Efficiency, technical progress and productivity of Arab banks: A non-parametric approach

Efficiency, technical progress and productivity of Arab banks: A non-parametric approach

G Model ARTICLE IN PRESS QUAECO-1232; No. of Pages 18 The Quarterly Review of Economics and Finance xxx (2019) xxx–xxx Contents lists available at...

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ARTICLE IN PRESS

QUAECO-1232; No. of Pages 18

The Quarterly Review of Economics and Finance xxx (2019) xxx–xxx

Contents lists available at ScienceDirect

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Efficiency, technical progress and productivity of Arab banks: A non-parametric approach Rana Mansour ∗ , Chawki El Moussawi Faculty of Economics and Business Administration, Lebanese University, Lebanon

a r t i c l e

i n f o

Article history: Received 28 July 2018 Received in revised form 29 January 2019 Accepted 24 February 2019 Available online xxx Keywords: Efficiency Technical progress Productivity Malmquist index Arab banks Panel data

a b s t r a c t The objective of this article is to focus on the evolution of the Arab banks’ productive efficiency and the factors relating to their total productivity over the period 2000–2014. In order to study banks’ efficiency and productivity, we adopt estimation techniques based on the notion of distance function to calculate the technical efficiency, allocative efficiency, cost efficiency and the total productivity of the factors. The obtained results reveal that first, technical, allocative and cost inefficiencies of Arab banks are located around 13%, 21% and 30% respectively and, second, the dispersion of efficiency is important over the study period since the coefficient of variation ranges from 18% to 39%, depending on the measure of efficiency examined. We also observe that the productivity, measured by the Malmquist and Luenberger indexes, improved by around 2.44% and 1.79% respectively, but interestingly this improvement is fully explained by the positive variation in technical progress while the technical efficiency component registered a negative variation. The results obtained by the Data Envelopment Analysis (DEA) method indicate a level of efficiency and productivity comparable to those observed in the empirical literature. They reveal, also, the existence of a banking convergence procedure and catch-up to the best practices situated on the efficient frontier. Thus, restructuring and prudential reforms explain the amelioration of Arab banks’ efficiency and productivity. Finally, the factors found to be most important in explaining productivity improvements are bank size, profitability, equity ratio, and the economic growth rate, while the bank’s risk ratio is associated with the degradation of productivity. © 2019 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

1. Introduction During the last twenty years, Arab countries’ banking sectors have experienced deep structural modifications as a result of deregulation and technological innovation (González, Razia, Vivel, & Sestayo, 2017; Olson & Zoubi, 2011). These deregulation and technological trends relaxed the strong limitations on market forces that previously constrained banks and financial institutions through either control of prices (interest rates) or operations volume (credit rationing). The reforms on the banking sector include the suppression of price and quantity controls (i.e. the progressive easing of obligatory assets of banks, the liberalization of creditor and debtor rates). The reforms also reinforced government banking control through additional prudential regulations (i.e. the adoption of the Basel accord

∗ Corresponding author at: Faculty of Economics and Business Administration, President Rafic Hariri University City- Hadath, Lebanese University, Lebanon. E-mail addresses: [email protected] (R. Mansour), [email protected] (C. El Moussawi).

on the equity norms) and the liberalization of access to the banking market (removing restrictions on foreign bank entry). By cushioning the problems of monetary control, these reforms are thought to have increased the performance, the efficiency and the productivity of the banking and financial sector by favoring competition and removing the distortions that altered the change in resources (Haque & Brown, 2017). These reforms have transformed radically the environment of the Arab banks: the competition for collecting deposits and granting credits has been amplified; and the possibility of foreign bank entry to the banking market has increased competition within this market (Apergis & Polemis, 2016). An analysis of the Arab banks’ performance in terms of efficiency and productivity is of great interest. This is because it would enable banks to better understand the factors, which influence their productive performance, and would offer them better levers of action, control and prevention. By determining which explanatory factors such as bank size, equity ratio, and macroeconomic growth are important in explaining bank efficiency and productivity, our results can guide regulatory authorities to implement the best policies to increase system efficiency and stability (Doan, Lin, & Doong, 2018; Haque & Brown, 2017; Tan & Floros, 2013). A

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Please cite this article in press as: Mansour, R., & El Moussawi, C. Efficiency, technical progress and productivity of Arab banks: A non-parametric approach. The Quarterly Review of Economics and Finance (2019), https://doi.org/10.1016/j.qref.2019.02.002

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better understanding of the drivers of bank efficiency in the Arab world would inform how best to channel the available resources to investment projects in an efficient way, improving the country’s economic activity and, therefore, its economic growth. In light of the significant deregulation trends on banking activity that have occurred in the Arab world over the past twenty years, our aim is to evaluate the effect on bank efficiency and productivity. The objective of this study is to evaluate the factors relating to Arab banks’ productive efficiency, technical progress and total productivity over the period 2000–2014 in a framework where we explicitly model inputs and outputs to bank production and intermediation. We conducted this study by using a non-parametric approach based on the linear programming. The main advantage of this two-stage approach is that it combines a Data Envelopment Approach (DEA) model and a regression analysis (Coelli, Rao, O’Donnel, & Battese, 2005; Pastor, 2002). In the first stage of our analysis, we determine efficiency scores and we then use a regression approach to predict the factors that explain efficiency in the second stage. The results permit us to establish the internal (bankspecific) factors such as size and the external (environmental) factors such as economic growth that determine efficiency. We are also able to distinguish between the principal sources of the change in productivity, namely arising from two sources: the change in technical efficiency and the change in technical progress. The first change in technical efficiency measures the change to the bank’s distance function linking it to the given estimated frontier, while the second change in technical progress is defined by the shift in the efficiency frontier. For this purpose, we constructed a panel of 12 Arab countries in order to determine for each country the degree of banking efficiency and productivity. Therefore, this study aims essentially to: • Determine and compare the change of the degree of efficiency in relation to the factors affecting the Arab banks’ total productivity. • Identify and compare the factors that explain the change in the total productivity. It is a question of determining the role of efficiency and technical progress in explaining the change in the Arab banks’ productivity. • Verify the existence of Arab banks convergence and catch up phenomena. • Identify the explanatory factors of Arab banks efficiency and productivity. By context, efficiency is made up of three components: technical efficiency, allocative efficiency and cost efficiency (Coelli, Rao, & Battese, 1998; Farrell, 1957). A bank is called technically efficient if it exists on the production possibilities frontier, which means that starting by a precise quantity of production factors; it obtains the best realizable level of output. Allocative efficiency concerns the bank’s capacity to combine the inputs and outputs in the optimal proportions, taking into account the prices on the market. Finally, cost efficiency measures the bank capacity’s to produce at minimum cost. In other words, cost efficiency evaluates the cost economies that a bank can realize by choosing the combination of inputs corresponding to minimal cost. The total productivity of production factors is the ratio of the production (outputs) to the used production factors (inputs) (Coelli et al., 1998; Kumbhakar & Lovell, 2000). In order to study the Arab banks’ efficiency and productivity, we adopted estimation techniques based on the notion of distance function. This permits us to determine the factors of technical efficiency, allocative efficiency, cost efficiency and the total productivity. We measured the efficiency and the productivity not only by their past level of performance but, also, by the best practices observed in the sample without imposing a priori either a functional form of the technology of production or a restrictive

hypothesis concerning the nature of returns of scale. The adoption of a Malmquist-Luenberger productivity index lets us propose an indicator of banking productivity, which accurately reflects the role of production technology in the Arab banks’ growth. The results, obtained by using the Data Envelopment Analysis (DEA) method and the Malmquist-Luenberger index, indicate a level of efficiency and productivity comparable to those observed in the empirical literature. These results reveal, also, the existence of the banking convergence procedure and catch-up effects to the best practices located on the efficient frontier. The recent studies, which analyzed the effects of deregulation on the banks’ efficiency and productivity, led to ambiguous results (Chen, 2012). Following deregulation, Norwegian banks have experienced an increase in their efficiency and productivity (Berg, Forsund, & Jansen, 1992) and the same thing happened with Turkish banks (Zaim, 1995). On the other hand, following deregulation in the 1980 s, the United States of America (USA)’s banking efficiency was relatively stable (Bauer, Berger, & Humphrey, 1993; Elyasiani & Mehdian, 1995). The deregulation of interest rates led to an increase in competition between banks which, in turn, led them to pay higher interest rates on deposits. This was not accompanied by either a corresponding reduction in banking services or an immediate increase in the level of deposits. In this way, the benefits of productivity, which could have been captured by the bank, were given instead to depositors. This led to a reduction in banking productivity (Humphrey, 1993; Humphrey & Pulley, 1997; Wheelock & Wilson, 1999). Spain experienced similar results to those of the USA (Griefell-Tatjé & Lovell, 1996). Finally, for India, Bhattacharyya, Lovell, and Sahay, (1997) focused their study on the impact of deregulation on the type of bank. The authors demonstrated that foreign banks saw an increase in efficiency during that time. On the contrary, private and public banks saw a reduction in their productivity. Therefore, it seems that the consequences of bank liberalization differ according to the country and the type of bank. In certain cases, liberalization appears to have a negative impact on bank productivity and, in other cases, it has a positive impact. The rest of the paper is organized as follows. Section 2 provides a review of the literature in the area of banking efficiency in different countries. Section 3 presents the research methodology. Section 4 presents data, variables specification and descriptive statistics. Section 5 describes empirical results, and section 6 concludes the paper with a number of policy implications.

2. Literature review An extensive body of literature exists on efficiency and productivity of banks using single-country or multi-country samples. Many studies on bank efficiency and its explanatory factors have been conducted in developed, emerging and developing countries. The structure of Arab banking sector has substantially changed over the last two decades, mainly as a result of financial liberalization, mergers and acquisition, and banking deregulation. These series of reforms in the banking industry had the main objective of improving the efficiency of banks. However, there are only a limited number of studies in which attempts have been made to measure and evaluate the efficiency and productivity of banks operating in GCC, Arab or MENA countries (El Moussawi & Saad, 2008; Limam, 2002; Olson & Zoubi, 2011; Turk Ariss, Rezvanian, & Mehdian, 2007). Among studies in other regions specifically the United States of America, Devaney and Weber (1996) analyze the evolution of American rural bank productivity over the period 1990-1993. They estimate the growth of productivity on this period as 11.5%, meaning approximately 3.6% on average annually. This increase

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of productivity is explained by technological progress and not by technical efficiency of American rural banks. Wheelock and Wilson (1999) apply the Malmquist productivity index on a sample of American banks over the period 1984-1993. Their results show a degradation of productivity of around 15% over the period. These results conform to those of Berger and Mester (1997) and can be explained, according to these authors, by the increase of costs, the adoption of new technologies and the decrease of technical efficiency. In fact, the increase in the technological progress component of around 30% has been more than compensated by the decrease of the technical efficiency of banks. It seems that banks could not adapt quickly enough to the evolution of technology, the regulatory reforms and competitive conditions. Carvallo and Kasman (2005) adopt a frontier of stochastic cost to measure the cost efficiency for a sample composed of 481 banks in Latin America and the Caribbean over the period 1995-1999. Their results show a map of banking efficiency with geographic disparities. In fact, the obtained scores of efficiency vary between 90.7% for Honduras and 79.7% for Venezuela. These results are explained, according to the authors, by specific factors to each bank (e.g. size, profitability, risk, and so on) but also by differences in the socioeconomic and political environment (e.g. economic growth, inflation, corruption). The evolution of productivity seems also to characterize Australian banks. Worthington (1999) shows that Australian banks experienced a decrease of productivity of about 2.14% over the period 1993-1997. The source of this decrease in productivity is the degradation of used technology in order to 1.95, the level of banks efficiency remaining nearly constant over this period. Allen and Rai (1996) present a comparative analysis of efficiency measures estimated with the free distribution frontier and the stochastic frontier for 11 developed countries. For a sample of 194 banks over the period 1988–1992, they observe that cost inefficiency explains costs better than that costs are the result of an under-optimal exploitation of returns of scale and scope. Pastor, Perez, and Quesada, (1997) compare the efficiency of many European banks to these of American banks for the year 1992. Under the hypothesis of constant returns of scale, the French banks are the most efficient (average technical efficiency equal to 95%) followed by the Spanish, Belgian, Italian, German, American, Austrian, and British ones. On the other hand, the authors report weak productivity of French banks measured by the Malmquist index. These are found, in fact, before the last position just in front of the Spanish banks. Maudos, Pastor, Perez, and Quesada, (2002) examine the cost efficiency and the profit efficiency of banks in 11 countries in European Union using a sample of 832 banks over the period 19931996. The authors estimate both cost and profit translog functional form. Their results vary depending on the estimation method and the envisaged truncations. If the truncation of 5% is considered, cost efficiency and profit efficiency scores are on the interval [82%, 87%] and [21%, 51%], respectively. Koutsomanoli-Filippaki, Margaritis, and Staikouras, (2009) use the parametric distance function to measure the technical efficiency and the productivity of banks operating in the Central and Oriental Europe over the period 1998-2003. The efficiency scores are dispersed with little homogeneity and exist on an interval varying from 28% to 80%. The evaluation of the productivity with the Luenberger index shows that the majority of banking sectors experienced a degradation of their productivity over the sub-period 1998–2000 due to incomplete reforms and due to the Russian financial crisis. But this tendency is reversed over the sub-period 2000–2003 when banking productivity improved due to technological progress. Fiordelisi, Marques-Lbanez, and Molyneux, (2011) analyze the relation between the risk, capital and efficiency of European banks over the period 1995–2007 using a stochastic frontier of cost and profit. The estimations lead to a level of cost and profit efficiency around 39% and 50% respectively. The authors use the Granger

