Large Bank Efficiency in Europe and the United States - Dnb

Directorate Supervision

Large Bank Efficiency in Europe and the United States: Are There Economic Motivations for Geographic Expansion in Financial Services?

J.W.B. Bos, J.W. Kolari

Research Series Supervision no. 61 July 2003

De Nederlandsche Bank

Samenvatting In dit paper maken we gebruik van stochastische grensmodellen om schaalvoordelen, scope voordelen en X-effficiëntie te schatten voor zeer grote Europese en Amerikaanse banken over de periode 1995-1999. Met betrekking tot schaalvoordelen tonen onze empirische resultaten dat grote Europese en Amerikaanse banken deels zeer veel overeenkomsten vertonen. Schaalvoordelen zijn positief, zowel voor onze winst- als voor onze kostenmodellen. Scope voordelen zijn negatief voor onze kostenmodellen en positief voor onze winstmodellen. Wanneer we vervolgens onderzoek in hoeverre stochastische winst- en kostengrenzen vergelijkbaar zijn, ontdekken we dat van een gezamenlijke winstgrens mogelijk sprake is. Voor een gezamenlijke kostengrens vinden we echter overtuigend bewijs. Bovendien zijn Amerikaanse banken gemiddeld genomen meer winst-efficiënt dan banken in de meeste Europese landen. Uit onze verdere analyse blijkt dat potentiële efficiëntievoordelen mogelijk te behalen zijn middels geografische expansie van grote Europese en Amerikaanse banken.

Key words: X-efficiency, scale economies, scope economies, distance, stochastic frontiers, banking JEL classification: G21, L11, L22, L23

Large Bank Efficiency in Europe and the United States: Are There Economic Motivations for Geographic Expansion in Financial Services?

J.W.B. Bos a,1,2, J.W. Kolari b,1 a [email protected],

Banking and Supervisory Strategies, De Nederlandsche Bank, P.O. Box 98, 1000 AB, Amsterdam, the Netherlands

b

Prof. Dr. James Kolari, [email protected], Texas A&M University, Lowry Mays College and Graduate School of Business, College Station, TX 77843-4218, USA.

Abstract This paper employs stochastic frontier cost and profit models to estimate economies of scale as well as X-efficiency for multi-billion dollar European and U.S. banks in the period 1995-1999. Empirical results with respect to separate analyses of large European and U.S. banks are strikingly similar with decreasing (increasing) cost (profit) returns to scale. We find that large banks in Europe and the U.S. have cost and profit functions that are similar with increasing returns to scale and decreasing (increasing) scope economies for the cost (profit) model. Further analyses evaluate the reasonableness of estimating a combined cost or profit frontier for European and U.S. banks. We find that, while a single profit frontier may exist, separate cost frontiers are implied. Although profitability in absolute terms is equal, large U.S. banks tend to exhibit higher average profit efficiency than European banks on average. Moreover, banks in the U.S. are more profit efficient than banks in most individual European countries. We conclude that the empirical results tend to support the notion that potential efficiency gains are possible via geographic expansion of large European and U.S. banks.

Key words: X-efficiency, scale economies, scope economies, distance, stochastic frontiers, banking JEL classification: G21, L11, L22, L23

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Introduction

Deregulation of the banking industry in Europe and the United States in the 1980s and 1990s stimulated an unprecedented merger and consolidation wave (see Berger and Strahan (1998)). However, most geographic expansion has been limited to continental borders, rather than crossing over the Atlantic Ocean (Berger and Humphrey (1997)). In view of recent changes in European and U.S. banking laws, the next expansion wave may involve an international consolidation. 3 Over the past half-century, financial systems in Europe and the U.S. have become increasingly integrated by virtue of the international flow of money and capital in securities markets. In the near future the process of integration may be completed by increasing structural overlap among financial institutions via cross-Atlantic consolidation. 4 The prospect of joint European-U.S. consolidation of financial services raises numerous questions. Will such inter-continental expansion result in public gains in terms of the quality and prices of financial services? What are the driving forces motivating large banks in Europe and the U.S. to expand? In this regard, is it possible that mega-institutions combining geographicallydispersed operations are more efficient in terms of costs and profits than otherwise? If increasing returns to scale are available to large banking institutions, an economic rationale for further geographic expansion would exist. Moreover, efficiency differences between large banks in Europe and the U.S. would 1

The authors thank seminar participants at Maastricht University and De Nederlandsche Bank for their helpful remarks. We are especially thankful for valuable comments from Jaap Bikker, Lawrence Goldberg, Clemens Kool, Iman van Lelyveld and Erik de Regt on earlier drafts of this paper, as well as from participants at the 2002 annual meetings of the Financial Management Association in Copenhagen, Denmark. We also thank Alan Montgomery for excellent research assistence. The authors gratefully acknowledge financial support from the Netherlands Organization for Scientific Research (NWO) and Center for International Business Studies, Mays Business School, Texas A&M University. The usual disclaimer applies. 2 The views expressed in this article are personal and do not necessarily reflect those of De Nederlandsche Bank. 3 In the European Union, recent banking legal reforms are the Second Banking Coordination Directive of 1988 and the establishment of the single market for financial services in 1993. In the U.S., the Riegle-Neal Interstate Banking and Branching Act of 1994 and the Financial Services Modernization Act of 1999 have expanded geographic and financial services powers of banking institutions. 4 We refer to geographic expansion as any means by which a bank can expand geographically, both within and across borders. As such, geographic expansion is more encompassing than bank mergers. A second and related reason for our focus on geographic expansion is the fact that there have been very few cross-Atlantic mergers to date, which would worsen the Lucas-critique in any test of these mergers’ success.

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imply that there is an opportunity to enhance efficiency via cross-Atlantic expansion. 5 Of course, aside from cost and profit considerations, cross-Atlantic consolidation of large banks could be motivated by a variety of other factors, including market power, diversification, and management incentives (e.g., see Milbourn, Boot, and Thakor (1999)). This paper seeks to examine the question of economic motivations for crossAtlantic geographic expansion by conducting stochastic frontier cost and profit analyses to estimate economies of scale as well as X-efficiency for multi-billion dollar European and U.S. banks in the period 1995-1999. Based on separate analyses of large European and U.S. banks, we find that cost and profit functions for banks in both regions are strikingly similar with increasing returns to scale and decreasing (increasing) scope economies for the cost (profit) model. Importantly, X-efficiency scores derived from these cost and profit models reveal that on average European banks have lower cost and profit efficiencies compared to U.S. banks, and the dispersion of both profit and cost efficiency scores is considerably smaller for U.S. banks than for European banks. Further analyses evaluate the reasonableness of estimating a combined cost or profit frontier for European and U.S. banks. A necessary condition for comparable-shaped frontiers is for economies of scale to be similar among efficient banks in Europe and the U.S. We therefore test for differences in economies of scale by moving progressively closer to the frontier in an effort to evaluate the appropriateness of estimating a single frontier for both regions. In brief, we find that, while no single cost frontier exists, a single profit frontier is implied. U.S. banks tend to exhibit higher average profit and cost efficiency than European banks in general and compared to most individual European countries. In a separate analysis, we find that small banks are less cost and profit efficient than large banks in Europe and the U.S.. We conclude that, based on profit model evidence of both increasing returns to scale and differences in cost and profit X-efficiency, our empirical results tend to support the notion that potential profit efficiency gains are possible in cross-Atlantic bank 5

The literature on the performance benefits of bank mergers is mixed (see Berger and Humphrey (1992), Rhoades (1994), and Peristiani (1997)). However, a recent study by Rhoades (1998) performed case studies of nine large U.S. bank mergers and found that cost efficiency improved in each case. Seven of the bank merger cases exhibited increased profitability relative to their peers. In general, acquiring (acquired) banks were more (less) efficient than their peer group. Thus, while not all mergers yielded increased performance, most large bank combinations provided economic benefits relative to peer banks. Moreover, Siems (1996) has reported evidence that shareholder wealth significantly increased in response to megamergers of U.S. banks in 1995. The author inferred that the results tended to support in part the synergy hypothesis, whereby acquiring banks reap economies of scale and scope via cutting costs of redundancies and duplication of operations.

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mergers between European and U.S. banks. Section II overviews related literature on the efficiency of European and U.S. banks. Section III describes our methodology, Section IV gives details of the data, and Section V reports the empirical results. The last section gives conclusions. 2

Related Literature

A large body of literature that spans a half-century exists on banking efficiency in the U.S. (e.g., see surveys in Berger and Humphrey (1997), Berger and Strahan (1998), and Berger, Demsetz, and Strahan (1999)). Likewise, a more recent but growing literature on European banking efficiency is developing (e.g., see Molyneux, Altunbas, Gardener (1997), Sheldon (1999), and Altunbas, Gardener, Molyneux, and Moore (2001)). Studies prior to the 1980s tended to report U-shaped cost curves with economies of scale exhausted at total assets worth $100-$500 million for the most part. Results for economies of scope (i.e., joint production of outputs) were mixed, with most authors concluding that banks do not gain efficiencies from providing multiple financial services to the public. Altering the path of efficiency research, Berger and Humphrey (1991) showed that U.S. banks could improve their cost efficiency more by reducing frontier inefficiencies than by reaching some optimal level of scale and scope economies to minimize average costs. Subsequent research further investigated this issue using both parametric and non-parametric frontier estimation methods (e.g., see Lovell (1993)). Moreover, recent research has expanded the analyses to consider both cost and profit efficiency (e.g., see Berger and Mester (1997), Berger and Humphrey (1997), and others), as well as risk variables (e.g., see Berg, Førsund, and Jansen (1992), McAllister and McManus (1993), Mester (1996), Berger and DeYoung (1997), and others). In general, studies have confirmed Berger and Humphrey’s result that cost and profit frontier inefficiencies outweigh output inefficiencies associated with scale and scope economies by a considerable margin. A major gap in early bank efficiency literature was the scant evidence on large banks. This shortfall was substantial due to the pivotal role of large institutions in shaping the structure of the banking industry. If large bank size is closely related to the efficient production of financial services, the implication is that the post-deregulatory consolidation movement will result in a highly concentrated banking industry dominated by a relatively small number of institutions. Conversely, if inefficiencies occur as banks expand output beyond some size threshold, it is likely that the organizational structure of the industry would be less concentrated with a larger number of banks offering services to the public. Without research on large bank cost and profit efficiencies, no inferences about deregulation and related policy implications to banking industry structure could be made. 4

In the 1980s and 1990s numerous studies on large U.S. banks were published to overcome this shortfall in the literature. 6 Summarizing this literature, scale economies were found for banks between $1 billion and $15 billion in assets with diseconomies thereafter. The existence of scope economies was more elusive with most studies reporting insignificant results. One exception is that, upon resolving some econometric problems plaguing previous work in this area, Pulley and Humphrey (1993) reported significant scope economies in the joint production of two types of deposit services and three credit areas. Finally, consistent with Berger and Humphrey, studies have confirmed that frontier inefficiencies are far greater than scale and scope inefficiencies. To our knowledge no European studies have focused on large bank efficiency per se; instead, banks of different sizes are comparatively examined. 7 Only a few studies have been published on the subject of large bank efficiency outside the U.S. Allen and Rai (1996) estimated a global cost function for 194 banks in 15 countries (including the U.S.). Banks were divided by asset size into two groups (i.e., small versus large banks below and above the median asset size of a country’s banks, respectively) and by regulatory environment (i.e., countries with functionally integrated or universal banking versus those with functionally separated banking). Since the median bank asset size in most countries exceeded 40 billion U.S. dollars, most banks in the study can be considered large banks. They found that universal banking countries were more efficient than non-universal banking countries, which implies that banks offering a variety of financial services (e.g., loans, deposits, insurance, securities investment, real estate, etc.) are more competitive than banks offering selected financial services. Also, France, Italy, the U.K., and the U.S. had the most inefficient banking institutions. Another study by Saunders and Walters (1998) measured scale and scope 6

See studies by Hunter and Timme (1986), Shaffer and David (1986), Kolari and Zardkoohi (1987), Noulas, Ray, and Miller (1990), Hunter, Timme, and Yang (1990), Elyasiani and Mehdian (1990), Evanoff and Israilevich (1990), Berger, Hunter, and Timme (1993), Saunders and Walters (1994), Hunter (1995), Jagtiani, Nathan, and Sick (1995), Jagtiani and Khanthavit (1996), Miller and Noulas (1996), Mitchell and Onvural (1996), Rhoades (1998), and Rogers (1998). 7 For example, see studies by Berg, Førsund, and Jansen (1992), Fecher and Pestier (1993), Berg, Førsund, Hjalmarsson, and Suominen (1993), Pastor, Pérez, and Quesada (1994), Vander Vennet (1994), Altunbas and Molyneux (1996), Griffell-Tatje and Lovell (1996), Ruthenberg and Elias (1996), Soares de Pinho (1996), Molyneux et al. (1997), Economic Research Group (1997), Mendes and Rebelo (1997), Pastor, Pérez, and Quesada (1997), Resti (1997), Altunbas and Chakravarty (1998), Dietsch, Ferrier, and Weill (1998), Battese, Heshmati, and Hjalmarsson (1998), Altunbas, Goddard, and Molyneux (1999), Bikker (1999), Sheldon (1999), Vander Vannet (1999), Berger, DeYoung, Genay, and Udell (2000), Hassan, Lozano-Vivas, and Pastor (2000), and Altunbas, Gardener, Molyneux, and Moore (2001).

