population countries like South Africa and Nigeria have relatively more developed financial sectors.
  - Poverty headcount positively related to the financial development gap; poverty negatively associated with financial development (poverty headcount shown in Table 1, regression (12); infant mortality used as proxy in Table 2, regression (7)).
- Technological and infrastructure indicators:
  - Power consumption per capita significant and positively related to financial development (Table 1, regression (11)); including power consumption can render income per capita insignificant due to correlation.
- Attribution analysis (Figure 6; calculated as one standard deviation increase × estimated coefficient from Table 2, regression (11)):
  - Improvement in Rule of Law could decrease the financial development gap by about 15 percentage points.
  - Natural resources deteriorate the gap by about 7 percentage points.
  - Inflation deteriorates the gap by about 14 percentage points.

### Empirical evidence and selected statistics
- Summary statistics (Table 4, 4-year averages, 1997-2012):
  - Private credit (% GDP): No obs 164; Mean 17.07; Standard dev. 16.39; Median 12.65; Min 0.81; Max 90.68.
  - Financial Gap**: No obs 163; Mean 1.95; Standard dev. 1.86; Median 1.28; Min 0.28; Max 11.42.
  - GDP per cap. (log): No obs 167; Mean 6.63; Standard dev. 1.13; Median 6.26; Min 4.79; Max 9.82.
  - Inflation (%): No obs 168; Mean 11.57; Standard dev. 28.39; Median 6.18; Min 0.37; Max 265.76.
  - Natural Resources (% GDP): No obs 168; Mean 7.64; Standard dev. 17.01; Median 0.00; Min 0.00; Max 85.22.
  - Deposits (% GDP): No obs 164; Mean 25.93; Standard dev. 22.99; Median 18.90; Min 1.99; Max 120.50.
  - Branches* (per 100,000 adults): No obs 100; Mean 4.02; Standard dev. 4.95; Median 2.38; Min 0.13; Max 29.81.
  - Operational costs (% assets): No obs 143; Mean 5.30; Standard dev. 2.41; Median 5.08; Min 0.02; Max 15.99.
  - Cost-income ratio (%): No obs 146; Mean 58.81; Standard dev. 17.38; Median 59.27; Min 4.30; Max 118.30.
  - Mobile Subscriptions (per 100 adults): No obs 168; Mean 22.71; Standard dev. 29.42; Median 9.30; Min 0.00; Max 134.20.
  - Internet Use (%): No obs 168; Mean 3.94; Standard dev. 6.90; Median 1.14; Min 0.00; Max 43.75.
  - Poverty Headcount* (% population): No obs 71; Mean 48.60; Standard dev. 14.41; Median 48.40; Min 13.40; Max 76.80.
- Regression coefficient highlights (selected from Table 1 and Table 2):
  - log GDP per capita (lag) positive and significant in many Private Credit/GDP regressions (examples: 9.305***, 9.227***, 9.586***, 8.943***, 7.291***, 7.501***, 8.945***, 9.145***, 8.782***).
  - Consumer Prices (lag, %) often negative and significant (examples: -0.0449***, -0.0420***, -0.0332**, -0.0521***, -0.0352***, -0.0403***, -0.0461***).
  - Natural Resource GDP/Total GDP (lag) negative and significant in several specs (examples: -0.224***, -0.211***, -0.239***, -0.191***, -0.161***, -0.153***, -0.250***).
  - Credit information depth positive in specific regressions (examples: 0.425***; 0.0112**).
  - Poverty Headcount (%) coefficient reported as -0.378*** in a specification (Table 1).

### Financial inclusion snapshots (2012, selected examples, Table 5)
- Kenya: Share of adults with an account at any formal financial institution — Overall 42.3; poorest 20% 18.7; richest 20% 85.0.
- Mauritius: Overall 80.1; poorest 20% 66.0; richest 20% 93.9.
- Mozambique: Overall 39.9; poorest 20% 20.5; richest 20% 56.4.
- SSA (simple average): Overall 21.0; poorest 20% 11.0; richest 20% 36.8.
- SSA (weighted average): Overall 24.2; poorest 20% 11.6; richest 20% 46.2.

