Emerging Markets Finance and Trade Effect of Internationalization

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Effect of Internationalization on the Cost
Efficiency of Taiwan’s Banks
Cheng-Ping Cheng, Lien-Wen Liang & Chen-Ta Huang
Published online: 08 Apr 2015.
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To cite this article: Cheng-Ping Cheng, Lien-Wen Liang & Chen-Ta Huang (2014) Effect of
Internationalization on the Cost Efficiency of Taiwan’s Banks, Emerging Markets Finance and Trade,
50:sup6, 204-228, DOI: 10.1080/1540496X.2014.1013857
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Effect of Internationalization on the Cost
Efficiency of Taiwan’s Banks
Cheng-Ping Cheng, Lien-Wen Liang, and Chen-Ta Huang
ABSTRACT: We focus on the effect of internationalization on the cost efficiency of banks by
studying Taiwan as a sample for developing countries. We find that (1) increasing overseas
businesses and foreign exchange deposits increases cost efficiency; (2) expanding offshore
banking units increases bank efficiency; and (3) the profitability of a bank’s overseas branch
is not a critical factor behind the differences in cost efficiency across both financial holding
company (FHC) banks and non-financial holding company (non-FHC) banks. Finally, our
metafrontier empirical results illustrate that FHC banks in Taiwan show better technical
performance in cost control than non-FHC banks.
KEY WORDS: banking efficiency, internationalization, metafrontier approach, stochastic
frontier approach.
The internationalization of banks began several centuries ago and is still undergoing
a continuous process of development (Lothian 2002). Many studies in the literature
explore the motivations and modes of entry of internationalization (Batten and Szilagyi
2011; Focarelli and Pozzolo 2008), the relationship between internationalization and
the performance of banks (Bayraktar and Wang 2004, 2005; Outreville 2010), and the
bank risks resulting from national regulations and internationalization (Klomp and de
Haan 2012; ul-Haq et al. 2012). While several researchers have attempted to analyze the
profitability and profit efficiency of globalized banks (Buch et al. 2011; Claessens et al.
2001), there are limited research studies covering the effect of internationalization on
the cost efficiency of banks.
The literature of internationalization explores a variety of topics with respect to
developed countries, such as Australia, Canada, Germany, Japan, and the United States
(Batten and Szilagyi 2011; Buch et al. 2011), but few look at these topics with respect
to developing countries. Taiwan is one of the most successful examples of a developing
country. The internationalization of its financial system started in the 1980s but has only
in the 2000s reached its peak. Therefore, studying the effect of internationalization on
Taiwan’s economic performance provides a good sample for developing countries.
Financial internationalization has been achieved in Taiwan through several means. At
its early stage in the 1960s, Taiwan’s government opened the market to foreign banks,
enabling them to set up business branches. By the end of 2010, 31 foreign banks had set
up 132 branch offices in Taiwan. Following the boom in outflowing foreign direct investment (FDI) by Taiwan businesses and the potential foothold in international financial
markets in the 1990s, Taiwan banks set up branch offices abroad. As of 2010, Taiwan
Cheng-Ping Cheng (cpcheng@yuntech.edu.tw) is an associate professor in the Department of
Finance at National Yunlin University of Science and Technology, Douliou, Taiwan. Lien-Wen
Liang (liang.lienwen@gmail.com), corresponding author, is an associate professor in the Department of Banking and Finance at Chinese Culture University, Taipei City, Taiwan. Chen-Ta Huang
(y0826@ms33.hinet.net) is an authorization clerk with the Hong Kong and Shanghai Banking
Corporation Limited, New Taipei City, Taiwan.
Emerging Markets Finance & Trade / November–December 2014, Vol. 50, Supplement 6, pp. 204–228.
© 2014 Taylor & Francis Group, LLC. All rights reserved. Permissions: www.copyright.com
ISSN 1540–496X (print) /ISSN 1558–0938 (online)
DOI: 10.1080/1540496X.2014.1013857
November–December 2014, Volume 50, Supplement 6 205
banks had 251 overseas affiliates, including 88 overseas branches, 36 overseas representative offices, and 127 subsidiaries. Another important means for internationalization of
Taiwan’s banks is using offshore banking units (OBUs) to handle international business.
The OBU channel is extremely important for Taiwan’s outward FDI into China because
China has not largely opened up its financial market. Following the dramatic increase in
trade and FDI between Taiwan and China, the scale of OBUs has grown quickly and has
become an important path for Taiwan’s financial internationalization.
Although not many studies target the effect of internationalization on the cost efficiency
of banks, many clues can be found from the literature to support the possible relationship
between cost performance and internationalization. For example, going international
implies that firms can spread fixed costs, such as operating overhead and research and
development expenditures, through a greater scale and scope (Markusen 1984). Internationalization offers firms opportunities to advance administrative technology about
domestic markets from their international market experience, thus likely improving their
competitiveness (Yuan 2008). Operating in foreign jurisdictions allows firms to access
production factors at a lower cost (Porter 1990). Internationalization also permits firms
to cross subsidize their domestic operations and provides greater opportunities for price
discrimination and tax and price arbitrage (Yuan 2008). Internationalization attracts more
stable sources of funds to expand the scale and scope effects of the domestic financial
sector. Moreover, since internationalization is related to domestic financial deregulation,
it influences the quality and competitiveness of domestic financial services providers
(Claessens and Glaessner 1998).
Taiwan in recent years has adopted financial policies seeking to liberate restrictions on
trade with, and investment in, China. Policy highlights include the financial Memorandum
of Understanding sealed between Taiwan and China in November 2009 and the Economic
Cooperation Framework Agreement signed in June 2010. The Taiwan government and
local banks now have to find a way to ensure a solid foothold in China’s massive financial
market in the context of competition from giant international players.
After the Financial Holding Act was instituted in 2001, many Taiwan banks were
merged into financial holding companies (FHCs) in an attempt to integrate their business
and to save management costs. Although a major part of the relevant literature shows
that there is a significant difference in the general structure and in many other parts of
management between FHC banks and non-FHC banks, most studies on the management
performance of banks do not clearly distinguish between these two groups. Therefore, this
paper intends to explore the issues of whether different internationalization effects exist
between the two groups of banks and on the possibility of constructing a metafrontier
cost function based on the two groups’ cost frontier functions.
We use the stochastic frontier analysis method to investigate the uncertain relationship
between the extent of internationalization and the cost efficiency of Taiwan’s banks. We
treat a bank as an intermediate financial institute for the transfer of resources from inputs
to financial services. We set up a translog-type cost frontier function that specifics multiple inputs and outputs. Our data are mainly from the database of the Taiwan Economic
Journal in which we look at thirty-two banks between 2002 and 2009.