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causality test to examine the causality between the risk, the capital and the efficiency of European banks. They conclude that the less managed banks are more risk taking to compensate their inefficiency. Otherwise, the results of the causality test show the existence of a positive relation between the efficiency and the level of equity retained by the European banks. These results corroborate the results obtained by Kwan and Eisenbeis (1997) who examine the interaction of the same variables through a representation in terms of simultaneous equations. They also find a positive relation between cost inefficiency and risk-taking. They observe also a positive link between efficiency and capitalization; so that the best managed banks accumulate equity. Andries¸ and Ca˘praru (2012a); Andries¸ & Ca˘praru, 2012b) apply the stochastic frontier for 154 banks from 11 Central and Eastern European countries and new members of European Union over the period 2004-2010. They consider a translog cost function to measure and evaluate the process of cost efficiency convergence. Their results show that the average cost efficiency is between 88% and 93% according to the considered productive combination. Moreover, their results show convergence of cost efficiency between the Central and Eastern European countries. Tsionas, Assaf, and Matousek, (2015) evaluate the technical and allocative efficiency of European banks over the period 2005–2012 using a dynamic cost frontier. This method presents the advantage of distinguishing the level of efficiency at the short term from that at the long term. The results obtained from a Vector Auto-Regressive (VAR) model show that the efficiency scores calculated at short term (varying between 65% and 78%) are inferior to those observed at long term (varying between 87% and 88%). Moreover, the impulse response functions applied to the sample show that the shocks are absorbed quickly and banks can find their level of efficiency at long term. Degl’Innocenti, Kourtzidis, Sevic, and Tzeremes, (2017) analyze the convergence of productivity for European banks during the subprime crisis (20072008), the world financial crisis (2009–2010) and the sovereign debt crisis (2010–2012) using the Malmquist productivity index. The results show an improvement of productivity during the subprime crisis but a decline of productivity during the world financial crisis. The application of convergence tests ( convergence) and   ˇ convergence reveals the existence of a strong convergence process coming principally from the catch-up process of Eastern Europe banks and the performance of Western Europe banks. Berg et al. (1992) analyze the technical efficiency and productivity of three Norwegian banks: Sweden, Norway and Finland over the period 1980-1989. Their principal conclusion is that Swedish banks had the highest efficiency. In fact, their results show that the Swedish, Norwegian and Finnish banks, register, on average, 78%, 57% and 53% respectively as scores of efficiency. The authors use the Malmquist productivity index to analyze the evolution of productivity. They find, on average, little growth of productivity over the period of study, but this growth has been faster for big banks than small ones. Doan et al. (2018) study the interaction between cost efficiency and banking diversification for a sample composed of banks operating in developed and developing countries over the period 2003-2012. The estimation of the stochastic cost frontier leads to cost efficiency scores on the order of 73.5% and 69% for banks operating in developed and developing countries respectively. Moreover, the results show, from one side, a positive relation between efficiency, the diversification, the size, the economic growth and the inflation. Finally, the results reveal the existence of a negative relation between the efficiency and the capital. Thus, a well-diversified and efficient bank does not necessitate the retention of a high level of equity to maintain its level of insolvency constant. These results conform to those of Hughes, Lang, Mester, and Moon, (1996), and Hughes and Mester (1998) but contradict

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those obtained by Berger and DeYoung (1997), and Tan and Floros (2013). Matthews and Zhang (2010) analyze the convergence of technical efficiency and the productivity of Chinese banks over the period 1997–2007 using a non-parametric frontier. They estimate an average annual productivity growth between 3.5% and 20.3%, depending on the used estimation method (Malmquist productivity index or method of Bootstrap). This growth of productivity is explained by an improvement in technological progress, with an increase varying between 1% and 20% over the period of study. The application of convergence tests (ˇ − convergence and − convergence) on the sample shows that the convergence speed is important. This result means that the less efficient and less productive banks rejoin the more efficient and productive banks and reveals the existence of a catch-up phenomenon at their productivity frontier. Tan and Floros (2013) examine the relation between the risk, capital and efficiency in Chinese banking using DEA approach and the Malmquist productivity index. The results show, on average, a score of technical efficiency close to 89% with an improvement of productivity close to 0.6% over the period 20032009. Moreover, the different estimations show a positive and statistically significant relation between the efficiency, the risk, the capital and the size of banks. Dong, Hamilton, and Tippett, (2014) estimate the cost efficiency of 41 Chinese banks over the period 1994–2007 using the stochastic frontier and the DEA method. The obtained results show the efficiency scores issued from the stochastic frontier and the DEA method are near to 90% et 88% respectively. The correlation study between the two efficiency scores conducted by the authors show a positive and statically significant correlation at the 1% confidence degree. Fujii, Shunsuke, and Matousek, (2014) use a non-parametric distance function to estimate technical efficiency and productivity of Indian banks over the period 2004-2011. Accounting for undesirable output (non efficient loans) in the production technology leads to technical efficiency scores on an interval of [8%, 43%]. The estimation of the productivity by the Luenberger index shows that Indian banks experienced an increase of their productivity on the order of 4% explained by an improvement of technological progress but not by the technical efficiency. This result is very similar to that obtained by Chang, Hu, Chou, and Sun, (2012) who use the Malmquist and Luenberger productivity indexes to measure the productivity of Chinese banks over the period 2002-2009. Their results show an improvement of productivity by nearly 3.9% explained by an improvement of technological progress but also not of technical efficiency. Zaim (1995) uses DEA and the Malmquist productivity index to measure the efficiency and the productivity of Turkish banks over the period 1981-1990. The author finds that the financial reforms appear having success to impulse banks adopting measures to improve their efficiency and their productivity. Limam (2002) analyzes the technical efficiency of banks operating in GCC countries: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and United Arab Emirates. The author uses the DEA method to calculate the technical efficiency under the hypothesis of variable returns to scale for the year 1999. The obtained results show that the average efficiency varies between 82% and 94%. They show also that the size measured by the logarithm of total assets, and the equity ratio are the two factors which explain the technical efficiency of banks operating in GCC countries. Turk Ariss et al. (2007) use a non-parametric frontier approach to compare and contrast the efficiency performance, efficiency and technological change, and productivity growth of banks in GCC countries over the period 1999-2004. Their results indicate that banks in Oman, on average, have been the most efficient among GCC countries followed narrowly by banks from Bahrain and to a lesser extent by banks from Kuwait. While the efficiency scores of banks in Bahrain

have been rising, banks from Oman and Bahrain dominate the common efficient frontier since a larger percentage of banks from these countries lie on the frontier. Examination of returns to scale measures provides evidence to indicate that there is very limited opportunity for banks to improve their scale efficiency, given that only a handful of banks are operating at increasing returns to scale. The result of the Malmquist productivity index reveals that banks on average have experienced a decline in productivity due to technological regress and to a lesser extent caused by a decline in overall technological efficiency. El Moussawi and Saad (2008) use the DEA method and the Malmquist productivity index to measure the technical efficiency and the productivity of 11 Arab banking sectors over the period 1994-2004. Their results show that the average value of efficiency is high enough, around of [0.87, 0.95], meaning that the inefficiency exists in average around of [13%, 5%]. Concerning the productivity, the application of Malmquist productivity index on a sample of Arab banks shows weak gains in productivity varying between 2% and 7% over the period of study. The evolution of the productivity is, before all, explained by technological progress, the evolution of technical efficiency, meaning organizational and managerial efficiency of banks and not the evolution of their efficiency of scale. Olson and Zoubi (2011) use the translog function to measure the cost efficiency and the profit efficiency of banks operating in MENA region over the period 2000-2008. The empirical study estimates, on average, cost efficiency and profit efficiency scores of around 73.2% and 63% respectively. The correlation study between cost efficiency and profit efficiency and profitability, measured by the return on assets (ROA) and return on equity (ROE), shows a negative and statistically significant correlation at the 5% confidence degree. Apergis and Polemis (2016) test the interaction between the competition and the cost efficiency of 217 commercial banks operating in MENA region over the period 1997-2011. The authors use a non-parametric approach of DEA method to measure the banking cost efficiency of banks. The authors observe an average efficiency of 77.5% over the period of study. Next, the authors try to test the relation between competition, measured by the H statistic of Panzar and Rosse (1987), and cost efficiency using the Granger causality test. The realized different estimations show the absence of a significant causal relation between competition and cost efficiency. Haque and Brown (2017) use the DEA method to measure the cost efficiency of banks operating in MENA region over the period 20022012. Their results lead to cost efficiency scores about 38% largely inferior to those observed in the empirical literature. Then, the authors try to explain the cost efficiency through internal, external, and institutional factors. Their results show that equity regulation, deposit insurance, the public ownership, and credit activity affect cost efficiency positively. On the other hand, the results reveal the existence of a negative relation between efficiency, market discipline, revenue level and bank size. Our study is a significant improvement over the existing studies on Arab banks efficiency and productivity and differs from them on several fronts. First, we consider technical, allocative, and cost efficiency and total productivity of Arab banks. Second, we pass from Malmquist to Luenberger index in evaluating banking productivity to reflect the role of production technology growth in Arab banks. Third, we examine the existence of a banking convergence procedure and catch-up compared with the best practices situated on the efficient frontier. Thus, restructuring and prudential reforms have contributed to the improvement in Arab banks’ efficiency and productivity. Fourth, we study the relationship between the Arab banks’ cost efficiency and productivity scores and the internal and external explanatory factors of banking efficiency. Finally, in contrast to previous studies, our sample contains a large number of Arab banks over a recent period 2000–2014 enabling us to draw a number of practical implications on the basis of our findings for

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managers and policy makers through the use of IBCA data that facilitates international comparisons. 3. Research methodology Habitually, economists evaluate banking performance by using the method of ratios and partial indicators of productivity. This method’s analysis of ratios certainly has the advantage of simple implementation but it poses a problem in the measure where it uses only one input or production factor. Consequently, the ensuing results can induce either under- or over-estimation of performance. By using numerous inputs to produce several outputs, this method remains very restrictive in giving a global vision of the performance of a sector or an industry. Moreover, the use of indicators of partial and global productivity fails to show the impact of technical progress on the bank’s productivity and performance. Finally, the method of ratios does not take into account the nature of returns to scale (constant, increasing or decreasing), which affect the costs of production and, therefore, the productive performance. In order to avoid the problems related to using the indicators of partial productivity of factors and the method of ratios, we opted for a measure based on the frontiers of production which allowed us to calculate a score of efficiency for each bank. To achieve this notion, it is advisable to consider the production function not as a relationship between inputs and outputs but as a frontier of the production set. In this way, a bank, which is situated on the frontier of the production set, is considered to be efficient while it is inefficient if it is situated inside this frontier. In this case, the distance, which separates a sector from the frontier, constitutes a measure from its productive inefficiency. The efficiency term indicates the efficient use of available resources. When applied to the banking industry, this term shows how banks efficiently use the inputs, which they possess, to maximize their potential outputs. Therefore, the inefficiency is not an absolute measure but a relative concept and its measurement requires a « benchmark » of performance which allows comparisons to be made between the relative performances of different banks (Forsund & Hjalmarson, 1974). This standard can be either the best actual performance of a sector or a theoretical maximum. Thus, by productive efficiency, we refer to a comparison of observable and optimal values of different inputs and outputs used in the production process. There are two approaches to measure the productive efficiency approaches: namely, the parametric approach; and the non-parametric approach. The literature review emphasizes the following three principal characteristics that allow distinctions to be made between these different methods: • The hypothesis imposed on data in terms of functional form (parametric/non-parametric); • Accounting for random errors which allow temporary effects on the levels of inputs, outputs and costs; • If a random error is included, which distribution of inefficiency probability should be assumed to isolate them from the random errors? If we consider a parametric approach so that the functional form specifies the frontier of the production set in a probabilistic perspective, the frontier is deterministic and the productive inefficiency or over-efficiency relates to a stochastic hazard (Jondrow, Lovell, Materov, & Schmidt, 1982; Kalirajan & Shand, 1999). In this case, the coefficients are estimated by the usual econometric methods. On the other hand, the non-parametric approach widens the traditional concept of the factors of productivity by introducing a global efficiency. This is applied generally to a technology with

