the impact of non-traditional activities on the estimation ... · the impact of non-traditional...

The impact of non-traditional activities on the estimation of bank efficiency: international evidence

Ana Lozano-Vivas & Fotios Pasiouras

University of Bath School of Management Working Paper Series

2008.01 This working paper is produced for discussion purposes only. The papers are expected to be published in due course, in revised form and should not be quoted without the author’s permission.

University of Bath School of Management

Working Paper Series

School of Management Claverton Down

Bath BA2 7AY

United Kingdom Tel: +44 1225 826742 Fax: +44 1225 826473

http://www.bath.ac.uk/management/research/papers.htm

2008

2008.01 Ana Lozano-Vivas & Fotios Pasiouras

The impact of non-traditional activities on the estimation of bank efficiency: international

evidence

3

The impact of non-traditional activities on the estimation of bank efficiency: international evidence

Ana Lozano-Vivas1, Fotios Pasiouras2*

1Department of Economics, University of Malaga, Spain

2School of Management, University of Bath, UK

Abstract We use a sample of 4,960 observations from 752 publicly quoted commercial banks

operating in 87 countries between 1999 and 2006 and estimate cost efficiency and

alternative profit efficiency using a global best-practice frontier while controlling for

cross-country differences in regulations, macroeconomic conditions, concentration,

activity in the banking sector, and country’s overall level of development. In each

case, we estimate a traditional function that considers loans and other earnings assets

as the only outputs, and two additional functions that account for non-traditional

activities, by considering either OBS items or non-interest income as an additional

output. The results indicate that on average cost efficiency increases whether we use

OBS or non-interest income. However, with respect to profit efficiency the results are

mixed. The inclusion of OBS does not have a statistically significant impact on profit

efficiency, whereas non-interest income results in higher and statistically significant

different profit efficiency scores compared to the ones of the traditional model.

Additionally, regarding the impact of environmental conditions on cost and profit

inefficiency, we reveal two important issues. First, the inclusion or not of non-

traditional outputs does not influence the direction of the impact of the determinants

of cost (profit) inefficiency. Second, since we have taken control for regulations

related with the three pillars of Basel II and restrictions on bank activities the results

suggest that those regulations ensure banks are well run (improve cost and profit

efficiency, on average).

Keywords: Efficiency, non-interest income, non-traditional activities, Off-balance sheet

* © Copyright: Ana-Lozano-Vivas, Fotios Pasiouras, 2008; Author for correspondence. E-mails: [email protected] (F. Pasiouras), [email protected] (A. Lozano-Vivas)

4

1. Introduction Over the last years, numerous banks around the world have broadened their portfolio

to offer non-traditional services. For instance, as Clark and Siems (2002) mention,

off-balance sheet (OBS) activities such as loan origination, securitization, standby

letters of credit, and derivative securities among others are expanding in a rapid pace.

As a result, the share of fee-based and other non-interest income to total income has

increased dramatically.

Rogers (1998) points out that an area of bank research that has largely ignored

non-traditional activities is the estimation of efficiency with most previous bank

efficiency models and measures based only on traditional balance sheet figures. Siems

and Clark (1997), Rogers (1998) and more recently Isik and Hassan (2003) among

others, imply that models that ignore such non-traditional outputs may penalize banks

that are heavily involved in such activities. The reason is that while the resources used

to produce these non-traditional outputs are considered in the input vector, the outputs

generated using these inputs are not part of the output vector. Furthermore, one policy

implication, highlighted by Rogers (1998) is that the increase (decrease) in cost and

profit efficiency indicates that banks tend to be producing and selling non-traditional

output better (worse) than traditional output, on average. Thus, policy makers may

want to consider such changes in efficiency when developing regulations related to

restrictions on bank activities.

Consequently, some recent studies have addressed this issue of increased

importance of non-traditional activities, by including the value of off-balance sheet

items or non-interest income in the output vector (e.g. Altunbas et al., 2000, 2001;

Akhigbe and McNulty, 2003; Drake and Hall, 2003; Bos and Kolari, 2005). However,

many other studies continue to estimate efficiency frontiers without accounting for

non-traditional activities (e.g. Maudos et al., 2002; Carvallo and Kasman, 2005; Fries

and Taci, 2005; Kasman and Yildirim, 2006; Lensink et al., 2007).

One potential reason is that relatively few studies provide a comparison of

models developed with and without proxies of non-traditional activities, allowing us

to have a complete view of the significance of their inclusion as outputs, in bank

efficiency estimates. Most of these studies generally report that the exclusion of

measures of non-traditional activities during the estimation of banks’ efficiency may

be misleading, although at least two studies find no impact of OBS (Jagtiani et al.,

5

1995; Pasiouras, 2008a), and the results of Clark and Siems (2002) depend on whether

they examine cost or profit efficiency. However, as these studies examine mainly the

U.S. (Jagtiani et al., 1995; Siems and Clark, 1997; Rogers, 1998; Stiroh, 2000; Clark

and Siems, 2002) and a few developed countries such as Spain (Tortosa-Ausina,

2003), Sweden (Rime and Stiroh, 2003), Taiwan (Lieu, et al, 2005), and Greece

(Pasiouras, 2008a), our knowledge with respect to other developed countries and

especially transition and developing countries remains limited.

The purpose of the present study is to provide, to the best of our knowledge

for the first time, international evidence on the relevance of non-traditional activities

on the estimation of banks’ efficiency. By using a sample from 87 countries, we can

also compare the impact of non-traditional activities across different levels of

economic development (e.g. advanced, transition) as well as geographic regions. The

main propose of our research is to attempt to show how the fact that banks around the

world have been diversifying, at the margin, away from traditional financial

intermediation (margin) business and into “off-balance-sheet” and fee income

business affects banking performance. In particular, we are interested in deriving bank

specific measures of cost as well as profit efficiency, with and without the inclusion of

non-traditional outputs.

We model bank efficiency using a global best-practice frontier. The first

advantage of this approach is that it increases the number of available observations.

As Berger and Humphrey (1997) argue a second advantage is that “a frontier formed

from the complete data set across nations would allow for a better comparison across

nations, since the banks in each country would be compared against the same

standard” (p. 187-188). However, to make cross-country comparisons meaningful one

has to account for differences in the environment in which the banks operate. Taking

in mind this important fact shown in international banking efficiency comparison

studies (Dietsch and Lozano-Vivas (2000), Lozano-Vivas et al. (2002) among others),

a second goal of the paper is to perform a wide international cross-country banking

analysis, which does not misstates the relative efficiency, by adjusting for diverse

environments where the banks develop their activities. Although, many recent cross-

country studies make an effort to account for environmental conditions during the

estimation of efficiency, they mainly incorporate country-level variables that account

for cross-country distinctions only in terms of demographics and economic conditions

(Dietsch and Lozano-Vivas, 2000; Drake and Hall, 2003; Carvallo and Kasman, 2005;

6

among others) However, as Pasiouras (2008b) points out, the importance of

differences in the regulatory environment has been ignored, to a large extent, on

cross-country bank efficiency studies. At the same time, while Pasiouras et al. (2007)

and Pasiouras (2008b) have recently examined the influence of regulations on bank

efficiency; they followed a two-step procedure, which does not allow one to conduct

cross-country comparisons of efficiency levels with confidence. Consequently, in the

present study we attempt to perform cross-country efficiency comparisons while

considering differences in regulatory conditions among countries during the

estimation of efficiency. To accomplish this task we use the approach of Battese and

Coelli (1995) that allows the single-step estimation of efficiency in which firm effects

are directly influenced by a number of variables. In the present study, we control for

various cross-country characteristics such macroeconomic conditions, concentration,

activity in the banking sector, country’s overall level of development as well as

regulations related to the three pillars of Basel II (i.e. capital adequacy requirements,

official supervisory power, private monitoring), and restrictions on bank activities.

