Empirical Study of Determinants of Management Behaviour in African Commercial Banks

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Table of Contents

  • Introduction
  • Africa: Economic Background Information
    • Overview of Banking in Sub-Saharan Africa
  • Efficiency and Management Behaviour
  • Literature Overview
    • Methodology
    • Date Sources
    • Conclusion
    • Appendices
    • References

Introduction

This paper seeks to determine management behaviour in African commercial banks between 1995 and 2010. Management behaviour is one crucial factor goes a long way in determining bank’s performance. Hence using dynamic panels and Granger causality techniques, the study will explore four different types of management behaviour as identified by Berger and DeYoung (1997) namely: bad management, bad luck, skimping and moral hazard.

Each type will be pinpointed through an examination of the inter-temporal ordering of the relationships between loan loss provision (asset quality), efficiency, and capitalisation. It is envisaged that the findings there-of will provide necessary ingredients for policy prescription and implementation towards enhancement of performance in banks operating on the African continent. Further, the study will add to the body of literature as it focuses on African managers that operate subject to unique economic conditions of low GDP per capital growth, high inflation rates, and low levels of information sharing, rule of law, government effectiveness and corruption, in contrast to other economies that other studies have focused on (See Berger and DeYoung (1997), Williams (2004) and Fordelisi et al (2011)).

The remainder of this paper is organised as follows: Section 2.0 covers brief introduction to African economy, section 2.1 overviews the African banking sector, section 2.2 covers the theoretical aspect of the study 3.0 covers literature review, section 4.0 covers methodology, section 5.0 will provide data sources and finally, section 6.0 concludes.

Empirical Study of Determinants of Management Behaviour in African Commercial Banks

Africa: Economic Background Information

Most countries in Africa were colonised by western countries and majority obtained independence in the early 1960s. Over the years, economic performance has been slow as countries develop new economic policies. According to statistics of the IMF (2010) on Sub Saharan Africa (SSA), real GDP per capita growth for 1995-2009 was on average higher, at 2.3% as compared to -0.2% registered over 1980-1994. Foreign direct investment in the region has also boomed to 4.5%  in 1995-2009 as compared to 1.0% of GDP recorded in 1996-2008. Africa has maintained trade relations amongst its member countries as divided into regional and resource groups (see Table 1 and 2 in the Appendix). However, trade partnerships with the rest of the world currently tilt more towards China and other developing countries in Asia. For instance, 3.4% trade relations with China was recorded in 2000 and increased to 13.6% in 2009.

The economic performance of Africa in the aftermath of 2007-2009 financial crisis was resilient, owing to the sound economic policies in most of the countries in the region, (IMF, 2010). Notwithstanding, the impact of the crisis as evidenced in the macro-economic indicators of deteriorating levels of unemployment and fiscal balances, economic growth is forecasted at 6% in 2012.

Overview of Banking in Sub Saharan Africa (SSA)

Many financial systems in African countries are bank dominated, usually accounting for over 50% of the financial sectors assets. One distinctive feature of African Banking systems is high concentration (Domansti 2005(BIS); with over 50% domination by few foreign owned banks, although the distribution varies across countries (Table 3).  According to IMF (2010), performance of the financial sector as measured by domestic credit to private sector, particularly in the SSA was 20.8% of GDP in 1995-2009, a decrease from 41.2% registered in 1980-1994. Overall, the performance of banks has improved although they vulnerabilities to macroeconomic variables remain. Many banking sectors are well capitalised, liquid, and profitable although the prevalence of non-performing loans remain high (Tables 4-6). African banks have loan portfolios that are concentrated in a few sectors which thrive on donor aid, exports earnings, resulting into high exposure to credit risk in cases of exogenous shocks (inflation) to the economy. Further, weaknesses in the legal and judiciary frameworks (rule of law, government effectiveness, bureaucracy, corruption) for enforcing creditor rights, deficiencies in the infrastructures for assessing borrower credit worthiness (credit ratings) and shortcomings in banks risk management capabilities further worsen the credit risk exposure. The probability of these risks has been increased by the global financial crisis, (Quintyn and Verdier, 2010).  Countries like Botswana, Lesotho, Zambia and Chad with more foreign bank ownership remain more exposed to credit risks through contagion effects, (Aryeetey and Ackah, 2011).

