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\title{Prudential Regulation and Banking Efficiency in MENA Countries}
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             \author[1]{Dr. Nadia Mansour  Bouzaida}

             \author[2]{Pr. Ezzeddine  Zouari}

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\date{\small \em Received: 16 December 2017 Accepted: 2 January 2018 Published: 15 January 2018}

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\begin{abstract}
        


This paper analyses the impact of the prudential rules on bank efficiency and the relationship between profit efficiency and banking risks by introducing the financial and the macroeconomic determinants (real GDP growth rate, inflation, real GDP growth rate, inflation, the governance indicator ...).For this reason, we used a sample of 146 conventional banks in MENA countries during the 2003-2014 period, whose purpose was to determine the specificities of these countries concerning the determinants of profit efficiency.

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\keywords{bank regulation, profit efficiency, MENA countries.}

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\section[{Introduction}]{Introduction}\par
n recent years, financial crises have multiplied, affecting more and more the financial stability and economic performance of many countries.\par
In this context, history has shown that the banking system can not remain immune to this instability. Indeed, the financial liberalization, the decommissioning of the credits, the variation of the interest rates has been at the origin of new threats. Also, these developments pose a challenge for both supervisors and banks.\par
Indeed, since the 1990s, the banking systems have undergone many restructuring resulting in concentration operations. Along with this, they also experienced a significant increase in competition, especially following the deregulation movement and liberalization, \hyperref[b10]{Berger and Mester, (1997)}. Increasingly subject to the different demands of globalization processes and surrounded by an uncertain environment, banks are forced to increase their efficiency to enhance their performance and preserve their sustainability.\par
As a result, the efficiency of intermediation has become a key element in the success of financial liberalization movements.\par
Indeed, in a context in which the liberalization of economies has widened the scope and manifestations of competition, banking firms are increasingly subject to the need to improve their productive behavior, \hyperref[b51]{Lesueur and Plane (1997)}.\par
As is the case with businesses, some banks are considered better than others. Banks' efficiency depends on the quality of their organization, as this allows them to manage effectively the flows where the operations involve transformation. In this context, banks were considered "efficient" when they have a good command of the technical aspects of their activities and therefore come up with the maximum number of services from a minimum level of resources.\par
Indeed, efficiency was defined as: "An internal measure of company performance, it is very frequently appreciated regarding production, profit or productivity costs and was measured by the number of resources used to produce a unit of goods or services,"  {\ref Johnson \& Scholes, (1997)}. Thus, the analysis of this notion makes it possible to make comparisons between the competitiveness of the banks.\par
However, this is only the first notion of efficiency in the banking business, as only physical quantities of resources were taken into account. Indeed, a bank was considered technically efficient when it comes to adapting to different constraints that surround it, taking into account risks. Thus, the efficiency of a bank is measured in two ways, firstly, the quality of the organization and position, and secondly, market knowledge.\par
In recent years, MENA countries have adopted new reforms in the context of financial liberalization and restructuring of the banking system. Therefore, the overarching goal of financial regulation is to push banks to improve the level of liquidity and solvency, \hyperref[b49]{Lee and Chih, (2013)}. To this end, banks are required to put in place strategies involving the optimal allocation of resources and effective monitoring of environmental changes.\par
Hence the question arises as to the impact of prudential regulations on banks' profit of these countries and the relationship between bank risk and efficiency.\par
This situation leads us to ask the following questions: What is the relationship between profit efficiency and banking risks? What is the impact of prudential regulation on bank efficiency?\par
The problem developed is that of assessing the impact of banking regulation in the MENA countries on bank efficiency. 
