Download Characteristics determining the efficiency of Foreign Banks in Australia

Characteristics determining
the efficiency of Foreign Banks
in Australia.
#
by
Jan-Egbert Sturm* and Barry Williams**
This Version: July, 2006.
Abstract:
The factors determining foreign bank efficiency are investigated using a three stage
research method. Following clients (defensive expansion) was found to increase host
nation efficiency, and new trade theory tended to, (but not conclusively), dominate
comparative advantage theory. The limited global advantage hypothesis of Berger, et
al. (2000) was found to apply for United States bank revenue creation efficiency, but
not for their transformation of physical inputs into outputs. Competitor market share
reduces host nation efficiency and positive parent bank attributes such as size, credit
rating and profits are associated with lower host nation efficiency, as is home nation
financial development.
Key words: Foreign bank efficiency, distance functions, factor analysis
JEL code: G15, G21, C15, C52
#
Corresponding author: Barry Williams, School of Business, Bond University, Gold Coast,
Queensland, Australia. [email protected]; Tel. +61 7 5595 2259; Fax + 61 7 5595
1160.
*
KOF – Swiss Institute for Business Cycle Research, ETH Zurich, Switzerland and CESifo
Munich, Germany.
**
School of Business, Bond University, Gold Coast, Queensland, Australia.
1
1. Introduction.
Factors determining the efficiency of foreign banks in the host nation is an area that has
received relatively little attention to date. This paper responds to (and extends) the
research agenda commenced by Berger, et al. (2004a) for cross border bank mergers, by
considering both country-level and firm-level characteristics of multinational banks that
determine differences in multinational bank efficiency in the host nation. This research
will commence from the frameworks offered by both comparative advantage theory and
new trade theory and so provide a new dimension to this growing literature. The current
work will extend this literature in two dimensions; (i) by considering firm-level as well
as country-level characteristics, and (ii) by considering the efficiency of multinational
banks. This research will have the further advantage of providing a test of the limited
global advantage hypothesis of Berger, et al. (2000). The limited global advantage
hypothesis argues that banks from some nations are able to overcome the diseconomies
of cross-border operations due to various unspecified advantages. By responding to
Berger, et al. (2004) within the context of the limited global advantage hypothesis, this
paper will identify at least some of the sources of these unspecified advantages.
The stylised facts relating to cross-border banking generally conclude that in developed
nations foreign banks generally under-perform their domestic counter-parts, with the
opposite occurring in developing nations (Berger, et al., 2000). Further, Berger, et al.
(2000) find that banks from some developed nations (particularly the United States)
tend to perform well in developed host nations, even when benchmarked against host
nation domestic banks.
An exception to this body of research is provided in the
Australian case by Sturm and Williams (2004), who found foreign banks to be more
efficient, on average, than domestic banks. Thus the Australian environment provides
an interesting location for further investigation of multinational bank performance. This
is particularly important given the importance of multinational banks in a globalised
financial marketplace, yet by contrast, the relatively limited evidence regarding factors
determining foreign bank performance in the host market. As far as these authors are
aware, to date, there is no published evidence addressing this question from the
perspective of foreign bank efficiency.
This paper will consider the relevance of comparative advantage theory and new trade
theory (Casson, 1999, Dunning, 1977, Markusen and Venables, 1996, Williams, 1997),
2
to the study of the efficiency of foreign banks in Australia. An important feature of this
study is that efficiency estimates of foreign banks will be drawn from efficiency
estimates from a larger bank population that includes both domestic and foreign banks.
In this way the negative impact of the liability of foreignness will be controlled for
(Miller and Parkhe, 2002).
The framework to model foreign bank efficiency will be based upon the work of Buch
(2003), Berger, et al. (2004) and Williams (2003). By applying bank-level data to
estimate bank efficiency as well as to estimate the impact of parent bank-level effects,
this paper provides further insights to the country-level approaches of Buch (2003),
Berger, et al. (2004), both of whom considered bank mergers across borders rather than
bank efficiency from a country-level perspective.
It is found that following clients (defensive expansion) tends to increase host nation
bank efficiency and that new trade theory tends to dominate comparative advantage
theory in explaining difference in bank efficiency. However, as factor analysis was not
able to separate these two theories conclusively, further research is necessary in other
settings. Some support was found for the limited global advantage hypothesis, in that
banks from the United States were found to be more efficient in revenue creation
efficiency, but less efficient in transforming physical inputs into outputs. Competitor
market share was found to reduce efficiency in the host market, consistent with
Williams (2003).
Home nation financial development and positive parent bank
attributes such as size, credit ratings and profits were found to be associated with lower
host nation efficiency.
The rest of this paper is structured as follows; the next section provides a review of the
relevant literature. The third section will detail the method that will be used to address
the research question posed by this paper. The fourth section discusses the nature of the
sample that will be employed. The fifth section will present the results; while the final
section will provide a conclusion and suggests directions for further research endeavour.
2. Literature Review.
This paper will consider the efficiency of foreign banks in Australian within the
contexts offered by comparative advantage theory and new trade theory. The traditional
3
approach to modelling multinational banks is based upon Ricardian comparative
advantage theory.
In this approach the multinational bank seeks to exploit its
comparative advantage and minimise its opportunity costs. Recent examples of this
framework within the context of multinational banking are offered by Williams (2003)
and Kosmidou, et al. (2006). New trade theory, (Markusen, 1995), emphasises national
similarities in determining investment flows. Recent studies of cross-border mergers by
Buch (2003) and Berger, et al. (2004) have considered that both of these theoretical
approaches are relevant to multinational banking.
Accordingly this issue can be
considered within these two frameworks.
2.1 Comparative Advantage and Opportunity Costs.
Within the structure offered by comparative advantage theory, multinational banks can
be considered to be subject to home nation effects, parent bank effects and host nation
effects, each providing a mix of advantages, opportunity costs and barriers to entry.
Home Nation Effects.
In multinational banking the hypothesis that banks follow their client’s abroad
(defensive expansion) is a traditional approach. This is usually measured by the use of a
vector of measures that represent the host nation’s trading and investment relationship
with the home nation. As surveyed by Williams (2002) this hypothesis is usually
supported for size studies but results for the impact of defensive expansion upon bank
profits are less unambiguous. Defensive expansion relationships are usually measured
using portfolio investment relationships, as these are most likely to result in the
multinational bank requiring a physical presence to defend its bank-client relationship.
It is this defence of the bank-client relationship that forms the core of the defensive
expansion hypothesis.
Export of financial practices and financial sophistication is also argued as a rationale for
multinational banking.
It is argued that those nations from more developed and
sophisticated financial systems are more likely to be able to export efficient financial
practices and so overcome the negative impact of the liability of foreignness. Nations
with higher levels of GDP per capita are more likely to possess sophisticated and
efficient financial systems, so GDP per capita is the usual measure of this home nation
effect. Buch and DeLong (2004) and Berger, et al. (2004) found that banks from
4
nations with higher levels of GDP per capita are more likely to acquire banks in other
nations, which was presumed by Buch and DeLong (2004) to be due to their higher
levels of efficiency.
Parent Bank Effects.
As compared to Williams (2003), Buch (2003) and Berger, et al. (2004) did not employ
bank-level data, instead confining their study to macroeconomic data. While parent
profits were not found by Williams (2003) to impact upon host nation profits, Focarelli
and Pozzolo (2001, p 2326) argued that parent profit offers a readily available measure
of parent efficiency. Thus, if a bank operates in the home market more efficiently than
other banks, this efficiency provides a potential source of comparative advantage when
expanding offshore. Overall, it is possible that parent profitability does not translate
into host market profitability, but does impact upon host market efficiency.
