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ScienceDirect Procedia - Social and Behavioral Sciences 235 (2016) 198 – 207
12th International Strategic Management Conference, ISMC 2016, 28-30 October 2016, Antalya, Turkey
Technical efficiency assessment using data envelopment analysis: an application to the banking sector of Côte d’Ivoire Gahé Zimy Samuel Yannicka , Zhao Hongzhongb , Belinga Thierryc , a* a, b,c
Wuhan University of Technology, Wuhan, 430070, China
Abstract Côte d’Ivoire is one of the largest economies in the West African Economic and Monetary Union, its banking sector as well. However, it seems that the banking sector faces some difficulties to transform the deposits at its disposal into credits for clients. In this context, the present study tried to address the issue of transformation of deposits into credits efficiently. To do so, 14 banks have been assessed using Data Envelopment Analysis method from 2008 to 2010. Taking into account two main approaches of this technique, the results revealed that Ivorian banks do not operate efficiently in terms of loans allocation. Also, a classification of banks, by ownership and origin, allows to discover that foreign ownership private banks are relatively more efficient than public ownership ones. Further analyses attribute the source of inefficiency to an incompatibility of production scale. ©©2016 Published by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license 2016The TheAuthors. Authors. Published by Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ISMC 2016. Peer-review under responsibility of the organizing committee of ISMC 2016. Keywords: Côte d’Ivoire; Bank; Efficiency; Data envelopment analysis
1. Introduction Financial ratios are often used to measure the overall financial soundness of bank and the quality of its management. Bank regulators, for example, use financial ratios to help evaluating a bank’s performance. According to Stainer (1997), such ratios face a fundamental problem since external factors may affect their computation and have no relationship to efficient resource usage. Yeh (1996), without dismissing the importance of these ratios, notes that their major demerit is the reliance on benchmark ratios which could be arbitrary and may mislead an analyst. In the same
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1877-0428 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ISMC 2016. doi:10.1016/j.sbspro.2016.11.015
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vein, Sherman and Gold (1985) argue that financial ratios do not capture the long-term performance and aggregate many aspects of performance such as operations, marketing and financing. Besides, evaluating the economic performance of banks is a complicated process. Often a number of criteria such as profits, liquidity, asset quality, attitude toward risk, and management strategies must be considered. The changing nature of the banking industry has made such evaluations even more difficult, increasing the need for more flexible alternative forms of financial analysis. To deal with this issue, various researches related to productivity led to the development of other measures that incorporate all the important factors in aggregated forms. These measures offer more insight about technical and financial performance of an organization. Thus in the recent years, there is a trend towards measuring bank’s performance using these new patterns like the frontier analysis. Some of the important applications pertaining to efficiency assessment in banking context include Parkan (1987) for a Canadian bank, Oral and Yolalan (1990) for a Turkish bank, Vassiloglou, Giokas (1990, 1991) for a Greek bank, Sherman and Ladino (1995) for a US bank, Tulkens (1993) for a Belgian bank, and Schaffnit et al. (1997) for a Canadian bank. All of them except Tulkens used the Data Envelopment Analysis (DEA) method for efficiency / performance analysis. Within the literature, studies adopting DEA in analyzing bank efficiency in Côte d’Ivoire solely are lacking. It is rather in the context of one sub-regional investigation with Kablan (2009) and one regional work with Kiyota (2009) that allusion is made to this country. However motivations to analyze the Ivorian banking sector do exist. Firstly the country is the majority stockholder of the West African Economic and Monetary Union (WAEMU) with 40% of shares. Secondly some unfortunate events have weakened further its financial system that was already trying to get rid of the aftermaths related to the 90s developing countries debt crisis and the last global economic recession. Thirdly as a result of important bank regulation policies, banks’ liquidity rate has increased and more financial institutions are coming into the country heightening competition. Finally as the main intermediation channels between saving and investment in a country, financial institutions play an important role in the economic development process. The best financial systems limit, quantify, gather and negotiate all operation risks, and incite the savers to invest by offering them a proportional payment to the scale of the incurred risks (Kablan, 2009). Financial intermediaries when they are efficient allow mobilizing