\section[{II.}]{II.} 
\section[{Literature Foundation}]{Literature Foundation}\par
There are many researches focus on the determinants of bank efficiency. Among the first studies, there are those of \hyperref[b57]{Miller and Noulas (1996)}, \hyperref[b22]{Dietsch and Lozano -Vivas(2000)}, \hyperref[b33]{Grigorian and Manole (2002)}...all I aim to study the effects of environmental variables on bank efficiency. However, there are other studies more recent, such as the example of \hyperref[b35]{Halkos and Salamouris (2004)}, \hyperref[b27]{Fries and Taci (2005)}, \hyperref[b38]{Havrylchyk (2006)}. The purpose of these latest studies is to explain, from internal and bank-specific variables, the various efficiencies between banks compared to other environmental variables.\par
Also, as a result of increased globalization and the opening of financial frontiers, banks are continually trying to diversify their income while maintaining a defined level of capital.\par
The notion of efficiency explains the presence of excessive profits. When firms are efficient, they can gain market share while increasing concentration. As with businesses, bank efficiency could be measured at different levels. However, it is essential to present the concept of profit efficiency. a) Profit efficiency \hyperref[b10]{(Berger and Mester, 1997)} Profit is the broadest concept of efficiency. \hyperref[b56]{Maudos and al. (2002)} indicate that the profit efficiency calculation offers a source of information for the management of a bank more than the partial vision of the cost efficiency. This type of efficiency refers to the maximization of value while accounting for errors in outputs and inputs. Profit efficiency requires a great deal of managerial attention from the banks, especially about increasing or decreasing the marginal price of income.\par
There are usually two types of profit boundary modeling in the banking literature. The first type of modeling is the standard profit function . This function was based on determined product prices, so each bank offers products without reducing them, to increase quantities. However, this assumption is not sufficient when competition is imperfect in banking markets. Indeed, according to \hyperref[b10]{Berger and Mester (1997)}, banks that carry out an activity are obliged to reduce prices and cannot maximize profits.\par
To solve this problem, other authors like Humphrey and Pulley (1997) proposed an alternative profit function. In this alternative model, the prices of banking products were no longer considered given; it is the quantities that are. As a result, under this approach, banks are expected to have the market power to set their prices. Thus, alternative-profit efficiency incorporates the differences in market power between banks and their ability to exploit them.\par
The function of the standard profit efficiency is as follows:\par
(1) 
\section[{With:}]{With:}\par
The variable : The price vector of variable outputs : Inefficiency that reduces profit The error variable. \hyperref[b10]{Berger and Mester (1997)} take into account all the interests and revenues obtained from the outputs.\par
In this function, it should been noted that the concept of profit assumes that all banks implement the same type of technology. However, at the practical level, this is unworkable given that foreign banks use more sophisticated than domestic banks. In this context, profit efficiency is the ratio between the current profits of the banks studied and the maximum that can be made by the banks if they are as efficient as the best bank of the sample studied.\par
This function makes it possible to consider the bank profit efficiency as a portion of the maximum profit generated by the bank that is the best on the market.\par
Unlike cost efficiency, profit efficiency can be negative, since it is possible that firms waste more than 100\% of their substantial profits. 
\section[{b) Prudential regulation and banking efficiency}]{b) Prudential regulation and banking efficiency}\par
Public authorities intervene in the banking field in three forms: prudential regulations, deposit insurance, and central bank interventions as lender of last resort. The adjustments made by these different interventions are often controversial. However, they all revolve around the fragility of banks.\par
Indeed, most regulatory systems require financial institutions to transfer financial information to supervisory bodies. This situation needs a permanent and high quality work. However, complying with different regulatory rules makes it easier for financial institutions to access external sources of financing such as equity or debt.\par
In this perspective, several banks have seen an improvement in their financial structure, including an increase in equity. Prudential regulation programs are an external governance system designed to compensate for failures, valuation systems and internal control of banks.\par
Under this prudential regulation, information asymmetry allows banking organizations and borrowers, regardless of size, institutional form and function in the economy, to behave opportunistically.\par
In this case, the delegation of control to a regulatory body has the main effect of improving banking efficiency.\par
Thus, prudential regulations allow all financial institutions to be protected from relative risks because of the nature of their activities while allowing them to avoid the advent of a systemic crisis. The different regulations require them to maintain a certain level of equity and liquidity.\par
In other words, prudential regulation aims at optimizing the soundness of the banking system to encourage financial institutions to efficiently and effectively assume the risks related to their activities while having a solid base concerning the different banking risks.\par
In this framework, economic theory provides many forecasts on the impact of regulatory and supervisory policies on bank efficiency. Some studies show that a low level of capital increases the risk of bank failure, while a high level can cause unnecessary costs to banks and, as a result, reduce the efficiency of the banking system.\par
Bath and al. (2006) studied how banking regulation works and how it can affect banking activity. Their research in most countries shows that standard regulation does not improve the efficiency of banks.\par
However, according to \hyperref[b3]{Awdeh and al. (2011)}, there is a positive correlation between bank profitability and capital increase. For \hyperref[b6]{Beltratti and Stulz (2009)}, banks with higher capital and more stable financing would obtain better results.  {\ref Altunbas and al. (2007)}, Hughes and Mester (1998), emphasized the importance of analyzing the impact of efficiency on risk and capital. They conclude a positive relationship between risk and capital level, which reflects the preference of regulators for a high level of this last by limiting risk-taking activities.\par
Shepherd and De Young (1997) and \hyperref[b68]{Williams (2004)} indicated that a decrease in efficiency increases costs because banks do not adequately monitor credits and control expenditures effectively.\par
