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Advances in Politics and Economic 
ISSN 2576-1382 (Print) ISSN 2576-1390 (Online) 

Vol. 1, No. 2, 2018 
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141 
 

Original Paper 

Bank Excess Liquidity and Economic Growth in Waemu 

Countries: A Panel Data Approach 

PRAO Yao Séraphin1* 

1 Department of Economic Sciences and Development, Alassane Ouattara University, Bouaké, Ivory 

Coast 
* Prao Yao Séraphin, Department of Economic Sciences and Development, Alassane Ouattara 

University, Bouaké, Ivory Coast 

 

Received: October 10, 2018    Accepted: October 30, 2018    Online Published: November 6, 2018 

doi:10.22158/ape.v1n2p141              URL: http://dx.doi.org/10.22158/ape.v1n2p141 

 

Abstract 

This paper provides an empirical assessment of the relationship between banking, liquidity, investment, 

terms of trade, bank solvency ratio, financial development and economic growth in the WAEMU zone. 

The analysis focuses on 7 countries of the West African Economic and Monetary Union (WAEMU) and 

covers the period 1994-2015. Using the panel data approach, we show that economic growth is positively 

related with banking on liquidity. In addition, the results highlight the impact of bank liquidity on 

economic growth but mitigate when it is associated with the investment. 

Keywords 

bank, excess liquidity, economic growth, West Africa 

 

1. Introduction 

Since the work of McKinnon (1973) and Shaw (1973), the relationship between financial development 

and economic growth has gained renewed interest. Empirically, the meaning and/or magnitude of the 

relationship between financial development and growth may depend on income level (Deida & Fattouth, 

2002) or financial development (Shen & Lee, 2006). Huang and Lin (2009) find an insignificant 

correlation between financial development and economic growth in low-income countries, while this 

relationship is positive and robust in the case of high-income countries. A World Bank study has shown 

that despite the low level of development of the banking sector in African countries, it is excessively 

liquid (Honohan & Beck, 2007). But if a bank’s performance stems from its liquidity situation (Goldstein 

& Pauzner, 2005), it is possible to assume that the bank’s performance factors are those of bank over 

liquidity. This one can be defined as an excess of bank cash held beyond what is needed to cope with 



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withdrawals of economic agents (Saxegaard, 2006). According to Doumbia (2011), the excess liquidity 

of WAEMU banks can be explained by three main factors, namely the constraints related to the defense 

of parity of the CFA franc (Note 1), the inflow of capital and the high level of interest rate. This excess 

liquidity of banks results in insufficient loans. In the WAEMU zone, on average, during the period 1991 

to 2006, the banking sectors of the different countries held excess liquidity ranging from 0.8% to more 

than 3.7% as a proportion of GDP.  

In a context of an embryonic (Note 2) financial market where there are no compartments for very small 

and medium-sized companies. These companies are the most rationed in terms of credit (Miesel & 

Mvogo, 2007). But over the last decade, WAEMU countries have recorded relatively high growth rates. 

According to the report of the Banque de France (2015), the real GDP growth rate in the zone is 6% in 

2013 compared to 3% for the world, 6.5% in 2014 and 7% in 2015. The result is that the good 

macroeconomic performance of the zone contrasts with the weak bank financing of the activity. The 

economic growth of the countries of the Union is therefore achieved in a context of bank excess liquidity. 

This raises the question of what is the impact of bank over-liquidity on the economic growth of WAEMU 

Member States? Therefore, the general objective of this study is to analyze the impact of bank 

over-liquidity on the economic growth of the countries of the Union. We assume that bank over-liquidity 

has a positive impact on economic growth in WAEMU countries. In addition, financial development is 

beneficial to economic growth.  

Methodologically, we apply a panel error correction model with an interactive variable. This article 

contributes to the empirical literature on finance and growth by studying the relationship between bank 

over-liquidity and economic growth in WAEMU countries over the period 1994-2015. The results 

obtained suggest a positive correlation between bank over-liquidity and economic growth. However, the 

association between bank over-liquidity and investment has a negative impact on economic growth. 

This article is organized as follows: Section 2 is devoted to reviewing the literature on the contribution of 

the banking sector to economic growth. Section 3 will introduce the methodology of the study. Section 4 

will discuss the empirical results, particularly the econometric analysis of the relationship between bank 

excess liquidity and economic growth. 

 

2. Literature Review 

In this section, we successively address the review of theoretical literature and the review of empirical 

literature. 