saving from diverse sources and allocate it to more productive activities that benefits not only investors and beneficiaries of the investments but also the whole economy (Gulde et al., 2006). Indeed, a banking system which efficiently channels financial resources to productive use is a powerful mechanism for economic growth (Levine, 1997). The aforementioned reasons along with the notoriety gained by the DEA fond our interest in conducting the present study. Thus the main purpose of this paper is to assess banks’ technical efficiency in Cote d’Ivoire using Data Envelopment Analysis. Furthermore it proposes to investigate on the factors explaining the outputs of this assessment. Doing so will allow us to know whether Ivorian banks operate effectively in their intermediation role while transforming deposits into loans and to understand what practices or policies underlie their level of performance. In the next sections we discuss first of all the theoretical foundations of our topic based on the literature. Then the methodology adopted is presented. The last stage consists of analyzing our results before making a conclusion. 2. Literature review 2.1. From economic efficiency to technical efficiency From a general point of view, effectiveness describes the capability of an individual, a group or a system to achieve the assigned goals with the disposable resources. Therefore to be effective would be to produce the expected results and to achieve the goals set. Beyond this concept, efficiency reflects the quality of a productivity, which makes it possible to fulfill objectives with the minimum of resources committed: delimitation between effectiveness and efficiency is done by the notions of non-idleness of resources (full employment) and non-waste (using the right amount needed, not more) (Guerrien, 2002). In other words, effectiveness requires goals’ achievement, while efficiency introduces minimization of the resources used.
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Before the 1950s, economists believed that firms always exploited their resources effectively and efficiently, in accordance with their mission, which is to make profits. Economic efficiency is a notion that appears after the 1950s (Amara and Romain, 2000). It refers to the use of resources in a way to maximize production. Actually, to be economically efficient, a system must meet three conditions (Sullivan et al., 2003): x x x
The production system must be in Pareto equilibrium. That is to say, production must be in such a way that one cannot improve it without spending more; No input can be added without a corresponding increase in outputs ; Finally production must be done at a minimal unit cost.
In most economic or financial lexicons, economic efficiency is defined as the state of an economy which obtains a maximum output with limited resources, considering costs and benefits stemming from various decisions. It is a concept that includes both technical efficiency and allocative efficiency. 2.2. Technical efficiency A Decision Making Unit (DMU) is considered as technically efficient if, from the basket of inputs it holds, it produces the maximum of outputs possible or if, to produce a given quantity of outputs it uses the smaller quantities possible of inputs (Atkinson and Cornwell, 1994). Measuring the degree of efficiency of a DMU enables therefore to determine if this one is able to increase its production without consuming more resources, or reduce the use of at least one input while maintaining the same level of production (Farell, 1957). 2.3. Typology of technical efficiency Pure technical efficiency and technical efficiency of scale Taking into account, returns to scale allows separation of the concept of technical efficiency into pure technical efficiency and scale technical efficiency. Pure technical efficiency reflects the way in which production unit resources are managed while scale efficiency or scale technical efficiency determines whether production unit operates at an optimal scale or not. The optimal scale is understood here as the best situation that can achieve the production unit by increasing proportionally the quantity of all its factors. Technical efficiency, input and output orientations Technical efficiency can be considered according to two principal approaches (Kamgna and Dimou, 2008). Firstly, it measures the ability of a production unit to get the maximum outputs possible with a given combination of inputs and production technology: that is its "output oriented" definition, which answers the question of knowing: "of how much can one modify quantities of output without modifying quantities of input that are being used". Secondly, it measures its ability to achieve a given output level with the smallest quantities possible of inputs: that is the "input oriented" definition, answering the question: "of how much quantities of input can be proportionally reduced, without any variation in quantities of output produced” (Coelli et al., 2005). Thus, technical inefficiency corresponds to either a production below what is technically possible with a quantity of inputs and a given technology, or a use of inputs’ quantities above the necessary, with a given level of output.