Regarding \hyperref[b56]{Maudos et al. (2002)}, higher risk banks present a higher level of profit efficiency. Banks with higher deposit credit ratios are therefore more profitable and less under pressure to control costs.\par
According to , big banks tend to be closer to the efficient frontier than smaller banks. Indeed, big banks generally have high market power and can, therefore, have their inputs cheaply \hyperref[b37]{(Hauner, 2005)}. However, \hyperref[b18]{Cook et al. (2000)} found a different result by analyzing the effects of financial liberalization on the efficiency of Tunisian banks. According to this study, big banks are created, first, for political purposes. From where they will grant credits without taking into account their profitability.\par
For the ratio of costs to revenues, it could be used as a tool in bank performance analyzes when reviewing its operational efficiency. \hyperref[b24]{Francis (2004)} has shown an inverse relationship between this ratio and profit efficiency. Shehzada and De Haan (2012) found that if the ratio of costs to revenues decreases, managerial efficiency will improve.\par
Regarding the macroeconomic variables,  {\ref Athanasoglou et al. (2008)}, \hyperref[b59]{Perry (1992)} argued that the variables used are inflation, interest rate, and GDP rates. \hyperref[b61]{Revell (1979)} introduced the relationship between efficiency and benefit inflation banks. An inflation rate fully anticipated by the bank's management implies that banks can adjust interest rates appropriately to increase their revenues faster than their costs and thus gain higher economic profits. Most studies \hyperref[b12]{(Bourke, (1989)}, \hyperref[b58]{Molyneux and Thornton, (1992)}) have shown a positive relationship between inflation and long-term interest rate and profit efficiency.\par
Recently, Demirguc-Kuntand Huizinga (2000) tried to identify the effect of annual GDP growth rates and GDP per capita of bank efficiency. They found a positive relationship between this last and these two macroeconomic indicators.\par
Regarding the governance indicator developed by Kaufmann and al.,  {\ref (2008)} and Kaufmann and al., (2012), the studies conducted by  {\ref Ciancanelli and Reys (2001)} and \hyperref[b47]{Lassoued and al. (2015)} found a positive relationship between this indicator and the efficiency level of banks.\par
Also, these studies have focused on developed countries. This observation leads us to propose, on the one hand, a new light on the relationship between banking regulation and banking efficiency, and on the other hand, a relationship between banking efficiency and risks by studying the banking sector in the MENA zone. 
\section[{c) Measure of efficiency}]{c) Measure of efficiency}\par
Generally, banking efficiency could be determined by two types of methods: parametric methods and non-parametric methods . These two methods were distinguished by the assumptions imposed on the data. A first difference was observed at the modeling level. Then there are the differences in whether random errors were taken into account or not.\par
On the other hand, non-parametric models were based on the production boundary using linear programming on which all observations were made without necessarily taking into account the functional form of the production function.\par
Indeed, the DEA or Data Envelopment Analysis approach is one of the nonparametric methods. It was Farell's (1957) work that highlighted this method of measuring bank performance. In recent years, the DEA method has been highly successful, particularly after development and the various modifications made to it,  {\ref Seiford and Thrall, (1990)}.\par
The DEA method of measuring bank efficiency gives banks the opportunity to evaluate their performance by the efficiency frontier. Its purpose is to determine a synthetic and comprehensive measure of the performance of financial institutions that use various resources to create different results.\par
The purpose of the DEA approach is to synthetically and comprehensively calculate the performance of an organization that implements a multitude of resources with the goal of producing multiple outcomes. As part of a financial institution, the DEA method is used to identify best practices. The purpose of this is to set the target values and the indicators that will have to appear in the banks' dashboards. Among the main advantages of this approach, we can distinguish: ? The method suitable for a small sample, and that does not require a cost specification a priori, ? The method to ensure simultaneous management of inputs and outputs, and that can distinguish between technical and scale inefficiency.\par
The DEA model is in the form of a ratio maximization program, as follows, Charnes and al., (1978):\par
(2) Under the constraints:\par
For any DMU (Decision Making Units), k = 1, ? n. With : : They represent respectively the efficiency score of the DMU 0, the output vector of the DMU k and the input vector of the DMU k.\par
Represent respectively the weights relating to n: is the number of DMUs. Among these various advantages, some authors note that the application of this approach is also appropriate for point-of-sale networks since it had generally based on the principle of comparison. Thus, it gives financial institutions the opportunity to realize the " benchmarking "Internally within the distribution network. Thus, the gap between inefficient banks and the efficiency frontier had determined from an efficiency score. In this context, the efficiency measure is the comparison between the observed values and the optimal values of inputs and outputs, \hyperref[b54]{Lovell and al., (1980)}.\par
Moreover, this approach is the most used at the level of the banking sector by making it possible to calculate a synthetic measure of performance, \hyperref[b10]{Berger and Mester, (1997)}.\par
The results of the DEA method could be considered according to two hypotheses: the assumption of constant returns to scale (CRS model) or variable returns to scale (VRS model).\par
? CRS model (Kalaitzandonakes and al., 1992) considers a sample of K firms, each of which uses M inputs to have N different outputs. The baskets of inputs and outputs are reduced by the DEA method to a couple: fictitious input and fictitious output \hyperref[b14]{(Charnes and al., 1978)}. Hence, for a company in the sample, the mathematical programming model of this measure is as follows:\par
(3)\par
Under constraint:For: j = 1.2 ... K With:\par
? and ?: vectors of the coefficients to be estimated : vectors of inputs and outputs of the company "i".\par
For each company, this program maximizes the virtual output / virtual input ratio without exceeding 1. Thus, the companies in the sample were necessarily located on or below the efficiency frontier.\par
According to \hyperref[b17]{Coelli and al. (2005)}, this method assumes that all firms operate on an optimal scale. However, imperfect competition may be, for example, a constraint for this kind of operation. \par
Under constraint:\par
With: any ? ? ? 0 and ? is a vector of N * 1 units.\par
Moreover, in this work, we will use the DEA method according to the VRS model since it makes it possible to test the hypothesis with variable returns of scale. This method is more consistent with the imperfectly competitive environment in which banks operate in the MENA zone. 