Finally, we differentiate our study from previous ones by investigating

whether the above-mentioned environmental conditions have a different influence on

inefficiency across different specifications of the model. That is, when non-traditional

outputs are introduced or not in the cost and profit equation. If the results will show

that that the determinants of inefficiency differ across our specifications then this will

imply that, the non-traditional activities will not only affect technology but also the

direction in which environmental factors determine inefficiency.

The rest of the paper is structured as follows. Section 2 describes the

methodology and Section 3 presents the dataset used in our study. Section 4 discusses

the results, and Section 5 concludes the study.

2. Methodology

The purpose of this study is to investigate the impact of off-balance-sheet activities on

the measurement of X-efficiency by performing a wide cross-country international

comparison taking into account country specific differences. In particular, we are

interested in deriving bank specific measures of cost as well as profit X-efficiency,

with and without the inclusion of non-traditional activities while taking into account

country-specific characteristics in terms of macroeconomic conditions, development,

regulations, and conditions in the banking sector.

7

2.1. The Battese and Coelli (1995) model

As Fries and Taci (2005) mention, most applications in bank efficiency use a two-step

procedure, in which the estimated efficiency scores obtained from the stochastic

frontier are then regressed during a second step on a set of explanatory variables.

However, Coelli (1996) mentions that the two-stage estimation procedure is unlikely

to provide estimates which are as efficient as those that could be obtained using a

single-stage estimation procedure. Furthermore, in a cross-country context, a two-

stage estimation approach does not allow for meaningful comparisons of efficiency

scores across countries or group of countries (e.g. by geographical region). The reason

is that on the one hand the efficiency scores obtained over the first stage on the basis

of a global (i.e. common) frontier have not been adjusted for country-level differences

in environmental conditions. On the other hand, if nation-specific frontiers will be

estimated over the first stage, then one cannot draw conclusions about whether banks

in one nation are more efficient than those in other nations because they are measured

against different frontiers (Berger, 2007).

Consequently, as mentioned earlier, in order to perform a systematic

comparison of efficiency measures across countries, based on the conjecture that

efficiency differences between banking industries are determined by country specific

differences, we use the Battese and Coelli (1995) specification that allow us to control

for environmental factors in a single stage during the estimation of efficiency, as in

Cavallo and Rossi (2002), Fries and Taci (2005), Lieu et al. (2005), and Lensink et al.

(2007).

In its general form, the Battese and Coelli (1995) cost model can be written as

follows1:

tititititi vuwqCC ,,,,, );,(ln ++= β , ;,...,2,1 Ni = Tt ,...,2,1= (1)

where: tiC , is the total cost of bank i at time t; tiq , is a vector of outputs; tiw , denotes a

vector of values of input prices associated with a suitable functional form;β is a

1 For brevity of space, we present only the cost function here. Under the alternative profit approach, we only have to replace total costs by profit before taxes in the case of the dependent variable, and change the sign of the inefficiency term, in order to estimate profit efficiency. These issues are discussed in more details latter on. .

8

vector of unknown scalar parameters to be estimated; sv ti, are random errors,

assumed to be i.i.d. and have ),0( 2vN σ ; su ti, are the non-negative inefficiency effects

in the model which are assumed to be independently (but not identically) distributed,

such that tiu , is obtained by truncation (at zero) of the ),( 2, utimN σ distribution where

the mean is defined by:

δtiti zm ,, = (2)

where tiz , is a )1( xM vector of observable explanatory variables that influence the

inefficiency of bank i at time t; and δ is an )1(Mx vector of coefficients to be

estimated (which would generally be expected to included an intercept parameter).

The parameters of equations (1) and (2) are estimated in one step using maximum

likelihood2. The individual bank (in)efficiency scores are calculated from the

estimated frontiers as CEkt= exp(ui).

For estimating profit efficiency, equations (1) and (2) are estimated taking

profit before taxes (PBT) as the variable to be explained. As in most previous studies,

we estimate an alternative profit frontier, which ignores output price data by assuming

imperfect competition3. Consequently, the specification of the profit frontier model is

the same as that of the cost frontier (equation (1)) with PBTit replacing TCit as the

dependent variable. However, the sign of the inefficiency term now becomes negative

(-uit), PEFkt = exp(-ui).. Additionally, since a number of banks in the sample exhibit

negative profits (i.e. losses), the dependent variable in the profit model is transformed

to ( )( )1ln min ++ PBTPBT , where min)(PBT is the minimum absolute value of PBT

over all banks in the sample4.

2 See Battese and Coelli (1995) and Coelli et al. (1999, 2005), for further details. 3 Berger and Mester (1997) argue that alternative profit efficiency may provide useful information and be preferred when one or more of the following conditions are applicable: (a) there are substantial unmeasured differences in the quality of banking services; (b) outputs are not completely variable; (c) output markets are not perfectly competitive; (d) output prices are not accurately measured. Based on these arguments, Maudos et al. (2002) and Kasman and Yildirim (2006) point out that in international comparisons with a diverse group of countries and competition levels it seems more appropriate to estimate an alternative rather than a standard profit function. Furthermore, DeYoung and Hasan (1998) point out that output quantities tend to vary across banks to a greater extent than input prices, thus explaining a larger portion of the variation in profits in regression analysis. 4 This transformation is common in the literature, so that the dependent variable is ln(1)=0 for the bank with lowest PBT, and positive for all other banks.

9

The cost (in) efficiency, CEkt= exp(ui), takes a value between one and infinity

and the profit (in) efficiency, PEFkt = exp(-ui) , between zero and one, whereas in

both cases, values closer to one indicate higher efficiency. Thus, to make our results

comparable, we present an index of cost efficiency calculated as follows: CEFkt= 1/

CEkt.

