Nontraditional activities and bank efficiency revisited: a distributional analysis for Spanish financial institutions

Journal of Economics and Business 55 (2003) 371–395

Nontraditional activities and bank efficiency revisited: a distributional analysis for Spanish financial institutions Emili Tortosa-Ausina∗ Departament d’Economia, Universitat Jaume I, Campus del Riu Sec, 12071 Castelló de la Plana, Spain Received 26 July 2001; received in revised form 18 December 2002; accepted 4 February 2003

Abstract This article analyzes the importance of nontraditional activities (e.g., fee-based services) when measuring bank cost efficiency. For this, we use both parametric and nonparametric location tests to compare results yielded by two different models—one of them accounting for nontraditional activities. We also analyze how the entire distributions of efficiency scores differ when this type of activities are considered, in order to draw more accurate conclusions on firms’ individual behaviour. Results show that efficiencies vary according to both models, as well as firms’ relative positions—compared with the average, some firms’ efficiency is enhanced, whereas in other cases it worsens. In addition, the issue of time must be accounted for, as the importance of these activities, and their effect on bank efficiency, increases over the sample period. © 2003 Elsevier Science Inc. All rights reserved. JEL classification: C14; C61; G21 Keywords: Banking; Efficiency; Nontraditional activities

1. Introduction The decline of traditional activities (making loans and funding them by issuing short-dated deposits) and a more widespread entry into nontraditional activities (e.g., fee-based services) in US banks has been widely reported in recent years, and is so well known that it is taken for granted in discussions on banking. Not only the economic press but also research studies ∗

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have documented this issue (DeYoung & Roland, 2001; Edwards & Mishkin, 1995; Gorton & Rosen, 1995; Kaufman, 1993; Kaufman & Mote, 1994), as bank income is increasingly generated through nontraditional activities. In Western Europe empirical evidence on these issues is scarce. Despite the deregulation process, which has reshaped most European banking industries, research studies have practically disregarded its importance. Bank efficiency was one of the most thoroughly studied issues during the deregulation and post-deregulation periods, but the extant literature has not often explicitly taken the relevance of nontraditional activities into account. However, if we consider the fact that the competition facing most Western European financial institutions today is stronger than before deregulation took place, and that many firms have reacted by engaging in new, nontraditional, activities, their omission turns out to be of a paramount importance. In addition, this new debate would fuel the long-standing disagreement on our conceptions of what banks produce, as the precise choice of their outputs would be further biased if all new services and products offered by banks were omitted. In contrast, the attention given to this matter has been much greater in the US, as revealed in the contributions of DeYoung (1994), Hunter and Timme (1995), Rogers (1998) and, more recently, Clark and Siems (2002). Other valuable contributions are those by Mester (1992), Jagtiani, Nathan, and Sick (1995) and Jagtiani and Khanthavit (1996), although they did not specifically deal with efficiency issues. The empirical focus of this paper is on Spain for three reasons. First, it is one of the Western European countries in which regulation was tightest and, accordingly, changes may have affected banking firms more markedly. For the purposes of this paper, most banks have reacted by engaging in these new, nontraditional activities, which are distinct from those they previously carried out. Second, the extant literature has not reported efficiency gains after deregulation took place. This would be at odds with the conventional wisdom that increased competition enhances X-efficiency (see Leibenstein, 1966), and a plausible explanation could be that new activities have been omitted. Third, we should bear in mind that mutual funds, as an important new bank activity, have experienced a dramatic increase over recent years to the detriment of deposits—a conventional proxy for the provision of payments services, regarded as traditional activities. If this tendency were accounted for when estimating efficiency, results could differ substantially. We should bear in mind that, since Rogers (1998), many papers examining efficiency and productivity issues take the relevance of these activities for granted. For instance, Altunba¸s, Evans, and Molyneux (2001a) and Altunba¸s, Gardener, Molyneux, and Moore (2001b) consider off-balance sheet items as an additional output, in order to avoid understating total output. However, some of these authors, prior to the studies by Jagtiani and Khanthavit (1996) or Rogers (1998), considered only traditional output (Altunba¸s & Molyneux, 1996). What we attempt to stress is that explicit comparisons—at least in the European case—are scarce, and that the features of the industry may considerably vary between Western Europe and the US In fact, as suggested by Allen and Santomero (2001), comparing investor portfolios across countries shows that US household allocations are similar to those in the UK but quite different from those in Japan, France and Germany. This might lead to variations across countries in the alleged decline in the traditional role of banks of taking deposits and making loans, which might also have quite an impact on the efficiency of banks if nontraditional activities are omitted. Yet the contribution of the paper is not only the analysis of whether the omission of nontraditional activities understate bank efficiency. Furthermore, we consider an alternative technique

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which analyzes the entire distributions of efficiency scores, rather than drawing conclusions from only summary statistics such as mean, standard deviation, or correlation coefficients. Other papers dealing with this issue base their final comments on the increase in mean efficiency due to the inclusion of new outputs, and the different rankings of firms as provided by rank correlation tests (Fernández de Guevara, 2001). But knowing how an average behaves gives little information about the extremes, both high and low, of the distributions of efficiency scores, which might have important economic implications. In addition, correlation coefficients do not encode information on exactly where firms lie according to the different models considered of what banks produce. The paper is set out as follows. After this introductory section, Section 2 briefly discusses nontraditional banking activities, provides a rationale for their inclusion as part of bank output, and outlines the most important deregulatory initiatives to occur in the Spanish banking system. Section 3 presents the methodology to measure cost efficiency for the Spanish banking industry, as well as the choice of inputs and outputs, and the resulting efficiency scores. The distributions of these scores are compared in Section 4 by assessing whether firms’ positions relative to the mean vary according to either of the output definitions. This is carried out in two ways: by means of transition probability matrices and stochastic kernels. Section 5 outlines the conclusions.

2. Deregulation in the Spanish banking industry and the importance of nontraditional activities Conventional wisdom considers the financing of loans for borrowers with deposits from depositors, or savers, as traditional banking activities (Gorton & Rosen, 1995). Conversely, nontraditional activities would consist of all other fee-generating banking activities. Hence, the essential distinction consists of the type of income generated by banking activities, i.e., interest or noninterest (fee) income. Accordingly, all products and services offered by banks would be classified into these two broad categories. However, although most items belong to a single category—loans are undoubtedly traditional output, whereas brokerage or underwriting activities should be treated as nontraditional—others, such as letters of credit and lines of credit, are more ambiguous, as they may generate both types of income. While examples of traditional output are clear, some insights on what we might consider nontraditional activities should be provided. These would include trust related services, the securities business (involving brokerage services and underwriting), securitization, or the participation in the market for pension or mutual funds. Firms’ engagement in such activities may differ greatly, depending on factors as important as size (Rogers & Sinkey, 1999). But, considering all firms in each banking industry, a growing importance can be seen in many industrialized countries, although their significance may vary according to specific circumstances. In our particular setting, in which deregulatory initiatives were being implemented, the significance of this type of activities could be magnified. Indeed, our sample period (1986–1997) was quite turbulent in terms of deregulation. As stated by Danthine, Giavazzi, Vives, and von Thadden (1999), post-World War II banking regulation in Europe can be divided into three periods. Before the late 1970s, regulation was tight, although it varied markedly from one country to another, and was quite uncoordinated.