2.1 The Theoretical Literature Review 

Since the pioneering studies of Bagehot (1873) and Schumpeter (1911), financial intermediation has 

been seen as a factor in draining funds into productive investments (Gurley & Shaw, 1955; Goldsmith, 

1969). In his work on Britain, Goldsmith (1969) concludes that there is a positive relationship between 

the financial sector and the level of economic development over the period 1860-1963. Inspired by the 

theory of endogenous growth, King and Levine (1993) emphasize the importance of financial sector-led 



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innovation, through the pace of capital accumulation and factor productivity. According to these authors, 

financial systems make it possible to channel savings towards more productive uses on the one hand and 

to diversify the various risks associated with productive activity on the other. Through the various 

services it provides, the financial sector plays an indispensable role in economic growth by contributing 

to the improvement of productivity under the influence of financial innovation (Eschenbach, 2004). But 

financial liberalization policies that were supposed to allow developing countries to finance their 

development have led to financial underdevelopment characterized by a situation of over liquidity in the 

financial and banking sector (Hugon, 2007). If excessive liquidity holding by financial intermediaries is 

aimed at reducing two major risks of microeconomic crisis linked to their activity, namely: a liquidity 

crisis and a solvency crisis, however, it hinders the economic circuit (Doumbia, 2011). The link between 

the development of the banking sector and economic growth could be the result of bank optimization 

behavior in a risky context. Indeed, in a general context of information asymmetry, risk is the main 

concern of the bank which has an imperfect readability of its customers. Thus, risk is the most studied 

factor in the banking sector and credit risk is the most important for a bank. The lower a bank’s own funds 

are compared to its customer receivables, the greater the credit risk and the greater the probability that the 

bank will not be over-liquid or efficient (Greuning & Bratanovic, 2004) in so far as the own funds would 

not be able to support any unpaid debts. 

2.2 The Empirical Literature Review 

Empirically, several studies have focused on the determinants of bank excess liquidity. They can be 

grouped into two categories: the first insisting on the voluntary holding of excess liquidity and the second 

on exogenous causes in the banking sector. 

Regarding the first, Ouédraogo (2011), indicates that a liquidity level of 1% (compared to the deposit) is 

accompanied by a decrease of 1.75% of the supply of credit and 2.24% % of loans to the private sector in 

WAEMU, highlighting a crowding out effect. In addition, banks tend to overestimate the risk associated 

with the financing of a large proportion of companies, which results in insufficient lending vis-à-vis 

deposits and, therefore, increased liquidity. Beyond the required threshold (Doumbia, 2011). Based on a 

modified Klein model (1971), from a sample of eight developing countries, khemraj (2008) attributes the 

causes of excess liquidity to the oligopolistic structure of the banking system. The high levels of interest 

rates on loans could be explained by the oligopolistic structure of the banking system. 

On the second, the causes of excess liquidity are perceived as the result of the reluctance of commercial 

banks in a context marked by the respect of regulatory prudential standards. In the case of the Central 

African Economic Community (CEMAC), Beguy (2014) identifies as causes of bank excess liquidity, 

the precaution of commercial banks and other exogenous factors, including soaring oil prices. Using 

Vector Autoreg Ression (VAR) model and a bootstrap technique, Kamgna and Ndambendia (2008) 

indicate in the case of CEMAC, a heterogeneity of the causes of bank over-liquidity. In addition to the 

non-significance of certain factors, the factors behind the excess liquidity in the CEMAC differ from one 

country to another. Only credit to the private sector reduces excess liquidity, the cost of credit, credit to 



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government and paid deposits are certainly significant, but do not have the expected sign. 

In terms of the implications of bank excess liquidity on the real economy and monetary policy, studies are 

not lacking. Agénor and El Aynaoui (2009) studied the implications of bank excess liquidity on the 

effectiveness of monetary policy in a simple model with imperfections in the credit market. They show 

that in situations of bank over-liquidity, the effectiveness of monetary policy is diminished. Barik et al. 

(2012) analyze the causes of excess liquidity in the Indonesian banking sector and its impact on monetary 

policy. Adopting a T-VAR modeling approach over the period 2000-2010, they conclude that the excess 

liquidity of banks in Indonesia is justified by precautionary grounds. Indeed, the volatility of deposits and 

that of growth as well as the cost of resources for banks, explain the excessive holding of liquidity. In 

addition, they show that in excess liquidity, the effectiveness of monetary policy in controlling inflation 

is reduced. Using Vector Autoregression (VAR) model over the period 2000 to 2012, Jayaraman et al. 