\section[{III.}]{III.} 
\section[{Methodology}]{Methodology}\par
From the 1980s, MENA countries began implementing financial liberalization policies as part of C : , W 0, Q 0, X t Ut, Vt: k, X k. Q\par
Yi and Xi and the adjustment plans put in place by the Bretton Woods institutions. These reforms had based on the improvement of monetary policy, the establishment of a prudential framework and the restructuring of the banking system \hyperref[b67]{(Touhami and Solhi, 2009)}.\par
However, few studies have examined the determinants of bank efficiency in developing countries, particularly countries in the MENA region. Also, it is interesting to study the banking system, which is an essential element in the growth and sustainable development of this area, for many reasons. On the one hand, it is a bridge between Europe and Asia, and on the other hand, the region is experiencing rapid growth regarding population and wealth with a relatively young banking sector.\par
Also, the selection is focused exclusively on conventional banks, at the level of this study, to avoid the difficulties due to the lack of homogeneity of banking practices \hyperref[b16]{(Cihak and Hesse, 2010)}.\par
Our sample will cover 146 conventional banks in 17 MENA countries (Algeria, Saudi Arabia, Bahrain, Djibouti, Egypt, United Arab Emirates, Jordan, Kuwait, Lebanon, Malta, Morocco, Oman, Palestine, Qatar, Syria, Tunisia, Yemen) over the period 2003-2014, which gives a panel of 1752 observations. Indeed, this period is rich in events in the MENA zone going from the Iraq war to The Arab Spring While going through the global financial crisis of 2009. Hence the motivation to work in the MENA zone. 
\section[{a) Presentation of the model and definition of the variables}]{a) Presentation of the model and definition of the variables}\par
Over the last twenty years, the majority of MENA countries have gradually implemented policies to transform their financial and banking landscape with the goal of modernizing their systems to make them more efficient and dynamic. In this framework, and to evaluate the profit-making efficiency of the banks, we used the quality of the assets, the capital ratio, the liquidity ratio, the size and the age of the bank as a control variable.\par
Thus, the models used for the study of banking risk, inspired by the research of \hyperref[b49]{Lee and Chih (2013)} and  {\ref Klomp and Hann (2012)} At this level, it should been mentioned that our study consists of estimating models by the Tobit regression method to determine the relationship between financial regulation and profit efficiency since the dependent variable (profit efficiency) is a binary variable. :\par
Indeed, the values of the efficiency scores resulting from the DEA method are between 0 and 1, and, consequently, the dependent variable can not follow a normal distribution. Thus, the MCO method will result in biased and inconsistent estimates of parameters, \hyperref[b32]{Greene, (1981)}. In this study, we will, therefore, use the simple and censored Tobit regression model, Fried, Schmidt, and Yaisawarng, (1999), Lin, (2002), \hyperref[b17]{Coelli and al., (2005)}.\par
In economics, this model had developed by James \hyperref[b66]{Tobin (1958)}, but the term Tobit only appeared in an article by \hyperref[b31]{Goldberger and al., (1964)}. 
\section[{So, the model proposed by Tobin is the following:}]{So, the model proposed by Tobin is the following:}\par
With:\par
The model estimates will be made, therefore, by the Tobit model using the maximum likelihood method. The latter is the most used today. We will begin by defining the log-likelihood associated with the simple Tobit model  {\ref (Bourbonnais, 2015)}:\par
With: Disturbances distributed according to Consider a sample of N observations yi, denoted y = (y 1 ..., yN). The likelihood of this model is defined by: Prudential Regulation and Banking Efficiency in MENA Countries (5) \hyperref[b6]{(6)} : Disturbances distributed according to N .\par
:\par
N .\par
(8) 
\section[{59}]{59}\par
Year 2018\par
© 2018 Global Journals 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XVIII Issue VII Version I ( ) 
\section[{C}]{C}\par
The first product is similar to that obtained by the Probit model since the two modelizations are identical for the event Y i = 0. Indeed, if one defines a dichotomous variable probit zi such that:\par
Then, the probability that the variable yi takes positive values takes the following form:\par
Hence, the probability that yi takes a value of zero was written as follows:\par
The second product corresponds to that obtained by the linear model since it is the likelihood of a Gaussian sample. Therefore, the Log-Likelihood was written as follows:\par
To achieve these goals, we adopted the STATA software in its 13th release.\par
a) The hypotheses In recent years, the banking environment in the MENA zone has undergone a series of restructuring programs aimed at improving the level of efficiency to align with the international financial landscape. Indeed, with the succession of financial crises, it is necessary to find indicators capable of measuring the banking efficiency and in particular "profit efficiency" which is the object of this study. At this level, one must also consider an essential element which is the size of the bank and its effect on the level of profit efficiency.\par
Hence the question underlying this study is whether there is a significant link, on the one hand, between banking regulation and efficiency and, on the other hand, between this last and bank risks for big and small banks in the MENA zone between 2003 and 2014. 