According to Murinde (2012), regulation to ensure capital adequacy and reasonable risk taking in African banks has evolved in three overlapping phases. The first was the pre-1960s colonial phase where bank business was controlled from abroad; Second, the post-independence 1960s-1970s where local governments intervened to address market failure by launching state development banks to direct credit to local entrepreneurs; and third, the Basel regime which includes transition from Basel I to Basel II and now to Basel III. Cornford (2008) indicates sporadic implementation of Basel II. Although most African countries complied with Basel I and II but post-crisis, countries are said to be at crossroads as going back to Basel II is not an option and the theme for Basel III appears to carter more for concerns of developing economies.

Efficiency and Management Behaviour

This section discusses the theory underpinning the study. We discuss efficiency in relation to the four different modes of management as suggested by Berger and DeYoung (1997), and how these are identified in the inter-temporal relationships of asset quality, efficiency and capital.

The measure of quality of management can be seen by the combination of technical and allocative efficiency, commonly referred to as X-inefficiencies. Technical efficiency results from bank management employing too much input to produce output whereas allocative inefficiency arises from management failure to react optimally to the relative price of input (See Berger and DeYoung, 1997, Bauer et al, (1998), Williams (2004), Northcott A. C. (2004). Further, the difference between banks cost relative to the cost of the best practice bank when both banks produce the same output given the same conditions is measured by cost efficiency and the concept of profit efficiency is the ratio of predicted actual profit to predicted maximum profit which could be earned if a bank was as efficient as the best practice. Management behaviour is one critical factor that determines bank efficiency. Berger and DeYoung (1997) identify four different types of management behaviour namely: bad management, bad luck, skimping and moral hazard behaviour. Each type is said to be easily pinpointed through the inter-temporal ordering of the relationships between loan loss provision (asset quality), efficiency, and capitalisation. Bad management is when management fails to control operating costs and this may show up as low cost efficiency in the short term. However, it results into larger amounts of loss provisioning in the long term. In cases where a bank is badly managed, low levels of cost efficiency may imply incompetent senior management quality. Incompetent management implies a lack of adequate control or monitoring strategies over operating expenses, selection of non-viable investments, improper valuation of collateral and a general laxity in reinforcing loan agreements.

On the other hand, managers that engage in skimping behaviour are reluctant to spend financial resources on monitoring and underwriting lending business on the premise of being cost efficient and with an ulterior motive of realising a higher profitability in the long term. Managers engaging in skimping behaviour postpone dealing with deteriorating asset quality until an unspecified future date. However this might give a false impression of being cost efficient in the short term and might lead to deterioration of asset quality in the long term.

According to the bad luck hypothesis, macro-economic shocks increase loan loss provision there-by translating into lower cost efficiency level. These shocks force managers to allocate additional resources to monitor delinquent borrowers, re-value collateral, allocate extra resources to protect the quality of existing loans and this generally causes managers to lose focus on their day to day responsibilities. Mester and Hughes (2008) identify accounting practices, government regulations, property rights and market conditions as some of the factors that also exogenously influence management efficiency and differences in these factors across countries may translate into differences in the efficiency of banks.

The fourth type of management behaviour termed as moral hazard suggests that managers whose institutions are thinly capitalised are less risk averse due to the fact that the upside of low capitalisation counterbalances the downside risk. In other words there is a positive correlation between expected returns and the amount of risk assumed by bank management.  It must be noted that the hypotheses are however, not mutually exclusive and any one of the four could dominate the behaviour of a given subset of banks at any given period.

Literature Review

Findings as per existing literature surrounding the interaction between bank efficiency, capital, asset quality, risk and management behaviour vary across studies. Berger and DeYoung (1997) study the linkage between management behaviour and efficiency on US banks in the 1990s and using Granger causality techniques to test the four management behaviour hypotheses. They find consistent results with the bad luck hypothesis in that after loans become due or non-accruing, operating costs rise because of the difficulty in dealing with these loans. The effect subsequently leads to a higher percentage of NPL from 0.0168 to 0.037 which effectively predicts reduction in cost efficiency from 0.9924 to 0.9211 or 1.7 per cent in predicted cost inefficiency. The study further finds that after cost efficiency declines, NPL increase possibly due to poor loan portfolio management as predicted by the bad management hypothesis (i.e. incompetent management implies a lack of adequate control or monitoring strategies over operating expenses, selection of non-viable investments, improper valuation of collateral and a general laxity in reinforcing loan agreements). The economic impact of the finding subsequently results in reduction of X-efficiency from 0.9224 to 0.8843, basically predicting a higher level of NPL. However, for the entire data set the analysis finds that bad management hypothesis dominates skimping behaviour. Consistent with respect to moral hazard hypothesis, the study finds that thinly capitalised banks take increased portfolio risk which eventually results in higher levels of problem loans. For example, they find that for typically low capitalised bank, reduction in capital from (0.0712 to 0.0578) predicts a cumulative increase in the NPL ratio over four years from (0.0186 to 0.0193) or a 3.8 per cent increase in NPL. In a nut shell, the study finds that management policies that result in both reduction in capital and cost efficiency results in low asset quality.