2.2. Model specification

We estimate two models, one for cost (Model A) and one for profit (Model B)

efficiency. In each case, we estimate three specifications. Model A1 is a “traditional”

cost model under the intermediation approach where we assume that banks have two

outputs, namely loans (Q1) and other earning assets (Q2). Model A2 is identical to

Model A1 but OBS (Q3a) is used as an additional output. In Model A3, we replace

OBS by non-interest income (Q3b). Thus, in models A2 and A3 we consider

(interchangeably) as an additional output the two most commonly used measures of

banks’ non-traditional activity. The so-called OBS which is as a measure of

aggregated off-balance-sheet activity, and the non-interest income as proxy of the

OBS fee services (Clark and Siems, 2002)

Models B1 to B3 are re-estimations of Models A1-A3 but they correspond to

profit rather than cost functions. Thus, the variable to be explained is the profit before

taxes (PBT).

In all the specifications, we use three input prices. Consistent with most

previous studies these are: cost of loanable funds (W1), calculated as the ratio of

interest expenses to customer deposits and short term funding; cost of physical capital

(W2), calculated by dividing overhead expenses other than personnel expenses by the

book value of fixed assets; and cost of labour (W3), calculated by dividing the

personnel expenses by total assets5. To impose linear homogeneity restrictions we

normalize the dependent variable and all input prices by W3.

A time trend with its square root is used. The time trend (T=1 for 1999, T=2

for 2000, …, T=8 for 2006) is included in the function to allow for changes in

technology over time. As in Lensink et al. (2007), since the translog function that we

use is a second order approximation, the trend is included with both T and T2 terms.

5 In calculating W3, we use total assets rather than the number of employees due to data unavailability. Our approach is consistent with several other studies such as Carbo et al. (2002), Maudos et al. (2002), Weill (2004), Carvallo and Kasman (2005), Becalli et al. (2006) to name a few.

10

Following Berger and Mester (1997) and Lozano-Vivas et al. (2007) among others,

we specify equity as a quasi fixed input to control for differences in risk preferences6.

Since we perform our analysis for a wide sample of countries around the

world, instead of introducing country dummy variables we classify the set of countries

in four groups, on the basis of their level of economic development, and we introduce

dummy variables for each group. MADV indicates whether a country belongs in the

group of major advanced economies (MADV=1) or not (MADV =0). ADV indicates

whether a country belongs in the group of advanced economies (ADV=1) or not

(ADV= 0). TRANS indicates whether a country belongs in the group of transition

economies (TRANS =1) or not (TRANS= 0). Emerging-developing countries form

the reference category and are represented by zero values in all three dummy

variables7. The coefficient on these dummy variables shows whether the technology is

different depending on the economic development of the group.

Using the multi-product translog specification8, Equation (1) in the case of

Model A2 becomes9:

6 As suggested in Mester (1996), and consistent with Berger and Mester (1997), Altunbas et al. (2000), Rime and Stiroh (2003), Lozano-Vivas et al. (2007) among others, we use the level of equity rather than the equity to assets ratio. 7To assign countries in the four categories we combine information from the International Monetary Fund (IMF) and the European Bank for Reconstruction and Development (EBRD). IMF classifies countries in the following three categories: (i) major advanced economies, (ii) advanced economies, (iii) other emerging and developing economies. According to the EBRD classification, 29 of the IMF emerging and developing economies can be characterized as transition economies (e.g. Czech Republic, Estonia, Kazakhstan, etc.). We therefore create a fourth category (i.e. TRANSITION) that includes these countries. 8Some other studies rely on the Fourier Flexible (FF) specification to estimate efficiency (e.g. DeYoung and Hasan, 1998; Carbo et al., 2002; Akhigbe and McNulty, 2003). Berger and Mester (1997) found that both the translog and the FF function form yielded essentially the same average level and dispersion of measure efficiency, and both ranked the individual banks in almost the same order. However, Altunbas and Chakravarty (2001) compare the FF and translog specifications and urge caution about the growing use of the former to investigate bank efficiency. We therefore use the tranlog specification as in several other recent studies such as Dietsch and Lozano-Vivas (2000), Cavallo and Rossi (2002), Bos and Kolari (2005), Carvallo and Kasman (2005), Fries and Taci (2005). 9 We arbitrarily select this model for presentation, the remaining models being subject to similar transformations.

11

( ) titi

a

a

aa

a

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vuTRANSADVMADVEQUITY

xTWWxT

WWxTQxTQxTQ

TTWWQ

WWQ

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WW

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WTC

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22221201918

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122

1110

2987

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543210

ln32ln

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21

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32ln)1ln(

31ln)1ln(

32ln

21

32ln

31ln

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21))3(ln(

21)3ln()2ln(

))2(ln(21)3ln()1ln()2ln()1ln())1(ln(

21

32ln

31ln)3ln()2ln()1ln(

3ln

++++++

⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛++++

++⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛+

⎟⎠⎞

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⎠⎞

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⎞⎜⎜⎝

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ββββ

βββββ

βββββ

ββββ

ββββ

ββββ

ββββββ

In the present study, to control for country-specific environmental factors such as

macroeconomic conditions activity and concentration in the banking sector,

regulatory conditions, as well as bank-level risk-preferences, itm in Equation (2) is

defined by:

7121110987

6543210 3)(lnTRANSADVMADVRESTRPRMONSPOWER

CAPRQCCLAIMSEQUITYGDPGRINFmit

δδδδδδδδδδδδδ

++++++++++++=

INF is the annual rate of inflation and GDPGR is the real GDP growth, both capturing

macroeconomic conditions. EQUITY is the bank-level amount of equity to control for

individual bank risk-taking. CLAIMS measures the activity in the banking sector and it

is calculated by dividing the bank claims to the private sector with GDP. C3 is the

concentration in the banking sector, as measured by the proportion of total assets held

by the three largest banks in the country. CAPRQ, SPOWER, PRMON and RESTR are

variables that control for the main regulatory conditions in each country’s banking

industry. CAPRQ is a measure of capital requirements that accounts for both initial

and overall capital stringency10. SPOWER is a measure of the power of the

10 For the construction of the capital requirements (CAPRQ), power of supervisory agencies (SPOWER) and private monitoring (PRMON) indices, we use the summation of the 0/1 quantified answers as in Gonzalez and Fernandez (2005), Barth et al. (2001, 2007), Pasiouras et al. (2006, 2007) and Pasiouras (2008b). An alternative would be to use the principal component as in Levine (2004) and Beck et al. (2006a). Barth et al. (2004) have followed both approaches. They mention that on the one hand the

12

supervisory agencies indicating the extent to which they can take specific actions

against bank management and directors, shareholders, and bank auditors. PRMON is

an indicator of private monitoring and shows the degree to which banks are forced to

disclose accurate information the public and whether there are incentives to increase

private monitoring. RESTR is a proxy for the level of restrictions on banks’ activities.

It is determined by considering whether securities, insurance, real estate activities, and

ownership of non-financial firms are unrestricted, permitted, restricted, or prohibited.

Additionally, we also introduce the dummy variables of the countries group (MADV,

ADV and TRANS), defined above, in the inefficiency term to control for the fact that

different levels of economic development can influence X-efficiency.

