




































American Research Journal of Economics, Finance and Management 

Volume 10 Issue 3, July-September 2022 

ISSN: 2836-9416 

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18 | P a g e  

EXAMINING THE IMPACT OF INTEREST RATE 
LIBERALIZATION ON PRIVATE SECTOR CREDIT IN THE 

WAEMU REGION 
 
 

Dr. Seraphin A. Prao and Dr. Eugene Kamalan 
Professors-Researchers at Alassane Ouattara University, Ivory Coast 

 
Abstract: The financing policies of African countries' development have historically been rooted in 
Keynesian economic theory, with low-interest rates and government control over the financial system 
aimed at stimulating investment and economic growth. However, these policies have often resulted 
in low or even negative real interest rates. This study draws on the seminal works of McKinnon (1973) 
and Shaw (1973) to argue that financial repression is a key factor hindering economic growth in 
developing nations. 
Financial repression discourages savings due to their poor performance and inhibits the efficient 
allocation of capital by financial intermediaries. To foster economic growth, a shift towards financial 
liberalization is recommended. This entails removing interest rate caps, reducing compulsory set-
asides, and eliminating directed credit programs, thereby allowing financial markets to operate 
freely and determine credit distribution based on market dynamics. 
This research sheds light on the detrimental impact of financial repression on economic development 
in African countries and advocates for policy changes that prioritize financial market autonomy and 
efficiency. 
Keywords: Financial repression, Economic development, Financial liberalization, Interest rate 
caps, African countries. 
  
 
1. Introduction  
From independence, the financing policies of African countries development were defined in a 
theoretical background inspired by Keynesian economies. Interest rates were capped at a very low level 
in order to foster investment and economic growth. Hence, the government controlled the entirety of 
the financial system and managed the development strategy of its economy. The results of these policies 
led to low – and even negative – real interest rates. The works of McKinnon (1973) and Shaw (1973) 
identified financial repressions the principal cause of low performances in terms of economic growth 
in developing countries. According to the authors, financial repression impedes economic development 
in many ways. First, savings are discouraged because of their low performance. Secondly, financial 
intermediaries are not encouraged to effectively spread savings. Logically, economic growth is fostered 
by adopting a financial liberalization policy, because in interest rates increase will facilitate savings 
mobilization and more effective capital distribution. Therefore, governments should cut out interest 
rates caps, reduce compulsory set-asides and abolish directed credit programs. It is about “freeing” 
financial markets from any intervention and allowing the market to determine credit distribution.   

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Under the auspices of the big financial institutions, the majority of sub-Saharan economies initiated 
financial liberalization programs from the 1980s. Apart from interest rates liberalization, many other 
measures were implemented in Africa as part of financial reforms (bank restructuring, abolition of 
direct monetary control, strengthening supervision). Nevertheless, monetarist neoliberal policies did 
not bring a miracle solution to economic development. Reinhart and Tokatlidis (2003), talking about 
sub-Saharan Africa, claimed that financial reforms had only slight effects on economies. The main 
reason of that failure is the existence of imperfect and incomplete markets, asymmetric information 
and an unstable economic environment, not conducive to the private sector.After more than two 
decades of liberalization in WAEMU, globally, the situation of banks in the banking system is satisfying. 
The bank credit to GDP ratio went from 11.63% in 2001 to 26.73% in 2013 (BEI, 2016). Between 2001 
and 2007, the average annual growth rate of credits ratio was at 13.7%. In the meantime, the average 
real interest rate of bank credits settled at 13.7% in the Union, against 5.81% in Morocco.   
From the preceding, the central issue of this study is around the following fundamental question: in 
what ways does interest rate liberalization stimulate credit to the private sector in the WAEMU zone? 
Hence, the general objective of this study is to analyze the effect of interest rate liberalization on credit 
supply to the private sector in the WAEMU zone. Specifically, on the one hand, we will examine the 
effect of financial repression on bank credit supply to the private sector in the face of credit request. On 
the other hand, we will appreciate the impact of financial savings on banks’ capacity to supply bank 
credit. In relation with our objectives, we formulated the two following hypotheses. First of all, 
financing the economy increases when the level of financial repression decreases. Second of all, an 
increase of financial savings is favorable to credit supply to the private sector.   
Interests and stakes do not lack in this study. In fact, the constraints of financing the economy remain 
a central issue in sub-Saharan Africa, particularly in the WAEMU countries where those constraints 
imply excess bank liquidity. The study contributes in moving forward the literature on the link between 
interest rates and bank credit supply in the WAEMU zone.   
At the methodological level, the study adopts the Pool Mean Group (PMG) and Mean Group (MG) 
methods respectively proposed by Pesaran et al. (1999) and Pesaran and Smith (1995). The advantage 
of these estimation methods is the introduction of heterogeneity in coefficients dynamics. By using the 
PMG method, the article highlights the convergence of long-term determinants on credit supply within 
the Union, while the short-term dynamics remain heterogeneous. This hypothesis seems reasonable 
for the WAEMU countries sharing the same monetary policy and aiming at the convergence of their 
economies in the long run. However, the study cannot be done a priori, it must be empirically tested. 
We used annual data over the period from 1982 to 2015. The choice of this period is according to the 
availability of data.   
The present article is organized in the following way: Section 2 is dedicated to the literature review of 
the relationship between interest rate liberalization and bank credit supply to the private sector. Section 
3 will present the methodology of the study. Section 4 presents data source and variables description. 
Section 5 will speak about empirical results, particularly those of the econometric analysis of the 
relationship between interest rate liberalization and bank credit supply to the private sector. Section 6 
is devoted to the conclusion of the study.   
2. Literature  Review  