 Similarly, Williams (2004) uses Granger-causality techniques to study the hypotheses for European savings banks between 1990 and 1998. However, they find weak statistical evidence to suggest the presence of the four different types of management behaviour although a detailed analysis shows that, in contrast with findings by Berger and DeYoung (1997), there is strong statistical evidence for bad management behaviour (i.e evidence that increase/decrease in loan loss provision led to decrease/increase in cost efficiency). They also find a statistically weak negative relationship between loan loss provision and lagged loan to assets (an indicator of credit risk), suggesting that banks with more loan-intensive balance sheets have higher asset quality.

A re-test of bad management and skimping behaviour using an estimated profit efficiency as opposed to using cost efficiency measure, shows no evidence of bad management (relationship between loan loss provision and lagged profit efficiency was found positive but statistically weak). Similarly, no skimping behaviour was identified (i.e found an inverse relationship between loan loss provision and lagged credit risk). This implies that the most profit efficient managers are dexterous at managing credit risk while on the other hand, the results point to the fact that cost efficient banks are subject to bad management behaviour at 10 percent significance level. Contrary to findings by Berger and DeYoung (1997), Williams (2004) results for moral hazard behaviour in European banks are statistically weak i.e no significant evidence to suggest that increases in bank capitalisation improves asset quality (less loan provision). However, they find a significant inverse relationship between loss provision and lagged cost efficiency i.e thinly capitalised banks are characterised by bad management. It must be noted that the differences in findings by Berger and DeYoung (1997) and Williams (2004) arise from differences in sample size and use of different proxies for asset quality. In addition, using Granger-causality methodology in a panel data framework, Fiordelisi et al.(2010) assess the inter-temporal relationship between bank efficiency, capital and risk in a sample of banks in 26 European countries in 1995 to 2007 and find that lower bank efficiency with respect to costs and revenues Granger-causes higher bank risk (moral hazard) and that increase in bank capital precede cost efficiency improvements. They also find that more efficient banks eventually become better capitalised and higher capital levels tend to have a positive effect on efficiency levels.

However, Rossi et Al. (2009) uses Arellano-Bond dynamic panel data model to determine managerial behaviour for Austrian large banks over 1997-2003 and find no evidence for moral hazard hypothesis, although they find evidence for bad luck hypothesis. Similar to findings by Willams (2004), the whole data set neither supports skimping nor moral hazard hypothesis. Altunbas et al. (2007) uses static simultaneous equation framework to analyse the relationship between capital, loan provisions and cost efficiency for a sample of European banks over the period 1992-2000. The study finds no positive relationship between inefficiency and bank risk taking and on the contrary to findings as shown above, inefficient European banks are found to hold more capital and take on less risk.

Other studies have considered the effect on efficiency if managers also happen to hold shares in the same company. DeYoung et al. (2001) examine the relationship between managerial shareholding and financial performance at 266 predominantly small state chartered commercial banks in the tenth Federal Reserve district, in the USA. Using confidential data from bank examination reports to establish ownership profiles and managerial responsibilities at these banks, they find profit efficiency was low for banks run by hired managers with little or no ownership stake but improved substantially as managers accumulated shareholding and presumably became more aligned with the owner’s objectives. However profit efficiency declined as managers accumulated additional shareholding and presumably became entrenched. Similarly, Kauko (2009) study’s the impact of manager characteristics on bank efficiency on Finnish savings and cooperative banks in 1999 – 2004 and found a positive correlation between managers’ behaviour and bank efficiency in the sense that manager’s age and education have strong but yet complicated effects on efficiency. For instance, they find that more mature managers outperformed their young counterparts in making sound decisions that led to cost efficiency. On the other hand, Kwan (2006) conducts a study using a stochastic frontier approach to investigate the cost efficiency of multi branch banks operating in Hong-Kong from 1992 to 1999. The study finds that the X-efficiency of Hong-Kong banks to be in the order of 16 to 30 per cent of observed costs.  However the efficiency figures were found to decline even more due to increased use of technological towards the end of the sampling period. However, the X-efficiency was found to edge up following the Asian financial crisis implying that some were unable to adjust their labour and capital inputs quickly amid falling loans demands while at the same time additional resources might have been needed to address the worsening portfolio problems (bad luck hypothesis). Foos et al. (2010) using data from Bankscope on more than 16000 individual banks from 16 major countries during 1997-2007, study the relationship between loan growth and riskiness of banks, they find that abnormal loan growth had a positive and highly significant influence on subsequent loan losses. They also find that abnormal loan growth is significantly negatively related to bank solvency In 14 out 16 countries higher abnormal loan growth leads to lower capital ratios indicating a decrease in bank solvency (moral hazard). Loan growth eventually leads to deterioration in banks risk return structure. Similarly, Cebenoyan and Strahan (2004) conduct a study US commercial banks totalling to 13126 over 1988 to 1993. The study sets out to find how active management of credit risk as proxied by loan sales and purchases affects financial institutions capital structure, lending, profits and risk. They find that banks that rebalance their loan portfolio exposures by both buying and selling loans, that is banks that use the loans sales market for risk management purposes rather than to alter their holdings of loans hold less capital than other banks, they also make more risky loans to businesses as a percentage to total assets than other banks. All things constant, banks that are active in the loans sales market are seen to have lower risk and higher profits than other banks.