2.4. Main assessment techniques Two main approaches to measure technical efficiency are distinguished (Berger and Humphrey, 1997).
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Parametric approaches The Parametric approaches propose an approximation of the effective production function by an a priori known functional form (Cobb Douglas, Translog, etc.). That is to say, a mathematical equation gives a form to the efficient frontier, regardless of the data. Therefore, an easier specification and a better analysis of the various algebraic properties of this function become possible. They can be deterministic or stochastic. They are deterministic when attributing any deviation from the frontier to inefficiency, and stochastic when the deviation from the frontier is the resultant of inefficiency on the one hand, and hazard and measurement errors on the other hand. Several authors such as Farrell (1957), Timmer (1971), Afriat (1972) and Richmond (1974) suggested different techniques to find an approximation of the efficient frontier as per the deterministic methods. However, parametric and deterministic approaches have some limitations related, in particular, to their strong sensitivity to extreme observations and to the restrictive character of the functional form attributed to the frontier function. As for the stochastic parametric approach, it corrects some shortcomings of the deterministic approach, particularly by putting the origin of the deviation from the efficient frontier into perspective. This method therefore postulates that the error term is composed of two independent parts: a purely random component which is in any relation and is distributed on each side of the production frontier (two-sided error term), and a component representing technical efficiency and is distributed on one side of the frontier (one-sided error term) (Amara and Romain, 2000). In either case, the parametric approaches have this unfortunately: they require, absolutely, writing a cost or profit function of the company under investigation. But this is not always possible or practical for any type of business. the non-parametric approach comes in response to this issue Non-parametric approaches A non-parametric approach considers a frontier that is not related to any functional form: the isoquant is estimated by the ratio outputs/inputs of each DMU. It is generally of a deterministic type. The method consists in placing all DMU in a sample, and representing each of their performances by a point on a graph. An efficient frontier is then drawn. In the case of DEA, this frontier connects all points which envelop the points’ cloud from the top. The points on this frontier represent effective units. Other points, located below this border, represent "ineffective" or "under effective" units. Moreover, the distance of each point from the frontier is a measure of its technical efficiency level. This efficiency is relative, since it depends on the most efficient units within the sample. DEA is an edifying example of non-parametric approach. It encompasses two main models. Those are CRS (Constant Return to Scale) model and VRS (Variable Return to Scale) model. According to Coelli et al. (2005), the difference between technical efficiency ratio obtained through DEA-CRS type and that of the same firm obtained by DEA-VRS type is a good measure of scale efficiency of this firm. To get such a measure, they suggest performing on the same database, both DEA-CRS and DEA-VRS types. If for a given firm, there is a difference in efficacy ratios measured by these two types of DEA, this indicates that the firm is not operating at an optimal scale. So scale inefficiency is then given by the difference between CRS technical inefficiency and VRS technical inefficiency. By following this pattern one can investigate the cause of efficiency / inefficiency level. This method has extended the analysis of technical efficiency to multi-products and non-constant returns to scale situations (Amara and Romain, 2000) like in the environment of banks. 2.5. Data envelopment analysis and bank’s technical efficiency assessment Definition, advantages and limitations Data envelopment analysis consists in using mathematical programming to build a frontier in fragments (piecewise surface) from the data set of production units. Then the efficiency of one production unit is calculated in relation to this border in fragments.