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This section revisits the theoretical and empirical literature on the relationship between interest rate 
liberalization and bank credit. But before that, we examine the impact of interest rates liberalization.   
2.1. Theoretical Literature Review of the Impact of Interest Rate Liberalization   
“Financial repression” compels banks to set low – and sometimes negative – interest rates (McKinnon, 
1973). It discourages savings and is harmful to the accumulation of the production capital. The analysis 
of McKinnon and Shaw aims at showing that within the framework of a financially repressed economy, 
setting rates below their equilibrium value reduces savings (reduction of bank deposits) for the benefit 
of current consumption. Such a measure reduces the quantity of funds available for investments, which 
is a consequence of the reduction of bank deposits. Conversely, interest rate liberalization favorably 
acts on savings. It ensures better mobilization of resources and increased investments. Thus, it permits 
income growth and economic development. According to financial liberalization theoreticians, 
underdeveloped countries suffer less from lack of financial resources than from a banking 
intermediation which is now ineffective due to distortions associated with the administration of interest 
rates. In addition, the model of Shaw (1973) is based on a debt-intermediation financial system. It is a 
model in which investors are not compelled to auto financing, but financial intermediaries fully play 
their role of turning savings to investments. The initial models of McKinnon and Shaw were taken up 
and enriched by a great number of authors (Kapur, 1976;  
Fry, 1978; Galbis, 1977; Mathieson, 1979). The McKinnon/Shaw approach was questioned by post 
Keynesians and Neostructuralists. The interest rate liberalization approach neglected many of the most 
distinctive foundations of developing economies. The first foundation is highlighted by post Keynesians 
(Burckett and Dutt, 1991). According to these authors, interest rates increase does not forcefully lead 
to credit and investments increase. Indeed, according to Keynesian concepts, they consider that 
investment does not depend on the amount of deposits but rather on the anticipated demand. Hence, 
interest rates increase would definitely lead to savings increase, but also to consumption reduction 
since the substitution effect outweighs the income effect. In other words, if savings remunerations 
consistent enough, households are prompted to assign a part of their consumptions for the benefit of 
increasing their savings. So, according to the post Keynesians, interest rate liberalization leads to 
economic slowdown due to investments reduction induced by the reduction of global demand.   
In addition, interest rates increase following financial liberalization will weigh down the cost of credit 
(Davidson, 1986). The second one is related to asymmetric information suitable for financial markets 
(Stiglitz and Weiss, 1981). According to these two authors, the McKinnon/Shaw approach does not take 
into account market imperfections. In this criticism, they pay particular attention to the microeconomic 
foundations of macroeconomic policies. The authors show that imbalances on the credit market do not 
only come from governments’ intervention but also from the adverse selection and incentive effects. 
They consider that, in a context of information asymmetry, it is difficult for interest rate liberalization 
to effectively operate through enhanced resource allocation and steering of savings towards more 
productive sectors. The third foundation is related to the existence of the informal sector (Taylor, 1983; 
Van Winjbergen, 1983). This criticismtakes into account the existence of informal financial markets 
and their greater effectiveness in terms of resource allocation. Interest rates increase in the formal 
sector leads to interest rates increase in the informal sector, which brings about higher investment 
credit and, therefore, an increase of the general price level (cost-push inflation).  

