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Volume 12 Issue 1, January-February 2024 

ISSN: 2836-9416 

Impact Factor: 5.57 

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STRIKING A BALANCE: CAPITAL, RISK, AND EFFICIENCY IN 

THE EVOLUTION OF CAMEROONIAN BANKING 
 

 
1Samuel Emmanuel Mbong, 2Felicity Amina Tchinda 

1,2Department of Monetary Economics and Banking, Faculty of Economics and Management, 

University of Ngaoundéré, Cameroon. 

DOI: https://doi.org/10.5281/zenodo.10619106 

 

Abstract: Cameroon, a member of the Community of Central African States (CEMAC), underwent 

transformative financial reforms during the 1990s in response to the economic and banking crisis of 

the late 1980s and the structural adjustment program (SAP) initiated under the influence of the 

International Monetary Fund (IMF). These reforms, positioned as a strategic component of the SAP, 

aimed to cultivate more efficient, resilient, and extensive financial systems. Advocates of these 

measures envisioned substantial economic benefits, envisioning enhanced bank efficiency and 

effectiveness for a more proficient mobilization and allocation of resources across diverse economic 

activities. 

This study delves into the multifaceted reforms executed in Cameroon, emphasizing their focal points 

on governance, risk management, and banking efficiency. The key components included financial 

deregulation, restructuring of banks, and bolstering capitalization to fortify banking soundness. The 

primary goal was to foster an environment conducive to improved economic performance and 

resource allocation. Over the ensuing decades, these initiatives induced profound structural and 

institutional shifts within Cameroon's banking sector, reshaping the governance landscape for banks 

operating in the country. 

The analysis traverses the evolution of the banking industry in Cameroon, meticulously examining 

the impacts of the implemented reforms. It scrutinizes the alterations in governance structures, risk 

management practices, and overall efficiency within the banking sector. The study unfolds the 

dynamics of financial deregulation, the restructuring of banks, and enhanced capitalization as 

integral components influencing the soundness and efficacy of banking operations in Cameroon. 

As the financial landscape of Cameroon evolved, this study underscores the intricate interplay of 

governance, risk management, and efficiency in shaping the trajectory of the banking industry. By 

elucidating the transformative journey propelled by these reforms, the study contributes to a nuanced 

understanding of the economic and institutional shifts that have defined Cameroon's banking sector 

in the wake of the 1990s financial reforms. 

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American Research Journal of Economics, Finance and Management 

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ISSN: 2836-9416 

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Keywords: Financial Reforms, Banking Industry, Governance, Risk Management, Economic 

Transformation  

 

 

INTRODUCTION  

 Cameroon is an African country belonging to the Community of Central African States (CEMAC). 

Following the economic and banking crisis at the end of the 1980s and as a component of the structural 

adjustment program (SAP) implemented mostly in response to the external pressure of the 

International monetary fund (IMF), this country underwent financial reforms during the 1990s. These 

reforms were considered as a means to build more efficient, robust and deeper financial systems.  

Indeed, for their proponents, such reforms would bring about significant economic benefits through 

improved bank efficiency and effectiveness to guarantee a more effective mobilization and efficient 

allocation of resources among    various economic activities. Consequently, implemented measures 

aimed at addressing governance, risk management and more efficiency in banking and were around 

financial deregulation, banks restructuring and firming up capitalization to improve soundness in 

banking. As a result, over the last decades, banking industry in Cameroon has experienced major 

structural and institutional transformations that alter governance of banks operating on this country.   

Domestic mergers, acquisitions and increase in foreign capital participation were among major 

observed structural changes in this country. The last state-owned bank in Cameroon was sold in 

January 2000 and this was the last step in a Structural Adjustment Programmed (SAP) recommended  

by  the  Bretton  Woods Institutions for the country to reach the completion of the Highly Indebted Poor 

Countries Initiative (HIPC).This initiative was recommended to re-launch the country’s economy after 

a decade of economic crisis that seriously affected its banks.  This crisis also led to liquidation of giants 

such as Cameroon Bank, Banque Meridien, Rural Development Fund and the split- winding of the Bank 

of Credit and Commerce of Cameroon (BCCC), with transfers of its good assets to Standard Chartered 

Bank of Cameroon (SCBC).  

Relative to institutional changes going with financial reforms, an attention was given to strengthening 

the regulatory and supervisory institution. The power to supervise the banking system initially carried 

out by the Cameroonian Loans National Council (CNC) was transferred to a community institution: 

The Banking Commission of Central African States (COBAC) created in 1992. As a result of this 

institutional change, observed failure of banks during this period was followed by a raising of the initial 

capital requirement of commercial banks from CFAF 300 million to CFAF 1 billion and later by an 

increase of the bank’s minimum capital requirement vis a vis their risk- weighted assets, 8 per cent as 

prescribed by the Basle committee of banking in 1995.   

Moreover since the early 1990s, financial liberalization implementation in Cameroon, driven by 

financial deregulation and technological change, has made Cameroonian banking markets increasingly 

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more competitive.  As a result, there has been tremendous emphasis on the importance of improved 

efficiency in the banking sector. But at the same time, this increase in competition could lead to 

incentives for greater bank risktaking implying potential risk- efficiency tradeoffs in Cameroonian 

banking. To address this potential threat to the bank system stability, the banking commission of 

Central African states gave capital adequacy a more preeminent role in the prudential regulatory 

process. The question then arises of whether or not the level of bank capital has a significant impact on 

risk-efficiency tradeoffs in Cameroonian banking?   