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multi-factors and many products. This approach is based on the techniques of linear programming (primal or dual approach of the production frontier) and does not require the definition of a functional form of the production, cost or profit function. This approach aims to obtain the more efficient points that define the possibilities of the production possibilities frontier. This approach entered the field of operational research through Charnes, Cooper and Rhodes’ (1978) famous article, which uses either linear or fractional programming as the technique. Since this is contrary to the notion of average performance retained in the majority of econometric works, we envisage the notion of the best practices frontier as referring to the estimation of the production technology. In other words, the frontier is calculated and not estimated and the efficiency measures are relative to the calculated frontier. In our study, we favor the non-parametric approach and, more particularly, the Malmquist-Luenberger index to measure Arab banks’ productive performance. This approach has the advantage of calculating not only the productive efficiency but, also, the total productivity of factors under the hypothesis of variable returns to scale. Moreover, it allows the role of technical progress in the evolution of global productivity to be taken into account. Finally, the non-parametric approach does not require the definition a priori of the production frontier. However, this technique is based on a set of implicit hypotheses that constrain to some extent the shape of the production frontier but do not affect the obtained results (Charles & Kumar, 2012). Accordingly, the three hypotheses are as follows: H1: observability: All observed data belong to the same production set and are considered without errors. H2: free disposition: It is always possible to produce fewer outputs from the same quantity of inputs. H3: convexity: All fictive units, constructed by a linear combination of two or more real units, are realizable. 3.1. Measuring Malmquist and Luenberger indexes productivity and DEA technologies The importance of the notion of productivity in economics since the 1950s, explains the considerable development of the works on its measurement by means of the theoretical plan rather than the empirical one. In the context of general economics, as result of Solow’s (1957) and Arrow’s (1962) works, the notion of productivity has been developed firstly in the macroeconomic context. This context of analysis postulates that we define a functional form of the set of production. Thus, it is important to study how the modification and the transformation of the production function occur. In the macroeconomic literature, we distinguish between technological progress on the labor factor and technological progress on the capital factor. In the context of the non-parametric approach, the development of the measure of productivity relates closely to the theory of numerous indexes. It appears that Moorsteen (1961), who by repeating Malmquist’s (1953) suggested idea in the consumer theory, proposes to compare the inputs used by a production unit in two different periods. He names this ratio the Malmquist inputoriented and defines it as the maximum amount of factor which we can deflate by using inputs at a given period of time and by maintaining the constant level of production in both periods. However, this formalization does not authorize the movement of the structure of production over time. It is in Caves, Christensen, and Diewert’s (1982) article where we find the first formalization of the Malmquist Productivity Index (MPI). By repeating Malmquist’ original idea, they added that the structure of production could be modified by time and, in this case, the productivity was the object to be measured. However, their formalization was limited by the fact that, in the benchmark period,

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the production unit was influenced by the hypothesis concerning the efficient frontier. Implicitly, changes of productivity could occur from variation in technological progress and not from the improvement in the production unit’s performance. Färe, Grosskopf, Lindgren, and Ross, (1992) extended Caves et al.’s (1982) work to measuring productivity by the DEA method by defining the Malmquist index from the directional distance function to the maximization of outputs over the two periods. We are inspired by Färe, Grosskopf, Norris, and Zhang’s (1994) article to formalize mathematically the methodology. have the vector Supposing that at  each date nt = 1, ...,t T, we m . At each moment input-output xt , yt with xt ∈ R+ , and y ∈ R+ t, the  graph oftechnology is defined   by the set of potential vectors: xt , yt xt can produceyt . GR = The directional distance function to the maximization of output at the date t is defined by: Dt0



 

 yt x , y = min ı  xt , ∈ GRt ı ı t

t



The Malmquist productivity index at the date t is defined as the ratio of two distance functions:











MPI t xt , yt , xt+1 , yt+1 = D0t xt+1 , yt+1 /D0t xt , yt



Consequently, we compare the distance functions of a productive unit at two successive dates in reporting the technology on the date t. In the same way, we can define the Malmquist productivity index on the date t + 1:











MPI t+1 xt , yt , xt+1 , yt+1 = D0t+1 xt+1 , yt+1 /D0t+1 xt , yt



As we search to calculate an index in relation to the chosen production technology, we take the geometric average of the two indexes calculated previously. We define the Malmquist productivity index as follows: MPI

 t,t+1

xt , yt , xt+1 , y

 t+1



=



M t xt , yt , xt+1 , yt+1

×

MPI

t,t+1



t

t

x ,y ,x

t+1

,y

t+1



=



M t+1 xt , yt , xt+1 , yt+1



D0t (xt , yt )







·

D0t+1 xt+1 , yt+1



D0t+1 (xt , yt )

As the authors, we acknowledge from the hypothesis that the observed production unit is inevitably on the efficient frontier, after some arrangements, we can break up the Malmquist productivity index into the following two components:



MPI t,t+1 xt , yt , xt+1 , yt+1



=

D0t xt+1 , yt+1 D0t (xt , yt )





D0t (xt , yt ) D0t+1 (xt , yt )

 · t+1 

D0t xt+1 , yt+1

D0

constant returns to scale. The illustration adopts the implicit hypothesis that technological progress is positive and not regressive. It is possible to calculate the Malmquist index by using linear programming. We can decompose the variation in technical efficiency to the effect due to the variation of the pure performance and the effect due to the change in returns to scale. For this, we calculate a linear programming under the hypothesis of constant returns to scale and, thereafter, variable returns to scale. The difference between the two means we can measure the change realized only from returns to scale. By exploiting the duality relationship between the directional distance function, the minimization of inputs and the cost function, Färe et al. (1992) demonstrated that the Malmquist productivity index could be written as the ratio of two Fisher indexes: M

t,t+1



t

t

x ,y ,x

t+1

,y

t+1





=

 

 

xt+1 , yt+1

This formulation allows us to separate the two components in the concept of productivity. There is a first component, which represents the change of technical efficiency (TEFFCH) between two periods. On the other hand, the second component relates to the modification of the efficient frontier, through which technical progress (TECH) can be understood. Improvement in one of the two components translates to a value of the Malmquist index superior to one, while deterioration translates to a value of the Malmquist index inferior to one. Fig. 1 illustrates the decomposition of the index under the hypothesis that n = m = 1 and the production technology is at

 

Fi wt+1 , xt+1 , wt , xt F0 pt+1 , yt+1 , pt , yt



with Fi wt+1 , xt+1 , wt , xt =



D0t xt+1 , yt+1

Fig. 1. Breaking up of the Malmquist productivity index.

p1 y1 p0 y1 p1 y0 p0 y0



Chambers, Färe, and Grosskopf, (1996) and Chambers (2002) extended the existing works by calculating a Malmquist productivity index from the directional distance function. Thus, they calculated a productivity index in the figure that they named the Luenberger Productivity Index (LPI). At each period, the figure of technology is defined by GRt . The directional distance function at the date t is defined by:



 



DD xt , yt ; gx , gy = sup ˇ  xt − ˇgx , yt + ˇgy



∈ GRt



ˇ

To measure productivity growth, the directional distance function is evaluated in different periods. Given the additive nature of the directional distance function, the LPI equals the sum of an efficiency change indicator (EFFCH) and a technical change indicator (TECH): LPI = EFFCH + TECH. Efficiency change equals the difference in the directional distance function between the periods. Technical change equals the average “shift” in the frontier from period to period. The indicators’ values greater than zero indicate productivity growth, greater efficiency, or technical progress. The indicators’ values less than zero indicate a decline in productivity, less efficiency, or technical regress (Park & Weber, 2006). We can define LPI now as follows:



t

t

LPI x , y , x

t+1

,y

t+1





1 = 2









t+1 DD xt , yt ; gx , gy

t + DD xt , yt ; gx , gy



t+1 − DD xt+1 , yt+1 ; gx , gy



t − DD xt+1 , yt+1 ; gx , gy





According to the LPI, the increased productivity is indicated by the positive values and the declining productivity by the negative values.

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As for the Malmquist productivity index, we can decompose the LPI into two terms: the first term measures the technical efficiency change (TEFFCH) and the second term measures the technological progress change (TECH).







t+1 t TEFFCH = DD xt , yt ; gx , gy − DD xt+1 , yt+1 ; gx , gy

 1 TECH = 2











t+1 t x t , yt ; g , g + DD xt , yt ; gx , gy − DD x y





3.2. Components of productive efficiency The models, which enable us to calculate the Arab banks’ different components of the productive efficiency (technical efficiency (TE), allocative efficiency (AE) and cost efficiency (CE)), can be written as follows: min  ,

s.c. xj ≥ X yj ≤ Y  ≥ 0,



≥0

 ∈ 



Where: • X and Y are matrix with dimension respectively (m x l) and (m x l)  is a weight vector with dimension (l × 1) who serves to make convex the  set of production.   =



l  

1 , ..., l :

i = 1, i ≥ 1, i = 1, ..., l

to take into

i=1

account the variable returns of scale. To calculate the efficiency-cost, the model of linear programing can be written as follows: Min C (w∗ , x) under constraint : Y ≤ zY X ≥ zX k 

zi = 1

i=1

z ∈ k+ Where z is a vector of intensity parameters (of weight attached to each bank for the determination of the minimum cost). For the calculation of the total productivity of factors, we can use the following linear program to calculate the different components of productivity and each directional distance function: Dt+1 (xt+1 , yt ; h, k) = max ı s.t. xt+1 − ıh ≥ yt+1 + ık ≤



 j

j = 1

j

j ≥ 0,

ı≥0

 j j

j

j xt+1 − h,

j yt+1 + h,

In this program, the technology is considered to be the date t + 1, the observation of output is realized at the date t and this input at the date t + 1.



t+1 t xt+1 , yt+1 ; g , g DD xt+1 , yt+1 ; gx , gy − DD x y



7

4. Data, variables specification and descriptive statistics Our empirical study is about the calculation of the global productivity of factors for 12 Arab countries over the period from 2000 to 2014. These countries are: Algeria (13 banks), Bahrain (9 banks), Egypt (20 banks), Jordan (11 banks), Kuwait (6 banks), Lebanon (25 banks), Morocco (7 banks), Oman (6 banks), Qatar (7 banks), Saudi Arabia (9 banks), Tunisia (12 banks) and United Arab Emirates (14 banks). The data came from the BankScope database, Fitch of London rating agency, International Bank Corporate Analysis (IBCA) and the World Bank. The advantage of IBCA data is that it has more statistics at the bank level compared with aggregated data elsewhere. These data appear equally more appropriate for our empirical study since the IBCA measures reflects a harmonization of balance sheets and income statements, which facilitates international comparisons. We view the construction of a homogenous sample to be a necessary condition for the principle of DEA model application. In the measure, where such methodology is based fundamentally on the comparison, it is indispensable for us to evaluate comparable productive units. It is clear that for a very dispersed sample, composed of bank samples with either very different sizes or different basic activities, it is a necessary requirement to measure, also, very dispersed performance which can be explained automatically by a poor relative efficiency. To select our study’s inputs and outputs for DEA, we concentrate on two approaches: namely, the production approach and the intermediation approach. The production approach, initiated by Benston (1965) and Bell and Murphy (1968), applies the concepts of an industrialized economy to the bank and considers it to be an ordinary firm whose objective is to maximize its profits or to minimize its costs of production. According to this optic and by using as inputs its labor and capital, the bank produces demand deposits, term deposits, credits, securities, others. This approach, largely used in the 1970s and 1980s, does not represent totally the banking activity because of the restrictive character of the definition of banking production. In fact, it does not take into account certain activities notably those related to the off-balance sheet operations and ancillary activities. The production approach obstructs, also, the vision of banking activity by ignoring spending on the bank’s interests and taking account only of the operating costs. In addition, it does not take into account the heterogeneous characteristics of the credits and deposits and supposes implicitly the divisibility of the costs between the products. The intermediation approach, developed by Sealey and Lindley (1977), treats the bank as a financial intermediary, which collects deposits to transform them into credits. Therefore, this approach is regarded as a classical financial intermediation of banks’ activities and enables the specificities of the banking activity to be highlighted by reporting to the non-banking financial institutions and non-financial sectors. According to this approach, the banking costs are composed not only of operating costs but, also, of financial costs since the deposits are considered to be inputs. In this approach, the banking outputs are represented by the customers’ credits and the banking investments. With respect to the inputs, the different empirical studies consider often the following three factors of production: the physical capital; the labor; and the banking deposits. Then, the more appropriate measure of banking outputs becomes the monetary value of assets and no longer the number of managed accounts as was previously the case.