While there are several studies that have focused on the three other modes of management behaviour as shown above, few have focused on effect of exogenous factors on efficiency. Beccalli (2004) studies cross country comparison of efficiency between cost efficiency of UK and Italian investment firms over the period 1995-1998. The study reveals that a set of structural and institutional factors significantly influence on cost efficiency and profitability. Similarly, Pesola (2011) studies the data of nine European countries from 1982 to 2004 to show the effect of financial fragility and macro-economic shocks on bank loan losses. Results support the view that adverse aggregate shocks contribute heavily to loan losses when banks are highly exposed to such shocks.

Methodology

Applying time series data dynamic panel models, the study will employ the Granger-causality test to determine the relationship between the variables in the hypotheses and be tested by the following equations:

LLRi = b1 + b2EFFIi + b3CAP, lag + b4LTA, lag + b5oYEARrti + b6infli + b7crediti + ui……………………………..…(1)

EFFi = b1 + b2LLPi + b3EFFi, lag + b4CAPi, lag + b5LTAi,lag + b6YEARi + b7crediti + ui…………………………………….……………………………….…….(2)

CAPi = b1 + b2LLPi, lag + b3EFFi, lag + b4CAPi, lag + b5oLTAi, lag + b6YEARi + b6infli + b7crediti + ui…………………………..………..……..(3)

 

Note: Where the i subscript in all the equations denotes the cross-sectional dimension of ith, bank and t denotes the time factor.

Equation (1) tests the bad management hypothesis. A prior bad management predicts a negative relationship between loan loss provision and lagged X efficiency. A positive relationship between the two variables however suggests skimping behaviour in banks.

Equation (2) tests the bad luck hypothesis.

Equation (3)

LLP =            Loan loss provision

CAP =            per capita GDP (constant prices: chain series) in 1990

LTA=             Loan to Assets ratio

EFFI =           Efficiency

lNFL =           inflation rate

YEAR =          (Controlling variables in the sub sample  – interest, low GDP per capital growth, high inflation rates, and low levels of information sharing, rule of law, government effectiveness and corruption, credit )

Data Sources

A yearly panel data for a selected group of commercial banks in Africa will be obtained from Bank scope data base. The data coverage will target over 150 banks with various styles of ownership for example, state owned, joint stake, and private; for the period 1995 to 2010. Although Bank scope will be the main primary source of data extraction, verification of missing or suspicious data from other websites and annual reports of the banks is not ruled out.

Conclusion

The study aims to analyse management behaviour in African commercial banks by empirically analysing the inter-temporal relationships between asset quality, capital and efficiency. African managers operate subject to unique economic conditions of low GDP per capital growth, high inflation rates, and low levels of information sharing, government effectiveness and corruption, in contrast to other economies that other studies have focused. Hence, the findings will be necessary ingredients for policy prescription and implementation in a quest to improve performance in African commercial banks.

Appendices

        TABLE 1: SUB-SAHARAN AFRICA:  COUNTRY GROUPINGS.