3. Data

Our sample consists of an unbalanced dataset of 4,960 observations from 752 publicly

quoted commercial banks operating in 87 countries between 1999 and 2006. We focus

on publicly quoted banks because as mentioned in Laeven and Levine (2006) it

enhances comparability across countries. Furthermore, we focus on commercial banks

for two reasons. First, because it allows us to examine a more homogenous sample in

terms of services, and consequently inputs and outputs, enhancing further the

comparability among countries. Second, as mentioned in Demirguc-Kunt et al.

(2004), since the regulatory data of the Barth et al. (2001, 2006, 2007) database are

for commercial banks, it is more appropriate to use bank-level data only for this type

of banks.

Panel A of Table 1 presents the observations by year and geographical region

whereas Panel B presents them by year and country groups in terms of their level of

economic development.

[Insert Table 1 Around Here]

drawback of using the summation for the construction of the index is that it assigns equal weight to each of the questions whereas on the other hand the disadvantage of the first principal component is that it is less transparent how a change in the response to a question changes the index. While they only report the empirical reports using the principal component indexes, they mention that “we have confirmed all this paper’s conclusions using both methods” (p. 218), implying that there are no differences in the results. Consequently, in the present study, we rely on the summation of the individual zero/one answers as it is requires less computational efforts and we basically do not think that the use of principal components will lead to significantly different results. Further information on the calculation of these variables is available is Appendix A.

13

We collect information from four sources. All bank-specific data were

obtained from Bankscope database of Bureau van Dijk and were converted to US

dollars. We select unconsolidated data wherever available and if these are not

available, consolidated data are used. Second, we use reports prepared under

international accounting (or international financial reporting) standards (IAS/IFRS)

wherever available, but if only reports prepared under local generally accepted

accounting principles (GAAP) are available we use these. Finally, as in Altunbas et al.

(2001), Casu and Molyneux (2003), Hauner (2005), and Pasiouras et al. (2007), we

express the data in real 1995 terms using country GDP deflators. Our starting list

consisted of the population of publicly quoted commercial banks that appeared to

have financial records in Bankscope. After excluding banks that: (i) had missing,

negative or zero values for inputs/outputs, and (ii) had missing values in the case of

the country-specific control variables, we obtained a sample of 752 banks operating in

87 countries.

Information on bank regulations and supervision (i.e. CAPRQ, PRMON,

SPOWER, RESTR) is obtained by the WB database developed by Barth et al. (2001)

and updated by Barth et al. (2006, 2007). However, this database is available in only

three points in time. Version I was released in 2001 and contains information for 117

countries (Barth et al., 2001). For most of the countries, information corresponds to

1999, while for others information is either from 1998 or 2000. Version II describes

the regulatory environment at the end of 2002 in 152 countries (Barth et al., 2006).

Version III describes the situation in 142 countries in 2005/06 (Barth et al., 2007).

Consequently, we had to work under the assumption that the scores of our regulatory

variables (CAPRQ, PRMON, SPOWER, RESTR) remain constant within short

windows of time. More precisely, we used information from Version I for bank

observations from the period 1999-2000, from Version II for bank observations from

the period 2001-2003, and from Version III for bank observations from 2004-2006. In

the case of a few countries for which information was not available in all versions, we

used information from the most appropriate one. More detailed, information for

Armenia, Bangladesh, China, Costa Rica, Indonesia, Jamaica, and Malawi are

available only in Versions I and III. For these countries, we use Version I for bank

observations from 1999-2002 and Version III for bank observations from 2003-2006.

Information for Nepal and Zambia is available only in Version I, for Sudan only in

Version II and for Uganda only in Version III so we use the available version over the

14

entire period of our analysis. While acknowledging this shortcoming, we do not

believe that it has an impact on our results. Other studies that have used this database

across a number of years have obviously worked under a similar assumption (e.g.

Demirguc-Kunt and Detragiache, 2002; Demirguck-Kunt et al., 2004; Fernandez and

Gonzalez, 2005; Beck et al., 2006c; Pasiouras et al., 2007).

Data for concentration (i.e. C3) are collected from the updated version (Beck

et al., 2006b) of the WB database on financial development and structure initially

constructed by Beck et al. (2000). Data for the macroeconomic conditions and

financial development indicators (i.e. GDPGR, INF, CLAIMS) are obtained from

Global Market Information Database (GMID).

4. Results

4.1. Cost efficiency and alternative profit efficiency

Panel A in Table 2 presents the average scores for cost and profit efficiency. In both

cases, the average efficiency increases with the inclusion of either the OBS or the

non-interest income in the output vector. More detailed, the mean cost efficiency

obtained from Model A1 is 0.8622, and increases to 0.8716 in the case of Model A2

and 0.8778 in the case of Model A3. As for profit efficiency, the corresponding

figures are 0.7532 (Model B1), 0.7540 (Model B2), 0.7929 (Model B3).

These results suggest that costs are, on average, around 13% higher than at the

best-practice banks. Profits are, on average, around 25% lower than at the best-

practice banks. Thus, our results are consistent with the ones of previous studies, with

cost efficiency being higher than profit efficiency. However, the mean profit

efficiency estimates obtained in our study, are in general higher than the ones

obtained in past studies. Early studies in the US report profit efficiency estimates

below 50% (e.g. Berger and Mester, 1997; DeYoung and Hasan, 1998) while Maudos

et al. (2002) also report an average profit efficiency between 21.7% and 52% for ten

EU countries. The differences between our findings and the ones of those studies

could be due to three reasons: (i) In relation to the US studies, the difference could be

due to the large number of countries that we examine; (ii) In general, the difference in

the results, in relation to all the above mentioned studies, could be explained by the

different periods that we examine. However, it should be mentioned that Rogers

(1998) and Clark and Siems (2002) report profit efficiency estimates that are similar

to the ones obtained in the present study. More detailed, the profit efficiency in the

15

study of Rogers (1998) ranges between 69% and 71% for the model that includes non-

interest income, and 65% to 68% for the traditional model. Clark and Siems (2002)

also report profit efficiency estimates that range between 0.6462 and 0.7642; (iii) In

relation to the study of Maudos et al. (2002) the differences could additionally be

attributed to the fact that the authors have estimated a pan-European frontier without

controlling for environmental differences across the ten countries11.


To assess whether the differences in the mean efficiency scores between the

traditional models (A1 and B1) and the models that account for non-traditional

activities (i.e. A2, A3, B2, B3) are statistically significant we conduct a non-

parametric Kruskal-Wallis test. The results in Panel B of Table 2 indicate that for cost

efficiency, the mean scores obtained from Model A1 are significantly lower than the

ones obtained from either Model A2 or Model A3. In the case of profit efficiency, the

results are mixed. First of all, consistent with Clark and Siems (2002) we find that the

inclusion of OBS in the profit function (Model B2) does not result in efficiency scores

that are statistically significant different from the ones obtained from the traditional

model B1. However, the profit efficiency scores obtained from Model B3 are

statistically significant higher than the ones obtained from Model B1. Thus, the two

proxies for non-traditional activities appear to measure different bank attributes.