### Policy implications and recommendations
- Structural and institutional reforms to reduce information asymmetries and foster financial development:
  - Enhance credit registry coverage and the depth of credit information.
  - Improve legislation, property rights and documentation to enhance access to finance.
- Address supply barriers to financial access:
  - National or regional strategy with measurable targets and a coordinating institution.
  - Examples of successful approaches:
    - Rwanda’s Financial Sector Development Program (FSDP) launched in 2006: adults not using any financial product or service fell from 52 percent in 2008 to 28 percent in 2012 (FinStats), driven by banking uptake and non-bank formal institutions (SACCOs).
    - Kenya’s Equity Bank expanded microfinance-focused commercial banking.
  - Common effective features: only requiring photo ID to open accounts; no minimum balance; regulatory imposition of free bank accounts (South Africa).
- Regulatory and supervisory priorities:
  - Protect consumers and properly supervise innovative banking and microfinance.
  - Foster collaboration among commercial banks, microfinance institutions and telecommunication companies.
  - Regulations should support innovative finance (mobile banking), interoperability among providers, and mobile-money-based cross-border remittances to lower transaction costs and widen usage.
- Operational recommendations:
  - Move forward with implementation of electronic payment systems for taxes and utilities.
  - Encourage expansion of banking branch networks.
  - Improve business environment and judicial framework, boost supervision capacities, reduce information asymmetry, and facilitate loan recovery.
- Worldwide context:
  - More than half of financial regulatory frameworks globally include measures for promoting financial inclusion and relevant best practices can be identified.

*Source: Finstats database; and authors’ calculations (content unit: _wp15235 - 1. Private credit to GDP  2. Private Credit to GDP).*

### 1. Private credit to GDP  2. Private Credit to GDP

### _wp15235 - 1. Private credit to GDP  2. Private Credit to GDP

### Benchmarking and observed patterns
- Figures illustrate benchmarking financial development in selected CEMAC countries (2004-2012) and financial depth and access in sub-Saharan Africa (1997-2012, four-year averages).
- The benchmark financial development indicator is the expected median of the private credit to GDP ratio obtained from yearly cross-country regressions.
- A financial gap is defined as the ratio between the benchmark and actual private credit to GDP level:
  - A financial gap at unity indicates that a country’s financial development is at its expected level.
  - A ratio above unity indicates underdevelopment relative to the benchmark and scope for policies to boost financial development.
- Figure 4 and Figure 5 show that most SSA countries deviate substantially from their benchmark; the average financial gap in SSA shrinks over time but remains above unity.
- The financial development gap is larger in CEMAC countries; some countries (e.g., Equatorial Guinea and Gabon) experienced rapid GDP growth that did not translate into equally rapid financial sector growth.

### Data and methodology
- Data:
  - Yearly observations during 1997 – 2012.
  - Country dimension covers 42 SSA countries.
  - All variables are averaged over four-year non-overlapping intervals: 1997-2000; 2001-2004; 2005-2008; and 2009-2012.
- The 42 countries are listed in the source (Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cabo Verde, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Republic of Congo, Côte d'Ivoire, Equatorial Guinea, Eritrea, Gabon, The Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, São Tomé and Príncipe, Senegal, Seychelles, Sierra Leone, South Africa, Swaziland, Tanzania, Togo, Uganda, and Zambia).
- Averaging over four-year intervals follows Levine (2005) to assess long-run relationships.

### Dependent variables
- Primary dependent variables:
  - Ratio of private credit to GDP.
  - Financial development gap = ratio between benchmark and observed private credit to GDP level.
- Data limitations prevent use of bank branches measure in regressions; focus is on credit to GDP as the primary measure.
- Access and depth measures are highly correlated in sample countries; factors associated with higher credit to GDP are expected to be associated with larger measures of access.