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economies for 133 of the largest 200 banks in the world at year-end 1988. Based on a translog cost model, they found that, while banks with loans less than $10 billion and more than $25 billion exhibited scale diseconomies, banks in the middle range had scale efficiencies. Also, scope diseconomies between fee-earning and interest-earning financial services existed. Given that their sample banks typically operated in multiple countries, they concluded that international expansion may well offer economies of scale opportunities for many financial institutions. They also concluded that it was too early in the 1980s to make clear inferences about potential scope economies, which they believed might materialize after some initial fixed costs of expanding beyond traditional commercial banking activities had been incurred. The aforementioned large bank studies are unique from other European work in that consolidated data are employed in the analyses. By consolidating the data from the entire organization, they provide insight into bank efficiency at the firm level, rather than at divisional or branch levels. There have been other efficiency studies of European banking that provide some large bank results, but they typically have employed unconsolidated data. For example, Berger, DeYoung, Genay, and Udell (2000) reported efficiency results for European banks with assets exceeding $100 million; however, they are careful to observe that their results are only relevant for subsidiaries of banking organizations due to the use of unconsolidated data. In this regard, they noted that transfer pricing can shift profits from one affiliate to another and affect efficiency estimates for subsidiaries. While they did not believe that this potential bias was significant in their study, it is not possible to determine the extent to which transfer pricing, shared inputs, and other intra-organizational arrangements might impact efficiency assessments. The authors estimated separate cost and profit functions for 2,123 commercial banks (i.e., other types of banks are excluded) with data available for the period 1993-1998 in France, Germany, Spain, U.K., and the U.S. Comparative analyses revealed that domestic banks on average had higher cost and profit efficiency than foreign banks in these countries. However, when the results were disaggregated on a country-by-country basis, they found that foreign banks from some countries were equal to or more efficient than domestic banks. Interestingly, U.S. banks overseas tended to be more efficient than domestic banks in their respective countries. They inferred that, since there is not necessarily a home field advantage, additional global consolidation is likely in the future. Relevant to our analyses, they recommended that future empirical work in this area should expand the analyses to a substantial number of countries. Another study by Sheldon (1999) used unconsolidated data for 1,783 commercial and savings banks in the EU, Norway, and Switzerland for the period 1993-1997. Data envelopment analysis (DEA) was employed to examine cost and profit efficiency. They found that large banks, specialized banks, and retail 6

banks are more cost and profit efficient than small banks, diversified banks, and wholesale banks, respectively. Average frontier efficiency was fairly low, at about 45 percent for costs and 65 percent for profit. Banks in Denmark, France, Luxembourg, and Sweden had the highest average efficiency, and banks in Greece, Italy, Portugal, Spain, and U.K. had the lowest average efficiency. Furthermore, estimates of economies of scale in costs and profits indicated that most banks in their sample were sub-optimal in size, with optimal scales in the range of 0.5 to 1.5 billion U.S. dollars. Decreasing cost and profit returns to scale were reported for most multi-billion dollar banks. These and other results led them to conclude that inefficient operations, rather than unexploited economies of scale, explain cost and profit differences across European banks. The authors inferred that diseconomies of large scale with respect to both costs and profits prohibit a high degree of industry concentration in the European banking market. In view of previous literature, the present study contributes new empirical evidence on large bank efficiency. Comparative analyses of European and U.S. large banks provide perspective in understanding similarities and differences between institutions located in these two regions of the world. Unlike previous European bank studies, we employ consolidated data for independent banks, as opposed to all entities contained within the organization. While some studies do use consolidated data, they do not exclude banking entities for which the organization does not have a controlling interest (e.g., at least 25 percent ownership of common stock). 8 Instead, they include banks that are under controlling interest by others as separate entities. Consequently, these studies suffer from double counting and tend to include banks that are not independent organizations. To a significant extent, this also runs counter to the profit-maximization and/or cost-minimization assumptions behind the efficiency measures employed, where banks are assumed to choose the input/output mix that is most efficient. Finally, we extend prior work by Allen 8

We select independent bank accounting statements for European banks by only including entities that are not controlled by other firms. Most research has used consolidated data from IBCA/BankScope. However, BankScope has ownership data that are kept on record for only the last year of a sample period; moreover, as noted by the publisher of BankScope, or Bureau van Dijk, ownership data are incomplete and dependent upon availability. National accounting standards require majority ownership to be published in some countries, whereas for others this information is provided on a voluntary basis. With the kind assistance of Mark Wessels and Patrick Oosterling of Bureau van Dijk, we were able to manually retrieve updated ownership and control data from the 1995-2000 issues of BankScope. Gaps in these reports were filled by gathering further data from Dunn and Bradsheet Linkages, Reuter’s News, and banks’ annual reports. In this way we constructed a complete set of large, independent commercial banks for each of the years included in our sample.

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and Rai (1996) and Saunders and Walters (1998) by collecting larger samples of multi-billion dollar banks and updating analyses to the last half of the 1990s. Like Allen and Rai, we give some comparative results for small, independent banks also. We next turn to further details of our methodology. 3 3.1

Methodology Bank Efficiency 9

The general concept of efficiency refers to the difference between observed and optimal values of inputs, outputs, and input/output mixes. Efforts to measure how efficiently a firm produces outputs from its inputs have led to the development of a number of efficiency concepts, including scale efficiency, scope efficiency, and X-efficiency. Berger, Hunter and Timme (1993) have defined Xefficiency as the economic efficiency of any single firm minus scale and scope efficiency effects, thereby allowing for sub-optimal (beneath-frontier) operations. 10 In this paper we employ stochastic frontier models that allow us to measure scale and scope efficiency as well as X-efficiency. According to Berger and Humphrey (1991) and Berger, Hunter, and Timme (1993), the significance of scale and scope inefficiencies (amounting to about 5 percent) is less important in the banking industry than X-inefficiencies (in the range of 20-25 percent). 11 We use stochastic frontier (SFA) models for two important reasons: (1) they allow for measurement error, which is an important feature in light of the fact that measuring bank production can be difficult due to data availability and the choice of a set of inputs and outputs; and (2) they generate firmspecific efficiency estimates, which allow us to test for differences in efficiency among banks from different countries as well as measure the scale and scope economies of banks that operate close to the frontier. 12 9

We refer to Lovell (1993) and Coelli, Prasado Rao, and Battese (1998) for an in-depth discussion of different efficiency measures. See also Berger, Hunter and Timme (1993) for an excellent overview of the use of different efficiency concepts in banking. 10 Economic efficiency is the sum of technical and allocative efficiency. Technical efficiency is a measure of a bank’s distance from the frontier, minimizing inputs given outputs or vice versa. Allocative efficiency measures the extent to which a bank is able to use inputs and outputs in optimal proportions given prices and the production technology. 11 See also Berger and Humphrey (1997) and Molyneux, Altunbas and Gardener (1997). 12 Concerning the measurement of X-efficiency, Bauer, Berger, Ferrier, and Humphrey (1997) imposed six consistency conditions and examined the extent to which stochastic frontier (SFA) models, thick frontier models (TFA), distribution free models (DFA), and data envelopment analysis (DEA) meet these consistency

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3.2

Model Specification

We employ SFA cost and profit models similar to those in Humphrey and Pulley (1997), Berger and Mester (1997), and DeYoung and Hassan (1998). Banks are assumed to face perfectly competitive input markets but operate in output markets where price differentiation is potentially possible. This framework easily accommodates our cost model with only trivial modifications. It also allows for market power in our profit model. Hence, banks can compete via their output pricing strategies by adjusting prices and fees according to market conditions. The extent to which they can influence prices depends on output quantities, input prices, and other factors, all of which are given at the time of price setting. Additional features of the profit model are that it can account for differences in the quality of outputs (to the extent that it is reflected in prices) as well as correct for scale bias. Also, output prices, which are subject to severe measurement problems according to Berger and Mester (1997) and Vander Vennet (1999), are not required for the empirical analysis. 13 For the estimation of the cost and profit frontier functions, we use the translog functional form. This form has been widely employed and allows for the necessary flexibility when estimating frontier models. Berger and Mester (1997) have compared the translog to the Fourier Flexible Form (FFF). Despite the latter’s added flexibility, the difference in results between these methods appears to be negligible (see also Swank (1996)). Moreover, previously cited bank efficiency studies have shown that the translog cost and profit functions are locally stable in large bank applications. We define profit before tax as PBT, outputs as Y, and input prices as W. 14 Also, let control variable Z reflect differences in risk-taking behavior of banks. We also include linear and quadratic trend terms. For the specific choice of variables, we refer to the next section. In the specification below, the optimal profit level for bank k in period t is now a function of the number of outputs, input prices, and the control variable Z. In a three-input, three-output translog setting, u and v are the inefficiency and random error terms, respectively, and ai , aij , bi , bij , ci, di , dij , ei , fi , gi , and hi are parameters: conditions. They found that the choice between these different models did not appear to significantly alter efficiency measures. In a study comparing DEA and SFA, Eisenbeis, Ferrier, and Kwan (1999) reported that SFA efficiency scores were more closely related to risk-taking behavior, managerial competence, and bank stock returns than those for DEA. 13 For a theoretical framework for the SFA models used here, see Coelli, Prasado Rao, and Battese (1998) and Bos (2002). 14 With respect to notation, we use lower case symbols in italics to denote logarithms. Upper case symbols represent actual values of the variables.

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3 P 3 3 1P P aij wikt wjkt + bi yikt (1) 2 i=1 j=1 i=1 i=1 3 P 3 P 3 3 1 P 1 P 2 + bij yikt yjkt + c0 zkt + c1 zkt + dij wikt yjkt 2 i=1 j=1 2 i=1 j=1 3 3 3 1 P P P + ei wikt zkt + fi yikt zkt + g0 T + g1 T 2 + di wikt T 2 i=1 i=1 i=1

pbtkt (y, w, z) = a0 +

+

3 P

i=1

3 P

ai wikt +

hi yikt T + c2zkt T + vkt − ukt

The error term vkt is normally distributed, i.i.d. with vkt ∼ N(0, σ 2v ). The inefficiency term ukt is drawn from a non-negative half-normal distribution truncated at µ and i.i.d. with ukt ∼ |N (µ, σ2u )|. The trunctation mean µ results from the MLE. It carries a negative sign because all inefficient firms will operate below the efficient profit frontier. For the estimations involving U.S. and European banks, we also use n-1 dummies for each of the European countries. For the cost model the left-hand side is replaced with the log of total costs (after an identical transformation) and the inefficiency term ukt carries a positive sign, as all inefficient firms operate above the efficient cost frontier. Duality requires the imposition of symmetry and linear homogeneity in input prices to estimate our cost and profit models (see Beattie and Taylor (1985) and Lang and Welzel (1999)):

aij = aji ∀i,j , bij = bji ∀i,j , 3 P

i=1

aij = 0 ∀j ,

3 P

i=1

3 X

ai = 1,

i=1

dij = 0 ∀j ,

3 P

i=1

3 P

i=1

ei = 0,

aij = 0 ∀i , 3 P

i=1

di = 0

We impose linear homogeneity in input prices by normalizing the dependent variable and all input price variables (W) before taking logarithms (see Coelli, Prasado Rao, and Battese (1998)). 15 Profit efficiency for bank k at time t is defined as: 16 15

Each of these variables is included as a ratio to one of the input price variables, and the coefficient for each input price is inferred ex post from the imposed restriction. This procedure only ensures homogeneity of degree one in factor prices. Imposing constant returns to scale would require normalization of the output variables as well. 16 The complete derivation of the maximum likelihood estimator is available upon request.

