This study measures the efficiency and technology gap of 28 mobile operators in APEC countries from 2003 to 2008 using data envelopment analysis and meta-frontier approaches. It finds that operators in countries with high mobile penetration rates had higher technical efficiencies and smaller technology gaps than those in low penetration countries. Specifically, Rogers Wireless and Bell Wireless in low penetration countries had the largest technology gaps of all APEC mobile operators.

1. Measuring the Efficiency and Technology Gap of APEC Mobile
Telecommunications Firm
Ya-Ting Chao1
Abstract
The Asia-Pacific Economic Cooperation (APEC) mobile operators play an influential
and fundamental role in global telecommunications industry and show pretty well
performances both in penetration and growth of mobile subscribers. This study is to
measure the efficiency and technology gap of 28 APEC’s mobile operators during the time
period of 2003 to 2008, using the DEA and Meta-frontier approaches. Two output
variables are operating revenue and number of mobile subscribers, and three input
variables are number of employees, total assets and capital expenditures. The Meta-frontier
results showed that the operators in the group of high penetration rate had higher technical
efficiencies and smaller technology gap on average than those in the group of low
penetration rate. This implies that the operators in the high penetration rate group had
higher technical efficiencies and their technologies were closer to the metafrontier as
compared with those in the low penetration rate group. Further, Roger Wireless and Bell
Wireless in the low penetration rate group are the two operators which had the largest
technology gap among all APEC mobile operators. This indicates that the technologies of
these two operators were farther away from the metafrontier.
Keywords: Efficiency, Technology gap, APEC mobile operator, Meta-frontier
1. Introduction
1.1 Background and motivation
Productive efficiency is a measure relating a quantity or quality of output to the inputs
required to produce it. In nowadays competitive environment, measuring productive
efficiency helps a firm or an organization to know how to improve its capability in the
process of producing and to find the way to use its resources and inputs more efficiently.
Extensive researches have measure the efficiency of firms in diverse fields. In particular,
the liberalization and privatization in global telecommunications markets in the last two
decades have attracted academician attention on the productive efficiency in
telecommunications.
Various methodologies have been used to measure the efficiency, including the total
factor productivity (TFP) measurement, the data envelopment analysis (DEA) and other
measurements. For instance, Tsai, Chen and Tzeng (2006) adopted traditional DEA, A&P
efficiency measure and efficiency achievement measure to discover the productivity
ranking of 39 leading telecommunication operators in Forbes 2000. The results indicated
that Asia-Pacific telecom operators have better productivity efficiency than those in
Europe and America.
There may be a bias in measuring and comparing the efficiency of firms in different
business environments. In practice, the frontiers estimated for two different regions or
countries are rare to be similar enough to facilitate the use of a single frontier. The
difference can be attributed to the available stocks of physical, human, and financial capital,
economic infrastructure, resource endowments, and any other characteristics of the
physical, social, and economic environment in which production takes place (O’Donnell,
1
Institute of Telecommunications Management, National Cheng Kung University, Tainan 70101, Taiwan
(E-mail: vivian0326@yahoo.com.tw).

2. Rao and Battese, 2008). The Meta-frontiers are used to compare the performance of firms
across different countries or regions. The technique entails the estimation of a
metatechnology and the frontiers of relatively homogenous groups. These estimated
frontiers give an empirical representation of the world and the group specific technologies,
respectively. The estimation of a metafrontiers, group frontiers, and the relative efficiency
levels with respect to both, allows the construction of a measure of the technology gap
between countries with efficiency effects removed to give a clearer picture of the relative
rates of technology differences between production entities.
The measurement of technology gap for businesses also attracted a lot of academic
attention. Battese, Rao, and O’Donnell (2004) measured the technical efficiency and
technology gaps of the garment industry in five regions of Indonesia from 1990 to 1995 by
the stochastic frontier analysis (SFA) and Meta-frontier. The results showed that garment
firms in Jakarta achieved the highest mean technical efficiencies relative to the
metafrontier. But, garment firms in East Java had the highest mean technical efficiency
relative to their regional stochastic frontier, but they tended to be furthest from the
potential outputs defined by the meta-frontier function. O’Donnell, Rao and Battese (2008)
considered both DEA and SFA approaches to estimating both metafrontier and group
frontiers, and for decomposing differences in performance into technical efficiency and
technology gap effects from 1986 to 1990. Food and agriculture production in 97 countries
in different continents were classified into four groups. The results showed that agricultural
producers in Europe operated in relatively restrictive production environments. Countries
in the Americas were found to be the most technically efficient when assessed against both
the group frontier and the metafrontier.