It is often argued that in multinational banking size is an important variable (Hirtle,
1991), with parent size found to determine host nation size (Cho, 1985, Ursacki and
Vertinsky, 1992). Further, Tschoegl (2004) suggested that the largest bank in each
nation is the most likely candidate for successful offshore expansion. It would be
therefore expected that banks with larger parents are more likely to be efficient in the
host nations than smaller banks.
Experience of operating internationally is another possible parent characteristic that
enables a multinational bank to succeed in the host nation. Tschoegl (1982) considered
that this experience can have two dimensions; (i) general experience of international
operations and (ii) experience of operating in a particular host nation. Experience
measures of the first type have been found to be highly correlated with size measures
(Cho, 1985), while Williams (1998a, and 1998b) found no support for host nation
experience effects for either size or profits of foreign banks in Australia.
Again,
however, it is possible that host nation experience impacts upon efficiency in the host
nation, but not upon size or profits.
Host Nation Effects.
Barriers to entry act to reduce the benefits of firm-specific and nation-specific
comparative advantages. As this study focuses upon a single nation, many of the
5
country-specific variables used by Berger, et al. (2004) are a constant for each year. To
alleviate statistical and interpretive problems associated with this issue, Williams (2003)
applied a competitor market share variable. This measures the degree of competition
confronting a foreign bank in the host market; considering the market share of the four
dominant banks in Australia, plus the market share of all other banks of the same
nationality. This measure reflects the degree of competition from the major Australian
banks, plus the additional competition from other banks of the same nationality seeking
to establish a beachhead in the host nation, (Fieleke, 1977), via defensive expansion to
follow their national client base. Williams (2003) found that foreign bank profits in
Australia were reduced by the impact of this competitor market share. This result may
not necessarily apply for efficiency, as this level of competition may engender increased
efficiency while depressing profits. Foreign banks in Australia are disproportionately
active in wholesale banking, which is a price competitive sector of the banking market
with an emphasis upon efficiency.
Thus, the barrier to entry represented by this
measure may reduce profits while increasing efficiency.
2.2
New Trade Theory.
New trade theory represents the alternative approach to comparative advantage theory.
Unlike comparative advantage theory, new trade theory considers that relative
characteristics of nations determine the patterns of investment, that markets are no
longer competitive and homogenous and that firms can choose between trade with or
investment in a nation (Berger, et al., 2004). This approach has also been termed
eclectic theory (Dunning, 1988) or the internalisation approach (Casson, 1990), with
Williams (1997) discussing each of these approaches within the context of the
multinational firm. From this basis Markusen and Venables (1996) developed the
convergence hypothesis in which increasing similarity increases investment as opposed
to trade. As multinational banking is based upon intangible assets such as skills,
knowledge, brand names and communications, new trade theory lends itself particularly
well to multinational banking (Berger, et al., 2004, Buckley, 1988). Berger, et al.
(2004) concluded that in the case of cross-border mergers and acquisitions both new
trade theory and traditional comparative advantage theory were supported.
6
3. Method.
This paper employs a three-step research design.
In the first step foreign bank
efficiency is estimated from a sample panel of data that includes both domestic and
foreign banks in Australia between 1988 and 2001. In the second step foreign bank
characteristics will be analysed using factor analysis.
In this step the variables
employed will be endogenous to those used in the first step. This step will estimate a
series of common factors representing distinctive features that are specific to foreign
banks operating in Australia. In the third step the estimates of foreign bank efficiency
drawn from the first step are used as dependent variables, while the common factors
estimated in the second step are used as independent variables in unbalanced panel
regressions.
Step 1. Bank Efficiency Estimation.
Given that this study is focussing upon foreign bank operations in the host nation, the
choice of efficiency estimation technique is limited by data availability. In this case the
data does not contain input or output prices, thus cost, profit or alternative profit
efficiency techniques are not available. As a result parametric input distance functions
from Coelli and Perelman (1999) will be used.
This approach applies maximum
likelihood estimation of efficiency allowing for multiple inputs and multiple outputs to
achieve an index of relative efficiency ranging from 100 to 0, with 100 representing
complete efficiency. Such an approach allows for the multiple input and output nature
of banking and has the additional advantage of allowing different specifications of
inputs and outputs which allows for some sensitivity analysis. We will include a time
trend in the efficiency estimation to allow for the impact of changing technology and
other time-dependent circumstances. The parameterisation of Battese and Corra (1977),
σ
is used, replacing σ V2 and σ U2 with σ 2 = σ V2 + σ U2 and γ = σ 2 +Uσ 2 . The appendix in
2
V
U
Battese and Coelli (1993) presents the log-likelihood function of this model.
Efficiency of banks in Australia will be modelled using the intermediation approach.
From this sample of efficiency estimates the efficiency estimates of the foreign banks in
Australian will be drawn. In the intermediation approach a bank is viewed as using
inputs such as deposits, staff and equity to produce outputs such as loans and offbalance sheet items. This approach is commonly used in modelling bank efficiency
(Berger and Humphrey, 1992, Berger and Mester, 1997). As efficiency estimation can
be sensitive to input and output specification (Berger, et al., 1993), several different
7
combinations of inputs and outputs will be applied to the available data. In the base line
approach, following Allen and Rai (1996), banks will be considered to use equity,
employees and deposits to produce loans and off-balance sheet items (Model 1).
Expanding upon this approach outputs will be expanded into housing loans and other
loans (Model 1a) and in Model 1b outputs will include wholesale activity. A revenue
based model of inputs and outputs will also be estimated, following Avkiran (2000, and
1999) and Sturm and Williams (2004); inputs will be considered to be interest expenses
and non-interest expenses, while outputs will be considered net interest income and
non-interest income (model 2). Table 1 summarises these models.
TABLE 1 ABOUT HERE.
Step 2. Factor Analysis.
Given the theory discussed above, there are a number of potential variables possible to
explain the efficiency of foreign bank in Australia. Factor analysis will be applied to
determine the common characteristics of these variables and to obtain a few
uncorrelated variables that are linear combinations of the original variables.
This
approach will have the advantages of reducing the number of possible variables to a few
key measures, of reducing the problems associated with multicollinearity in the third
stage of this study, and (hopefully) ensuring that any complex interrelationships are
reduced to a few variables representing the fundamental effects of concern. Factor
analysis will be conducted using both image factoring and alpha factoring with the
commonly used varimax rotation (Tabachnick and Fidell, 2001). Those factors selected
for inclusion in the third stage of this research will have eigenvalues of greater than one.
Step 3. Model Estimation.
In step three the estimated factors from step two will be used as independent variables
with the efficiency estimates from step one employed as dependent variables in
unbalanced pooled regressions. Because the efficiency estimates from step one were
estimated from a sample that includes both domestic and foreign banks, the
diseconomies of the liability of foreignness will be controlled for. In order to achieve a
parsimonious model specification, general to specific modelling (Hendry, 1995) will
also be employed.
8
4. Data.
The sample for the first stage of this study will be drawn from banks operating in
Australia between 1988 and 2001. Following Sturm and Williams (2004) these banks
will be classified as Big Four (the dominant four banks in Australia), Other Domestic
(mainly regional banks) and Foreign Banks. The data will be sourced mainly from the
bank’s individual annual reports, with some additional details, such as housing loans,
obtained from the Reserve Bank of Australia (RBA) Bulletin and the website of the
Australian Prudential Regulation Authority (APRA). Details regarding this data are in
the appendix as tables A1 and A2.