This question is of real importance in Cameroon for at least two reasons: Firstly, despite the great 

number of papers dealing with the issue of whether or not higher capital ratios reduces or increases 

overall banking risk, this issue remains largely unsolved. Moreover, the recent streams of the literature 

introducing the efficiency of banks into the debate just led to conflicting theoretical hypothesis. For a 

significant part of researchers convinced by the bad luck hypothesis, increase in risk determined by 

exogeneous factors negatively affects bank efficiency. Conversely, for the proponents of the bad 

management hypothesis, bank efficiency is determined by internal behavior in banks. Therefore, it is 

the reduction of efficiency caused by bad management that induces increase in bank risk taking. In the 

third hypothesis (the skimping hypothesis), if this negative relationship   between   efficiency   and  bank  

risk  taking  Donatien and exists in the short term, it turns into a positive one in the long term. As the 

empirical evidence remains contradictory, this paper will therefore add empirical evidence in the 

Cameroonian context and allow comparisons with what is observed in other countries. Furthermore, 

despite the importance of this topic, with regard to financial instability and systemic bank crises 

observed in this country during the 90s and recent reported cases of bank distress (IMF, 2018), there 

is a lack of subsequent research to guide bank authorities’ interventions.   

Secondly, despite underwent reforms, if the excess liquidity of banks is a striking feature of the 

Cameroonian banking system at the end of the restructuring process as pointed by Avom and Eyeffa 

Ekomo (2007), in recent years the question of loan quality and of its implicit risk consequences still 

occupy a prominent place. In the Cameroonian context, the level of non-performing loans first declined 

from an average of 405 of total credit in 1995 to around 12% at the end of 2006 following the 

restructuring of the banking sector and the transfer of impaired loans to a loan recovery agency in the 

late 1990s.  

But, Cameroon’s structurally high ratio of nonperforming loans was later aggravated in the first quarter 

of 2018 to 15 percent far from observed averages in North America (0.07%), Europe and Central Asia 

(3.8%) or even Sub-Saharan Africa (11.7%) (IMF, 2018). In more recent years and according to COBAC 

statistics, nonperforming loans have increased by 45 billion between 2020 and 2021.    

This observed increase in bad loans might not rely on the bad luck hypothesis of Berger and DeYoung 

(2007) in Cameroon. As IMF (2018) noted, the Cameroonian banking system has proven its resilience 

to exogeneous shocks even resulting from foreign economic behavior. Face to the twin recent oil price 

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Volume 12 Issue 1, January-February 2024 

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and security shocks, bank reaction was an improvement of prudential ratios. More specifically, after a 

declining to 9 per cent at the end of 2016, the system wide capital adequacy ratio increased to 10.7% at 

the end of March 2018 (IMF, 2018). Indeed, there are variations across banks on meeting the prudential 

ratios. In 2015 seven banks did not have enough capital to meet capital requirement of the bank 

Commission of Central Africa states (COBAC), and four banks (13% of banks’ total assets) were in 

distress in 2018 with 3 of them having negative capital.  This seems to be in relation with bank 

ownership.  Following the restructuring process in the Cameroonian banking system, the capital 

ownership structure was modified in favor of foreign participation. Table 1 illustrates the selected banks 

in Cameroon, and the ownerships structure of capital in 2019.   

This preeminence of foreign capital in banking can potentially expose the country to external shocks, 

as investors might at any time move their funds to correct imbalances in their domestic economies. But 

this was not the case in Cameroon even during the international financial crisis of subprime. Indeed, 

despite the importance of foreign banks with parents that have been hit, the reaction of commercial 

banks in Cameroon to this external shock was to increase collateral requirements, to widen their spread 

and refocus their portfolios on blue chip companies and high network clients, making access to credit 

even more difficult for SMEs.    

 Source: COBAC.  

Overall, faced with exogeneous shocks, the reaction of banking authorities is, in many cases, to increase 

capital adequacy ratios to cope with bank risk taking. This shows  their adhesion is not only to the idea 

Table 1. Ownership structure of capital in selected Cameroonian banks (2019).  

  
 Banks  Government Foreign capital Domestic 

capital  Others   

 
BICEC  17,50  70  7.5  5  

SGBC  25,60  58,06  16,32    

AFRILAND    74  4  22  

CBC  98,09    1,91    

BGFI BANK  20  70,69    9,31  

ECOBANK    79,80  9,35  10,85  

UBC    54  37  9  

UBA  17.5  70  7.5  5  

SCBC    100      

SCB  2.49  97.51      

CITIBANK    99,98%  0,02%    

 

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of a negative relationship between bank capital and risk-taking behavior of banks in accordance with 

traditional theoretical banking models, but also to the idea that such an action can help reaching at the 

same time more efficiency as required by the reforms. Furthermore, by arguing that non-performing 

loans are not linked to external shocks, IMF (2018) implicitly suggests a determining role of the 

dynamics observed at the very level of Cameroonian commercial banks as described by the bad 

management hypothesis.    

 The following hypotheses can therefore be formulated;  

 H1: Increase in bank capital reduces commercial banks’ risk taking in Cameroonian banking system 

H2: There are tradeoffs between bank efficiency and bank risk taking in Cameroonian banking system 

H3: Inefficient banks run with higher level of capital in Cameroonian banking system.  