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2.2. Empirical Literature Review on the Relationship Between Interest Rate and Bank 
Credit  
There are a considerable number of empirical studies to confirm or infirm McKinnon/Shaw 
hypotheses. Demirgüç-Kunt and Detragiache (1998) brought to notice that interest rate liberalization 
policies exacerbated competition between banks, which incites to take a lot of risks, thereby leading to 
serious financial crises. In the same logic, Guillaumont and Kpodar (2006) show that interest rate 
liberalization positively influences economic growth. However, the latter in penalized by the financial 
instability deriving from it. Conversely, in the case of developing countries, Giovannini (1983, 1985) 
finds that savings do not significantly answer to real interest rate increase. According to Greene and 
Vallanueva (1991), interest rates increase reduced investments in thirty three developing countries. 
Demetriades and Devereux (1992) also reach a similar conclusion on a sample of sixty four developing 
countries. In the same vein, the study of Nadem, al. (2016) on credit supply in Pakistan revealed a 
harmful effect of interest rate raise on credit to the private sector, both in the short and long run.  
In Africa, empirical studies do not lack. Mwega and Ngola (1991) used Kenyan data to test the 
relationship between interest rates and savings. The results reveal that real lending rate has marginal 
influence on savings in Kenya. They also noticed that high interest rates impede credit request and 
therefore impede investment. On a sample of thirty African countries, Diery and Yasim (1993) indicate 
that the real interest rate on deposits has a positive and significant impact on savings. Moreover, they 
find that savings have a strong impact on investment, but interest rates have a negative impact on the 
latter.  
In Nigeria, Onwumere et al. (2012) find that interest rate liberalization had a non-significant impact of 
savings but a robust and negative impact on investment. Consequently, Yazid (2007) find a lowly 
significant and negative relationship between financial liberalization and household savings in Algeria. 
According to this author, financial liberalization reduced household savings. This result is explained by 
the fact that liberalization allowed households’ easier access to consumer credit.  
3. Methodology  
 In this section, we firstly present the model specification and secondly, the PMG estimation 
methodology.  
3.1. Model Specification   
The model to estimate in this paper can be specified in the following way:  
   (1)  
Where is the bank credit granted to the private sector to GDP, the bank lending rates in annual 
percentage,  is a financial repression indicator measured by banks’ reserves in the M2 money supply 
percentage,  a financial savings indicator measured by the volume of deposits in GDP percentage, 

the non-performing loans measured by delinquent credits in credit granted percentage, , the 

consumer price index and the budget deficit measured by the gap between public revenues and 
expenditures.  
3.2. The Pooled Mean Group Estimation  

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The estimation technique chosen is the one proposed by Pesaran et al. (1999), the PMG estimator. 
Following Pesaran et al. (1999), Eq.(1) can be seen as an autoregressive distributed lag (ARDL) model 
whose form is : is a vector of explanatory variables; is represents the fixed effect (country). The 

following long term  
If variables are co-integrated, then the term is a stationary process. In that case, the model can be re-
specified under the form of an error-correction model in which the short-term dynamics is influenced 
by the long-term relationship gap: 
 
 
 
 
 
 
 
 

 

 
 
Where  is the coefficient of adjustment, is the vector of long-term coefficients and is the variation 

operator between two successive dates. We expect that One of the advantages of ARDL models is that 
short- 

  
Where 

a  vector  of  coefficients;  ascalar  and 

relation ship is derived from this model:   

  