Data for the factor analysis in the second stage was obtained from a number of different
sources. Details regarding parent banks were obtained from Moody’s Credit Opinions:
Financial Institutions. Trade and investment stocks and flows from the parent bank’s
home nations were obtained from Australian Bureau Statistics publications. Parent
nation data was obtained from the International Financial Statistics of the International
Monetary Fund.
Several measures of home nation comparative advantage are possible. These include
trade measures and investment measures. Following the defensive expansion approach
to multinational banking, several measures of following clients will be employed. The
first measure will be home nation investment income, measured according to the IMF’s
Balance of Payments convention, in that flows from Australia to the home nations will
be measured as an outflow, with a negative value. A second measure of defensive
expansion effects will be total home nation trade (exports plus imports) as a share of
Australian GDP. Additional measures of defensive expansion effects will be home
nation capital flow into Australia, and home nation capital stock in Australia, both in the
relevant calendar year in billions of Australian dollars. In each case the investment
measures will reflect direct investment rather than portfolio investment, reflecting the
argument of Williams (2002) that direct investment is more likely to result in a need for
a physical presence to satisfy the requirements of defensive expansion. As a last
measure of home nation comparative advantage, the arguments of Buch and DeLong
(2004) and Berger, et al. (2004) will be applied, in that nations with higher GDP per
capita are more likely to have efficient domestic financial systems and so are able to
export efficient financial practices into the host nation. Both Buch and DeLong (2004)
9
and Berger, et al. (2004) found banks from nations with higher GDP per capita are more
likely to acquire banks in other nations and that banks from nations with lower GDP per
capita are more likely to be targets in cross-border mergers. Given that cross border
mergers are conducted with some expectation of gains, this observed regularity was
argued by Buch and DeLong (2004) to reflect efficiency of the acquiring bank. This
study will determine if this effect translates into observed increases in efficiency in the
host nation.
Measures of parent bank comparative advantage encompass parent profitability, parent
size and parent experience. Measures of this type were not used by Buch and DeLong
(2004) and Berger, et al. (2004). Following Focarelli and Pozzolo (2001, p 2326),
parent profits acts as a possible measure of parent efficiency. While Williams (2003)
did not find any evidence that parent bank profits affected foreign bank profits in
Australia, it is possible that foreign bank efficiency in the host market is increased by
parent profits, but that this does not translate into host nation profits. Two measures of
parent profits in the home nation will be used, parent Return on Assets and parent Net
Interest Margin. With parent bank size found to have in important role in multinational
banking (Cho, 1985, Ursacki and Vertinsky, 1992), two measures of parent size will be
used, log of parent assets and log of parent capital. In each case the foreign currency
values will be converted into Australian dollars using the average of the relevant year’s
exchange rate. A further parent bank advantage may be its experience of operating in
the host nation (Tschoegl, 1982). This will be measured as the number of years since
the first transaction based activity in Australia. The alternative measures of experience,
those representing multinationality in general, are usually found to be highly correlated
with measures of parent bank size (Cho, 1985). While Williams (1998a, and 1998b)
found no evidence that this measure of experience impacted upon profits in the host
nation, following the line of reasoning above, it is possible that efficiency but not profits
in the host nation is increased by host nation experience.
As a measure of host nation effects, this study will consider the barriers to entry
confronting foreign banks in Australia. In this case these barriers to entry will be
measured as competitor market share. As with Williams (2003), this will be specified
as the market share of the Big Four banks, which dominate the Australian banking
system, plus the markets share of all other banks of the same nationality. This will
10
measure the impact of market domination, as well as the competitiveness of the foreign
banks with the other banks seeking to establish a beachhead (Fieleke, 1977) in the host
nation by following their clients abroad.
New trade theory emphasises the role of similarity between home and host nations in
encouraging international investment. Following Berger, et al. (2004) three measures of
similarity will be employed, (i) differences in home nation growth (Home nation real
GDP growth – Australian real GDP growth); (ii) similarity of economic size (GDP);
and, (iii) similarity of GDP per capita. Both of the last two measures will represent
similarity as an index ranging between 0 and 1, with 1 representing complete similarity.
In the case of GDP this will be calculated as [1 – abs (Home real GDP – real Australian
GDP) / max(Home real GDP, Australian real GDP). Similarity of real GDP per capita
will be calculated in the same manner.
Some control variables will also be included to ensure that there are no nation- or firmspecific effects excluded from our consideration. It is possible that a combination of
parent characteristics that are represented by its credit rating impact upon efficiency in
the host nation. Thus a measure of ranked credit rating will be included, this will be
constructed as having a value of 1 for all banks with a rating of AAA, if there are three
such banks, then the banks with the next lowest rating (Aa1) will be ranked 4 and so
forth.
Further analysis will also be conducted in the third stage of this paper to
determine if dummy variables representing nationality have any additional explanatory
power after the variables represented by the factor analysis are considered.
The
descriptive statistics for the variables in the second stage factor analysis, as well as the
additional control variables, are shown in Table 2.
TABLE 2 ABOUT HERE.
5. Results.
The efficiency estimates from the first stage estimation are summarised in the
Appendix, Table 3.
Consistent with Sturm and Williams (2004) the correlations
between Models 1, 1a and 1b are the highest and the correlations with Model 2 are
lower.
The average estimated efficiency of between 80 and 85 per cent is also
consistent with previous Australian studies, with some differences due to differences in
11
estimation technique and increases in sample population. Foreign banks are on average
less efficient than the domestic incumbents, also consistent with previous global studies.
However, one foreign bank is also the most efficient for each model, consistent with the
limited global advantage hypothesis (Berger, et al., 2000). It should be noted that it is
not the same foreign bank that is the most efficient for each model.
The Kaiser’s measure of sampling adequacy and Bartlett test of sphericity both indicate
factorability of the data. As the Kaiser’s test value is above the value of 0.6 and the
Bartlett’s test is significant, the factorability of the correlation matrix is indicated.1 The
factor analysis results are shown in Table 3. In the case of each of Alpha factoring and
Image factoring, factors are chosen on the basis of an eigenvalue of one or greater. In
each case five factors were extracted, with the extracted factors after varimax rotation
showing high levels of commonality across the two factor analysis methods. In the case
of Alpha factoring the five factors extracted accounted for over seventy percent of
sample variance, while with the Image factor analysis five factors explained over sixty
per cent of sample variance.2
In this study variables with a factor loading
approximately equal to or greater than 0.5 will be interpreted, as shown by the shaded
elements in Table 3, this critical value is greater than the value of 0.32 discussed in
Tabachnick and Fidell (2001, p 625).
TABLE 3 ABOUT HERE.
The first factor found generally loaded on macroeconomic factors, with a combination
of trade, home GDP per capita and the two similarity indices all loading on the same
factor, as well as parent size. The macroeconomic factors had positive loadings while
the similarity factors had negative values for their loading. This indicates that it is not
possible to readily separate the impacts of comparative advantage and new trade theory.
The second factor represented defensive expansion effects, with a positive loading on
the two measures of direct investment into Australia (stock and flow) and negative
loading on the measure of Home Nation Investment income. Given that this third
variable is measured according to the IMF’s Balance of Payments conventions, this sign
reflects the fact that outflows from Australia to the bank’s home nation are reported
with a negative sign. The third factor in Alpha factor analysis loaded on the same
1
2
These results in the Appendix Table A4.
These results are shown in the Appendix, Table A5.