 THEORETICAL ARGUMENTS  

 For a great number of researchers, risk-taking behavior and cost efficiency are adversely related in 

banking.  At least, two alternative theoretical arguments allow the rationality of such a position to be 

established.  

Firstly, the Berger and DeYoung (1997)’s bad luck hypothesis in which, an external event increasing the 

amount of problem loans may result in efforts to service these loans. This implies higher incurred costs.  

According to this argumentation, such exogenously determined increase in risk therefore impacts 

negatively the observed cost efficiency of banks: hence the idea of efficiency- risks tradeoffs in banking. 

Thereby, the causality runs from increase in bank risk due to external shocks to cost efficiency decrease.   

Secondly, the bad management hypothesis in this alternative argument is an increase in the amount of 

problem loans caused by unwished internal bank behaviors. In such a case, the lower cost efficiency is 

a signal of poorly performing management, which has also poor control over its loan portfolio. 

Moreover, decrease in efficiency can motivate the bank to boost its risk in order to offset the lost levels 

of efficiency (Nguyen and Nghiem, 2015). Bank risk taking and efficiency relationships are therefore 

negative. Finally, as noted by Tan and Floros (2013), a part from credit, poor managerial practice can 

tarnish banks’ reputation and cause market problems. Therefore, and unlike the bad luck hypothesis, 

in the bad management hypothesis, internal lower cost efficiency leads to an increase in problem loans.  

Unlike the arguments developed so far, let us now differentiate short term from long term 

consequences. Monitoring of loans has an impact on both the amount of non-performing loans and cost 

efficiency, and this would imply possible intertemporal tradeoff between the quality of loans and the 

cost efficiency of the bank. In fact, bank may skimp on the resources devoted to underwriting and 

monitoring loans, reducing operating cost and increasing cost efficiency in the short run. But such a 

behavior may have an impact on the riskiness of the portfolio in the long run because non-performing 

loans increase as poorly monitored borrowers fall behind in loan repayment. Hencloans  monitoring  

appear to be more efficient in the short term (Bashir and Hassan, 2017; Kolia and Papadopoulos (2020). 

But in the long term, they take on higher risk as this management behavior affects the quality of future 

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loans. This theoretical position called skimping hypothesis in the literature implies a positive 

relationship between the considered variables and consequently a rejection of the idea of tradeoffs 

between efficiency and bank risk taking in banking.   

  

Source: Authors.  

    The mediating effect of risk taking in the capital- efficiency relationship  

 Seminal researches to test the alternatives theoretical predictions in any US (Berger and DeYoung, 

1997; Kwan and Eisenbeis, 1997) or European countries (Williams, 2004; Altunbas et al., 2007; 

Fiordelisi et al., 2011) yield contradicting results most explained by the differences in econometric 

methods. An alternative explanation in this paper is that the rationality of capital, risk and efficiency 

relationships builds both on the long-lasting bank capitalbank risk controversy in the banking 

literature, and in the more recent idea of bank risk-efficiency tradeoffs.   

Two dominant and opposed hypotheses characterize the capital-risk relationships in the banking 

literature. For the proponents of negative relationship or proponents of moral hazard hypothesis (Lee 

and Hsieh, 2013), banks may have the incentives to increase their portfolio risk and leverage due to 

moral hazard because financial contracts are incomplete. In fact, bank managers usually exploit the 

rights of depositors that they primarily favor their interest in managerial compensation and support 

the benefit of shareholders for their wealth maximization. On the contrary, proponents of the regulatory 

approach suggest that banks are required to increase their capital in increased risk taking. Regulators 

therefore suggest the positive bank capital- risk relationship to reduce the problem of bankruptcy owing 

to higher risk and lower capital.  

Hence, linking these two strands of the banking literature might help to establish the mediating effect 

of risk in the capital efficiency relationships, connecting definitively the three variables.  We clearly 

distinguish the  case tradeoffs hold from the case tradeoffs is rejected.  

Table 2. Theoretical bank capital, risk taking and efficiency interlinks.  

                      Risk-Capital - Negative relationship positive relationship  

No relationship  

Risk-efficiency  (hazard moral hypothesis) (Regulatory theory)  

Trade offs        

Bad management hypothesis Bad 

luck hypothesis  
Lower efficiency  Higher efficiency  No effect  

No trade offs        

Skimping hypothesis  Higher efficiency  Lower efficiency  No effect  

No relationship  No effect  No effect  No effect  
 

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If the tradeoffs hold and bank capital and risk are related negatively, an increase in capital requirements 

will result in a deterioration of bank risk taking behavior. The higher level of bank risk will in turn 

decrease bank cost efficiency.  Let us now suppose in the same case, a positive capital-risk relationship. 

An increase in capital requirements in this case improves the bank risk-taking behavior (decrease of 

risk) and hence, leads to higher bank cost efficiency in the long term.  

Let us now suppose that the bank efficiency-bank risk tradeoffs do not hold. If bank capital and risk are 

related negatively, an increase in capital requirements improves bank risk behavior. The lowering of 

risk deteriorates in this case bank cost efficiency. On the contrary, if there is a positive capital-risk 

relationship, changes in capital requirements affect in the same direction bank risk. Therefore, increase 

in capital requirements results in higher bank cost efficiency. Table 2 summarizes the theoretical 

relationships between the three variables in the banking literature.  

