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\title{Estimation of the Islamic Banks' Competitive Structure VS Conventional Banks' One}
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\begin{document}

             \author[1]{Asma  Sghaier}

             \affil[1]{  IHE Sousse (TUNISIA)}

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\date{\small \em Received: 5 February 2015 Accepted: 1 March 2015 Published: 15 March 2015}

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\begin{abstract}
        


The objective of this studies is to measure the competitive structure of the Islamic and conventional banks in the MENA region and its determinants. For this, firstly (1) we will use the measuring of the contestability ratios, then (2), we will estimate the model Panzar & Ross (1987). The results show similar changes in two different measures of competition. The banks category analysis revealed that conventional banks are more efficient than Islamic banks. Despite technological changes experienced by the banking system in the MENA region, the analysis of bank competition shows that the concentration of banks in the MENA region is sensitive to variables such as crisis, deposits, capitalization and including variables related to business lines.

\end{abstract}


\keywords{competition, achievement, IHH, CRK, islamic banking, risk management.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
slamic banks follow certain ethics in their operations. Islamic banks are exposed to risks that differ from those that conventional bank are exposed; this is because of the constraints they impose and the specificity of their management which induces Unlike bank returns.\par
In this paper, we analyse the structure of competition (depending on concentration indices on one hand, and modelling Panzar and Rosse, on the other hand.\par
The objective of this study is to investigate the nature of the relationship of the structure of competition between the two types of banks. 
\section[{II.}]{II.} 
\section[{The Literature Review}]{The Literature Review}\par
Bikker and Groeneveld (1998) found, for a sample of European banks and in the period 1989-1996, monopolistic competition for the majority of European banking markets.  {\ref Bikker and Haaf (2002)} confirmed the results proved by De Bandt and Davis  {\ref (2000)}, the results show a monopolistic competition, which becomes weaker on local markets and stronger in international markets.\par
Al-Muharrami et al.\par
(2006) estimate monopolistic competition GCC banks (Gulf countries) for a period of 1993 to 2002, using the Panzar and Rosse approach  {\ref (1987)}. They showed that the banking market in Kuwait, Saudi Arabia and the UAE operate under perfect competition, while banks in Bahrain and Qatar operate in monopolistic competition, which is not the case in Oman. Saeed Al-Muharrami (2008) found a monopolistic competition in the banking market of Saudi Arabia during the period 1993-2006. The assessed value of H-statistic is equal to 0.23 during the period studied.\par
Saeed Al-Muharrami (2009) found a perfect competition in the Kuwaiti banking market in the period 1993-2002. He showed that bank mergers (= concentration) can improve bank services. He also suggested that the Central Bank of Kuwait has to stop allowing the opening of new branches because the country will eventually adjust. The following table summarizes what has already been treated: 
\section[{I}]{I}\par
Based on the model of Panzar and Rosse  {\ref (1987)}, Vesala (1995) several studies showed a monopolistic competition in the Finnish banking sector in 1985-1992. In the same study Molyneux et al  {\ref (1994)} show a monopolistic competition for the UK banking market compared to other markets its sample elements (the sample is composed of German banks, French, Italian, Spanish and British) for a period 1986-1989.\par
In the following, we will examine the differences between Islamic and conventional banking' structures. An important assumption in the theory of classical industrial organization regarding the profit maximization, it can be argued that the new empirical industrial organization techniques such as RA H-statistic (Hstatistique Panzar \& Rosse) and Lerner index can't be applied to Islamic banks. In our work we will study the art of IHH, CRk and H-Statistics PR. The objective of Islamic banks is to ensure social and economic justice rather than being primarily guided in principle, by the principle of profit maximization, and this can be achieved mainly through the promotion sharing of financing techniques risks (PLS). However, a close look at the balance sheet of Islamic banks shows that creditbased financing (Murabaha or cost plus sales) is the dominant form of funding applications while financing profit and loss (or sharing risks) as mudaarabah and Musharaka on average less than 10\% of assets  {\ref (Dar and Presley, 2000)}, this, in fact, could be an Achilles heel for Islamic banks, which have been criticized in the past three decades to ignore the social aspect of their mission, but rather the search for quick profits and safer thanks a Murabaha financing.\par
Traditional measures of concentration include concentration ratios and the Herfindahl Index -Hirschman (HHI). Using the rate of bank concentration n, especially CR3 index (the three largest banks concentration ratios) according to their share of the assets, deposits and loans in the banking sector. We also calculated the HHI index summing the squares of the market shares of all banks (using total assets, deposits and total credit).  
\section[{Estimation of the Competition' Structure}]{Estimation of the Competition' Structure}\par
We analyse the structure of competition for conventional banks (BC) and non-conventional banks (BNC) in the MENA region. Our sample is distributed as follows : "An important aspect to consider when evaluating efficiency is competition. All things being equal, a more competitive market generally means greater efficiency "(Allen and Engert 2006). In this section we present the results of a comparative study of the state of competition between 132 CB and 52 NCB in the MENA region. To conduct our study we will proceed in two steps, (1) we will conduct an analysis based on the evolution of the index of CRK concentration and Hirfindahl Hirshman Index (HHI), and (2) we'll try to validate the econometric model Panzar and Rosse (1987). 