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The 1980s brought a period of deregulation, at a national level as well as through E.U.-wide measures. In the case of Spain, deregulation was essentially evident in the late 1980s, immediately following its incorporation into the former European Economic Community, when some of the most important deregulatory initiatives, such as the removal of restrictions on the geographic expansion of savings banks, took place. Finally, from the late 1980s and into the 1990s the trend was to harmonize bank regulation—and partly, as suggested by Danthine et al. (1999), to re-regulate the industry. As noted in Section 1, the Spanish banking system was one of the most tightly regulated banking systems in Europe in terms of interest rates, entry, branching, and investment and reserve requirements.1 Limits on entry were relaxed and, after 1992, any bank authorized by its home member state was able to provide a wide range of banking services in any E.U. country— the so-called “single banking license”. Limits on branching were completely removed in 1989; since then, in much the same way as in the US following the Riegle-Neal Act of 1994, all banks (both commercial and savings banks) may operate outside their home region. This reform significantly affected savings banks, leading to an engagement in territorial expansion processes by all but the smallest ones (Fuentelsaz & Gómez, 2001; Fuentelsaz, Gómez, & Polo, 2002). Interest rates were also affected by liberalization, which had ended by 1987, and by the “macro-accounts’ war” (guerra de las supercuentas) triggered off by one of the largest commercial banks. Certain legal requirements, such as those affecting reserves, were also reformed. By 1990, both commercial and savings banks were required to keep 19% of a subset of their liabilities as deposits in the Bank of Spain. By 1992, this level had declined to about 5% and today it is only 2%. Other requirements were investment requirements—completely removed by 1993—or capital requirements, which are settled in accordance with the Basle Agreement. Finally, savings banks were previously subjected to more severe restrictions than private commercial banks with regard to specialization issues; today, they can essentially carry out the same operations. Recently, Kumbhakar et al. (2001) provided excellent insights into the deregulation initiatives experienced by Spanish banking firms in general and savings banks in particular. This differing deregulation has resulted in the greater success of savings banks in gaining market share at the expense of the private commercial banks (Kumbhakar et al., 2001). The shift has occurred mainly since the early 1990s, not only because deregulation initiatives have virtually ceased since then, but also because the consolidation process among savings banks was virtually over at that point. Indeed, the number of savings banks decreased from 77 in 1986 to 53 in 1992, and to 50 in 1997. This decrease, along with the timing of deregulation, provides a rationale for splitting the sample period into two distinct subperiods, namely, 1986–1991 and 1992–1997, which hereafter will be referred to as the deregulatory and post-deregulatory periods, respectively. Furthermore, the consolidation process in the Spanish banking sector has been quite singular, as commercial banks cannot acquire savings banks due to their particular type of ownership (they are a mix between publicly- and privately owned firms), but the opposite is possible. Consequently, savings banks may expand geographically either through new branch openings or by consolidating with already established firms, regardless of their type of ownership. In contrast, commercial banks can only acquire other commercial banks and, consequently, their ability to expand territorially is obviously hindered. This contrasts sharply with what has happened in Europe (if we consider the European banking industries altogether), where differences in language, culture, currency (until recently), regulatory/supervisory structures, and

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Fig. 1. Noninterest income as a percentage of total bank income (1986–1997).

explicit or implicit rules against foreign competitors “have offset most of any potential efficiency gains from cross-border consolidation” (Berger, DeYoung, & Udell, 2001). Banks’ responses to the changing financial systems are reflected not only in their balance sheets but also in their off-balance sheet activities, which increase as banks diversify their product range in order to maintain their degree of competitiveness and to increase their customer base and their fee income. This diversification includes items related to traditional types of business (loan commitments, guarantees, etc.) and derivative activities, and has been growing at remarkably high rates in many E.U. countries. However, these reactions are also reflected in the development of noninterest income, which more closely reflects nontraditional output. In many E.U. countries, the competition from non-bank financial institutions, and also among banking institutions themselves, together with the pressure on intermediation margins—coming from different sources—has led banks to offset the decrease in their interest income by shifting to other sources of income such as fees and commissions (European Central Bank, 2000). Fig. 1 presents the evolution of noninterest income as a percentage of total bank income for Spanish commercial and savings banks—accounting roughly for 95% of total industry assets—over the 1986–1997 period. On aggregate, real noninterest (fee) income2 rose from 5.52% to 10.86% of total bank income. Results vary depending on the type of firms considered, as the importance of these activities is always higher for commercial banks. On balance, these data reveal a substantial increase in the level of nontraditional output to the detriment of more traditional output, for both commercial banks and, more particularly, savings banks. However, as noted earlier, such a phenomenon might not be constant across all banks. This point will be vigorously debated throughout the paper. Despite the growing relevance of banks’ nontraditional activities, reflected in many banking industries in other countries, the extant literature tends to consider these activities separately, focusing only on how they affect the level of risk at an individual level (Avery & Berger, 1991; Boot & Thakor, 1991; Hassan, 1992, 1993; Hassan & Sackley, 1994; Hassan, Karels, & Peterson, 1994). Some recent studies (Allen & Santomero, 1998, 2001) have analyzed the importance of nontraditional activities by considering their relevance for traditional theories of intermediation (Santomero & Trester, 1998). The literature on bank efficiency and productivity

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has also largely omitted nontraditional activities, as reported by Rogers (1998). This becomes more intriguing if we consider the important debate arising out of the definition of bank output when measuring efficiency, which has largely ignored nontraditional output. 3. Nonparametric estimates of cost efficiency: restricted versus unrestricted models 3.1. Methodology We have chosen the nonparametric data envelopment analysis (DEA) technique to measure cost efficiency (Charnes, Cooper, & Rhodes, 1978), because of its ability to envelope data reasonably closely, and despite its inability to disentangle inefficiency from random error. Parametric methods do this but, in turn, they must impose a functional form on the distribution of inefficiency which, in principle, involves less flexibility. In fact, no one methodology predominates, as reported by several research studies.3 A separate analysis is performed for each year of the data as this allows the cost frontier to change annually with advances in technology. But the number of annual observations is too small to use a flexible parametric approach. Thus, we use a DEA analysis, which allows us to construct a reasonably flexible cost frontier with fewer observations, and does not impose any parametric shape on the frontier. We are aware that DEA techniques are incapable of disentangling random error from inefficiency. However, this should not cause serious bias in our study because, as will be shown later in the paper, we normalize the annual efficiency scores to the average score, and most of our analysis focuses on average results over time. This technique estimates efficiency by solving the following program: Minλ,x ∗s s.t.

ωs x ∗s y s ≤ Y λs , x ∗s ≥ Xλs , − → 1 λs = 1 λs ≥ 0,

(1)

where Y = [y 1 , . . . , y S ], X = [x 1 , . . . , x S ], with x s and y s denoting (n × 1) and (m × 1) vectors of (observed) inputs and outputs, respectively. The (n × 1) vector of input prices is denoted by ωs , and x ∗s , which is calculated by the linear program, is the cost-minimizing vector of input quantities for the s-firm. Finally, λ = [λ1 , . . . , λS ] is an (S × 1) vector of intensity variables which serves to form a piecewise linear estimate of the production set boundary. Computing the individual cost efficiency scores requires program (1) to be solved for each s-firm and year in our sample with common frontiers for all banking firms taken into consideration. The solution is given by the x ∗s cost minimizing vector, given the price vector ωs and outputs vector y s . Accordingly, the economic (cost) efficiency scores are given by ESs = ωs x ∗s /ωs x s . Previous studies analyzing the importance of nontraditional activities when measuring bank efficiency have considered not only cost but also profit and/or revenue efficiency (Rogers, 1998). The rationale for which profit/revenue efficiency may be important for this type of operations is that banks producing high-quality output will be able to charge a high price and earn high

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revenues; but producing high-quality output requires larger amounts of expensive inputs, so these banks will have low cost efficiency scores even though they may be very profitable. This effect is likely to be strongest for banks with nontraditional outputs for which highly skilled labor is necessary. We recognize the relevance of also analyzing revenue and/or profit efficiency, but we consider that cost efficiency will also be strongly affected by the misspecification of outputs. If some banks are producing high quantities of nontraditional output, using highly skilled labor which raises their costs, the omission of nontraditional output will clearly yield low cost efficiency scores. However, if an additional output accounting for nontraditional activities is included in the analysis, their efficiency scores will increase, reducing the magnitude of the bias. Despite the relevance of both revenue and profit efficiency, we confine our analysis to cost efficiency for several reasons. First, in the case of cost efficiency, measuring efficiency by means of DEA requires prices for each input. The same would hold for the revenue side if the objective were either profit or revenue efficiency, i.e., we need output prices, for which detailed information on the income generated by each output is required, and which unfortunately, is unavailable. In addition, the variable used to proxy nontraditional output (fee-generated income) is a revenue in itself, as we do not know exactly which quantities generate it. Moreover, studies applying nonparametric techniques to measure either profit or revenue efficiency are extremely scarce (Devaney & Weber, 2002). We also coincide with Bauer et al. (1998), who state that cost minimization is a more commonly specified and accepted efficiency concept in the literature than profit maximization and, in addition, would strain the limits of space. In the light of the above, we could switch to econometric techniques to measure efficiency, as in this case we do not necessarily need output prices. We do not do this for different reasons. First, as noted earlier, the annual number of observations is too small for, for instance, a flexible parametric approach to be used. Second, a previous study by Fernández de Guevara (2001) already considered this type of technique. He found that accounting for nontraditional output led to misleading results, as profit efficiency was understated. However, cost efficiency gains were not only virtually negligible but also insignificant. Unfortunately, conclusions were drawn only from mean values. We argue that means may hide relevant information, a point which will be forcefully made throughout the paper. 3.2. Data and specification of inputs and outputs Two bank output definitions are considered, one accounting for traditional output only, whereas the other treats nontraditional activities as an additional output. For the sake of simplicity, and to keep to the same notation as Rogers (1998), the former will be labeled as the restricted model, and the latter, the unrestricted model.4 Our choice of the first model or intermediation approach followed the ideas of Sealey and Lindley (1977), who consider the banking firm primarily as an intermediary between savers (depositors) and borrowers. As some authors recognize, this approach is more relevant for financial institutions, as it is inclusive of interest expenses which often account for one-half to two-thirds of total costs (Berger & Humphrey, 1997; Sathye, 2001). Hence, according to the restricted model, banking firms are assumed to produce two outputs, namely, loans (all forms of loans to customers, y1 ) and other earning assets (securities and