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P Ekt = E [exp (−υ κτ )] |εκτ ]

(2)

This measure takes on a value between 0 and 1 (fully efficient) and indicates how close a bank’s profits (conditional on its outputs, input prices, and the control variable) are to the profits a fully efficient bank under the same conditions. Cost efficiency also takes on a value between 0 and 1 (fully efficient) and is defined as 17 :

CEkt = {E [exp (υ κτ )] |εκτ }−1

(3)

In estimating our profit and cost models, we apply the usual reparameterization by replacing σ 2u and σ 2v with σ2 = σ 2u + σ2v and γ = σ 2u /(σ2u + σ2v ). 18 We evaluate CE and PE for the whole sample as well as for four asset quartile size classes. 3.3

Scale economies for Profit and Cost Frontiers in Europe and the U.S.

As mentioned earlier, in order to evaluate whether a single profit frontier and a single cost frontier for Europe and the U.S. can be estimated, it is necessary to determine the shape of the individual frontiers. We should only estimate a joint frontier if estimated European and U.S. frontiers are similar in shape. Characteristics of cost and (to a lesser extent) profit functions are often expressed in terms of economies of scale measures. Output-specific economies of scale are calculated by taking the derivative of the profit (or cost) model with respect to an output. For example, based on equation (1), scale economies for output Y1 can be estimated as: 17

A bank that lies above the cost frontier has cost efficiency in the range from 1 (efficient) to ∞. We invert this measure in order to get efficiency scores comparable to those of our profit model. 18 The parameter γ represents the share of inefficiency in the overall residual variance and ranges between 0 and 1. A value of 1 for γsuggests the existence of a deterministic frontier, whereas a value of 0 represents evidence in favor of a standard OLS estimation. Note that a deterministic frontier is by no means necessarily identical to a DEA model, given the latter’s restrictions on the shape of the frontier and the distribution of the inefficiency. See Coelli, Prasado Rao, and Battese (1998) for further discussion. As part of our robustness tests, we vary maximum likelihood convergence criteria (one-by-one) from 0.1 to 10−4 to see whether our results are robust. In these estimations the likelihood value, model coefficients, and efficiency measurements do not change significantly but the number of iterations does vary.

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∂ pbtkt 1 1 = a1 + a11 y1kt + a12y2 + a13 y3 + d11w1kt +d21 w2kt +d31 w3kt + f11zkt ∂ y1 2 2 (4) For the profit function a value larger (smaller) than one indicates increasing (decreasing) returns to scale, and unity indicates constant returns to scale. For the cost function scale estimates are oppositely interpreted. Overall economies of scale are simply the sum of output-specific economies of scale. When measured at the frontier, economies of scale are a good indicator of the shape of the frontier. Berger, Hunter, and Timme (1993) identified four aspects of the measurement of economies of scale that are relevant to our analyses. First and foremost, research has confirmed that banks have U-shaped cost curves. Economies of scale increase up to a relatively modest size, often estimated in the range of $100-$500 million in total assets, after which they tend to decrease (albeit slowly). Since the large banks in our samples normally operate well beyond the minimum average cost point, we need only match one segment of the cost curve, rather than the entire curve. Second, risk variables are often excluded when measuring economies of scale. Following Mester (1996) and Berger and Mester (1997), we attempt to overcome this problem by including an equity/total assets ratio that enters scale measures via interaction terms. Third, many studies base their scale measures on observations that do not lie on or close to the efficient frontier. As such, economies of scale (i.e. the marginal effects of outputs on profits or costs) cannot be separated from Xefficiency (i.e., the distance from the efficient frontier). In this case economies of scale will be biased to the extent that banks do not lie on or close to the efficient frontier. Unlike DEA, in which actual observations make up the edges of the frontier, the frontier is estimated in SFA. For this reason even the most efficient bank is rarely 100 percent efficient and, in turn, identifying the banks that are most important in shaping the frontier is not possible. This problem is worsened by the fact that the distribution of efficiency measures, when truncated towards the frontier, is generally highly skewed, with the majority of banks lying close to the frontier. 19 For these reasons choosing a set of efficient banks is an arbitrary decision. Interestingly, we do not need a single cut-off point beyond which we have a number of efficient banks; instead, our solution to this problem derives from the fact that, if for example the cost frontiers in Europe and the U.S. have the same shape, we should expect to see a decrease in the difference in economies of scale as we get closer to the frontier. 19

This is reflected by a positive, significant value for µ/σ u .

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By measuring cost and profit efficiencies as we gradually move closer to the frontier, we decrease the bias from X-efficiency but simultaneously decrease the number of observations in our independent sample tests. We therefore start with the 50th percentile as a cut-off point and end with the 90th percentile, which still provides sufficient observations to obtain reliable estimates. At each increment we test the significance of the difference in average economies of scale for European and U.S. banks, both with and without correction for the difference in variance. If the frontiers do have the same shape, this significance should decrease as attention is narrowed to the most X-efficient banks. Fourth, the most reliable measure of economies of scale is an overall estimate, defined as the sum of output-specific economies of scale. The sum of the partial derivatives of each output is less dependent on changes and differences in the output mix. Therefore, we report overall economies of scale, rather than output-specific economies of scale, to compare the results for Europe and the U.S. and to evaluate the shape of the frontiers. We report both results for the whole sample as well as for four asset quartile size classes. 3.4

Scope economies

Unfortunately, calculating scope economies is not as straightforward as calculating scale economies. However, scope economies are not crucial to evaluating whether European and U.S. banks operate under a single frontier. In addition, Berger and Humphrey (1991) and Berger, Hunter, and Timme (1993) have shown that scope economies are far less significant than X-efficiencies. Berger, Hanweck, and Humphrey (1987) observed that for translog functions complementarities cannot exist at all levels of output. Berger and Humphrey (1994) noted that an additional problem with scope estimation is the possible existence of zero outputs. Also, another potential pitfall is that there is often an extrapolation problem. Given a sample containing both universal banks and other banks, only the former banks typically offer the full range of financial services. Consequently, the economies of scope derived from the cost (or profit) function tend to overestimate the true economies of scope among most sample banks. A further problem is that the measurement of average economies of scope yields values that are biased due to the inclusion of X-(in)efficiencies. In the search for a better functional form, some researchers have used a Box-Cox transformation for outputs, while others have used a composite function with a separate fixed-costs component of scope economies. For cost models Molyneux, Altunbas, and Gardener (1997) proposed a comparison of the separate cost functions for individual outputs with the joint cost of production. However, the plant and firm level data required for this type of analysis are not available for our sample banks. We cannot claim to solve all these problems. Instead, we propose a rather simple way of measuring economies of scope that overcomes some problems and mitigates other 13

problems. In our models we have three outputs, or Y1, Y2, and Y3, which sum to Y. We start by taking the ratios Y1/Y (= a), Y2/Y (= b) and Y3/Y (= c). If such a ratio is high, a bank is relatively specialized. For overall scope economies, we calculate d = a2 +b2 +c2 . This measure is bounded between 1/3 (not specialized) and 1 (specialized). We define ‘high’ [H] as referring to the upper 25th percentile, and ‘low’ [L] for the remainder of the observations. 20 Next, for the cost model we calculate the ratio (T CH − T CL )/T CL for Y1, Y2, Y3, and Y. Likewise, for the profit model we calculate the ratio(P BTL − P BTH )/P BTL . Total costs and total profit are divided by total revenues to adjust for the possibility that banks in high and low bank groups may have different size. In both cases, if scope economies exist, the ratio is greater than 0. Note that these ratios can only be constructed using averages; as such, the scope measure itself does not have a standard deviation. This is a common problem, as recognized in Berger and Humphrey (1994). Instead, we report a t-value for an independent samples test for TCH -TCL (similar to our x-efficiency tests). Note that by varying the cut-off point to more or less than the 25th percentile, we can check for extrapolation problems. Also, by measuring scope economies for four size classes as well as for the whole sample, we control for some of the X-(in)efficiencies which can differ between size classes. 4

Geographic Expansion

As noted by an anonymous referee, a major factor potentially affecting differences in efficiency among large banks is the geographic distance between banking offices. Recent papers by Berger and DeYoung (2001, 2002) have argued that efficient lead banks in a multi-bank organization can export management skills, technology, and operating practices that enhance the efficiency of smaller affiliated banks. On the other hand, geographic distance could diminish lead bank control, cause organizational diseconomies, strain relationships and related information monitoring, and lead to agency costs in overseeing junior managers. In general, their empirical findings reveal that, even though organizational control over affiliates does decrease as distance from the lead bank increases, the effect is small. Also, affiliates tend to benefit from efficient organizations, which suggests that technology overcomes barriers to efficient operation of widely dispersed banking facilities. More benefits were found in terms of profit than cost efficiency. Following Berger and DeYoung, we conduct multivariate regression tests with cost and profit efficiency scores (CE and PE ) as dependent variables and geographic distance (in miles) between banks in banking organizations as the 20

We tested for the robustness of our results by taking other cutoff points. Our results stay qualitatively the same for a range of approximately +/- 10%.

14

focal independent variable. We also include the following as control variables: asset size (rescaled to a 0,1 range) and cost and profit levels (i.e., total cost or total profit divided by size defined as total assets plus off-balance sheet activities). Since distance and size are highly correlated with one another, we orthogonalized these variables by regressing distance on size and using the residual as the distance measure. Data was available for 516 European banks and 410 U.S. banks. For both Europe and the U.S. distance data have been gathered for 1997, the middle year in the sample period. In this regard, data were not available on European bank affiliate locations in all five sample years. If locational data were missing in 1997, data for the closest available year were available. For U.S. banks this approach simplified the collection of geographic distances for thousands of banking offices. As in the Berger and DeYoung study, geographic distance for Europe is measured by the sum of distances from the lead bank to affiliated banks (including affiliated banks outside of Europe) divided by the total assets of the banking organization. For the U.S. we compute the sum of distances from the lead bank to affiliated banks (including foreign bank affiliates) as well as branch offices divided by total assets. The reason for the latter definition is that many U.S. banks converted their organizations to one-bank holding companies in the 1990s by merging bank affiliates into the lead bank as branch offices. 5 5.1

Data Sample Data

Data on European banks are collected from IBCA reports found in BankScope. For U.S. banks data are gathered from the Call Reports for Income and Condition provided by the Federal Reserve System. Data for both samples are pooled for the period 1995 to 1999. The beginning year 1995 was chosen due to the advent of interstate banking in the U.S. in 1995 as well as a single market for financial services in the European Union in 1993. Because the Financial Modernization Act was effective in 2000 and fundamentally altered the organizational structure of U.S. banks to include greater securities and insurance powers, we ended our analyses in 1999. During our sample period, few regulatory changes occurred in European and U.S. banking markets, with the exception of the introduction of the Euro in 1999. It should also be mentioned that economic conditions were stable in both regions during this period. 21 Since we comparatively examine both cost and profit efficiency, sample banks must be profit maximizing. Also, in order to test for one cost frontier and one profit frontier for all banks, it is necessary to include banks with similar 21

An exception is the Asian crisis in 1997. Since we do not estimate a panel data model, and given the fact that this crisis affected all of the countries involved in our study, we do not expect it to significantly affect the comparative results.

