Abstract : Waves of mergers have been under way for many years, the most standard hypothesis to explain mergers and acquisitions is allowing the consolidating banks to enhance their efficiency and profitability. This paper examines the efficiency and performance in the European banking sector. It tries to focus on the X-efficiency explanations for merger and acquisition activities in Europe, which investigates the role of cost or profit efficiency as a motive for M&As. Both distribution free and stochastic econometric frontier estimation methods are used in order to evaluate the efficiency in European financial industry.
Nowadays banks deal with new market dimensions as they expand beyond national borders and encounter new macroeconomic, regulatory and demographic environments. Banking industry has started experiencing underlying changes since 1970’s, which is due to large number of bank and thrift failures. As a result of regulations and deregulations, banks now expand their business in previously unapproachable domestic and foreign markets through merger and acquisition. In many nations around the glove, waves of mergers between banks, securities firms, insurance companies and other financial service providers has been under way for many years, including some mega mergers among large banks. These merger acquisitions activities are mostly due to some big changes such as serious industry crisis or economic recession, and driven by the desire to achieve greater cost and revenue synergies. Capital base and market access became the traditional motives for marriage of convenience in the 1980’s.
Another important key driver of the consolidation process in banking is deregulation, both geographically and functionally. In the USA, the RiegleNeal Interstate Banking and Branching Efficiency Act of 1994 effectively eliminated the barriers to cross-state banking, which composed an important step in the process of deregulation of interstate banking in the years before 1994. In the Europe, the geographical restrictions were abolished by the Second Banking Directive of 1989 through the introduction of a single banking license and the principle of home country control. These changes bring an opportunity for the companies to diversify their products. In order to cope with the challenges come from new products and new forms of competition brought by the advances in technology, the industry need to define new demand patterns and new customer dimensions which management has to continuously adjust with the updating trend. In addition, the enormous pace of progress in information and telecommunication technology has further contributed to blurring the geographical and functional borders in the financial services industry world wide. The consequent need to adapt the industry structure has generated a wave of mergers in Europe as well as in the rest of the world. During the period of 1990 to 2001, more than 10000 financial service companies were merged and acquired in the world’s 13 leading industrialized nations.
Efficiency of banking sector influences the costs of financial intermediation and the overall stability of the financial markets. A frequent reason given for bank mergers is the potential cost synergies that may result from economies of scale, economies of scope, managerial or technological efficiency sources, which called X-efficiencies. The event studies by Berger in 1993 claims that it is rationalize if mergers are succeed in improving banking industry efficiency, which substantial benefits may accrue to the customers and claimholders of these banks, and the level of competition within the banking industry may be considerably increased. By looking at the post merger results in the 1990s, Berger found that mergers have not increased cost efficiency but have increased profit efficiency by mending the inefficient management, by permitting those consolidated banks to shift their outputs mixes from lower rate securities to higher rate loans, and by improving diversification of their products and investments.
On the other hand, Shull (2001) found that all of the mergers did result in cost cutting, but that only half of them were clearly successfully in improving cost efficiency, which cost cutting alone is distinguished from cost efficiency which relates cost to assets or revenue. Furthermore, the evidence from Spajic’s research in 2002 indicated that the large EU banks were, n average, closer to the EU efficient cost frontier than their smaller counterparts. He suggested that the reason behind this could be the idea that these large banks compete against others in similar markets and are monitored by similar Markey tests of efficiency such as credit rating. Hence, they are subject to more market and peer group assessment or pressure on their performance – an element of which is cost efficiency.
The reason of focusing on X-efficiency in this study is partly because the banking researches to date suggest that X-efficiency appears to be large and tends to dominate scale and scope efficient. According to Hadad (2005), X-efficiency refers to the fact that given a current volume of output, a firm is not operating with maximum cost efficiency, ie. the cost structure is too high. This source of efficiency is often cited as the prime motivation for domestic merger, as two banks merging can more easily coordinate the reduction of the size of too large branch network. In the studies of banking industry cost structure based on the measures of cost scale efficiency and X-efficiency, Williams emphasis the importance of X-efficiency across different sized institutions, which rise the attention for policymakers and bankers alike to consider the source of future efficiency gains by improving scale efficiency and X-efficiency as the most important source of potential cost savings for European savings banks. This result suggest about the future direction of banking consolidation within the European industry.