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According to Charnes et al. (1978), this technique is a mathematical programming model applied to observed data that provides a new way of obtaining empirical estimates of extreme relationships such as the production functions and/or efficiency production, possibility surfaces that are the cornerstones of modern economics. Throughout the literature the new technique is very often coveted for measuring technical efficiency of banks (Joumady, 2000). Many studies dealing with banks efficiency analysis called on this method around the world and more specifically in francophone Africa. Among those ones we can cite Joumady (2000), Tanimoune (2003), Kamgna and Dimou (2008), Dannon (2009) and Kablan (2009). This enthusiasm is due to the edges of DEA over the other methods. On the one hand it requires no a priori assumption about the functional form of the estimated frontier. It is therefore a method particularly suitable in case of uncertainty about the functional form of the production technique. This specificity extends measure of technical efficiency to firms that have production functions not yet known or difficult to estimate, such as banks. Indeed, banks make complex products and services with multiple inputs and outputs, at very disparate scales, making obviously difficult the theoretical determination of their efficient frontier. On the other hand DEA method is suitable in investigations dealing with small samples (Ludwin and Guthrie, 1989). This is actually our case with only 14 financial institutions active during the study period with available and accessible data. Despite the aforementioned there are some limitations when using this technique. Due to its deterministic nature there is an extreme sensitivity to potential data errors. Also the method dismisses measurement of allocative efficiency, and therefore does not take into account cost of different factors. Approaches to bank’s efficiency assessment with DEA Technical efficiency of bank can be measured in several ways. One can distinguish the production approach from the intermediation approach or also cost efficiency from profit efficiency. All these approaches contribute to evaluate performance of banks in one way or another. However, since we have chosen the intermediation approach, the nonparametric DEA method is sufficiently adapted. Within the intermediation approach, the role of bank is to raise funds and transform them into credits. In this case bank production is measured in monetary unit. Inputs are deposits collected and funds borrowed, and the volume of loans granted constitutes the principal output. Besides theorists introduce a new approach, the modern approach, which incorporates elements of information theory in banks activity, risk management, etc (Freixas et al., 1999). According to the production approach, the role of banks is to provide services to their customers. The output of the bank is the volume of services provided to savers (managed accounts) and borrowers (loans granted). To obtain those ones bank transforms physical capital and labor. Then fixed assets and wages may therefore be considered as inputs (Kamgna and Dimou, 2008). 3. Methodology 3.1. Mathematical framework There are several forms of DEA models. In order to better simplify our analysis, we will adopt the following presentation, which uses the notation proposed by Kablan (2009) and Coelli (2005). Let us suppose that there are K production factors (Inputs) and M goods (Outputs) for each bank i (i = 1, 2... n). Let us denote, by xi and yi the vector of inputs used by bank i and the vector of goods offered by the same bank, respectively. Let us note by K * N the matrix of inputs X and by M * N the matrix of outputs Y. The best way to introduce DEA is via the ratio form. Thus for each bank we would like to obtain a measure of the ratio of all outputs
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over all inputs, such as u'yi / v'xi, where u and v are the vectors M * 1 and K * 1 of outputs and inputs weights respectively. The optimal weights are determined by solving the following mathematical programming problem:
Maxu ,v (u' yi /v'xi ), St u' y j /v'xj d1, j 1,2...n u ,vt0
(1)
This involves finding values of u and v, so that the efficiency measure of the i-th bank is maximized, subject to the constraint that all efficiency measures must be less than or equal to one. One problem with this particular ratio formulation is that it has an infinite number of solutions. That to say if (u*, v*) is a solution, then (αu*, αv*) is another solution and so on. To avoid that, one can impose the constraint v’x i =1, which provides:
MaxP ,Q ( P' yi ), Q 'xj 1 St P' yj Q 'xj d0, j 1,2...n
(2)
where the notation’s change from u and v to μ and ν reflects the transformation. This form is known as the multiplier form of the linear programming problem. Because solving the problem in this form will be difficult, one can use the duality in linear programming, and derive an equivalent envelopment form of this problem:
MinTO T , St yi Y O t0 T xi X O t0 O t0
(3)