In summary, the results so far support the importance of including a measure

of non-traditional activities in the cost function that is consistent with past studies.

However, the results obtained from the profit efficiency are less supportive, as their

impact on efficiency estimates depends on the proxy for non-traditional activities. To

explore our findings further we present in Tables 3 and 4, the mean cost and profit

efficiency scores disaggregated by group of countries on the basis of their economic

development, and their geographical region.


11While a number of recent studies, estimate pan-European frontiers without controlling for environmental factors, Dietsch and Lozano-Vivas (2000) have shown that even in the case of two large EU banking sectors, namely France and Spain, efficiency scores were significantly different once they controlled for environmental conditions.

16

Table 3 presents the average efficiency scores by country’s level of

development. Consistent with Table 2, the average cost efficiency scores increase in

all cases when we control for non-traditional activities either by OBS or non-interest

income. The increase is always higher in the latter case. The results of the Kruskal-

Wallis test indicate that the null hypothesis of no difference in the mean efficiency

can be rejected in all cases.

The inclusion of OBS in the profit function results in an increase in the

average scores of major advanced and advanced countries and a decrease in the case

of transition and developing countries. The Kruskal-Wallis test indicates that the

differences are statistically significant only in the case of the most and least developed

groups of countries, namely the major advanced and developing economies. As

before, the comparison of models B1 and B3 indicate that the inclusion of non-interest

income in the output vector results in mean profit efficiency scores that are higher and

statistically significant different irrespective of the level of development.

Since we have a wide sample of countries, to investigate more deeply the

influence of non-traditional activities on the X-efficiency (cost and profit) in different

countries we perform a second exercise by analyzing the level of efficiency by

geographical region.

The results in Table 4 indicate that, as before, cost efficiency increases with

the inclusion of OBS irrespective of the geographical region that we examine. The

highest difference between models A1 and A2 is recorded in the case of Eastern

Europe and equals 0.191; the lowest difference is observed in the case of North

America and equals 0.0025. With the exception of Australia, the cost efficiency scores

increase further when we replace OBS by non-interest income. When we compare

models A1 and A3 the highest and lowest differences are observed again in the cases

of Eastern Europe and North America. However, while the cost efficiency scores

increase in all cases, the results of the Kruskal-Wallis test reveal that in the case of

Africa and Middle East, Australia, and North America, the differences between

Models A1 and A2 are not statistically significant12. When we replace OBS by non-

12 The insignificant difference in the case of North America that contradicts the results of past US studies might be due to one or a combination of more than one of the following reasons. First, the region of North America in our study includes banks from both Canada and the US. Second, we have included data for only twelve publicly quoted U.S. commercial banks for which complete data were available in the version of Bankscope to which we had access. Third, we have examined a different time period. Fourth, we have estimated a global frontier rather than a US country-specific frontier as in past studies.

17

interest income, only the difference in the cost efficiency estimates that correspond to

Australian banks remains insignificant.

Turning to the estimates of the profit function the null hypothesis of no

difference in efficiency between models B1 and B2 can be rejected in only three of

the seven geographical regions. Furthermore, even in those cases that the null

hypothesis is rejected, there is no consistency in the change of the efficiency scores.

The inclusion of OBS reduces the profit efficiency of banks from Latin America and

Caribbean, while the opposite occurs in the case of North America and Western

Europe. The inclusion of non-interest income in the profit function results in mean

efficiency scores that are significantly higher than the ones obtained from the

traditional model B1 in all cases.


4.2. Impact of Environmental Conditions on Cost and Alternative Profit

Efficiency with and without OBS activities

Our efficiency measurements shown in the previous section are obtained by

considering environmental conditions and bank-level capitalisation as explanatory

variables of differences in the efficiency levels. As discussed in the introduction of the

paper, an additional important task of our analysis, that extends the work of previous

studies, is to test whether those conditions have a different influence on inefficiency

across different specifications of the model.

Table 5 contains the estimation results of the influence of bank-level

capitalization and country-specific environmental factors such as macroeconomic

conditions, concentration in the banking sector and regulatory conditions as well as

different country levels of development, on cost (Panel A) and profit (Panel B)

inefficiency. The results reveal that irrespective of the model used to obtain cost

(profit) inefficiency (with or without non-traditional activities) the explanatory

variables retain their sign. So, the inclusion or not of non-traditional activities does

not influence the direction of the impact of the determinants of cost (profit)

inefficiency.

Panel A in Table 5 shows that while inflation has a negative impact on cost

efficiency, we find that the growth of GDP is positively associated with efficiency. In

accordance with the findings of Mester (1996), cost inefficiency is inversely

18

correlated with the level of bank’s financial capital. Financial development (i.e.

CLAIMS), appears to have a positive influence on cost efficiency. Hence, it seems that

the activity in the banking sector matters for cost efficiency. Furthermore, more

concentrated banking industries are more cost efficient ones. Finally, the regulatory

factors: (i) capital adequacy requirements, (ii) private monitoring, (iii) disciplinary

power, and (iv) restrictions on banking activities, affect cost inefficiency as follows.

As expected by the regulators, higher capital requirements have a positive impact on

cost efficiency. Additionally, the results show that private monitoring also improves

cost efficiency. Those results should mean that regulations that improve private

monitoring of banks are associated with greater bank development, lower net interest

margins and small non-performing loans (Bath et al., 2004). On the other hand, the

results reveal that granting broad powers to supervisors have a negative impact on

cost efficiency. Those results should be explained by the fact that powerful

supervision might impede bank operations (Bath et al., 2004). Moreover, in

accordance with Bath et al. (2004) greater regulatory restrictions on bank activities are

associated with lower banking sector efficiency.

In terms of the influence of the explanatory variables on profit inefficiency

(Panel B in Table 5), the results show that differently to cost inefficiency, profit

inefficiency is positively correlated with the level of financial capital. Additionally,

higher concentration in the banking sector results in lower profit efficiency. On the

other hand, the macroeconomic environmental conditions, inflation (INF) and growth

of GDP, maintain their relationship with efficiency across the profit and cost

specifications. The inflation has a negative impact on profit efficiency and the growth

of GDP is positively associated with profit efficiency which is intuitive, since in

countries that are more prosperous, banks have better access to new technology

(Lensink et al., 2007). Financial development (CLAIMS), has a positive influence on

profit efficiency. Thus, it seems that consistent with the findings of Pasiouras (2008b)

for technical efficiency, the activity in the banking sector matters for cost and profit

efficiency as well. As for the influence of the regulatory conditions on profit

inefficiency, we observe a positive association, with CAPRQ being the only

exception. Higher capital requirements have a negative impact on profit efficiency

(less profit due to captive earning resources). As in the case of cost efficiency, an

improvement in private monitoring conditions improves profit efficiency as well.

19

However, the results reveal that granting broad powers to supervisors and greater

restrictions on bank activities are associated with higher profit efficiency.