The importance of improving X-efficiencies has been stated in Berger, Hunter and Timme’s survey article in 1993, which their empirical result shows that X-inefficiencies count for 20% of costs in banking industry whereas scale elasticities and scope economies count for only approximately 5%. The research from European bank cost literature by Altunbas (2001) tends to support this proposition as well. In addition, the European Investment Bank also shows that x-efficiencies are much larger than scale economies, which means banks can improve their overall cost efficiency to a greater extent if they emulate industry best practice, which by improving managerial and technological factors rather than by increasing their size. This paper points out that mainly US based literature does suggest that big banks are relatively more X-efficient, which on average most likely to be closer to the best cost practice of banks with similar size and product mix. Besides, there is increasing evidence that large European banks have efficiency advantages over their smaller counterparts. As well as appear to benefit more from technological progress.
However, Williams (2002) has a different opinion on it; His studies on cost structure of banks has found out that scale efficiency is more material than X-efficiency across different sized institutions, which the average savings bank could reduce costs by 38% f it attained cost scale efficiency compared with 16.5% if it lay on the X-efficient cost frontier. Nevertheless, it is still undeniable that X-efficiency is still an important factor in studying bank cost. Moreover, the efficiency effect of mergers constitutes an important policy question on its own, since merger applicants often cite prospective efficiency benefits as a justification for merger approval. Therefore, it is important to construct further researches to find out the factors that predict efficiency benefits for policy purpose and bank management. The research paper by Berger and Hannan (1996) which linked x-efficiencies to market concentration and the empirical research on the relationship of X-efficiencies to merger and acquisition by Peristiani (1997) are both suggesting that X-efficiencies have potentially important implications for public policy and bank management. These researches also indicates that X-efficiency is found to decline over the sampling period, indicating that banks are operating closer to the cost frontier than before, which is consistent with the existence of technological innovation in banking during the sampling period. However, it found that X-efficiency is to be decline with bank size, deposit –to-asset ratio, loan-to-asset ratios, provision for loan loss and loan growth and increase with off-balance sheet activities. Nevertheless, after controlling for on-and off-balance sheet ratios and growth, bigger banks tend to be more efficient than smaller banks.
In order to measure the X-efficiency in a meaningful way, OBS activities cannot be ignored in the models. In the past, banks obtained short term funds from depositors and lent them long term to the industry sector, where this old fashioned intermediation functions are focused mainly on credit risk assessment. However, nowadays the net income of bank focus shifted away from margins towards security management, fee income from placing instruments, off balance sheet (OBS) activities (options, futures, etc); Bank expand into securities activities, purchase non-banking companies such as mutual funds, mortgage banks, credit card companies and other permitted activities that will generate additional fee income and provide economies of scale. Given the growth of OBS activities, estimating bank cost and profit efficiency without incorporating these activities may not be accurate or meaningful. Boyd and Gertler (1994) and Kaufman and Mote (1994) argue that significant portions of the banking business have moved beyond the traditional balance sheet. They argue that industry output is thus significantly mis-measured by focusing only on the on-balance activities of banks. Both studies note that off-balance sheet activities such as loan origination, sales, servicing, securitization, standby letters of credit, and derivative securities are expanding rapidly. Omitting OBS activities could seriously understate actual bank output and seriously bias empirical estimates of the relationships between bank size and both cost and profit efficiency.
Clark and Siems (2002) stated that M&A activities in banking industry may be geared to exploit economies of scale or scope, improve the X-efficiency of the consolidating banks or may enable the merged banks to exercise increased market power. Their studies have tried to measure the impact of the off –balance sheet activities on the measurement of X-efficiency in the banking industry. However, the research paper fails to confirm that excluding OBS activities from the profit function will introduces a systematic bias in the estimates of profit X-efficiency. Moreover, it found that neither the mix of off –to on balance sheet activities nor the size of the banking organization have a statistically significant correlation with either cost or profit x-efficiency. But interestingly, their study did show that there’s a negative relationship for x-efficiency with derivatives activities and positive relationship with loan guarantees, credit commitments and lines of credit.