### Explanatory variables (categories and expectations)
- Macroeconomic variables:
  - Included: income per capita, inflation, debt-to-GDP ratio, natural resources GDP (ratio of non-renewable resources GDP to total GDP).
  - Expected signs: higher income → more financial development; higher inflation, debt-to-GDP, and resource GDP → negative for private credit to GDP and increase financial gap.
  - Variables used in lags to address endogeneity.
- Institutional variables:
  - Included: government effectiveness, rule of law, political stability (Worldwide Governance Indicators ranges between -2.5 and 2.5).
  - Expectation: better institutions → higher private credit-to-GDP and lower financial gap.
- Banking infrastructure and financial sector–specific governance:
  - Variables: deposits, interest rate spreads, registry coverage, depth of credit information, cost-to-income ratios, operational costs.
  - Expectations: higher deposits and better information → higher private credit-to-GDP; higher cost-to-income and operational costs → negative for financial development.
- Geography and population:
  - Variables: country size, population density, share of urban population, poverty headcount, infant mortality.
  - Expectations: population density and urban share → positive for access and financial development; high poverty and infant mortality → lower inclusion.
- Technological advances:
  - Variables: mobile phone subscriptions, internet utilization rates.
  - Purpose: assess association with financial sector development.
- CFA franc zone specific variables:
  - CEMAC identified by CFA franc zone dummy.
  - Interaction variables created (e.g., CFA * Rule of Law, CFA * Government Effectiveness, CFA * Political Stability, CFA * Deposits, CFA * Operational costs) to detect differential effects in CFA members.

### Empirical model
- Panel specification for private credit to GDP:
  - phi_it = alpha + X_it beta + country fixed effects + time fixed effects + error term.
  - Country fixed effects (alpha_i) and time fixed effects (tau_t) control for unobserved time-invariant country characteristics and country-invariant time-specific variables.
  - X_it includes macroeconomic, structural, banking, institutional variables and interaction terms with CFA dummy.
  - Averaging over four-year intervals and lagging macroeconomic regressors used to mitigate endogeneity.
- Financial development gap:
  - Gap_it = Benchmark_it / Observed_it.
  - Regression similar to equation (1) regressing gap on macroeconomic, institutional, and banking-specific variables with country and time fixed effects.
- When limited observations (on average only two observations per country) exist for some variables, random effects panel analysis is used under the assumption that regressors are not correlated with country effects.

### Results: Macroeconomic determinants
- Main findings:
  - Countries with higher income per capita have a more developed financial sector.
  - Inflation is negatively and significantly correlated with credit to the private sector.
  - Share of natural resources GDP is negatively associated with private credit to GDP and associated with a larger gap from the benchmark.
- Interpretation:
  - Higher income reduces financial constraints and increases demand for services.
  - Higher inflation reduces demand for credit and saving behavior and reduces returns to lending.
  - Natural resources can mechanically boost GDP without parallel financial sector development; foreign-dominated resource sectors may access external financing, reducing domestic credit expansion.
- Specific quantitative notes:
  - In the first set of regressions in Table 1, income per capita, inflation, and natural resources GDP are significant and robust across specifications.
  - In regressions of the financial gap, previous period inflation remains significant: the R2 of regression (3) indicates almost 30 percent of the financial gap is explained by lagged inflation.
- Additional note:
  - Country income level is an insignificant determinant of the financial gap in Table 2; no linear relationship between financial gap and income per capita.

### Results: Institutional determinants
- Main findings:
  - Significant relationship between financial gap and rule of law for all SSA countries.
  - CFA franc zone countries with better government effectiveness and property rights tend to have a more developed financial system (based on interaction variables reported in Table 3).
- Interpretation:
  - Institutional quality (rule of law, government effectiveness, property rights) is associated with closer performance to the benchmark and higher private credit to GDP.