\section[{a) Measured by concentration indices}]{a) Measured by concentration indices}\par
Traditional measures of concentration include CRK concentration ratios and Herfindahl-Hirschman IHH.\par
i. 
\section[{The CR3 concentration' index}]{The CR3 concentration' index}\par
CRk The index takes into account the market share of K first banks in the sector. Generally, studies take a value of k equal to 3 or 5. In our sample, some countries don't have more than three NCB, therefore, we will choose K = 3.\par
The concentration ratio CR3 consider the relative market share of the three largest banks. This share is approached to the case of banks according to their share of the assets and deposits and Net Loans.\par
The table below shows the evolution of the CR3 index in terms of total assets, total loans and total deposits during the period from 2005 to 2011 BC (Conventional Bank) and BNC (Non Conventional Bank) for the MENA region. It allows us to identify three main findings.\par
First, the BC market structure is not characterized by intense competition. In fact, between 2005 and 2011, the value of the CR3 index is quite high. It varies between 0.36 and 0.49 and it doesn't mark a significant downward trend. Considering the "total assets" criterion and "Total loans", the highest values are recorded in 2006. During this year's three overly banks (the largest) account for over 49\% of allocations and capitalize more than 44\% of total assets.\par
Second, NCB market structure isn't characterized by an intense competition. The three largest banks have over 33\% of assets and provide more than 32\% of loans. The market trend is for greater concentration. It peaked in 2011 considering the criterion "Total deposits".\par
Third, despite the trends of similar concentration, we note that the NCB market structure is more competitive than CB. This leads us to ask, if the difference in concentration of the BC market and BNC leads to efficiency difference.  The CR3 index certainly allows to observe and to compare the state of the competition, but its use is followed by the fact that it does not account for all banks. The CR3 index can, indeed, hide a false competition or concentration. Thus, we continue our analysis by characterizing the state of competition in the banking market by HHI. We also allow the analysis of the state of competition in the country using section as the HHI is more complete and to avoid duplication of results. 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
ii. 
\section[{The HHI concentration index}]{The HHI concentration index}\par
In the following section, and based on the HHI index, we will analyse the state of competition that characterizes the market for BC and BNC. This index is used in several studies to analyse competition in the financial sector, particularly the banking sector in different countries. This in my study we include in particular the study of Nathan and Neave (1989) about the Canadian financial system, the study of Molyneux, Altunbas and Gardener (1996) relating to the Japanese banking sector during 1986-1988, the study of Rime ( 1999) of the Swiss banking system and finally that of Ben Ali. M.S and SGHAIER.A (2012) about the Tunisian banking system.\par
Our analysis with the HHI index has two parts. In the first we will meet the general trends in the entire region. In the second part, we will try to describe the state of competition in the MENA countries separately. iii. 
\section[{The state of the banking competition for MENA}]{The state of the banking competition for MENA}\par
The HHI is calculated by summing the squares of the market shares of all banks. To improve this calculation banks must first be sorted in descending order of sales. In our application on the banking sector we will calculate the HHI by approaching the size of banks by three indicators: total assets (Asset), total deposits (Deposits) and total loans (Loans\textunderscore Net). The following table indicates the evolution of the HHI 2005-2011 period for BC and BNC. C\par
The analysis of the above table allows us to address three key findings about the market structure of BC and BNC. First, among the 132 BC retained in our analysis, 3-5 banks dominate the market and lead the competition. Whatever the size of endpoint, the indicated trend is almost the same and shows a fairly stable market concentration between 2005 and 2001. In 2009, the market is in the closest state of the oligopoly. Indeed, only three banks account for the largest share of the votes and the largest share of deposits.\par
Second, among the 52 NCB included in our analysis, only two banks dominate the market throughout the period 2005-2011. This state of the concentration is indicated by the size of three evaluation criteria. Third, we note that the index and the CR3 HHI give conflicting results. The CR3 index indicates a false competition in the market and BNC false trend of concentration for the BC market for 2006. Given that the HHI is more comprehensive than the CR3 index, we will remember trends identified by the HHI. The state of the banking competition for MENA\par