To select a set of effective variables to represent complicated international strategies,
we use principal component analysis (PCA) to extract the main components of a variety
of internationalization extent indexes. To build an inefficiency model, in addition to the
internationalization variable, we input some control variables in the model, such as the
factors of branches, profit, and risk.
206 Emerging Markets Finance & Trade
Following Battese and Coelli (1995), we implement the maximum likelihood estimation
method to simultaneously estimate the stochastic cost efficiency model and the inefficient
model. By respectively estimating the effect of the extent of internationalization on the
cost efficiency of Taiwan’s FHC banks and non-FHC banks, we are able to determine
the critical factors that affect the cost inefficiency of these two kinds of banks. Finally,
to compare the different performances among all of Taiwan’s banks, we calculate the
technology gap ratio and meta-cost efficiency for all FHC subsidiary banks and non-FHC
banks using the metafrontier approach of Battese et al. (2004).
Review of the Literature
Internationalization and Efficiency of Banks
The internationalization of finance and the globalization of financial markets are not new
phenomena. They are part of an evolutionary process that began in the eleventh century
and has continued. What we are seeing today is the latest and most advanced manifestation of this process (Lothian 2002). There are many studies throughout the literature that
cover bank internationalization.
One of the important issues in the literature is the motivation for bank internationalization.1 There are two major but opposite theories about cross-border bank expansion. The
“follow the client” thesis states that the cross-border expansion of banks is a by-product of
internationalization in manufacturing because banks follow their home clients when they
operate abroad. A more recent trend emphasizes that in a number of cases, the pattern of
cross-border expansion is independent of the relationship with home country clients and
is instead shaped by the opportunity to profit from financial services in the foreign market.
Batten and Szilagyi (2011) find that internationalization of Japan’s banks appears to be at
odds with customer-related motivations, although such a low-risk strategy is consistent
with the effects of asymmetries in information and risk aversion. However, this debate
has not yet reached a definitive conclusion (Focarelli and Pozzolo 2008).
Many research studies examine profitability resulting from the entry of foreign banks
into the domestic banking sector (Levine 1996; Peek and Rosengren 2000). DemirgüçKunt and Huizinga (1999) show that foreign banks in developing countries tend to
have greater profits and higher interest margins compared to domestic banks, while the
opposite situation is true in developed countries. Claessens et al. (2001) examine foreign
bank operations in eighty countries and find that foreign banks experience lower (higher)
net interest margins, overhead expenses, and profits than domestic banks in developed
(developing) countries. Peek et al. (1999) support the perspective that domestic banks
have better performance than foreign banks for banks operating in the United States.
Nolle (1995) also concludes that, according to aggregate profits, foreign-owned banks
are not as profitable as domestically owned banks in the United States.
After the global financial tsunami in 2008, some researchers began to target the
contagion of risk in the international banking context, thus exploring the relationship
between bank internationalization and risk (ul-Haq et al. 2012). Klomp and de Haan
(2012) study the effect of bank regulations and supervision on banking risk by examining
twenty-five indicators of banking risk. They find that banking regulation and supervision
affect the risks of high-risk banks. Berger et al. (2013) investigate the effects of bank
internationalization on risk taking. They show that internationalization increases bank
risk taking: the Z-score of U.S. banks that engage in foreign activities is lower than that
of their purely domestic peers. Their results are consistent with the empirical dominance
of the market risk hypothesis, whereby internationalization increases banks’ risk due to
market-specific factors, rather than the diversification hypothesis, whereby internationalization allows banks to reduce risk through increased diversification of their operations
(Berger et al. 2013).
Whether internationalization increases efficiency and productivity is part of the
story of the general performance and competitiveness of banks. Berger et al. (2001)
consider two alternative hypotheses to explain differences in the performance of foreign
and domestic banks. The home-field-advantage hypothesis argues that domestic banks
generally outperform foreign banks because of informational and cost advantages. The
global-advantage hypothesis states that foreign banks possess sufficient efficiency gains
to overcome any home-field advantages. Berger et al. (2001) find that domestic banks
exhibit higher cost and profit efficiencies than foreign banks, supporting the home-fieldadvantage hypothesis. However, they also present some support for the global-advantage
hypothesis; that is, foreign banks from the United States generally exceed the cost and
profit efficiencies of domestic banks.
Bayraktar and Wang (2004, 2005) show that foreign banks play a statistically and
economically significant role in improving the efficiency and competitiveness of domestic
banks by reducing costs, profits, and net interest margins. Lensink and Hermes (2004)
note that foreign banks might increase the quality of human capital in the banking system either by importing highly skilled bank managers to work in their branches or by
training local employees.
Outreville (2010) suggests that the relationship between international diversification
and performance may follow a S-shaped curve. Firms can further their own internationalization using the strong competencies that they have developed over time in foreign
markets. However, it is difficult for a firm to assess when it is overinternationalized.
Most research shows that foreign bank entry increases the efficiency of the domestic
banking sector. However, some studies conclude that banks with higher foreign bank
ownership involvement are associated with higher efficiency (Claessens et al. 2001;
Hasan and Marton 2003).
Regarding the theory of productivity and efficiency, the effect of internationalization on
a bank’s performance can be analyzed by technical efficiency, cost efficiency, and profit
efficiency. Many research studies analyze technical efficiency by the data envelopment
analysis approach, but the stochastic frontier analysis (SFA) approach is also an effective
method for analyzing profit efficiency and cost efficiency. For example, using a profitefficiency model, DeYoung and Nolle (1996) conclude that foreign banks have a distinct
disadvantage in terms of input inefficiency rather than output inefficiency, primarily as
a result of expenditures on purchased funds.
The Internationalization of Taiwan Banks
When Taiwan joined the World Trade Organization in 2001, it committed to several actions
within the financial services sector: (1) deregulation of the restrictions on foreign banks
establishing branches and representative offices in Taiwan; (2) elimination of the ceiling
level of New Taiwan dollar (TWD)–denominated deposits that foreign banks may hold;
(3) enablement of foreign insurance companies to operate in Taiwan; (4) elimination
of the bans on banks providing underwriting and certification services for commercial
paper; (5) enablement of foreigners to establish billing companies in the financial ser-
vices sector; and (6) relaxation of limitations on foreign investment in foreign currency
brokerages. Since then, Taiwan’s banking sector has continued in the direction of further
deregulation and liberalization.
Table 1 shows that, as of the end of March 2010, 31 foreign banks had set up 132
branch offices in Taiwan. Another 13 foreign banks had established representative offices
on the island. These foreign banks have brought with them a great number of techniques
and experiences in handling financial asset securitization.
As of March 2010, Taiwan’s financial sector boasts 88 overseas branches, 36 representative offices, and 127 other affiliates. Figure 1 shows the distribution.