### Results: Financial sector variables
- Main findings:
  - Variables related to financial sector infrastructure and governance affect financial development in SSA.
  - Higher deposits are associated with higher private credit to GDP ratio.
  - Banking-system specific factors (e.g., cost-to-income ratio, operational costs, interest spreads, registry coverage, credit information depth) influence both the level of financial development and the financial gap.
- Quantitative note:
  - In Table 1, a one percent increase in deposits translates into a (statement truncated in source)—the source indicates a positive and significant translation of deposits into higher private credit-to-GDP.

### Estimation approach and robustness
- Fixed effects regressions with robust standard errors are provided for each dependent variable.
- When independent variables have less than 100 observations (equivalent of less than 3 time-period observations per country), estimation results are provided based on panel regressions with random effects to maximize sample size.
- The assumption for random effects regressions was checked for robustness.

*Source: Finstats database; and authors’ calculations (content unit: _wp15235 - 1. Private credit to GDP  2. Private Credit to GDP).*

### 0.4 percent increase in private credit.

### 0.4 percent increase in private credit.

### Key empirical findings on determinants of financial development
- Higher operational costs and cost-income ratio affect negatively the growth of financial development as high operational costs may be transferred to the clients, and demand for loans reduced.
- The performance of the regressions improves when financial sector variables are added.
  - The highest R2 found in Table 1 is around 66 percent when income per capita, inflation, natural resource GDP, deposits and banks’ cost-income ratio are jointly included (regression (7)).
- Deposits-to-GDP ratio and banks’ operational costs are important determinants in explaining the financial development gap (Table 2):
  - Higher operational costs are associated with a wider gap.
  - The financial gap narrows as the share of deposits to GDP increases.
- Better information availability is positively associated with financial development and significantly narrows the underdevelopment gap:
  - Credit information depth and credit registry coverage address information asymmetries between lenders and borrowers and improve prospects for financial development.
- Population characteristics:
  - Total population (log) is found in negative relationship with the financial gap (consistent with Allen, et al. (2014)). This may be driven by countries with high populations like South Africa and Nigeria where financial sectors are relatively more developed than the rest of SSA; economies of scale are another possible explanation.
  - Poverty and the financial development gap are positively related.
  - The negative relationship between financial development and poverty is evident: poverty headcount (shown in Table 1, regression (12); or proxied by infant mortality in Table 2, regression (7)) indicates the need for measures to make financial services more inclusive.
- Technological advances:
  - Power consumption per capita is found to be significant and is positively related to the level of financial development (Table 1, regression (11)).
  - When including per capita power consumption, income per capita becomes insignificant because power consumption is associated with economic development and is highly correlated with income, thus capturing the income-related effect.
- Attribution analysis (effects from Figure 6; calculated as one standard deviation increase × estimated coefficient from Table 2, regression (11)):
  - Improvement in Rule of Law could decrease the financial development gap by about 15 percentage points.
  - Natural resources deteriorate the gap by about 7 percentage points.
  - Inflation deteriorates the gap by about 14 percentage points.