  Empirical review  

 Bank capital and risk taking  

 Empirical evidence on the relationship between capital requirement and risk taking is far from being 

conclusive. In the case of USA, Calem and Rob (1999) quantified the effect of capital-based regulation 

and find that an increased capital requirement, whether flat or risk based, tends to induce more risk 

taking by ex-ante well capitalized banks that comply with the new standard.  In fact, undercapitalized 

banks took higher risk because the cost of bankruptcy is shifted to deposit insurance. But well 

capitalized banks also took higher risk because it is more profitable and there is low probability of 

bankruptcy.   

Koehn and Santomero (1980) and Kahane (1977) concluded that risk-based capital boosts risk-taking. 

Shrieves and Dahl (1992) and Jokipii and Milne (2011) confirm the positive relationship between 

capital and risk changes  while  studying  the  USA   banking  data.  Blum (1999) advocates that capital 

adequacy requirements increase the riskiness of banks. Matajesak et al (2009) favor a positive 

association between risk-taking and capital ratio in the case of US and 15 European countries. This is 

also the conclusion of Ugwuanyi (2015), who examined the relationship between risk and capital in the 

post-crisis setting. In contrast, Jacques and Nigro (1997) and Aggarwal and Jacques (1998) applied a 

similar methodology and concluded on an inverse relationship between risk and capital. Lee and Hsieh 

(2013) examined the effect of capital ratio on risk-taking of Asian commercial banks covering 1994 and 

2008. They documented an inverse relationship between risk and capital ratio in support of the moral 

hazard hypothesis. Tan and Floros (2013) found an inverse relationship between capital and risk. 

Recent empirical contributions also favor the negative relationship between risk-taking and bank 

capital (Ding and Sickles, 2018; Jiang et al., 2020).  

  Bank efficiency and bank risk  

 If the aforementioned empirical contributions were mainly interested in the relation between risk and 

capital, for Hughes and Mester (1998), the stress should also be on the analysis of the tradeoff between 

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https://www.tandfonline.com/doi/full/10.1080/23311975.2021.1947557
https://www.tandfonline.com/doi/full/10.1080/23311975.2021.1947557
https://www.tandfonline.com/doi/full/10.1080/23311975.2021.1947557
https://www.tandfonline.com/doi/full/10.1080/23311975.2021.1947557
https://www.tandfonline.com/doi/full/10.1080/23311975.2021.1947557


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risk and efficiency. The result of their empirical test shows a negative relationship between the two 

variables. More generally, empirical test of the efficiency-risk trade off yields conflicting results in the 

banking literature. For instance, in examining the same link in a large sample of European banks 

between 1992 and 2000, Altunbas et al. (2007) noted that inefficient European banks seem to 

undertake less risk. William (2004), Le (2018) and Tan and Floros (2013), in their empirical 

contributions, confirm this result and suggest that efficiency and risk are adversely related.   

Deelchand and Padgett (2009) using a sample of 263 Japanese cooperative banks over the period 2003 

through 2006, confirm the belief that risk, capital and efficiency are simultaneously determined, but 

suggest a positive relationship between efficiency and risk in banking as argued in the hazard moral 

hypothesis. In fact, the results of their research show that inefficient Japanese cooperative banks take 

more risk, contrasting with evidence in Europe. This result is also in line with that of Kwan and 

Eisenbeis (1997) in the case of US commercial banks. For Bashir and Hassan (2017) or Nguyen and 

Nghiem (2015) the relation is also positive. They argue that banks not spending resources on risk 

monitoring seem to be more efficient in the short term, but, they take higher risks in medium and long 

term.  

  Bank capital and bank efficiency  

 The empirical   evidence   on  bank  efficiency  and  bank  capital also remains mixed even in recent 

contributions of literature. Berger and Di Patti (2006), in their study of the relationships between 

capital ratio and profit efficiency in US banking industry over the period 1990-1995, find that higher 

capital has negative effect on efficiency. Also interested by profit efficiency, Fiordelisi et al. (2011), using 

granger tests of causality in a GMM dynamic panel framework, examine the reverse causality between 

the two variables. Their findings emphasize that the less efficient banks tend to take more risk and 

better capitalized banks perform better in terms of efficiency.  

However, Barth et al. (2013), in their study of whether or not bank supervision, regulation and 

monitoring enhances or impedes bank operating efficiency in a sample of 72 countries over the period 

1992-2007, find that a more stringent capital requirement is marginally and positively associated with 

bank efficiency. This was also the result of Haque and Brown (2017)’s study while Triki et al. (2017) find 

this true only for large banks. Pasouiras (2008) also states that capital stringency improves efficiency 

but their result was not robust over all specifications. Sufian (2016), in the case of Malaysian banks for 

the period 199-2008 or Banker et al. (2010) in the case of Korean banking institutions, suggest that 

efficiency is positively related to capital. Pasouira et al. (2009) discuss the impact of capital stringency 

not only on cost efficiency, but also on profit efficiency. As a result, capital stringency increases cost 

efficiency and decreases profit efficiency.  Onio (2017) seems to confirm Berger and Di Patti (2006)’s 

findings of a negative association between capital and financial performance in the case of European 

banks. Bashir and Hassan (2017) state that an increase in capital increases agency costs and the free 

cash at the disposal of managers, leading to a decrease of efficiency. More recently, Djalilov and Piesse 

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(2019), in their study of the impact of bank regulation on bank efficiency, consider 04 regulations: 

activity restrictions, capital requirements, market discipline and supervisory power. The paper finds 

bank activity restrictions to be the only regulation improving banking efficiency, using a sample of 21 

transition countries for the period 2002-2014.   