The structure of the banking market is not the same in all the countries of the MENA region. Thus, we share further our analysis by considering IHH each country separately and as shown in the table below. We found in our calculations that the size indicator "Total assets", "Total deposit" and "Total credit" all show the same trends. Thus, we will limit our interpretation that the evolution of the HHI index calculated on the basis of "total assets" for BC and BNC.\par
We note that the trends in competitive intensity was almost stable for most countries marking sometimes small changes they can be explained by the fusion acquisition movements knows that the banking market countries.\par
Indeed, in the case of Jordan and Qatar, it should be noted that they have the BC market most concentrated in the MENA region. Tunisia (9 banks out of 14), Saudi Arabia (7 banks of 9) and the United Arab Emirates (9 out of 17 banks) have the most competitive conventional banking market.\par
considering the case of Tunisia, in 2005, competition in the banking sector is not achernée and is led by nine commercial banks of similar size among the 14 commercial banks used in our sample. This result is similar to A.SGHAIER (2010). Indeed, "Since 1985, Tunisia, like many developing countries, introduced a structural adjustment program (SAP) for the restructuring and the total liberalization of all economic sectors of the country, including the banking system. This liberalization was seen by economic and monetary authorities, above all, as a strategic choice dictated by the need to step up investment, diversify the economy in view of the comparative advantages of the country, but especially to increase the efficiency of the banking sector, creating a more competitive environment among financial institutions and strengthening the capital base of banks. The axes of this strategy, initiated in 1987 and strengthened especially towards the 90s, focused on the removal of credit controls, liberalization of the banking business, the revision of the refinancing policy and strengthening the efficiency. Following these reforms, the situation has improved dice 2006 and remained the same until 2011. In fact, these reforms have been introduced mainly by the Tunisian monetary authorities to liberalize the banking system and to promote banking competition. Regarding non-conventional banks analysing the chart below shows that the HHI index records for most countries rather large values. This shows that the NCB market is fairly concentrated and particularly in Tunisia, Jordan, Egypt and Saudi Arabia. The lowest values indicating a competitive market are raised for Bahrain, Sudan and the United Arab Emirates. In addition, we find that for most countries the value of the HHI is not stable. This could be explained by the fact that the BNC market has not yet reached a stage of maturity.\par
2006 and from 32 to 36. This number has decreased slightly from 2007 and remains almost unchanged up at the end of the period of our study. We note the same observation HHI calculated in terms of total credit. Indeed, in 2006 there was 34 of 52 dominant BNC and this number has been declining ever since the end of the period (2005-2011) to move to NCB 27 that have the same size and that engage in competition in terms of lending (as Murabaha, Musharaka ...). After calculating the concentration ratios in the period 2005-2011, we will now estimate the Panzar and Rosse H-statistic (1987) for the CB and NCB during the same period, according to a data panel. In the MENA region, the number of NCB influencing the market recorded a significant increase in Where ROA is the pre-tax return on assets, and since the ROA ratio can take negative values, we, therefore, calculate the dependent variable ln (1+ ROA). According Claessens and Laeven (2004), "the measure of ROA included in the above equation is equal to ln (1 + ROA) and thus adjusted for small negative values because of bank losses in any given year. 
\section[{IV. Analysis by Modeling Panzar \& Rosse}]{IV. Analysis by Modeling Panzar \& Rosse}???? (???? ???? ) = ?? + ?\par
The H statistic is equal to ?1 + ?2 + ?3, the sum of the inputs of price elasticity's of the total income. These statistics measure the sensitivity of bank earnings compared to the prices of inputs. A less than or equal to 0 H-statistic is interpreted as a sign of monopoly; by constante if the H statistic is equal to 1, this indicates a situation of perfect competition, and if the H value is between 0 and 1 the sector is monopolistic competition (Shaffer (2004 a, b) ).\par