Following the trend for expansion, Taiwan’s domestic banks have quickened their
pace of internationalization and rapidly increased their number of overseas affiliates in
the form of overseas branches, representative offices, and other affiliates. Taiwan banks
have 251 overseas affiliates as of March 2010. Of the 88 overseas branches, the largest
number are in the United States (22), followed by Hong Kong (18). Of the 36 overseas
representative offices, Vietnam has the most (13), followed by China (9). Of the 127
subsidiaries, the largest number are in the United States (47), followed by the Philippines
(25) and Vietnam (25). Most Taiwan banks select the United States as a host country (70),
then Vietnam (46), the Philippines (29), and Hong Kong (26).
To bring Taiwan’s financial regulations closer in line with international business
practices, Taiwan’s government launched the Financial Institutions Merger Law and
Financial Holding Company Act on December 13, 2000, and July 9, 2001, respectively.
These two legislations were formed by making reference to the operational systems of
financial holding companies with stipulations pertaining to the mergers and acquisitions
(M&As) of financial institutions in Japan, the United States, and other countries. The
enacted regulations allow foreign banks to set up wholly owned subsidiaries in Taiwan
through direct investment or M&As.
To promote OBUs as a capital control center for Taiwan businesses, the government
has gradually amended parts of the Regulation for the Implementation of the Offshore
Banking Act in recent years. For instance, in March 2008, the Financial Supervisory
Commission loosened OBUs’ restrictions on credit limits for Taiwan businesses operating overseas. Collaterals are not a concern anymore. The maximum is universally set
at 30 percent of the net value of assets. The government also allows OBUs to conduct
a factoring of business for any transactions inside China. It also permits OBUs to grant
credits to Taiwan businesses after accepting local stocks, real estate properties, and other
TWD-denominated assets as collateral.
The overseas branch offices of Taiwan banks involved in international banking not
only serve as capital control centers for Taiwan businesses, but also boost profits for the
banking industry back home. These overseas branches have played an essential role in
ensuring a foothold for Taiwan banks in the international financial sector.
While Taiwan’s economy had been growing rapidly in the 1980s, its old-fashioned
banking sector was unable to cope with the needs that accompanied soaring economic
development. In 1989, the government decided to initiate deregulation in the banking
industry and approved the establishment of new banks. By 1992, sixteen new banks were
allowed to operate, and state-owned banks were becoming privatized. Trust investment
firms, small- and medium-size enterprise banks, and credit cooperatives transformed
themselves into commercial banks, one after another. Taiwan’s financial industry thus
officially entered an era of fierce competition. In 2001, Taiwan became a member of the
World Trade Organization, and in the same year, the government passed the Financial
36
36
35
36
33
32
32
32
31
68
69
67
68
64
83
141
133
132
Branches
104
105
102
104
97
115
173
165
163
Total
78
80
80
79
82
82
84
88
88
Branches
30
31
33
38
39
34
35
35
36
Representative
offices
70
73
78
86
101
120
123
126
127
Others
178
184
191
203
222
236
242
249
251
Total
Number of overseas branches of domestic banks
Source: Overview of Banking Sector, Banking Bureau, Financial Supervisory Commission.
2002
2003
2004
2005
2006
2007
2008
2009
2010 (March)
Head office
Number of local branches of foreign banks
Table 1. Overview of the internationalization of banks in Taiwan
0.50
0.63
0.69
0.70
0.77
0.91
0.99
0.95
—
Assets of OBU
(billion U.S.
dollars)
209
Figure 1. Overseas branches of Taiwan banks (March 2010)
Source: Overview of Banking Sector, Banking Bureau, Financial Supervisory Commission.
Holding Company Law and approved the establishment of fourteen financial holding
companies, opening a new chapter in the development of Taiwan’s financial industry.
The literature relating to studies on banking internationalization for Taiwan is limited
in comparison to papers on the same issue for Europe and the United States. Huang
(2010) stresses that entry location factors may affect the internationalization strategy
of Taiwan banks and the relationship between the banks’ internationalization and their
performances. He uses the least square dummy variable method to explore the relationship between internationalization and efficiency, although the definition of efficiency is
given a less financial meaning.2 He finds that internationalization improves operational
efficiency, which in turn increases financial performance (Huang 2010).
Accompanying the large amount of outward FDI by Taiwanese firms, Taiwan’s banks
have recently been rapidly raising their internationalization level to serve their domestic
customers abroad. However, there is still a lack of any strict research on how to analyze
the effect of internationalization of Taiwan’s banks on their efficiency. Therefore, we find
it is worthwhile to investigate the appropriate strategies of internationalization and whether
internationalization affects the cost efficiency of FHC and non-FHC banks differently.
Model Specification and Variable Selection
Stochastic Cost Frontier Function
Following Battese and Coelli (1995), we specify the following stochastic translog cost
function with three inputs and three outputs:
3
3
n =1
m =1
ln TCit = α 0 + ∑ α n ln Yn,it + ∑ β m Pm ,it +
1 3 3
∑ ∑ δ nj ln Yn,it ln Yj ,it
2 n =1 j =1
(1)
3
3
1 3 3
+ ∑ ∑ γ mk ln Pm ,it ln Pk ,it + ∑ ∑ ρnm ln Yn,it ln Pm ,it + νit + uit .
2 m =1 k =1
n =1 m =1
Here, TCit represents the total cost of the decision-making unit; Yn is the nth output (loans,
investment, and noninterest income, respectively); Pm is the mth input price (price of
funding, labor, and capital, respectively); and i is the banking firm. Moreover, α, β, δ, γ,
ρ are the parameters to be estimated, whereby νit and uit are random error terms assumed
to be mutually independent, and uit is a function of firm-specific factors that affect cost
inefficiency. Specifically, uit belongs to a truncated normal distribution—that is, given
by uit ∼ N+(mit,σ2u).
We also specify the following regression model to capture the main determinants of
X-inefficiency in the internationalization of banks in Taiwan:
mit = b0 + b1B1it + b2B2it + b3B3it + b4B4it + b5B5it + b6B6it.
(2)
We select six characteristic variables that might affect the cost inefficiency of banks.
Detailed definitions of these variables are provided in a later section.
We adopt principal component analysis to solve the multicollinearity problems that
internationalization variables might have. Principal component analysis is a procedure for
transforming a set of correlated variables into a new set of uncorrelated variables. This
transformation is a rotation of the original axes to new orientations that are orthogonal
to each other, and therefore there is no correlation between variables.3
The translog cost function is known as being flexible in the sense that it provides a
second-order approximation of the true function. Taking these estimated parameters as
given, we can examine whether the estimated cost function is concave in input prices.