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Table 1 Definition of the relevant variables (1997) Variable

Variable name

Outputs Restricted model y1 Loansa y2 Other earning assestsa y3 Nontraditional outputa Inputs (common to both models) Labor x1 x2 Fundinga x3

Physical capitala

Definition

Mean

SD

All forms of loans to customers Securities and loans to financial institutions Fee-generated income

325772 327350 5542

859701 660983 12743

Number of employees Savings deposits, other deposits, and interbank deposits Fixed assets

1787 632848

3518 1463146

17412

37500

5.058 0.043 0.471

1.006 0.011 0.254

Inputs’ prices (common to both models) ω1 Price of labor Labor expenses/number ofemployees ω2 Price of funds Financial costs/x2 ω3 Price of physical capital (Amortizations + other non-interest expenses)/x3 a

In millions of 1990 pesetas.

loans to financial institutions, y2 ). The unrestricted model includes a further output, namely, fee-generated income (y3 ), which is considered as a proxy of the nontraditional activities performed by banks. The definition of inputs is common to both models, as banks are assumed to use labor (number of employees, x1 ), loanable funds (savings deposits, other deposits, and interbank deposits, x2 ), and physical capital (furniture, fixtures, and materials, x3 ) to perform their activities. Prices of inputs are obtained by dividing the expenses of each input by their respective quantities. This selection of inputs and outputs follows the studies by Berger and Mester (1997) or, more specifically, Mester (1997). All variables are described in Table 1, which also reports descriptive statistics. Data are provided by the Spanish commercial banks association (AEB, Asociación Española de Banca) and the Spanish savings banks association (CECA, Confederación Española de Cajas de Ahorro). Our sample consistently covered around 90% of the total assets of commercial and savings banks. Although the period under study witnessed an enormous spurt in the number of mergers and acquisitions (M&As), we finally decided to consider an unbalanced panel data. Hence, the number of savings banks decreases from 77 in 1986 to 50 in 1997. The number of commercial banks is more variable and, although large banks have recently undergone an important consolidation process, many banks are still entering and exiting the industry. In addition, most of these commercial banks were extremely small and, besides this, their data were either unavailable or unreliable and, consequently, they were dropped from the sample. Along with the usual summary statistics reported in Table 1, we have added an additional table (Table 2) displaying the inputs and outputs as a percentage of assets, for all commercial banks, savings banks, and all banking firms, and also for both the subperiods 1986–1991 and 1992–1997. According to these figures, nontraditional activities, as measured by y3 , seem to be far more important for commercial banks, and this importance grows over time. We have tested these propositions by means of a parametric test (t-test) and a nonparametric test

Commercial banks 1986–1991

Savings banks 1992–1997

Total

1986–1991

1992–1997

1986–1991

1992–1997

Unweighted mean

Weighted mean

Unweighted mean

Weighted mean

Unweighted mean

Weighted mean

Unweighted mean

Weighted mean

Unweighted mean

Weighted mean

Unweighted mean

Weighted mean

Outputs y1 /assets y2 /assets y3 /assets

44.94 42.64 0.85

42.83 45.95 0.87

46.91 45.62 1.04

51.94 41.19 0.88

37.10 45.29 0.37

40.60 42.24 0.38

40.34 52.08 0.53

43.15 49.15 0.57

41.23 43.85 0.63

42.04 44.65 0.69

44.30 48.18 0.84

48.54 44.27 0.76

Inputs x1 a /assets x2 /assets x3 /assets

0.42 85.13 2.45

0.42 88.05 2.18

0.37 83.74 3.08

0.29 89.90 2.34

0.44 88.99 3.43

0.35 88.08 3.78

0.36 90.45 3.39

0.29 90.25 3.60

0.43 86.91 2.91

0.39 88.06 2.75

0.36 86.39 3.20

0.29 90.03 2.83

a

Labor expenses.

E. Tortosa-Ausina / Journal of Economics and Business 55 (2003) 371–395

Table 2 Inputs and outputs as percentages of total assets

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Table 3 Statistical tests of equality of noninterest income over assetsa t-testb

Mann–Whitney testb

Commercial banks versus savings banks 1986–1991 1992–1997 1986–1997

15.353∗∗ 12.804∗∗ 19.862∗∗

−17.081∗∗ −11.948∗∗ −20.180∗∗

1986–1991 versus 1992–1997 Commercial banks Savings banks

−8.841∗∗ −1.255

−10.321∗∗ −2.562∗

−7.029∗∗

−9.398∗∗

Total

y3 /assets. The null hypotheses are that the means, and the medians, respectively, of the noninterest income over total assets are equal. ∗ Coefficients significant at the 0.01 level. ∗∗ Coefficients significant at the 0.05 level. a

b

(Mann–Whitney). Table 3 contains the test statistics for these tests. They confirm the propositions outlined earlier. On one hand, the ratio y3 /assets differs substantially between commercial banks and savings banks, as shown by the upper panel of Table 3. On the other hand, the lower panel indicates that these nontraditional activities are increasingly important over time for commercial banks, but in the case of savings banks the significance does not reach the 1% level. 3.3. Efficiency results The estimates of mean efficiency (weighted and unweighted) as well as standard deviation are provided in Table 4 for both the restricted and unrestricted models.5 Differences observed between mean efficiency show that there appear to be two distinct subperiods in which the importance of nontraditional activities is different. Over the 1986–1991 subperiod, (slight) differences exist, especially for commercial banks, whereas differences are larger over the 1992–1997 subperiod. For an analysis of how cost efficiency differences have evolved over time, see Tortosa-Ausina (2002). Since the deregulatory changes are scattered primarily over the second half of the 1980s, we proceed in the same way as Kumbhakar et al. (2001), by considering 1986–1991 as the deregulatory subperiod and 1992–1997 as the post-deregulatory subperiod. Accordingly, the differences observed in Table 4 were tested for statistical significance, with consideration of not only the restricted versus unrestricted models, or savings banks versus commercial banks versus all banking firms, but also comparison of results for 1986–1991 versus 1992–1997. To do this, following Elyasiani and Mehdian (1992, 1995), we carried out both parametric (t-tests) and nonparametric tests (Wilcoxon and Mann–Whitney).6 Results are set out in Tables 5–7, which report different, although related, kinds of information. They broadly confirm that observed differences in efficiencies are indeed significant at the 1% level. This result holds when comparing efficiencies yielded by the restricted and

Table 4 Efficiency evolution, banking firms (1986–1997)a 1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