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The choice between these two approaches is a difficult task since authors do not agree on the classification of certain variables (whether inputs or outputs). For Ferrier and Lovell (1990), the production approach is appropriate for studying a bank’s efficiency costs for a bank the operating costs are the bank’s only banking activity. The intermediation approach is applied rather to the global banking costs and is used simultaneously to answer the questions concerning the bank’s viability. Generally, the authors do not justify very succinctly their methodological choice. For this reason, we decided to adopt a hybrid approach for our study (Nathan & Neave, 1992) and a modern one (Freixas & Rochet, 1997; Mester, 1996; Paradi, Wilson, & Yang, 2014), which allows us to exceed the problem of choosing variables. This has the advantage of taking into account variables reflecting agency theory, bank managers’ risk preferences, macroeconomic activity, and so on. In our study, we use the modern approach to define the necessary inputs and outputs for the calculation of the productive efficiency scores (technical efficiency, allocative efficiency and cost-efficiency) and the Malmquist-Luenberger productivity indexes. The inputs (production factors) are the physical or fixed capital approximated by the fixed assets. We measure the financial capital by the total interest expenses and we measure the labor factor by the personnel expenses. The outputs are total earning assets, total customer deposits and impaired loans. The banks’ risk preferences are taken into account by a measure for impaired loans. We measure the price of deposits by the ratio of total interest expenses and total customer deposits, the price of labor by the ratio of personnel expenses and total assets, and the price of fixed capital by the ratio of total non-interest expenses and fixed assets. Our variable choice is governed by what we believe captures the most important measures for our modern approach according to the specificities of Arab banks and the data availability in IBCA. Table 1 indicates the descriptive statistics for different inputs and outputs used to measure the banking efficiency. Table 1 indicates, also, that the used sample is homogeneous since the coefficients of variation change between [0.002, 2.45] over the period from 2000 to 2014. Otherwise, this dispersion between the different variables is relatively homogenous. In fact, the coefficient of variation, established for each input and each output, remains in a narrow interval of value: [0.53, 1.70] for Algeria, [0.51, 1.29] for Bahrain, [0.31, 2.01] for Egypt, [0.30, 1.59] for Jordan, [0.19, 1.26] for Kuwait, [0.31, 1.48] for Lebanon, [0.36, 1.35] for Morocco, [0.21, 1.05] for Oman, [0.39, 1.87] for Qatar, [0.28, 1.12] for Saudi Arabia, [0.38, 1.30] for Tunisia and [0.4, 2.45] for United Arab Emirates. To ensure the validity of the DEA model specification, an isotonicity test was conducted establishing the validity of the selected inputs and outputs for our study (Avkiran, 1999). This test involves the calculation of all inter-correlations between inputs and outputs to identify whether increasing amounts of inputs lead to greater output. Table 2 indicates the Pearson correlation matrix of Arab banks over the period 2000–2014 to know whether the selected inputs positively and significantly correlate with the selected outputs. The inter-correlation between fixed assets, interest expenses, personnel expenses as inputs and total earning assets, total customer deposits, impaired loans as outputs was positive and significant at the 1% confidence degree; the isotonicity test was passed, and the inclusion of inputs/outputs justified.

5. Empirical results 5.1. Measure of the productive efficiency by the method of Data Envelopment Analysis (DEA) Table 3 indicates the distribution of the values of the Arab banks’ productive efficiency scores over the period 2000–2014 obtained by using the Data Envelopment Analysis (DEA) method. It appears that, on average, the Arab banks have a technical efficiency score equal to 87%. We can conclude that over the period from 2000 to 2014, the sampled banks could have saved on average 13% of their inputs to produce the same level of outputs. This result corroborates those obtained by Pastor (1999) on the Spanish banks, by El Moussawi and Saad (2008) on the Arab banks, by Chiu and Chen (2009) on the Taiwanese banks, by Fungáˇcová, Pessarossi, and Weill, (2013) on the Chinese banks, by Tsolas and Charles (2015) on the Greek banks and by Stewart, Matousek, and Nguyen, (2016)\ on the Vietnamese banks. Although the technical efficiency average is relatively high, it hides deep disparities between the banks in the sample since the dispersions, measured by the standard deviation and the coefficient of variation, are 15% and 18% respectively. The analysis by country for this type of efficiency shows that the scores of technical efficiency vary considerably between the sampled banks from the different countries (Table 4). These countries can be classified into three groups. A first group, which is composed of poorly performing banking sectors, consists of Algeria, Bahrain, Egypt, Kuwait, Oman, Qatar and Tunisia. A second larger group is composed of the countries with, on average, efficient banking sectors, namely Lebanon, Morocco and Saudi Arabia. Finally, a third group of countries, which is composed of highly performing banking sectors, consists of Jordan and the United Arab Emirates. Concerning allocative efficiency, we observe that the Arab banks have a level of allocative efficiency close to 80%. This score is very close to those obtained by the European banks studied by Tsionas et al. (2015) and the Turkish banks studied by Batir, Volkman, and Gungor, (2017). This result shows that over the period from 2000 to 2014, on average, the combination of inputs used by the Arab banks was in false proportions when compared to the optimal combination allowing the minimization of banking costs. Moreover, the values in the second column of Table 3 indicate a high dispersion of the scores of allocative efficiency as attested by the high values of standard deviation (25%) and coefficient of variation (31%). The analysis by country for this type of efficiency shows a large variation in allocative efficiency ratios (Table 4). Certain countries do better than others in terms of allocative efficiency. This is the case notably of Egypt, Jordan, Lebanon and the United Arab Emirates (UAE) that are classified in the top group with allocative efficiency scores superior to 90%. Other countries, such as Algeria, Morocco, Tunisia and Oman, have a poor classification and register allocative efficiency scores of less than 65%. Concerning cost efficiency, Table 3’s results show a cost inefficiency of about 30%. This result confirms those obtained by Olson and Zoubi (2011) on banks operating in the MENA region, by Fujii et al. (2014) on the Indian banks, by Doan et al. (2018) on a sample of banks operating in the developed and developing countries, and by Dong et al. (2014) on the Chinese banks. This result indicates that a 13% reduction in the banks’ capacities would allow Arab banks to restore their performance in the long term. The analysis by country for this type of efficiency shows that certain countries do better than others in terms of cost efficiency (Table 4). This is the case of Jordan, Lebanon and United Arab Emirates (UAE) that are classified in the top group. Other countries, such as Algeria, Morocco, Oman and Tunisia, have a poor classification. In fact, these results are found in numerous studies using this approach and they show that Arab banks do not operate on the frontier of efficient costs due, probably, to managerial deficiencies and poor use of pro-

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Algeria

Bahrain

Jordan

Kuwait

Lebanon

Morocco

Oman

Qatar

Saudi Arabia

Tunisia United Arab Emirates

Total Assets

Total Interest Expenses

Personnel Expenses

Total Earning Assets

Total Customer Deposits

ImpairedLoans

Total Non-Interest Expenses

PD

PL

PFK

73008 91632 1.26 84323 112898 1.34 34799 40088 1.15 94349 128957 1.37 145312 153089 1.05 155643 168304 1.08 80892 87526 1.08 51997 39959 0.77 130781 221512 1.69 92407 106303 1.15 67247 82075 1.22 47295 64423 1.36

5306467 7616062 1.44 10409930 10309420 0.99 5798651 9409597 1.62 8087582 12583117 1.56 15079047 12656416 0.84 3976936 5686406 1.43 11921374 9802049 0.82 4172096 4385047 1.05 12033684 20538305 1.71 24320018 18694201 0.77 2036488 1657026 0.81 13345945 20121730 1.51

55863 72158 1.29 268781 346207 1.29 286730 514895 1.80 184052 292733 1.59 295820 221996 0.75 152410 190816 1.25 217226 195709 0.90 73945 64401 0.87 203318 282907 1.39 312335 244742 0.78 49210 40345 0.82 245385 342604 1.40

24920 27778 1.11 73925 78694 1.06 47661 78557 1.65 72404 104463 1.44 92672 85407 0.92 30438 45099 1.48 121813 128537 1.06 43928 38136 0.87 62286 71693 1.15 191489 139049 0.73 30338 26324 0.87 96440 138715 1.44

4123523 6337279 1.54 9738882 9662084 0.99 5146019 8350219 1.62 6841346 10701441 1.56 13322822 10522125 0.79 3234723 4627798 1.43 10594261 8833834 0.83 3731638 3816415 1.02 11138420 18973187 1.70 22032503 16651072 0.76 1833085 1481260 0.81 11854837 17658087 1.49

3576893 6090815 1.70 5066167 5071145 1.00 4733654 7855149 1.66 5547596 8588408 1.55 8832406 6774384 0.77 3276992 4790686 1.46 7618526 6792547 0.89 2842751 2753638 0.97 7999925 14949817 1.87 18174216 14577535 0.80 1412461 1163351 0.82 8618301 12907893 1.50

50018 75975 1.52 212500 218253 1.03 326250 655849 2.01 230302 315506 1.37 485880 610128 1.26 96964 140254 1.45 313584 422646 1.35 166168 147872 0.89 155349 205779 1.32 406516 453671 1.12 124273 161161 1.30 505514 1237734 2.45

62769 65925 1.05 118439 121221 1.02 79325 122798 1.55 139745 200623 1.44 164532 155771 0.95 55876 81034 1.45 245665 218485 0.89 78689 70353 0.89 117745 137219 1.17 343683 275076 0.80 49031 42651 0.87 159495 224773 1.41

0.03 0.05 1.73 0.08 0.09 1.17 0.06 0.02 0.31 0.04 0.02 0.48 0.04 0.02 0.54 0.05 0.02 0.31 0.04 0.04 1.02 0.03 0.02 0.64 0.04 0.04 1.03 0.02 0.02 0.72 0.04 0.02 0.63 0.03 0.02 0.59

0.01 0.004 0.53 0.01 0.004 0.51 0.01 0.01 1.16 0.01 0.003 0.30 0.01 0.001 0.19 0.01 0.004 0.40 0.01 0.004 0.36 0.01 0.002 0.21 0.01 0.003 0.39 0.01 0.002 0.28 0.02 0.01 0.38 0.01 0.004 0.45

0.84 0.78 0.93 0.70 0.49 0.69 1.70 1.72 1.01 0.84 0.51 0.61 0.54 0.18 0.33 0.55 0.76 1.37 0.56 0.36 0.64 1.14 0.78 0.68 0.93 0.67 0.73 0.69 0.26 0.38 0.64 0.57 0.89 0.60 0.37 0.61

Note: M, SD, CV represent respectively the mean, the standard deviation and the coefficient of variation for the variables. PD, PL, PFK represent respectively the price of deposits, the price of labor and the price of fixe or physical capital. The data in thousands of American dollar are issued from the database of BankScope, Fitch of London rating agency International Bank Corporate Analysis (IBCA) and the World Bank.

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Egypt

M SD CV M SD CV M SD CV M SD CV M SD CV M SD CV M SD CV M SD CV M SD CV M SD CV M SD CV M SD CV

Fixed Assets

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Please cite this article in press as: Mansour, R., & El Moussawi, C. Efficiency, technical progress and productivity of Arab banks: A non-parametric approach. The Quarterly Review of Economics and Finance (2019), https://doi.org/10.1016/j.qref.2019.02.002

Table 1 Descriptive statistics of Arab banks over the period 2000–2014.