15

output mixes. In this respect we exclude brokerage firms with a banking license due to their large securities activities and relatively small commercial lending services. Likewise, while general banks and bank holding companies are included in the analyses, cooperative banks, credit unions, mutual banks and credit card banks 22 are excluded. To ensure that variables are computed similarly across countries and over time, all values are expressed in constant 1995 U.S. dollars. 23 We then set a bank asset size cut-off point of $1 billion 1995 U.S. dollars. Although this restriction substantially reduces our sample size, multi-billion dollar banks represent the dominant share of the banking assets in Europe and the U.S. due to concentration of resources among larger banks and are most likely to engage in cross-Atlantic expansion. As recommended by an anonymous reviewer, we conducted tests of the possible effect of the Euro’s introduction on January 1, 1999. A dummy variable equal to 1 in 1999 and 0 in other sample years was included in the U.S., Europe, and combined cost and profit models. The dummy variable was mixed in sign and rarely significant across models. Importantly, the correlation of X-efficiency scores generated from models with and without the Euro dummy exceeded 0.90 in all cases and was more than 0.96 in five out of the six models. Further analyses on the correlation of predicted values of the dependent variables (i.e., the total derivative) for the sample periods 1995-1998 and 1995-1999 yielded results exceeding 0.97 in all models. Rank tests for both X-efficiency and predicted values with and without the year 1999 did not significantly affect rank orders. Thus, we infer our results were not significantly affected by the advent of the Euro. Only independent banks and bank holding companies (BHCs) that are not owned or controlled by other firms are included in our sample. In this regard, our models presume banks have some degree of freedom to choose their inputs and set output prices, which would not be entirely true of component 22

In the U.S. the Comptroller of the Currency defines credit card banks as those banks with credit card loans totaling 6.5 percent or more of total assets. We excluded banks that exceeded this threshold from the analyses. 23 As a robustness check, we also used German marks as a reference currency with no change in results. The dollar exchange rate in the sample period was relatively stable for the 17 European countries with banks in our analyses. CPI and exchange rate series were taken from EIU Country Data. All European data and U.S. data are expressed in 1995 dollars using the U.S. CPI. Note that, since both dependent and independent variables are expressed in the same currency, our transformation to constant 1995 U.S. dollars only has an impact on the cutoff value of 1 billion 1995 U.S. dollars. As shown in Table 1, only a fraction of our sample banks are close to this cutoff point. In this regard, changing the cutoff point to slightly higher values did not change our results.

16

Table 1: Descriptives A. European Banks (N = 519)a Variable

Skew

Kurtosis

Minimum

Maximum

4,791,070

8,124,660

2.27

8.11

16358.6

49,246,900

831,208

1,443,620

2.66

11.03

655.76

9,177,220

Y1 (loans)

32,366,000

56,840,900

2.53

9.77

13382.7

Y2 (investments)

12,396,900

26,578,300

3.31

15.58

116.78

Y3 (off-balance sheet)

19,342,800

42,105,800

3.78

22.51

2058.87

W1 (labor price)

0.013

0.013

3.69

21.03

0.0003

W2 (financial capital price)

0.041

0.039

4.89

32.44

0.0013

0.38

W3 (physical capital price)

0.012

0.014

4.22

26.62

0.0003

0.13

0.083

0.093

3.84

18.51

0.01

0.66

2.7

10.62

1,012,440

TC (total cost) PBT (profit before taxes)

Z (equity/assets) ASSETS (total assets)

Mean

Std. Dev.

64,400,000

0.11

B. U.S. Banks (N = 476) a Variable TC (total cost) PBT (profit before taxes) Y1 (loans) Y2 (investments)

Mean

Skew

Kurtosis

Minimum

Maximum

2,684,750

9,692,600

6.28

50.14

18426

98,457,300

854,247

3,124,390

6.76

59.64

3654

34,826,600

24,701,600

89,628,300

6.69

57.58

42092

92,70,800

32,095,400

5.91

42.34

1715

2.50E+09

9.53

100.49

24638

3.13E+10

Y3 (off-balance sheet)

Std. Dev.

W1 (labor price)

0.011

0.004

-0.92

5.34

8.29E-06

0.03

W2 (financial capital price)

0.024

0.007

-1.47

5.63

4.65E-04

0.05

W3 (physical capital price)

0.003

0.001

-0.07

5.8

5.39E-08

0.01

Z (equity/assets)

0.094

0.027

3.99

22.04

0.05

0.26

ASSETS (total assets) 42,986,500 6.37 52.55 1,020,880 1.60E+09 = Total costs, profit before taxes, outputs, and total assets are measured in thousands of 1995 U.S. dollars.

a

subsidiaries within the organization. Also, and as cited earlier, Berger, DeYoung, Genay, and Udell (2000) have noted that transfer pricing and other intra-organizational funds flows could affect efficiency estimates. We therefore use consolidated statements and set limits on the maximum ownership and control by outside parties. With respect to ownership, U.S. law requires banks that are 25 percent or more owned by a single shareholder to be included in a BHC. For U.S. bank data we applied this legal criterion to the construction of our sample banking organizations. In Europe, under the First Banking Coordination Directive of 1977, similar rules allow us to apply the same criterion to European banks. 24 Due to numerous mergers and takeovers in Europe and the U.S. during the sample period, ownership thresholds are checked for all large banks in each year of the sample period. 24

The First Banking Directive of 1977 and further requirements set forth in 1983 state that: 25 percent ownership of a bank’s shares constitutes official participation; 50 percent ownership requires that proportional consolidation take place; and ownership below 50 percent is left to the discretion of member states concerning the consolidation procedure, with most member states requiring proportional consolidation for ownership between 25 percent and 50 percent.

17

We also exclude all observations with variable values missing and values less than or equal to zero. A negative or zero value for a variable implies that the respective bank’s production function is quite different from other sample banks. Moreover, if such observations were included in the analyses, an arbitrary transformation would be required due to the fact that zero and negative values are disallowed in the translog model. For the combined data set only 10 observations were dropped for this reason. Lastly, outliers are considered by estimating the models with all observations and checking for outliers in the efficiency scores. As found in other studies, outliers tend to have higher scores than other sample banks. If outliers in the independent variables are consistent with outliers in the dependent variable, we omit the observations as long as skewness is not substantially altered. We then re-estimate the models and report the results with all observations (with outliers omitted) if the coefficients have not changed (have changed) significantly. To avoid heteroskedasticity problems we use weighted least squares, with weights based on the log of total assets. 25 5.2

Variables

Consistent with the intermediation approach used by most SFA studies, bank outputs are defined as follows: loans (Y1), investments (Y2), and off-balance sheet activities (Y3). Loans aggregate commercial and industrial, real estate, consumer, agriculture, and other outstanding credit. Investments include securities, equity investments, and all other investments reported on the balance sheet. Off-balance sheet activities are credit items and other guarantees, loan commitments, derivative securities, and other loan and securities exposures not reflected on the balance sheet. Of course, these activities are particularly important among large banks. Turning to input prices, it is not possible to calculate a traditional measure of the price of labor due to incomplete data on the number of employees in BankScope. Even if the number of employees were available, part-time employees are not counted despite their increasing usage in the banking industry. Also, given the dispersion in the types of jobs at large banks (from cashier to investment banker), the price of labor as measured by average labor cost is not likely an accurate proxy for the marginal cost of labor. In an effort to overcome these difficulties, we express prices as the input-specific cost per unit of total output. Therefore, we define the price of labor (W1) as total employee expenses divided by the sum of assets and off-balance sheet activities. As such, difficulties related to missing employee data, part-time workers, and average labor costs are mitigated. 25

In the presence of heteroskedasticity, the least squares estimator is not efficient but still unbiased, consistent, and asymptotically normally distributed.

18

Likewise, the price of financial capital (W2) equals total interest expenses divided by the sum of assets and off-balance sheet activities, and the price of physical capital (W3) equals non-interest operating expenses divided by the sum of assets and off-balance sheet activities. As justification for this approach, we argue that, to the extent that inputs are substitutes for one another, they individually contribute to the cost per dollar output in the same way. Because all input prices have the same denominator, we checked for multicollinearity but found it was not important among input prices. We also performed a robustness check by re-running the models using observations that had data available for the number of employees but the results were unchanged for the most part. Finally, as mentioned earlier, the control variable equity/total assets (Z) is included in the model to adjust for differences in equity capital risk across banks. Table 1 contains descriptive statistics for input prices, outputs, and dependent variables. Total costs, profit before tax, outputs, and input prices as well as total assets are in thousands of 1995 PPP dollars. Our samples consist of 519 European banks and 476 U.S. banks, or 995 banks in total. As shown in Table 1, the size distributions of European and U.S. banks in our samples differ to some extent. Compared to U.S. banks, the European bank distribution has a larger mean size (i.e., on average $20 billion larger), is less skewed, and has a lower maximum size.

6

Bank Efficiency Results

Forthcoming results and discussion focuses on how the X-efficiency of large European and U.S. banks compares in the period 1995-1999. To begin we estimate separate frontiers for Europe and the U.S., respectively. Next, economies of scale and scope are calculated, and economies of scale are compared as measured at the frontier for Europe and the U.S., respectively. In this respect the scale estimates are evaluated for whether they are similar enough to estimate a single profit frontier and cost frontier. Additionally, profit and cost efficiency results are compared for each of the European countries separately and for Europe as a whole and the U.S. too. OLS models were initially run to examine goodness of fit. In all cases adjusted R2 values exceeded 95 percent, with most models exceeding 99 percent. These results provide strong support for our model design. Since our focus is on frontier analyses, to conserve space we only report the SFA models and results (i.e., OLS results are available from the authors upon request). Also, we should mention that the price of financial capital (W2) is used to ensure linear homogeneity in input prices. 19

6.1

European Banks

Panel A of Table 2 reports results for the estimated SFA cost and profit models for large European banks. 26 For both economies of scale/scope and X-efficiency, we report results for four asset quartiles as well as for the total sample. Importantly, X-efficiency estimates based on the cost model for European banks average 0.947, which implies that frontier efficient banks could further reduce operating costs by 5.3 percent on average. As in other SFA studies, the distribution of efficiency scores is highly skewed (e.g., the minimum is 0.064). Relative to the cost results, X-efficiency estimates based on the profit model for European banks are on average considerably lower at 0.721, which implies potentially large profit improvements of 27.9 percent on average are possible for frontier banks. With a standard deviation of 0.219 (i.e., approximately twice that of the cost model), the distribution of profit efficiency scores is quite large. Apparently, large banks in Europe had wide disparities in profit X-efficiency but much smaller disparities in cost X-efficiency during our sample period. These results are generally consistent with previous European studies of bank cost and profit X-efficiency (e.g., see Ruthenberg and Elias (1996), Dietsch, Ferrier, and Weill (1998), and Vander Vennet (1999)). However, some studies have reported profit X-efficiencies ranging between 0.20 and 0.25 for large European banks (e.g., see Griffell-Tatje and Lovell (1996), Resti (1997), and Altunbas, Gardener, Molyneux, and Moore (2001)). Assuming that profit efficiency is a function of not only internal production (as in the case of cost efficiency) but external market forces, higher cost efficiency and lower profit efficiency could be attributable in part to differences in market power among large banks. Alternatively, Sheldon (1999) has argued that these results could be due to high-cost outputs with service and quality features that are not in demand by bank customers. Panel A of Table 2 also contains average scale estimates derived from the cost and profit SFA models for large banks in Europe. As discussed earlier, scale estimates are derived from the estimated coefficients for the entire cost 26

Since estimated coefficients for λ (i.e., the ratio of the variance of the truncated normal inefficiency term to the variance of random noise) are significantly greater than zero (at the 0.01 level) in both models, we infer support for our frontier approach relative to OLS. The total variance of the error term (or σ) is low. Similar results hold for all our estimations. Results with respect to individual variables are difficult to interpret due to second order and interaction variable effects. For example, in the cost model the estimated coefficient for loans (Y1) was negative and significant, which implies lower operating costs as loans are increased, all else the same. However, this interpretation does not take into account the nonlinear cost implications of loans captured in squared loans (Y1Y1) and multiple interactions of loans with other variables in the model. For these reasons we do not report estimated coefficients for the cost and profit models.