The Asia-Pacific Economic Cooperation (APEC) has a great influence on the world’s
economical growth and development. APEC’s mobile operators play an influential and
fundamental role in global telecommunication industry. Mobile operators currently face
fierce challenges from different industries and international competition. For instance,
entering WTO is a significant step towards the further development and reform of a
country’s mobile market. The commitments to join the WTO have rendered investment
environment more suitable for international investments in this sensitive field. The
restrictions of foreign-capital investment on telecommunication operators have been lifted
due to the WTO's protocol. Foreign mobile operators bring positive effects of raising funds
and equipment/technology upgrade in these countries by entering the domestic market.2
In
addition, the merger and alliance between operators enhance the business competitiveness.
Accordingly, mobile operators are able to upgrade the telecommunications systems and
provide better services. On the other hand, to pursue a faster bandwidth and full coverage,
a new generation of mobile systems has a much shorter life cycle. In sum, mobile operators
bear higher infrastructure costs and mobile market becomes increasingly competitive.
Therefore, to find a suitable way to measure the operator's the efficiency, productivity
change and technology gap is thus important.
1.2 Development of mobile telecommunications in APEC
Global economy stably developed from 2004 to 2008 with a growth rate of around
4.0%. However, the subprime mortgage crisis from the U.S. seriously struck various
countries resulting in global financial downturn. The emerging economies in Asia,
2
Take Vietnam as an example. “WTO accession will lure foreign investors to telecom market”, the
statement made by the Post and Telematics Minister in 2009. Vietnam's telecom and information technology
sectors have many opportunities for development, especially in drawing foreign investment, after the country
joined the WTO, that almost US$2 billion from foreign enterprises have been invested in telecom services.

3. especially China, India and Russia, were still strong with high growth rates and played the
role of a driving engine for the global economy. APEC, established in 1989, is the premier
forum for promoting economic growth, cooperation, trade and investment in the Asia
Pacific region. The 21 members in APEC, accounted for 40.5% of the world's population,
approximately have 54.2% (28.6 trillion) of world gross domestic product (GDP) and
43.7% of world trade volume in 2007 (APEC, 2008). The average economic growth rate
from 2000 to 2007 in the APEC was 4.71%, higher than the global value of 3.2% (The
World Bank, 2009).
According to International Telecommunication Union (ITU, 2009), the number of
global mobile subscribers has reached to 4 billion in 2008, with the penetration rate of
59.34 percent in the world's population of 6.77 billion. It reveals that mobile services
significantly affect human being’s life and technology, and bring enormous economic
benefits and communicating convenience. The main mobile systems adopted include
global system for mobile communications (GSM), general packet radio service (GPRS)
and enhance data GSM environment (EDGE) system with 3 billion subscribers and 78
percent of market share. The first generation (1G) analog system is fast diminishing with
only 1 million subscribers left in the advanced mobile phone system (AMPS), total access
communication system (TACS) and Nordic mobile telephony 450/900 (NMT450/900). The
second generation (2G) system is also decreasing and being replaced by the third
generation (3G) services. The wideband code division multiple access (WCDMA) and high
speed packet access network (HSPA) systems have 315 million subscribers with 8.2
percent of market share because of better service quality and download speeding.
Mobile telecommunications industry in the APEC shows pretty well performances
both in penetration and growth of mobile subscribers. The APEC mobile penetration rate in
2008 was 90.46 percent, a 31 percent higher than the one in the global market. Although
major APEC members have low growth rate in subscribers due to the saturated markets,
the growth rate of 26.73% on average from 2003 to 2008 still surpassed the world’s value
of 23.2%.
There are eight members in APEC which mobile penetration rate are fully saturated:
Hong Kong, Singapore, Russia, Thailand, Taiwan, New Zealand, Australia and Malaysia
with the respective penetration rates of 162.9, 138.15, 132.61, 118.04, 110.31, 109.22,
104.96 and 100.41 (ITU, 2009). In particular, Japan and Korea are the most well developed
in the mobile service market, and their mobile broadband penetration rates were 56.8 and
48.58 in 2007, ranking in the world's top two. Japan, Korea, Taiwan, Hong Kong and
Singapore are currently facing an issue in mobile services that their markets almost reach
to the status of full saturation. As a result, these mobile operators focus on upgrading the
mobile systems and the revenue growth in mobile data services. Given that the HSPA, a
3.5 generation service (3.5G), is of almost the full coverage in these countries, mobile
operators and information and computer technology (ICT) companies work together to
promote mobile data services with mobile Internet device (MID). For example, netbook3
boosts the demand for subscribers’ second phone number and stimulates the revenue in
mobile data services.