(2012) examine the effects of excess liquidity on interest rates on loans, the share of loans in GDP, the 

exchange rate and the general price level in the Fiji Islands. The results indicate a negative relationship 

between excess bank liquidity and loan rates. A positive shock to liquidity strengthens the level of 

lending and causes a depreciation of the local currency. However, the excess bank liquidity does not 

significantly explain the level of inflation, especially in the short and medium term. 

 

3. Methodology and Data 

In this section, we present the specification of the model and the methodology of the research. 

3.1 Model Specification 

In econometric form, the regression model is as follows: 

1 2 3 4 5 6( ) ( / )it it it it it it it it itTCPIB RALI INV RALI INV TE FP ENG IDF                      
(1) 

Where TCPIB is the gross domestic product growth rate, RALI the excess liquidity ratio (RALI) 

proposed by Wanda (2007), INV, gross fixed capital formation as a ratio of GDP, TE, terms of trade, 

 solvency ratio and IDF, a financial development index and RALIxINV, a multiplicative variable to 

capture the conditioning effect of private investment on bank excess liquidity and economic activity. The 

variables are summarized in the table below. 

 

Table 1. Summary of Study Variables and Expected Signs 

Endogenous variable: TCPIB: GDP growth rate 
Exogenous variables Description of variables Expected signs 
RALI Over-liquidity ratio + 
INV Private investment + 
RALI×INV Interaction variable + 
IDF Financial development index + 
TE Terms of the Exchange - 
FP/ENG Solvency ratio + 

Source: Author’s presentation.  

 



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3.2 The Panel Estimation 

Our study was performed using the panel data, under their double dimensions, including an individual 

dimension and temporal dimension. The panel data, by this feature, proved so particularly suited 

whenever you want to estimate models and test underlying theories (Nerlove & Balestra, 1995). 

Therefore, the model (1) can be again specified as follows: 

1 2 3 4 5 6( ) ( / )it it it it it it it it itTCPIB RALI INV RALI INV TE FP ENG IDF                   
(2) 

With i = 1, ..........T, t = 1, .......22; x1it, the value of the j-ième variable for I-ième unit to the period t, j 

= 1, ...... 7. K is the number of explanatory variables model and αit, βi, the coefficients of exogenous 

variables for the individual i. finally, μit is the term errors for the individual i on the datet.  

This model assumes that each individual has a specific behavior, which is differentiated from one 

period to another. Denuded of any economic interest, the model becomes interesting once it imposes 

identifying restrictions, which correspond to the different hypotheses that one wishes to test. The 

economic literature most often uses three hypotheses that we present in our study. 

First the homogeneous model. This type of model assumes the presence of uniform behavior between 

individuals. Estimating model returns to be applied simply the Ordinary Least Squares (OLS) on all the 

data set end-to-end without worrying their specific nature or that of hazard and. in this model, all 

coefficients are identical. In this case we can write: 

 
It follows that our model is given by: 

   (3) 

If one assumes the existence of difference of behavior between individuals, then this model is no more 

suitable. Then, the individual effect model which assumes that the individual estimates differ only in the 

constant. We distinguish in this case the fixed-effect model where the individual effect is constant over 

time and the random-effect model where the constant term is a random variable. 

Concerning the fixed effects model, it takes into account the heterogeneity of the behaviors of the 

individuals composing the sample by considering that the equations that govern the relations the 

explained variable and the explanatory variables, stand out, from an individual to another by a constant. 

Therefore, the method of estimating the parameters depends on the structure of the error terms. If the 

errors are homoscedastic, not auto correlated in the temporal dimension and in the individual dimension, 

the MCO method on the dummy variables (LSDV = Least Square Dummy Variable) or the within 

estimators is used. On the other hand, if the errors are heteroscedastic and/or auto correlated in the 

temporal dimension but independent in the individual dimension, we use the Generalized Least Squares 

(GCM) method on the dummy variables (LSDV) or the Within estimators. In the case where the 

systematic effects are represented by the ordinates at the origin αi for each individual one can write the 

hypotheses in the following way: 



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The equation is also given by:  

     (4) 

In the absence of structural specificity of the endogenous variable that differs according to individuals, 

it is possible to retain another hypothesis: that of the random effect. The random effect model assumes 

that the relationship between the variable to be explained and the explanatory variables are no longer 

fixed but random. The individual effect (constant) is no longer a fixed but random parameter. This 

model can be written as follows: 

 
The equation is also given by: 

   (5) 

The Hausman test is a specification test that determines whether the coefficients of the two estimates 

(fixed and random) are statistically different. The statistic of this test is a chi-square with k degree of 

freedom. If the probability of the test is less than 5%, then the within estimators are unbiased. In the 

opposite case, we will retain the MCG estimators or the random effect model. 