12
variables as the fifth factor in Image factor analysis, and represented a mix of
macroeconomic, defensive expansion and host nation effects. Home Nation Investment
income again entered this factor with a negative loading and the factor analysis again
was not able to distinguish between defensive expansion effects and new trade theory
effects. In this case, the macroeconomic factors included home GDP per capita and
differences in real GDP growth rates.
This factor also included the impact of
competitor market share.
The fourth factor represented parent bank factors, loading on parent bank profits;
experience in Australia and ranked home credit rating. The fifth factor in Alpha factor
analysis loaded on the same variables as the third factor in Image factor analysis. The
variables in this factor were again mixed, with the factor representing a mix of home
nation (investment income), host nation (competitor market share) and parent bank
effects (parent return on assets). As with the previous factors Home Nation Investment
Income had a negative sign. Overall, the factor analysis was not able to separate the
variables as cleanly as the theoretical framework depicted, in particular, macroeconomic
factors representing comparative advantage theory tended to be included in the same
factor as new trade theory variables. This outcome is consistent with Berger, et al.
(2004), who concluded that both types of effects were relevant to cross-border mergers.
However, factor loadings for parent effects and defensive expansion effects each tended
to be more concentrated into a single factor.
The third stage of the analysis of this study then employed the efficiency scores from
the first stage analysis as dependent variables and used the calculated factors for each
bank-year as the independent variables in a series of unbalanced panel regressions.
Each of these regressions were estimated as random effects models with bank and time
specific effects, as this was indicated by the Hausman and LM tests to be the most
appropriate. In this third stage, the fully specified model including all the factors was
estimated, together with a second model that also included the nation specific dummy
variables.3 These results are shown in Table 4. Following these estimations a second
set of regressions were estimated applying the general to specific approach outlined in
Hendry (1995). Again two sets of models were estimated, one including the factors
3
The regressions results for the fully specified models without the nationality dummy are shown in the
Appendix, Table A6.
13
from the second stage and one including these factors and the nation specific dummy
variables.4 These results are shown in Table 5.
TABLES 4 and 5 ABOUT HERE.
In general the results for Models 1, 1a and 1b were different to those for Model 2.
Models 1, 1a and 1b represent the efficiency of banks in transforming physical inputs
and deposits into loans, off balance sheet items and investments (Model 1b), while
Model 2 represents the efficiency of banks in transforming expenses into revenue. Thus
they each reflect different aspects of bank efficiency, as is borne out by the empirical
results in this study. Overall it was found that factor five, representing a mix of home
host and parent variables had a consistently negative and significant relationship with
bank efficiency measured using Models 1, 1a and 1b.
Given that Home Nation
Investment Income has a negative loading in this factor, this result shows that following
clients increases bank efficiency in the host nation, consistent with the defensive
expansion hypothesis. Further, competitor market share reduces bank efficiency in the
host market, consistent with the profit results of Williams (2003), indicating that
increased dominance of the host market by the incumbent banks results in the foreign
entrants increasing expenditure on inputs to produce the same level of outputs, resulting
in lower profits and efficiency. In the case of Model 1a, the retail focussed model, it
was also found that factors one and two had negative relationships with foreign bank
efficiency. This indicates that following clients, with the exception of Home Nation
Investment Income (factor 2) reduces bank efficiency in the host nation. Further, home
nation financial development, as measured by GDP per capita is associated with
reduced efficiency in the host market, as is parent bank size. However, new trade
theory is supported, with increased economic similarity increasing bank efficiency in
the host nation.
In the case of Models 1, 1a and 1b, dummy variables representing
nationality indicate that banks from the United States tend to be less efficient, contrary
to Berger, et al. (2000), who found banks from the United States to be more efficient in
Europe. However Berger, et al. (2000) estimated cost and profit efficiency, which is
closer in general approach to Model 2, which will be discussed below.
4
The regressions results for the general to specific models without the nationality dummy are shown in
the Appendix, Table A7.
14
In the case of Model 2, representing the efficiency of revenue creation, factor 4,
representing parent variables were found to be consistently negative and significant.
This indicates that increased experience in the host nation, higher parent profits (as
measured by parent Net Interest Margins) and higher credit ratings tend to result in
lower revenue creation efficiency. The General to Specific reduced form model also
showed that factor 3 has a significant and negative relationship with bank revenue
creation efficiency. This indicates that following clients, as measured by Home Nation
Investment Income, increases revenue creation efficiency. Consistent with the above
results, competitor market share reduces revenue creation efficiency and higher levels of
home nation financial development, as measured by GDP per capita, is associated with
lower host nation revenue creation efficiency. Supporting new trade theory, it was also
found that increased similarity in terms of real GDP growth rates increases revenue
creation efficiency.
Dummy variables representing nationality are positive and
significant in the reduced form General to Specific regression for the United States and
Japan. The result for the United States dummy variable is consistent with Berger, et al.
(2000) and supports the limited global advantage hypothesis.
6. Conclusion.
Overall, these results indicate that following clients will increase bank efficiency and
that new trade theory tends to be a stronger explanation of differences in bank efficiency
than the comparative advantage approach.
However, the comparative advantage
approach should not be rejected on the basis of these results, as factor analysis was not
able to separate these two effects. Further, this is a study of a single host nation, unlike
Berger, et al. (2000), who considered a multi-county sample. Banks from the United
States are less efficient at transforming physical inputs into loans and other items, but
more efficient in revenue creation efficiency. This second result is consistent with the
limited global advantage hypothesis. Competitor market share reduces bank efficiency
in the host nation, resulting in over use of inputs to produce outputs, and as shown by
Williams (2003), resulting in lower profits. It was also found that home nation financial
development is associated with reduced efficiency in the host nation and that positive
attributes of parent banks such as increased size, higher profits and higher credit ratings
have a negative impact on foreign bank efficiency in the host nation.
15
As with Berger, et al. (2004) (in the case of cross-border bank mergers) it is hoped that
this paper will provide a call to research into factors determining foreign bank
efficiency. This study has considered foreign bank efficiency from the perspective of
one host nation, Australia, and using parametric distance functions and factor analysis.
Thus there is a need to address this research question in a variety of nations and using
different techniques. The techniques applied in this study were the results of the
limitations imposed by data availability and it is possible that the results of this
approach, combined with the impact of the single nation focus, will mean different
outcomes in different settings. Thus, an important extension to this study will be to
determine how robust these results are to differences in research design and institutional
settings.
References.
Allen, Linda, and Anoop Rai, 1996, Operational efficiency in banking: An international
comparison, Journal of Banking and Finance 20, 655-672.
Avkiran, Necmi K., 2000, Rising productivity of Australian trading banks under
deregulation 1986-1995, Journal of Economics and Finance 24, 122-140.
Avkiran, Necmi Kemal, 1999, The evidence on efficiency gains: The role of mergers
and the benefits to the public, Journal of Banking and Finance 23, 991-1013.
Battese, George, and Tim Coelli, 1993, A stochastic frontier production function
incorporating a model for technical efficiency effects, Working papers in
Econometrics and Applied Statistics (Armidale).
Battese, George, and G Corra, 1977, Estimation of a production frontier model: With
application to the pastoral zone of Eastern Australia, Australian Journal of
Agricultural Economics 21, 169-179.
Berger, Allen N., Claudia M. Buch, Gayle DeLong, and Robert DeYoung, 2004,
Exporting financial institutions management via foreign direct investment
mergers and acquisitions, Journal of International Money and Finance 23, 333366.