where Ɵ is a scalar and λ is a N*1 vector of constants. This envelopment form involves fewer constraints than the multiplier form (K+M < N+1), and hence is generally the preferred form to solve. The value of Ɵ obtained will be the efficiency score for the ith bank. It will satisfy Ɵ є [0, 1], with a value of 1 indicating a point on the frontier and hence a technically efficient bank. Not that the linear programming problem must be solved N times, once for each bank in the sample. This method assumes that returns to scale are constant (Constant Returns to Scale). However, to take into account changes in scale economies (Variable Return to Scale), the convexity constraint N1’λ = 1 can be added to formulate the following program:
MinTO T , St N1'O 1 yi Y O t0 T xi X O t0 O t0
(4)
where N1 is a N*1vector of 1. This formulation allows forming a convex hull of intersecting planes which envelop the data points more tightly than the CRS conical hull and thus provides technical efficiency scores which are greater than or equal to those obtained using the CRS model. The VRS specification has been the most commonly used specification in the 1990’s. On the basis of Berg et al. (1993), we will estimate technical efficiency under these two assumptions of CRS and VRS. All in all, the general empirical estimated model will be:
Volume of Loans f (Volume of Deposits )
(5)
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3.2. Data and sample Data of loans and deposits come from monthly reports of the Ivorian association of banks and financial institutions and from the Treasury of Côte d’Ivoire. Indeed these documents provide a clear state on assets and liabilities of banks every month. Moreover a classification of banks by ownership and origin is done. The sample includes four (4) banks with public capital (public banks), only one (1) private bank with American capital (American private bank). There are three groups of private banks respectively Ivorian, African and European. Each one of them includes three (3) banks. In summary we have four (4) public banks and (10) private banks. 3.3. Model specification The Efficiency measurement is done over time, from 2008 to 2010, meaning we are in the case of time series analysis. In DEA there are at least two ways to deal with this kind of situation: Window Analysis and Malmquist Productivity Index. For our study, we chose the first one. It stands as the easiest of these techniques. It is a moving average pattern of analysis, and is described by Charnes et al. (1985). A bank in each year is treated as if it is a different DMU. Thus its performance in a given year is compared to its performance in the others years. That works as if a DMU competes against itself from one year to another. In addition, each bank is compared with the others in the same period. The orientation chosen to assess technical efficiency is that of intermediation: it is question to maximize loans (outputs) granted to clients regarding deposits (inputs). Because of the increasingly competition within Ivorian banking market, it seems that distribution of loans is an alternative solution (besides sale of services, the most preferred mean actually) to guarantee profitability. In summary, the key analysis tool is DEA. Since performances are considered over time (time series), our analysis is dynamic. So the way provided by DEA that we prefer is DEA-Window method. In addition, given that we are interested in maximizing loans (outputs) granted by banks under the constraint of deposits (inputs) available - also known as intermediation approach, we are in the case of DEA output oriented. Moreover, we take into account both Constant Return to Scale (CRS or C) and Variable Return to Scale (VRS or V). Finally, the method is called DEAWindow output oriented under VRS (CRS), shortly written: Window-O-V (C). On this basis after entering all data in the software DEA-Solver Learning Version, we will get efficiency scores of each bank. 4. Results and analyses Many studies have decomposed technical efficiency (inefficiency) scores obtained from a CRS DEA (total technical efficiency) into two components. One is due to pure efficiency (inefficiency) (VRS DEA) and expresses the proportion of total technical efficiency that is purely due to technical efficiency, as indicates by its name. The second one is provided by scale efficiency (inefficiency) and represents the part of total technical efficiency explained by the compatibility of the production scale at which the bank operates. As in this study, this may be done by conducting both a CRS and a VRS DEA upon the same data. If there is a difference in the two technical efficiency score for a particular DMU, then this indicates that the DMU has scale inefficiency (Coelli, 2005), and that the scale inefficiency can be calculated from the difference between the VRS DEA score and the CRS DEA score. The results are summarized in table 1. Statistics on input (output) data show a strong correlation between the variables deposits and loans. In fact the coefficient of linear correlation is more than 0.95 over the study period. That stands for the two models.