3.3 Stationarity and Co-Integration Test  

The robustness of the results depend on the compliance with the characteristics stochastic series. To do 

this, should be applied the stationarity tests and if any, co-integration test. 

i) study of the stationarity  

There is stationarity of the variables if the characteristics (expectancy and variance) are not modified 

over time. It is difficult, if not impossible, to clearly identify the stochastic characteristics of a series if it 

is not stationary. In panel, the most widely used test to analyze the stationarity of the variables is that of 

Im-Pesaran and Shin (2003) (when the intertemporal dimension is weak) and Levin Lin and Chu (2002). 

ii) The co-integration test 

Co-integration makes it possible to identify the true relationship between two variables by searching for 

the existence of a co-integrating vector and eliminating its effect, if any. Co-integration tests can detect 

the presence of a long-term relationship between variables. However, it is very interesting to know the 

evolution in the short and medium term of this relationship. The tool required for this purpose is the 

Error Correction Model (ECM). This type of model makes it possible to highlight how the short-term 

dynamics of the variables of the system is influenced by the long-term equilibrium. Thus, when the 

series are co-integrated, their relationship has to be estimated through an error correction model. For 

the co-integration test we adopt the tests proposed by Westerlund (2007) to test the long-term 



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relationship between private investment and bank excess liquidity. Indeed, Westerlund (2007) 

developed four co-integration tests that are based on the structural dynamics of relationships and not on 

residuals. This is to test the null hypothesis of non-co-integration by checking whether the error 

correction term in the error correction model constructed for this purpose is significantly equal to zero. 

The first two tests (Gt and Ga) test the existence alternative of at least one individual for which the 

variables are co-integrated. The last two tests (Pt and Pa) test the null hypothesis of no co-integration 

against the alternative hypothesis that the panel as a whole is co-integrated. 

3.4 Data and Descriptive 

In this sub-section, we first present the source of the data and, secondly, the different statistical 

characteristics of our study variables 

i) Data sources 

Our study will be conducted from a panel of seven WAEMU countries over the period (1994-2015) and 

the choice of this period is related to the availability of data. Togo is excluded from the study for reasons 

of lack of data. Data from the study come mainly from the World Bank (WDI), the International 

Monetary Fund (IMF data base) and the Central Bank of West African States (BCEAO) which publishes 

information on the rate member countries and financial data on states and financial institutions. Thus, 

panel data will be processed according to the usual panel data methodology that is, stationarity analysis, 

co-integration, application of the presence of specific effects test and the Hausman test to discriminate 

between the fixed effects model and the random effects model. 

ii) Descriptive statistics 

Table 2 below gives us the statistical description of the variables used in the realization of our study. 

The analysis in the table shows that the average growth rate of GDP is at a low level, around 4% over 

the study period, far below the 7% required to achieve the Sustainable Development Goals. Private 

investment as a percentage of Gross Domestic Product (GDP) does not exceed 20%, averaging 18.65%. 

This level of investment is below the level set by ECA and recently by the Clarke (2013) study which is 

25%. On average, the banks of the Union are strong in terms of compliance with prudential standards, 

the ratio Cooke (1.054), exceeding 8% of Basel 1. On the other hand the financial depth remains low 

with an average of 0.17. On average, however, WAEMU banks are liquid with a liquidity ratio of 

around 1.3483 on average over the study period. 

 

Table 2. Description of Variables 

Variables Observations Average Standar deviation Minimum Maximum 
INV 154 18.6467 7.8029 4.0072 38.8954 
RALI 154 1.3483 0.6465 0.8409 4.8218 
IDF 154 0.1795 0.0549 0.0486 0.3587 
FP/ENG 154 1.0547 0.3974 0.3817 2.2728 
TE 154 114.5149 24.9872 65.9527 193.8547 
TCPIB 154 3.9211 4.5364 -28.0999 14.6612 

Source: Author’s estimation. 