Berger, Allen N., Robert DeYoung, Hesna Genay, and Gregory F. Udell, 2000,
Globalisation of financial institutions: Evidence from cross-border banking
performance, Brookings-Wharton Papers on Financial Service 3, 23-120.
Berger, Allen N., and David B. Humphrey, 1992, Measurement and Efficiency Issues in
Comercial Banking, in Zvwi Griliches, ed.: Output measurement in the service
sector (University of Chicago Press, Chicago).
Berger, Allen N., William C. Hunter, and Stephen G. Timme, 1993, The efficiency of
financial institutions: A review and preview of research past, present, and future,
Journal of Banking and Finance 17, 221-249.
Berger, Allen N., and Loretta J. Mester, 1997, Inside the black box: What explains
differences in the efficiencies of financial institutions, Journal of Banking and
Finance 21, 895-947.
Buch, Claudia, 2003, Information or regulation: What drives the international activities
of commercial banks?, Journal of Money, Credit and Banking 35, 851-869.
Buch, Claudia M., and Gayle L. DeLong, 2004, Cross-Border Bank Mergers: What
Lures the Rare Animal?, Journal of Banking and Finance 28, 2077-2102.
16
Buckley, Peter J., 1988, The limits of explanation: testing the internalization theory of
the multinational enterprise, Journal of Business Studies 19, 181-193.
Casson, M, 1990, Evolution of multinational banks: A theoretical perspective, in G
Jones, ed.: Banks as Multinationals (Routledge, London).
Cho, Kang-Rae, 1985. Multinational banks; Their identities and determinants (UMI
Research Press).
Coelli, Tim, and Sergio Perelman, 1999, A comparison of parametric and nonparametric distance functions: with application to European railways, European
Journal of Operational Research 117, 326-339.
Dunning, John H., 1977. Trade, location of economic activity and the MNE: a search
for an eclectic approach (The Macmillan Press Ltd.).
Dunning, John H., 1988, The eclectic paradigm of international production: a
restatement and some possible extensions, Journal of International Business
Studies 1-31.
Fieleke, Norman S., 1977, The growth of US banking abroad: an analytical survey, Key
issues in international banking.
Focarelli, Dario, and Alberto Franco Pozzolo, 2001, The patterns of cross-border bank
mergers and shareholdings in OECD countries, Journal of Banking and Finance
25, 2305-2337.
Hendry, David, 1995. Dynamic Econometrics (Oxford University Press, Oxford).
Hirtle, Beverly, 1991, Factors affecting the competitiveness of internationally active
financial institutions, Federal Reserve Bank of New York (FRBNY) Quarterly
Reveiw 16, 38-51.
Kosmidou, Kyriaki, Fotios Pasiouras, and Angelos Tsaklanganos, 2006, Domestic and
multinational determininants of foreign bank profits: The case of Greek
banksoperating abroad., Journal of Multinational Financial Management
forthcoming.
Markusen, J, and A Venables, 1996, The increased importance of direct investment in
North Atlantic economic relationships: a convergence hypothesis, in M
Canzoneri, W Ethier, and CV Grilli, eds.: New Transatlantic Economy
(Cambridge, London).
Markusen, James, 1995, The boundaries of multinational enterprises and the theory of
international trade, Journal of Economic Perspectives 9, 169-189.
Miller, Stewart R., and Arvind Parkhe, 2002, Is there a liability of foreignness in global
banking? An empirical test of banks' X-efficiency, Strategic Management
Journal 23, 55-76.
Sturm, Jan-Egbert, and Barry Williams, 2004, Foreign banks entry, deregulation and
bank efficiency: Lessons from the Australian experience, Journal of Banking
and Finance 28, 1775-1799.
Tabachnick, Barbara, and Linda Fidell, 2001. Using Mulitivariate Statistics (Allyn and
Bacon, Boston).
Tschoegl, Adrian E., 1982, Foreign bank entry into Japan and California, in Alan M.
Rugman, ed.: New theories of the multinational enterprise (Croom Helm).
Tschoegl, Adrian E., 2004, Who owns the major US subsidiaries of foreign banks? A
note, International Financial Markets Institutions and Money 14, 255-266.
Ursacki, Terry, and Ilan Vertinsky, 1992, Choice of entry timing and scale by foreign
banks in Japan and Korea, Journal of Banking and Finance 16, 405-421.
Williams, B., 1998a, Factors affecting the performance of foreign-owned banks in
Australia: A cross-sectional study, Journal of Banking and Finance 22, 197-219.
Williams, B., 1998b, A pooled study of the profits and size of foreign banks in
Australia, Journal of Multinational Financial Management 8, 211-231.
17
Williams, Barry, 1997, Positive theories of multinational banking: Eclectic theory
versus Internalisation theory, Journal of Economic Surveys 11, 71-100.
Williams, Barry, 2002, The defensive expansion approach to multinational banking:
Evidence to date, Financial Markets, Institutions & Instruments 11, 127-203.
Williams, Barry, 2003, Domestic and international determinants of bank profits:
Foreign banks in Australia, Journal of Banking and Finance 27, 1185-1210.
18
Table 1: Summary of Models Employed.
Model 1
Model 1a (retail model)
Model 1b (wholesale model)
Model 2 (revenue model)
19
Inputs
(i) employees,
(ii) deposits and borrowed
funds,
(iii) equity capital.
(i) employees,
(ii) deposits,
(iii) equity capital.
(i) employees,
(ii) deposits,
(iii) equity capital.
(i) interest expenses,
(ii) non-interest expenses.
Outputs
(i) loans,
(ii) off balance sheet activity.
(i) loans less housing loans,
(ii) housing loans,
(iii) off balance sheet activity.
(i) loans,
(ii) investments,
(iii) off balance sheet activity.
(i) net interest income,
(ii) non-interest income.
Table 2.
Descriptive statistics: Parent Characteristics.
Home Country
Home Nation Investment Income
127
-0.43
1.74
-3.53
2.34
Trade with Australia as a share of Aust. GDP
129
0.03
0.02
0.00
0.06
Home nation capital flow
128
1.28
1.27
-0.67
6.05
Home nation capital stock
129
21.87
12.70
0.75
57.06
Log (Home Nation GDP per capita)
132
10.30
0.50
7.89
11.10
Home Return on Assets
132
0.63
0.62
-1.66
3.02
Home Net Interest Margin
121
2.52
1.37
-1.62
4.93
Log(Home Assets) (Avg. Annual Ex Rate)
132
12.13
1.04
9.04
14.04
Log(Home Capital) (Avg. Annual Ex. Rate)
127
9.44
1.10
6.79
12.71
Experience in Aust. (years)
132
19.50
16.93
0.00
66.00
Ranked Home Credit Rating
109
97.38
54.74
16.00
183.50
132
0.58
0.08
0.46
0.71
Dummy English Language
132
0.58
0.49
0.00
1.00
Real growth differential to Australia
132
-0.30
2.61
-6.37
9.06
Index of comparative economic size
132
0.19
0.17
0.02
1.00
Index of comparative economic development
132
0.76
0.18
0.08
1.00
Dummy Canada
132
0.02
0.12
0.00
1.00
Dummy Germany
132
0.05
0.21
0.00
1.00
Dummy Hong Kong
132
0.01
0.09
0.00
1.00
Dummy Japan
132
0.26
0.44
0.00
1.00
Dummy Jordan
132
0.02
0.15
0.00
1.00
Dummy Singapore
132
0.05
0.21
0.00
1.00
Dummy Switzerland
132
0.02
0.15
0.00
1.00
Dummy UK
132
0.32
0.47
0.00
1.00
Dummy USA
132
0.25
0.43
0.00
1.00
Dummy Bank WA
132
0.06
0.24
0.00
1.00
Dummy ING
132
0.02
0.12
0.00
1.00
Parent Bank
Host Nation
Competitor Market share
New Trade Theory
Control
20
Table 3 Factor Analysis Results.