### Implications for policy and institutional reform
- Structural reforms and improvement of the institutional framework are necessary to reduce information asymmetries and foster financial development, including:
  - Enhancing credit registry coverage and the depth of credit information.
  - Improving legislation, property rights and documentation to enhance access to finance.
- The public policy agenda should address main supply barriers to financial access:
  - A national or regional strategy with measurable targets and a coordinating institution is needed.
  - Example: Rwanda’s Financial Sector Development Program (FSDP) launched in 2006 aimed at extending financial services to the unbanked.
    - In 2008, 52 percent of Rwandan adults (18 years or older) did not use any financial product or service.
    - This number dropped to 28 percent in 2012 (FinStats).
    - Progress was driven by uptake of banking products and products offered by non-bank formal financial institutions, particularly newly created savings and credit cooperatives (SACCOs).
  - Example: Kenya’s Equity Bank focused on the microfinance segment significantly helped increase the proportion of households with bank accounts.
  - Common features that increased access: only requiring photo ID for opening a bank account and not requiring a minimum balance; regulatory imposition of a type of free bank accounts in South Africa.
- Regulatory and supervisory priorities:
  - Protect consumers and ensure proper supervision of innovative banking or microfinance.
  - Continue efforts to create a good environment for microfinance and mobile banking by fostering collaboration between commercial banks, microfinance institutions and telecommunication companies.
  - Regulations should foster innovative finance such as mobile banking as they lower transaction costs and allow offering financial services at lower costs, thereby widening their usage.
  - Interoperability among providers promotes competition and reduces costs; mobile-money-based cross-border remittances can be enabled by appropriate regulation.
- Worldwide context:
  - Worldwide, more than half of financial regulatory frameworks include measures for promoting financial inclusion and a set of relevant best practices can be identified.

### Peer benchmarking and lessons
- Peer selection:
  - Peer countries (listed in Table 11, Appendix) were chosen for having similar population size and economic development as CEMAC countries.
- Rapid progress examples among peers (Figure 7):
  - Mozambique, Rwanda and Kenya have made rapid progress in widening access to financial services in recent years.
- Institutional and sectoral measures that worked in peers:
  - Expansion of SACCOs and microfinance-focused commercial banks.
  - Simplified account opening requirements (photo ID only; no minimum balance).
  - Regulatory approaches encouraging free or low-cost accounts.
  - Interoperability and supportive rules for mobile-money services.

*Source: _wp15235 - 0.4 percent increase in private credit.*

### 2. Private Credit to GDP

### 2. Private Credit to GDP

### Key findings on private credit and financial development
- Private credit to GDP is used as the proxy for financial development.
- Macroeconomic variables explain most of the private credit to GDP ratio: inflation, income, and natural resources are important determinants.
- Financial development is positively associated with:
  - Number of bank branches.
  - Availability/depth of credit information.
  - Registry coverage (public registry coverage (% adults)).
- Financial development is negatively associated with:
  - Banks’ operational costs (Operational costs / Total Assets (%)).
  - Cost-income ratio (Cost-income ratio (%)).
  - Poverty headcount (Poverty Headcount (% population)).
- Inflation, new technology, and operational costs are important determinants of the financial development gap in Africa.
- CFA franc zone countries with stronger institutions (government effectiveness and property rights) developed more inclusive financial systems.
- Income does not explain the financial development gap: richer and poorer countries may be equally far from their expected financial development levels.
- Improved financial supervision and financial sector governance contribute to promoting financial sector development.
- Measures to improve availability of credit information could boost the likelihood of financial development.
- Policies focused on financial inclusion should help to alleviate poverty.

### Policy recommendations and measures
- Move forward with ongoing implementation of the electronic payment system for taxes and utilities.
- Encourage expansion of banking branch networks.
- Further reforms should aim to:
  - Improve the business environment and judicial framework.
  - Boost supervision capacities.
  - Reduce asymmetry of information.
  - Facilitate loan recovery.
- Turn to best practices among peer countries with proven recent reform experience to develop the financial sector in an inclusive, sound, and sustainable manner.
- Improve availability of credit information (credit registers / depth of credit information).