As it concerns the factors that influence inefficiency, an interesting and more

realistic scenario to be investigated is the average effect that the different

determinants of inefficiency have, depending on level of the economic development

of the countries analyzed, viz. Major Advanced, Advanced, Transition, and Emerging-

Developing countries. To perform this exercise, we start by computing and comparing

the average effect of regulatory conditions and the total average effect (taking into

account all the explanatory variables including the regulatory ones as well) on cost

and profit inefficiency by group of economic development. Since we have used

dummy variables to control for these different country groups (Major Advanced,

Advanced, and Transition countries) and we have introduced an intercept, the omitted

category (Emerging-Developing) becomes a base or benchmark against which all the

others are compared. In this case, the estimated coefficient of each country group

must be interpreted as reflecting the difference between each country group and the

Emerging-Developing countries group, taking into account the rest of the control

variables. By considering this fact, Table 6 shows the results of the average effect of

regulatory conditions and the total average effect on cost and profit efficiency (Panel

A and B) by country group for each one of the estimated models (evaluated at the

average level of each explanatory variable).

In terms of the impact of each country group on cost inefficiency (Panel A in

Table 6) the results show that banks operating in Transition countries experience an

improvement in cost efficiency that is lower than in the rest of the country groups, in

the three models. More detailed, while comparing models A1-A2-A3 banks in Major

Advanced countries have improved their cost efficiency between 1.71% and 2.10%; in

Advanced countries between 1.38% and 1.78% and in Emerging-Developing

countries between 0.96% and 1.45%; the banks in Transition countries have improved

their cost efficiency between 0.36% and 0.81%. These results suggest that the banks

in Transition countries are the ones with attain lower cost efficiency, given the rest of

environmental variables, which is in agreement with the results presented in Table 3

20

(Panel A) about the estimated mean value of cost efficiency by level of economic

development.

In terms of the average effect of regulatory conditions in each country group,

taking as given the rest of explanatory variables for all the three models (A1-A3) the

regulatory conditions have a positive effect on cost efficiency except for Emerging-

Developing countries in model A3. On average, the regulatory conditions have

exercised a lower positive effect on Transition and Emerging-Developing countries

than on Major Advanced and Advanced countries. Moreover, the regulatory

conditions exercise a higher effect on model A2 and A1 than on model A3. The net

effect of all explanatory variables on cost efficiency is positive being again the

Transition countries the ones with the lower positive effect on cost efficiency.

Moreover, this positive effect is higher in model A3 followed by model A2, showing

a lower net positive effect in the case of model A1 (without non-traditional activities).

Those results suggest that although the determinants of inefficiency have the same

sign in all three models however, the impact is higher when OBS is taking into

account.

Regarding the results of the average effect of regulatory conditions and the

total average effect (taking into account all the explanatory variables including the

regulatory variables as well) on profit inefficiency, the picture changes drastically

(Panel B in Table 6). The Transition countries are the only ones that have a positive

effect on profit efficiency, taking as given the rest of the environmental variables

(column 1). The Major Advanced and the Emerging-Developing countries are the

ones that experience the highest decrease in profit efficiency. Those results are again

in harmony with the results presented in Table 3 (Panel B) about the estimated mean

value of profit efficiency by level of economic development. However, the regulatory

conditions have a higher impact on profit efficiency rather than on cost efficiency

when the rest of explanatory variables are taken as given. In contrast, when the net

average effect of all determinants of profit inefficiency is accounted for, the results

show a deterioration of profit efficiency. The model which shows a higher decrease of

profit efficiency is Model 3 (when non-traditional activities are taken into account and

are measured by non-interest income).


21

5. Conclusions

This study investigates the inclusion of proxies for non-traditional activities as an

output in studies of bank efficiency. As a consequence of the increase in such

activities, a handful number of recent studies have included off-balance sheet items or

non-interest income as part of bank output providing evidence on the impact of non-

traditional activities on bank efficiency estimates. The present paper attempts to

contribute to this literature by analyzing, for the first time, a wide sample of different

countries that include major advanced, advanced, transition and emerging-developing

countries, aiming to examine the impact of such activities in the estimated cost and

profit efficiency of the banking industry over the world.

We use a sample of 4,960 observations from 752 publicly quoted commercial

banks operating in 87 countries between 1999 and 2006 and estimate cost efficiency

and alternative profit efficiency using the Battese and Coelli (1995) specification that

allows us to control for specific cross-country environmental conditions. In each case,

we estimate a traditional function that considers loans and other earnings assets as the

only outputs, and two additional functions that account for non-traditional activities,

the first through the inclusion of OBS, and the second through the inclusion of non-

interest income in the output vector.

We find that on average cost efficiency increases whether we use OBS or non-

interest income as an indicator of non-traditional activities. With respect to profit

efficiency, the results are mixed. On the one hand, the inclusion of OBS does not have

a statistically significant influence on profit efficiency. On the other hand, non-interest

income results in higher and statistically significant different profit efficiency scores

compared to a traditional model. When we explore the results further, by

distinguishing banks on the basis of countries’ overall level of economic development

and geographic regions we find that there is a consistency as for the impact of non-

interest income. Nevertheless, the influence of OBS on cost and profit efficiency is

not robust across either geographical regions or different levels of development.

To conclude, the results imply that the estimation of traditional models that do

not account for non-traditional activities through non-interest income will

underestimate both cost and profit efficiency. However, the impact of OBS on

efficiency is in most cases insignificant and its influence can be either positive or

negative and varies across geographical regions and level of economic development,

as well as the specification of the model.

22

Since we consider environmental conditions as determinants of cost and profit

efficiency, we are able to further analyze not only the impact of such country specific

conditions on inefficiency but also whether those conditions have different impact on

inefficiency depending whether non-traditional activities are included as banking

output, or not. The results shown that the inclusion or not of non-traditional activities

hold unchanged the direction of the impact of the determinants of cost (profit)

inefficiency. However, it is found that the impact of the environmental conditions on

inefficiency is higher when non-traditional activities are taking into account.

An important issue undertaken in the present paper has being to account for

the regulatory environment where banks perform their banking activity. Additionally

to macroeconomic conditions, concentration, activity in the banking sector, and

country’s overall level of development we have controlled as well for regulations

related to the three pillars of Basel II (i.e. capital adequacy requirements, official

supervisory power, private monitoring) and restrictions on bank activities. The results

show that regulatory conditions affect positively cost and profit efficiency. Moreover,

we find that those regulatory conditions exercise a much higher impact on profit

efficiency rather on cost efficiency. Since we have taken control for regulations

related with the three pillars of Basel II and restrictions on bank activities the results

suggest that those regulations ensure banks is well run (improve cost and profit

efficiency, on average).