Rotated Factor Matrix (a)
Factor
1
Home Nation
Investment
Income
Trade with
Aust. As a
share of Aust.
GDP
Home nation
capital flow
Home nation
capital stock
Log (home
nation GDP
per capita)
Home ROA
Home NIM
2
3
4
5
-.068
-.498
-.433
-.124
-.543
.963
.147
-.112
-.108
-.157
.064
.661
-.128
.084
.108
-.003
.863
.190
.388
.194
.750
.139
.485
-.108
.230
-.088
.133
-.087
.079
.848
-.391
.141
-.002
.561
.118
.261
-.385
-.242
.078
.605
.053
.680
.120
.525
-.711
-.101
.130
.141
-.113
-.017
-.274
.106
.014
.085
.724
.011
Log (Home
assets) (Avg.
.461
.381
Annual Ex.
Rate)
Experience in
-.073
.203
Aust (years)
Competitor
.092
.246
Mkt. Share
Real growth
differential to
-.011
.093
Aust.
Index of
-.746
-.039
similarity of
GDP
Index of
similarity of
-.926
.121
GDP per capita
Ranked Home
.126
.042
Credit Rating.
Extraction Method: Alpha Factoring.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 7 iterations.
21
Table 3 Factor Analysis Results (cont’d).
Rotated Factor Matrix (a)
Factor
1
Home Nation
Investment
Income
Trade with
Aust. As a
share of Aust.
GDP
Home nation
capital flow
Home nation
capital stock
Log (home
nation GDP
per capita)
Home ROA
Home NIM
2
3
4
5
-.069
-.481
-.563
-.160
-.341
.897
.117
-.131
-.099
-.058
.068
.617
.118
.077
-.087
.010
.726
.271
.364
.122
.729
.092
.289
-.096
.465
-.093
.127
.648
.109
-.066
.086
.583
-.039
-.188
-.287
.282
.085
.516
.046
.576
.124
.561
.006
-.102
-.587
.007
-.154
.177
-.018
.095
-.266
.071
.603
.119
-.425
.160
Log (Home
assets) (Avg.
.509
.333
Annual Ex.
Rate)
Experience in
-.077
.217
Aust (years)
Competitor
.085
.257
Mkt. Share
Real growth
-.037
.047
differential to
Aust.
Index of
similarity of
-.781
-.055
GDP
Index of
-.883
.115
similarity of
GDP per capita
Ranked Home
.101
.014
Credit Rating.
Extraction Method: Image Factoring.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 6 iterations.
Alpha Factor 1 = Image Factor 1: Macroeconomic Factors.
Alpha Factor 2= Image Factor 2: Defensive Expansion
Alpha Factor 3= Image Factor 5: Mixed Factor; Home and Host Effects
Alpha Factor 4= Image Factor 4: Parent Factors
Alpha Factor 5= Image Factor 3: Mixed Factor; Home / Host / Parent
22
Table 4.
Random effects models with bank- and time-specific effects.
Including dummy variables for nationality. t-statistics in parentheses.
TE_M1
TE_M1
TE_M1A
alpha
img
alpha
0.69
0.76
(3.58)
(3.41)
Constant
Constant
0.02
0.05
ALPHAFAC1 IMGFAC1
(0.38)
(0.67)
0.01
0.03
ALPHAFAC2 IMGFAC2
(0.20)
(0.87)
-0.02
-0.02
(-1.15)
(-0.73)
ALPHAFAC3 IMGFAC5
0.01
0.04
(0.18)
(0.85)
ALPHAFAC4 IMGFAC4
-0.05
-0.08
ALPHAFAC5 IMGFAC3
(-2.30)
(-2.66)
0.21
0.16
(1.06)
(0.66)
DUMUK
-0.08
-0.20
(-0.35)
(-0.76)
DUMUS
0.12
0.04
DUMJP
(0.51)
(0.14)
0.27
0.29
(1.07)
(0.97)
DUMCAN
0.26
0.20
(1.10)
(0.71)
DUMDE
-0.09
0.01
DUMING
(-0.35)
(0.04)
Alpha Factor 1 = Image Factor 1: Macroeconomic Factors.
Alpha Factor 2= Image Factor 2: Defensive Expansion.
Alpha Factor 3= Image Factor 5: Mixed Factor; Home and Host Effects.
Alpha Factor 4= Image Factor 4: Parent Factors.
Alpha Factor 5= Image Factor 3: Mixed Factor; Home / Host / Parent
TE_M1A
TE_M1B
TE_M1B
TE_M2
img
alpha
Img
alpha
0.88
(6.08)
0.00
(-0.05)
0.06
(2.04)
-0.03
(-1.33)
0.00
(0.02)
-0.05
(-2.05)
-0.03
(-0.21)
-0.13
(-0.76)
-0.09
(-0.45)
0.09
(0.53)
0.14
(0.87)
-0.13
(-0.70)
0.91
(5.92)
-0.04
(-0.47)
0.08
(2.57)
-0.01
(-0.22)
0.04
(1.01)
-0.06
(-2.18)
-0.10
(-0.61)
-0.17
(-0.89)
-0.04
(-0.19)
0.06
(0.32)
0.09
(0.53)
-0.13
(-0.49)
23
0.82
(5.82)
-0.06
(-1.22)
0.00
(0.16)
0.00
(-0.28)
0.02
(0.93)
-0.03
(-1.87)
0.00
(0.03)
-0.06
(-0.35)
0.13
(0.70)
0.11
(0.61)
0.04
(0.26)
-0.11
(-0.57)
0.92
(6.39)
0.00
(0.07)
0.03
(1.01)
0.00
(-0.11)
0.03
(0.79)
-0.06
(-2.68)
-0.03
(-0.20)
-0.20
(-1.16)
-0.04
(-0.24)
0.07
(0.36)
0.01
(0.03)
-0.13
(-0.56)
TE_M2
img
0.35
(1.56)
-0.15
(-1.80)
-0.09
(-2.31)
0.03
(1.21)
-0.11
(-2.59)
0.03
(1.01)
0.42
(1.74)
0.76
(2.82)
0.59
(2.09)
0.23
(0.82)
0.00
(0.00)
0.00
(0.00)
-0.36
(-0.94)
-0.29
(-2.48)
-0.04
(-0.97)
0.10
(1.98)
-0.14
(-2.34)
0.06
(1.20)
0.96
(2.84)
1.56
(3.27)
1.44
(2.92)
0.61
(1.82)
0.00
(0.00)
0.00
(0.00)
Table 5.
General to Specific Reduced Form Model.
Random effects models with bank- and time-specific effects (t-statistics in parentheses).