Unweighted mean Weighted mean Standard deviation

75.70 85.97 15.32

74.36 86.23 16.19

73.78 89.34 16.41

79.33 91.53 14.53

80.79 94.22 15.47

75.69 92.86 17.43

72.21 91.79 18.76

71.65 93.59 21.31

68.43 91.51 20.75

72.46 91.90 18.20

74.39 92.55 17.45

70.33 91.38 18.79

Savings banks

Unweighted mean Weighted mean Standard deviation

58.68 67.19 10.66

57.96 68.97 11.01

57.62 71.09 10.56

67.84 78.59 9.38

71.80 85.53 10.40

71.75 83.79 9.52

73.07 85.89 11.85

78.97 89.18 12.04

73.29 85.52 12.83

75.99 88.24 13.65

78.25 88.68 12.06

71.89 85.48 13.05

Total

Unweighted mean Weighted mean Standard deviation

67.61 80.10 15.77

66.27 80.80 16.09

65.80 83.17 16.00

73.55 87.07 13.47

76.53 91.21 14.01

74.15 89.74 14.93

72.54 89.72 16.41

74.39 91.93 18.68

70.34 89.28 18.16

73.84 90.55 16.59

75.97 91.10 15.53

70.98 89.09 16.59

Unweighted mean Weighted mean Standard deviation

81.40 90.69 13.54

77.24 90.12 16.22

75.26 90.81 16.07

83.09 93.95 14.17

83.13 94.95 14.03

82.52 95.05 16.20

80.61 93.53 15.54

80.90 95.74 17.97

74.41 94.39 19.54

79.30 94.04 16.48

82.92 95.02 15.76

82.82 95.04 16.27

Savings banks

Unweighted mean Weighted mean Standard deviation

59.04 67.40 10.46

58.31 69.17 10.88

57.80 71.25 10.48

68.03 78.63 9.28

72.37 85.78 10.93

72.16 84.00 9.12

74.02 85.97 10.44

79.84 89.32 10.70

74.35 85.76 11.75

77.43 88.67 12.56

79.19 89.25 11.21

73.36 85.92 12.34

Total

Unweighted mean Weighted mean Standard deviation

70.77 83.41 16.52

67.89 83.54 16.75

66.64 84.20 16.14

75.51 88.67 14.11

78.03 91.78 13.71

78.46 91.24 14.73

78.08 90.88 14.13

80.50 93.33 15.60

74.38 91.19 16.86

78.56 92.07 15.04

81.39 92.86 14.15

78.88 91.49 15.43

Restricted versus unrestrictedb Commercial banks Unweighted mean Weighted mean

5.70 4.72

2.88 3.89

1.48 1.47

3.76 2.42

2.34 0.74

6.83 2.18

8.40 1.75

9.26 2.16

5.97 2.89

6.84 2.14

8.53 2.47

12.48 3.66

Unrestricted Commercial banks

Savings banks

Unweighted mean Weighted mean

0.36 0.21

0.35 0.20

0.18 0.16

0.19 0.04

0.57 0.25

0.41 0.20

0.95 0.09

0.87 0.14

1.06 0.24

1.45 0.43

0.94 0.58

1.47 0.44

Total

Unweighted mean Weighted mean

3.16 3.31

1.63 2.73

0.84 1.03

1.97 1.60

1.50 0.57

4.32 1.50

5.54 1.16

6.11 1.40

4.04 1.90

4.72 1.51

5.42 1.76

7.90 2.41

No. of firms

Commercial banks Savings banks Total

a b

85 77

79 77

79 77

75 76

71 64

87 56

85 53

85 51

79 51

77 50

72 50

70 50

162

156

156

151

135

143

138

136

130

127

122

120

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Restricted Commercial banks

Efficiencies have been estimated for each year separately, and common frontiers for commercial banks and savings banks are specified. Differences have been computed as unrestricted efficiency scores minus restricted efficiency scores.

381

382

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Table 5 Statistical tests of equality of efficiency measures, restricted versus unrestricted models 1986–1991 t-test

a

1992–1997 Wilcoxon test

a

t-test

a

1986–1997 Wilcoxon test

a

t-testa

Wilcoxon testa

Commercial banks Savings banks

−10.077∗∗ −3.339∗∗

−14.505∗∗ −8.333∗∗

−14.640∗∗ −8.634∗∗

−16.237∗∗ −9.063∗∗

−17.389∗∗ −8.184∗∗

−21.763∗∗ −12.295∗∗

Total

−10.175∗∗

−16.715∗∗

−14.778∗∗

−18.584∗∗

−17.617∗∗

−24.988∗∗

a The null hypotheses are that the means, and the medians, respectively, of the efficiency measures for each type of firm aggregate are equal. ∗∗ Coefficients significant at the 0.01 level.

unrestricted models (Table 5). However, when comparing commercial banks versus savings banks (Table 6), test statistics decrease substantially in the post-deregulatory period. In the case of the restricted model, we reject the hypothesis of equal medians only at the 5% significance level in 1992–1997 according to the nonparametric Mann–Whitney test. This suggests that efficiency differences between commercial banks and savings banks shrink over time, an outcome consistent with the fact that both types of firms currently face the same regulatory environment, despite still maintaining the differences. Finally, when comparing the deregulatory versus post-deregulatory periods (1986–1991 versus 1992–1997, Table 7), differences in means and medians are not significant for commercial banks, i.e., their efficiency was not enhanced after deregulation. Unfortunately, this assertion is partly thwarted by the fact that the efficiency measurement is conducted for each year separately (see Grifell-Tatje & Lovell, 1996).7 Table 4 also provides information regarding the role of size, through weighted mean values. This may be important, as in Spain, in contrast to the US, very few large firms dominate the industry. The figures suggest that large firms are more efficient than their smaller counterparts. Statistical tests support this hypothesis—although they have been left out in order to save space. This proposition is robust over time, across types of firms, and across production models. However, some interesting facts emerge here. According to the restricted model, discrepancies are quite large when weighted and unweighted values are compared, and widen over time. This holds primarily for commercial banks. In the case of savings banks, this tendency is Table 6 Statistical tests of equality of efficiency measures, commercial banks versus savings banks

Restricted model Unrestricted model

1986–1991

1992–1997

t-test

Mann–Whitney testa

t-test

−12.270∗∗ −15.481∗∗

−3.171∗∗ 3.603∗∗

a

13.673∗∗ 18.026∗∗

a

1986–1997 Mann–Whitney testa

t-testa

Mann–Whitney testa

−2.490∗ −4.607∗∗

7.237∗∗ 15.369∗∗

−7.264∗∗ −14.492∗∗

a The null hypotheses are that the means, and the medians, respectively, of the efficiency measures for each type of firm aggregate are equal. These tests assume independent samples. ∗ Coefficients significant at the 0.01 level. ∗∗ Coefficients significant at the 0.05 level.

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Table 7 Statistical tests of equality of efficiency measures, 1986–1991 versus 1992–1997 Restricted model t-test Commercial banks Savings banks Total

a

Unrestricted model Mann–Whitney test

a

t-testa

Mann–Whitney testa

4.253∗∗ −12.514∗∗

−3.602∗∗ −11.742∗∗

0.316 −13.880∗∗

−0.434 −12.843∗∗

−3.227∗∗

−4.426∗∗

−7.741∗∗

−8.382∗∗

a The null hypotheses are that the means, and the medians, respectively, of the efficiency measures for each type of firm aggregate are equal. These tests assume independent samples. ∗∗ Coefficient significant at the 0.01 level.

less accentuated. In contrast, for the unrestricted model, differences shrink substantially for commercial banks, suggesting that the omission of nontraditional activities might be crucial for most smaller banks, as the unweighted mean is pushed upwards. Hence, several features emerge. Among them, we observe that the importance of nontraditional activities varies over time, and it appears to be increasingly important. This importance also varies across firms, as differences are much lower if we focus solely on savings banks. Consequently, although common regulation provides a rationale for analyzing commercial banks and savings banks altogether, differences still subsist. Some of them were presented in Section 2, others have emerged here. Thus, despite the fact some commercial banks may be focusing on activities in which savings banks have traditionally specialized (retail banking), and vice versa, we will carry out analyses for commercial banks, savings banks, and all banking firms throughout the paper. These results partly coincide with those of the only study which covers nontraditional activities in the Spanish banking industry (Fernández de Guevara, 2001), in which significant— although very slight—differences in mean cost efficiency are found when nontraditional activities are included. However, the two studies are not directly comparable, as they differ not only in the technique used to measure efficiency (this study uses parametric as opposed to our nonparametric methods) but also in the way nontraditional activities are accounted for. In Fernández de Guevara (2001), nontraditional activities are measured by the inclusion of off-balance sheet activities.8 It would be also misleading to confine the analysis to location tests that compare mean (or median) efficiency for both models, and to the estimation of rank correlation coefficients. Regarding the former, distributions may be far more complex than can be summarized in a single statistic. Indeed, some of the tests undertaken to compare means require assumptions for the residuals to be met. In addition, if we estimate rank correlation coefficients, we have (once more) a single statistic which does not inform on the exact firms’ relative positions. Our approach of comparing results yielded by each production model is rooted in the comparison of the entire distributions of efficiency scores—in some sense, our analysis is more global than local, as we do not confine the analysis to the comparison of centrality measures. The analysis is performed by estimating nonparametrically stochastic kernels to assess better firms’ relative positions according to both models.