Particularly, the Hessian matrix must be negative semidefinite.
In estimation, the translog cost function should satisfy the regularity condition that an
input share equals the derivative of the log cost function with respect to the corresponding
log input price (Allen and Rai 1996).4 We impose homogeneity restrictions by normalizing total costs and input prices through one of the input prices. In particular, we select
labor price as the normalizing factor.
After the normalizing process, we estimate Equations (1) and (2) simultaneously using
Frontier 4.1 software. We calculate the cost inefficiency of each bank by defining the
cost inefficiency function as CEit = e–uit, with 0 < CEit < 1, meaning that as CE increases,
cost efficiency rises.
Metafrontier Approach
Following Battese et al. (2004), we use the metafrontier cost function to compare the
performance among banks with different technologies. We define the observed cost
function for the ith bank at the tth period by the stochastic frontier for the kth group in
Equation (3):
Cit ( k ) = f ( xit ( k ) , ϕ ( k ) )e
νit ( k ) + uit ( k )
≡e
xit ( k ) ϕ( k ) + νit ( k ) + uit ( k )
.
(3)
The metafrontier function is an overarching function of a given mathematical form
that encompasses the deterministic components of the stochastic frontier cost functions
for firms that operate under the different technologies. Thus, we express the metafrontier
cost function model for FHC and non-FHC banks by
C *it = f ( xit , ϕ*) = e xit ϕ* i = 1, 2,..., N , t = 1, 2,..., T . (4)
Here, Cit* is the minimum expenditure incurred by the ith firm at the tth period, and ϕ*
denotes the vector of parameters for the metafrontier function such that
Xitϕ(k) ≥ Xitϕ*.
(5)
We formulate the measure of cost efficiency (CE ) for the ith bank at the tth period
as the ratio of the minimum cost to the observed cost, adjusted by the corresponding
random error:
*
CE *it ( k ) =
e
xit ϕ*+ νit ( k )
=e
Cit ( k )
e xit ϕ*
.
e xit ϕ ( k ) − uit ( k )
(6)
We calculate the technology gap ratio (TGR) for the observation of the sample bank
involved by
e xit ϕ*
TGR
=
.
(7)
it ( k )
e xit ϕ ( k )
This results in bank k ’s meta efficiency (CE *):
CE * = CE × TGR.
(8)
Thus, the meta-efficiency scores are the technical efficiencies of each bank in different
groups corrected by the technological gaps of the banks in a given group relative to the
technology available to the industry as a whole.
According to this reasoning, the metafrontier should be an enveloping curve of group
frontiers. The parameters of Equation (3) can be obtained by solving the following linear
programming (LP) equation provided by Battese et al. (2004):
T
N
� ( k ) ) − ln f ( xit , ϕ*) ,
min L ≡ ∑ ∑ ln f ( xit ϕ
t =1 i =1
s.t. ln f (xit, ϕ*) ≤ ln f (xit, ϕˆ(k)).
(9)
In the above equation, ϕˆ(k) is the estimated coefficient vector associated with group k’s
stochastic frontier. Since estimated coefficient vectors are fixed for the above equation,
Battese et al. (2004) define and operate an equivalent form of the linear programming.
Economies of Scale and Scope
We next investigate whether banks possess economies of scale and economies of scope.
The economies of scale (SE) measure used here is as follows:
SE =
C * ( P, Y )
3
.
(10)
∑ Y C ( P, Y )
*
i
i
i =1
*
Here, C (P, Y) is the optimal cost function, and Ci* (P, Y) is the optimal cost function
for output i of partial differential. If SE > 1, then a bank is facing increasing returns
to scale, implying that the larger its size, the lower the cost for the bank to operate. If
SE = 1, then a bank is operating at constant returns to scale. If SE < 1, then an opposite
situation occurs for decreasing returns to scale, implying that the bank is at the stage of
diseconomies of scale.
Economies of scope exist when the total cost of a firm producing more than one output
jointly is lower than the sum of the costs for producing each output separately. In the
case of a bank producing three outputs (Y1, Y2, and Y3), as suggested by Mester (1996),
the estimate for the degree of economies of scope (SC) is
(
)
(
)
, P ) − C (Y , Y , Y , P )
.
C * Y1 − 2Y1m , Y2m , Y3m , P + C * Y1m , Y2 − 2Y2m , Y3m , P
SC =
(
+ C Y , Y , Y3 − 2Y
*
m
1
m
2
m
3
C (Y1 , Y2 , Y3 , P )
*
(11)
*
1
2
3
Here, Yi is the volume of output i, i = 1, 2, 3; Yim is the minimum amount of output i produced by any bank in the sample; and C *(⋅) is the optimal cost function.5 An estimate
of SC greater than or less than zero indicates scope economies or scope diseconomies,
respectively.
Variables and Data Resources
Our data come from the database of the Taiwan Economic Journal and the Central Bank
of the Republic of China (Taiwan). It is unbalanced panel data with fourteen subsidiary
banks of financial holding companies (FHC banks) and eighteen independent banks (nonFHC banks) for the period 2002–9. We adopt an intermediation approach (Rezvanian and
Mehdian 2002) to define factor inputs and outputs of financial institutions.
According to Lang and Welzel (1999), the typical input factors for a bank include
labor, capital, and funding. The output variables for a bank consist of loans, total investment (including short- and long-term investments), and noninterest income (including
transaction fees and other forms of commercial income). Table 2 lists the definitions
and summary statistics for these variables. Since they are in nominal values, we use the
consumer price index to convert them into real values.6
Analysis of Empirical Results
Extracting the Factors of Internationalization
Following Ruigrok and Wagner (2003), Sullivan (1994), and Welch and Luostarinen
(1988), we select eleven indexes that are closely related to internationalization of Taiwan’s banks: (1) the ratio of personnel costs of overseas branches to the bank’s overall
personnel costs, (2) the ratio of business revenues obtained overseas to the bank’s overall
business revenues, (3) the assets of overseas subsidiaries, (4) the ratio of assets of overseas
subsidiaries to the bank’s overall assets, (5) the pretax profits of OBUs, (6) the number of
overseas branch offices, (7) foreign exchange deposits, (8) the ratio of personnel costs of
OBUs to the bank’s overall personnel costs, (9) the ratio of business revenues of OBUs to
the bank’s overall business revenues, (10) the ratio of pretax profits of overseas branches
to the bank’s overall profits, and (11) the pretax profits of overseas branches. To simplify
the preceding variables, we use the PCA method to extract the principal components from
the eleven indexes as the representative indexes of bank internationalization.