\section[{The relationship between asset quality and profit efficiency:}]{The relationship between asset quality and profit efficiency:}\par
Lee and Chih (2013): The higher the quality of assets, the higher the efficiency level of banks. 
\section[{H1: Improving the quality of assets has a positive influence on the profit efficiency of banks.}]{H1: Improving the quality of assets has a positive influence on the profit efficiency of banks.}\par
The relationship between liquidity and banking efficiency: 
\section[{Ayadi and Pujals (2005), Caprio, D'Apice and al. (2014)}]{Ayadi and Pujals (2005), Caprio, D'Apice and al. (2014)}\par
and \hyperref[b49]{Lee and Chih (2013)}: liquid assets tend to have low returns. An increase in liquidity ratio may imply a decrease in profit efficiency. 
\section[{H2: Liquidity has a negative influence on profit efficiency.}]{H2: Liquidity has a negative influence on profit efficiency.}\par
The relationship between the ratio of costs to revenues and "profit efficiency": \hyperref[b30]{Ghosh and al. (2003)} and  {\ref Shehzada and Haan (2012)}: an inverse relationship between the cost/income ratio and profit efficiency.Francis (2004), 
\section[{H3: A decrease in the cost/income ratio positively influences banking efficiency.}]{H3: A decrease in the cost/income ratio positively influences banking efficiency.}\par
The relationship between capital ratio and profit efficiency:  {\ref Pessarossi and Weill (2015)}: A positive relationship between the ratio of capital and profit efficiency. Hence, the capitalized banks are more efficient. 
\section[{H4: The variable «capital ratio» has a positive influence on "profit efficiency".}]{H4: The variable «capital ratio» has a positive influence on "profit efficiency".}\par
The relationship between macroeconomic variables and "Z-score": Ciancanelli and Reys (  {\ref 2001}) and \hyperref[b47]{Lassoued and al, (2015)}: Global governance indicator developed by Kaufmann et al, (2008.) That refers to the government's ability to formulate and effectively implement of approved policies, has a positive effect on the level of efficiency banks profit.  {\ref Demriguc and al., (2000)};  {\ref Athanasoglou and al., (2008)}: GDPGR and GDPPC have a positive impact on banking efficiency in developed markets. \hyperref[b50]{Lee and Hsieh (2013)}: A positive relationship between inflation and bank profit efficiency. Indeed, an increase in the rate of inflation pushes banks to charge more profits to customers. Also, the interest rate is positively related to the profit efficiency of the banks. Also, the latter will adjust their interest rates in response to the increase in the general price level to mitigate the negative effect of inflation. 
\section[{H5: Macroeconomic variables significantly influence profit efficiency.}]{H5: Macroeconomic variables significantly influence profit efficiency.}\par
IV. 
\section[{Empirical Results}]{Empirical Results}\par
This study involves presenting the results of the analysis to examine the impact of asset quality, efficiency, liquidity, prudential regulation, size and time factor on profit efficiency. Also, we present the significant statistics followed by the models constructed concerning the regression of the variables defined previously on conventional banks of the MENA zone with the empirical results obtained and their interpretations. 
\section[{a) Descriptive analysis of variables and econometric tests i. Descriptive statistics}]{a) Descriptive analysis of variables and econometric tests i. Descriptive statistics}\par
This study will expose the descriptive analysis of the different variables. The table below gives the mean, the standard deviation, the maximum and the minimum of the variables studied during the study of the previously defined models (see appendix 1).\par
Indeed, we notice the disparity of the average values of the explanatory variables and their standard deviations. These two variables suggest that the sample structure is not homogeneous and that additional tests are required to select the appropriate estimator. 
\section[{b) Econometric Tests}]{b) Econometric Tests}\par
We will rely on econometric following: Multicollinearity test, stationarity test, and heteroscedasticity test. 
\section[{i. Multicollinearity test}]{i. Multicollinearity test}\par
According to \hyperref[b11]{Bourbonnais (2009)} to decide on a problem of collinearity between the independent variables included in a regression model, it is necessary that the correlation coefficient exceeds the order of 0.7.\par
Examination of the correlation matrix and the VIF test (see Appendix 2 and 3) highlights the absence of a multicollinearity problem. 
\section[{ii. Stationarity test}]{ii. Stationarity test}\par
To do this, we would be based on the Dickey-Augmented Float (ADF) and Phillips-Perron (PP) tests. 