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We should mention that there is a potential source of bias in the results, namely, the technique used to measure efficiency. As Alam (2001) indicates, a well-known DEA phenomenon is that “as the number of variables increases, average efficiency rises because each firm has a greater and greater opportunity to be efficient in some dimension of production. Thus, when comparing average efficiency across studies, attention must be paid to the number of variables”. Hence, by including an additional variable to account for nontraditional activities we may expect efficiency scores to increase. As will be noted later, we deal with this issue by normalizing the annual efficiency scores to the average score.9 We therefore assess each firm’s position relative to the mean.

4. Changes in firms’ relative positions We first consider using rank correlation coefficients to compare distributions of efficiencies. In fact, the information on where firms lie according to the different models is not available through the analysis of summary statistics. We have computed ρrank between them for each data set.10 The correlation between the rankings ranges from 0.774 for savings banks in the 1992–1997 subperiod to 0.955 for commercial banks in the 1986–1991 subperiod. Hence, the unrestricted model shows that firms’ relative positions vary, and this pattern is more marked in the 1992–1997 subperiod, regardless of the type of firm aggregate considered. This was touched on by Rogers (1998), who stated that rank correlation coefficients significantly different from one implied that there was some relative movement in the rankings between the two models across the samples. However, rank correlation coefficients are also summary statistics which do not capture some important features. They do not allow us to appreciate which firms are changing with regard to their relative positions. Although mean efficiency increases for the unrestricted model, this might occur because only the efficiency of some firms specializing in nontraditional activities is enhanced. Alternatively, there could also be some very efficient firms with relatively poorer performances when accounting for these operations. Put another way, we do not know the economic facts underlying a correlation coefficient, i.e., we do not know if firms improve or worsen and, more importantly, as noted earlier, which firms improve, or worsen, and the magnitude of such variations. For instance, the case could arise of a cluster of firms becoming more efficient according to our unrestricted model. This would clearly affect correlation coefficients. However, if what occurs is that all firms change in their relative positions, correlation coefficients would be also affected, and perhaps in the same magnitude. But the economic meaning for these two facts is not the same. Thus, this type of summary statistics reveal significant, but definitely incomplete, information. Questions such as the aforementioned are easily dealt with by using transition probability matrices, which encode information on changes in firms’ relative positions according to both models. In other words, in our study, the Markov model tracks changes in the relative efficiency of banking organizations when moving from one relative efficiency class to another according to the different production models (Robertson, 2001). Hence, matrices do not show transitions over time—the usual purpose of this instrument. However, their computation is analogous. Specifically, we consider two grids each made up of five states which define a set of states, or classes, E = {e1 , e2 , . . . , e5 }, which array, in increasing order, relative efficiencies according

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Table 8 Transition probability matrices across restricted and unrestricted models, commercial banks Upper endpoint restricted

(a) 1986–1991 0.792 0.879 0.986 1.216 1.520

(b) 1992–1997 0.779 0.958 1.069 1.207 1.422

Upper endpoint unrestricted

Number

0.784

0.878

1.003

1.230

1.502

0.89 0.15 0.00 0.00 0.00

0.07 0.74 0.16 0.00 0.00

0.01 0.08 0.76 0.10 0.00

0.01 0.03 0.07 0.81 0.09

0.02 0.00 0.01 0.08 0.91

0.836

0.971

1.050

1.184

1.344

0.73 0.14 0.00 0.00 0.00

0.11 0.65 0.33 0.00 0.00

0.06 0.14 0.55 0.41 0.00

0.01 0.06 0.07 0.54 0.24

0.09 0.01 0.04 0.05 0.76

95 95 94 96 96

94 93 94 93 94

to the restricted model and unrestricted model, respectively. Each state accounts for 20% of probability. Consequently, the limits, or boundaries, between states vary. Each cell in the matrix is computed by averaging transitions observed between both models, as follows: pij =

Nij,t Ni,t

(2)

where Nij,t is the number of firms crossing from state ei to state ej in the subperiod considered (t), for i = 1, . . . , 5, j = 1, . . . , 5, and Ni,t is the total number of firms initially affiliated to ei , in the subperiod concerned. Table 8a–10b display transitions across both models for the deregulatory and post-deregulatory subperiods, and for commercial banks, savings banks, and all banking firms. If these matrices were the identity matrix, then the outcome would be persistence, and firms’ relative positions would be the same according to either model. What can be appreciated is that persistence is greater in 1986–1991, as diagonal entries average to 0.82, 0.74, and 0.80 for commercial, savings, and all banking firms, respectively, suggesting that, on average, firms remained in their relative positions with that probability. However, persistence decreases substantially over time. During 1992–1997, diagonal entries average to 0.65, 0.55 and 0.62 for each type of firm aggregate. The precise interpretation of each entry in each matrix is as follows: they show the probability of movement—or of persistence, if they are diagonal entries—from one state, or class, to another. For instance, the top left hand entry in Table 8a shows that the 20% of commercial banks with an efficiency of below 79.2% of the industry average, according to the restricted model, remained in that range with a probability of 0.89 according to the unrestricted model. The following entry in that row would show the probability (0.07) of firms initially in state 1 that transited to state 2, with efficiencies in the range between 79.2% and 87.9% of the industry average.

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Table 9 Transition probability matrices across restricted and unrestricted models, savings banks Upper endpoint restricted

(a) 1986–1991 0.848 0.935 1.025 1.156 1.361

(b) 1992–1997 0.813 0.968 1.056 1.194 1.408

Upper endpoint unrestricted

Number

0.840

0.925

1.028

1.172

1.325

0.79 0.22 0.00 0.00 0.00

0.07 0.72 0.25 0.00 0.00

0.03 0.02 0.65 0.24 0.00

0.03 0.02 0.07 0.71 0.15

0.07 0.01 0.04 0.06 0.85

0.839

0.961

1.040

1.213

1.272

0.67 0.28 0.00 0.00 0.00

0.18 0.52 0.44 0.03 0.00

0.03 0.13 0.37 0.41 0.00

0.03 0.00 0.11 0.46 0.28

0.08 0.07 0.08 0.10 0.72

86 85 85 85 86

61 60 62 61 61

This increasing mobility is higher in the case of savings banks, as shown by Table 9a and b. As noted earlier, the diagonal entries for savings banks average to 0.55. This poor persistence is primarily generated by central classes (e2 , e3 , e4 ), for which persistence decreases to 0.45, due to downward movements. For instance, entry a33 in Table 9b indicates that savings banks with relative inefficiencies in the (0.961, 1.040) range remained there only with probability 0.37, whereas 0.44 moved downward to another class of lower relative efficiency (e2 ). This pattern Table 10 Transition probability matrices across restricted and unrestricted models, total Upper endpoint restricted