Resource RichNon-Resource Rich
OILNON- OILCOASTAL LANDLOCKED
AngolaBotswanaBeninBurkina Faso
CameroonCôte d‘IvoireCape VerdeBurundi
ChadGuineaComorosCentral African Rep
Congo Rep of DRCNamibiaGambiaCom Dem of Rep of
Equatorial GuineaSierra LeoneGhanaEthiopia
GabonZambiaGuinea BissauLesotho
NigeriaKenyaMalawi
MadagascarMali
MozambiqueNiger
São Tomé and PríncipeRwanda
SenegalSwaziland
SeychellesUganda
South AfricaZimbabwe
Tanzania
Togo

Source: IMF Report October, 2011

TABLE 2: SUB SAHARAN AFRICA:  MEMBER COUNTRIES REGIONAL TRADE GROUPINGS.

The West African Economic and Monetary Union.  (WAEMU)Economic and Monetary Community of Central African States (CEMAC)East African Community(EAC)Southern African Countries Union (SACU)Southern African Development Community (SADC)
BeninCameroonBurundiBotswanaAngola
Burkina FasoCentral African RepublicKenyaLesothoBotswana
Côte d‘IvoireChadRwandaNamibiaCongo DRC
MaliCongo Republic DRCTanzaniaSouth AfricaLesotho
NigerEritreaUgandaSwazilandMadagascar
SenegalEthiopiaMalawi
TogoKenyaMauritius
MadagascarMozambique
MalawiNamibia
MauritiusSeychelles
RwandaSouth Africa
SeychellesSwaziland
SwazilandTanzania
UgandaZambia
ZambiaZimbabwe
Zimbabwe

Source: IMF Report October, 2011.

Table 3:  Countries with concentrated foreign bank Assets, 2008 (in Per cent).
Host CountryAssets held by foreign banks (per cent)Largest foreign banksHome countries of the largest foreign banks
Angola68.0Angolan Development BankPortugal
Espiritu Santo Bank of Angola (BESA)Portugal
Totta Angola Bank (BTA)Portugal
Botswana99.0Barclays Bank Of BotswanaUnited Kingdom
Standard Chartered Bank BotswanaUnited Kingdom
First National Bank of BotswanaSouth Africa
Cameroon70.0BICECFrance
Societe GeneraleFrance
Attijariwafa BankMorocco
Cape Verde74.0Banco Commercial AtlanticoPortugal
Banco IntertlanticoPortugal
Banco gaboverdiano de NegociousPortugal
Chad75.0Societe Generale Tchadienne de Bangue (SGTB)France
EcobankTogo
Commercial Bank TchadCameroon
Comoros92.0Bangue Pour I’I Industrie et le Commerce (BIC)France
EXIM Bank TanzaniaTanzania
Congo, Demo. Rep of90.0Bangue CongoliaseUnited States
Bangue Commerciale du Congo (BCC)Belgium
RwabankLuxemburg
Ghana55.0Barclays BankUnited Kingdom
Standard Chartered BankUnited Kingdom
SSB BankFrance
Lesotho97.0Standard  BankSouth Africa
Madagascar71.0Mauritius Commercial Bank (MCB)Mauritius
Bangue Malgeche deL’ocean Indien (BMOI)France
BFV-Societe Generale (SG)France
Mauritius72.0Barclays BankUnited Kingdom
Hong Kong and Shanghai Banking Corporation (HSBC) MauritiusUnited Kingdom
Standard Chartered BankUnited Kingdom
Mozambigue100.0Banco International de Mozambigue (BIM)Portugal
BCI-FomentoPortugal
Standard BankSouth Africa
Namibia73.0Standard Bank NamibiaSouth Africa
First National BankSouth Africa
Sao Tome&Principe100.0Banco International de STP (BISTP)Portugal
Afriland First BankCameroon
Island BankNigeria
Senegal65.0SGBSFrance
BICISFrance
AttijariwafaMorocco
Swaziland70.0Standard Chartered Bank of Swaziland LtdUnited Kingdom
Nedbank Swaziland LtdSouth Africa
First National BankSouth Africa
Swaziland Ltd
Tanzania52.0NBC LtdUnited Kingdom
StanchartUnited Kingdom
Barclays BankUnited Kingdom
Note: Only those countries for which share of banking system assets held by foreign banks that exceed 50% are shown.
Source: IMF, African Department Financial Sector Survey results.
Table 4: Bank regulatory capital to risk weighted assets ( in Per cent)
2002200320042005200620072008Latest month
Angola20.118.119.616.518.521.9December
Congo DRC-3.46.87.710.5December
Ethiopia11.711.511.420.4December
Gabon17.219.922.319.817.814.319.6September
Ghana13.59.313.916.215.815.713.9September
Kenya173.016.616.416.518.018.1November
Lesotho22.025.019.014.015.0September
Mozambique14.017.018.716.012.514.214.3June
Namibia14.114.815.414.614.215.415.8September
Nigeria18.117.814.717.822.621.022.0September
Rwanda12.514.610.59.27.211.312.3September
Senegal15.511.711.911.113.1136.013.7June
Sierra Leone15.527.336.035.736.038.746.0September
South Africa32.512.414.012.712.312.812.5June
Swaziland12.614.014.015.020.023.0June
Tanzania20.621.015.415.116.316.215.7September
Uganda22.117.020.518.318.019.520.8June
Zambia28.023.022.228.420.418.617.0June
Zimbabwe15.635.722.625.425.4October
Cameroon9.39.88.011.212.0December
Equitorial Guinea7.211.012.49.213.0No
Benin6.29.51.43.0
Burkina Faso7.911.010.213.013.0June
Cote d’Ivoire16.317.013.712.49.510.0June
Guine-Bissau53.338.733.624.8
Mali7.97.49.79.27.27.4December
Niger15.713.317.913.712.8
Togo-6.6-6.0-0.8-1.9-16.0
Note: Due to differences in national Accounting, taxation and supervisory regimes, FSI data are not strictly comparable across countries.
Source: IMF Staff estimates