20

or profit equation, rather than a single estimated coefficient. Consistent with prior studies of large banks (e.g., Allen and Rai (1996), and Altunbas, Gardener, Molyneux, and Moore (2001)), cost model estimates suggest decreasing economies of scale for outputs. An increase of 1 USD in total output results in an increased cost of almost 1.13 USD for European banks. Since overall economies of scale are negative and significant, the results imply cost diseconomies of scale. Contrary to the cost model findings, overall scale economies for the profit model are increasing and significant at 1.15 USD for large European banks. This result suggests that large banks can increase profits by expanding their size. 27 The scale economies results were similar across quartile size ranges. Finally, scope economies are negative but insignificant for the cost model. European banks that produce a disproportionate amount of a particular output have total costs that are approximately 34 percent lower (but statistically insignificant) than banks that have a more balanced output mix. These results are in line with the large international bank results of Allen and Rai (1996), as well as work by Vander Vennet (1999). For the profit model scope economies are positive (with total profits approximately 37 percent higher among banks with a more balanced output mix) but again insignificant. This result is consistent with the finding of higher revenues and profitability among universal banks compared to specialized banks by Vander Vennett (1999). The insignificance of the cost and profit scope estimates suggests that scope economies are small in general, which agrees with the consensus in the empirical banking literature (e.g., see Berger (2003)). 6.2

U.S. Banks

Panel A of Table 2 further reports the economies of scale/scope and Xefficiency results from estimating the cost and profit SFA models for large U.S. banks. For the cost model the average X-efficiency score is 0.976 (or higher than European banks) with a standard deviation of 0.047 (or lower than European banks). 28 When evaluated at their respective cost frontiers, 27

The negative relationship between cost and profit scale economies as bank output expands is consistent with work by Berger and Mester (1997), who found that cost and revenue inefficiencies can be negatively correlated (i.e., cost inefficiencies do not necessarily imply profit inefficiencies). On the other hand, our results differ from Sheldon, who reported decreasing returns to scale for both costs and profits. 28 For the U.S. data we observe very high kurtosis for the dependent variables and outputs. The variable assets used in weighted least squares (WLS) also has high kurtosis, potentially aggravating estimation problems arising from heteroskedasticity. This latter condition appears to be the case for our U.S. cost model. We checked for robustness of our results by using different WLS estimations; in general, this problem does not significantly affect our efficiency estimates and our coefficients, which

21

Table 2: Comparison of Independent, Large Banks in Europe and the U.S. A. Efficiency a

Cost Models Europe

Quart. Scale

Scope

X-eff.

Mean

Profit Models U.S.

t

Europe

Mean

t

Mean

U.S. t

Mean

t

Q1

1.143

4.767

1.055

17.108

1.208

3.646

1.134

11.186

Q2

1.126

4.699

1.071

17.376

1.159

3.497

1.148

11.322

Q3

1.153

5.661

1.049

13.572

1.169

4.387

1.098

8.856

Q4

1.095

7.494

0.938

8.376

1.100

5.986

0.907

5.237

Total

1.127

5.991

1.042

9.294

1.151

4.564

1.099

5.808

Q1

-0.231

0.327

-0.947

3.357

0.455

1.658

0.895

0.404

Q2

-0.009

0.680

-0.975

1.038

0.245

1.516

0.934

1.135

Q3

-0.166

0.986

-0.989

11.006

0.248

0.264

0.982

4.808

Q4

-0.631

0.561

-0.867

15.932

0.534

0.714

0.865

15.955

Total

1.362

-0.340

0.083

-1.024

10.484

0.367

0.590

0.950

Q1

0.924

5.755

0.973

12.281

0.636

2.169

0.676

3.711

Q2

0.924

5.573

0.976

21.321

0.600

1.977

0.754

5.381

Q3

0.962

15.571

0.978

35.126

0.714

3.074

0.778

6.542

Q4

0.957

12.235

0.978

190.13

0.831

7.353

0.866

15.232

Total

0.947

8.454

0.976

18.098

0.721

2.964

0.749

4.790

SD

Mean

B. Frontier Test a Independent samples test Perc. Cost

Profit

F

t

Europe

N europe

N us

Mean

U.S. SD

50th

26.3

8.934

259

238

1.156

0.186

1.032

60th

12.9

8.957

207

191

1.163

0.186

1.023

0.113 0.122

70th

7.2

7.986

155

143

1.167

0.193

1.016

0.129

80th

3.6

6.595

104

95

1.176

0.213

1.007

0.145

90th

8.2

5.282

51

47

1.227

0.249

1.009

0.153

50th

40.7

2.246

260

238

1.167

0.260

1.124

0.151

60th

27.8

1.766

208

190

1.164

0.262

1.126

0.153

70th

28

1.899

155

143

1.186

0.285

1.137

0.145

80th

22.3

0.876

104

95

1.173

0.311

1.144

0.142

90th

13.6

1.027

52

47

1.201

0.383

1.14

0.185

C. X-Eff. Test a Independent samples test F Cost

t 59.8

-5.005

Europe

N europe 519

N us 476

Mean 0.947

U.S. SD

Mean

0.112

0.976

SD 0.054

Profit 96.3 -2.181 519 476 0.721 0.243 0.749 0.156 a = Asset quartiles (in millions of U.S. dollars) are distributed as follows: Q1 = [1012435,1982082], N europe =93, N us =155; Q2 = [1982083,4420897], N europe =84, N us =166; Q3 = [4420898,30193469], N europe =165, N us =84; Q4 = [30193470,160096490], N europe =177, N us =71. All independent samples tests use Levene’s test (at the 5% level) for the equality of variances. We only report the absolute t-values for mean differences that are not rejected by this F test. Scope t-values correspond to an independent samples test that expected cost (or profit) is equal for specialized and non-specialized banks.

we infer that large U.S. banks have higher cost efficiency on average compared to European banks. For the profit model the average X-efficiency score are still unbiased and consistent. Also, the mean efficiency and efficiency rankings were not significantly affected.

22

is also relatively higher compared to European banks at 0.749 with a standard deviation of 0.092. 29 This result contrasts with that of Miller and Noulas (1996), who conducted a DEA analysis of large U.S. banks and found profit Xefficiency of 0.97 with almost half the banks 100 percent technically efficient. Not surprisingly, the U.S. market is more homogeneous than the European market, as confirmed by the lower standard deviation for both cost and profit X-efficiency. Also, as implied by the descriptive data in Table 1, whereas average return on assets is higher in the U.S., the standard deviation of the return on assets is lower than in Europe. The average cost ratio of U.S. banks is lower than for European banks also. Cost model results in panel A of Table 2 show that overall economies of scale for U.S. banks significantly decrease but are smaller in magnitude than those for the European cost model (i.e., 1.127 and 1.042 for European and U.S. banks, respectively). 30 Average scale economies for U.S. banks generated from the profit model are positive, but somewhat smaller than those for European banks — for example, a 1 USD increase in total outputs results in an almost 1.10 USD increase in profits (compared to 1.15 USD for European banks). As in Europe, it appears that large U.S. banks’ expansion tends to boost profits. One exception is the profit economies of scale result of 0.907 for U.S. banks in the largest size quartile. For these banks profit scale diseconomies appear to exist. Scope economies for large U.S. banks are negative and significant (at the 0.01 level) for the cost model, whereas they are positive but insignificant for the profit model. Cost scope diseconomies for U.S. banks are about three times larger than for European banks. This difference in results could be due to greater number of specialized banks in the U.S. and universal banks in Europe. Previous U.S. studies on large U.S. banks and scope economies are generally mixed, with relatively small economies or diseconomies as mentioned earlier (e.g., see Pulley and Humphrey (1993) and Mitchell and Onvural (1996) and citations therein). Our results are likewise mixed with scope economies in profits but scope diseconomies in costs for both Europe and the U.S. Also, like most previous work, most scope economies are not significant. In general, our efficiency results for large European and U.S. banks reflect more similarities than differences. Estimates of scale economies for costs and profits reveal functional relationships between output level and efficiency that are strikingly comparable to one another. However, on average European banks 29

Comparing the values for µ/σ u , truncation is very similar for the U.S. and Europe bank samples, with a large proportion of efficient banks. 30 Our finding of decreasing returns to scale is consistent with numerous prior studies of large U.S. banks (e.g., see Noulas, Ray, and Miller (1990), Hunter, Timme, and Yang (1990), Jagtiani and Khanthavit (1996), and Miller and Noulas (1996)).

23

have lower cost and profit X-efficiencies compared to U.S. banks, and the dispersion of both profit and cost efficiency scores is considerably smaller for U.S. banks than for European banks. Consistent with previous literature, we do not normally find scope economies, with the exception of significant cost diseconomies of scope for U.S. banks. We should mention that our results do not necessarily imply that U.S. banks are more efficient than European banks, as each sample of banks is evaluated against its own efficient frontier. It is possible that the most efficient U.S. banks are only average when compared to the European banks. In order to compare efficiency results for European and U.S. banks, we next turn to a combined analysis of both samples. 7

European and U.S. Banks Combined

Panel B of Table 2 reports the results for a series of independent sample t-tests for mean differences in economies of scale estimates among European and U.S. banks. As discussed earlier, these tests are based on bank samples that progressively move closer to the frontier (i.e., from the 50th to the 90th percentile). The t-statistics test for the mean differences in average scale estimates for European and U.S. banks that are not rejected by Levene’s F-test for equality of variances. The cost models’ t-tests for all percentile cut-off points are significant, which is evidence against a single cost frontier. For the profit models the evidence is mixed, with significant t-tests for the 50th , 60th , and 70th percentile samples but insignificant t-tests for higher percentile samples. Placing greater weight on the 90th percentile banks that are closest to the profit frontier, due to insignificant differences in mean scale economies, we infer that the evidence favors estimating a single profit frontier for European and U.S. banks. We next estimate the cost model for the combined sample with the addition of dummy variables for 17 countries and a dummy variable for Europe (versus the U.S.), respectively. These tests seek to capture geographic differences in cost scale estimates. The left-hand side of Table 3 reports the results. While some of the country-specific dummy variables are significant, it is noteworthy that the Europe dummy variable is not significant. Since average X-efficiency is 0.961 for the country dummy cost model and 0.958 for the Europe dummy cost model, overall cost efficiency results are not affected by the inclusion of country-specific dummy variables. 31 In both estimations µ/σu is significant and negative, as a relatively large number of bank observations can be found in the tail of the efficiency distribution. 31

As a further test, we also estimated the European model with country dummies where Germany is the reference country. Efficiency scores and rankings were not significantly different using the model without dummies as presented in Table 2. Thus, whereas Europe’s banking market as a whole may be quite heterogeneous, the estimation of a single European frontier poses no problems.

24

Table 3: Combined Cost and Profit Frontiers Cost Models a Variable Intercept

Profit Modelsa

Country dummy

Europe dummy

Country dummy

Europe dummy

b

b

b

t

b

11.074

16.822

t 4.767

12.948

Europe

t 2.334

5.934

0.031

1.598

Austria

0.197

2.336

-0.160

-0.910

Belgium

-0.017

-0.472

-0.149

-1.428

0.099

4.878

0.016

0.320

-0.110

-4.694

-0.295

-5.582 -3.662

Switzerland Germany Denmark

0.167

5.263

-0.237

Spain

0.173

9.552

0.102

2.097

Finland

0.084

1.466

0.078

1.265

France

0.270

9.942

-0.488

-9.374 4.298

Great Britain

-0.042

-2.179

0.187

Greece

0.116

4.48

0.676

9.010

Ireland

0.094

3.161

0.452

4.130

-0.063

-2.827

-0.164

-3.715

0.156

2.847

-0.144

-0.903 -2.908

Italy Luxembourg Netherlands

-0.067

-2.189

-0.204

Norway

-0.092

-1.718

-0.440

-2.027

Portugal

-0.176

-2.739

-0.33

-2.656

Sweden

t 0.048

0.068

0.225

4.497

-0.023

-0.709

0.218

3.287

W1

0.663

8.060

0.582

7.551

0.885

4.319

1.555

8.639

W3

0.303

3.895

0.270

3.712

0.090

0.517

-0.847

-5.085

Y1

-0.106

-1.912

0.110

1.701

-0.875

-7.762

0.384

3.202

Y2

0.374

10.694

0.561

17.510

0.179

2.304

0.425

6.002

0.384

11.739

0.243

6.554

0.628

8.512

0.106

1.354

-0.703

-7.83

-0.765

-7.380

-0.166

-0.913

-1.563

-9.038

Y3 Z T

0.020

1.470

0.014

0.893

0.094

2.924

0.129

3.537

0.5T2

-0.005

-1.251

-0.004

-0.750

-0.012

-1.127

-0.027

-2.117

µ/σ υ

-2.423

-4.476

-1.754

-3.824

2.139

3.437

0.500

0.841

2.936

22.914

2.362

21.074

4.208

13.345

1.203

12.694

0.249

23.422

0.239

22.660

0.625

13.881

0.364

24.387

λ σ LLF

743.850

611.660

90.430

-129.170

ss(v)

0.009

0.009

0.021

0.054

ss(u)

0.076

0.048

0.369

0.078

Mean

0.961

0.958

0.735

0.853

Std. Dev.