Finally, Miah and Sharmeen (2015) using a sample of banks from year 2001 to 2011 in the case of 

Bangladesh concluded that, capital, risk and efficiency are interrelated. One explanation of such a 

situation is that, the tree variables could depend on other factors such as moral hazard, asymmetric 

information, ownership structure and agency problems.  

  MATERIALS AND METHODS  

 Research design and sample size  

 At the end of 2020, 15 commercial banks operated in Cameroun. As the bank population is not large 

enough, the authors are constraint  to  test  their  hypotheses  using  a  small  sample. Small samples 

are generally associated with low statistical power and increased margin of errors that can render the 

study meaningless.    

Furthermore, there is also a possibility of vibration effects with small samples. Vibration effects refer 

to a situation of change of results as a consequence of even minor analytical manipulation. In the case 

of Cameroonian commercial banks, the authors expect a very low sampling variability as commercial 

banks share the same regulatory environment imposed by the Banking Commission of Central African 

Table 3. Sample representativeness.  

 Banks  Capital   Assets  Deposits  Loans  

BICEC  49.1  726,5  602,7  320,9  

SGBC  12,5  1055,4  830,2  621,1  

AFRILAND  20  1260,1  997,6  603,7  

CBC  12  458,1  336,6  311  

BGFI 

BANK  

20  376,5  250  273,5  

ECOBANK  10  466  369,2  191,7  

UBC  20  118,1  57,8  2,8  

UBA  10  480,6  376,3  136,9  

SCBC  10  224,3  168,8  93,1  

SCB  10,5  624  509,5  324,1  

Sample  174,1  4733,6  4498,7  2878,7  

 All banks  260,9  7010,7  5398,8  3443,7  

Percentage  66,84  67,51  83,32  83,59  
 

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States (COBAC). A major challenge raised notably by Van de Schoot and Miocević (2020) remains 

however to increase information in data by using reliable measures and a smart sampling approach. In 

this study, they use a non-probabilistic sampling approach. They therefore excluded five banks because 

of unavailability of information and data on key variables included in the model. Their panel is therefore 

constituted of 10 banks with yearly data in millions of Fcfa from 2014 to 2020 on all the variables 

included in their econometric model. The authors therefore have enough observations to obtain reliable 

results when estimating their econometric model. COBAC database is used to obtain banks’ balance 

sheets data and income statements. The financial statements published on the website of each bank are 

also used to have reliable data on included variables. In this case, data are first converted in Fcfa when 

needed, and then presented in millions of Fcfa. In 2020, four of the banks considered in the sample 

(Afriland First Bank, SGBC, BICEC and SCB) remain the most important banks in the Cameroonian 

banking system in terms of activity. These four institutions account for 52% of the banking system's 

consolidated balance sheet, 54.3% of total loans and 54.5% of total customer deposits. As shown in 

Table 3, taken together, the sample banks represent 83.3% of deposits 83.59% of loans and almost 68% 

of assets of the whole banking industry.  

  Measurement of variables  

 The measure of endogenous variables was discussed briefly (Bank risk, capital and efficiency) and 

included control variables.  

  Bank risk measure  

 There is until now no consensus on how to measure bank risk in the literature. If some recent papers  

are  based  on  insolvency  risk  (Moyo, 2018), (Barra and Zotti, 2018), others still rely on more 

traditional measures. Insolvency risk is measured by distance to  

default indicator as follows  where  

and  Standard deviation of ROA.  Concerning more traditional approaches, the most 

widely used indicator is portfolio risk. Bank risk measure is hereby given by the ratio of riskweighted 

assets to total assets (Jacques and Nigro, 1997; Rime, 2001; Aggarwal and Jacques, 2001).  The 

standardized approach to calculating risk-weighted assets consists in multiplying the amount of an 

asset by the standardized risk weight associated with that type of asset.  A high proportion of RWA 

indicates a higher share of riskier assets.  However, a limit generally reported of the risk weighting 

methodology is that it can be manipulated.   

Liquidity risk is generally measured by the loans to deposits ratio (LDEP). Banks with higher loans to 

deposits are usually viewed as riskier due to potential shortage of liquidity. In the Cameroonian case, 

bank excess liquidity observed in recent years does not comply with the use of such indicator. Moreover 

this over-liquidity goes with credit rationing accentuated by the risk aversion of bankers, suggesting 

that bank risk indicator based on credit risk might be more appropriate in Cameroonian banking. This 

last option includes among others, as in Abedifar et al. (2013), Tan and Floros (2013) or Bitar et al. 

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(2018), the possibility to use loan loss reserves as a fraction to total assets as a proxy of credit quality.  

Higher values of this ratio can be a sign of a precautionary reserve policy in the bank or an anticipation 

high non performing revenues (Anginer and Demirguc-Kunt, 2014). The problem with this ratio in the 

Cameroonian case is that its variations between banks may be related to different banking policies 

regarding non-performing loans, reserves and write-offs.  

Following Bashir and Hassan (2017) and Kabir and Worthington (2017), non-performing loan ratio was 

used in this paper that is, the non-performing loans as a fraction of total loans as a risk indicator. The 

advantage of this ratio in Cameroonian banking is that it might contain information on risk differences 

between banks not caught notably by RWA.  

Non-performing loans are measured by loans past due 90 days or more and non-accrual loans and 

reflect the ex-post outcome of lending decisions. As noted by Ding and Sickles (2018), higher values of 

the NPL ratio indicate that banks ex-ante took higher lending risk and, as a result, have accumulated 

ex-post higher bad loans.  