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In the case of VRS, scores vary between 0.36 (lower) and 1 (highest). The average pure efficiency score is 0.79. As for the case of CRS, scores are bounded by 0.29 (lower) and 0.93 (upper). And the average total efficiency score is 0.48. So efficiency scores decrease under CRS assumption. Let us see closer the distribution of banks in each case. Table 1. Summary of efficiencies scores Banks
BIAO BICICI SGBCI SIB BNI CITIBANK VERSUS BANK COFIPA BANK OF AFRICA ECOBANK BACI BFA STANDARD CHARTERED BHCI Averages (rounded up)
VRS Average scores 0.926273579 0.816754023 1 0.903696718 0.542943809 0.951645738 0.362836314 0.999985666 1 0.936463927 0.78664507 0.62738745 0.822903356 0.451710842 0.7949
Ranks 6 9 1 7 12 4 14 3 1 5 10 11 8 13
CRS Average Scores 0.434299203 0.365864687 0.392598783 0.497738395 0.295185351 0.933344459 0.323319921 0.412320099 0.63774412 0.44136742 0.357745099 0.518731831 0.733435911 0.325782292 0.4764
Ranks 7 10 9 5 14 1 13 8 3 6 11 4 2 5
Scale (rounded up) 0.469 0.448 0.392 0.552 0.544 0.981 0.891 0.412 0.638 0.471 0.455 0.827 0.891 0.721 0.6151
The Distribution of banks by ownerships gives further results as in table 2. Within the first model, all the ten private banks’ (100%) scores are superior to 0.5. Better the only two banks on the frontier (score = 1) are parts of the private banks. 50% (2 banks) of the public banks have scores inferior to 0.5, meaning they get the lowest scores. The two others public banks have scores over 0.5. All in all 12 banks (85.7%) among the 14 banks are over 0.5, hence the rest (14.3%) stay under that figure. As for the second model, 10 banks (71.4%) have scores less than 0.5. Then only 4 banks (28.6%) stand over 0.5. Among these 4 banks, the main part, 3 banks (75%) are private banks. Regarding the results obtained, we can make some comments. First of all a strong positive correlation between our variables means that the volume of deposits and that of loans move the same way. Then, one can say we were right to attempt to explain volume of loans by deposits considering intermediation approach of banks. Table 2. Distribution of banks by ownership under VRS and CRS assumptions Banks
Ivorian Private Foreign private Public Total
VRS Score < 0.5
Score > 0.5
Total
CRS Score < 0.5
Score > 0.5
3
On the frontier score = 1 0
0
3
3
0
5
2
9
2 2
2 10
0 2
4
Total
0
On the frontier score = 1 0
4
3
0
7
3 10
1 4
0 0
4
3
Secondly, Most of the banks included in the sample do not operate efficiently in terms of transformation of deposits into loans because whatever the model, scores are generally less than 1 and averages efficiency scores are respectively 0.79 (under VRS) and 0.48 (under CRS). This is consistent with the idea according to which Ivorian banks do not provide loans to their clients as much as possible. In other words, to catch up with the best practice in the sector (score = 1), efforts of 0.21 (under VRS) and 0.52 (under CRS) must be done on average. Since the level of efficiency is not equal to 1, they can improve their policies to reach the optimum level of loans. In other words they can do better. Thirdly some banks do transform deposits more than the others. They are mostly foreign ownership private banks (African, European and American private banks). The few number of public ownership banks which make some effort in providing enough loans on the basis of the deposits at their disposal is not sufficient to qualify the public banks in