\section[{Table 3: ADF \& PP tests}]{Table 3: ADF \& PP tests}\par
Significant at the 1\% level, ** significant at the 5\% level, * significant at the 10\% level.\par
This results show that some of the variables were stationary in level for the two tests ADF \& PP and others were stationary in difference. 
\section[{iii. Heteroscedasticity test}]{iii. Heteroscedasticity test}\par
This is to test the variance of the standardized residuals is constant or homosedasticity, \hyperref[b23]{Evrard and al., (2003)}.\par
To do this, we adopted the "Breush-Pagan" test, the value of chi2 displays a value of 101.05 having a level of significance of 0.000 below the critical threshold of 5\% (see  {\ref Appendix 4)}. This leads us to reject the hypothesis of homoscedasticity and to confirm the presence of a problem of heteroscedasticity. To solve this problem, the estimation of the model will be carried out by the Robust command. 
\section[{c) The results of the estimates}]{c) The results of the estimates}\par
The results of the Tobit model estimation using the maximum likelihood method, with the Robust command, are:   
\section[{C}]{C}\par
For the global model:\par
The size variable was positively related to profit efficiency. This result corroborates with the studies of  {\ref Altunbas et al. (2007)} who argue that big banks are more efficient than small banks. Indeed, the largest banks have better asset portfolio management as well as better performance when choosing investment projects. Any increase of one unit of this variable, will increase the probability that the bank will be efficient of 1.96312\%.\par
For the RES-LOAN variable, it had negatively related to the profit efficiency score. This result contrasts the findings of \hyperref[b49]{Lee and Chih (2013)}. This result means that any decrease of one unit of the variable RES-LOAN will generate a decline in the bank chance that it is efficient at 0.9126\%. Indeed, the higher the ratio, the higher the amount of non-performing loans, which will degrade the efficiency of the bank.\par
For the LIQ variable, it was negatively related to the profit efficiency of the global sample, according to the findings of \hyperref[b49]{Lee and Chih (2013)}. This result means that any decrease in a unit of the general liquidity ratio will generate a decline of the bank chance that it is efficient at 0.2095\%. As a measure of liquidity, this ratio may reflect the proper use of funding sources. Indeed, the increase in this ratio can significantly impact the efficiency of banks through inactive funds.\par
However, the LDR ratio was positively related to profit efficiency. This result means that any increase in this ratio will improve the level of banking efficiency. Indeed, a high LDR ratio reflects, on the one hand, efficient banking intermediation and, on the other hand, loans financed by unregistered sources, which can affect banks' financial stability (Caprio, D'Apice and al., (2010)). Any increase of one unit of this variable will increase the probability that the bank will be efficient at 11.82257\%.\par
The CIR variable was positively related to profit efficiency, which puts into question the efficiency level of banks in the MENA zone. Any increase of one unit in the ratio of costs to revenues, will increase the probability that the bank will be efficient of 0.13337\%. Indeed, according to the study conducted by \hyperref[b29]{Girardone et al. (2004)} on Italian banks during the period 1993-1996, inefficient banks tend to have high-interest margins and extended branch networks compared to efficient banks. 
\section[{Comparison between big and small banks:}]{Comparison between big and small banks:}\par
Concerning the variable RES\textunderscore NPL, it has a significant positive effect on profit efficiency in the big banks. This result means that as the ratio increases, the level of profit efficiency increases. Any raise of one unit of this ratio will augment the probability that the bank will be efficient at 0.32729 \%. However, this ratio has no significant effect on profitability in small banks.\par
For the CIR variable, it was negatively related to profit efficiency for big banks, according to the results of \hyperref[b49]{Lee and Chih (2013)} who argue that a decrease in this ratio positively influences banking efficiency. Hence, the big banks in the MENA countries are more efficient. Indeed, a decrease of one unit of the variable CIR will generate an increase of the chance that the bank is efficient at 0.2048 \% for the big banks and a decrease at 0.11944 \% for the small banks.\par
The LIQ ratio has a significant negative effect on the efficiency of small banks. Indeed, the higher the ratio, the lower the score efficiency because of inactive funds in these banks. This result means that any decrease of one unit of the LIQ variable generates a decrease at 0.2180 \% of the chance that the bank is efficient.\par
Regarding the LDR ratio, it only affects small banks. Indeed, it is positively related to bank efficiency in the latter. This result means that any one-unit increase in the LDR variable will generate an increase in the probability that the bank will be efficient at 12.77617 \% in profit efficiency. The higher the ratio, the higher the level of bank efficiency is improved which means that funds are financed, firstly, by deposit O costs and therefore ensures the stability of funding. This result corroborates with the findings of Caprio, D'Apice and al., (2014). However, this ratio does not affect big banks because their sources of financing are more stable.\par
For the variable RES-LOAN, it had negatively related to the efficiency score of small banks. Any increase in this ratio will worsen the efficiency of the bank. This result means that any decrease of one unit of the variable RES-LOAN will generate a decline of the bank chance that it is efficient at 0.9395 \%. Indeed, the higher the ratio, the higher the number of bad debts, which will degrade the efficiency of the bank.\par
On the macroeconomic determinants, the results showed a positive relationship between inflation and profit efficiency of the big banks, which marginally supports thefindings of Lee and Hsieh (2013) who argue that when the inflation rate increase, banks tend to charge more to customers. Any raise of one unit of this variable will increase the probability that the bank will be 0.20501\% efficient. Also, the interest rate is positively related to the profitability of small banks. Any increase of one unit of this variable will increase the probability that the bank will be efficient at 0.29005\%. This result means that the latter can adjust their interest rates in response  
\section[{C}]{C}\par
For macroeconomic conditions, the GDPGR had positively related to the efficiency of banks. Any increase of one unit of this rate will increase the probability that the bank will be efficient at 1.18422\%. Banks in countries with higher levels of economic development are more efficient. Our results are in agreement with the results of Johnes, Izzeldin and Pappas (2013), and  {\ref Barth and al. (2013)}.\par
to the increase in the general price level to mitigate the effect of inflation.\par
The real GDP growth was positively related to the efficiency of small banks. Any increase of one unit of this rate will increase the probability of the bank being efficient at 11.375\%. Also, in countries with higher levels of economic development, small banks are more efficient.\par
V. 