The measure of capital  

 Capital ratio is generally measured in three ways. Tier1 risk based - ratio based (proportion of total 

capital to risk-weighted assets), total risk-based ratio (proportionoftier1 and tier2 capital of risk 

weighted assets) and tier 1 leverage ratio (ratio of tier1 capital on total assets). Following Nguyen and 

Nghiem (2015) and Zheng et al. (2017), the authors calculated   capital as the ratio of core capital to 

total assets (capital adequacy ratio).  

 Efficiency scores  

 The authors further computed Individual bank efficiency (EFF) as the distance of a firm’s observed 

operating costs to the minimum or ‘best-practice’ efficient cost frontier. Efficiency scores are derived 

using the stochastic frontier approach. Based on Aigner et al.  

(1977), the cost function of a firm is as follows:  

 )                                                                              (1)  

 Where CTi represents the bank i total operational costs, Yi the  

vector of quantity of bank output variables and Pj the vector of  prices of bank input variables.  hereby 

denotes the compound random error. This error is divided into endogenous (  and exogeneous factors 

(    that influence bank production costs. Endogenous factors or inefficiency factors are therefore 

related to an increase of bank production cost because of an error of management that causes 

inefficiency. Exogeneous factors represent an increase or a decrease of bank cost due to random factors 

(mistakes on data’s, on measurement of unexpected or uncontrolled factors).     are supposed 

separable. Taking the logarithmic form of the relation (2), we then have:  

 ) +  + (2)  

 One remaining problem to solve to estimate this relation is that of the functional form of the production 

function.  

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To measure cost efficiency in Cameroonian banking, the authors specify a cost frontier model with two 

outputs and three inputs. In fact, they suppose that, in this country, bank’s production function uses 

labor and physical capital to attract deposits. The collected deposits are used to fund loans and other 

earning assets. Inputs and outputs are therefore specified using the intermediation model presented by 

Sealey and Lindley (1977).  The translog specification of the used cost frontier model (relation 3) is as 

follows:  

  In this relation, i stands for banks and CTit is the total cost of bank i at the year t where t represents 

years. As j is an index for labor (lab), physical capital (cap) or financial capital (fin), Plabit denotes labor 

price in bank at the year t, Pcapit the price of physical capital of bank at year t and Pfinit the 

remuneration of financial capital of bank i at time t. The authors further noted Yit the output of bank i 

at the year t, v the random error term that incorporates measurements errors and luck and u a firm 

effect representing the bank inefficiency level, that is the distance of an individual to the efficient cost 

frontier. Indeed, cost efficiency measures the distance of a bank relative to the cost of the best practice 

bank when both banks produce the same output under the same conditions. The cost efficiency scores 

are therefore computed as:     

 among sample  

banks. Table 4 recapitulates variables included in the cost function and their measure. Table 5 presents 

the cost frontier estimated efficiency scores in the Cameroonian banking.   

The level of estimated efficiency scores varies all along the study period and between banks. The highest 

level is attained in 2017. Concerning bank analysis, Commercial Bank Cameroon (CBC) with more than 

98% state participation in the capital, that was not regulatory compliant in 2009 and goes into a 

restructuring process and a temporarily management until 2018 is also the less efficient bank of the 

studied sample.   

 Control variables  

 For the explanatory variables the authors used a broad range of bank-specific and country - specific 

variables that are believed to be important in explaining performance and risk. These include loans 

growth (loang) as rapid loan growth may increase risk and impact adversely on capital and bank 

efficiency.  Bank size, through economies of scale, may influence the relationship between capital, risk 

and efficiency so we control for the assets size of banks (size). Big banks, typically hold less capital than 

smaller banks; they may also be more diversified and gain from other size advantages so it is important 

to control for this factor. Table 6 provides a synthetized description of the variables includes in the 

system of equation to be estimated.  

Modelling framework  

                                                  (3)  

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 The modelling framework adopted to test the hypotheses in this study is based on the various 

approaches suggested by the strand of the literature aiming to criticize the earlier causality approach 

proposed by Berger and DeYoung (1997) in their seminal contribution and implemented by several 

researchers. As a response to causality approach and taken all together, a significant part of proposed 

approaches in this empirical literature implicitly suggest that, as bank capital risk and efficiency are 

determined simultaneously, examining the investigated relationships should best be evaluated in an 

appropriate system of simultaneous equations, further estimated by efficient estimators (Tan and 

Floros, 2013), Altunbas et al. (2007), Moudud-Ul-Huq (2019), Moudud-Ul-Huq (2020). The authors 

therefore specify a system of equations and estimate these using the   three stage least squares panel 

data estimator technique. This allows for simultaneity between banks’ risk, capital and efficiency while 

also controlling for important other bank specific factors and endogeneity. The system of equations 

estimated is as follows:  

  

                                                                         (4)                  

                 
                                                                                                       

(5)   

               (6)  

 The relations (4), (5), and (6) satisfy the order conditions required for the identification in 

simultaneous equations system.  

RESULTS AND DISCUSSION  

Bank risk equation results  

 In this equation, the authors are interested by the sign of the capital variable coefficient. If this 

coefficient is significant and negative, they will assert that Hypothesis H1 is validated. The estimated 

coefficient of bank capital variable (∆CAPt) is however significantly positive on 5% level, suggesting 

that the changes in risk and capital are positively related. The hypothesis H1 is therefore not validated.  