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estimator allows heterogeneity both in short-term parameters and long-term coefficients. The MG 
estimator estimates the equation for each country of the sample and then calculates the unweighted 
means of coefficients on the whole panel. The homogeneity hypothesis of long-term coefficients is 
empirically tested. To this end, one recourses to a Hausman test applied to the difference between MG 
and PMG estimators. Under the null hypothesis, this difference is not significant, and then the PMG 
estimator in preferable.   
4. Data and Variables Description  
The empirical study uses the annual data of 7 WAEMU countries excepting Guinea Bissau. The 
countries are Cote d’Ivoire, Senegal, Niger, Mali, Burkina Faso, Togo and Benin. The study data come 
principally from two major sources: BCEAO and the World Bank’s World Development Indicator 
(WDI). The study is on the 1982-2015 period with34 observations. The descriptive statistics of all 
variables are consigned in Table 1. In this table, one notices that the average interest rate is 11.31% on 
the period of study. That rate indicates high cost of credit in the WAEMU zone. In the meantime, credit 
granted to the private sector as related to GDP has a mean of 17.65%. This very low level can be 
associated to the very high cost of credit in the zone.  
Table 2 shows high correlation between explanatory variables. Of all those variables, the pair bank 
deposits (DEP) and consumer price index (CIP) presents the higher correlation coefficient (0.63), but 
below 0.8. The pairs bank credit to the private sector (CRED) and bank deposits (DEP), then lending 
rate (R) and non-performing loans (NPL) respectively present correlation coefficients of 0.34 and 0.44. 
The inclusion of explanatory variables in our model is thus justified in addition with their theoretical 
interest. 
Table 1. Descriptive Statistics  
 Variables  Obs.  Mean  Std.Dev.  Min  Max  

 

Bank credit to private sector 
(CRED)   
Financial Repression (FR)  
Bank Deposits (DEP)  
Lending Rate (R)  

238  17.65622  8.290538  3.302083  46.2638  

233  14.58288  11.54087  0.1493967  68.7819  
229  15.21742  9.725433  2.751303  47.6188  
238  11.31017  2.852473  0.85  18.779  
232  5.303428  3.001261  0.2529732  17.80329  

term and long - term indicators are jointly estimated. Moreover, these models allow the presence of variables that can  

be integrated in different orders,  and , or co - integrated (Pesaran et Shin, 1999). Th e PMG estimator allows  

short - term coefficients and the adjustment coefficient to vary according to the countries, but long - term coefficients  
are identical for all countries ( In this study, the PMG estimator is based on the following error - corre ction  

model:   

  
Where   

It was shown that imposing an identical coefficient to the restoring force could lead to bias (Kiviet, 1995). The MG  

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Table 2. 

Matrix 
of 

Pearson Correlation Coefficients 
 
 
 
 
 

   
 
 
 
 
 
 
 
 

CRED  R  FR  DEP  CPI  NPL  DB  
Note: * 5% threshold significance   
5. Empirical Results   
The empirical analysis follows the subsequent approach. Firstly, we apply unit roots tests to the series 
in order to study the stationarity of variables. Second, we estimate long-term coefficients with the PMG 
estimator. The integration order of variables is tested according to the tests of Im, Peseran and Shin 
(IPS, 2003). The null hypothesis of the test assumes that all series are non-stationary against the 
alternative hypothesis which states that only one fraction of series is stationary. The test results 
summarized in Table 3 show that at the 5% threshold, the null hypothesis confirming the presence of 
unit root cannot be rejected for all level variables. These results show that credit to private sector 
(CRED), deposits (DEP) and inflation (CPI) are not stationary in level. However, variables are all 
stationary in first difference; they are I(0) and I(1). This implies that there is a presumption of co-
integration relationship between the different variables. We apply the co-integration test of Pedroni 
(1999) and the results are consigned in Table 4. Over all variables, on the seven statistics, four are in 
favor of the existence of a long-term relationship between credit supply and other variables. This 
suggests that variables are co-integrated and we will use an error-correction model to estimate the long 
term relationship1. Once the presence of co-integration is detected, the objective following is to estimate 
the long-term relationships between variables.  
 Table3. Results of Panel Unit Root Tests with Im,Pesaran and Shin (2003)  
  

                                                      
1 Pedroni (1999) shows that the statistics panel-ADF and group-ADF have better finite distance properties than other tests statistics.  