Constant
Constant
ALPHAFAC1
IMGFAC1
ALPHAFAC2
IMGFAC2
ALPHAFAC3
IMGFAC5
ALPHAFAC4
IMGFAC4
ALPHAFAC5
IMGFAC3
DUMUK
DUMUS
TE_M1
TE_M1
TE_M1A
TE_M1A
TE_M1B
TE_M1B
TE_M2
TE_M2
alpha
img
alpha
img
alpha
img
alpha
img
0.73
(15.89)
0.83
(18.65)
0.80
(29.72)
0.05
(2.67)
0.82
(35.90)
-0.04
(-2.03)
0.05
(2.20)
0.88
(39.63)
0.88
(40.60)
0.84
(18.17)
-0.02
(-1.85)
-0.07
(-2.50)
-0.06
(-3.87)
0.14
(1.66)
-0.08
(-4.64)
-0.04
(-2.73)
-0.05
(-2.81)
-0.19
(-2.28)
-0.05
(-3.94)
-0.13
(-3.07)
-0.12
(-3.02)
-0.18
(-1.80)
-0.17
(-1.73)
DUMCAN
0.19
(1.68)
DUMING
Alpha Factor 1 = Image Factor 1: Macroeconomic Factors.
Alpha Factor 2= Image Factor 2: Defensive Expansion.
Alpha Factor 3= Image Factor 5: Mixed Factor; Home and Host Effects.
Alpha Factor 4= Image Factor 4: Parent Factors.
Alpha Factor 5= Image Factor 3: Mixed Factor; Home / Host / Parent.
24
0.10
(2.07)
-0.14
(-2.66)
-0.06
(-4.29)
DUMJP
DUMDE
-0.29
(-0.77)
-0.26
(-2.41)
0.90
(2.74)
1.44
(3.15)
1.32
(2.78)
0.61
(1.73)
Appendix.
Table A1: Sample Characteristics.
2
1
3
6
2
3 13 18
4
8
7 19
3
8 15 26
3
4 12 19
3
8 15 26
4
9
8 21
3
8 13 24
3
8 13 24
3
8 13 24
4 10
7 21
4
9 13 26
4
9 13 26
4
9 13 26
4 10
7 21
4
9 12 25
4
9 12 25
4
9 12 25
4 10
7 21
4
9 11 24
4
9 11 24
4
9 11 24
4 12
7 23
Total
3 13 18
Total
Foreign
Big4
Other
Domestic
Model 2
2
Total
Foreign
Big4
Other
Domestic
Model 1b
Total
Big4
Other
Domestic
Model 1a
Foreign
198
8
198
9
199
0
199
1
199
2
199
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
Foreign
Big4
Other
Domestic
Model 1
4 10 11 25
4 10 11 25
4 10 11 25
4 10
7 21
4 10
9 23
4 10
9 23
4 10
9 23
4 11
5 20
4 10
6 20
4 10
6 20
4 10
6 20
4 11
4 19
4
7
6 17
4
7
6 17
4
7
6 17
4
8
4 16
4
5
4 13
4
5
4 13
4
5
4 13
4
9
5 18
4
5
5 14
4
5
5 14
4
5
5 14
4
7
6 17
4
5
4 13
4
5
4 13
4
5
4 13
4
8
7 19
4
5
3 12
4
5
3 12
4
5
3 12
4
8
4 16
Model 1: Inputs: (i) employees, (ii) deposits, (iii) equity capital. Outputs: (i) loans, (ii)
off balance sheet activity. Model 1a: Inputs: (i) employees, (ii) deposits, (iii) equity
capital. Outputs: (i) loans less housing loans, (ii) housing loans (iii) off balance sheet
activity. Model 1b Inputs: (i) employees, (ii) deposits, (iii) equity capital. Outputs: (i)
loans, (ii) investments, (iii) off balance sheet activity. Model 2: Inputs: (i) interest
expenses, (ii) non-interest expenses. Outputs: (i) net interest income, (ii) non-interest
income.
25
Table A2.
Descriptive Statistics.
Australian Bank data.
All values in $A,000 except Employees.
Panel A: All banks
Series
Deposits
Employees
Housing loans
Loans
Non-interest income
Off balance sheet activity
Equity capital
Interest income
Interest expense
Investments
Non-interest expense
Obs
334
341
361
334
321
304
364
324
322
334
283
Mean
16,627,473
7,497
4,080,611
17,246,386
494,091
7,925,736
1,699,438
2,030,574
1,324,700
3,599,697
872,726
Std. Error Minimum Maximum
31,670,293 2,607 191,000,000
14,146
43
50,366
7,811,910
0
47,679,000
33,448,542 188,471 208,000,000
961,216
1,678
6,522,999
17,324,183
0
96,141,000
3,484,645 21,999 23,556,999
3,564,893 31,235 19,918,999
2,272,543
6,150
12,958,999
6,158,195
2,700
45,165,999
1,440,134
8,131
8,348,999
Panel B: Big four banks:
Series
Obs
Mean
Std. Error Minimum Maximum
Deposits
56 78,631,183 34,541,815 27,577,499 191,000,000
Employees
56
38,552
6,608
22,500
50,366
Housing loans
56 18,759,232 9,994,634 4,370,841 47,679,000
Loans
56 81,615,712 38,934,619 26,445,398 208,000,000
Non-interest income
56 2,124,322 1,125,816 626,499
6,522,999
Off balance sheet activity 52 41,359,625 19,934,037 5,510,000 96,141,000
Equity capital
56 8,674,520 4,393,153 2,491,899 23,556,999
Interest income
56 9,281,054 2,835,641 4,902,799 19,918,999
Interest expense
56 5,834,337 2,030,585 3,103,399 12,958,999
Investments
56 14,866,742 6,556,399 6,403,099 45,165,999
Non-interest expense
56 3,450,101 1,160,910 1,799,699 8,348,999
Panel C: Other domestic banks
Series
Obs Mean
Deposits
134 6,803,024
Employees
134
2,242
Housing loans
139 2,544,646
Loans
134 6,655,576
Non-interest income
133 233,326
Off balance sheet activity 119 1,392,368
Equity capital
157 597,805
Interest income
133 763,401
Interest expense
133 538,757
Investments
134 1,932,073
Non-interest expense
131 334,516
26
Std. Error
7,403,338
2,368
3,461,435
7,763,194
565,918
1,877,616
815,295
764,347
550,861
3,369,209
574,008
Minimum
267,770
45
0
188,471
1,678
0
21,999
46,361
6,150
54,484
22,322
Maximum
37,853,918
11,495
20,300,100
39,698,998
4,331,999
9,826,000
3,858,999
3,310,999
2,457,599
29,246,999
4,260,999
Panel D: Foreign banks
Series
Deposits
Employees
Housing loans
Loans
Non-interest income
Off balance sheet activity
Equity capital
Interest income
Interest expense
Investments
Non-interest expense
Obs
144
151
166
144
132
133
151
135
133
144
96
Mean
1,657,115
644
414,926
2,069,206
65,217
699,486
258,060
271,368
211,849
769,886
103,689
27
Std. Error Minimum Maximum
2,037,772 2,607 12,322,799
788
43
3,311
1,093,813
0
6,386,400
2,564,837 295,810 16,633,098
107,813
2,121
580,545
893,698
5,772
5,086,258
304,297
21,999 1,576,768
276,123
31,235 1,344,199
198,942
21,494
947,099
914,925
2,700
5,051,665
111,618
8,131
568,217
Table A3.
Average efficiency scores.
All Banks.
Series
Obs Mean Std.Error Minimum Maximum
Model 1
280 0.83
0.12
0.24
0.96
Model 1a 261 0.83
0.09
0.51
0.96
Model 1b 280 0.86
0.08
0.51
0.97
Model 2
272 0.87
0.10
0.16
0.97
Correlation between efficiency scores: All Banks.
Observations \ Correlation Model 1 Model 1a Model 1b Model 2
Model 1
280
0.70
0.63
-0.03
Model 1a
261
261
0.61
-0.01
Model 1b
280
261
280
-0.08
Model 2
232
221
232
272
Foreign Banks.