The mobile penetration rate in the U.S. was 86.79 in 2008. In accordance with Forbes
2000 (2009), American Telephone & Telegraph (AT&T) and Verizon Communications are
ranked as the first and third largest telecommunications operators in the world based on a
3
A netbook is a laptop computer designed for wireless communication and access to the Internet. It ranges in
size from below 5 inches to over 13, typically weighs 2 to 3 pounds and is often significantly cheaper than
general purpose laptops.

4. mix of four metrics: sales, profit, assets and market value, indicating that the U.S.
operators have determinable power in the global market. Mobile penetration rate in North
America was about 75.65 percent in 2008. As compared with the markets in other APEC’s
regions, the ratio of owing the second phone number is relatively low. Consequently, the
strategies for these mobile operators are to increase wireless terminal connections for each
user and to promote the demand of mobile broadband service.
There are two generations of commercial mobile service systems used in the APEC
nowadays, including the 2G and the 3G. The GSM and cdmaOne are the two main systems
in the 2G services. The 2G standard allows a maximum data rate of 9.6 kbps, which is
possible to transmit voice and low volume digital data, for example, short message service
(SMS) or multimedia message service (MMS). The WCDMA and CDMA2000 1X are the
two main systems in the 3G services. The 3G standard increases the transmission rate up to
2 mega bit per second (Mbps), which is compatible with all mobile systems in the world
and with the 2G networks. Due to its high data transmission rate, the 3G system is able to
provide multimedia services, such as video transmission, video conferencing, and
high-speed Internet access, and is widely applied to the other aspects of the daily life. Their
extended versions (3.5G) are the HSPA and CDMA2000 1x EV-DO.
The major mobile system adopted in Asian markets is GSM, which accounted for the
market share of 76.2 percent in 2008 (MIC, 2009). The other system technologies by
subscriber share are cdmaOne and CDMA 2000 1X (12 percent), WCDMA and HSPA (7.4
percent), and CDMA2000 1x EV-DO (3.7 percent). SK Telecom and Korea Telecom
Freetel (KTF) in Korea actively deploy WCDMA and HSDPA networks, as well as
advocating the user to switch CDMA2000 1X system to WCDMA and HSDPA systems.
So, the CDMA users in Asia are expected to slowly decrease in the future. The unique
system, TD-SCDMA, offered by China Mobile in China, has grown in a tardy pace,
because of its incomplete industry chain and communication quality. There were only
330,000 subscribers by the end of 2008. The main mobile system in North America
markets is still the GSM, which accounted for the market share of 31.1 percent in 2008.
The other system technologies by subscriber share are cdmaOne and CDMA 2000 1X
(29.3 percent), and CDMA2000 1x EV-DO (21.2 percent) (MIC, 2009). The future
technology developed by Verizon Wireless and Telecom Mobile(T-Mobile) in the U.S.,
and Telus and Bell in Canada are moving towards long term evolution (LTE), the fourth
generation (4G) system.
Mobile service, being needed in our daily lives, has enormous impacts on world
economy. Mobile services connect and communicate with people anytime and anywhere.
The revenues of world mobile communication have steadily increased, reaching the total
values of US 1,391 billion in 2008 (MIC, 2009). In 2007, global revenue (692 billion) of
mobile service surpassed that (647 billion) of fixed-line service. Mobile service continually
grows because of the newly developing markets and the various contents in 3G service.
Undoubtedly, mobile service plays the mainstream role now and will do so in the future.
1.3 Research objective
The purpose of this study is to measure the efficiency and technology gap of 28
mobile operators during the time period of 2003 to 2008, using the DEA and Meta-frontier
approaches. The operators are Telstra, Optus, CSL, NTT DoCoMo, KDDI, SK Telecom
(SKT), KTF, Celcom, Telecom New Zealand, SingTel, Chunghwa Telecom (CHT),
Taiwan Mobile (TMB), AIS, Total Access Communication (DTAC), Mobile TeleSystems
(MTS), Vimpelcom, Verizon Wireless, AT&T Mobility, Telkomsel, Indosat, China Mobile,
China Unicom, Smart Communication, Globe Telecom, Rogers Wireless, Bell Wireless,

5. Telcel, Movistar. There are two output variables and three input variables adopted in this
study. Two output variables are revenue and number of mobile subscribers, and three input
variables are number of employees, total assets and capital expenditures, as commonly
adopted in the literature.