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our sample as good examples. It is rather private banks (all the private banks included Ivorian ownership private banks), more exclusively foreign private banks (all the private banks excepted Ivorian private ownership banks), which are the best benchmarks. Fourthly efficiency scores change significantly when we change assumption (VRS or CRS). However this reality does not change too much the key idea of the previous findings. The main part of the banks remains inefficient, more, they become worse and foreign private banks are still the best. Rather, the change indicates that Ivorian banks have scale inefficiency (Coelli, 2005). That is to say their production scale is not compatible to convert deposits into loans as much as possible. Finally Computation of scale efficiency scores proves that on average Ivorian commercial banks suffer from scale inefficiency at a level of 38%. Broadly speaking they do not use scale economy to improve their outputs. Further analyses allow finding some explanation as per the reasons of the general level of efficiency or inefficiency. Management practices change over the study period (Dem, 2003) and affect the efficiency level. Scale efficiency (inefficiency) explains mostly the total efficiency (inefficiency). So both management practices (pure technical inefficiency) and production scale (scale inefficiency) affect efficiency level. However, the production scale is the more significant explanatory variable. When we calculate the efficiency adjustment rate by the ratio of inefficiency score to efficiency score times 100, we find that banks could increase their total efficiency by 108%, if they kept constant the level of deposits, under assumption of constant return to scale. Under assumption of variable return to scale, they could raise up their pure technical efficiency until 25% only. As for the scale technical efficiency adjustment, it is possible to increase it by 61% without changing the transformation rate of deposits, but only by adopting an optimal level of production. In one word, if they operate under constant return to scale, Ivorian banks could provide more loans. In summary, Ivorian banks suffer from an incompatibility of production scale more than a problem of management (in terms of use of resources for credits). Inefficiency is more related to a problem of “under optimal” scale than to a problem of management practices. We talk here about practices which consist of optimization of transformation of disposable resources into loans. 5. Conclusion Through the present study we tried to assess the banking sector of Côte d’Ivoire in terms of technical efficiency. Thus after examining the theoretical background of the main concepts we covered some key aspects pertaining to the data envelopment method which served as analysis tool. Broadly speaking technical efficiency is the ability of a firm to maximize its outputs with a fixed quantity of inputs, or conversely, its ability to waste minimum quantity of inputs with a constraint of fixed quantities of outputs. Technical efficiency’s measurement is done by using whether parametric approach or non-parametric approach. The first one considers an efficiency function known in advance, while the second one assumes that efficiency is made up with the best units of the sample. We found out that DEA is part of the non-parametric approach and can be very useful to measure banks’ technical efficiency under intermediation perspective. Applying this framework to the context of Ivorian banks gave us some answers related to the performance level of these banks as well as the reasons of the results when considering intermediation role. Our analyses revealed an average efficiency score of 48%, from 2008 to 2010, if we consider the hypothesis of constant return to scale. With respect to variable return to scale, this score is 79%. Also, it seems that foreign ownership private banks are relatively more efficient or relatively less inefficient than public ownership ones. All in all the scores proved that generally speaking the commercial banks assessed were technically inefficient in converting resources into loans, during the study period. That is mostly due to an incompatibility of production scale with a scale inefficiency of 38% on average. The same statement may probably hold if the remaining banks, which have not been included in the sample, were considered, since the main largest banks that have the best practices in the sector have already been taken into account.