Budget Deficit (DB)  
Consumer Price Index (CPI)  
Non Performing Loans (NPL)  

238  74.10916  24.1144  31.19  116.06  
226  11.68508  11.60192  0.7622925  61.75492  

CRED  
R  
FR  
DEP  
CPI  
NPL  
DB  

1.00              

-0.2939*  1.00            
-0.1194  -0.1627  1.00          
0.3460*  -0.1213  -0.0669  1.00        
-0.1487  0.0502  -0.2768*  0.6302*  1.00      
-0.0680  -0.4474*  0.2503*  -0.2223*  -0.4721*  1.00    
-0.0143          -0.1525  0.0525  0.3086  0.2152  0.0433  1.00  

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Variable  

Level   First Difference   

Statistics  P-values  Statistics  P-values  

LCRED  
LR FR  
LDEP  
LCPI  
LNPL  
LDB  

3.381  
-4.029**  
-3.402**  
-0.122  
-0.604  
-1.924*  
-3.933**  

0.999 0.000 
0.000 0.451 
0.272 0.027  
0.000  

-10.009**  
-14.579**  
-13.181**  
-13.124**  
-7.507**  
-13.122**  
-12.297**  

0.000 
0.000 
0.000 
0.000 
0.000 
0.000  
0.000  

          Source: Auther’s computation   
Note: * (**) means that the rejection of the unit root hypothesis at the 5% threshold (1%). The choice of 
lags is based on the Akaike Info Criterion.  

 
                Source: Auther’s computation   
Note : *(**) shows the test significance at the 10% threshold (5%). The choice of lags is based on the 
Akaike Info Criterion.  
The PMG and MG estimates are consigned in Table 6. The Hausman test presented in Table 5 shows 
that the homogeneity hypothesis of long-term coefficients cannot be rejected, which means that PMG 
estimations are the most appropriate ones. This result was expected a priori and reasonable for 
WAEMU countries that share the same monetary policy and aim at the convergence of their economies 
in the long run2. In that case, the results interpretation will be on the PMG method.  
Table 5: Hausman Test Result  

Variables  Coefficients  Difference (b-B)  

MG (b)  PMG (B)  

LR  -0.670  -0.733  0.063  
LFR  0.007  -0.210  0.217  
LDEP  0.567  0.509  0.058  
LCPI  -0.838  -0.702  -0.136  
LNPL  -0.159  -0.416  0.257  
LDB  -0.108  -0.066  -0.042  

                                                      
2 MG estimators only give coherent results when the panel dimension approaches infinity (Pesaran and Smith, 1995).  

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chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 
12.05 Prob>chi2 = 0.0609  

 

                  Source:  Auther’s computation  
Note: The Hausman test is applied to the difference between MG and PMG. Under the null hypothesis, 
the difference between the estimated MG and PMG coefficients is not significant and PMG is more 
effective. The test probability is superior to the 5% threshold. The PMG estimator, the effective 
estimator under the null hypothesis, is preferred.  
Most long-term effects have the same sign in the two regressions, but their scale is varying. In the “PMG 
estimators” regression where 7 countries are compelled to have the same long-term relationship, long-
term coefficients are practically all significant. The estimates seem satisfying. The coefficient values 
obtained are nearly all significant, except public expenditures. The phi adjustment coefficient is 
statistically significant at the 1% threshold. This confirms the existence of the long-term relationship 
between variables. phi is equal to -0.311, which implies that an imbalance coming after a shock is 
completely corrected in the first term of the fourth year following a 31.1% rate per year.   
Table 6. Estimates of the Long-Term Relationship  
  
Variables  PMG  MG  
Coef. S.E p-value Coef. S.E p-value LR -0.733 ** 0.080 0.000 -0.670* 0.277 0.016  
LFR  -0.210**  0.030  0.000  -0.007  0.085  0.929  
LDEP  0.509**  0.075  0.000  0.567  0.298  0.057  
LCPI  -0.702**  0.183  0.000  -0.838  0.588  0.155  
LNPL  -0.416**  0.036  0.000  -0.159  0.145  0.273  
LDB  -0.066  0.043  0.131  -0.108  0.281  0.699  
              