Maudos and Perez  {\ref (2005,} {\ref 2007)} and Berger, Klapper and Turk-Ariss (2008) estimated the "total assets" output according to production approach which is calculated as the total income of banks on assets, d where the Translog cost function is: We will follow the study of Gelos and Roldós (2002) in the estimate of the revenue function. The application of the model will be distributed in two stages: first, we will check the validity of the competitive balance in our sample. Second, we will assess the value of the index H.???? (?? ???? ) = ?? ?? +\par
Although there is a vast literature that uses nonstructural measures to assess the competition in many developed countries and some developing countries, "throughout our research we found" only three papers that lead this type of analysis MENA. These three studies calculated the H-statistic as a measure of the competition.\par
Murjan and Ruza (2002) study the degree of competition during the period 1993-1997 in nine banks in the MENA region (Bahrain, Egypt, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Tunisia, and United Arab Emirates) they find that the banking sectors in the MENA operate in monopolistic competition.\par
Analysis   In the previous section, concentration ratios indicate that some Islamic institutions dominate the banking markets, the HHI index in the section above, also shows that the concentration on the Islamic world market is higher than for banks classics. The table (N°8) shows the evolution of the Herfindahl-Hirshman that the concentration is higher for Islamic banks than conventional banks, and all the HHI measures are almost twice as large.\par
To have a better clearer understanding of the conditions of competition between the two banking sectors, one should refer to RA H-Statistical which confirm the results.\par
According to the preceding table (the correlation matrix) although we can see there is a significant correlation between W3 and W1 ie between the price of financial capital and labour prices (measured in personnel costs related to the size of the bank i). As against the profitability ratio ROA was significantly correlated with almost all variables except the W3 and W1.\par
The following table shows the H-statistic for the countries of the MENA region during the period 2005-2011. In addition, the table shows our model of variable values for testing: if H is 0 so the market can be considered as a monopoly and if H is 1 so the market operates in pure competition perfectly.\par
Referring to Panzar and Ross model (H = 0.0116567) H (0.1), banks in our sample are monopolistic. Therefore, any increase in costs of inputs induced a disproportionately low increase in revenues. "In such a market structure, each firm seeks to differentiate its products from competitors' products, by make its unique product to escape the homogeneity and thus obtain a separate application from other competing products. In addition, several economic analyses and empirical research agree that a liberal and competitive economy type supports the efficient use of production factors, lower costs, diversification of risk, the growth of the national product and the emergence of creativity. This performance contrasts with that of command economy, cartelized or compartments. A market dominated by cartels or monopolies hinders productivity growth and growth of the national product. In addition, cartels or monopolies impede the implementation of macroeconomic policies. " (L.Daly 2006). From this table, it appears a balance index E = 0.011519 nonzero, the banking system is therefore not studied in long-term equilibrium (we are in the situation where 0 <H <1). The H-statistics calculated are consistent with those reported by previous studies and suggest that monopolistic competition best describes the market structure in Islamic and conventional banking sectors worldwide.\par
The model results using ROA as the dependent variable indicate that the observations are in long-term equilibrium. These results could provide more clarity on the degree of competition. They show that the estimates of Islamic banks are more significant than their conventional counterparts, suggesting a high degree of Islamic financial market power.\par
To explain the differences in the levels of profitability between Islamic and conventional banks, we combined the two samples enveloping 1288 observations, and then we made multivariate regressions in the last equation, and finally present the results (following tables).\par