0.090

0.081

0.207

0.118

Obs. 995 995 995 995 a = Variables are defined as follows: Y1 — loans, Y2 — investments, Y3 — off-balance sheet activities, W1 — price of labor, W2 — price of financial capital, W3 — price of physical capital, Z — equity/total assets ratio, and T — time. Other terms are: LLF — the log likelihood ratio test, ss(v) — the variance of random noise, or σ 2ν , ss(u) — the variance of the truncated efficiency term, or σ 2υ , µ/σ υ — the truncation point for υ divided by the standard deviation of the truncated efficiency term, — the ratio of standard deviations of the truncated normal efficiency term and random noise, or σ υ /σ ν , and σ — the total variance of the error term equal to the sum of the variance of random noise plus the variance of the truncated efficiency term, or σ 2 = σ 2υ + σ 2ν . The full estimation results are available from the authors upon request.

In panel C of Table 2, we employ the results from the country dummy cost model to test whether the European banks jointly are on average as cost efficient as U.S. banks. Mean efficiency for European banks is 0.947, which 25

is significantly less than U.S. banks at 0.976, albeit by only 2.9 percent. We infer that European banks are slightly less cost efficient than U.S. banks on average but the difference is not economically meaningful. The left-hand side of Table 4 compares each European country’s bank cost efficiency to U.S. bank cost efficiency. Given the relatively low number of observations for some countries, and in order to take into account the different market structures, we weighted the efficiency scores for each bank in a particular country by its share of the country’s total banking assets. For the U.S. the weighted cost efficiency is 0.974. The F-statistic tests for the equality of variances fail to accept the null hypothesis (except for Italy); as such, Type II t-tests under the assumption of unequal variances are most appropriate. A negative and significant t-statistic means a country’s banks are less cost efficient than U.S. banks. Excluding some countries due to relatively low sample sizes (i.e., Austria, Finland, Luxembourg, and Portugal), the Type II t-test results are mixed, with five (eight) countries’ banks having cost efficiency similar to (different from) U.S. banks. Only banks in Italy and the Netherlands had significantly higher average cost efficiency scores than U.S. banks. 32 Banks in Belgium stand out as the least efficient among the European countries. Finally, while banks in Germany, Denmark, France, Great Britain, and Norway were less efficient than U.S. banks from a statistical standpoint, the magnitude of the difference appears to be fairly modest in economic terms. Turning to the estimated profit models for the combined sample, recall that earlier tests moving progressively closer to the profit frontier implied differences between European and U.S. banks that became insignificant. The righthand side of Table 3 shows the estimated models with the addition of dummy variables for 17 countries and a Europe dummy (versus U.S.), respectively. Profit X-efficiency scores averaged 0.735 for the country dummy model and 0.853 for the Europe dummy model. Hence, relative to the cost model results, country-specific dummy variables (which are generally statistically significant) provide incremental information that is averaged out upon lumping together the European countries. Focusing on the Europe dummy variable t-test, Xefficient frontier U.S. banks’ scale economies are not significantly higher than those for European banks. Also, µ/συ is positive and significant, implying that most banks are efficient. This evidence tends to confirm our earlier finding of a single profit frontier for European and U.S. banks. As mentioned earlier, panel C of Table 2 shows that for U.S. banks the average profit efficiency score is 0.749 compared to 0.721 for European banks, which are significantly different at the 5 percent level. 32

As shown in Table 8, these findings are not related to the individual coefficients for the country dummies, as these dummies capture country-specific effects but not X-efficiency.

26

Figure 1: Mean Weighted Cost an Profit Efficiency for Large Banks 0.90 U.S. Spain 0.85

Profit Efficiency

0.80

Ireland Netherlands

Great Britain Portugal Sweden & Switzerland Germany Italy Denmark France Finland

Belgium

Norway Greece

0.75

0.70 Luxembourg 0.65

0.60

0.55 Austria 0.50 0.87

0.89

0.91

0.93

0.95

0.97

0.99

Cost Efficiency

We next consider the independent sample tests for mean differences in weighted profit efficiency scores between individual European countries’ banks and U.S. banks, where the latter have a profit efficiency weighted by assets of 0.865. Again excluding selected countries due to small sample sizes (i.e., Austria, Finland, Luxembourg, and Portugal), the Type II t-test results in Table 4 clearly indicate that U.S. banks are significantly more profit efficient than banks in eleven countries and that only banks in Ireland and Sweden had similar profit efficiency to U.S. banks. In sum, the profit efficiency results for banks in Europe and the U.S. are consistent with the cost efficiency results. As a robustness check, we also estimated the cost and profit models using traditional labor prices for U.S. banks as well as using flow outputs (e.g., substituting interest earnings on loans for the stock of loans, securities earnings for the stock of investments, and noninterest income for the stock of off-balance sheet activities), but the cost and profit efficiency results remained qualitatively the same. Figure 1 graphically summarizes the mean cost and profit efficiency scores for large banks by country. Casual inspection of this graph suggests that cost efficiency and profit efficiency are correlated with one another to some extent. Viewing countries in the upper right quadrant of the graph as operating within a relevant range of cost and profit efficiency, there is considerable dispersion among sample countries’ cost and profit efficiency. Banks in the U.S., Ireland, and the Netherlands tend to dominate other countries’ banks in cost/profit efficiency space. The relative strength of U.S. banks is consistent with Berger, DeYoung, Genay, 27

Table 4: Independent Sample Tests of X-efficiencies Compared to the U.S. Cost Model Country a

N

Mean

Austria

5

0.902

Belgium

16

Switzerland

46

Profit Model

F

t

Mean

F

777.18

-1.23

0.518

2313.37

t

0.877

813.72

-2.79

0.791

316.61

-1.73

0.974

1874.98

0.23

0.824

26.14

-4.94

-1.69

Germany

64

0.940

1363.42

-4.20

0.805

386.44

-6.09

Denmark

47

0.956

5848.73

-5.42

0.796

47.91

-9.86

Spain

52

0.973

626.19

-1.60

0.849

10.66

-3.54

Finland

10

0.971

674.15

-0.76

0.772

176.69

-1.89

France

44

0.969

240.28

-6.09

0.783

325.63

-5.37

Great Britain

63

0.947

714.38

-4.28

0.831

60.53

-6.83 -12.96

Greece

22

0.973

215.42

-1.32

0.739

41.74

Ireland

16

0.976

1222.10

0.66

0.865

8.68

0.03

Italy

54

0.976

0.06

13.20

0.803

194.26

-7.47

Luxembourg

13

0.937

1905.46

-4.11

0.683

843.00

-2.96

Netherlands

24

0.981

205.55

2.67

0.845

17.94

-2.38

Norway

16

0.971

205.55

-2.67

0.751

327.08

-4.45

Portugal

11

0.971

606.82

-0.93

0.855

0.18

-1.22

Sweden 18 0.974 244.06 -0.31 0.823 218.79 -1.35 = The F-statistic is Levene’s test (at the 5% level) for the equality of variances. We only report the t-values for mean differences in average scale economies that are not rejected by the F test. Efficiency scores are weighted by assets per country in each year to avoid small banks in small countries biasing results. The reference country is the U.S. with an average weighted cost efficiency of 0.974 and an average weighted profit efficiency of 0.865. a

and Udell (2000), who found that U.S. banks tended to be relatively efficient compared to banks in France, Germany, Spain, and U.K. Belgian banks have relatively low cost efficiency but their profit efficiency is well within the range of profit efficiency of other countries’ banks. Only banks in Luxembourg and Austria, for which the number of observations is small, appear to be outside relevant ranges for cost and profit efficiency. While differential regulatory and economic environments in the past no doubt influenced the relative efficiency of banks in these and other countries, harmonization of banking services as European countries move toward a single market will likely lead to increasing convergence of cost and profit efficiency among large banks in the future. In the meantime differential competitive advantages exist for large banks located in different European countries; moreover, large banks in the U.S. are competitive relative to the most efficient countries in Europe. 8

Geographic Dispersion and Bank Efficiency

Here we report the results for empirical tests on the relationship between geographic dispersion as measured by distance between banks within a banking organization and efficiency measures. Table 5 gives the descriptive statistics for the six independent variables discussed previously. U.S. banks have higher mean distances than European banks; nonetheless, U.S. banks’ average cost and profit efficiency scores are higher, average cost ratios are lower, and aver28

age profit ratios are higher compared to European banks. Table 6 shows the correlation coefficients between these variables. As may be expected, a higher cost efficiency is negatively and significantly correlated with the cost ratio. Table 5: Descriptive Statistices: Geographic Distance Analyses A. European Banks a (516 obs.)

Mean

Distance Size

Std. Dev.

Minimum

Maximum

14,356.000

30,041.000

0.0000

165,215.0000

84,235,200.000

151,781,000.000

1,071,080.0000

956,470,000.0000

CE

0.949

0.105

0.1059

0.9953

PE

0.721

0.244

0.0153

0.9895

Cost ratio

0.0652

0.056

0.0051

0.5028

Profit ratio

0.0136

0.016

0.0001

0.1322

B. U.S. Banksa (410 obs.)

Mean

Distance Size

Minimum

Maximum

226,378

0

1,454,040

454,657,000

2,799,080,000

1,045,520

31,869,400,000

0.9744

0.058

0.2009

0.9902

0.766

0.1459

0.0583

0.9874

0.0448

0.0134

0.0009

0.1188

CE PE Cost ratio a

Std. Dev. 53,662

Profit ratio 0.014 0.0066 0.0003 0.084 = Distance is the sum of geographic distances in miles between banks within the banking organization. Size is total assets and off-balance sheet items in thousands of dollars. CE and PE are cost and profit efficiency scores, respectively. Total costs and total profits divided by total assets and off-balance sheet items are the cost and profit ratios.

Likewise, a higher profit efficiency is positively and significantly correlated with the profit ratio. Interestingly, the cost and profit ratios are positively and significantly correlated, with a value of 0.645. Distance was not significantly correlated with any of the other variables. As defined previously, distance is orthogonalized with respect to size. Table 6: Correlations Between Independent Variables Distance a

CE

PE

Cost ratio

Distance

1.000

Size 0.000

-0.018

-0.026

-0.001

Size

0.000

1.000

0.013

0.076

CE

-0.018

0.013

1.000

-0.059

PE

-0.026

0.071

*

-0.059

1.000

Cost ratio

-0.001

-0.121

**

-0.262

**

-0.114

*

**

-0.121

Profit ratio 0.055

**

-0.108

-0.262

**

-0.245

**

-0.114

**

0.236

**

0.645

**

1.000

**

Profit ratio 0.055 -0.108 ** -0.245 ** 0.236 ** 0.645 ** 1.000 a = Distance is the sum of geographic distances in miles between banks within the banking organization. Size is total assets and off-balance sheet items in thousands of dollars. CE and PE are cost and profit efficiency scores, respectively. Total costs and total profits divided by total assets and off-balance sheet items are the cost and profit ratios. The symbols * and ** denote 5% and 1% significance (two-tailed), respectively. Distance is orthogonalized with respect to size (see also Table 7).