This result is consistent with Abbas et al. (2021), but do not confirm the findings of Ding and Sickles 

(2018) or Jiang et al. (2020). Therefore, faced with more stringent capital requirements in difficult 

times as noted during the 2007 crisis or Covid 19 pandemic, commercial banks in Cameroon seem to 

structure their activities in a way to reduce the regulation burden without a corresponding reduction in 

the underlying risk. This can explain the high level of non-performing loans observed in this country in 

recent years despite measures taken by COBAC.     

The authors are also interested by the sign and of the coefficient of the efficiency variable. A negative 

and significant coefficient would indicate that there is a tradeoff between the efficiency and risk and 

that this is explained by the bad management hypothesis. The results of the risk equation presented in 

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Table 7 do not support any relationship between the changes in bank’s efficiency and bank risk position 

in Cameroonian commercial banking. The coefficient  is  not  statistically  significant, albeit negative.   

This suggests that changes in bank’s efficiency do not lead to changes in bank risk-taking behavior in 

Cameroonian commercial banks.     

Moving to control variables, the change in the bank risk behavior is positively dependent on the net 

interest margin of a given year.  When facing favorable interest rate environment, commercial banks in 

Cameroon might be tempted to increase the amount of loans provided at the expense of decreased 

quality of such loans. The results   also imply that the change in RISK variable is determined by the loan 

growth (significant at 1% level) and bank size (significant at 5% level). Large banks are therefore less 

averse to risk in Cameroon. 

Table 4. Cost frontier inputs and output description.  

 Variable  Notation  Description  

Total cost  CT  Total of interest and non interest cost  

Output  

Total loans  

  

Y  

  

Gross loans-reserves for loan loss provisions  

Inputs prices  

Price of physical 

capital  

  

Pcap  

  

Expenditures on premises and fixed assets/premises 

and fixed assets  

Price of labor  Plab  Salaries on full time equivalent employees  

Price of borrowed 

funds  

Pfin  Interest expenses paid on deposits/total deposits  

 Source: authors.  

   Table 5. Cost frontier efficiency scores in Cameroonian banking (%).  

 Year  Mean  Med  Sd  Min  Max  

2014  0.595  0.634  0.114  0.356  0.754  

2015  0.660  0.650  0.145  0.448  0.857  

2016  0.746  0.749  0.126  0.514  0.897  

2017  0.791  0.810  0.075  0.672  0.881  

2018  0.727  0.757  0.149  0.420  0.872  

2019  0.718  0.759  0.172  0.351  0.859  

2020  0.773  0.759  0.095  0.620  0.937  

 Source: Author’s calculations based on Frontier 4.1.  

   Table 6. Variables included in the model.  

  Variable  Description  

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Eff  Estimated efficiency scores  

risk  Non-performing Loans ratio  

cap  Capital adequacy Ratio  

size  natural logarithm of total 

assets  

NIM  Net interest margin  

ROA  Return on assets  

loang  Loans annual’s growth rate  

Source: Authors.  

Bank efficiency equation results  

Table 8 presents the results of the second equation in the authors’ system, where the change in the 

bank’s cost efficiency is the dependent variable. They are interested in the estimated coefficient of the 

risk variable (∆RISKt) since this estimate is related to the bad luck explanation of the tradeoff’s 

hypothesis between bank efficiency and bank risk-taking behavior. For H2 to be validated, the 

estimated coefficient of the bank risk variable should be negative. This is the case in Table 8. This 

coefficient is negative with a value of -0.063  and significant at 10% level.  They may infer from this that 

change in bank’s cost efficiency is negatively affected by any change in bank risk taking behavior in 

Cameroon.  

Table 7. Risk equation results.  

 Variable  Coef.  SE  t-stat  Prob  

C  -1.194***  0.409  -2.917  0.004  

∆CAP  0.256**  0.105  2.441  0.016  

∆EFFIC  -0.067  0.183  -0.365  0.715  

Risk (-1)  0.153***  0.031  4.852  0.000  

Size  0.044**  0.018  2.418  0.017  

Loang  1.001***  0.085  11.725  0.000  

  

Source Authors calculations 

based on EView 

Table 8. Efficiency equation 

results.  

  

s 12 

software.  

  

Variable  Coef  SE  t-stat  Prob  

C  0.926***  0.241  3.839  0.000  

∆cap  0.127**  0.056  2.262  0.025  

∆Risk  0.063*  0.037  1.675  0.096  

Effic (-1)  -0.972***  0.134  -7.220  0.000  

Size  -0.009  0.009  -0.969  0.334  

 Source: Authors calculations based on EViews 12 software.  

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Hypothesis H2 is therefore validated. As IMF (2018) suggests that exogeneous shocks are not linked to 

commercial bank risk taking in Cameroon, this might be explained by unskilled management that is 

losing control over both the cost structure of the bank and the administration of its loan portfolio.   

From the table, it can be seen that the coefficient of bank capital (∆CAPt) is significant at 5% level and 

presents a positive sign with a value of 0.012.  This result suggests that commercial banks with higher 

capital operate more efficiently in Cameroon. This finding seems consistent with Shrieves and Dahl 

(1992), Berger and DeYoung (1997) Altunbar et al. (2007) or more recently Haque and Brown (2017), 

but do not support Bashir and Hassan (2017).   