9

--------0.2341 10.4559 0.0000 0.2212 9.8511 0.0000 0.2253

1 --------0.9106 95.6711 0.0000 0.9075

1 --------0.9683

1

15.6833 0.0000 0.2962 13.4648 0.0000 0.9087

10.0432 0.0000 0.1749 7.7145 0.0000 0.6483

93.8103 0.0000 0.6136 33.7398 0.0000 0.3072

168.5173 0.0000 0.5833 31.1847 0.0000 0.2987

--------0.5898 31.7140 0.0000 0.2837

1 --------0.2720

1

94.5675 0.0000 −0.0234 −1.0165 0.3095 −0.0180 −0.7829 0.4338 −0.0533 −2.3197 0.0205

36.9742 0.0000 0.0190 0.8264 0.4087 −0.0123 −0.5370 0.5913 −0.0481 −2.0924 0.0365

14.0180 0.0000 −0.0607 −2.6402 0.0084 −0.0406 −1.7645 0.0778 −0.0068 −0.2968 0.7666

13.593 0.0000 −0.0555 −2.4152 0.0158 −0.1232 −5.3941 0.0000 −0.0097 −0.4235 0.672

12.8456 0.0000 −0.0733 −3.1923 0.0014 −0.1229 −5.3808 0.0000 −0.0191 −0.8313 0.4059

12.2744 0.0000 −0.0345 −1.4997 0.1339 −0.0589 −2.5644 0.0104 0.0114 0.4974 0.6189

--------−0.0133 −0.5781 0.5632 0.0053 0.2334 0.8155 −0.0494 −2.1486 0.0318

1 --------0.2855 12.9371 0.0000 0.2107

1 --------0.7091

1

9.3592 0.0000 0.8378 66.6240 0.0000 0.7836 54.7624 0.0000 0.7817

43.6642 0.0000 0.3162 14.4710 0.0000 0.3531 16.3883 0.0000 0.3397

54.4347 0.0000 0.4940 24.6689 0.0000 0.2539 11.3987 0.0000 −0.0657 −2.8625 0.0042 −0.0951 −4.1479 0.0000 −0.1149 −5.0226 0.0000

Note: PD, PL, PFK represent respectively the price of deposits, the price of labor and the price of fixe or physical capital.

Impaired Loans

Total Non-Interest Expenses

PD

PL

PFK

1 --------−0.0096 −0.4180 0.6759 0.0094 0.4084 0.683

1 --------0.5344 27.456 0

1 ---------

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Total Customer Deposits

Total Interest Expenses

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Total Earning Assets

Total Assets

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Fixed Assets t-Statistic Probability Total Assets t-Statistic Probability Total Interest Expenses t-Statistic Probability Personnel Expenses t-Statistic Probability Total Earning Assets t-Statistic Probability Total Customer Deposits t-Statistic Probability Impaired Loans t-Statistic Probability Total Non-Interest Expenses t-Statistic Probability PD t-Statistic Probability PL t-Statistic Probability PFK t-Statistic Probability

Personnel Expenses

Fixed Assets

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Table 2 Correlation matrix of Arab banks over the period 2000–2014.

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Table 3 Scores of productive efficiency in Arab banks over the period 2000–2014.

Mean (M) Standard Deviation (SD) Coefficient of Variation (CV) Spearman’s Rank Correlation Coefficient between Technical and Allocative Efficiency

Technical Efficiency (TE)

Allocative Efficiency (AE)

Cost Efficiency (CE)

87 15 18 0.41**

79 25 31

70 27 39

Note: The data are in thousands of American dollar issued from the database of BankScope, Fitch of London rating agency International Bank Corporate Analysis (IBCA) and the World Bank. Numbers are in percentage (%). (**) represents the level of significance at 1%.

Table 4 Evolution of the technical efficiency (TE), allocative efficiency (AE) and cost-efficiency (CE) in Arab banks over the period 2000–2014. Country Algeria

Bahrain

Egypt

Jordan

Kuwait

Lebanon

Morocco

Oman

Qatar

Saudi Arabia

Tunisia United Arab Emirates

Efficiency

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

M

SD

CV

TE AE CE TE AE CE TE AE CE TE AE CE TE AE CE TE AE CE TE AE CE TE AE CE TE AE CE TE AE CE TE AE CE TE AE CE

0.97 0.35 0.34 0.87 0.81 0.71 0.84 0.89 0.75 0.91 0.91 0.84 0.82 0.70 0.57 0.89 0.95 0.85 0.96 0.44 0.44 0.71 0.75 0.54 0.75 0.63 0.44 0.90 0.77 0.70 0.97 0.77 0.76 0.96 0.86 0.83

0.96 0.46 0.46 0.88 0.79 0.70 0.84 0.89 0.74 0.95 0.91 0.88 0.88 0.61 0.54 0.90 0.95 0.86 0.98 0.46 0.46 0.70 0.67 0.47 0.69 0.52 0.35 0.95 0.71 0.69 1.00 0.78 0.78 0.97 0.81 0.79

0.82 0.45 0.41 0.86 0.76 0.66 0.76 0.87 0.66 0.96 0.87 0.85 0.95 0.62 0.60 0.90 0.94 0.84 0.96 0.45 0.45 0.64 0.56 0.36 0.71 0.61 0.46 0.93 0.72 0.69 0.91 0.59 0.57 0.97 0.85 0.82

0.91 0.30 0.29 0.90 0.84 0.76 0.80 0.92 0.73 0.98 0.94 0.92 0.90 0.77 0.70 0.91 0.95 0.87 0.96 0.44 0.43 0.59 0.79 0.46 0.76 0.71 0.56 0.91 0.76 0.70 0.80 0.60 0.55 0.97 0.86 0.83

0.81 0.43 0.34 0.92 0.81 0.75 0.81 0.93 0.75 0.98 0.96 0.95 0.98 0.75 0.73 0.89 0.93 0.83 0.96 0.45 0.45 0.62 0.80 0.50 0.84 0.71 0.60 0.94 0.75 0.71 0.76 0.45 0.38 0.96 0.86 0.83

0.88 0.38 0.34 0.95 0.81 0.77 0.80 0.92 0.73 0.98 0.95 0.94 0.86 0.75 0.64 0.87 0.95 0.83 0.98 0.65 0.65 0.70 0.78 0.55 0.88 0.75 0.66 0.93 0.83 0.79 0.85 0.41 0.38 0.95 0.93 0.88

0.83 0.40 0.33 0.93 0.84 0.79 0.84 0.91 0.76 1.00 0.94 0.94 0.87 0.64 0.56 0.89 0.92 0.81 0.96 0.63 0.62 0.82 0.62 0.51 0.89 0.63 0.56 0.89 0.82 0.74 0.82 0.45 0.40 0.98 0.90 0.88

0.82 0.44 0.37 0.94 0.75 0.71 0.78 0.85 0.65 0.98 0.93 0.91 0.80 0.69 0.57 0.90 0.88 0.79 0.97 0.62 0.62 0.89 0.46 0.41 0.89 0.58 0.52 0.92 0.70 0.64 0.78 0.46 0.40 0.96 0.88 0.85

0.88 0.50 0.43 0.93 0.71 0.67 0.83 0.88 0.73 0.98 0.92 0.91 0.77 0.67 0.51 0.89 0.85 0.75 0.88 0.56 0.51 0.87 0.55 0.48 0.88 0.63 0.55 0.93 0.65 0.60 0.80 0.53 0.46 0.98 0.92 0.90

0.85 0.50 0.42 0.86 0.72 0.66 0.84 0.90 0.76 0.98 0.95 0.93 0.74 0.78 0.58 0.83 0.81 0.67 0.86 0.62 0.55 0.78 0.66 0.50 0.91 0.83 0.75 0.91 0.74 0.68 0.75 0.57 0.46 0.97 0.93 0.90

0.79 0.52 0.41 0.81 0.73 0.65 0.85 0.90 0.76 0.93 0.96 0.90 0.76 0.75 0.57 0.90 0.88 0.79 0.83 0.64 0.55 0.84 0.62 0.49 0.91 0.87 0.80 0.93 0.74 0.69 0.80 0.53 0.45 0.97 0.95 0.92

0.77 0.73 0.56 0.67 0.75 0.53 0.86 0.91 0.78 0.95 0.96 0.91 0.71 0.81 0.58 0.89 0.84 0.76 0.82 0.77 0.65 0.66 0.55 0.34 0.85 0.84 0.72 0.94 0.75 0.71 0.78 0.71 0.57 0.96 0.95 0.91

0.81 0.67 0.55 0.63 0.75 0.51 0.89 0.91 0.81 0.97 0.96 0.94 0.65 0.91 0.60 0.97 0.97 0.93 0.82 0.74 0.63 0.58 0.46 0.26 0.87 0.85 0.76 0.95 0.76 0.72 0.77 0.75 0.59 0.97 0.96 0.93

0.89 0.55 0.49 0.75 0.82 0.64 0.86 0.89 0.77 0.98 0.95 0.94 0.82 0.88 0.73 0.95 0.96 0.91 0.85 0.69 0.60 0.70 0.86 0.59 0.92 0.95 0.88 0.97 0.80 0.78 0.81 0.63 0.51 0.98 0.95 0.93

0.92 0.50 0.46 0.78 0.80 0.63 0.85 0.90 0.76 0.99 0.95 0.94 0.86 0.83 0.72 0.94 0.94 0.88 0.82 0.68 0.58 0.67 0.87 0.57 0.91 0.93 0.84 0.96 0.85 0.82 0.77 0.64 0.50 0.95 0.97 0.92

0.85 0.49 0.42 0.83 0.77 0.66 0.83 0.90 0.75 0.97 0.94 0.92 0.81 0.75 0.61 0.90 0.91 0.82 0.90 0.60 0.55 0.72 0.66 0.47 0.85 0.76 0.66 0.93 0.76 0.71 0.80 0.57 0.48 0.97 0.91 0.88

0.19 0.29 0.29 0.24 0.24 0.30 0.13 0.13 0.16 0.07 0.10 0.14 0.14 0.12 0.16 0.13 0.12 0.17 0.12 0.37 0.37 0.16 0.18 0.13 0.15 0.18 0.22 0.09 0.14 0.17 0.18 0.32 0.34 0.07 0.13 0.15

0.22 0.60 0.68 0.29 0.31 0.45 0.16 0.15 0.22 0.08 0.10 0.15 0.18 0.16 0.26 0.14 0.14 0.21 0.14 0.61 0.67 0.22 0.28 0.29 0.18 0.24 0.34 0.10 0.19 0.24 0.22 0.56 0.70 0.07 0.15 0.17

Note: M, SD, CV represent respectively the mean, the standard deviation and the coefficient of variation for the variables.

duction factors (technical inefficiency and allocative inefficiency). These deficiencies impede banks from minimizing their costs of production. In fact, in the Arab banking sector, the allocative inefficiency increases the average costs on the order of 21%. As shown in Table 3, this inefficiency contributes more than the technical inefficiency to an increase of costs. In fact, we find that banks close to their production frontier are those with the highest scores in terms of allocative efficiency. As shown in Table 3, the correlation coefficient of Spearman ranking for the scores of technical and allocative efficiency is equal to 0.41 over the period from 2000 to 2014. This is both a statistically significant and positive result at 1% and shows that the Arab banks, which choose the optimal combinations of production factors and in the more appropriate proportions, form the efficient frontier. This result is different from those obtained by Dietsch and Chaffai (1999) in the case of European banks and by Chaffai (1998) in the case of Tunisian banks because they show a negative correlation between technical efficiency and allocative efficiency.