2. Literature Review
Most state-owned telecommunications operators worldwide experienced competitive
changes through the deregulation and the privatization in the market. Traditional rate of
return regulation was replaced with new price cap regulations. The digital convergence and
liftoff of international investment restriction in telecommunications make the market
fiercely competitive from all aspects. The efficiency is important for telecommunication
operators. With the knowledge of the strength and weakness, the operators are able to
modify their managerial strategies to increase the efficiency and to achieve higher profits.
The issue of measuring efficiency of an industry is crucial to both the economic theorist
and the economic policy maker (Farrell, 1957). However, the efficiency performance may
be changed when facing different production opportunities and technology, which will
result technology gap. Therefore, to find a suitable way to measure the operator's
efficiency, productivity change and technology gap is thus important.
The measurement of technology gap for businesses attracted a lot of academic
attention. These literatures demonstrated how metafrontier and group frontiers can be
estimated using the DEA and SFA techniques in various industries of garment, banking,
agriculture and pharmacy, etc. To my best knowledge, this methodology has not been
applied to telecommunication industry in the existing literature. Battese, Rao, and
O’Donnell (2004) measured the technical efficiency and technology gaps of the garment
industry in five regions of Indonesia from 1990 to 1995. The methodologies of the SFA
and Meta-frontier were adopted to model a metafrontier production function of one output
(total manufacturing value) and four inputs (capital, labor, material and investment). The
results showed that garment firms in Jakarta achieved the highest mean technical
efficiencies relative to the metafrontier. But, garment firms in East Java had the highest
mean technical efficiency relative to their regional stochastic frontier, but they tended to be
furthest from the potential outputs defined by the meta-frontier function.
Bos and Schmiedel (2007) estimated comparable efficiency scores for more than
5,000 European large commercial banks operating in the Single Market in the EU over the
period 1993-2004. Three outputs (loan, investment and off-balance sheet items), three
input prices (labor, financial capital and physical capital) and two dependent variables
(total operating cost and profit before taxes) were used to estimated profit and cost
efficiency scores by the models of the SFA and Meta-frontier. It was found that cost
efficiency had decreased over the period under consideration, but profit efficiency
appeared to move with the economic cycles. The small technology gap ratios indicated that
the very efficient in their home country will have a hard time being equally successful
abroad. Finally, they concluded that traditional efficiency techniques based on pooled
frontier efficiency scores tended to underestimate cost and profit efficiency levels and, thus,
very efficient and very inefficient banks may be wrongly identified.
O’Donnell, Rao and Battese (2008) considered both DEA and SFA approaches to
estimating both metafrontier and group frontiers, and for decomposing differences in
performance into technical efficiency and technology gap effects. The closeness of group
frontiers to the metafrontier was measured as metatechnology ratios for the different
groups. The data used were one output variable (an aggregate of 185 agricultural
commodities) and five input variables (land, machinery, labor, fertilizer and livestock)

6. from 1986 to 1990. Food and agriculture production in 97 countries in different continents
were classified into four groups. The results showed that agricultural producers in Europe
operated in relatively restrictive production environments. Countries in the Americas were
found to be the most technically efficient when assessed against both the group frontier and
the metafrontier.
Mazumdar and Rajeev (2009) examined the technical efficiency, technological gap
ratio and productivity change of Indian pharmaceutical firms from 1991 to 2005 by the
DEA, Malmquist productivity index and Meta-frontier. The data used were one output
variable (total output produced by the firms plus the change in the stock) and four input
variables (material, energy, labor, capital). A total of 2,492 firms were classified into two
groups of firms engaged in R&D and firms not engaged in R&D. The results indicated that
R&D did not provide much benefit for achieving greater efficiency, but large firms that
invested more R&D benefited from technological growth. In other words, increasing the
scale of operation backed by sufficient R&D activity could have helped firms to expand
their production possibilities, enabling themselves to realize higher margins. In this
industry, it was suggested that merging vertically with downstream raw material firms
could have been an extremely effective strategy for firms to benefit from efficiency and
technological gains. Finally, vertically integrated firms that produce both bulk drug and
formulation exhibit higher technological innovation and efficiency. However, in contrast
with popular belief, it was found that increased export earnings do not necessarily lead to
higher efficiency. It can be found that the DEA and Meta-frontier approach were used
more frequent than other methodologies for the evaluation of business performance.