\section[{Conclusion}]{Conclusion}\par
Regarding financial profitability, the introduction of the various regulatory requirements aims to reduce exposure to different banking risks. However, most regulatory arrangements require financial institutions to transfer financial information to supervisory bodies.\par
In this context, we have tried throughout this work to highlight the relationship between profit efficiency, as a means of assessing bank performance, and banking risks. Also, the link between profit efficiency and prudential regulation.\par
We found a lack of relationship between prudential regulation and bank efficiency in MENA countries.\par
During the last twenty years, the majority of the countries in this zone have progressively transformed their financial and banking landscape with the aim of modernizing their systems. They were involved in the implementation of a reform to liberalize their banking and financial system. It also allows them to encourage competition and open their financial system to foreign investors (Solhi and Mehdi, 2012).\par
However, improvements in the context of financial and banking reforms are irregular. Hence, the governments of this region are called upon to lighten the legal system to give more opportunities to the banks to diversify their activities, to satisfy better their customers while keeping a well-defined level of the capital to face the possible ones financial crises.\par
It is also important to note that for the majority of MENA countries, the banking sector is an axis of the financial system. Indeed, the region was weakly exposed to many financial crises, but with a financial area characterized by increasing openness to investment and foreign players, the contagion effect continues to amplify. As a result, banking systems are forced to put in place early warning indicators to prevent any possible banking crisis.   \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.85\textwidth}}
With:\\
: Financial determinants\\
: Macroeconomic determinants\\
And:\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.08661137440758293\textwidth}P{0.2618483412322275\textwidth}P{0.5015402843601896\textwidth}}
variables\tabcellsep Definitions of variables\tabcellsep Descriptions\\
Input\tabcellsep Fixed asset\tabcellsep Fixed asset\\
\tabcellsep Funds\tabcellsep Deposits \& short-term funding\\
\tabcellsep Price of fixed assets\tabcellsep Other operating expenses / fixed assets\\
Input Price\tabcellsep Price of FUNDS\tabcellsep Total interest expenses / Deposits \& short-term funding\\
Output\tabcellsep Total loans\tabcellsep Net loans\\
\tabcellsep Investment\tabcellsep Other earning assets\\
Output price\tabcellsep Price of loans Price of investment\tabcellsep Interest income of loans / Other operating income / other earning assets\end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.22385496183206105\textwidth}P{0.27576335877862596\textwidth}P{0.0681297709923664\textwidth}P{0.20114503816793894\textwidth}P{0.08110687022900763\textwidth}}
Variables\tabcellsep ADF\tabcellsep \tabcellsep PP\\
\tabcellsep In level\tabcellsep Indifference\tabcellsep In level\tabcellsep Indifference\\
Vrs (profit efficiency)\tabcellsep 0.0197 **\tabcellsep \tabcellsep \tabcellsep 0.0146 **\\
\tabcellsep (With variation)\tabcellsep \tabcellsep \\
RES\textunderscore NPL\tabcellsep 0.0064 ***\tabcellsep \tabcellsep 0.0072 ***\\
RES\textunderscore Loan\tabcellsep 0.0002 ***\tabcellsep \tabcellsep 0.0002 ***\\
CIR\tabcellsep 0.0271 **\tabcellsep \tabcellsep \tabcellsep 0.02 **\\
\tabcellsep (with variation)\tabcellsep \tabcellsep \tabcellsep (with trend)\\