 

Table 5:  Non- Performing Loans to total assets  ( in Per cent)
2002200320042005200620072008Latest month
Angola10.49.08.16.74.82.9December
Congo DRC1.52.06.83.0December
Ethiopia27.820.014.010.16.8December
Gabon14.613.916.014.110.77.67.9September
Ghana22.718.316.313.07.98.77.6September
Kenya34.929.325.621.310.98.4November
Lesotho1.02.02.01.73.5September
Mozambique22.014.46.43.83.32.60.9June
Namibia3.53.92.42.32.62.83.2September
Nigeria21.420.521.618.18.88.46.1September
Rwanda57.052.029.931.128.018.510.6September
Senegal18.513.312.611.916.818.619.0June
Sierra Leone11.07.416.526.826.831.721.4September
South Africa2.92.41.81.51.11.41.0June
Swaziland2.03.02.03.66.48.4June
Tanzania8.34.53.54.96.86.36.3September
Uganda3.07.32.22.33.04.1June
Zambia11.45.37.68.911.38.5June
Zimbabwe15.635.722.625.46.4October
Cameroon15.213.713.512.112.4December
Equitorial Guinea13.517.214.3No
Benin19.719.719.818.5June
Burkina Faso12.415.113.413.019.420.2June
Cote d’Ivoire21.325.126.221.020.021.521.5June
Guine-Bissau27.423.812.810.5
Mali15.619.630.225.025.125.3December
Niger26.521.321.621.821.217.7
Togo41.334.833.629.1
Note: Due to differences in national accounting, taxation and supervisory regimes, FSI data are not strictly comparable across countires.
Source: IMF Staff estimates

 

Table 6:  Bank return on assets  2002-08 ( in Per cent)
2002200320042005200620072008Latest month
Angola0.74.74.13.12.72.7December
Congo DRC0.6-1.11.72.7December
Ethiopia1.62.02.82.32.9December
Gabon2.80.72.82.62.52.7December
Ghana6.86.24.53.03.32.92.8June
Kenya-8.92.32.12.42.83.02.8November
Lesotho3.02.02.02.62.4September
Mozambique1.61.21.41.83.53.52.7September
Namibia4.53.62.13.51.53.53.2September
Nigeria2.41.73.10.91.62.12.4September
Rwanda-5.01.40.60.91.61.31.9September
Senegal1.81.81.81.81.61.6December
Sierra Leone10.010.59.98.15.86.42.1December
South Africa0.40.81.31.21.41.41.8December
Swaziland4.02.93.15.92.93.6June
Tanzania1.82.13.13.93.94.73.8September
Uganda3.04.54.33.63.43.93.5June
Zambia6.55.43.16.55.14.75.0June
Zimbabwe6.39.713.414.614.6September
Cameroon1.11.92.32.21.2December
Equitorial Guinea2.42.21.91.91.7No
Benin0.40.2June
Burkina Faso1.41.20.61.0June
Cote d’Ivoire0.60.31.10.9June
Guine-Bissau2.5-0.6
Mali-1.81.0December
Niger0.60.70.51.5
Togo8.00.71.5
Note: Due to differences in national accounting, taxation and supervisory regimes, FSI data are not strictly comparable across countires.
Source: IMF Staff estimates
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