The empirical assessment by Punt and Rooij (1999) has used several market power and efficient structure theories to explain the profit-structure relationship. In their studies they indicate that the profit structure relationship is found because of the positive effect of x-efficiency on both profitability and market shares and possibly on market concentration. In the article, the efficient-structure-X-efficiency (ESX) hypothesis states that firms are able to realize higher profits as a result of higher X-efficiency. X-efficiency measure to what extent the firm is successful in earning maximum profits given input and output prices, or in minimizing costs given input prices and output quantities. Hence, it is often interpreted as an indication of the level of managerial efficiency. In addition to increasing profitability, higher x-efficiency levels also enable firms to increase their market share, at the expense of less efficient firms, which may result in a higher level of concentration. By testing several hypothesis the empirical results shows mergers prove to have significant effects on bank performance, which lead to higher profitability and also to a significant improvement of a bank’s level of x-efficiency. Hence, the empirical evidence corroborates the efficient structure x-efficiency hypothesis as a valid theory providing an economic rationale for bank mergers, as this theory predicts that bank mergers lead to improved profitability through higher x-efficiency.
Berger (1995) clearly divided the efficiency hypothesis into X-efficiency and scale efficiency hypothesis. Under the x-efficiency hypothesis, the costs incurred by banks with efficient management and /or technologies are lower, with resultant higher profitability. More x-efficient banks acquire larger market shares, which increases market concentration. As stated by More and Nagy (2003), X-efficiency hypothesis can be verified when there is positive correlation between x-efficiency and profit, with the other efficiency variable and the coefficient of market structure variables being irrelevant. Similar empirical studies have been done by Gildberg and Rai (1996), Punt and Van Rooij (2001) as well as Vander Vennet (2002), and all of their studies tend to support this conclusion.
Other than the concerns on impact of profit or cost efficiency, political interest is involved as well. In Europe, all mergers with a so-called Community dimension must be notified to the Commission and are subsequently reviewed under the merger regulation of Council Regulation 4064/89 of 21 December, 1989, on the control of concentrations between undertakings. According to Article 2(1)(b), the Commission shall make an appraisement where, amongst other things take into account ‘the development of technical and economic progress provided that it is to consumers’ advantage and does not form an obstacle to competition. This clause has triggered a debate about whether the merger regulation allows for a so-called efficiency defense; Could important cost savings or other efficiencies save an otherwise anti-competitive merger. Moreover, economic theory suggests that cost saving and other efficiencies are more likely to dominate the anti-competitive effects of a merger, the lower the concentration. Since the Commission has introduce the concept of joint dominance, which including joint or oligopolistic dominance, the importance to consider efficiencies has been increased.
This paper will examine particularly the merger and acquisition activities in European financial industry. The period was chosen as 1990-2001 since it was concentrated around the completion of the single market program (early 1990s) and the EMU (1999). Over the period 1995 to the first half of 2000, ECB records 2153 mergers and acquisitions of credit institutions in the European Union. The advances of technology, the introduction of the single European currency and the demographic trend will become the three key macro variable to shape the European banking sector in the near future. The specific goal of this paper is to look at the impact on X-efficiency from pre and post acquisition activities. Up to the present, most of the researches on the performance effects of bank M&As comes from scrutiny of the US market. European bank mergers have attracted less attention, partly caused by the methodological difficulties in studying the fragmented European bank markets. However, the US experience cannot be automatically applied to the European environment since the regulation and the structure of European banking markets are different. A review of the literature suggests that the alleged gains have not been verified and they should be unique on countries or markets under study.
As in most developed economies, the European banking sector is going through a process of restructuring, mainly caused by pervasive trends such as deregulation, disintermediation, technological progress and intensified competition. The most visible manifestation of the restructuring is the enhanced pace of mergers and acquisitions among banks and other financial services providers across the European banking markets. Following the single market program (1992) and the introduction of the euro (1999) the expectation was that consolidation in banking would take a pan-European dimension. The single market program was designed to integrate the financial services market and to facilitate cross-border trade in financial services. The intention of the program was to eliminate regulatory and other impediments to cross-border financial activity. The book written by Spajic in 2002 also mentioned that although the level of x-inefficiency varies over time and across countries, in almost all cases, the largest categories of banks were, on average, more efficient than their smaller counterparts. Thus, if SMP stimulated an increase in M&A activity, particularly in terms of absorption of smaller banks, then one would expected it to result in efficiency, and therefore, welfare gains.