3. Meta-Frontier Model
The DEA method is used for analyzing the efficiency of different DMUs with the
same market conditions, e.g., technology, service and market structure. These DMUs are
put together to figure out the efficiency values. However, DMUs in different regions face
different production opportunities that force them to make choices between different sets
of feasible input-output combinations, or called as technology sets. The following reasons
results in the differences between countries or group technologies. Firstly, firms in
different countries or regions could be operating in a different regulatory environment,
labor laws and other different market conditions. Secondly, due to physical and
infrastructural differences, e.g., land quality, physical infrastructure, human capital
endowments, financial structures and the different technology, firms operate under
different environments, countries or regions. Third, due to the constraints on foreign
exchange and/or stringent import restrictions in some countries, these firms cannot access
globally available technology and are forced to use out-dated technology. Hence, it tends
to empirically reject the null hypothesis of constancy of the production frontier across
different regions. Applying the Meta-frontier model can overcome the barriers resulting
from the environment variables and measure the differences among various DMUs which
have different source properties.
3.1 Meta frontier
The efficiency measurement is deeply rooted in production theory and the concept of
distance functions. Following the Meta-frontier framework proposed by O’Donnell, Rao
and Battese (2008), the metafrontier and group frontiers are defined in terms of output sets
and output distance functions. Output distance functions are used to define technical
efficiencies and technology gap ratios (TGR). Let y and x be nonnegative output vector
(M×1) and input vector (N×1), respectively. The entire metatechnology set contains all

7. input-output combinations that are technologically feasible, as defined below:
{ }( , ) : 0, 0;T x y x y x can produce y= ≥ ≥
Associated with this metatechnology set are input and output sets. For example, the input
set is defined for any output vector, y, as:
{ }( ) :( , )L y x x y T= ∈
The boundary of this input set is referred as the input metafrontier. To measure efficiency,
it is convenient to represent the technology using the input metadistance function, defined
as:
{ }( , ) sup 0:( ) ( )D x y x L yλ λ λ= > ∈
This function gives the minimum amount by which a firm can radially reduce its input
vector, given an output vector. The distance function inherits its regularity properties from
the regularity properties of the input set. An observation (x, y) can be considered
technically efficient with respect to the metafrontier if and only if D(x, y) = 1.
3.2 Group frontier
It is possible to conceptualize the existence of sub-technologies that represent the
production possibilities of groups of firms. Consider a set of firms which can be divided
into K (> 1) groups, and suppose that resource, regulatory or other environmental
constraints may prevent firms in certain groups from choosing from the full range of
technologically feasible input–output combinations in the metatechnology set, T. Rather,
the input–output combinations available to firms in the kth group are contained in the
group-specific technology set:
{ }( , ) : 0, 0; inT x y x y x can beused by firms groupk to produce y= ≥ ≥ｋ
The K group-specific technology can also be represented by group-specific input sets and
input distance function, which is defined as follows:
{ }( ) :( , ) , for 1,2, ,k k
L y x x y T k K= ∈ =  ,
{ }( , ) sup 0:( ) ( ) , for 1,2, ,k k
D x y x L y k Kλ λ λ= > ∈ = > .
The boundaries of the group-specific input sets are referred as group frontiers. If the input
sets, ( )k
L y , k = 1,2,…,K, satisfy standard regularity properties, then the distance function,
( , )k
D x y , k = 1,2,…,K, also satisfy standard regularity properties. Irrespective of the
properties of these sets and functions, it is clear that the frontier formed by the input set of
these group firms is called group frontiers, and it has following properties:
 If ( , ) k
x y T∈ for any k, then ( , )x y T∈ ;
 If ( , )x y T∈ , then ( , ) k
x y T∈ for some k ;
 { }1 2
.... K
T T T T= ∪ ∪ ∪ ;
 For any k , ( , ) ( , )k
D x y D x y≥ ;
 Convex ( )L y does not necessarily imply convex group input sets, has
convexity and does not always represents that ( )k
L y , k = 1,2,…,K; and vice
versa.