Table 7 reports the results for the multivariate regression models using cost efficiency and profit efficiency as dependent variables. We ran three models for each efficiency measure due to the inclusion of the following three dummy variables: (1) outside country (i.e., 1 for banks with affiliates outside their 29

home country in Europe or the U.S, 0 otherwise), (2) outside EU (i.e., 1 for European banks with affiliates outside the European Union or U.S. banks with offices outside the U.S., 0 otherwise), and (3) outside Europe (i.e., 1 for European banks with affiliates outside of Western and Eastern Europe or U.S. banks with offices outside the U.S., 0 otherwise). Table 7: Geographic Expansion and Bank Efficiency Cost Efficiency Dependent Variablea Model (1) b

Model (2) t

b

Model (3) t

b

t

Intercept

0.102

22.80

2.920

30.13

7.919

25.11

Distance

-8.26E-09

-0.66

-2.17E-07

-0.57

-4.48E-07

-0.41

5.68E-04

2.47

1.47E-02

-2.19

3.11E-02

0.47

-3.62E-02

-1.97

-1.172

-2.15

-3.239

-2.06

Total Profit Ratio

-0.338

-4.75

-9.725

-4.75

-26.450

-4.43

Outside Country

-0.178

-6.33 -0.452

-6.49 -1.231

-5.61

Size Total Cost Ratio

Outside EU Outside Europe F Value

19.33

18.61

18.17

Adjusted R 2

0.090

0.087

0.085

Profit Efficiency Dependent Variable a Model (1) b

Model (2) t

b

Model (3) t

b

t

Intercept

0.388

31.94

0.389

33.17

0.389

33.18

Distance

-1.57E-07

-2.29

-1.50E-07

-2.19

-1.54E-07

-2.23

1.85E-02

2.43

1.61E-02

2.27

1.65E-02

2.27

-1.883

-11.72

-1.96

-12.25

-1.945

-11.85

Total Profit Ratio

9.360

13.21

9.603

13.59

9.446

12.38

Outside Country

0.030

2.54 0.048

4

Size Total Cost Ratio

Outside EU Outside Europe F Value

0.048 43.27

45.35

3.87 45.57

0.186 0.193 0.194 Adjusted R 2 = We estimate a truncated regression model, with truncation at 1 from above. Mean cost efficiency is 0.960, and mean profit efficiency is 0.741. Distance is the sum of geographic distances in miles between banks within the banking organization. It is computed orthogonal to size (i.e., the residual of a regression of distance on size and a constant) to avoid multi-collinearity problems. Size is total assets plus off-balance sheet items. CE and PE are cost and profit efficiency scores, respectively. Total costs and total profits divided by total assets plus off-balance sheet items are the cost and profit ratios. Three dummy variables are defined as follows: (1) outside country (i.e., 1 for banks with affiliates outside their home country in Europe or the U.S, 0 otherwise), (2) outside EU (i.e., 1 for European banks with affiliates outside the European Union or U.S. banks with offices outside the U.S., 0 otherwise), and (3) outside Europe (i.e., 1 for European banks with affiliates outside of Western and Eastern Europe or U.S. banks with offices outside the U.S., 0 otherwise). We use a White estimator to correct for possible heteroskedasticity problems. a

The estimated distance coefficients have negative signs for all cost and profit efficiency models, with significant coefficients (at the 0.01 level) for all profit models. Hence, as banks become more geographically diversified, profit efficiency tends to significantly diminish. For example, an increase of total distance within the banking organization of 1,000 miles would decrease profit 30

efficiency by about 0.016 percent, and vice versa for a decrease in distance. The results for the dummy variables reflecting foreign versus domestic banking operations are negative (positive) and significant in the cost (profit) efficiency models. These results suggest that cost (profit) efficiency diminishes (increases) as banks move beyond their domestic geographic area. The positive effect on profit efficiency may reflect the net positive diversification benefits banks reap from their geographical expansion. Regarding the control variables, all three control variables (i.e, profit ratios, cost ratios, and asset size) are significant in both the cost and profit models, except for cost model 3 in which asset size is insignificant. The latter finding as well as the relatively low spread in cost efficiency explain the higher OLS adjusted R2 values in the profit models compared to the cost models. In sum, based on the distance variable results, we infer that geographic dispersion tends to negatively affect banks’ profits. The dummy variable results further reveal that expansion outside of a bank’s domestic area can have negative effects on cost efficiency but positive effects on profit efficiency. These findings are generally consistent with Berger and DeYoung, who found more profit than cost efficiency benefits of geographic expansion. 8.1

Small Bank Results

In this section we broaden our analyses to include small banks. International mergers and acquisitions are most likely to occur between large institutions and smaller banks as the former seek a foothold position in new markets. Historically, small, independent banks are much more common in the U.S. than in Europe. Our European sample includes all small banks not affiliated with a bank holding company or other banking organization in the 19951999 period for which data was available. A total of 383 observations met these criteria. 33 For the U.S. we randomly sampled 500 small, independent banks. Compared to our large bank samples, the size distribution is much less dispersed. Repeating the empirical methodology applied to large banks, Table 8 contains the results for small, independent commercial banks with less than $1 billion in constant 1995 dollars. 34 The estimated cost and profit frontier X-efficiency results for small banks in Europe, the U.S., and combined reveal relatively higher cost efficiency but lower profit efficiency compared to large banks, especially among European banks. Cost X-efficiency is on average 0.743 and 0.871 for 33

Most small, independent European banks are located are in Germany, Switzerland and Denmark. No observations were obtained for Finland, Greece, Ireland and Sweden. 34 Complete details of the empirical results are available upon request from the authors.

31

Table 8: Comparison of Independent, Small Banks in Europe and the U.S. A. Efficiencya

Cost Models

Profit Models

Europe Quart. Scale

Scope

X-eff.

Mean

U.S. t

Europe

Mean

t

Mean

U.S. t

Mean

t

Q1

0.895

3.814

1.149

6.387

0.359

0.836

1.324

6.337

Q2

0.996

4.243

1.142

6.343

0.252

0.589

1.295

6.198

Q3

1.026

5.096

1.144

9.927

0.413

0.976

1.308

11.077

Q4

1.111

6.85

1.142

6.938

0.601

1.324

1.339

8.586

Total

1.059

5.068

1.145

8.007

0.490

1.079

1.311

7.701

Q1

-0.113

1.286

-0.437

3.312

0.091

0.519

0.485

0.115

Q2

0.739

2.383

-0.573

0.585

-1.608

0.102

0.55

1.097

Q3

0.699

3.297

-0.596

0.297

-0.429

1.714

0.554

1.016

Q4

1.501

0.872

-0.578

0.370

-1.201

0.299

0.663

0.884

Total

0.816

3.037

-0.599

2.369

-0.507

1.777

0.608

0.805

Q1

0.814

6.292

0.877

9.699

0.527

2.448

0.615

3.933

Q2

0.817

4.165

0.854

7.361

0.564

2.561

0.65

4.278

Q3

0.710

3.273

0.882

9.514

0.586

2.753

0.692

6.362

Q4

0.740

3.416

0.892

8.351

0.638

3.554

0.625

3.156

Total

0.743

3.495

0.871

8.519

0.607

3.042

0.644

4.249

B. Frontier Test a Independent samples test Perc. Cost

Profit

F

t

Europe

Neurope

Nus

Mean

U.S. SD

Mean

SD

50th

25.6

4.561

191

260

1.061

0.211

1.136

0.141

60th

12.3

3.274

153

208

1.073

0.199

1.136

0.152

70th

6.4

2.772

115

156

1.071

0.206

1.136

0.17

80th

10.6

3.763

76

104

1.043

0.213

1.145

0.119

90th

6.2

2.526

38

52

1.034

0.25

1.147

0.132

50th

163.8

26.459

191

259

0.496

0.44

1.304

0.189

60th

180.6

22.026

153

207

0.497

0.441

1.312

0.144

70th

162.4

18.441

115

156

0.487

0.465

1.319

0.154

80th

122.4

14.94

76

104

0.436

0.49

1.308

0.161

90th

57.7

12.25

38

51

0.344

0.469

1.326

0.180

C. X-efficiency Test a Independent samples test F Cost

t 261.3

Europe

Neurope 11.95

383

Nus 519

Mean 0.743

U.S. SD

Mean

0.213

0.871

SD 0.102

Profit 47.2 2.999 383 519 0.607 0.200 0.644 0.151 = Asset quartiles (in millions of U.S. dollars) are distributed as follows: Q1 = [4069,45720], N europe = 24, N us =201; Q2 = [45721,102338], N europe = 39, N us = 187; Q3 = [102339,264629], N europe = 122, N us = 103; Q4 = [264630,990942], N europe = 198, N us = 28. All independent samples tests use Levene’s test (at the 5% level) for the equality of variances. We only report the absolute t-values for mean differences that are not rejected by this F test. Scope t-values correspond to an independent samples test that expected cost (or profit) is equal for specialized and non-specialized banks. a

small European and U.S. banks, respectively. Cost economies of scale are negative, especially for small U.S. banks. While our sample tests show significant differences between the European and U.S. cost frontiers, these differences become less significant as we approach the frontier. Cost scope economies are also more negative in the U.S. with mixed significance in Europe and the U.S. 32

Figure 2: Mean Weighted Cost and Profit Efficiency for Small Banks 0 .70 0 .68

L u xem b our g N eth erla nds

0 .66

U .S. D en m ark

Profit Efficiency

0 .64 G rea t B r itain

0 .62

N orw a y

A ustr ia G e rm an y

S w itzerla nd

Italy Po rtug al

0 .60 0 .58 0 .56

B elg ium Spa in Fra nce

0 .54 0 .52 0 .50 0 .60

0.65

0.70

0.7 5

0.80

0.85

0 .90

C ost E fficiency

Consistent with our large bank results, cost-efficient small banks in Europe and the U.S. are comparable. However, based on joint frontier results, there are more inefficient banks in Europe than in the U.S., as reflected by the difference between the average cost X-efficiency of U.S. (0.871) and European (0.743) banks. Regarding the small bank profit efficiency results, average profit X-efficiency is 0.607 in Europe and 0.644 in the U.S. As in the large bank analyses, European small banks have lower profit X-efficiencies compared to U.S. small banks. However, for all percentile sample cutoff points we find significant differences between the two frontiers. For the separate profit frontier results, small U.S. banks gain more than small European banks on a dollar of additional output (i.e., the estimated coefficients are 1.31 and 0.49, respectively). This higher profit efficiency among small U.S. banks may well be due to the fact that independent, small U.S. banks tend to operate in rural areas in which there is less competition than experienced by their European counterparts. When these two groups of banks are compared using a joint frontier, we find that small U.S. banks are also significantly more profit efficient than small European banks, albeit by a small absolute margin (i.e., 0.644 for U.S. banks versus 0.607 for European banks). Scope economies are also higher for U.S. banks but are not significant. Figure 2 shows the mean cost and profit efficiencies of each country’s small banks, which are weighted by assets as before. Similar to the large bank results, 33

there appears to be a positive correlation between cost and profit efficiency scores. Also, like the results for large banks, small banks in the U.S. and the Netherlands dominate other banks in the cost/profit efficiency space. Luxemburg was relatively inefficient among large banks but is highly efficient in small bank comparisons. Small banks in Spain and France are relatively cost/profit inefficient. Unlike the large bank results, there is much higher dispersion of countries’ cost/profit efficiency scores for small banks. In general, the small bank efficiency results are similar in some ways to those for large banks in Europe and the U.S. but different in other respects. Small U.S. banks tend to have greater cost and profit X-efficiencies than small European banks, which is similar to our findings for large banks. One difference in results is the normally lower average cost and profit X-efficiency scores for small banks relative to large banks. We infer that small banks tend to be less efficient than large banks. One possible implication of these findings is that small banks face less competitive pressure to be cost and profit efficient than large banks. If this is indeed true, an efficiency motive for large banks to merge or acquire small banks exists. 9