Berger (1993) has suggested that there are at least four different approaches have been employed in the analysis of financial institution efficiency, all of which differ in the assumptions placed on the probability distributions of the X-efficiency differences and unrelated random errors. These are : the Stochastic Econometric Frontier Approach; the Thick Frontier Approach; the Distribution Free Approach; and the Current Data Envelopment Analysis or DEA approach. This paper will adopt both stochastic frontier approach and the Distribution Free Approach (DFA) to derive bank specific measures of cost and profit X-efficiency. Basic models of these two approaches assume that the total cost spent by a bank is different from the optimal cost because of random noise vi and inefficiency component ui.
Total cost of the bank can be written as
In Ci = f (In Qi, In Pi ) + ei
Where:
Cn = total cost of bank i
Q = Output quantity
P = Input price
e = error component, where
ei = ui + vi
vi = uncontrollable random factor
ui = controllable error factor, which means inefficiency factor
Our method assumes that all banks have the same access to the underlying production technology and hence face the same cost frontier.
(HK X-efficiency)
There continues to be some debat about what constitutes the outputs and inputs in a banking firm. In this paper, the intermediation approach is used, which views the bank as employing labor, physical capital, and borrowed funds to produce earning aasets. This is the approach most commonly used in the conventional bank cost function literature.
Three outputs are included in the model :
Y1 = loans to finance imports, exports, re-exports, merchandising trade
Y2 = loans for non-trade related financing
Y3 = earning assets including negotiable cerificate of deposits, all other negotiable debt instruments and equity investments.
The average volume of each these are……xx
The inputs (whose prioces are used to estimate the cost frontier) include labor, physical capital, and borrowed money ( invcluding deposits and all other interest-bearing liabilities) used to fund the outputs
W1 = price of labor, proxied by staff expenses / nubmer of employees
W2 = price of capital, constructed as rental and other expenses / number of employees
W3 = borrowed money price, constructed as interest expenses / total liabilities
The average costs …..median…
Our sample consists of annual data on banks in nine EEC countries, namely Belgium,
Denmark, France, Germany, Greece, Italy, Netherlands, Portugal and Spain.
Tc total cost
Pbt profit before taxes
Y1 loans
Y2 investments
Y3 off-balance sheet items
W1 labor price
W2 financial capital price
W3 physical capital price
Assets total assets
Z equity/assets
In the literature, the definition of bank inputs and outputs varies across studies and mainly depends on what a researcher pricturers a bank to be. This study follows the so-called intermediation appraoch, which views a bank as an intermediary between depositors and borrowers. Accordinly, bank outputs are defined as loans y1 and investments y2 and off-balance sheet items. More precisely, loans comprise commerical and industrial, real estate, consumerand other outstanding credits. Investments aggregate securities, equity investments, and other investments. Off-balance sheet items refer to credits and other guarantees, which are not reported on the balance sheet. Concerning input prices, the price of labor w1 equal the total employee expenses scaled by the toal sum of assets. Similarly, the price of financial capital w2 is measured as the total interest expenses per unit of total assets, and the price of physical capital w3 represents all non-interest operating expeses divided by the sum of assets. Finally the variable equity/total assets (z) controls for differences in equity capital risk across banks. In order to estimate profit and cost efficiency scores, we use the total operating cost TC and profits before taxes PBT as our depended variables.
Sources of data
IBCA database:
Name of operating banks in each year.
The Bankers Almanac, Bank Base, several years:
Number of branches for each single bank, k.
Information about the history of each bank (for instance on M&A).
OECD, Bank Profitability, last issue:
Total number of branches for each country (overall banking industry), N,
Labor costs in the banking sector, Wage.