ROA ratio was used as a dependent variable, and then to determine the method of parameter estimation and after estimating the Hausman test (to get an idea about the behavior of random variables and the study of the structure variances and covariances of the errors), which confirmed that this is a fixed effects model (hypothesis H1 is accepted: fixed effect model [Prob> chi2 = 0.0001] there is no type coefficients estimated by fixed effects and those estimated by random effects), so the fixed effects were used. Second step we tested the from the table below, we see that F = 0.0305 is significantly different from zero, the financial factor positively affects the total income of the bank, while the coefficient on the size (W2) is significant at 10\% and a negative effect on income. This leads us to conclude that the errors are serially correlated between them. This encouraged us to estimate our model taking into account the two problems (heteroscedasticity and autocorrelation).\par
In the second stage, and to clarify the extent of our sub samples (Islamic banks and conventional banks), a dummy variable was induced in the previous model (As did R.Turk Ariss, 2010 and L.Weill 2010) and we get a random effects model because of the existence of the Dummy taking into account the two preceding problems (heteroscedasticity and autocorrelation).\par
One notices a linear relationship between competition and bank profitability. The results indicate that the coefficient estimated for all variables are positive, and that the relationship between the profitability of the bank and the labor price is negative. Given our study period  {\ref (2005)} {\ref (2006)} {\ref (2007)} {\ref (2008)} {\ref (2009)} {\ref (2010)} {\ref (2011)}, we are able to reject the null hypothesis that the banking sector is best characterized by a monopoly (H = 0). Also, we are able to reject the hypothesis of perfect competition in all countries.\par
The significance of the parameter PRstatistique H indicates a low degree of association between the two measures of competition and bank profitability, and that bank yields increase with increasing degree of market power.\par
The meaning of the parameters is maintained during the measurement of the bank performance. This has an impact on the general concept of the higher degree of market power. Thus, the strategies used to enter new market sectors where the level of competition is low, are likely to be rewarding for banks. However, this finding does not provide sufficient grounds to conclude that Islamic banks are more profitable than conventional banks. Although the coefficient on the dummy variable (or dummy) Islamic is positive in our model, it is more significant at 1\%, the regression results do not provide evidence that Islamic banks can usually achieve a higher level profitability compared to their commercial counterparts. As a robustness test, an interaction term was added in all regressions, ie the Dummy variables and each provides measures of competition.\par
V. 
\section[{Conclusion}]{Conclusion}\par
Our conclusion is almost identical with the results of the empirical literature: Islamic banks are more cost effective compared to conventional \hyperref[b14]{(Samad, 1999;}\hyperref[b14]{Samad \& Hassan, 1999;} {\ref Iqbal, 2001;} {\ref Hassoun, 2002)}.\par
Finally, the parameter estimates of the size of banks and market shares are significantly positive when considering the ROA as the dependent variable. The significant difference in market power between Islamic and conventional banks. In addition, the regression of market power indices even suggests a lower market power for Islamic banks.\par
We explain the lower market power of Islamic banks by their different religious and economic incentives. Islamic banks are expected to adhere to Islamic norms of behavior, such as the obligation to charge fair prices and sharing of loss and profit. Compliance with this rule could limit their ability to charge high prices. In addition, Islamic banks have an incentive to charge lower borrowing rates than size of banks and market shares appear to be a significant determinant of bank profitability.\par
Diego Anzoategui, Maria Soledad Martinez Peria and Roberto Rocha (2010) in their article "Bank Competition in the Middle East and Northern Africa Region" concluded that, "Comparing the MENA region to other regions, we find that the H-statistic for MENA is much lower than that of the countries of Eastern Europe, the former Soviet Union, Latin America and South Asia in the most recent period  {\ref (2002 2008)}, and the study period of 1994-2008. On the other hand, we find no difference in the H statistic for the MENA region, East Asia and sub-Saharan Africa, the two regions with the lowest level of competition in the banking sector by H Statistics.\par
In this part, we compared the market power of Islamic and conventional banks by concentration indices such as HHI and CR3 for a large sample of countries and banks where the two types of banks coexist. The studies do confirm that market power is more important conventional banks and a higher risk of moral hazard behavior of borrowers.\par