(a) 1986–1991 0.818 0.910 1.013 1.185 1.520

(b) 1992–1997 0.799 0.965 1.065 1.200 1.422

Upper endpoint unrestricted

Number

0.813

0.905

1.015

1.205

1.502

0.86 0.16 0.00 0.00 0.00

0.04 0.76 0.20 0.00 0.00

0.04 0.06 0.70 0.17 0.00

0.02 0.02 0.06 0.77 0.11

0.04 0.01 0.03 0.06 0.89

0.836

0.964

1.048

1.194

1.344

0.70 0.21 0.00 0.00 0.00

0.14 0.58 0.36 0.01 0.00

0.06 0.13 0.48 0.34 0.00

0.02 0.05 0.10 0.57 0.25

0.09 0.03 0.06 0.07 0.75

181 180 181 180 181

155 154 155 154 155

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also exists for commercial banks, although to a lesser extent. In their case, entry a33 in Table 9b shows higher persistence (0.55), and movements to state e2 are also substantial, amounting to 0.33. However, the important point is what is taking place in the case of savings banks. For this type of firm, the discrepancies between efficiency scores yielded by each model is quite low, reaching a peak of only 1.47% in 1997, whereas in the case of commercial banks such a discrepancy is far higher, reaching a peak of 12.48% (see Table 4). However, as revealed by Table 9a and b, savings banks switch much more in their relative positions, especially in the post-deregulatory period. Therefore, the omission of this type of activity seems to affect each type of institution aggregate differently. In the case of commercial banks, a generalized efficiency enhancement is appreciated, via an increase in mean efficiency. This was partly predictable, as they produce more y3 than savings banks. In the case of savings banks, the relevance is appreciated via distribution mobility: many firms change in their relative positions. It therefore seems that during the deregulatory period, accounting for nontraditional activities does not greatly affect firms’ performance, at least when all firms are considered—banks persist in their relative positions; but during the post-deregulatory period, there are marked changes: some firms become more relatively efficient (i.e., compared to the mean), whereas others do not improve, or fail, in their rankings.11 These findings might suggest not only that nontraditional activities are increasingly important over time, but also that firms are specializing differently. Therefore, studies accounting for this type of activity should perhaps note that performance may be understated for many firms, but simultaneously some others might perform relatively more poorly when they are included, as they emphasize different lines of business more deeply. The relevance of these results could be thwarted because of the bias involved by the selection of the grids, or limits between classes. Furthermore, widths of the states vary, not only within each matrix, but also across them, i.e., over time. The way to overcome this bias or discretization is to consider the continuous counterpart of these transition probability matrices, known as stochastic kernels (Stokey & Lucas, 1989). These are mathematical operators that map one distribution into another. In our case, they describe transitions between the distributions of efficiencies yielded by each model. Thus, they quantify the effect on firms’ efficiency of considering an alternative model of bank production.12 Stochastic kernels are estimated by considering bivariate density functions, and dividing by the implied marginal. To estimate densities we also consider nonparametric methods, and smoothing is performed by means of kernel smoothing. For computational ease, we chose the Epanechnikov kernel. The controversial decision regards bandwidth selection. On this point, the subject is in its preliminary stage, much more so than the univariate case. We followed the solve-the-equation plug-in approach developed in Wand and Jones (1994), but we recognize that the literature on this matter is still growing at a fast rate. Figs. 2a–4b report stochastic kernels for the deregulatory and post-deregulatory periods. Results are presented separately for commercial banks, savings banks, and all banking firms, given the differing results presented earlier in the paper. Their interpretation is analogous to that corresponding to the transition probability matrices. Here persistence is suggested by probability overwhelmingly concentrated along the positive sloped diagonal. All Table 10a and b and Figs. 2a–4b are conceptually the same: all represent stochastic kernels. However, the latter refer to the continuous version, whereas the former represent their discrete counterparts.

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Fig. 2. Efficiency transitions across restricted and unrestricted models, commercial banks.

Fig. 3. Efficiency transitions across restricted and unrestricted models, savings banks.

Fig. 4. Efficiency transitions across restricted and unrestricted models, total.

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More specifically, what can be appreciated is that persistence is higher in the deregulatory period, whereas firms vary more markedly in their relative positions in 1992–1997, as probability mass tends to abandon the positive sloped diagonal. We also observe that most firms with efficiencies below 60% of the industry average for the restricted model are more efficient for the unrestricted model, although this tendency is less marked in the 1986–1991 period (as might have been expected). This pattern is verified by looking at the lower left hand side in each figure, and is more important in the case of savings banks. But the opposite also occurs: there are many firms with efficiencies of above 140% of the industry average that are less efficient (below 130% of the industry average) when accounting for nontraditional activities. Probability concentrated along the 45 degree line, occurring especially during the deregulatory period, for both commercial banks and savings banks, suggests that the output misspecification does not make much difference for most of the banks. But there is a clear node of banks off the 45 degree line. For these banks, the output misspecification substantially understates performance. In the case of savings banks, the node in this period is quite far from the 45 degree line, indicating the existence of a cluster of firms whose performance was obviously understated. Furthermore, this node appears to get larger in the 1992–1997 period, a fact consistent with the lower rank order correlations. The clear advantage of this type of nonparametric techniques is that they allow tendencies that are barely captured by traditional econometric techniques to be disclosed. Therefore, these graphical devices turn out to be of paramount importance, as they remove the bias involved by the grid selection of the transition probability matrices, while maintaining, and expanding, their contributions. We have also estimated OLS regressions in order to test the determinants of and the size of the misspecification bias involved by the omission of nontraditional activities.13 To do this, we consider two equations. The first one can be written as: MBi,t = α0 + β1 T + β2 CBi + ui,t

(3)

where MBi,t equals the unrestricted efficiency score minus the restricted efficiency score for bank i in year t (i.e., the magnitude of bias), T = 1, . . . , 12 is a time trend, CBi is a dummy variable equal to 1 for commercial banks and ui,t is the error term. The second equation is as follows: MBi,t = α0 + β1 T + β2 NONTRADi,t + ui,t

(4)

where NONTRADi,t equals the ratio of nontraditional output-to-assets for bank i in year t. If we consider the null hypothesis β1 > 0 and β2 > 0 in both equations, the magnitudes of the coefficients can be interpreted as the amount of bias due to the misspecification. Results are shown in Table 11. They clearly confirm the conclusions drawn from the graphical analysis. Although for both equations β1 and β2 coefficients are positive and significant, in the first case, the low value of β2 , at least compared with the high value of β2 in Eq. (4), suggests the ownership structure is not so important for nontraditional activities. The magnitude of the differences in Table 4 between the mean efficiency of commercial banks and savings banks seems to be caused by some firms, as suggested by Figs. 2–4. These firms constitute an upward bias for the average. Again, we note that the careful analysis of the entire distributions turns out to be of paramount importance. On the other hand, the β2 coefficient in Eq. (4) suggests that the exclusion of nontraditional activities does clearly understate efficiency. In this case, not

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Table 11 Regressions on the magnitude of bias due to misspecification Dependent variable MB Eq. (3) α0 β1 β2 F -statistic R2

Eq. (4) ∗∗

−0.018 (−3.938) 0.004∗∗ (7.174) 0.053∗∗ (13.102) 119.204 0.125

−0.041∗∗ (−13.102) 0.001∗ (1.962) 10.207∗∗ (44.952) 1077.154 0.563

t-statistics appear under coefficient estimates. ∗ Coefficients significant at the 0.01 level. ∗∗ Coefficients significant at the 0.05 level.

only is the coefficient higher but also the fitness of the regression improves dramatically. The conclusions drawn from Figs. 2–4 are also reinforced by the β1 coefficients in both regressions.

5. Conclusions This paper examines the importance of nontraditional activities in the analysis of bank cost efficiency using an approach based on the analysis of the entire distributions. Efficiency scores were estimated nonparametrically, and distributions were assessed by means of kernel smoothing, transition probability matrices, and stochastic kernels. The application is performed over a sample of Spanish financial institutions, both commercial and savings banks, for the 1986–1997 period. The application is relevant given this period’s turbulence in terms of changes re-shaping the industry, coming primarily from deregulation. Among them, we must highlight the considerable number of mergers and acquisitions, savings banks’ territorial expansion processes (which were forbidden in the regulatory period), or the drop of entry barriers for financial institutions from other European Union countries. The results partly corroborate those of Rogers (1998) and, in the case of the Spanish banking industry, Fernández de Guevara (2001), namely, average cost efficiency is enhanced when considering an alternative model which includes the nontraditional activities performed by banks. However, this result varies over time, and between the size (weighted mean) and type (commercial/savings banks) of firms. A more detailed picture is yielded by statistical tests (both Wilcoxon and Mann–Whitney) enabling different comparisons. Specifically, we find that excluding nontraditional activities biases efficiency estimates for all financial institutions, and that commercial banks and savings banks’ efficiencies differ greatly. However, over time savings banks have improved their efficiency more than did commercial banks. Yet the most interesting results come from the analysis of distributions. According to their bivariate nonparametric estimation, differences seem to be more important than those reported by summary statistics such as mean, or standard deviation. When assessing changes in firms’

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relative positions, by means of the estimation of transition probability matrices and stochastic kernels, we can observe remarkable features, as firms vary greatly in their positions relative to the mean. It is difficult for the rank correlation coefficient to completely capture this. These findings suggest that the inclusion of these types of activities is more important for the efficiency analysis than stated in previous studies. There are clusters of firms whose relative efficiency (relative to the mean) is substantially enhanced, while for others it increases to a lesser extent and, for many others, it worsens. Thus, it seems that an additional issue could be convoluted when analyzing these types of activities, namely, each firm’s intrinsic specialization.