8. This is illustrated in Fig. 1. It reveals the production possibilities available to
single-input, single-output firms from three different groups. The group-k frontier is
labelled k-k’ and is assumed to be convex (k = 1, 2, 3). If the three groups are exhaustive
(i.e., if K = 3) then the group-specific frontiers envelop all the input–output combinations
that could be produced by any single firm, implying the metafrontier is the nonconvex
piecewise frontier, 1-B-3’. However, if the three groups are not exhaustive, then other
input–output combinations may be feasible and the metafrontier could conceivably be the
convex frontier, M-M’.
Figure 1 Illustration of meta-frontier and individual frontiers
The meta-frontier is the outer envelope of all of the group frontiers. It consists of the
boundary points of the free disposal convex hull of the input-output vector of all firms in
the sample. An input-orientated measure to the technical efficiency of an observed pair (x,
y) with respect to the metatechnology, can be indicated as below:
* 1
( , )
( , )
TE x y
D x y
=
As for the frontier of group k, the input orientation efficiency of viewpoint (x, y) can be
indicated as follow. The higher (lower) K
TE is, the actual input level of firms in group k
is closer (further) to the minimum input of group k.
1
( , )
( , )
k
k
TE x y
D x y
=
In view of the fact that the metafrontier production possibility set contains every group
production possibility set, it is obvious that ( , ) ( , )K
D x y D x y≥ , and hence, k
TE ≤ *
TE ,
for every group. In other words, firms cannot be more technically efficient when assessed
against the metafrontier than when evaluated against a group frontier. Due
to ( , ) ( , )K
D x y D x y≥ , the frontier of group k is included in metafrontier; hence, there is a
technology gap between them. The input orientation technology gap ratio (TGR) of each
firm in group k is defined as follows:

9. *
( , ) ( , )
( , )
( , ) ( , )
k
K
k
D x y TE x y
TGR x y
D x y TE x y
= =
Because the technical efficiency relative to the metafrontier is always less than the
technical efficiency relative to the regional frontier, TGR is bounded within 0 and 1. And if
it is close to 1, then it means the production frontier of group k is close to the metafrontier,
in other words, the production technology level is more advanced. Contrarily, the closer it
is to 0, the further the group frontier of group k is from the metafrontier, indicating a less
developed production technology level.
3.3 Input and output variables
To examine an operator’s efficiency, many studies used total revenue (Pentzaropoulos
and Giokas, 2002; Tsai, Chen and Tzeng, 2006; Liao and González, 2009) and number of
calls or minute of calls (Uri, 2000, 2001 and 2002) as the output variables. Nevertheless,
both number of calls and minute of calls are unavailable for most of the operators studied
in the current analysis. Total revenues and subscribers are the most frequently used output
variables in the related studies and they indicate the operating strengths and scales of an
operator. Every mobile telecommunications operator needs sufficiently large amounts of
revenues and subscribers to maintain its service operation of any scale. Subscribers of a
mobile operator are the number of users who use its mobile services. Total revenues of an
operator, defined as the operating revenues earned from the charge for these services,
reflect the technology-variation characteristics of mobile operator and, in particular, the
development of mobile market. However, not all of the operators would be willing to
publish their detailed revenues due to the fierce competition in the market; hence, this
study uses operating revenues (y1) and mobile subscribers (y2) as output variables instead.
As for input variables, the number of employees (x1), total assets (x2) and capital
expenditures (x3) are chosen in the study. Number of employees is referred to as the
manpower employed by mobile operators or by the mobile segment of integrated business
operators. It increases along with the operation scale of an operator and it is an important
input for mobile service provision. Without an appropriate allocation of resources,
redundant employees become burdens in operator’s expenditure. Total assets are defined as
the summation of current assets, fixed assets, long-term investment, intangible assets and
other investment in wireless segment. Capital expenditures are the total expenditures for
the purchases of property, plant and equipment, intangible assets and other assets in one
year of the wireless segment. Capital expenditures, used as investments, are fundamental to
mobile communication industry and significantly affect call quality such as coverage of
services, transmission speed, and network capacity. With more investments an operator can
expand its system and improve its service, resulting high quality of services in turn attracts
more subscribers and increases its revenues. Therefore, the number of employees (x1), total
asset (x2) and capital expenditures (x3) are used as input variables in the DEA and
Meta-frontier model.