5.2. Analysis of the total productivity of production factors through the Malmquist-Luenberger indexes The calculation of the Arab banks’ total productivity of production factors is carried out over the period from 2000 to 2014 by using the Malmquist and Luenberger productivity indexes (MPI) and (LPI) respectively. An index of productivity superior (inferior) to 1 indicates amelioration (deterioration) of the bank’s productivity banks over this sample period. Table 5 indicates the results of the Malmquist and Luenberger productivity indicators and their decomposition into the variations of the efficiency and the technological changes. For all the set of the sampled banks, both MPI and LPI have a positive sign over the period from 2000 to 2014. Moreover, the MPI values of 1.0244 and LPI values of 1.0179 indicate that, during the abovementioned period, productivity slightly improved by about 2.44% and 1.79% respectively. This result is very similar to those obtained by Koutsomanoli-Filippaki et al. (2009) on the Central and Eastern Europe banks of, by Matthews and Zhang (2010) on the Chinese

Please cite this article in press as: Mansour, R., & El Moussawi, C. Efficiency, technical progress and productivity of Arab banks: A non-parametric approach. The Quarterly Review of Economics and Finance (2019), https://doi.org/10.1016/j.qref.2019.02.002

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R. Mansour, C. El Moussawi / The Quarterly Review of Economics and Finance xxx (2019) xxx–xxx

Table 5 Average results of the total productivity of production factors in Arab banks over the period 2000–2014. Malmquist Productivity Index (MPI)

Mean (M) Standard Deviation (SD) Coefficient of Variation (CV)

Luenberger Productivity Index (LPI)

EFCH

TECH

MPI

EFCH

TECH

LPI

0.9871 0.0529 0.0536

1.0377 0.0525 0.0506

1.0244 0.0758 0.0740

0.9901 0.0387 0.0391

1.0278 0.0337 0.0328

1.0179 0.0529 0.0520

Note: EFCH, TECH, MPI and LPI represent respectively the change of technical efficiency, the change of technological progress, the variation of the total productivity of production factors measured by the Malmquist and Luenberger indexes.

banks, and by Degl’Innocenti et al. (2017) on the European banks. This productivity improvement is fully explained by a positive variation of technological progress (3.77% for the MPI and 2.78% for the LPI index) and not by the technical efficiency which, as indicated by Table 5’s results, registered a negative variation of -1.28% for the MPI and of -0.98% for the LPI. Finally, we observe that the two methods of calculation of the productivity present homogenous results and are characterized by a weak dispersion. These are measured by the coefficient of variation of the productivity and its different components (the technological progress and the technical efficiency) which is situated on the very narrow interval of [5.06%, - 7.4%] for the MPI and of [3.2%, - 5.2%] for the LPI. However, this performance has not been uniform in all the countries. If almost all the countries maintained or increased their technological progress, it is not the same for their level of technical efficiency. Table 6 indicates the temporal (by year) and individual (by bank) evolution of the variations of technical efficiency, technological progress and the factors of total productivity. These results reveal an annual contrasted evolution of different components of the factors of total productivity whatever the studied banking sector and whatever the used calculation method. In fact, we remark that, with the exception of Algerian and Egyptian banks, all our sampled banks, have known a positive evolution of the factors of total productivity, due essentially to an amelioration of technological progress rather than to gains of technical efficiency. The decline in the Algerian and Egyptian banks’ productivity is explained by the fact that the negative variation of the technical efficiency, observed over the period from 2000 to 2014, seems to neutralize the positive effects that the technological progress can play on the bank’s productivity. Generally, according to the LPI, the annual evolution of banking productivity (the classification of countries remains almost the same when the productivity is measured by the index of Malmquist) is placed on the interval of [-0.05%, -0.16%] for Algeria and Egypt, [0.58%, 1.68%] for Bahrain, Jordan, Kuwait, Morocco and Tunisia [2.09%, 2.64%] for Lebanon, Oman and United Arab Emirates, [3.96%, 4.62%] for Qatar and Saudi Arabia. The dispersion of the productivity remains within a narrow interval of [2.78%, 14.62%] for the MPI and [2.47%, 9.32%] for the LPI. The results, presented in Table 6, reveal a negative evolution of the Arab banks’ technical efficiency over the period from 2000 to 2014. This decline of technical efficiency is relatively homogenous whatever the used estimation technique. In fact, we observe that the variation of technical efficiency remains on a very narrow interval of [-0.15%, -0.45%] for Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates, of [-0.87%, -1.42%] for Bahrain, Jordan, Lebanon, Morocco, and Tunisia and of [-2.49%, -4.23%] for Algeria and Egypt. Moreover, we remark that the dispersion of the change in technical efficiency, measured by the coefficient of variation, is considered to be constant and varies between 1.54% and 8.71%. The degradation of technical efficiency, observed over the period of study, indicates that the banks, experiencing a negative evolution of their technical efficiency, do not operate on the efficient production frontier due to several elements such as the technology and the quality of the factors of production, scale of production, allocation of resources,

the differentiation and the heterogeneity of products and the bank management (X-efficiency), and so on. Finally, Table 6 s results show that the technological progress evolves positively during the period of study and is considered to be a determinant factor of the factors of total productivity. In addition, for the 12 banking sectors, country, the index of technological progress registered positive variations going from 1.83% to 7.44% for the MPI and from 1.59% to 5.07% for the LPI. In fact, Algeria, Saudi Arabia and Qatar are classified in the first position with a technological progress score varying between 5.11% and 7.44%. For Lebanon, Oman and United Arab Emirates, the variations of the productivity, linked to the technical progress, vary between 3.13% and 2.57%. Finally, Bahrain, Egypt, Jordan Kuwait, Morocco and Tunisia are classified in the third position with scores varying between 1.59% and 2.32%. The dispersion of scores of technological change is very close between one country and another since the coefficient of variation remains on an interval of [1.87%, 11.50%]. However, we should point out that the dispersion of LPI results [1.87%, 6.74%] is less strong than those obtained from the MPI [2.08%, 11.50%]. 5.3. Banks efficiency and productivity convergence The improvement in banking productivity over the period from 2000 to 2014 suggests the existence of catch-up phenomenon where the less efficient banks catch up with the more efficient ones (the latter are those who contribute to the formation of the productive frontier). The convergence of banking productivities appears to be, also, a useful indicator to anticipate the policies and strategies of any additional banking restructuring to be implemented. To appreciate the adjustment costs in terms of employment and banking infrastructure, we test the convergence of the Arab banks’ scores of efficiency and productivity. In the literature, there are two techniques that allow us to verify the existence of a catch-up phenomenon. These are the ˇ -convergence and the  -convergence (Matthews & Zhang, 2010; Andries¸ & Ca˘praru, 2012a, 2012b; Carvallo & Kasman, 2017; Olson & Zoubi, 2017; Degl’Innocenti et al., 2017). The ˇ -convergence can be defined as the structural tendency of the less efficient banks to catch-up with the more efficient ones. By identifying the more efficient banks from a technical, allocative, and cost efficiency and productivity point of view as benchmarks (banks constituting the frontier), the others banks’ spreads to these frontiers measure their relative efficiency. If the distances between the inefficient banks and the efficient ones constituting the frontier decrease over time, they reveal a phenomenon of structural convergence. Then, ˇ -convergence exists if there is a negative and significant correlation between the initial level of productive efficiency and its growth rate over time. This analysis of ˇ -convergence can be completed by the  -convergence established by the diminution of the dispersion efficiency levels. If the standard deviation of the spreads at the efficiency frontier is reduced over time, there is  -convergence. The ˇ -convergence is a necessary condition but is insufficient to determine whether there is  -convergence. The results, concerning the dispersion of efficiency and productivity scores do not allow us to reach a conclusion of a phenomenon

Please cite this article in press as: Mansour, R., & El Moussawi, C. Efficiency, technical progress and productivity of Arab banks: A non-parametric approach. The Quarterly Review of Economics and Finance (2019), https://doi.org/10.1016/j.qref.2019.02.002

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

M

SD

CV

EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI

0.9439 1.0020 0.9458 0.9540 1.0014 0.9554 0.9726 1.0000 0.9726 0.9746 1.0000 0.9746 0.9870 1.0097 0.9965 0.9887 1.0087 0.9974 1.0149 1.0128 1.0278 1.0136 1.0109 1.0245 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9826 1.0344 1.0164 0.9879 1.0297 1.0176 0.9726 1.0000 0.9726 0.9746 1.0000 0.9746 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9953 1.0027 0.9980 0.9954 1.0027 0.9980

0.9725 1.0028 0.9752 0.9798 1.0018 0.9815 1.0576 1.0000 1.0576 1.0481 1.0000 1.0481 1.0170 1.0135 1.0306 1.0165 1.0118 1.0283 0.9996 1.0523 1.0519 0.9996 1.0449 1.0444 1.0000 1.0123 1.0123 1.0000 1.0118 1.0118 0.9975 1.0555 1.0529 0.9948 1.0467 1.0416 1.0576 1.0000 1.0576 1.0481 1.0000 1.0481 1.0000 1.0612 1.0612 1.0000 1.0551 1.0551 1.0047 1.0909 1.0960 1.0046 1.0750 1.0796

0.9675 1.0163 0.9833 0.9742 1.0109 0.9852 0.9930 1.0316 1.0244 0.9938 1.0279 1.0217 0.8991 1.1941 1.0737 0.9194 1.1262 1.0457 0.9992 1.0533 1.0525 0.9992 1.0459 1.0451 1.0000 1.0086 1.0086 1.0000 1.0083 1.0083 1.0027 1.0820 1.0850 1.0022 1.0662 1.0684 0.9930 1.0316 1.0244 0.9938 1.0279 1.0217 1.0000 1.0494 1.0494 1.0000 1.0440 1.0440 1.0000 1.1706 1.1706 1.0000 1.1278 1.1278

0.9405 1.1064 1.0406 0.9609 1.0704 1.0313 1.0003 1.0551 1.0554 1.0002 1.0430 1.0432 0.9308 1.0662 0.9924 0.9469 1.0456 0.9925 1.0012 1.0584 1.0597 1.0012 1.0516 1.0529 1.0000 1.0085 1.0085 1.0000 1.0083 1.0083 1.0004 1.0582 1.0586 0.9999 1.0484 1.0483 1.0003 1.0551 1.0554 1.0002 1.0430 1.0432 1.0000 1.0352 1.0352 1.0000 1.0323 1.0323 1.0000 1.0523 1.0523 1.0000 1.0440 1.0440

0.9138 1.1595 1.0595 0.9366 1.0890 1.0256 0.9474 1.0001 0.9475 0.9584 1.0001 0.9585 1.0227 1.0725 1.0969 1.0195 1.0448 1.0644 1.0000 1.0175 1.0175 1.0000 1.0167 1.0167 0.9992 1.0000 0.9992 0.9992 1.0000 0.9992 0.9925 1.0620 1.0540 0.9926 1.0519 1.0445 0.9474 1.0001 0.9475 0.9584 1.0001 0.9585 1.0000 1.0398 1.0398 1.0000 1.0362 1.0362 1.0000 1.0001 1.0001 1.0000 1.0001 1.0001

0.8578 1.1823 1.0141 0.9346 1.0818 1.0164 0.9609 1.0155 0.9758 0.9648 1.0134 0.9781 1.0012 1.0035 1.0047 1.0048 1.0031 1.0079 0.9087 1.0073 0.9153 0.9175 1.0070 0.9245 0.9599 1.0000 0.9599 0.9639 1.0000 0.9639 0.9749 1.0145 0.9890 0.9773 1.0128 0.9901 0.9609 1.0155 0.9758 0.9648 1.0134 0.9781 0.9793 1.0000 0.9793 0.9798 1.0000 0.9798 0.8788 1.0000 0.8788 0.8894 1.0000 0.8894

0.8771 1.0272 0.9010 0.9412 1.0133 0.9544 0.9286 1.0087 0.9367 0.9408 1.0069 0.9477 0.9668 1.0380 1.0035 0.9790 1.0271 1.0061 0.9613 1.0095 0.9704 0.9677 1.0092 0.9769 0.9723 1.0000 0.9723 0.9798 1.0000 0.9798 0.9294 1.0004 0.9298 0.9388 1.0004 0.9392 0.9286 1.0087 0.9367 0.9408 1.0069 0.9477 1.0096 1.0049 1.0145 1.0090 1.0047 1.0137 0.9606 1.0000 0.9606 0.9679 1.0000 0.9679

0.8464 1.0698 0.9055 0.9052 1.0279 0.9331 0.9944 1.0791 1.0730 0.9960 1.0532 1.0491 1.0100 1.0091 1.0192 0.9964 1.0086 1.0050 0.9244 1.0000 0.9244 0.9387 1.0000 0.9387 0.9726 1.0127 0.9850 0.9784 1.0124 0.9908 0.9837 1.0077 0.9913 0.9890 1.0066 0.9956 0.9944 1.0791 1.0730 0.9960 1.0532 1.0491 0.9818 1.0473 1.0283 0.9828 1.0430 1.0257 1.0414 1.0028 1.0443 1.0353 1.0026 1.0379

0.9178 1.0700 0.9820 0.9541 1.0283 0.9824 0.8797 1.0293 0.9055 0.9255 1.0227 0.9482 0.8541 1.0107 0.8632 0.9000 1.0086 0.9086 0.9435 1.0005 0.9439 0.9579 1.0005 0.9583 0.9565 1.0003 0.9568 0.9610 1.0003 0.9612 1.0440 1.0095 1.0539 1.0348 1.0086 1.0434 0.8797 1.0293 0.9055 0.9255 1.0227 0.9482 1.0215 1.0501 1.0727 1.0201 1.0442 1.0643 0.9985 1.0771 1.0754 0.9966 1.0613 1.0579