Notes 1. We have left out most of the details regarding the deregulation initiatives undergone by the Spanish banking industry, as good reviews already exist in the literature, such as those by Lloyd-Williams, Molyneux, and Thornton (1994), Vives (1990, 1991a, 1991b), Grifell-Tatje and Lovell (1996, 1997) or, more recently, Kumbhakar, Lozano-Vivas, Lovell, and Hasan (2001) and Danthine et al. (1999). 2. Unfortunately, this item includes charges on deposits, which constitute traditional output but cannot be isolated because of data limitations. 3. Ferrier & Lovell (1990), or Resti (1997), compare both types of techniques in applications to banking, and come to the conclusion that differences are attributable to the intrinsic features of the models. Berger and Humphrey (1997) survey 130 studies of frontier efficiency of financial institutions, of which more than half employed nonparametric techniques. More recently, Bauer, Berger, Ferrier, and Humphrey (1998) have suggested that nonparametric methods do not meet their consistency conditions and accordingly should not be used. In contrast, McAllister and McManus (1993), Mitchell and Onvural (1996) and Wheelock and Wilson (2001) test and reject the translog specification of bank cost functions, and suggest semi-nonparametric or nonparametric methods for estimating bank costs. However, other studies (Berger & DeYoung, 1997) show that a Fourier-flexible approach dominates the translog for banks. 4. We should nevertheless bear in mind that he uses that notation for a specific reason, namely, the type of frontier to estimate efficiencies, which cannot be considered here. 5. In the case of the Spanish banking industry, efficiency research studies vary widely in their aims and results, as there are differences regarding the techniques used, the outputs and inputs definitions, the firms being analyzed (savings banks and/or commercial banks), the sample period uncovered, etc. With regard to the technique, some consider a nonparametric approach (Grifell-Tatje & Lovell, 1996, 1997; Kumbhakar et al., 2001; Prior & Salas, 1994), while others consider an econometric approach (Dietsch & Lozano-Vivas, 2000; Lozano-Vivas, 1997, 1998). In some cases the focus lies only on savings banks, essentially due to greater homogeneity of firms and data reliability, while others consider the whole industry by also analyzing commercial banks. In addition, the time span uncovered by research studies varies widely, which constitutes a further source of variation. Hence, it is difficult to compare results. This has been performed on an international scope level by Berger and Humphrey (1997).

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6. We also performed nonparametric tests because the distribution of efficiency scores does not always follow a normal distribution. We performed either Wilcoxon or Mann–Whitney tests depending on each specific circumstance. For instance, when comparing only restricted versus unrestricted models, we have paired samples for which the Wilcoxon signed rank test turns out to be the most appropriate tool. If paired samples are not available, the Mann–Whitney (or Mann–Whitney–Wilcoxon) test is more suitable (Gibbons, 1993). 7. During the period analyzed, macroeconomic conditions also varied remarkably. By the mid-1980s, Spain was just coming out of the crisis of the late 1970s and early 1980s, and there was an expansion until the early 1990s when, again, there was a recession which lasted until approximately the mid-1990s. Traditionally, economic crises in Spain have most affected commercial banks. Savings banks are more committed to retail banking and the public sector. However, it is also a feature of Spanish banking that, apart from the banking crisis of the 1980s, this type of financial institutions have been able to cope quite well with macroeconomic downturns. 8. We also considered a third model which included this type of activities instead of noninterest income. Our results were practically the same as those of Fernández de Guevara (2001), as differences between the restricted and this other version of the unrestricted model were virtually negligible. 9. According to this, if the normalized efficiency score of a certain firm had a value of 2, it would indicate that such a firm is twice as efficient as the industry average. On the other hand, if such a value were 0.5, it would indicate that its efficiency is half the industry average. We do this for several reasons. Among others, it permits us to offset the distorting effects of outlying observations, which may have quite an effect in DEA. 10. Specifically, correlations between restricted and unrestricted models for commercial banks, savings banks and banking firms were 0.955, 0.846, and 0.914 in the 1986–1991 period, and 0.808, 0.774 and 0.795 in the 1992–1997 period, respectively. In all cases they were different from one at the 0.01 significance level. 11. These conclusions are drawn from the comparison of both models in the deregulatory and post-deregulatory periods. We are not comparing what happens to each particular bank before and after deregulation. Here we find a hurdle which is difficult to overcome, namely, due to the process of consolidation we do not have the same firms every year. If this were the case, we would be able to conclude whether firms improve or not after deregulation. 12. We should mention that the importance of these operators is higher than what might have been expected. As stated by Quah (1997), two distributions Y (for our purposes, the restricted model) and Z (unrestricted model) can be identical even if the operator transforming Y into Z is not the identity. In the analysis of transition probability matrices, an identity operator is reflected in an identity matrix. Accordingly, if one attempts to ascertain how Y becomes Z (i.e., if we ask for Y → Z) we are demanding more than when asking if Y and Z have the same distributions by means of standard statistical tests (such as Kolmogorov–Smirnov). 13. Regressions in which efficiency scores are the dependent variable, the so-called twostage analysis, are usually performed via Tobit estimation techniques. In this case,

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the dependent variable is not exactly an efficiency score, and our purpose is merely illustrative. Acknowledgments I am grateful for the comments and suggestions made by Luis Orea, David Conesa and, most particularly, for the help of Manuel Illueca, which contributed greatly to the improvement of this paper. I also acknowledge the careful review and insightful comments of the three anonymous referees and the editor, Elyas Elyasiani, which improved the quality and readability of this paper. Financial support from Spanish Ministry of Science and Technology (SEC2002-03651) and Generalitat Valenciana (GV01–129) is also gratefully acknowledged. Finally, I would like to thank the Instituto Valenciano de Investigaciones Económicas (IVIE), for kindly providing data. References Alam, I. M. S. (2001). A nonparametric approach for assessing productivity dynamics of large US banks. Journal of Money, Credit, and Banking, 33(1), 121–139. Allen, F., & Santomero, A. M. (1998). The theory of financial intermediation. Journal of Banking and Finance, 21, 1461–1485. Allen, F., & Santomero, A. M. (2001). What do financial intermediaries do? Journal of Banking and Finance, 25, 271–294. Altunba¸s, Y., Evans, L., & Molyneux, P. (2001a). Bank ownership and efficiency. Journal of Money, Credit, and Banking, 33(4), 926–954. Altunba¸s, Y., Gardener, E. P. M., Molyneux, P., & Moore, B. (2001b). Efficiency in European banking. European Economic Review, 45, 1931–1955. Altunba¸s, Y., & Molyneux, P. (1996). Economies of scale and scope in European banking. Applied Financial Economics, 6, 367–375. Avery, R. B., & Berger, A. N. (1991). Loan commitments and bank risk exposure. Journal of Banking and Finance, 15, 173–192. Bauer, P. W., Berger, A. N., Ferrier, G. D., & Humphrey, D. B. (1998). Consistency conditions for regulatory analysis of financial institutions: A comparison of frontier efficiency methods. Journal of Economics and Business, 50(2), 85–114. Berger, A. N., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking and Finance, 21(6), 849–870. Berger, A. N., DeYoung, R., & Udell, G. F. (2001). Efficiency barriers to the consolidation of the European financial services industry. European Financial Management, 7(1), 117–130. Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98, 175–212. Berger, A. N., & Mester, L. J. (1997). Inside the black box: What explains differences in the efficiencies of financial institutions? Journal of Banking and Finance, 21, 895–947. Boot, A. W. A., & Thakor, A. V. (1991). Off-balance sheet liabilities, deposit insurance and capital regulation. Journal of Banking and Finance, 15, 825–846. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. Clark, J. A., & Siems, T. F. (2002). X-efficiency in banking: Looking beyond the balance sheet. Journal of Money, Credit, and Banking, 34(4), 987–1013.