4. Empirical Results
4.1 Data collection
The study analyzes 28 major mobile operators in APEC. They are Telstra and Optus
in Australia; Bell Wireless and Rogers Wireless in Canada; China Mobile and China
Unicom in China; CSL in Hong Kong; NTT DoCoMo and KDDI in Japan; SK Telecom
and KT Freetel in Korea; Celcom in Malaysia; America Movil’s Telcel and Telefonica’s
Movistar in Mexico; Telkomsel and Indosat in Indonesia; New Zealand Telecom in New

10. Zealand; SingTel in Singapore; Smart Communications and Globe Telecom in Philippines;
MTS and VimpelCom in Russia; Chunghwa Telecom (CHT) and Taiwan Mobile (TMB) in
Taiwan; Advanced Info Service (AIS) and Total Access Communication (DTAC) in
Thailand; Verizon Wireless and AT&T Mobility in the U.S
The operating and financial data was mainly obtained from these operators’ annual
reports and the surveys released from telecommunications authorities and associations. The
units of currencies of these data are transferred into US dollars by using the exchange rates
announced by the Federal Reserve Bank of New York on the last business day of the fiscal
years. It is noticeable that a fiscal year for the operators in Japan, Singapore and Optus in
Australia ends on March 31 and for Telstra in Australia ends on June 30. Most importantly,
in order to measure the efficiency of operators exclusively for mobile services, the data of
integrated business operators which operate both fixed-line and mobile businesses used in
this study were calculated by mobile revenue proportion of total telecommunications
revenue.
4.2 Technology gap comparison
Firms make productive choices from a set of feasible inputs and outputs combinations
(i.e., technology sets). The literatures (Tsai, Chen and Tzeng, 2006; Lam and Shiu, 2008;
and Yang and Chang, 2009) used DEA approach to measure the relative efficiency scores
of firms’ performance. However, these literatures had neglected the fact that the
technology sets of firms may differ across regions/countries because of the differences in
physical infrastructure, human, and financial capital, economic infrastructure, and any
other characteristics of the physical, social, and economic environment in which firms
operate. To accommodate the potential variation of available technology across members
and to obtain comparable technical efficiencies for the members in APEC, the study uses
the Meta-frontier approach. The Meta-frontier is a frontier enveloping several groups of
observations whose technology differs, and comparing it with the respective frontier of
each group, for identifying technology gaps between each group’s frontier and the
enveloping metafrontier. The software adopted is DEA-SOLVER. The advantage of using
the Meta-frontier approach is to separate technical efficiency difference within groups from
technology difference within groups.
There are three steps to measure the efficiency and technology gaps between two
groups of observations. First, yearly technical efficiencies under respective frontiers are
calculated, indicating that a frontier is constructed for each group. It shows how DMUs in
each group perform with respect to their own group’s technology. Second, both groups are
merged in a unique sample and a common frontier is constructed. Then yearly technical
efficiencies under this metafrontier are calculated. Third, a technology gap ratio (TGR) is
computed for each DMU in each year: the ratio is the DMU’s technical efficiency
calculated under the metafrontier divided by the DMU’s technical efficiency calculated
under the respective group’s frontier. By construction, the ratios are equal or less than 1. In
the remainder of this study, the acronyms TE (the efficiencies estimated with respect to the
group frontier) and TE* (the efficiencies estimated with respect to the metafrontier) are
used to refer to DEA estimates of technical efficiencies relative to the group frontiers and
the metafrontier, respectively.
Three methods to classify groups of DMUs commonly used in the studies of
metafrontier efficiency are regions (Battese, Rao and O’Donnell, 2004; O’Donnell, Rao
and Battese, 2008), countries (Bos and Schmiedel, 2007) and firm’s size or development
strategy (Mazumdar and Rajeev, 2009). Their purposes are to classify firms of different
production technologies within different industries. Penetration rate in mobile market is a

11. suitable indicator for classifying groups in the Meta-frontier analysis. It does not only
reveal the strength of subscribers and revenues, but also the quality of systems and services
for mobile operators. Penetration rate reflects the current developments both in mobile
market and in economy. Indian Council for Research on International Economic Relations
(ICRIER, 2009), founded by Vodafone Group, stated that mobile penetration could boot
the growth of GDP in the economy.4
Developed countries usually have a more advanced
technology and better performance in mobile penetrations than those in developing
countries. Operators in markets of different penetration rates have different managerial
strategies and operating objectives. In the countries with high mobile penetrations, for
example, operators focus on upgrading mobile systems and revenue drive in data services
because of market status of nearly full saturation. However, operators in the countries with
low mobile penetrations focus on providing subscribers higher service coverage by
investing network infrastructures.