KMO (Kaiser–Meyer–Olkin) and Bartlett’s tests are two statistic benchmarks that
are often used to examine the effectiveness of the factor analysis model. In this paper,
the KMO value is 0.829 and the approximate chi-square is 2,913.805 (p < 0.000), thus
confirming that the data are suitable for factor analysis. Table 3 indicates the rotated
components matrix that the research seeks to analyze.
The first component’s major variables include the ratio of personnel costs of overseas
branches to the bank’s overall personnel costs, the ratio of business revenues obtained
overseas to the bank’s overall business revenues, the assets of overseas subsidiaries, the
ratio of assets of overseas subsidiaries to the bank’s overall assets, the pretax profits of
OBUs, the number of overseas branch offices, and foreign exchange deposits. Because
these variables are closely related to a bank’s overseas banking services, we name this
component “level of overseas branch.”
Loans
Investment
Noninterest income
645,731
186,868
52,998
0.3447
885,493
5,032
20,674
0.0179
1,137
Deposits + Borrowing
Total employees
Net fixed assets
Interest payments / (Deposits + Borrowing)
Employee salary / Total employees
Operating expense / Net fixed assets
26,842
Mean
(FHC)
Labor cost + Capital cost + Funding cost
Description
Note: The samples of 14 FHC and 18 non-FHC banks are 112 and 143, respectively.
Total cost (TC )
Input
Fund (X1) (billion TWD)
Labor (X2) (people)
Capital (X3) (billion TWD)
Price of funding (P1) (percent)
Price of labor (P2) (thousand
TWD)
Price of capital (P3) (percent)
Output
Output (Y1) (billion TWD)
Variable
Table 2. Definitions and descriptions of variables
397,298
137,364
4,354
0.1676
582,785
2,084
19,611
0.0062
306
16,247
Standard
deviation
(FHC)
330,880
53,481
1,541
0.3603
412,636
2,625
8,066
0.0175
988
12,009
Mean
(non-FHC)
424,504
70,908
1,336
0.2437
500,721
1,876
9,598
0.0055
275
12,942
Standard
deviation
(non-FHC)
214 November–December 2014, Volume 50, Supplement 6 215
Table 3. Rotated components matrix
Variable/
components
Assets of overseas
subsidiaries (OBU
included)
Ratio of personnel costs
of overseas branches
to the bank’s overall
personnel costs
Ratio of business
revenues obtained
overseas to the bank’s
overall business
revenues
Number of overseas
branch offices
Foreign exchange
deposits
Ratio of assets of
overseas subsidiaries
(OBU included) to the
bank’s overall assets
Pretax profits of OBUs
Ratio of personnel costs
of OBUs to the bank’s
overall personnel
costs
Ratio of business
revenues of OBUs
business revenues
Ratio of pretax profits
of overseas branches
profits
Pretax profits of
overseas branches
Total
Percent of variance
Cumulative percent
Level of
internationalization
OBU
operation
Profitability
of overseas
branches
Communalities
0.950
0.117
0.108
0.928
0.925
–0.005
–0.122
0.871
0.925
–0.036
–0.075
0.862
0.924
–0.028
0.154
0.878
0.899
0.150
0.071
0.836
0.854
0.422
0.010
0.908
0.754
–0.089
0.245
0.848
0.206
–0.035
0.671
0.728
0.315
0.735
0.038
0.641
0.180
–0.021
–0.843
0.744
0.414
–0.034
0.675
0.628
5.886
53.500
53.500
1.538
13.978
67.488
1.273
11.568
79.056
Notes: Extraction method: principal component analysis; rotation method: varimax with Kaiser
normalization.
The second component’s major variables include the ratio of personnel costs of OBUs
to the bank’s overall personnel costs and the ratio of business revenues of OBUs to the
bank’s overall business revenues. Since these variables are highly related to the business
development of a bank’s OBU operation, we name this component “OBU business.”
The third component’s major variables include the ratio of pretax profits of overseas
branches to the bank’s overall profits and the pretax profits of overseas branches. These
variables have a relationship with local banks’ intention to boost overall margins by
setting up overseas offices and expanding their overseas business. Thus, we name this
component “profitability of overseas branches.”
We next use the three components as the representative factors of internationalization
that will be put in the inefficiency model to analyze the effect of internationalization on
the cost efficiency of banks.
Cost Frontier Function and Inefficiency Model
Cost Frontier Function
Table 4 shows the empirical results of the stochastic cost frontier model and inefficiency
model. First, we use the likelihood ratio (LR) test to verify whether the proposed inefficiency model is well specified. Our LR statistic for FHC banks is 96.41; for non-FHC
banks, 114.79. Both significantly reject H0 and imply the suitability of the proposed
inefficiency model.
The Wald test results show that the majority of marginal effects of output are
consistent with the condition of monotonicity for both FHC and non-FHC banks. All
marginal effects of inputs satisfy the condition of nondecreasing in input prices for
both groups of banks. The Wald test results of the Hessian matrix are also consistent
with the conditions of concavity, although they are not all significant because we do
not simultaneously estimate the cost function with shared functions (Greene and Segal
2004). In sum, the estimated translog cost functions of FHC and non-FHC banks are
congruent with the cost theory.
Inefficiency Model
As with our above analysis, we use the method of PCA to extract three factors pertaining
to internationalization: (1) level of overseas branch (LOB), (2) OBU business (OBU),
and (3) profitability of overseas branches (POB). As in the related literature, in the inefficiency model we also include some control variables, such as the number of domestic
branches, profit (return on assets [ROA]), and risk (nonperforming loans [NPL] ratio
lagged for one period). The Pearson correlation analysis shows that all correlation coefficients among the variables are less than 0.6, meaning that no multicollinearity exists
among the selected factors.