Based on the estimate of size variable (SIZEt) coefficient, we might observe that the changes in the cost 

efficiency are not related to the size of the bank. This might suggest that behavior of the banks with 

respect to cost efficiency does not vary with increasing balance sheet size. This result is not consistent 

with the findings of Wheelock and Wilson (2012) or Hughes and Mester  

(2013).    

 Capital equation results  

 Let us move to the results of the capital equation presented in Table 9. The results show a negative and 

significant relationship between change in capital and change  in bank efficiency. Inefficient banks run 

therefore with higher level of capital in Cameroonian banking. H3 is validated. The authors also have a 

negative one with risk taking meaning that capital regulation is not binding strictly in Cameroon. In 

fact, there is a possibility that banks escape from COBAC’s measures. Banks with significant amount of 

non-performing loans are forced to provide more provisions leading to consequent evolution of their 

capital. Similarly, as observed in the risk equation, results of the estimation of the capital equation 

suggest a negative and significant relation with the size of the bank as generally found in the literature 

and notably by Aggrawal et al. (1998) or Rime (2001). The change in the bank capital is however not 

related to the bank’s return on assets in a given year. This last result is not consistent with Altunbas et 

al. (2007) who found that ROA and bank capital are sharply and positively related. It therefore seems 

that banks in Cameroon do no rely on earnings in order to increase their capital.    

Table 9. Capital equation results.  

 Variable  Coef  SE  t-stat  Prob  

C  0.642  0.504  1.273  0.205  

∆Effic  -0.551**  0.212  -2.591  0.010  

∆Risk  -0.525***  0.047  -11.047  0.000  

CAP (-1)  -0.182***  0.055  -3.274  0.001  

Size  -0.026  0.022  -1.153  0.251  

ROA  0.028  0.017  1.629  0.105  

  Source: Authors calculations based on EViews 12 software.  

Table 10. Capability of the model.  

 Equation  OBS  PARMS  RMSE  R-SQ  F-

STAT  

P  

Efficiency  54  5  0.131  0.479  12.59  0.000  

Risk  54  6  0.116  0.535  11.19  0.000  

Capital  54  5  0.472  0.513  14.27  0.000  

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Source: Authors calculations.  

Table 10 presents the capability of our model to link efficiency, capital and risk in Cameroonian 

commercial banks. All X2 are significant at 1% level. This means that at least one instrumental variable 

(IV) has non zero relationship with endogenous variables (Efficiency, Risk and Capital).  

 Conclusion  

In the aftermath of the financial deregulation aiming to improve bank efficiency in Cameroon, to 

address the potential implicit threat to the banking system stability, the Central African States banking 

commission (COBAC) placed    a     more     emphasis    on    bank   governance considerations and 

notably on a more preeminent role of capital adequacy ratios in the implementation of prudential 

regulation. However, neither theoretical studies nor empirical papers are until now conclusive on the 

effect of   more stringent capital requirements on bank efficiency and risk behavior.   

In this paper, the interrelationships between risk-taking, capital regulation and efficiency In 

Cameroonian commercial banks were examined. To reach target, based on theoretical contributions 

and an analysis of the Cameroonian context, three hypotheses are formulated:  

 H1: Increase in bank capital reduces commercial banks risk taking in Cameroonian banking.  

H2: There are tradeoffs between bank efficiency and bank risk taking in Cameroonian banking. H3: 

Inefficient banks run with higher level of capital in Cameroonian banking.  

 These hypotheses are tested on a sample of representative Cameroonian commercial banks from 2014 

to 2020 in a system of simultaneous equations approach. Estimation of the system relies on the use of   

the two stages panel data estimator technique to account for potential endogeneity and simultaneity 

and small samples approaches. Cost technical inefficiency is derived using the computer program 

named Frontier Version 4.1 developed by Coelli (1996). The authors also use proxy risk taking by a 

credit risk measure, capital by the capital adequacy ratio and control for bank-level variables that affect 

the relationship between the three considered variables.   

As a  result,  their  empirical  analysis  shows that bank capital does not lead to bank risk taking behavior 

in Cameroonian banking. In fact, there is a positive and significant relationship between the two 

variables (H1 is not validated). Moreover, there is a trade -off between bank risk and bank efficiency in 

  

Parms=parameters RMSE=Root mean square error  

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Cameroonian banking explained by the bad luck hypothesis (H2 is validated). Finally, there is a 

negative impact of change in efficiency on the yearly change in bank capital meaning that inefficient 

banks run with higher level of capital in Cameroonian banking (H3 is validated).     

Therefore, for a better contribution of bank policy to efficiency improvements, banking authorities in 

Cameroon might create conditions of bankers’ regulation arbitrage mitigation.  In this sense measures 

aiming to ensure that no risk spill over from non-regulated financial institutions to the banking system 

might be privileged. Specially, COBAC should look at the link between banks and insurance companies 

and address step-in risk. Furthermore, COBAC should also develop policies aiming to scrutinize more 

deeply what bankers do and examine individual transactions to see whether they might be an attempt 

to play by the rule.  

There are some limitations of this paper that need to be improved in future research. First, the analysis 

period is too short; it should be extended. Also the sample is limited. It can be extended to CEMAC 

countries. Secondly, an analysis at the macro-level might help taking into account many economic 

environmental variables not considered in this study. Finally, future researches might take into 

consideration bank capital structure as the literature suggests significant relationships with bank 

efficiency or ba 

CONFLICT OF INTERESTS  

 The authors have not declared any conflict of interests.  

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