394

E. Tortosa-Ausina / Journal of Economics and Business 55 (2003) 371–395

Danthine, J.-P., Giavazzi, F., Vives, X., & von Thadden, E.-L. (1999). The future of European banking (Vol. 9). Monitoring European Integration. London: Center for Economic Policy Research. Devaney, M., & Weber, W. L. (2002). Small-business lending and profit efficiency in commercial banking. Journal of Financial Services Research, 22(3), 225–246. DeYoung, R. (1994). Fee-based services and cost efficiency in commercial banks. Federal Reserve Bank of Chicago, Proceedings: Conference on Bank Structure and Competition. DeYoung, R., & Roland, K. P. (2001). Product mix and earnings volatility at commercial banks: evidence from a degree of leverage model. Journal of Financial Intermediation, 10, 54–84. Dietsch, M., & Lozano-Vivas, A. (2000). How the environment determines banking efficiency. a comparison between French and Spanish industries. Journal of Banking and Finance, 24, 985–1004. Edwards, F. R., & Mishkin, F. S. (1995). The decline of traditional banking: Implications for financial stability and regulatory policy. Federal Reserve Bank of New York Economic Policy Review, 1(2), 27–45. Elyasiani, E., & Mehdian, S. (1992). Productive efficiency performance of minority and nonminorityowned banks: A nonparametric approach. Journal of Banking and Finance, 16, 933–948. Elyasiani, E., & Mehdian, S. (1995). The comparative efficiency performance of small and large US commercial banks in the pre- and post-deregulation eras. Applied Economics, 27, 1069–1079. European Central Bank. (2000). EU banks’ income structure. Other publications. Frankfurt am Main, Germany: European Central Bank. Fernández de Guevara, J. (2001). Operaciones fuera de balance en el sistema bancario español. Revista de Economía Aplicada, 9, 209–221. Ferrier, G. D., & Lovell, C. A. K. (1990). Measuring cost efficiency in banking. Journal of Econometrics, 46, 229–245. Fuentelsaz, L., & Gómez, J. (2001). Strategic and queue effects on entry in Spanish banking. Journal of Economics and Management Strategy, 10(4), 529–563. Fuentelsaz, L., Gómez, J., & Polo, Y. (2002). Followers’ entry timing: Evidence from the Spanish banking sector after deregulation. Strategic Management Journal, 23(3), 245–264. Gibbons, J. D. (1993). Nonparametric statistics. An introduction. Number 090 in quantitative applications in the social sciences. Newbury Park, California: Sage Publications. Gorton, G., & Rosen, R. (1995). Corporate control, portfolio choice, and the decline in banking. Journal of Finance, 50, 1377–1419. Grifell-Tatjé, E., & Lovell, C. A. K. (1996). Deregulation and productivity decline: The case of Spanish savings banks. European Economic Review, 40(6), 1281–1303. Grifell-Tatjé, E., & Lovell, C. A. K. (1997). The sources of productivity change in Spanish banking. European Journal of Operational Research, 98, 365–381. Hassan, M. K. (1992). An empirical analysis of bank standby letters of credit risk. Review of Financial Economics, 2, 31–44. Hassan, M. K. (1993). The off-balance sheet banking risk of large US commercial banks. The Quarterly Review of Economics and Finance, 33, 51–69. Hassan, M. K., Karels, G. V., & Peterson, M. O. (1994). Deposit insurance, market discipline and off-balance sheet banking risk of large US commercial banks. Journal of Banking and Finance, 18, 575–593. Hassan, M. K., & Sackley, W. H. (1994). A methodological investigation of risk exposure of bank off-balance sheet loan commitment activities. The Quarterly Review of Economics and Finance, 34, 283–299. Hunter, W. C., & Timme, S. (1995). Core deposits and physical capital: A reexamination of bank scale economies and efficiency with quasi-fixed inputs. Journal of Money, Credit, and Banking, 27, 165–185. Jagtiani, J., & Khanthavit, A. (1996). Scale and scope economies at large banks: Including off-balance sheet products and regulatory effects (1984–1991). Journal of Banking and Finance, 20, 1271–1287. Jagtiani, J., Nathan, A., & Sick, G. (1995). Scale and cost complementarities in commercial banks: On- and off-balance sheet activities. Journal of Banking and Finance, 19, 1175–1189. Kaufman, G. G. (1993). The diminishing role of commercial banks in the US economy. In L. H. White (Ed.), The crisis in American banking (pp. 139–159). New York: New York University Press. Kaufman, G. G., & Mote, L. R. (1994). Is banking a declining industry? A historical perspective. Economic Perspectives, 18, 2–21.

E. Tortosa-Ausina / Journal of Economics and Business 55 (2003) 371–395

395

Kumbhakar, S. C., Lozano-Vivas, A., Lovell, C. A. K., & Hasan, I. (2001). The effects of deregulation on the performance of financial institutions: The case of Spanish savings banks. Journal of Money, Credit, and Banking, 33(1), 101–120. Leibenstein, H. (1966). Allocative efficiency versus “X-efficiency”. American Economic Review, 56(3), 392–415. Lloyd-Williams, D. M., Molyneux, P., & Thornton, J. (1994). Market structure and performance in Spanish banking. Journal of Banking and Finance, 18, 433–443. Lozano-Vivas, A. (1997). Profit efficiency for Spanish savings banks. European Journal of Operational Research, 98, 382–395. Lozano-Vivas, A. (1998). Efficiency and technical change for Spanish banks. Applied Financial Economics, 8(3), 289–300. McAllister, P. H., & McManus, D. A. (1993). Resolving the scale efficiency puzzle in banking. Journal of Banking and Finance, 17, 389–405. Mester, L. J. (1992). Traditional and nontraditional banking: An information-theoric approach. Journal of Banking and Finance, 16, 545–566. Mester, L. J. (1997). Measuring efficiency at US banks: accounting for heterogeneity is important. European Journal of Operational Research, 98, 230–243. Mitchell, K., & Onvural, N. M. (1996). Economies of scale and scope at large commercial banks: Evidence from the Fourier flexible functional form. Journal of Money, Credit, and Banking, 28, 178–199. Prior, D., & Salas, V. (1994). Eficiencia técnica de las cajas de ahorros españolas y sus factores determinantes. Papeles de Economía Española, 58, 141–161. Quah, D. T. (1997). Regional cohesion from local isolated actions. II. Conditioning. Discussion Paper 379, CEPR. Resti, A. (1997). Evaluating the cost-efficiency of the Italian banking system: What can be learned from the joint application of parametric and non-parametric techniques? Journal of Banking and Finance, 21, 221–250. Robertson, D. D. (2001). A Markov view of bank consolidation. Economic and Policy Analysis Working Paper 2001-4, Offce of the Comptroller of the Currency. Rogers, K. E. (1998). Nontraditional activities and the efficiency of US commercial banks. Journal of Banking and Finance, 22, 467–482. Rogers, K. E., & Sinkey, J. F., Jr. (1999). An analysis of nontraditional activities at US commercial banks. Review of Financial Economics, 8, 25–39. Santomero, A. M., & Trester, J. J. (1998). Financial innovation and bank risk taking. Journal of Economic Behavior and Organization, 35(1), 25–37. Sathye, M. (2001). X-efficiency in Australian banking: An empirical investigation. Journal of Banking and Finance, 25, 613–630. Sealey, C. W., & Lindley, J. T. (1977). Inputs, outputs and a theory of production and cost at depository financial institutions. Journal of Finance, 32(4), 1251–1266. Stokey, N. L., & Lucas, R. E. (1989). Recursive methods in economic dynamics. Cambridge, MA: Harvard University Press. Tortosa-Ausina, E. (2002). Exploring efficiency differences over time in the Spanish banking industry. European Journal of Operational Research, 139, 643–664. Vives, X. (1990). Deregulation and competition in Spanish banking. European Economic Review, 34, 403–411. Vives, X. (1991a). Banking competition and European integration. In A. Giovannini & C. Mayer (Eds.), European financial integration (Chap. 2). Cambridge: Cambridge University Press. Vives, X. (1991b). Regulatory reform in European banking. European Economic Review, 35, 505–515. Wand, M. P., & Jones, M. C. (1994). Multivariate plug-in bandwidth selection. Computational Statistics, 9, 97–116. Wheelock, D. C., & Wilson, P. W. (2001). New evidence on returns to scale and product mix among US commercial banks. Journal of Monetary Economics, 47, 653–674.