### Empirical evidence and quantitative highlights (select)
- Table 4 (Summary statistics, 4 year averages, 1997-2012):
  - Private credit (% GDP): No obs 164; Mean 17.07; Standard dev. 16.39; Median 12.65; Min 0.81; Max 90.68.
  - Financial Gap**: No obs 163; Mean 1.95; Standard dev. 1.86; Median 1.28; Min 0.28; Max 11.42.
  - GDP per cap. (log): No obs 167; Mean 6.63; Standard dev. 1.13; Median 6.26; Min 4.79; Max 9.82.
  - Inflation (%): No obs 168; Mean 11.57; Standard dev. 28.39; Median 6.18; Min 0.37; Max 265.76.
  - Natural Resources (% GDP): No obs 168; Mean 7.64; Standard dev. 17.01; Median 0.00; Min 0.00; Max 85.22.
  - Deposits (% GDP): No obs 164; Mean 25.93; Standard dev. 22.99; Median 18.90; Min 1.99; Max 120.50.
  - Branches* (per 100,000 adults): No obs 100; Mean 4.02; Standard dev. 4.95; Median 2.38; Min 0.13; Max 29.81.
  - Operational costs (% assets): No obs 143; Mean 5.30; Standard dev. 2.41; Median 5.08; Min 0.02; Max 15.99.
  - Cost-income ratio (%): No obs 146; Mean 58.81; Standard dev. 17.38; Median 59.27; Min 4.30; Max 118.30.
  - Mobile Subscriptions (per 100 adults): No obs 168; Mean 22.71; Standard dev. 29.42; Median 9.30; Min 0.00; Max 134.20.
  - Internet Use (%): No obs 168; Mean 3.94; Standard dev. 6.90; Median 1.14; Min 0.00; Max 43.75.
  - Poverty Headcount* (% population): No obs 71; Mean 48.60; Standard dev. 14.41; Median 48.40; Min 13.40; Max 76.80.

- Regression evidence (selected coefficient signs and significance from Table 1 and Table 2):
  - log GDP per capita (lag) enters positively and significantly in many Private Credit / GDP regressions (e.g., coefficients reported as 9.305***, 9.227***, 9.586***, 8.943***, 7.291***, 7.501***, 8.945***, 9.145***, 8.782***).
  - Consumer Prices (lag, % change) often enter negatively and significantly (e.g., coefficients -0.0449***, -0.0420***, -0.0332**, -0.0521***, -0.0352***, -0.0403***, -0.0461***).
  - Natural Resource GDP/ Total GDP (lag) coefficients are negative and significant in several specifications (e.g., -0.224***, -0.211***, -0.239***, -0.191***, -0.161***, -0.153***, -0.250***).
  - Public registry coverage and credit information depth show positive associations in specific regressions (e.g., Credit information depth coefficient 0.425***; Credit information depth coefficient 0.0112** in another specification).
  - Poverty Headcount (%) coefficient reported as -0.378*** in a specification (Table 1).

- Financial inclusion snapshot (2012, selected country examples from Table 5):
  - Kenya: Share of adults with an account at any formal financial institution Overall 42.3; poorest 20% 18.7; richest 20% 85.0.
  - Mauritius: Share of adults with an account Overall 80.1; poorest 20% 66.0; richest 20% 93.9.
  - Mozambique: Share of adults with an account Overall 39.9; poorest 20% 20.5; richest 20% 56.4.
  - SSA (simple average) Share of adults with an account Overall 21.0; poorest 20% 11.0; richest 20% 36.8.
  - SSA (weighted average) Share of adults with an account Overall 24.2; poorest 20% 11.6; richest 20% 46.2.

### Benchmarks and methodology (technical appendix summary)
- Methodology follows Beck, et al. (2008) and the FinStats database manual.
- Financial development indicator X (time t) is modeled as a function of:
  - Macroeconomic development characteristics (Y).
  - Policy variables (P).
  - Structural characteristics (Z).
  - Error term.
- The system accounts for endogeneity: macro characteristics depend on lagged policy and structural variables; policy innovations follow an autoregressive process.
- The 2014 FinStats reduced-form regression (A.4) is estimated using quantile regressions, and the median estimate is used as the benchmark for private credit to GDP.

*Source: _wp15235 - 2. Private Credit to GDP (PDF chapter/section).*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2015/_wp15235.pdf_