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Table 1 – Observations by year, geographical region and country’s economic development

Panel A: Observations by year and geographical region Africa &

Middle East Asia Pacific Australia Eastern

Europe Latin America & Caribbean

North America

Western Europe

1999 101 156 9 51 70 17 129 2000 106 214 9 63 82 18 127 2001 108 226 9 66 84 19 131 2002 105 228 9 70 78 20 135 2003 114 235 9 70 67 21 133 2004 112 235 9 66 79 21 113 2005 109 241 9 66 80 21 127 2006 93 230 8 51 73 19 109 Total 848 1,765 71 503 613 156 1,004 Panel B: Observations by year and country’s development Major

advanced Advanced Transition Developing

1999 74 104 59 296 2000 124 105 72 318 2001 137 106 77 323 2002 139 104 81 321 2003 143 99 82 325 2004 137 84 78 336 2005 141 94 78 340 2006 128 84 62 309 Total 1,023 780 589 2,568 Note: Countries in Panel A were assigned in the geographical regions following the classification of the Global Market Information Database (GMID). Countries in Panel B were classified in the four categories by combining information from the International Monetary Fund (IMF) and the European Bank for Reconstruction and Development (EBRD).

30

Table 2 – Cost and Alternative Profit efficiency estimates

Panel A: Cost and alternative profit efficiency estimates

Cost efficiency Model A1 Model A2 Model A3 Mean 0.8622 0.8716 0.8778 Profit efficiency Model B1 Model B2 Model B3 Mean 0.7532 0.7540 0.7929 Panel B: Kruskal-Wallis test of mean differences Chi-Square A1 vs A2 27.8700*** A1 vs A3 71.4680*** B1 vs B2 0.0010 B1 vs B3 141.6610*** Notes: *** Statistically significant at the 1% level, ** Statistically significant at the 5% level; *Statistically significant at the 10% level; Models A1 and B1 are traditional models with two outputs, namely, loans and other earnings; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Models A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS.

31

Table 3 – Disaggregation of Cost and Alternative Profit efficiency estimates by level of economic development

Panel A: Cost efficiency Model A1

(Mean) Model A2(Mean)

Model A3(Mean)

Kruskal-Wallis Chi-Square

Kruskal-WallisChi-Square

(A1 vs A2) (A1 vs A3) Major Advanced 0.8899 0.9010 0.9026 23.0850*** 25.8650***Advanced 0.8984 0.9041 0.9084 9.6310*** 31.4500***Transition 0.8139 0.8334 0.8431 7.9330*** 16.8550***Developing 0.8512 0.8588 0.8666 5.6700** 25.8270***Panel B: Alternative Profit efficiency Model B1

(Mean) Model B2 (Mean)

Model B3 (Mean)

Kruskal-Wallis Chi-Square

Kruskal-WallisChi-Square

(B1 vs B2) (B1 vs B3) Major Advanced 0.7270 0.7591 0.7846 13.4540*** 50.1650***Advanced 0.8251 0.8303 0.8414 0.6870 7.3330***Transition 0.8292 0.8211 0.8509 1.7830 10.4770***Developing 0.7243 0.7134 0.7682 4.9420** 91.9110***Notes: *** Statistically significant at the 1% level, ** Statistically significant at the 5% level, Statistically significant at the 10% level; Models A1 and B1 are traditional models with two outputs, namely loans and other earnings; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Models A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS; Countries were classified in the four categories by combining information from the International Monetary Fund (IMF) and the European Bank of Reconstruction and Development (EBRD).

32

Table 4 – Disaggregation of Cost and Alternative Profit efficiency estimates by geographical region

Panel A: Cost efficiency Model A1

(Mean) Model A2

(Mean) Model A3

(Mean) Kruskal-Wallis

Chi-Square Kruskal-Wallis

Chi-Square (A1 vs A2) (A1 vs A3) Africa & Middle East

0.8896

0.8941

0.8988

0.6170

3.2830*

Asia 0.8656 0.8729 0.8774 13.1990*** 24.1000*** Australia 0.8768 0.8916 0.8877 2.0960 0.7270 Eastern Europe 0.8110 0.8301 0.8396 6.8370*** 13.4480*** Latin America & Caribbean

0.8062

0.8217

0.8323

4.7750**

13.3600***

North America 0.9166 0.9192 0.9245 0.2030 7.2010*** Western Europe 0.8834 0.8928 0.8997 8.8080*** 39.4320*** Panel B: Alternative Profit efficiency Model B1

(Mean) Model B2

(Mean) Model B3

(Mean) Kruskal-Wallis

Chi-Square (B1 vs B2)

Kruskal-Wallis Chi-Square (B1 vs B3)

Africa & Middle East

0.7829

0.7758

0.8086

1.2470

21.3460***

Asia 0.8009 0.7973 0.8317 0.0800 64.6050*** Australia 0.7722 0.7743 0.8103 0.0260 4.7070** Eastern Europe 0.8266 0.8190 0.8501 1.2010 9.6500*** Latin America & Caribbean

0.6021

0.5823

0.6703

4.0150**

45.8270***

North America 0.5759 0.6350 0.6701 9.2720*** 19.0570*** Western Europe 0.7259 0.7489 0.7755 5.4160** 22.7020*** Notes: *** Statistically significant at the 1% level, ** Statistically significant at the 5% level; *Statistically significant at the 10% level; Models A1 and B1 are traditional models with two outputs, namely, loans and other earnings; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Models A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS; Countries were assigned to geographical regions on the basis of the GMID classifications.

33

Table 5: Effect of determinants on cost and profit inefficiency

Panel A: Cost inefficiency Model A1

Model A2

Model A3

Constant -1.2472***

-0.9580**

-1.4471***

INF 0.0341*** 0.0365*** 0.0298*** GDPGR -0.0468*** -0.0423*** -0.0479*** LN(EQUITY) -0.2224*** -0.0725** -0.1097*** CLAIMS -0.3927*** -0.4098*** -0.3146*** C3 -0.0138*** -0.0123*** -0.0070*** CAPRQ -0.1581*** -0.1745*** -0.1794*** SPOWER 0.0309** 0.0255** 0.0873*** PRMON -0.2724*** -0.2995*** -0.1874*** RESTR 0.2581*** 0.4272*** 0.3770*** MADV -0.4659*** -1.1218*** -0.6578*** ADV -0.1251 -0.4221*** -0.3296*** TRANS 0.5669*** 0.5969*** 0.6336*** Panel B: Alternative Profit inefficiency Model B1

Model B2

Model B3

Constant 2.6618***

2.5392***

3.1508***

INF 0.0311*** 0.0302*** 0.0451*** GDPGR -0.1076*** -0.1076*** -0.1501*** LN(EQUITY) 0.3102*** 0.2953*** 0.2062*** CLAIMS -2.3783*** -2.3119*** -2.0943*** C3 0.0039** 0.0030* 0.0205*** CAPRQ 0.0555** 0.0562** 0.0687** SPOWER -0.1389*** -0.1172*** -0.1269*** PRMON -0.5763*** -0.5675*** -0.7222*** RESTR -0.9696*** -0.9171*** -1.4887*** MADV 0.3320** -0.2064* 0.1583* ADV -1.3044*** -1.5161*** -2.2614*** TRANS -3.0141*** -2.8887*** -4.0082*** Notes: *** Statistically significant at the 1% level, ** Statistically significant at the 5% level; *Statistically significant at the 10% level; Models A1 and B1 are traditional models with two outputs, namely, loans and other earnings; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Models A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS; Countries were assigned to geographical regions on the basis of the GMID classifications.