Internationalization Variables
Level of Overseas Branch
The estimated coefficient of LOB displays a significantly negative relationship with cost
inefficiency for both FHC and non-FHC banks. This suggests that the more internationalized a bank is through its overseas businesses and foreign exchange deposits, the lower
its cost inefficiency, thus improving its overall operation efficiency.7
In terms of LOB, non-FHC banks are more capable of decreasing cost inefficiency
than FHC banks. While both groups of banks face intense competition in the overseas
markets, the FHC banks do not effectively execute strategies of diversification. In the
2000s, since a bank’s interest spread has stood at 1–2 percent, banks have limited room
for making profits in the Taiwan market. If the banks upgrade the level of their overseas
Coefficient
31.3487***
–1.3943
0.1604
–0.8556
2.8921**
–2.0909**
–0.0259
0.0714
0.0585
0.2687**
0.1909***
0.0423
0.1063
–0.0942*
–0.1145**
–0.0007
Variable
Constant
lnY1
lnY2
lnY3
ln(P1 /P2)
ln(P3 /P2)
1/2(lnY1)2
1/2(lnY2)2
1/2(lnY3)2
1/2ln(P1 /P2)2
1/2ln(P3 /P2)2
lnY1 × lnY2
lnY1 × lnY3
lnY2 × lnY3
ln(P1 /P2) × ln(P3 /P2)
lnY1 × ln(P1 /P2)
10.8122
1.5732
0.9373
0.9417
1.1510
1.0591
0.1758
0.0721
0.0689
0.1094
0.0677
0.0865
0.0956
0.0482
0.0584
0.0862
SE
FHCs
2.8994
–0.8863
0.1711
–0.9085
2.5128
–1.9742
–0.1474
0.9900
0.8489
2.4555
2.8179
0.4895
1.1117
–1.9542
–1.9605
–0.0087
t-value
Table 4. Empirical results of the stochastic cost frontier model and inefficiency model
29.7788***
–3.5522***
2.5704***
0.2524
1.8603**
0.9771
0.4591***
0.1514***
0.0009
0.0886
0.0430
–0.1890***
–0.1176
0.0051
0.0049
0.0329
Coefficient
1.2788
0.8710
0.6663
0.9135
0.9231
0.8647
0.1190
0.0352
0.0744
0.1342
0.0536
0.0497
0.0826
0.0280
0.0674
0.0637
SE
Non–FHCs
23.2862
–4.0781
3.8574
0.2763
2.0153
1.1300
3.8582
4.3030
0.0116
0.6601
0.8020
–3.8013
–1.4231
0.1830
0.0733
0.5170
(continues)
t-value
217
0.0140
0.0202
0.0068
0.0004
0.0079
0.0030
–0.0787***
–0.0325
–0.0022
0.0015***
–0.0062
0.0012
0.0057***
3.3792
–0.7862
0.4133
6.9142
–5.6276
–1.6073
–0.3305
–0.1745
0.3107
1.8208
–0.8651
1.3047
1.9266
t-value
0.0015**
–0.0338***
0.0024
0.0155***
–0.3554***
–0.1255***
–0.1054
–0.0718
0.0669
0.0970***
–0.1465**
–0.0516
–0.2766**
Coefficient
114.7946
0.0006
0.0091
0.0044
0.1242
0.0274
0.1773
0.0481
0.0454
0.0367
0.0632
0.0558
0.1258
SE
Non–FHCs
2.4033
–3.6992
0.5511
4.1097
–2.8612
–4.5832
–0.5945
–1.4924
1.4744
2.6470
–2.3179
–0.9241
–2.1982
t-value
Notes: LOB represents level of overseas branch; OBU is OBU business; POB represents profitability of overseas branches. The samples of 14 FHC and 18 non-FHC
banks are 112 and 143, respectively. * Statistical significance at the 10 percent level; ** statistical significance at the 5 percent level; *** statistical significance at the
1 percent level.
132.1589
0.0922
0.0559
0.0479
0.0446
0.0578
0.1175
–0.0161
0.0174
0.0872*
–0.0386
0.0754
0.2264*
lnY1 × ln(P3 /P2)
lnY2 × ln(P1 /P2)
lnY2 × ln(P3 /P2)
lnY3 × ln(P1 /P2)
lnY3 × ln(P3 /P2)
Constant
Internationalization variables
LOB
OBU
POB
Control variables
Domestic branches
Profit (ROA)
Risk (lag NPL)
σ2 = σ2u + σ2ν
Log likelihood function
SE
Coefficient
FHCs
Variable
Table 4. Continues
218 November–December 2014, Volume 50, Supplement 6 219
branches and launch international banking services, then we expect their cost efficiency
to be enhanced. The finding echoes Huang (2010) and Sullivan (1994).
OBU Business
For the first decade of the twenty-first century, since the governments across the Taiwan
Strait have not yet fully opened their financial markets, OBU transactions have become a
major channel for financing and tax avoidance by Taiwan firms and businessmen investing in China. OBUs are thus an important source of income for banks. We find that the
OBU business and cost inefficiency are negatively related for FHC and non-FHC banks,
implying that expanding OBU operations helps improve bank efficiency. In 2008, the
governments of Taiwan and China have relaxed restrictions on cross strait trade and
financial exchanges. Statistics show that both OBU assets and the ratio of OBU business revenues to banks’ business revenues have risen sharply. As trade codependency
across the Taiwan Strait deepens and liberalization and internationalization of the two
financial markets increase, expanding OBU operations is expected to benefit banks’ cost
performance.
Profitability of Overseas Branches
We find that POB displays a negative but not significant relation with operational inefficiency for subsidiary banks. For non-FHC banks, POB has a positive but not significant
relation with cost inefficiency. The insignificant effects in both groups are partly because
most Taiwan banks made profits only in the Hong Kong and Vietnam markets. More than
one-third of the overseas branches of Taiwan banks are set in developed countries, but
they did not earn significant profits.
Control Variables
Following Ruigrok and Wagner (2003) and Welch and Luostarinen (1988), we set the
number of domestic branches, profit (ROA), and risk (lag NPL) as control variables.
Domestic Branches
The number of domestic branches not only indicates a bank’s scale of operations, but also
helps a bank provide more-efficient services to its loan clients due to the widespread availability of branch offices. With an increase in branch offices, however, banks are burdened
with higher overhead costs such as personnel costs, management costs, and office rent.
In addition, instead of creating economies of scale, the head office might suffer from an
inefficiency of resource allocation and then weaker operational efficiency because of an
excess number of branch offices (Banker et al. 2003; Beck et al. 2005).
Profit
ROA is defined as a bank’s pretax profit divided by average total assets. Altunbas et al.
(2000) show that ROA and the inefficiency value have an inverse relationship. We also
find that ROA and cost inefficiency are negatively related. This implies that when banks
fully utilize their assets, the profitability of assets increases, and so there is an improvement in banks’ cost efficiency.
Risk
We used nonperforming loans to proxy for risk (Berg et al. 1992; Berger and DeYoung
1997; Berger and Mester 1997; Hughes and Mester 1993; Mester 1996). To resolve a high
NPL ratio, a bank in general will allocate undistributed earnings to make provisions for
loan losses. Our study indicates that the NPL ratio and cost inefficiency have a positive
relation, but they are not significant for FHC and non-FHC banks. However, several studies
find that banks with a larger number of problem loans face reductions in cost efficiency
(Berger and De Young 1997; Drake and Hall 2003; Hughes and Mester 1993).
Economies of Scale
Table 5 shows that the average values measuring economies of scale for FHC and non-FHC
banks are 1.2119 and 1.2459, respectively, implying they are at the stage of increasing
returns to scale. However, there are two FHC banks and one non-FHC bank that have
economies of scale less than 1 in 2010, implying they are at the stage of diseconomies
of scale, while the other twenty-nine banks are at the stage of increasing returns to scale.