1.0005 1.0077 1.0082 0.9784 1.0020 0.9805 1.1120 1.0170 1.1309 1.0821 1.0073 1.0894 0.9478 1.0220 0.9687 0.9743 1.0169 0.9912 1.0521 1.0206 1.0738 1.0381 1.0176 1.0556 1.0882 1.0043 1.0928 1.0660 1.0040 1.0700 1.0015 1.1109 1.1125 1.0030 1.0823 1.0853 1.1120 1.0170 1.1309 1.0821 1.0073 1.0894 0.9768 1.0301 1.0063 0.9792 1.0283 1.0076 0.8900 1.0684 0.9508 0.9135 1.0519 0.9653

1.0178 1.0189 1.0370 1.0150 1.0092 1.0243 1.0291 1.1133 1.1457 1.0273 1.0694 1.0968 0.9775 1.0301 1.0069 0.9912 1.0223 1.0135 1.0879 1.0227 1.1126 1.0635 1.0176 1.0811 1.0114 1.0427 1.0545 1.0111 1.0344 1.0455 1.0055 1.0365 1.0421 1.0042 1.0277 1.0318 1.0291 1.1133 1.1457 1.0273 1.0694 1.0968 0.9824 1.0296 1.0115 0.9834 1.0277 1.0111 1.0538 1.0350 1.0907 1.0394 1.0306 1.0700

1.1173 1.0033 1.1210 1.0745 1.0033 1.0778 0.9948 1.0469 1.0414 1.0044 1.0357 1.0402 0.9218 1.0000 0.9218 0.9516 1.0000 0.9516 1.0218 1.0017 1.0235 1.0172 1.0014 1.0186 1.0450 1.0616 1.1094 1.0390 1.0515 1.0905 0.9431 1.0717 1.0108 0.9560 1.0516 1.0076 0.9948 1.0469 1.0414 1.0044 1.0357 1.0402 1.0223 1.0559 1.0795 1.0202 1.0488 1.0690 1.0319 1.4676 1.5144 1.0186 1.2585 1.2771

1.0043 1.0086 1.0129 0.9969 1.0067 1.0036 0.9751 1.0158 0.9905 0.9781 1.0111 0.9892 0.9536 1.0000 0.9536 0.9772 1.0000 0.9773 0.9912 1.0000 0.9912 0.9945 1.0000 0.9945 1.0086 1.0942 1.1036 1.0073 1.0806 1.0878 0.9658 1.0016 0.9674 0.9754 1.0015 0.9769 0.9751 1.0158 0.9905 0.9781 1.0111 0.9892 0.9773 1.0384 1.0149 0.9778 1.0348 1.0126 0.8938 1.0738 0.9598 0.9117 1.0552 0.9669

1.0312 1.0410 1.0735 1.0083 1.0331 1.0415 0.9797 1.0477 1.0265 0.9837 1.0296 1.0133 0.9739 1.0017 0.9756 0.9855 1.0016 0.9871 0.9424 1.0000 0.9424 0.9501 1.0000 0.9501 0.9558 1.0672 1.0199 0.9600 1.0581 1.0181 0.9982 1.0042 1.0024 0.9994 1.0040 1.0034 0.9797 1.0477 1.0265 0.9837 1.0296 1.0133 1.0185 1.0016 1.0201 1.0180 1.0014 1.0194 1.2303 1.0000 1.2303 1.1645 1.0000 1.1645

0.9577 1.0511 1.0043 0.9724 1.0271 0.9995 0.9875 1.0329 1.0203 0.9913 1.0229 1.0142 0.9617 1.0337 0.9934 0.9751 1.0232 0.9983 0.9892 1.0183 1.0076 0.9899 1.0159 1.0059 0.9978 1.0223 1.0190 0.9976 1.0193 1.0168 0.9873 1.0392 1.0262 0.9897 1.0313 1.0210 0.9875 1.0329 1.0203 0.9913 1.0229 1.0142 0.9978 1.0317 1.0295 0.9979 1.0286 1.0265 0.9985 1.0744 1.0730 0.9955 1.0507 1.0462

0.0743 0.0597 0.0618 0.0423 0.0309 0.0393 0.0557 0.0330 0.0704 0.0414 0.0215 0.0495 0.0481 0.0517 0.0583 0.0347 0.0332 0.0381 0.0493 0.0212 0.0609 0.0397 0.0184 0.0492 0.0361 0.0310 0.0509 0.0294 0.0262 0.0419 0.0282 0.0349 0.0486 0.0230 0.0267 0.0387 0.0557 0.0330 0.0704 0.0414 0.0215 0.0495 0.0163 0.0217 0.0287 0.0153 0.0193 0.0254 0.0869 0.1236 0.1569 0.0671 0.0708 0.0976

0.0775 0.0568 0.0616 0.0435 0.0301 0.0393 0.0564 0.0320 0.0690 0.0417 0.0210 0.0488 0.0500 0.0500 0.0587 0.0356 0.0325 0.0381 0.0499 0.0208 0.0605 0.0401 0.0181 0.0489 0.0361 0.0303 0.0500 0.0295 0.0257 0.0412 0.0286 0.0336 0.0474 0.0233 0.0259 0.0379 0.0564 0.0320 0.0690 0.0417 0.0210 0.0488 0.0163 0.0210 0.0278 0.0154 0.0188 0.0247 0.0871 0.1151 0.1463 0.0674 0.0674 0.0933

Algeria

Bahrain

Bahrain

Egypt

Egypt

Jordan

Jordan

Kuwait

Kuwait

Lebanon

Lebanon

Morocco

Morocco

Oman

Oman

Qatar

Qatar

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Variable

Algeria

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Country

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Table 6 Estimation results of the total productivity of production factors for Arab banks over the period 2000–2014 by the Malmquist and Luenberger indexes.

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CV

0.0606 0.0484 0.0922 0.0477 0.0378 0.0715 0.0212 0.0333 0.0336 0.0167 0.0269 0.0281 0.0566 0.0428 0.0783 0.0419 0.0312 0.0574

SD

0.0604 0.0510 0.0970 0.0476 0.0394 0.0744 0.0209 0.0342 0.0340 0.0165 0.0275 0.0284 0.0564 0.0443 0.0808 0.0417 0.0320 0.0586 0.9977 1.0535 1.0524 0.9968 1.0429 1.0397 0.9858 1.0269 1.0120 0.9884 1.0231 1.0115 0.9971 1.0352 1.0328 0.9956 1.0257 1.0212

M 2014

0.9737 1.0107 0.9842 0.9792 1.0103 0.9894 0.9560 1.0270 0.9819 0.9628 1.0232 0.9861 1.0258 1.0527 1.0798 1.0158 1.0351 1.0509 0.9904 1.0130 1.0032 0.9939 1.0120 1.0059 0.9927 1.0261 1.0186 0.9942 1.0224 1.0166 1.0393 1.0483 1.0895 1.0330 1.0340 1.0670

2013 2012

0.9782 1.0136 0.9916 0.9795 1.0128 0.9924 0.9728 1.0034 0.9761 0.9763 1.0031 0.9794 1.0448 1.0347 1.0810 1.0276 1.0213 1.0488 0.9994 1.0864 1.0858 1.0007 1.0710 1.0717 0.9636 1.0463 1.0082 0.9782 1.0398 1.0180 1.1092 1.0352 1.1482 1.0615 1.0262 1.0877

2011 2010

1.1389 1.0889 1.2401 1.0989 1.0713 1.1702 0.9796 1.1331 1.1100 0.9864 1.1066 1.0930 0.9306 1.0452 0.9726 0.9534 1.0330 0.9863 1.0428 1.1005 1.1476 1.0407 1.0755 1.1162 1.0265 1.0149 1.0419 1.0205 1.0138 1.0343 1.0398 1.0000 1.0398 1.0370 1.0000 1.0370 1.0551 1.0450 1.1026 1.0379 1.0365 1.0744 0.9638 1.0494 1.0114 0.9702 1.0439 1.0142 0.9703 1.0386 1.0078 0.9784 1.0299 1.0083 0.9687 1.0023 0.9710 0.9784 1.0016 0.9800 0.9684 1.0241 0.9917 0.9713 1.0223 0.9936 0.9286 1.0012 0.9297 0.9467 1.0009 0.9476 0.8655 1.0000 0.8655 0.8876 1.0000 0.8876 1.0157 1.0221 1.0381 1.0095 1.0203 1.0297 0.9176 1.0000 0.9176 0.9279 1.0000 0.9279

2009 2008 2007 2006

0.9870 1.0311 1.0177 0.9891 1.0265 1.0156 0.9915 1.0128 1.0042 0.9952 1.0120 1.0072 0.9578 1.0085 0.9660 0.9626 1.0076 0.9702

2005 2004

0.9682 1.1324 1.0964 0.9700 1.1074 1.0775 0.9739 1.0128 0.9863 0.9770 1.0119 0.9889 1.0006 1.0456 1.0462 1.0004 1.0363 1.0367 1.0019 1.1495 1.1517 1.0028 1.1126 1.1154 1.0005 1.0011 1.0016 1.0005 1.0010 1.0016 1.0125 1.1722 1.1868 1.0100 1.1250 1.1350

2003 2002

1.0300 1.0718 1.1040 1.0273 1.0583 1.0855 0.9955 1.0000 0.9955 0.9955 1.0000 0.9955 1.0363 1.0086 1.0452 1.0328 1.0075 1.0403 0.9684 1.0043 0.9726 0.9699 1.0041 0.9740 1.0000 1.0031 1.0031 1.0000 1.0031 1.0031 0.9456 1.0029 0.9484 0.9509 1.0026 0.9535 United Arab Emirates

United Arab Emirates

Tunisia

Tunisia

Saudi Arabia

EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI EFCH TECH MPI EFCH TECH LPI Saudi Arabia

2001 Variable Country

Table 6 (Continued)

Note: EFCH, TECH, MPI, LPI represent respectively the technical efficiency change, the technological progress change. M, SD, CV represent respectively the mean, the standard deviation and the coefficient of variation for the variables.

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of  -convergence since the reduction of the performance spreads in the beginning of the period does not conform with the end of the period. The convergence of banks is modeled in the following way (Weill, 2009; Andries¸ & Ca˘praru, 2012a, 2012b; Carvallo & Kasman, 2017; Olson & Zoubi, 2017): EFFijt = ˛ + ˇLn(EFF ij,t−1 ) + it Where: EFFijt = Ln(EFFijt ) − Ln(EFF ij,t−1 ) This regression equation, linking the average annual growth rates of the banks’ scores with the initial levels of their productive efficiency and their productivity, provides an answer to the convergence phenomenon (Table 7). Table 7 indicates the existence of banks’ catch-up phenomenon as shown by the coefficients of different effectuated regressions. Whatever is the component of the retained productive efficiency, the less efficient banks, in terms of production costs, catch up undeniably with the more competitive ones. This global convergence of the production costs results from both technical and allocative components. Our results confirm that, over the study period, on the one hand, the less efficient banks would have saved progressively on expenses devoted to the purchase of production factors and, on the other hand, would have modified their productive combinations to better adjust them to their relative price structure. Our results show, also, that the less productive banks join the more productive ones. They reveal, also, a banks’ catch-up phenomenon to their productivity frontier. This result is explained essentially by not only the efforts of quantitative management of factorial resources but, also, by the introduction of technological progress in the production procedure at the level of each bank. 5.4. Explanatory factors of the banks efficiency and productivity The explanatory factors of banking efficiency and productivity are concerned at the same time with the internal factors. These should be searched in the organizational strategies proper to each bank. The external factors reflect the environment in which the bank operates. In this field, the usual estimation methods are either whether they are direct by integrating the exogenous factors of the econometric estimation of production frontier (Casu & Molyneux, 2003; Dietsch & Lozano-Vivas, 2000; Fries & Taci, 2005) or whether they are indirect whereby the efficiency indicators, in the second stage, are regressed on a set of exogenous variables (KoutsomanoliFilippaki et al., 2009; Pasiouras, Sifodaskalakis, & Zopounidis, 2007; Sufian, 2009; Tan & Floros, 2013). The appropriate method is one that takes into account internal and external factors in two stages. Notably, Pastor (2002) and Coelli et al. (2005) describe the advantages of this method which combines a DEA model and a regression analysis. In the first stage, a traditional DEA model is constructed. This model consists only of discretionary variables (inputs and outputs). In the second stage, the efficiency scores are regressed on the environmental variables. A regression of Tobit type is used normally in this second stage. This method is based on the hypothesis that the unitary efficiency score represents a censure. For their part, Simar and Wilson (2007) propose a method based on bootstrapping since the temporal dependency of efficiency scores prevaricates the parametric estimations. However, McDonald (2009) confirmed recently that the unitary efficiency score was not a censure but well and truly an observed value. In this case, the Ordinary Least Square (OLS) method is the more appropriate method especially when we proceed to the correction of heteroscedasticity by using the White test.