Series
Obs Mean Std.Error Minimum Maximum
Model 1
125 0.80
0.17
0.24
0.96
Model 1a 112 0.83
0.11
0.51
0.96
Model 1b 125 0.85
0.10
0.51
0.97
Model 2
85 0.85
0.13
0.16
0.97
Correlation between efficiency scores: Foreign Banks.
Observations \ Correlation Model 1 Model 1a Model 1b Model 2
Model 1
125
0.74
0.68
-0.15
Model 1a
112
112
0.64
-0.16
Model 1b
125
112
125
-0.17
Model 2
78
73
78
85
Model 1: Inputs: (i) employees, (ii) deposits, (iii) equity capital. Outputs: (i) loans, (ii)
off balance sheet activity.
Model 1a: Inputs: (i) employees, (ii) deposits, (iii) equity capital. Outputs: (i) loans less
housing loans, (ii) housing loans (iii) off balance sheet activity.
Model 1b Inputs: (i) employees, (ii) deposits, (iii) equity capital. Outputs: (i) loans, (ii)
investments, (iii) off balance sheet activity.
Model 2: Inputs: (i) interest expenses, (ii) non-interest expenses. Outputs: (i) net interest
income, (ii) non-interest income.
28
Table A4.
KMO and Bartlett's Tests of Factorability.
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
Bartlett's Test of
Sphericity
Approx. Chi-Square
df
Sig.
.664
1203.066
91
.000
29
Table A5.
Total Variance Explained
Factor
Initial Eigenvalues
Total
Extraction Sums of Squared Loadings
Total
Rotation Sums of Squared Loadings
% of Variance
Cumulative %
% of Variance
Cumulative %
1
3.881
27.720
27.720
3.589
25.633
25.633
Total
3.315
% of Variance
23.681
Cumulative %
23.681
2
3.354
23.954
51.674
3.152
22.517
48.150
1.782
12.727
36.408
3
1.555
11.110
62.783
1.236
8.832
56.982
1.639
11.705
48.114
4
1.439
10.275
73.059
1.051
7.505
64.487
1.605
11.466
59.580
5
1.111
7.938
80.997
.824
5.882
70.369
1.510
10.789
70.369
6
.658
4.701
85.697
7
.567
4.052
89.750
8
.421
3.006
92.755
9
.394
2.813
95.568
10
.185
1.322
96.891
11
.170
1.212
98.102
12
.116
.825
98.928
13
.091
.653
99.581
14
.059
.419
100.000
Extraction Method: Alpha Factoring.
Total Variance Explained
Factor
Initial Eigenvalues
Total
% of Variance
Cumulative %
Extraction Sums of Squared Loadings
% of
Cumulative
Total
Variance
%
Rotation Sums of Squared Loadings
% of
Total
Variance
Cumulative %
1
3.881
27.720
27.720
3.522
25.158
25.158
3.208
22.914
22.914
2
3.354
23.954
51.674
2.773
19.810
44.968
1.447
10.333
33.247
3
1.555
11.110
62.783
.974
6.955
51.923
1.311
9.366
42.612
4
1.439
10.275
73.059
.723
5.161
57.084
1.306
9.330
51.942
5
1.111
7.938
80.997
.502
3.583
60.668
1.222
8.726
60.668
6
.658
4.701
85.697
7
.567
4.052
89.750
8
.421
3.006
92.755
9
.394
2.813
95.568
10
.185
1.322
96.891
11
.170
1.212
98.102
12
.116
.825
98.928
13
.091
.653
99.581
14
.059
.419
100.000
Extraction Method: Image Factoring.
30
Table A6.
Random effects models with bank- and time-specific effects.
No Dummy variables for Nationality. t-statistics in parentheses
Constant
Constant
ALPHAFAC1
IMGFAC1
ALPHAFAC2
IMGFAC2
ALPHAFAC3
IMGFAC5
ALPHAFAC4
IMGFAC4
ALPHAFAC5
IMGFAC3
TE_M1
TE_M1
TE_M1A
TE_M1A
alpha
0.78
(18.64)
-0.02
(-0.74)
-0.01
(-0.33)
-0.01
(-0.62)
-0.01
(-0.33)
-0.05
(-2.52)
img
alpha
img
0.78
(15.59)
-0.01
(-0.21)
0.01
(0.41)
0.00
(0.02)
0.01
(0.25)
-0.08
(-2.73)
0.82
(27.50)
-0.04
(-2.00)
0.03
(1.40)
-0.02
(-0.94)
-0.01
(-0.56)
-0.04
(-1.93)
0.82
(25.61)
-0.04
(-1.71)
0.05
(2.02)
0.00
(-0.20)
0.00
(0.19)
-0.05
(-2.04)
Alpha Factor 1 = Image Factor 1: Macroeconomic Factors.
Alpha Factor 2= Image Factor 2: Defensive Expansion
Alpha Factor 3= Image Factor 5: Mixed Factor; Home and Host Effects
Alpha Factor 4= Image Factor 4: Parent Factors
Alpha Factor 5= Image Factor 3: Mixed Factor; Home / Host / Parent
31
TE_M1B
TE_M1B
TE_M2
TE_M2
alpha
img
alpha
0.86
(15.91)
-0.04
(-0.82)
-0.04
(-1.21)
0.00
(0.02)
-0.07
(-2.26)
0.03
(1.01)
Img
0.84
(30.06)
-0.04
(-1.67)
-0.01
(-0.28)
-0.01
(-0.48)
0.00
(-0.02)
-0.04
(-2.52)
0.84
(29.33)
-0.02
(-0.81)
0.01
(0.33)
0.01
(0.33)
0.00
(-0.07)
-0.06
(-2.70)
0.85
(14.64)
-0.01
(-0.15)
0.01
(0.20)
-0.03
(-0.85)
-0.07
(-1.58)
0.04
(0.93)
Table A7.
General to Specific Reduced Form Model.
Random effects models with bank- and time-specific effects
No Dummy variables for Nationality. t-statistics in parentheses
TE_M1 TE_M1 TE_M1A TE_M1A TE_M1B TE_M1B TE_M2 TE_M2
alpha
img
alpha
img
alpha
img
alpha
img
Constant
Constant
ALPHAFAC1
IMGFAC1
ALPHAFAC2
IMGFAC2
ALPHAFAC3
IMGFAC5
ALPHAFAC4
IMGFAC4
ALPHAFAC5
IMGFAC3
0.78
(19.82)
0.78
(20.52)
0.82
(35.62)
-0.05
(-2.35)
0.03
(1.96)
0.82
(35.90)
-0.04
(-2.03)
0.05
(2.20)
0.84
(36.41)
-0.03
(-1.88)
0.84
(47.03)
0.84
(18.17)
0.84
(15.49)
-0.07
(-2.50)
-0.07
(-1.99)
-0.02
(-1.80)
-0.06
(-3.86)
-0.08
(-4.64)
-0.04
(-2.82)
-0.05
(-2.81)
Alpha Factor 1 = Image Factor 1: Macroeconomic Factors.
Alpha Factor 2= Image Factor 2: Defensive Expansion
Alpha Factor 3= Image Factor 5: Mixed Factor; Home and Host Effects
Alpha Factor 4= Image Factor 4: Parent Factors
Alpha Factor 5= Image Factor 3: Mixed Factor; Home / Host / Parent
32
-0.04
(-3.52)
-0.06
(-4.36)