34

Table 6: Country, Regulatory and Net Total Effect on cost and profit inefficiency

by country group

Panel A: Cost inefficiency Country

Effect Regulatory

Effect Net Total

Effect

Model A1 Major Advanced -1.7131 -1.2739 -3.2888 Advanced -1.3723 -1.5090 -3.2734 Transition -0.6803 -1.1822 -1.2365 Developing -1.2472 -1.2869 -2.1693

Model A2 Major Advanced -2.0798 -1.1271 -4.0775 Advanced -1.3801 -1.4259 -3.7906 Transition -0.3611 -1.0696 -1.7913 Developing -0.9580 -1.1194 -2.5697

Model A3 Major Advanced -2.1049 -0.0154 -4.2871 Advanced -1.7767 -0.3261 -4.1506 Transition -0.8136 -0.0062 -2.3838 Developing -1.4471 0.0801 -3.2195 Panel B: Alternative Profit inefficiency

Model B1 Major Advanced 2.9937 -6.8001 4.6480 Advanced 1.3574 -6.4499 2.4385 Transition -0.3524 -6.1465 1.9638 Developing 2.6618 -7.2758 5.1441



35

Appendix A- Information on regulatory variables Variable Category Description

CAPRQ Capital requirements

This variable is determined by adding 1 if the answer is yes to questions 1-6 and 0 otherwise, while the opposite occurs in the case of questions 7 and 8 (i.e. yes=0, no =1). (1) Is the minimum required capital asset ratio risk-weighted in line with Basle guidelines? (2) Does the ratio vary with market risk? (3-5) Before minimum capital adequacy is determined, which of the following are deducted from the book value of capital: (a) market value of loan losses not realized in accounting books? (b) unrealized losses in securities portfolios? (c) unrealized foreign exchange losses? (6) Are the sources of funds to be used as capital verified by the regulatory/supervisory authorities? (7) Can the initial or subsequent injections of capital be done with assets other than cash or government securities? (8) Can initial disbursement of capital be done with borrowed funds?

PRMON Private monitoring

This variable is determined by adding 1 if the answer is yes to questions 1-6 and 0 otherwise, while the opposite occurs in the case of questions 7 and 8 (i.e. yes=0, no =1). (1) Is subordinated debt allowable (or required) as part of capital? (2) Are financial institutions required to produce consolidated accounts covering all bank and any non-bank financial subsidiaries? (3) Are off-balance sheet items disclosed to public? (4) Must banks disclose their risk management procedures to public? (5) Are directors legally liable for erroneous/misleading information? (6) Do regulations require credit ratings for commercial banks? (7) Does accrued, though unpaid interest/principal enter the income statement while loan is non-performing? (8) Is there an explicit deposit insurance protection system?

SPOWER Official disciplinary

power

This variable is determined by adding 1 if the answer is yes and 0 otherwise, for each one of the following fourteen questions: (1) Does the supervisory agency have the right to meet with external auditors to discuss their report without the approval of the bank? (2) Are auditors required by law to communicate directly to the supervisory agency any presumed involvement of bank directors or senior managers in illicit activities, fraud, or insider abuse? (3) Can supervisors take legal action against external auditors for negligence? (4) Can the supervisory authorities force a bank to change its internal organizational structure? (5) Are off-balance sheet items disclosed to supervisors? (6) Can the supervisory agency order the bank's directors or management to constitute provisions to cover actual or potential losses? (7) Can the supervisory agency suspend director’s decision to distribute dividends? (8) Can the supervisory agency suspend director’s decision to distribute bonuses? (9) Can the supervisory agency suspend director’s decision to distribute management fees? (10) Can the supervisory agency supersede bank shareholder rights and declare bank insolvent? (11) Does banking law allow supervisory agency or any other government agency (other than court) to suspend some or all ownership rights of a problem bank? (12) Regarding bank restructuring and reorganization, can the supervisory agency or any other government agency (other than court) supersede shareholder rights? (13) Regarding bank restructuring & reorganization, can supervisory agency or any other government agency (other than court) remove and replace management? (14) Regarding bank restructuring & reorganization, can supervisory agency or any other government agency (other than court) remove and replace directors?

RESTR Restrictions on banks activities

The score for this variable is determined on the basis of the level of regulatory restrictiveness for bank participation in: (1) securities activities (2) insurance activities (3) real estate activities (4) bank ownership of non-financial firms. These activities can be unrestricted, permitted, restricted or prohibited that are assigned the values of 1, 2, 3 or 4 respectively. We use an overall index by calculating the average value over the four categories.

Note: The individual questions and answers were obtained from the World Bank database developed by Barth et al. (2001, 2006, 2007)

University of Bath School of Management Working Paper Series

School of Management

Claverton Down Bath

BA2 7AY United Kingdom

Tel: +44 1225 826742 Fax: +44 1225 826473

http://www.bath.ac.uk/management/research/papers.htm

2007

2007.01 Fotios Pasiouras International evidence on the impact of regulations and supervision on banks’ technical

efficiency: an application of two-stage data envelopment analysis

2007.02 Richard Fairchild Audit Tenure, Report Qualification, and Fraud

2007.03 Robert Heath & Paul Feldwick

50 Years using the wrong model of TV advertising

2007.04 Stephan C. Henneberg, Daniel Rohrmus & Carla

Ramos

Sense-making and Cognition in Business Networks: Conceptualisation and Propositional

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2007.05 Fotios Pasiouras, Sailesh Tanna &

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Regulations, supervision and banks’ cost and profit efficiency around the world: a stochastic

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2007.07 Robert Heath Emotional Persuasion in Advertising: A Hierarchy-of-Processing Model

2007.08 Joyce Yi-Hui Lee & Niki Panteli

A Framework for understanding Conflicts in Global Virtual Alliances

2007.09 Robert Heath How do we predict advertising attention and engagement?

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2007.12 Fotios Pasiouras, Emmanouil

Sifodaskalakis & Constantin Zopounidis

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stage data envelopment analysis

2007.13 Fotios Pasiouras and Emmanouil

Sifodaskalakis

Total Factor Productivity Change of Greek Cooperative Banks

2007.14 Paul Goodwin, Robert Fildes, Wing

Yee Lee, Konstantinos

Nikolopoulos & Michael Lawrence

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2007.16 Stephen Brammer and Helen Walker

Sustainable procurement practice in the public sector: An international comparative study

2007.17 Richard Fairchild How do Multi-player Beauty Contest Games affect the Level of Reasoning in Subsequent

Two Player games?

2007.18 Klaus Meyer, Saul Estrin, Sumon

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