LIQ\tabcellsep \tabcellsep 0.02011 **\tabcellsep 0.02507 **\\
\tabcellsep \tabcellsep (With trend)\tabcellsep \\
LDR\tabcellsep 0011 **\tabcellsep \tabcellsep 0011 **\\
CAR\tabcellsep 0.0244 **\tabcellsep \tabcellsep 0.0220 **\\
SIZE\tabcellsep \tabcellsep 0.0763 *\tabcellsep \tabcellsep 0.08 099 *\\
GDPGR\tabcellsep 0.0127 **\tabcellsep \tabcellsep 0.0127 **\\
\tabcellsep (with trend)\tabcellsep \tabcellsep (with trend)\\
GDPPC\tabcellsep 0.02163 **\tabcellsep \tabcellsep 0.02163 **\\
INTEREST RATE\tabcellsep 0.0009 ***\tabcellsep \tabcellsep 0.0002 ***\\
GDP DEFLATOR\tabcellsep 0.0021 ***\tabcellsep \tabcellsep 0011 **\\
GOVERNANCE\tabcellsep 0.0000 ***\tabcellsep \tabcellsep 0.0535 *\\
\tabcellsep (with trend)\tabcellsep \tabcellsep (with trend)\end{longtable} \par
 
\caption{\label{tab_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.06666666666666667\textwidth}P{0.023655913978494623\textwidth}P{0.041935483870967745\textwidth}P{0.02150537634408602\textwidth}P{0.05268817204301075\textwidth}P{0.05322580645161291\textwidth}P{0.05268817204301075\textwidth}P{0.05483870967741935\textwidth}P{0.0510752688172043\textwidth}P{0.052150537634408606\textwidth}P{0.052150537634408606\textwidth}P{0.01827956989247312\textwidth}P{0.05376344086021505\textwidth}P{0.002150537634408602\textwidth}P{0.05430107526881721\textwidth}P{0.052150537634408606\textwidth}P{0.052150537634408606\textwidth}P{0.0564516129032258\textwidth}P{0.038172043010752686\textwidth}}
\tabcellsep \tabcellsep p-\tabcellsep value\tabcellsep 0246\tabcellsep 0310\tabcellsep 0542\tabcellsep 0007\tabcellsep 0001\tabcellsep 0041\tabcellsep 0007\tabcellsep \tabcellsep 0489\tabcellsep \tabcellsep 0850\tabcellsep 0003\tabcellsep 0424\tabcellsep 0719\tabcellsep 0.000\\
62 Year 2018 Volume XVIII Issue VII Version I Global Journal of Management and Business Research ( )\tabcellsep variables Global Model Big Banks Small Banks\tabcellsep Coefficient dy / dx T-p-value Coefficient dy / dx T-p-Coefficient dy / dx T-\tabcellsep statistic statistic value statistic\tabcellsep TIME 0.0005756 0.0005738 0.82 0. 410 -0.000258 -0.000258 -0.59 0. 555 0.0008325 0.0008299 1.16\tabcellsep CAR -0.001186 -0.001183 -0.66 0. 510 -0.000551 -0.000551 -0.73 0. 46 5 -0.00180 -0.001802 -1.02\tabcellsep RES-NPL 0.0014884 0.0014838 0.69 0. 490 0.0032729 0.0032729 2.18 0.0 29 0.001292 0.001288 0.61\tabcellsep RES-LOAN -0.009154 -0.009126 -2.64 0008 -0.001306 -0.001306 -0.71 0. 478 -0.009424 -0.009395 -2.70\tabcellsep CIR 0.0013378 0.0013337 3.19 0001 -0.002048 -0.002048 -1.84 0.0 67 0.0011982 0.0011944 3.20\tabcellsep LDR 0. 1185905 0.1182257 1.91 0056 -0.017611 -0.017611 -0.36 0. 722 0. 1281606 0.1277617 2.05\tabcellsep LIQ -0.002101 -0.002095 -2.57 0011 -0.000317 -0.000317 -0.36 0. 716 -0.002187 -0.002180 -2.72\tabcellsep size 0.0196312 0.0195708 2.35 0019\tabcellsep Interest 0.001875 0.0018693 0.70 0. 486 0.0020501 0.0020501 2.04 0.0 42 0.0018739 0.0018681 0.69\tabcellsep Rate\tabcellsep Inflation 0.0006727 0.0006706 0.23 0. 819 0.0029005 0.0029005 2.04 0.0 42 0.0005569 0.000552 0.19\tabcellsep GDPGR 0.0118787 0.0118422 3.11 0. 002 -0.000074 -0.000074 -0.09 0. 925 0.0114105 0.11375 2.98\tabcellsep GDPPC -1.13e-06 -1.13e-06 -1.25 0.21 2 5.21e-07 5.21e-07 0.87 0385 -7.22e -07 -7.20e-07 -0.80\tabcellsep Governance -0.000904 -0.000901 -0.03 0.976 -0.034845 -0.78 -0.010544 -0.36 0. 436 -0.010577 -0.034845\tabcellsep Constant 10.81 0.842538 15.73 0.0000 0.6845371 6.10 0.000 1.055496\\
© 2018 Global Journals 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 4 :}\end{figure}
 		 		\backmatter  			 
\subsection[{Appendix}]{Appendix}\par
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