Conclusion

The last two decades of the 20th century witnessed rapid bank consolidation around the world, which has taken place for the most part within countries and on an intra-continental basis. At some point, when regional concentration of bank resources becomes satiated, it is reasonable to believe that intercontinental mergers will be the next phase in the merger movement. In this paper we examine the question of economic motivations for further geographic expansion by conducting stochastic frontier cost and profit analyses to estimate economies of scale as well as X-efficiency for multi-billion dollar European and U.S. banks. Comparable data for input prices and outputs are gathered for large banks for the period 1995-1999. Our empirical results with respect to separate analyses for large European and U.S. banks’ cost and profit curves are strikingly similar. For the cost model in both regions, we found small, increasing and significant returns to scale. Returns to scale for the profit models are positive and significant for the most part. Generally speaking, these results suggest that large banks in Europe and the U.S. can increase profits via output expansion. Based on these cost and profit models, X-efficiency scores revealed that on average European banks have lower cost and profit efficiencies compared to U.S. banks. In absolute terms U.S. banks have lower (higher) cost (profit) ratios. Also, the dispersion of both profit and cost efficiency scores is considerably smaller for U.S. banks than for European banks. This result is not surprising in view of the greater homogeneity of banking markets in the U.S. compared to Europe. Scope estimates were generally not significant, with the exception of cost diseconomies 34

of scope among U.S. banks. Further analyses evaluated the reasonableness of estimating a combined cost or profit frontier for European and U.S. banks. For this purpose we measured economies of scale moving progressively closer to the cost or profit frontier, as banks closest to the frontier are most important in terms of assessing the feasibility of a single cost or profit frontier. We found that mean scale economies for European and U.S. banks measured very close to the profit frontier are not significantly different and, therefore, imply a single profit frontier. Profit X-efficiency scores revealed lower average efficiency among different European countries’ banks compared to U.S. banks. By contrast, the results for cost scale economies comparisons failed to accept the null hypothesis of a single cost frontier. Results of X-efficiency comparisons revealed that U.S. banks’ cost efficiency tended to be higher than that of European banks. We conclude that our empirical results tend to support the notion that potential profit efficiency gains are possible in cross-Atlantic bank mergers between European and U.S. banks. Thus, an economic motivation appears to exist in favor of geographic expansion in the years ahead. Likewise, in view of considerable heterogeneity in cost and profit efficiency, our evidence suggests that intra-European mergers among large banks are possible. Direct tests for how geographic distance affects cost and profit efficiencies of large banks in the U.S. and Europe indicated that efficiency tends to decline as banking organizations spread out their offices. However, profit efficiency tended to increase with respect to expansion beyond their home country. Since further analyses showed that small banks tend to have lower average cost and profit X-efficiency scores than large banks, large banks may well be expected to expand via small bank mergers. Today’s global banks developed gradually over time by expanding abroad to meet their domestic business clients’ international financial needs. As such, branch office acquisitions and mergers and de novo entry have traditionally been utilized to achieve a foothold in foreign locations. While other motivations may well exist for cross-Atlantic mergers among large banks, potential profit efficiency gains appear to be a plausible factor in decisions to merge beyond continental shores and form global organizations comprised of large Europe/U.S. bank combinations.

35

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Rogers, K. (1998), Product mix, bank powers, and complementarities at U.S. commercial banks, Journal of Economics and Business, 50, pp. 205-18. Ruthenberg, D., and R. Elias (1996), Cost efficiencies and interest margins in a unified European banking market, Journal of Economics and Business, 48, pp. 231-49. Saunders, A., and I. Walter (1994), Universal Banking in the United States: What Could We Gain? What Could We Lose?, Oxford University Press, New York. Siems, T. F. (1996), Bank mergers and shareholder wealth: Evidence from 1995’s megamerger deals, Financial Industry Studies, Federal Reserve Bank of Dallas (August). Shaffer, S. (1984), Scale economies in multiproduct firms, Bulletin of Economic Research, 36, pp. 51-8. Shaffer, S., and E. David (1986), Economies of superscale and interstate expansion, Research Paper No. 8612, Federal Reserve Bank of New York. Sheldon, G. (1999), Costs, competitiveness and the changing structure of European banking, Working Paper, Fondation Banque de France pour la Recherche. Soares de Pinho (1994), Economies of scale and scope and productive efficiency in Portuguese banking: a stochastic frontier approach, Ph.D. thesis, City University Business School, London, UK. Swank, J. (1996), How stable is the multiproduct translog cost function? Evidence from the Dutch banking industry, Kredit und Kapital, 29, pp. 153-72. Vander Vennet, R. (1994), Concentration, efficiency and entry barriers as determinants for EC bank profitability, Journal of International Financial Markets, Institutions, and Money, 4, pp. 21-46. Vander Vennet, R. (1999), Cost and profit dynamics in financial conglomerates and universal banking in Europe, University of Ghent, Belgium (mimeo). Waldman, D. (1982), A stationary point for the stochastic frontier likelihood, Journal of Econometrics, 18, pp. 275-79.

41

Verschenen in deze reeks Nr

Periode

Titel

Auteur(s)

1

Oktober 1997

De rol van rating instituten in een euro-kapitaalmarkt

2

November 1997

3 4

December 1997 Maart 1998

5 6 7

Maart 1998 Mei 1998 Mei 1998

8

Augustus 1998

9 10

Oktober 1998 December 1998

Ontwikkelingen in het bedrijfs-economisch toezicht op banken Financiële stabiliteit: een taak van centrale banken Fusies in het Europese bankwezen: achtergronden en implicaties Toezicht op liquiditeit van banken: nodig of overbodig? Kredietrisicomodellen: uitdaging voor het toezicht The Impact of Capital Adequacy Standards on Bank Behaviour in Continental Europe: A Brief Overview of the Literature Competition and Concentration in the EU Banking Industry Is de EU bronbelasting op rente waterdicht? Een anatomie van financiële crises

H.H.J. van der Hoorn, A.L. Touw A.L. Touw

11 12 13

December 1998 Januari 1999 Januari 1999

Toezicht in internationaal perspectief Een signaleringssysteem voor het renterisico Strategische ontwikkelingen bij Nederlandse banken

14

Januari 1999

15

Februari 1999

16 17 18

April 1999 April 1999 Mei 1999

19

Juli 1999

Restructuring of the Dutch banking sector: Implications for banks and the economy LTCM, hedge funds en het kredietrisicobeheer van banken De kernactiviteiten en toekomst van banken Interne rating van banken in Nederland Efficiency in the European Banking Industry: an exploratory analysis to rank countries De schuld van het Nederlandse huishouden?

20 21

Juni 1999 Juli 1999

Risicomanagement bij banken en toezicht daarop Financial Fragility and Macroeconomic Performance

22 23 24

Juli 1999 September 1999 November 1999

Het millenniumprobleem in de kredietportefeuille De veranderingen in het bancaire toezicht: de ervaringen A Tool for the Analysis of Systemic Country Risk

25

Januari 2000

Toezicht op securitisaties

26

Februari 2000

27

Mei 2000

28

Mei 2000

29

Juni 2000

Het kredietrisicomodel: een nieuw instrument voor risicobeheer Measures of Competition and Concentration: A Review of the Literature Beleggingsinstellingen en de stabiliteit van het financiële stelsel Op weg naar een Europese toezichthouder?

30

September 2000

Competition, concentration and their relationship: An empirical analysis of the banking industry

A.M.C. van der Zwet J.M. Groeneveld J.A. Bikker A.P. Huijser J.M. Groeneveld

J.A. Bikker, J.M. Groeneveld J.A. Bikker, S.G.A. Kaatee H. van Ees, H. Garretsen, J.M. Groeneveld, R. de Haas A.M.C. van der Zwet H.H.J. van der Hoorn J.M. Groeneveld, R.T.L. Moonen W.J. Jansen, R.T.L. Moonen R.T.L. Moonen J.M. Groeneveld A.P. Huijser J.A. Bikker J.M. Groeneveld, R.T.A. de Haas J.A. Bikker A.M.C. van der Zwet, J. Swank H.H.J. van der Hoorn R.C. Coppes R.T.L. Moonen, K. Haaf, M.E.E. Rijken R.C. Coppes J.A. Bikker K. Haaf E. Kersten M.P.H. de Vor, A.M.C. van der Zwet J.A. Bikker, K. Haaf

31

September 2000

Housing Prices, Bank Lending, and Monetary Policy

Nr

Periode

Titel

32 32

December 2000

33

December 2000

34

Januari 2001

35

Januari 2001

36 37

Maart 2001 Mei 2001

38

Juli 2001

39

Oktober 2001

40

December 2001

41

December 2001

42

December 2001

43

Maart 2002

44

Maart 2002

45

April 2002

46

Mei 2002

47

September 2002

48

Oktober 2002

49

November 2002

50

Oktober 2002

Financial Stability and Efficiency in the Twentieth Century: the Netherlands De jacht op zwart spaargeld: eindelijk een EU-brede belasting op rente in zicht Efficiency and cost differences across countries in a unified European banking market Financial Development and Economic Growth in Transition Economies. A Survey of the Theoretical and Empirical Literature Report on Crisis Management in the EU Repo-style transactions and capital adequacy requirements: Collateral, capital adequacy requirements, and supervisory policies Investor reactions to news: An analysis of the euro-dollar exchange rate Conjunctuur in winst en kapitaal en procycliciteit van nieuwe Bazelse kapitaaleisen Doelmatigheid en rechtvaardigheid van de depositoverzekering Banken, instituties en zachte budgetbeperkingen tijdens de transitie Technology and the (re)location of financial activity: a European perspective Foreign bank penetration and bank credit stability in Central and Eastern Europe De Nederlandse woningmarkt: sterk fundament of drijfzand? Economic versus regulatory capital for financial conglomerates The blurring of distinctions between different financial sectors: fact or fiction? Banking Supervision at the Crossroads: Background and Overview A Brief History of the Institutional Design of Banking Supervision in the Netherlands Risk in Financial Conglomerates: Management and Supervision Bank provisioning behaviour and procyclicality

51

Februari 2003

52

Februari 2003

53

Maart 2003

54

Maart 2003

55

Maart 2003

Risk measurement within financial conglomerates: best practices by risk type Testing for imperfect competition on EU deposit and loan markets with Bresnahan’s market power model Intermediation, integration and internationalisation: a survey on banking in Europe Depositor and Investor Protection in the EU and the Netherlands: A brief History Reviewing the Performance of Dutch versus UK Banks

I.J.M. de Greef, R.T.A. de Haas Auteur(s) H.M. Prast J.A. Bikker J.A. Bikker R.T.A. de Haas

J.G. Rotte, M.P.H. de Vor R.T.A. de Haas, T. Keijser

H.M. Prast, M.P.H. de Vor J.A. Bikker, H. Hu J.A. Bikker, H.M. Prast R.T.A. de Haas I.P.P. van Lelyveld, M.A. Donker R.T.A. de Haas, I.P.P. van Lelyveld I.J.M. de Greef, R.T.A. de Haas J.A. Bikker I.P.P. van Lelyveld A.M.C. van der Zwet K.H.W. Knot J. Mooij, H.M. Prast I.P.P. van Lelyveld, A. Schilder J.A. Bikker, P.A.J. Metzemakers Working group on Economic Capital Models J.A. Bikker J.A. Bikker and A.A.T. Wesseling Gillian Garcia and Henriëtte Prast J. Draulans, N. van Dijk, A. Dowdalls, D. Heymans, D. Rennie, J. Rushton

56

April 2003

Improving Market Power Tests: Does it Matter for the Dutch Banking Market?

J.W.B. Bos

57

Mei 2003

Comparing Efficiency in European Banking: A Metafrontier Approach

J.W.B. Bos, H. Schmiedel

58

Mei 2003

Foreign Banks and Credit Stability in Central and Eastern Europe: Friends or Foes? A Panel Data Analysis

R.T.A. de Haas, I.P.P. van Lelyveld

59

Juni 2003

Depositor and Investor Protection in the Netherlands: an assessment

Gillian Garcia, Henriëtte Prast

60

Juli 2003

The Future of Depositor and Investor Protection in the Netherlands

Gillian Garcia, Henriëtte Prast