Coef. of adjustment  

Phi  -0.311**  0.120  0.010  -0.571**  0.102  0.000  

  

-0.004  
-0.054  
0.050  
-1.332**  
0.100**  
0.004  

0.103 
0.028 
0.068 
0.209 
0.028  
0.029  

0.966 
0.056 
0.465 
0.000 
0.000  
0.881  

0.118  
-0.020  
-0.127  
-1.002**  
0.082  
0.042  

0.105 
0.034 
0.079 
0.274 
0.050  
0.063  

0.261 
0.553 
0.110 
0.000 
0.101  
0.501  

 
      Source: Auther’s computation  
Note: The upper pad shows long-term coefficients and the lower pad shows short-term coefficients. * 
(**) shows the non-rejection of the long-term coefficients’ null hypothesis of homogeneity at the 5% 
threshold (1%).   
In the long run, the repression level has a significant and negative impact on credit to the private sector. 
This means that an increase of the banks’ reserves ratio on money supply would reduce bank loans 
possibilities. According to McKinnon (1973), the higher that ratio, the more “financially repressed” the 

  

  

  

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bank system is. Financial repression is thus harmful to credit activity in the WAEMU zone. Concerning 
the effect of lending rate on bank credit supply to the private sector, the former does not have the 
expected sign. Indeed, interest rates increase negatively affects credit supply. The higher credit costs, 
the less banks are able to finance the activity. High credit costs discourage credit request which, as a 
last resort, negatively impacts on bank financing of the economy. At a given level of the lending rate, 
market balance might be characterized by credit rationing. Imperfections and the oligopolistic 
structure of credit market begin the expected advantages of liberalization. This result is in conformity 
with that of Tanimoune (2001) who shows that interest rates liberalization did not favor firms’ access 
to credit in the WAEMU zone. 
As for bank savings, it has a significant and positive effect on credit to the private sector in the zone. 
Bank deposits increase is favorable to bank financing of the activity in the Union. Liquidity 
management seems to be an important factor in credit decisions for the private sector in the WAEMU 
zone. This result was highlighted in the study of Saxegaard (2006); according to him, the “willful excess 
liquidity” of the Union’s banks is the consequence of the sociopolitical instability observed in the zone. 
Furthermore, the results show that inflation is harmful to bank credit to the private sector. Indeed, for 
given and fixed real interest rates, inflation increase demands an increase of nominal interest rates. The 
consequence would be weighing down credit cost and discouraging companies from borrowing. Most 
theories are conclusive enough on the harmful effect of inflation on credit.   
About the effect of non-performing loans, it is in conformity with our expectation. The impact of 
nonperforming loans on credit to the private sector seems very significant and negative in the long run. 
Bad credits have a crowding-out effect on private investments financing. Indeed, the more doubtful 
debts banks have, the less they are able to offer new credits, which reduces credit offer at the 
macroeconomic level. The effect of budget deficit is not significant, which means that it is not possible 
on the period of study to mention the notion of “crowding-out effect” between the public sector and the 
private sector. Public expenditures might be complementary with private investments.   
6. Concluding Remarks  
In this study, our objective was to analyze the effect of interest rates liberalization on bank credit to the 
private sector in the WAEMU zone, on the period from 1982 to 2015. To this end, we estimated a panel 
data model between six explanatory variables and bank credit granted to the private sector related to 
GDP. The results show that financial repression reduction, especially the reduction of compulsory set-
asides imposed to African banks, is favorable to bank financing of the private sector. The same applies 
for the reduction of non-performing loans and credit cost. Likewise, fighting inflation is favorable to 
bank financing of the activity. In sum, these results provide a given number of implications in terms of 
policies. First of all, the accommodative policy started by the Central Bank of West African States 
(BCEAO) should be pursued. BCEAO should reduce its compulsory set-asides rates in order to 
encourage bank credit to the private sector. Moreover, bank risk control is useful in increasing banks’ 
share in financing the activity. Secondly, macroeconomic stability is an important requirement for the 
bank financing of the activity. A future study could be dedicated to determining the threshold from 
which inflation negatively acts on bank credit supply to the private sector.   
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