It is therefore appropriate for Taiwan banks to expand their sizes. Increasing offshore
banking services is a good alternative under such a strategy.
If SC > 0, it is beneficial for banks to jointly produce the entire array of outputs.
However, if SC < 0, a bank should specialize in a single output to minimize its production costs. The average values of economies of scope for both FHC and non-FHC
banks are greater than zero, meaning that the diversification of financial products
always reduces costs. It is therefore beneficial for banks to expand into different lines
of domestic and foreign business such as loans, investments, and others that generate
noninterest income.
Metafrontier Estimation
Before estimating the metafrontier cost function, we first examine whether the stochastic frontier cost function of FHC banks is different from that for non-FHC banks.
We set the null hypothesis as H0 : βF = βN. Our LR test statistic of 97.28 is larger than
χ20.01 (28) = 48.28, so the null hypothesis is rejected. This means a difference in the
stochastic cost frontier function indeed exists between FHC and non-FHC banks.
Therefore, it is not appropriate to conduct a hybrid estimation by a single cost function.
We should adopt the metafrontier cost function to conduct a comparison for the two
groups of banks.
To compare the metafrontier cost function with conventional studies, according to
Equation (9), we use the LP to estimate the parameters of the metafrontier cost function.
We also use the SFA to evaluate banking efficiencies by pooling all the data across groups
without regard to possible technological differences. We obtain the standard errors of
the mathematical programming estimators through bootstrapping methods with 1,000
replications. The estimated standard error of a metafrontier parameter is the standard
deviation of the 1,000 new parameter estimates. Table 6 shows that there are substantial
Scale
FHC
Non-FHC
Scope
FHC
Non-FHC
1.3236
1.2220
3.5913
13.1084
3.5873
13.1071
2003
1.4253
1.2152
2002
3.3580
13.1048
1.1890
1.2857
2004
3.2711
13.1549
1.1640
1.2646
2005
3.2255
13.2330
1.1713
1.2199
2006
3.1587
13.2009
1.1291
1.2658
2007
Table 5. The economies of scale and economies of scope for FHC and non-FHC banks
3.1753
13.1654
1.1434
1.2948
2008
3.2087
13.1288
1.1492
1.1966
2009
3.3220
13.1506
1.2119
1.2459
Average
221
Coefficient
7.2782
0.4982
1.5114***
–0.3555
2.8014***
0.4596
0.1206
0.0875**
0.0636
0.3081***
0.1537***
–0.0887*
–0.0452
–0.0255
Variable
Constant
lnY1
lnY2
lnY3
ln(P1 /P2 )
ln(P3 /P2)
1/2 (lnY1 )2
1/2 (lnY2 )2
1/2 (lnY3 )2
1/2 ln(P1 /P2 )2
1/2 ln(P3 /P2 )2
lnY1 × lnY2
lnY1 × lnY3
lnY2 × lnY3
7.2083
0.8509
0.4790
0.5398
0.8518
0.5901
0.0874
0.0366
0.0455
0.0972
0.0367
0.0486
0.0504
0.0243
Standard error
SFA–POOL
1.0097
0.5856
3.1552
–0.6587
3.2888
0.7787
1.3788
2.3870
1.3969
3.1691
4.1905
–1.8256
–0.8967
–1.0532
t-value
Table 6. Empirical results of the metafrontier cost function
58.3585
0.3616
0.3090
–0.1396
1.4479
0.6756
0.6046
1.0618
1.7404
1.3156
0.9923
0.8900
0.4282
1.2269
Coefficient
4.728
0.240
0.977
0.435
0.597
0.649
0.279
0.857
0.429
1.022
0.377
0.468
0.513
0.647
Standard
deviation
LP
33.7384
–0.3815
–0.3240
–1.7185
0.5186
–0.0155
–0.1440
0.1728
–1.4893
–0.0336
0.4623
–0.1603
–0.1972
–0.4401
95 percent
confidence
82.9786
1.1047
0.9421
1.4393
2.3771
1.3667
1.3532
1.9508
4.9700
2.6649
1.5223
1.9402
1.0536
2.8939
Interval
222
–0.0905**
0.0074
0.0034
0.0683*
0.0178
–0.0608
–0.0374
–0.0323**
–0.0729***
0.0071
0.0019***
–0.0210***
0.0011
0.0123***
0.0460
0.0546
0.0410
0.0378
0.0306
0.0424
0.0377
0.0142
0.0196
0.0087
0.0003
0.0063
0.0023
0.0012
–1.9677
0.1354
0.0824
1.8059
0.5834
–1.4334
–0.9931
–2.2757
–3.7238
0.8239
5.8146
–3.3413
0.4735
10.4034
0.8534
1.0669
0.7424
–0.5128
0.8483
0.4303
0.7259
0.4196
0.7738
0.6712
1.5876
0.4504
0.0132
0.823
1.032
0.361
1.578
2.460
0.672
0.552
1.566
0.280
0.955
2.379
0.638
1.598
–1.0701
–2.5016
–0.5225
–2.2246
–0.0778
–0.3141
–0.2262
–0.1287
0.1728
–1.9665
0.9416
–0.4734
–2.7869
* Statistical significance at the 10 percent level; ** statistical significance at the 5 percent level; *** statistical significance at the 1 percent level.
ln (P1 /P2 ) × ln(P3 /P2 )
lnY1 × ln(P1 /P2 )
lnY1 × ln(P3 /P2 )
lnY2 × ln(P1 /P2 )
lnY2 × ln(P3 /P2 )
lnY3 × ln(P1 /P2 )
lnY3 × ln(P3 /P2 )
INT
OBU
Pro
Branches
ROA
Lag_NPL
σ 2 = σu2 + σν2
2.7769
4.6354
2.0072
1.1989
1.7744
1.1746
1.6780
0.9680
1.3747
3.3089
2.2337
1.3742
2.8133
223 Downloaded by [Chinese Culture University], [Lien-wen Liang] at 17:38 09 April 2015
Figure 2. Technology gap ratio of FHC banks and non-FHC banks
Figure 3. Meta-cost efficiency of FHC banks and non-FHC banks
differences between the metafrontier coefficients and the corresponding coefficients of
the SFA for the entire sample.
As shown in Figure 2, the estimated technology gap ratio (TGR) stands somewhere
between 0.969 and 0.661. The average TGR of FHC banks is 0.871, whereas that for
non-FHC banks is 0.826. From Figure 2, it is obvious that the TGR for FHC banks is
mostly greater than that of non-FHC banks, meaning FHC banks present a better technical
performance. Figure 2 also shows that, except in 2002, the average TGR of FHC banks is
larger than that of non-FHC banks. An important reason for this is that the passage of the
Financial Holding Company Act in 2001 allows FHC banks to provide one-stop shopping
like a financial department store, and this offers a significant cost advantage.