IFS Yearbook, several years:
Total deposits (demand and time deposits) for each country, S,
Real interest rates, Rirate.
ERE Summary Report:
Dummy for implementation of interest rate deregulation, IRD,
Dummy for implementation of capital control lifting, CFL,
Dummy for implementation of Second EEC Directive, SED.
Over the last decade or so, the financial sector in Europe has witnessed a series of fundamnetal changes such as deregulation and financial and technological innovation that have forced banks to modify their strategic objectives. The deregulation of financial industry in the European Union and the establishment of the Economic and Monetary Union has created a level-playing-field for banking services across the Europe, which aim on removing entry barriers and stepping up the competition and efficienct in the national banking markets. Consolidation activities have therefore been urged as a result of regulation changes, and meanwhile accompany with the technological innvoations which have converted the banking industry by saving cost and time of providing financial services and designing a range of new products. Both of these factors result in the recent wave of mergers and acquisitions. Banking institutions have responsed to greater competitive pressures with a privatisation and consolidation process, the diversification of banking portfolios, the offering of a wider range of products and services and the outsourcing of non-core activities. As a result, European banks managers have become increasingly demand-oriented and had to adjust their strategic goals towards cost reductions, profit and shareholder value maximisation. Such a study has important policy implications especially in light of the fact that the EU banking sector is experiencing profound structual changes and a full integration ahs not yet been achieved. In order to investiage the impact of increased consolidation on the competitive conditions of EU banking markets.
Increased cross-border capital flows, greater market contestability, as well as the on going process of privatisations of financial institutions have fostered an increased in market concentration. The number of banks in the European Union has decreased by 18% between 1997 and 2003, from 9100 to 7500 (ECB, 2004)
While scale and scope efficiencies have been extensively studies, primarily in the context of US financial institutions, relatively little attention has been apid to measuring what appears to be a much more important source of efficiency differences x-inefficiencies, or deviations from the efficient frontier (berger, et al. 1993, p.222) in terms of the estimation technique which follows, a stochastic frontier cost function appraoch is employed. The most notable feautures of this model are i) the estimation of a cost, rather than a production fuunction, and ii) the use of firm-specific variables to identify the sources of cost inefficiency.
In the past, European banking industry was highly regulated and was unharmonized. The primary breakthrough was the Treaty of Rome in 1957 that abolished the restrictions of capital movements. The next progress in deregulation came twenty years later, which the First Banking Directive in 1997 stipulated that collaborate between national bank supervisors is needed and the foreign identity is no longer allowed as a reason to refuse the bank license. The Second (1989) and Thrid (1993) Directives established the ‘single passport’ for financial services across the Europe. Meanwhile the Principle of Mutual Recognition is created to enforce the member states to recognie any financial institiution licensed in another member state. The Euroepan financial industry is in the trend of concentration, which it is believed that gaining size is an universal cure for all forthcoming challenges.
In the European Union, the First Banking Coordination Directive (1977), the Eu White Paper (1985) and the Second Banking Coordination DCXirective (1988) finally built to a climax with the establishment of the Single Marketfor Financial Services on January1, 1993. In this paper, we attempt to answer this question by analysing the differences in efficiency between commercial banks in 8 large European countries. As stated by Molyneux et al., 1997, efficiency is one of the “[I]mportant elements that impact on the effects of the single financial market place” (p. 9). Berger et al., 1993, and Berger and Humphrey, 1991, state that scale and scope inefficiencies in banking amount to approximately 5 percent. They are considered less important than X-inefficiencies, which account for roughly 20-25 percent. A recent study carried out for the European Commission, Economic Research Ltd., 1997, finds similar results. We therefore focus on X-inefficiency, also because it is conceptually appealing: in estimating X-inefficiency we allow banks to react to price changes, and we allow for and measure sub-optimal behavior. We therefore evaluate cost and profit X-inefficiency across all major European banking markets for the period 1993-2000.1
Countries included are Belgium, france, germany, itlay , the netherlands, spain , uk and switzerland.