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The terms of trade, at 114.51 on average, are typical of most developing economies, indicating that 

economies are highly foreign, making it vulnerable to external shocks. 

 

4. Empirical Results 

The purpose of this section will be to present the results of the different econometric tests as well as the 

results of the estimation of our model, then their statistical and economic interpretations. 

4.1 Results of Stationary Test and Co-Integration 

The results of the tests developed by Levin Lin and Chu (2002) and those of Im Pesaran and Shin (2003) 

are given in Table 5.  

 

Table 5. Results of Unit Root Tests in Panel 

Variables 
 

IPS LLC 
Coef P-value Coef P-value 

TCPIB -6.1988 0.0000 -3.4806 0.0003 
RALI -3.4204 0.0003 -3.9213 0.0000 
INV 0.2705 0.6066 -0.3080 0.3790 
TE -0.8471 0.1985 -1.9441 0.0259 
IDF 1.0802 0.4237 -0.9100 0.1814 
FP/ENG -2.4720 0.0067 -2.4926 0.0063 
RALI×INV     
DTCPIB -8.1663 0,000 -8.0813 0.0000 
DRALI -6.8197 0,0000 -6.6697 0.0000 
DINV -6.7676 0.0000 -3.8117 0.0001 
DRALI×DINV     
DTE -6.1455 0.0000 -6.1111 0.0000 
DFP/ENG -6.9208 0.0000 -7.7176 0.0000 
DIDF -6.4275 0,0000 -4.6839 0.0000 

Source: Author’s estimation. 

 

As a result, the RALI, TCPIB and FP/ENG variables are stationary in level according to the results of the 

LLC and IPS test. As for the TE variable, the two tests indicate contradictory results. Indeed, TE is level 

stationary according to the LLC test, while the IPS test indicates a contrary result. The variables INV and 

IDF are not stationary according to the two tests. In first difference, the unit root hypothesis is rejected by 

the two tests for all the analysis variables. It follows that all the panel series are integrated of order 1 

according to the tests of Levin Lin and Chu (2002) and Im Pesaran and Shin. For the co-integration test, 

as mentioned above, we adopt the test proposed by Westerlund (2007) and the results obtained are 

recorded in Table 6. 

 

Table 6. Panel Co-Integration Test Result (Westerlund, 2007) 

Tests TCPIB et TE TCPIB et FP/ENG TCPIB et IDF 
Coef P-value Coef P-value Coef P-value 

Gt -7.164 0.000 -5.218 0.000 -7.641 0.000 
Ga -26.412 0.000 -29.820 0.000 -25.549 0.000 
Pt -6.504 0.005 -8.579 0.000 -8.886 0.000 



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Pa -24.256 0.000 -34.262 0.000 -25.097 0.000 

Source: Author’s estimation. 

 

The results indicate that the non-co-integration hypothesis can be rejected at the 1% threshold for all the 

variables of the panel, taken as a whole, with regard to the statistics Pt and Pa. Which means the variables 

admit a dynamic specification of type error correction. In addition, the hypothesis of the existence of a 

long-term relationship between the series is validated. Therefore, the error correction model can be 

adopted for the entire panel. 

4.2 Specification Tests of Individual Effects 

We first test the homogeneous or heterogeneous specification of the data generating process. It will be a 

question of testing the equality of the coefficients of the studied model in the individual dimension. This 

amounts to determining from the specification tests, if one is entitled to assume that the theoretical model 

studied is perfectly identical for all individuals. The rejection of the null hypothesis of no individual 

effects (Prob > F = 0.0002) in this study leads to the Hausman test to discriminate between the fixed and 

the random effects model. Table 7.  

 

Table 7. Result of the Specific Effect Test and the Housman Test 

Specific effect test Prob > F = 0.0002 
Hausman Test  Prob > chi2 = 0.3812 

Source: Author’s estimation. 

 

Indicates that the results obtained lead to accept the null hypothesis, therefore to the estimation of the 

random effects model. Now we can estimate our 2-step panel error correction model. The first step is to 

estimate the short-term relationship while the second step is dedicated to long-term dynamics. 

4.3 Analysis and Interpretation of Results 

Here we proceed to analyzes and interpretations of the results of the estimation of the short-term 

relationship and the long-term one. 

Concerning the short-term dynamics, the results of the MCE estimation of the short-term relationship 

between economic growth and excess liquidity are summarized in Table 3. 