Thus, our results do not support the concerns of adverse effects resulting from the expansion of Islamic banks in terms of market power. Nevertheless, the results of this study should be taken with caution. Further work could help confirm or refute these findings and clarify our interpretations (L.WEILL 2010).\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Volume}\end{figure}
   \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.44849246231155776\textwidth}P{0.1665829145728643\textwidth}P{0.11532663316582914\textwidth}P{0.11959798994974874\textwidth}}
countries\tabcellsep Conventional banks\tabcellsep Islamic banks\tabcellsep Total\\
Bahrain\tabcellsep 11\tabcellsep 14\tabcellsep 25\\
Egypt\tabcellsep 23\tabcellsep 2\tabcellsep 25\\
Jordan\tabcellsep 11\tabcellsep 2\tabcellsep 13\\
Kuwait\tabcellsep 6\tabcellsep 7\tabcellsep 13\\
Qatar\tabcellsep 6\tabcellsep 3\tabcellsep 9\\
Saoudi Arabia\tabcellsep 9\tabcellsep 3\tabcellsep 12\\
Sudan\tabcellsep 11\tabcellsep 7\tabcellsep 18\\
Tunisia\tabcellsep 14\tabcellsep 1\tabcellsep 15\\
Turkey\tabcellsep 19\tabcellsep 4\tabcellsep 23\\
United Arab Emirates (UAE)\tabcellsep 17\tabcellsep 6\tabcellsep 23\\
Yemen\tabcellsep 5\tabcellsep 3\tabcellsep 8\\
Total\tabcellsep 132\tabcellsep 52\tabcellsep 184\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.0697089947089947\textwidth}P{0.1259259259259259\textwidth}P{0.16865079365079363\textwidth}P{0.09444444444444444\textwidth}P{0.12817460317460316\textwidth}P{0.16865079365079363\textwidth}P{0.09444444444444444\textwidth}}
B.C\tabcellsep CR3 (BC) Asset\tabcellsep \multicolumn{2}{l}{Loans\textunderscore Net Deposits}\tabcellsep CR3 (BNC) Asset\tabcellsep \multicolumn{2}{l}{Loans\textunderscore Net Deposits}\\
2005\tabcellsep 0.3941\tabcellsep 0.4191\tabcellsep 0.3978\tabcellsep 0.3439\tabcellsep 0.3261\tabcellsep 0.3574\\
2006\tabcellsep 0.4450\tabcellsep 0.4906\tabcellsep 0.4137\tabcellsep 0.3494\tabcellsep 0.3634\tabcellsep 0.3819\\
2007\tabcellsep 0.4229\tabcellsep 0.4502\tabcellsep 0.4219\tabcellsep 0.3324\tabcellsep 0.3548\tabcellsep 0.3817\\
2008\tabcellsep 0.4112\tabcellsep 0.4363\tabcellsep 0.4266\tabcellsep 0.3453\tabcellsep 0.3529\tabcellsep 0.3839\\
2009\tabcellsep 0.3892\tabcellsep 0.4181\tabcellsep 0.3824\tabcellsep 0.3679\tabcellsep 0.3655\tabcellsep 0.3655\\
2010\tabcellsep 0.4184\tabcellsep 0.4486\tabcellsep 0.3669\tabcellsep 0.3639\tabcellsep 0.3287\tabcellsep 0.4194\\
2011\tabcellsep 0.4215\tabcellsep 0.4527\tabcellsep 0.4015\tabcellsep 0.3635\tabcellsep 0.3325\tabcellsep 0.4204\end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.07877155172413794\textwidth}P{0.18868534482758623\textwidth}P{0.159375\textwidth}P{0.047629310344827584\textwidth}P{0.09159482758620689\textwidth}P{0.16120689655172413\textwidth}P{0.032974137931034486\textwidth}P{0.08976293103448275\textwidth}}
\tabcellsep \tabcellsep BC\tabcellsep \tabcellsep \tabcellsep BNC\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Années}\tabcellsep \multicolumn{6}{l}{Asset Loans\textunderscore Net Deposits Asset Loans\textunderscore Net Deposits}\\
\tabcellsep \multicolumn{2}{l}{IHH 0.217}\tabcellsep 0.2232\tabcellsep 0.2133\tabcellsep \multicolumn{2}{l}{0.4663 0.5276}\tabcellsep 0.5168\\
2005\tabcellsep n*\tabcellsep 5\tabcellsep 4\tabcellsep 5\tabcellsep 2\tabcellsep 2\tabcellsep 2\\
\tabcellsep \multicolumn{3}{l}{IHH 0.2259 0.2461}\tabcellsep 0.2076\tabcellsep 0.577\tabcellsep 0.5996\tabcellsep 0.6441\\
2006\tabcellsep n*\tabcellsep 4\tabcellsep 4\tabcellsep 5\tabcellsep 2\tabcellsep 2\tabcellsep 2\\
\tabcellsep \multicolumn{3}{l}{IHH 0.2184 0.2367}\tabcellsep 0.2058\tabcellsep 0.477\tabcellsep 0.5314\tabcellsep 0.5394\\
2007\tabcellsep n*\tabcellsep 5\tabcellsep 4\tabcellsep 5\tabcellsep 2\tabcellsep 2\tabcellsep 2\\
2008\tabcellsep \multicolumn{2}{l}{IHH 0.214 n* 5}\tabcellsep 0.2329 4\tabcellsep 0.2058 5\tabcellsep \multicolumn{2}{l}{0.4864 0.5307 2 2}\tabcellsep 0.543 2\\
\tabcellsep \multicolumn{3}{l}{IHH 0.2661 0.2901}\tabcellsep 0.2957\tabcellsep \multicolumn{2}{l}{0.4787 0.4998}\tabcellsep 0.5514\\
2009\tabcellsep n*\tabcellsep 4\tabcellsep 3\tabcellsep 3\tabcellsep 2\tabcellsep 2\tabcellsep 2\\
\tabcellsep \multicolumn{3}{l}{IHH 0.2417 0.2641}\tabcellsep 0.2452\tabcellsep \multicolumn{2}{l}{0.4806 0.4994}\tabcellsep 0.5767\\
2010\tabcellsep n*\tabcellsep 4\tabcellsep 4\tabcellsep 4\tabcellsep 2\tabcellsep 2\tabcellsep 2\\
\multicolumn{3}{l}{2011 IHH 0.23}\tabcellsep 0.2517\tabcellsep 0.2176\tabcellsep \multicolumn{2}{l}{0.4811 0.4953}\tabcellsep 0.5753\\
\tabcellsep n*\tabcellsep 4\tabcellsep 4\tabcellsep 5\tabcellsep 2\tabcellsep 2\tabcellsep 2\end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.09390243902439024\textwidth}P{0.07804878048780488\textwidth}P{0.09999999999999999\textwidth}P{0.09634146341463415\textwidth}P{0.09999999999999999\textwidth}P{0.0975609756097561\textwidth}P{0.09999999999999999\textwidth}P{0.0975609756097561\textwidth}P{0.08658536585365853\textwidth}}
coutries\tabcellsep index\tabcellsep 2005\tabcellsep 2006\tabcellsep 2007\tabcellsep 2008\tabcellsep 2009\tabcellsep 2010\tabcellsep 2011\\