These initiatives, do not settle adequately the issue of cross-border efficiency compaisons of banks having access to different types and standards of technologies in different countries. This paper attempts to add to the established literature by estimating truly comparable efficiencies across countries to account for different underlying technologies in the eu banking industry. Although efficiency studies have become popular at the national market level, only a very limited number of cross-country comparative studies can be deteced.
The introduction of euro in EMU is enhancing international competition between European banks, forcing banks to become more efficient. The managerial ability to decide on input and output in order to minimise cost or maximise revenues is referred to as X-inefficiency. The stochastic cost frontier analysis seeks to measure this type of inefficiency as deviations of the costs from the so-called efficient frontier, which is the estimated level of costs under optimal behavior. In this paper we focus on the relationship between concentration and efficiency in the EU banking markets. The empirical evidence on the links bewtween concentration and banking sector efficiency does not suggest an unambiguously positive or negative relationship ( Demirguc-Kunt and Levine, 2000). Furthermore, there are conflcting results on the impact of increased bank concnetration – through M&As – on efficiency, deposit rates and bank profitability ( berger and Humphrey, 1992; Pilloff, 1996)
In most previous studies technology is found to change the cost structure substantially. Regulatory changes having created larger markets also play a role. Studies have shown that inefficiencies are common among banks and that domestic mergers among equally sized partners significantly improved the performance of the merged banks to reach X-efficiency (when regardless of the scale of operation, input use is in line with the best practice of the industry / no waste of inputs given outputs). Efficiency may be a factor of greater relevance than economies of scale and scope.
Economies of scale are the main rationale for bank M&As. Institutions aim to achieve critical mass to explore synergies arising from size and diversification. These M&As are clearly related to cost reductions that are realized by cutting branch networks, staff and overheads in central head-office functions such as information technology departments, macroeconomic departments and legal departments.
Bank M&As often reflect a repositioning of the institutions involved. The pursuit of size increases reflects the perceived need to become big enough for the domestic market. Economies of scale also play a role. Banks aim at increased market power and a larger capital base, and thus there is a larger focus on increasing revenue. Together with the possibility of selling off peripheral business areas, M&A offers the advantage to owners of optimization of the capital structure, and thus increased shareholder value.
Ayala’s study has found on average acquirer firms increase their efficiency by approximately 5% three years after a merger. However, this is mainly due to increases in technological change and not to scale efficiency change as was originally hypothesized. According to the DEA under VRS assumption and to the SFA under the normal-truncated normal disturbance assumption, acquirer financial firms do not seem to have an increased normal disutrbance assumption, acquirer financial firms do not seem to have an increased efficiency in the period following a merger
Several merger and acquisition deals appeals in Europe over the sample period, and some of them are cross-border deals. However, it is not possible to reveal all M&A deals in the data set as there are difficulties to gether all banks involved except for large public banks. In our smaple there are xx banks involved in M&A deals. In xx cases the bank was involved in a merger, while in 58 in an acquisition deal. Among the acquisitions, in xx cases (xx cross-borders) the bank was active in the deal, while in xx cases (xx cross-borders) it was passive. ll acquisitions recorded in our sample refer to cases where the acquiring bank gained majority control over the acquired bank. Furthermore, only major mergers were taken into account, that is mergers involving two or more medium to big banks. Finally we do not have any information on deals involving banks in Greece and Portugal. Even though our list of M&A is not exhaustive, there still is a large enough number of deals to test the impact of M&A on branching costs. Notice that in the model there is no difference between opening (or closing) a new branch or acquiring (or selling) a branch through a M&A deal. We hence included in our data set a dummy variable that indicates if an observation is involved in a M&A deal.
Belgium, France, germany, italy, netherlands, spain , uk , luxembourg, switzerland
Under SFA, the random error obtained from the estimation of the production cost function is decomposed into two compoments: the first represents the cost inefficiency of the banks and the second represents the unpredictable random erro. This decomposition is achieved assuming a probability distribution function that characterizes each of the two componemets, where the prevailing approach is to assume that the first component representing inefficienct is distributed according to a truncated (at zero) normal distribution, while the second component , representing random erro, follows a normal distribution. Another methodology commonly used in the banking literature is the distributional free appraoch. Under this appraoch, the decomposition between inefficiency and random error is determined assuming a core or average efficienct for each bank, while the expected value of the random error component tends to cancel out
There are a few assumptions for SFA.