The meta-cost efficiency between the two groups is between 0.514 and 0.867, as shown
in Figure 3. The average meta-cost efficiency of FHC banks is 0.606, whereas that of
non-FHC banks is 0.733. From Figure 3, it is clear that the meta-cost efficiency value
of FHC banks is significantly lower than that of non-FHC banks. This shows that bank
liberalization and internationalization harmed FHC banks’ operations during Taiwan’s
credit card crisis in 2005, the U.S. subprime mortgage crisis in 2007, and the global
financial tsunami in 2008.
Conclusion
Taiwan’s banks have recently upgraded their internationalization levels and increased their
number of overseas affiliates due to the outward FDI clustering sites of Taiwan-based
firms in China and Southeast Asia countries (Huang 2010). Although some studies assert
that internationalization can successfully facilitate competition in a domestic banking
sector and achieve economies of scale and scope (Huang 2010), different factors of the
internationalization process might result in an inconsistent effect on different types of
banks. Through the PCA method, we use three factors pertaining to internationalization
of Taiwan banks: (1) level of overseas branch, (2) OBU business, and (3) profitability
of overseas branches.
We find that, in general, the greater the number of overseas branches a Taiwan bank
has, the higher its cost efficiency. This suggests that the more internationalized a bank is
through overseas businesses and foreign exchange deposits, the lower its cost inefficiency,
which thus improves its overall operation efficiency. We also find that non-FHC banks are
more able to decrease cost inefficiency than FHC banks. This indicates that FHC banks
must improve their efficiency in executing an overseas diversification strategy.
Expanding OBU operations greatly improves the performances for both groups of
Taiwan banks due to lower transaction costs in conducting international business through
OBUs. Although the economic relationship across the Taiwan Strait has significantly
improved, it is not expected that the governments of Taiwan and China will fully open
their financial markets in the immediate future. The channel of OBU is still mainly used
for financing and tax avoidance by Taiwan firms and individuals investing in China.
Therefore, Taiwan’s banks should try to expand their OBU assets and increase the ratio
of OBU business revenues to gain more profit.
Through the metafrontier model, the technology gap ratio (TGR) estimated herein is
greater for FHC banks than for non-FHC banks, meaning that FHC banks present a better
technical performance. However, the average meta-cost efficiency of FHC banks shows
that the group of non-FHC banks has a lower cost frontier. It is therefore the attainable
objective of FHC banks to reduce cost through a suitable administration adjustment.
Because banks’ interest spread in recent years has stood at 1–2 percent, Taiwan’s banks
have limited opportunities to make profits in the domestic market. To increase their profits
as well as their cost efficiency, these banks should closely follow Taiwan manufacturing firms investing abroad in order to upgrade their internationalization strategies, such
as launching international banking services and increasing their OBU business. Since
Taiwan signed the financial Memorandum of Understanding and Economic Cooperation Framework Agreement with China, another booming area for Taiwan banking is
expected to follow the local information technology industry over to China. Whether
Taiwan’s banking industry is able to take advantage of the great potential business across
the Taiwan Strait depends on the degree of internationalization.
Banks’ internationalization might involve additional risks that include external factors
such as political risks, economic risks, social risks, and environmental risks, and interior
risks such as credit risks and operational risks. Whether high risks are associated with
high efficiency needs to be examined by a strict new work. For example, how to exactly
measure bank risks is a huge job. Klomp and de Haan (2012) use twenty-five indicators of
banking risk, while Berger et al. (2013) use more than a dozen risk variables, to investigate
the effects of bank risks resulting from either regulation or internationalization.
Among those risk indexes, return on equity, ROA, the Sharpe ratio, nonperforming
loans, and the lease loss allowance are the most commonly seen indicators. In our paper,
since we mainly focus on the relationship between internationalization and efficiency, we
only take into account the risk variables of ROA and nonperforming loans. Therefore,
through our empirical results, we can partially see the effect of bank risks on efficiency,
but it is not a complete work. A complete story about internationalization, risk, and
efficiency can be more thoroughly unveiled by a future work.
Notes
1. Some research studies also focus on the relationship between bank size and internationalization. “Only a few, large banks have a commercial presence abroad, consistent with the size pecking
order documented for manufacturing firms. However, the relationship between internationalization and productivity also yields two inconsistencies with recent trade models. First, virtually all
banks hold at least some foreign assets, irrespective of size or productivity. Second, some fairly
unproductive banks maintain commercial presences abroad. The size and productivity distributions are dispersed and skewed, and this heterogeneity is mirrored in banks’ internationalization
patterns” (Buch et al. 2011).
2. Huang (2010) defines bank efficiency as involving two indexes: (1) the ratio of the number
of employees to its total assets and (2) the ratio of a bank’s noninterest expenses to its total revenue
(Huang 2010, p. 55). Both indexes differ from the strict definition that appears in the relevant
literature on productivity and efficiency.
3. There are K behavior variables (X1, X2, ..., Xk) transformed into an overall index (Y ), where
the overall index is a linear combination of behavior variables. Through principal component
analysis, if we have K behavior variables, then at most we could get K principal components. We
only pick up the principal component whose eigenvalue is greater than 1.
4. Since the duality theorem requires that the cost function must be linearly homogeneous in input
prices, we impose the following homogeneity restrictions on the parameters in Equation (1):
m
∑β
j =1
j
m
m
j =1
j =1
= 1, ∑ βij = 0, ∑ γ ij = 0, i = 1, 2,…, m.
Furthermore, the second-order parameters of the cost function in Equation (1) must be symmetric—that is, δnj = δjn, n, j = 1,2,3, γkm = γmk, m,k = 1,2,3.
According to Shephard’s lemma, an input share is equal to the derivative of the log cost function
with respect to the corresponding log input price. Each input share should lie between zero and
unity, and input shares should sum to 1. We define cost shares as follows:
Si =
3
3
∂ ln TC
= βi + ∑ γ ij ln Pj + ∑ ρij ln Yn + ηi , m = 1, 2, 3.
∂ ln Pm
j =1
n =1
5. Here, Yim is 10 percent of the minimum value of Yi in the sample. Using Yim instead of zero in
the equation avoids taking the logarithms of zero in the translog function (Mester 1996).
6. The consumer price index is from the Directorate-General of Budget, Accounting and Statistics, Executive Yuan, with 2000 as the base year.
7. See Akin et al. (2013) and Chang et al. (2013) for discussion of the relationship between foreign
financial institutions of well-diversified currency portfolios and highly effective regulation.
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Emerging Markets Finance and Trade Effect of Internationalization

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