Bahreïn\tabcellsep IHH n*\tabcellsep 0.2322 4\tabcellsep 0.217 5\tabcellsep 0.2114 5\tabcellsep 0.2018 5\tabcellsep 0.2303 4\tabcellsep 0.2349 4\tabcellsep 0.2183 5\\
Egypte\tabcellsep IHH n*\tabcellsep 0.1619 6\tabcellsep 0.1475 7\tabcellsep 0.1326 8\tabcellsep 0.1418 7\tabcellsep 0.1949 5\tabcellsep 0.1722 6\tabcellsep 0.178 6\\
Jordanie\tabcellsep IHH n*\tabcellsep 0.3132 3\tabcellsep 0.3128 3\tabcellsep 0.3147 3\tabcellsep 0.3254 3\tabcellsep 0.3849 3\tabcellsep 0.3614 3\tabcellsep 0.3539 3\\
Kuwait\tabcellsep IHH n*\tabcellsep 0.2408 4\tabcellsep 0.2497 4\tabcellsep 0.2658 4\tabcellsep 0.2693 4\tabcellsep 0.2379 4\tabcellsep 0.224 4\tabcellsep 0.2321 4\\
Qatar\tabcellsep IHH n*\tabcellsep 0.3374 3\tabcellsep 0.3383 3\tabcellsep 0.3456 3\tabcellsep 0.3332 3\tabcellsep 0.3588 3\tabcellsep 0.4011 2\tabcellsep 0.3755 3\\
Saudi arabia\tabcellsep IHH n*\tabcellsep 0.1422 7\tabcellsep 0.14 7\tabcellsep 0.1445 7\tabcellsep 0.1384 7\tabcellsep 0.1457 7\tabcellsep 0.1491 7\tabcellsep 0.1462 7\\
Sudan\tabcellsep IHH n*\tabcellsep 0.2902 3\tabcellsep 0.4546 2\tabcellsep 0.3557 3\tabcellsep 0.3094 3\tabcellsep 0.3425 3\tabcellsep 0.3499 3\tabcellsep 0.337 3\\
Tunisia\tabcellsep IHH n*\tabcellsep 0.1098 9\tabcellsep 0.1096 9\tabcellsep 0.1098 9\tabcellsep 0.1096 9\tabcellsep 0.3956 3\tabcellsep 0.1319 8\tabcellsep 0.123 8\\
Turkey\tabcellsep IHH\tabcellsep 0.1842\tabcellsep 0.1783\tabcellsep 0.1722\tabcellsep 0.1651\tabcellsep 0.1708\tabcellsep 0.1691\tabcellsep 0.1682\\
n*\tabcellsep 5\tabcellsep 6\tabcellsep 6\tabcellsep 6\tabcellsep 6\tabcellsep 6\tabcellsep 6\tabcellsep \\
IHH\tabcellsep 0.1171\tabcellsep 0.1088\tabcellsep 0.1256\tabcellsep 0.1229\tabcellsep 0.22\tabcellsep 0.2338\tabcellsep 0.162\tabcellsep \\
n*\tabcellsep 9\tabcellsep 9\tabcellsep 8\tabcellsep 8\tabcellsep 5\tabcellsep 4\tabcellsep 6\tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.10071090047393365\textwidth}P{0.5337677725118484\textwidth}P{0.062440758293838855\textwidth}P{0.025177725118483412\textwidth}P{0.05941943127962085\textwidth}P{0.03524881516587677\textwidth}P{0.009063981042654028\textwidth}P{0.024170616113744076\textwidth}}
Pays\tabcellsep 2005\tabcellsep 2006\tabcellsep 2007\tabcellsep 2008\tabcellsep 2009\tabcellsep 2010\tabcellsep 2011\\
Bahrain\tabcellsep \multicolumn{2}{l}{IHH 0.2409 0.227 n* 4 4}\tabcellsep \multicolumn{2}{l}{0.2055 0.178 5 6}\tabcellsep \multicolumn{3}{l}{0.1475 0.2033 0.1908 7 5 5}\\
Egypt\tabcellsep \multicolumn{3}{l}{IHH 0.6129 0.6001 0.588 n* 2 2 2}\tabcellsep \multicolumn{3}{l}{0.5765 0.6025 0.609 2 2 2}\tabcellsep 0.609 2\\
Jordan\tabcellsep \multicolumn{7}{l}{IHH 0.6506 0.5949 0.6049 0.6576 0.5628 0.5628 0.5628 n* 2 2 2 2 2 2 2}\\
Kuwait\tabcellsep \multicolumn{3}{l}{IHH 0.4294 0.4895 0.518 n* 2 2 2}\tabcellsep \multicolumn{3}{l}{0.6084 0.4169 0.444 2 2 2}\tabcellsep 0.4628 2\\
Qatar\tabcellsep IHH 0.478 n* 2\tabcellsep \multicolumn{6}{l}{0.4895 0.5123 0.5399 0.5512 0.5078 0.5078 2 2 2 2 2 2}\\
Saudi arabia\tabcellsep \multicolumn{7}{l}{IHH 0.5984 0.5565 0.5515 0.5814 0.7721 0.7522 0.7522 n* 2 2 2 2 1 1 1}\\
Sudan\tabcellsep \multicolumn{7}{l}{IHH 0.1474 0.1593 0.1618 0.1726 0.1932 0.1932 0.1932 n* 7 6 6 6 5 5 5}\\
Tunisia\tabcellsep IHH 1 n* 1\tabcellsep 1 1\tabcellsep 1 1\tabcellsep 1 1\tabcellsep 1 1\tabcellsep 1 1\tabcellsep 1 1\\
Turkey\tabcellsep \multicolumn{7}{l}{IHH 0.3042 0.2616 0.2633 0.2602 0.2615 0.2615 0.261 n* 3 4 4 4 4 4 4}\\
United arab emirates\tabcellsep \multicolumn{7}{l}{IHH 0.1667 0.1088 0.3171 0.2638 0.2515 0.2467 0.2467 n* 6 9 3 4 4 4 4}\\
Yemen\tabcellsep \multicolumn{7}{l}{IHH 0.5006 0.5258 0.5246 0.5122 0.5062 0.5062 0.5062 n* 2 2 2 2 2 2 2}\\
Total des n*\tabcellsep 32\tabcellsep 36\tabcellsep 30\tabcellsep 30\tabcellsep 32\tabcellsep 30\tabcellsep 30\end{longtable} \par
 
\caption{\label{tab_5}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.42588357588357584\textwidth}P{0.24033264033264035\textwidth}P{0.1837837837837838\textwidth}}
Roldos, 2004; Claessens \& Laeven, 2004; Schaeck et al,\tabcellsep \tabcellsep \\
2009; Jimenez et al, 2007; Berger, Klapper and Turk\tabcellsep \tabcellsep \\
Ariss, 2009, and Turk Ariss, 2010):\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep Year 2015\\
\tabcellsep \tabcellsep Volume XV Issue VII Version I\\
\tabcellsep \tabcellsep ( ) C\\
??\tabcellsep ??\tabcellsep Global Journal of Management and Business Research\\
\tabcellsep + ? ????(?? ???? ??\tabcellsep ) + ?? ????\\
??=??\tabcellsep ??=??\tabcellsep \\
Based on the methodology of Panzar and\tabcellsep \multicolumn{2}{l}{pursued by classes and Laeven (2004), we obtain the H-}\\
Rosse (1982, 1987) and following the empirical strategy\tabcellsep \multicolumn{2}{l}{statistic by the following estimating equation (Gelos \&}\end{longtable} \par