First, there is often the general assumption that firms included in a sample compete in some way.
Second, related assumption refers to the definition of the market these firms operate on. Are the products offered by the firms in the sample completely homogenous? Or are firms offering close substitutes also included?
Thirdly, once there is agreement on the degree of homogeneity of the outputs, we have to agree on the total production set. Depending on the degree of specialization not all firms in the sample may use the same inputs and outputs. Bearing in mind that most models cannot handle zero inputs or outputs, this generally involves limiting the sample to those firms that make use of the full gamma of inputs and outputs defined by the production set.
A fourth and related assumption then concerns the functional form of the prodcution function. Upon applying duality, the same holds for a cost or profit model. Not all firms may use the same prodcution techniques. Depending on the degree of specialization of the firm and the role of its environment, firms may have different transformation function and a larger or smaller opportunity set – even if they have the same production set. Thus we are faced with the paradoxical situation that in order to benhchmark the differences in efficiency of firms in our sample, we have to assume that these firms operate under the same frontier. This observation may seem trivial, but it is far less so when we keep in mind that in most benchmarking exercises we are most interested in those firms that are furthest removed from the frontier. But of course, especially those firms may not be operating under the same frontier. It is this potential paradox on which we focus in this paper.
In the last decade, this approach has been applied often in literature on efficiency of the banking industry. Stochastic cost frontier models enable us to calculate the degree of inefficiency for each individual bank in the sample and subsequently to draw general conclusions about x-inefficiencies for a country’s banking industry as a whole. Where stochastic cost frontier functions have been applied to the us banking industry manifold the number of applications to european countries is rather limited. Multiple countries studies which allow international comparison of inefficiency are rare. Bank cost efficiency analysis is based on the assumption that the technology of an individual bank can be described by a production function, which link banking outputs to available input factors. Production functions explain output as a function of inputs, like labour and capital. Knowing the frontier one can measure technical inefficiencies: the distances of individual cases to the frontier.
Schmiedel states that stochastic frontier models are a particular class of benchmarking models. As with most benchmarking models, SFA yields firm-specific estimates that are comparable. More precisely, it yield firm-specific efficiency estimates drawn from the same distribution, with the same transformation function T and the same pricing opportunity set H.
Difference between sfa and dfa
However, the stochastic frontier analysis has some drawbacks. Battese, Prasada Rao and Coelli (1997) has pointed out some problems with this approach. Firstly, the assumption of SFA has no a priori justification on concerning the probability distribution of the cost inefficient compnent, Ui. Besides, as the mode is equal to zero in the half-normal distribution the model, there is a high chance that this inefficient component will be the approximately zero as well. This implies that it is possible for only a minority of banks in the sample are truly efficient even though the assumption results show the majority of banks are being characterized as efficient. Moreover, this appraoch does not allow cost efficiency to vary over time as it does not fully take the advantage to the existence of panel data at our disposal. Furthermore, the decomposition between the inefficiency factor and random error factor is based only on technical concerns, which it ignore the corelation between efficiency and other variables such as profitability and risk, which also have considerable effects on the performance of banks in the industry Battese, G.E. and T.J. Coelli (1995): “A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data”, Empirical Economics 20, 325-332.
The data was compiled from the International Bank Credit Analysis Bankscope Database
In the last decade, this approach has been applied often in literature on efficiency of the banking industry. Stochastic cost frontier models enable us to calculate the degree of inefficiency for each individual bank in the sample and subsequently to draw general conclusions about x-inefficiencies for a country’s banking industry as a whole. Where stochastic cost frontier functions have been applied to the us banking industry manifold the number of applications to european countries is rather limited. Multiple countries studies which allow international comparison of inefficiency are rare. Bank cost efficiency analysis is based on the assumption that the technology of an individual bank can be described by a production function, which link banking outputs to available input factors. Production functions explain output as a function of inputs, like labour and capital. Knowing the frontier one can measure technical inefficiencies: the distances of individual cases to the frontier.
Credit:ivythesis.typepad.com
0 comments:
Post a Comment