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Further study may be conducted to analyze the efficiency scores obtained. The purpose would be to understand better this issue, and to try to control factors that managers and policy makers could use to affect efficiency levels of the banking industry. References Afriat, S. N. (1972). Efficiency Estimation of Production Functions. International Economic Review, 13(3), 568-598. Amara, N., Romain, R. (2000). Mesure de l'efficacité technique: Revue de la littérature. Série Recherche des Cahiers du CRÉA. Atkinson S., Cornwel, C. (1994). Parametric estimation of technical and allocative inefficiency with panel data. International Economic Review, 35, 231-244. Berg, S.A., et al. (1993). Banking Efficiency in the Nordic Countries. Journal of Banking and Finance. 17(2-3), 219-574. Berger, A.N., Humphrey, D.B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175-212. Charnes A., et al. (1978). Measuring efficiency of decision making units. European Journal of Operational Research, 98(2), 429-444. Coalli, T.J., et al. (1996). A guide to frontier, Version 4.1: a computer program for stochastic frontier production and cost function estimation. CEPA working paper. Coelli, T.J., et al. (2005). An Introduction to Efficiency and Productivity Analysis. Spring (2 nd ed), 349. Dem, I. (2003). Economies de coûts, économies d’échelle et de production jointe dans les banques de l’UMOA : Qu’est ce qui explique les différences de performance ? UMOA, Note d’information et statistiques, Etudes et Recherche, 537, 36 p. Farell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3), 253-281. Freixas, X., et al.(1999). Optimal Regulation of a Fully Insured Deposit Banking System. Journal of Regulatory Economics, 16(2), 111-34. Giokas, D. (1991). Bank Branch Operating Efficiency: A Comparative Application of DEA and the Loglinear Model. Omega, 19(6), 549-557. Guerrien, B. (2002). La théorie des jeux. Economica. Gulde, A., et al. (2006). Sub-Saharan Africa: financial sector challenges. International Monetary Fund working paper. Joumady, O. (2000). The sources of productivity change in Spanish banking. European Journal of Operational Research, 98, 364-380. Kablan, S. (2009). Mesure de l'Efficacité des Banques dans les Pays en Voie de Développement: Le Cas de l'Union Economique et Monétaire Ouest Africaine (UEMOA). African Development Review, 21(2), 367-399. Kamgna, Y., Dimou, L. (2008). Efficacité technique des banques de la CEMAC. University Library of Munich, Germany. Kiyota, H. (2009). Efficiency of Commercial Banks in Sub-Saharn Africa: A comparative Analysis of domestic and foreign banks. A Paper Presented in a CSAE Conference on Economic Development in Africa, Held at the University of Oxford, 22nd to 24th March 2009. Levine, R., (1997). Financial Development and Economic Growth: Views and Agenda. Journal of Economic Literature, 35(2), 688-726. Ludwin, W.G., & Guthrie, T.L. (1989). Assessing Productivity with Data Envelopment Analysis. Public Productivity Review, 12(4), 361-372. Oral, M., & Yolalan, R. (1990). An empirical study on measuring operating efficiency and profitability of bank branches. European Journal of Operational Research, 46(3), 282-294. Parkan, C. (1987). Measuring the efficiency of service operations: An application to bank branches. Engineering Costs and Production Economics, 12(1), 237-242. Richmond, J. (1974). Estimating the Efficiency of Production. International Economic Review, 5 (2), 515-521. Schaffnit, C., et al. (1997). Best practice analysis of bank branches: an application of DEA in a large Canadian bank. European Journal of Operational Research, 98, 267–289. Sherman, H.D., Gold, F. (1985). Bank branch operating efficiency: Evaluation with Data Envelopment Analysis. Journal of Banking & Finance, 9(2), 297-315. Sherman, H.D., & Ladino, G. (1995). Managing Bank Productivity Using Data Envelopment Analysis (DEA). Interfaces, 25(2), 60-73. Stainer, A. (1997). Capital input and total productivity management. Management Decision, 35(3), 224-232. Tanimoune, N.A. (2003). Les déterminants de la profitabilité des banques de l’UEMOA : une analyse sur données de Panel. BCEAO, NIS, Vol.539. Timmer, C.P. (1971). Using a Probabilistic Frontier Production Function to Measure Technical Efficiency. Journal of Political Economy, 79, 776-794. Tulkens, H. (1993). On FDH Efficiency Analysis: Some Methodological Issues and Applications to Retail Banking, Courts, and Urban Transit. Journal of Productivity Analysis, 4, 183-210. Vassiloglou, M., & Giokas, D. (1990). A Study of the Relative Efficiency of Bank Branches: An Application of Data Envelopment Analysis. Journal of the Operational Research Society, 41(7), 591-597. Yeh, Q. (1996). Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation. Journal of the Operational Research Society, 47(8), 980-988.
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