 

 

 

 

 
Tests 

TCPIB et RALI TCPIB et INV TCPIB et RALI×INV 
coef p-value Coef p-value Coef p-value 

Gt -6.188 0.000 -5.742 0.000 -5.563 0.000 
Ga -17.316 0.000 -23.193 0.000 -11.572 0.024 
Pt -13.106 0.000 -7.949 0.000 -12.354 0.000 
Pa -28.136 0.000 -25.893 0.000 -9.237 0.005 



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Table 8. Results of the Short-Term Relationship between Economic Growth and Bank Excess 

Liquidity 

Endogenous variables growth rate (TCPIB) 
Esplanatory Variables Coefficients P-value 
DTE -0.00255 0.918 
DFPENG -1.43312 0.161 
DIDF -3.17799 0.753 
DRALI 0.29829 0.082 
DINV 0.16564 0.090 
DINV×RALI -0.24438 0.082 
CONSTANTE 0.70765 0.716 

Source: Author’s estimation.  

 

It follows that in the short term, only three explanatory variables are significant. Bank excess liquidity is 

statistically significant at the 10% level with a positive coefficient as well as private investment. The 

multiplicative variable has a negative and significant coefficient at the 10% threshold. As a result, bank 

excess liquidity has a positive effect on economic growth but is reduced when private investment is taken 

into account. Regarding the long-term dynamics, the results are shown in Table 4. 

 

Table 4. Results of the Long-Term Relationship between Economic Growth and Bank Excess 

Liquidity 

Endogenous variables: Growth rate (TCPIB) 
Explanatory variable Coefficients P-value 
TE -0.00112 0.943 
FPENG -0.98861 0.296 
IDF 15.82892 0.037 
RALI 0.17667 0.864 
INV 0.05502 0.655 
INV*d RALI 0.00583 0.951 
CONSTANTE 0.70765 0.716 

Source: Author’s estimation. 

 

In the long term, it follows that the financial development (IDF) measured by the liquidity rate of the 

economy, is the only variable of the model which presents a statistical significance at the threshold of 5%. 

4.4 Verification of Assumptions and Economic Implications 

At this stage of the study, it is good to invalidate or validate the hypotheses postulated. The study found a 

positive effect of bank over-liquidity on economic growth even though private investments reduce this 

positive impact in the short term. As a result, our first hypothesis is verified. The study also concludes 

that our financial development indicator exerts a statistically significant influence at the 5% threshold on 

long-term economic growth, confirming our second hypothesis. 

But in the long run there is no relationship between economic growth and bank over-liquidity. As a result, 

our hypothesis 1 is verified. 

 



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5. Conclusion and Implications of Economic Policies 

The objective of this study was to analyze the impact of bank over-liquidity on the economic growth of 

the WAEMU countries. On panel data from seven countries over the period from 1994 to 2015, the 

results indicate that bank excess liquidity has a positive impact on economic growth. Considering bank 

excess liquidity as an indicator of banking performance, it follows that the more efficient they are, the 

more the economy benefits. The positive influence of bank excess liquidity on the economic growth of 

countries could also be explained by the recycling of banking resources through the underwriting of 

government securities. Indeed, to guard against credit risk, commercial banks have a strong preference 

for public securities in WAEMU. In doing so, the state with more resources undertakes investments in 

infrastructure which increases the overall demand and production. However, the positive impact of 

banking performance on economic growth is thwarted by the relationship between bank excess liquidity 

and investment. By equating investment with gross fixed capital formation, it follows that only long-term 

financing is profitable for capital accumulation. In the WAEMU zone, however, banks prefer to finance 

the trade and wholesale sector (import-export, etc.) mainly by granting short-term and low-value credits. 

As a result, there is a need to loosen credit risk constraints. Establishing credit bureaus, improving the 

institutional and macroeconomic framework could reduce banks’ aversion to credit risk. To this must be 

added the improvement of the institutional framework to promote a policy of capital accumulation. In the 

long run, it should also be noted that the liquidity of the economy positively influences economic growth. 

A policy of making available and managing monetary resources is necessary to stimulate economic 

growth. 

 

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Notes 

Note 1. The CFA franc is the currency of the eight countries in the WAEMU zone, namely Côte 

d’Ivoire, Togo, Burkina faso, Mali, Sénégal, Benin and Guinée-Bissau. 

Note 2. Financial markets in sub-Saharan African countries are embryonic, with bond markets 

dominated by sovereign bond issuance, private bond markets and stock markets reserved for a few 

large companies. 

 