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Table 7 Results of the ␤ convergence of Arab banks over the period 2000–2014.

˛



ˇLn EFFij,t−1



Observations Number R2

TE

AE

CE

TPF (MPI)

TPF (LPI)

−0.0123∗ (0.0050)

−0.0094 (0.0089)

0.0161 (0.0108)

0.0157∗∗ (0.0030)

0.7325∗∗ (0.0178)

−0.1058∗∗(0.0257)

−0.0463∗∗(0.0162)

−0.0409∗∗(0.0151)

−1.0412∗∗(0.0253)

−1.0186∗∗(0.0245)

1579 0.13

1579 0.12

1579 0.13

1601 0.56

1601 0.57

NB: TE: Technical Efficiency, AE: Allocative Efficiency, CE: Cost Efficiency. The TPF calculated according to the Malmquist productivity index, the TPF calculated according to the Luenberger productivity index. Standard errors are in parenthesis. (*) and (**) represent the level of significance at 5% and 1% respectively.

Table 8 Results of the cost efficiency and productivity of Arab banks over the period 2000–2014. Cost Efficiency Variables Constant ROA TA ER Risk EG INFR R2 Observations Number

Estimated Coefficients 1.0068 0.0352 0.0631 0.0782 −0.0291 0.0947 0.0021 0.76 1887

t-value **

2.9436 0.0667 2.0617* 4.2376** −3.0348** 2.0439* 0.03128

Malmquist Productivity Index (MPL)

Luenberger Productivity Index (LPI)

Estimated Coefficients

Estimated Coefficients

t-value

2.0837 0.03367 0.0108 0.0629 −0.0183 0.0143 0.00183 0.73 1744

4.9288** 2.8337** 2.7513** 3.7259** −3.2183* 2.3825* 0.9361

1.02206 0.04374 0.0629 0.03719 −0.0389 0.03577 0.0044 0.71 1744

t-value *

2.3042 2.0438* 3.0023** 1.0342 −2.6295** 1.8953* 1.2053

Note: ROA: Return on Assets, TA: Total Assets, ER: Equity Ratio, Risk: Banking Risk, EG: Economic Growth, INFR: Inflation Rate. (*), (**) represent the level of significance at 5% and 1% respectively.

To study the relationship between the Arab banks’ cost efficiency and productivity scores and the internal and external variables, we chose the following several variables: • The Total Assets (TA) which is a variable of size measured by the logarithm of TA. • The Return on Assets (ROA) defined by the ratio of the net result and the total assets. • The Equity Ratio (ER) measured by the ratio of equity and the TA. • The banking Risk (Risk) measured by the ratio of provisions for doubtful credits and the TA. • The Economic Growth (EG) measured by the growth rate of GDP. • The Inflation Rate (INFR) measured by the growth rate of the price index to the consumption. For our study, we favored the model with fixed effects to take into account each bank’s specificity when we searched to verify the link between the efficiency, the productivity and the internal and external variables. Table 8 shows the regression results by OLS method, which indicate that the estimated model is globally significant since the determination coefficient is about 70%. Table 8’s results show, also, that size has a positive effect on the bank’s efficiency and productivity. Thus, the large banks should be more efficient and more productive than the small banks notably because of the exploitation of returns to scale. This positive relationship conforms to Hauner and Shanaka’s (2005) findings and is explained by the fact that a greater bank size should lead to a reduction in production costs related to the existence of returns to scale, allowing larger banks to obtain capital at lower cost. We observe, also, both a positive and significant relationship at the 5% level between the profitability measured by the ROA and the factors relating to total productivity. Thus, higher profitability should lead to a productivity gain. These results corroborate those of Berger and DeYoung (1997), and Girardone, Georgios, Chortareas, and Garza, (2010), and imply that the reduction in banking costs by the sound management of used resources in the production procedure leads generally to an increase in profitability and an amelioration of the bank’s efficiency and productivity.

The results show, also, both a positive and significant relationship at the 5% level between the bank’s equity ratio, efficiency and productivity. Thus, a high equity ratio leads to a high level of productive performance. The positive effect of the bank’s capitalization ratio on the efficiency and the productivity confirm the results of several empirical studies (Altunbas, Carbo, Gardener, & Molyneux, 2007; Kwan & Eisenbeis, 1997). The positive relationship between the equity ratio and the measures of productive performance allow us to validate the theory of anticipated default costs (Myers & Majluf, 1984; Harris & Raviv, 1991). According to this theory, the increased level of bank capitalization increases its performance. This is explained on the one side, by the fact that the capital represents the most expensive source of financing for the bank. Thus, the increase of bank capital induces managers to reduce to a minimum the exploitation fees in order to increase the bank’s profits and to allow the payment of shareholders dividends. On the other side, the increase of capital reduces the bank’s default risk and reduces the financing cost of its activities. In the two cases, the decline of exploitation fees and the reduction of financing costs lead to a reduction of the bank’s total cost and, consequently, to an amelioration of the bank’s efficiency and productivity. The effect of banking risk on the bank’s efficiency and productivity is negative and statistically significant at the 5% level. The negative relationship between the bank risk and its productive performance indicates that the degradation of the bank’s productive performance can be partly attributed to the excessive increase of its credit risk. This result is explained by an increase in doubtful credits which is followed by an increase in provisions for banking risks, thus limiting the bank’s capacities to channel the available resources efficiently to the more profitable projects and leading to a reduction in its efficiency and productivity level. This result is consistent with those found in the empirical literature (Kwan & Eisenbeis, 1997; Fiordelisi, Girardone, & Radic, 2011). Thus, the bank’s excessive credit risk leads to a degradation of the quality of its assets portfolio and an accumulation of unpaid loans. For the bank, this situation leads to an increase of losses on granted loans, an increase of costs and, consequently, to a degradation of bank efficiency and productivity.

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For the macroeconomic variables, the estimations results show that the economic growth has both a positive and significant effect on the bank’s efficiency and productivity. The obtained results show that an amelioration of economic activity which is followed usually by a reduction in the probability of default by borrowers has a positive effect on the bank’s results and leads to a decline in banking costs and provisions for doubtful credits and an amelioration of the bank’s productive performance. Finally, the rate of inflation is not considered to be an explanatory factor of the bank’s efficiency and productivity. In fact, the obtained relationship between the rate of inflation and the bank’s productive performance is positive but insignificant whatever measure of performance indicator is used.

6. Conclusion The transformation and deregulation of Arab banks, observed during the last twenty years, invite us to question the effect on bank efficiency and productivity. Using the approach by the nonparametric distance function, we can compare the efficiency and productivity of these banks from the standpoint of the observed best practices and not from an absolute technical and economic objective. The obtained results reveal, on the one hand, that the technical, allocative and cost inefficiencies are located around 13%, 21% and 30% respectively and, on the other hand, the dispersion of the productive efficiency is important over the study period from 2000 to 2014. This means that the Arab banks are not relatively close to each other in terms of productive efficiency. The results also show that the productivity of Arab banks, measured by the Malmquist and Luenberger indexes, is located around 2.4% and 1.78% respectively. It appears that productivity improvements depend essentially on the positive evolution of technical progress. Our results reveal clearly that the more efficient Arab banks join the more competitive ones; this means that they constitute the frontier. They reveal, also, a phenomenon of banking catch-up to this frontier of efficiency and productivity. This is explained by the efforts of quantitative management of production factors and, more particularly, by the integration of technological progress in the activity of banking production. However, the convergence of banking efficiency and productivity, measured by the ˇ− convergence, is not accompanied by a significant reduction in the spreads between the Arab banks’ levels of performance. In fact, the evolution of the dispersion of efficiency and productivity scores does not allow us to conclude in favor of a − convergence since, over the study period, there was no decrease in the dispersion of obtained results. A number of policy implications arise out of this paper. First, the decomposition of Arab banking productivity shows that technical progress has been responsible for the improvement in productivity rather than technical efficiency. This seems like an area for policymakers and bank managers to consider further how to improve best practices on technical efficiency so that the benefits of technical progress can be best realized and captured. Second, the large banks (in terms of asset size) are more efficient than small and medium sized banks with small banks having the lowest efficiency scores in the Arab banking sector. This suggests that efficiency can be raised by restructuring the banking sector to reduce the number of smaller, less efficient banks. Third, equity regulation has a positive effect on the banking efficiency. Thus, the imposition of equity ratios by regulation explains the stability of Arab banks and an amelioration of its efficiency. Fourth, the relation between risk and efficiency is negative. Thus, banks should engage in strategies of risk reduction to avoid increasing provisions for risk and then, increasing banking costs, with harmful consequences on banking efficiency and stability. Particular attention should be paid to the behavior control of risk-taking by the regulatory authorities. Fifth and finally, the results show that the relation between the economic

growth rate and the efficiency is positive. Thus, the responsible authorities should implement economic policies to encourage further economic growth rate, allowing greater efficiency and productivity of Arab banks. In sum, the obtained results are of particular interest to banking managers and for control and regulatory authorities. In fact, they allow managers to understand the complexity of their bank performance. They should grant more consideration to organizational aspects and interactions between each of the banking efficiency determinants that we could isolate. Overall, policy makers in Arab countries can draw some lessons from the results of the study to promote efficiency and productivity by enhancing their efforts to reform the financial services regulatory and supervisory framework and to complete the restructuring process. Finally, the measurement of efficiency and productivity in future studies can be improved by using other productivity indexes like the Hölder index based on the metric distance function which has the advantage to take into account the duality of measuring the efficiency and productivity of banks. Acknowledgments The authors are grateful for the financial support received from the Lebanese University, which has enabled them to accomplish this research. The authors really appreciate the Editor-in-Chief, Professor Hadi Esfahani, the Editor, Professor Narjess Boubakri of the Quarterly Review of Economics and Finance for completing the review of their manuscript, and the anonymous reviewer for the valuable comments. They would like to express their sincere thanks to Dr. Nada Mora for her pertinent and helpful suggestions. Also, Dr. Rana Mansour would like to thank in particular Assistant Professor Adam Osman in University of Illinois at Urbana-Champaign for insightful comments and encouragements as well as being her mentor during the 2017 Fulbright program. The opinions expressed in this paper represent exclusively those of the authors and do not represent necessarily those of others. The authors are solely responsible for any remaining errors and deficiencies. References Allen, L., & Rai, A. (1996). Operational efficiency in banking: An international comparison. Journal of Banking & Finance, 20, 655–672. Altunbas, Y., Carbo, S., Gardener, E., & Molyneux, P. (2007). Examining the relationships between capital, risk and efficiency in European banking. European Financial Management, 13, 49–70. Andries¸, A. M., & Ca˘praru, B. (2012a). Competition and efficiency in EU 27 banking systems. Baltic Journal of Economics, 12(1), 41–60. Andries¸, A. M., & Ca˘praru, B. (2012b). Convergence of bank efficiency in emerging markets: The Central and Eastern European countries experience. Emerging Markets Finance and Trade, 50(4), 9–30. Apergis, N., & Polemis, M. (2016). Competition and efficiency in the MENA banking region: A non-structural DEA approach. Applied Economics, 48(54), 5276–5291. Arrow, K. J. (1962). The economic implications of learning by doing. The Review of Economic Studies, 29(80), 155–173. Avkiran, N. K. (1999). Productivity analysis in the services sector with data envelopment analysis (1st ed.). Camira, Queensland: NK Avkiran. Batir, T. E., Volkman, D. A., & Gungor, B. (2017). Determinants of bank efficiency in Turkey: Participation banks versus conventional banks. Borsa Istanbul Review, 17(2), 86–96. Bauer, P., Berger, N., & Humphrey, D. (1993). Efficiency and productivity growth in US banking. In Fried, Lovell, & Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 386–413). New York: Oxford University Press. Bell, F., & Murphy, N. (1968). Costs in commercial banking: A quantitative analysis of bank behavior and its relation to bank regulation. Research Report N◦ 41. Federal Reserve Bank of Boston. Benston, G. (1965). Branch banking and economies of scale. The Journal of Finance, 20(2), 312–331. Berg, S., Forsund, F., & Jansen, E. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980-89. Scandinavian J Economics, 94, 211–228. Berger, A., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking & Finance, 21, 849–870.

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Please cite this article in press as: Mansour, R., & El Moussawi, C. Efficiency, technical progress and productivity of Arab banks: A non-parametric approach. The Quarterly Review of Economics and Finance (2019), https://doi.org/10.1016/j.qref.2019.02.002