  {\small\itshape [Note: ???? (???? ???? ) = ?? ?? + ?? ?? ?????? ??,???? + ?? ?? ?????? ??,???? + ?? ?? ?????? ??,???? + ?? ?? ?????? ??,???? + ?? ?? ???? ?? ??,???? + ?? ????]} 
\caption{\label{tab_6}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.06839080459770115\textwidth}P{0.08793103448275862\textwidth}P{0.18563218390804598\textwidth}P{0.20191570881226054\textwidth}P{0.14980842911877393\textwidth}P{0.15632183908045977\textwidth}}
Variable\tabcellsep Obs\tabcellsep Mean\tabcellsep Std. Dev.\tabcellsep Min\tabcellsep Max\\
w1\tabcellsep 1288\tabcellsep 1.731296\tabcellsep 26.65433\tabcellsep -3.492754\tabcellsep 888.8889\\
w2\tabcellsep 1288\tabcellsep 0.6036735\tabcellsep 0.6606814\tabcellsep 0\tabcellsep 7.540467\\
w3\tabcellsep 1288\tabcellsep 0.0813828\tabcellsep 0.7340037\tabcellsep -0.140088\tabcellsep 13.40604\\
y1\tabcellsep 1288\tabcellsep 0.1924635\tabcellsep 0.1880487\tabcellsep -0.125674\tabcellsep 2.87398\\
y2\tabcellsep 1288\tabcellsep 0.5136035\tabcellsep 0.3507693\tabcellsep -0.05805\tabcellsep 5.786367\\
ROA\tabcellsep 1288\tabcellsep 0.0221594\tabcellsep 0.0485944\tabcellsep -0.3007\tabcellsep 0.5309\end{longtable} \par
  {\small\itshape [Note: C 2015 © 2015 Global Journals Inc. (US) 1]} 
\caption{\label{tab_8}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.10377906976744185\textwidth}P{0.20261627906976745\textwidth}P{0.1828488372093023\textwidth}P{0.12848837209302325\textwidth}P{0.1136627906976744\textwidth}P{0.07412790697674419\textwidth}P{0.04447674418604651\textwidth}}
Variable\tabcellsep w1\tabcellsep w2\tabcellsep w3\tabcellsep y1\tabcellsep y2\tabcellsep ROA\\
w1\tabcellsep 1.0000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
w2\tabcellsep 0.0002\tabcellsep 1.0000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
w3\tabcellsep 0.1653*\tabcellsep -0.0154\tabcellsep 1.0000\tabcellsep \tabcellsep \tabcellsep \\
y1\tabcellsep 0.0285\tabcellsep -0.0446\tabcellsep 0.0079\tabcellsep 1.0000\tabcellsep \tabcellsep \\
y2\tabcellsep -0.0243\tabcellsep 0.3404*\tabcellsep 0.0162\tabcellsep -0.2047*\tabcellsep 1.0000\tabcellsep \\
ROA\tabcellsep -0.0328\tabcellsep -0.3098*\tabcellsep 0.0109\tabcellsep 0.2688*\tabcellsep 0.1207*\tabcellsep 1.0000\end{longtable} \par
 
\caption{\label{tab_9}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.20139925373134326\textwidth}P{0.21884328358208957\textwidth}P{0.09990671641791045\textwidth}P{0.06977611940298507\textwidth}P{0.055503731343283576\textwidth}P{0.12052238805970149\textwidth}P{0.08404850746268656\textwidth}}
lroa\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep t\tabcellsep P>|t|\tabcellsep \multicolumn{2}{l}{[95\% Conf. Interval]}\\
lw1\tabcellsep 0.0025342\tabcellsep 0.0008208\tabcellsep 3.09\tabcellsep 0.002\tabcellsep 0.0009235\tabcellsep 0.004145\\
lw2\tabcellsep -0.0181163\tabcellsep 0.0028697\tabcellsep -6.31\tabcellsep 0.000\tabcellsep -0.0237475\tabcellsep -0.012485\\
lw3\tabcellsep 0.0039254\tabcellsep 0.0012393\tabcellsep 3.17\tabcellsep 0.002\tabcellsep 0.0014935\tabcellsep 0.0063573\\
ly1\tabcellsep 0.0290414\tabcellsep 0.0036047\tabcellsep 8.06\tabcellsep 0.000\tabcellsep 0.0219678\tabcellsep 0.0361149\\
ly2\tabcellsep 0.0087969\tabcellsep 0.0019446\tabcellsep 4.52\tabcellsep 0.000\tabcellsep 0.0049809\tabcellsep 0.0126128\\
cons\tabcellsep 0.0982227\tabcellsep 0.0085244\tabcellsep 11.52\tabcellsep 0.000\tabcellsep 0.0814951\tabcellsep 0.1149502\\
sigma\textunderscore u\tabcellsep 0.0210638\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
sigma\textunderscore e\tabcellsep 0.03030193\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
rho\tabcellsep \multicolumn{3}{l}{0.32578499 (fraction of variance due to u\textunderscore i)}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{F test that all u\textunderscore i=0: F(180, 1009) = 2.11}\tabcellsep \multicolumn{2}{l}{Prob > F = 0.0000}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_10}Table 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{10} \par 
\begin{longtable}{P{0.4868932038834951\textwidth}P{0.004126213592233009\textwidth}P{0.1691747572815534\textwidth}P{0.08252427184466019\textwidth}P{0.07014563106796116\textwidth}P{0.004126213592233009\textwidth}P{0.033009708737864074\textwidth}}
Estimated covariance's\tabcellsep =\tabcellsep \multicolumn{3}{l}{178 Number of obs}\tabcellsep =\tabcellsep 1192\\
\multicolumn{6}{l}{Estimated autocorrelations = 178 Number of groups =}\tabcellsep 178\\
\multicolumn{2}{l}{Estimated coefficients =}\tabcellsep 6\tabcellsep \multicolumn{3}{l}{Obs per group: min =}\tabcellsep 2\\
\multicolumn{3}{l}{avg = 6.696629}\tabcellsep \tabcellsep \\
max = 7\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Wald chi2(5)}\tabcellsep \multicolumn{2}{l}{= 2027.60}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Prob > chi2}\tabcellsep = 0.0000\end{longtable} \par
 
\caption{\label{tab_11}Table 10 :}\end{figure}
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