The 28 APEC mobile operators are classified into two groups: group of high
penetration rate and group of low penetration rate, using the boundary of penetration rate
85% in member’s mobile market. Groups of high penetration rate refer to Telstra, Optus,
CSL, NTT DoCoMo, KDDI, SKT, KTF, Celcom, Telecom New Zealand, SingTel Telecom,
CHT, TMB, AIS, DTAC, MTS, Vimpelcom, Verizon Wireless and AT&T Mobility, and
groups of low penetration rate refer to Telkomsel, Indosat, China Mobile, China Unicom,
Smart Communication, Globe Telecom, Rogers Wireless, Bell Wireless, Telcel and
Movistar. Tables 1 (a)-(b) report the metafrontier estimation of the APEC mobile operators
for each of the two groups and for all operators.
Three fully efficient operators, Telkomsel, KDDI and Smart Communication, had the
values of TE, PTE and SE equal to 1 throughout the study period, and they were still fully
efficient in the group of high penetration and low penetration, respectively. The
input-output allocation of KDDI is located at the points where the group frontiers of high
penetration rate are tangent to the metafrontier. Similarly, the input-output allocations of
Telkomsel and Smart Communication are located at the points where the group frontiers of
low penetration rate are tangent to the metafrontier. It indicates that the metafrontier
closely envelops the group frontiers and that the value of the TGR equals the maximum
value of one.
The technical efficiency (TE*
) of China Mobile with respect to the metafrontier
steadily decreased from 0.805 to 0.560 during 2003 to 2008. The TE*
(= 0.560) of China
Mobile2008 implies that to produce the same level of output, it had to used 44% more of
the input and the production technology available in the metafrontier. The TE (= 0.797) of
China Mobile2008 implies that it produced the same output using 20.3% more of the input
level and the production technology available in the low penetration rate group. The TGR
(= 0.702) of China Mobile2008 implies that the output produced by using the minimum
inputs of the China Mobile2008 and the technology of the low penetration rate group is the
same level of output produced by using 29.8% more of the minimum inputs and the
technology represented by the metafrontier. Moreover, its values of TGR decreased during
2003 to 2008, indicating that technology gap of the low penetration rate group to the
metafrontier increased.
4
Indian states with high mobile penetrations could be expected to grow faster in economies than the ones
with lower mobile penetration rates, by 1.2% points a year more on average for every 10% increase in the
penetration rate (ICRIER, 2009). The study suggested there were significant network effects which magnified
the impact of mobile services on economic development when the level of mobile penetration exceeded a
critical mass of around 25%.

12. Finally, overall technical efficiencies of the operators in the high penetration rate
group (TE* = 0.813) were higher than those in the low penetration rate group (TE* =
0.780), and overall technology gap ratios of the operators in the high penetration rate group
(TGR = 0.949) were higher than those in the low penetration rate group (TGR = 0.894).
This implies that the operators in the high penetration rate group had higher technical
efficiencies and their technologies were closer to the metafrontier as compared with those
in the low penetration rate group. Further, it is interesting to observe that Canadian
operators, Rogers Wireless and Bell Wireless, performed less efficiently in the
metafrontier (i.e., TE* = 0.568 and 0.594 on average) but performed more efficiently in the
low penetration rate group (i.e., TE = 0.898 and 0.892 on average). Hence, the TGR values
(= 0.635 and 0.663 on average) were the lowest during 2003 to 2008. This indicated that
technology gaps of these two operators relatively to the low penetration rate group and to
the metafrontier were the largest among all APEC mobile operators.

13. Table 1 (a) Summary of TEs and TGRs of the APEC mobile operators – Group of
high penetration rate
DMU 2003 2004 2005 2006 2007 2008 Average
Telstra
TE* 0.666 0.521 0.534 0.514 0.431 0.522 0.531
TE 0.668 0.532 0.536 0.514 0.431 0.522 0.534
TGR 0.997 0.979 0.998 1.000 1.000 1.000 0.996
Optus
TE* 1.000 1.000 1.000 1.000 0.882 0.796 0.946
TE 1.000 1.000 1.000 1.000 0.882 0.796 0.946
TGR 1.000 1.000 1.000 1.000 1.000 1.000 1.000
CSL
TE* 0.835 0.662 0.558 0.784 1.000 0.737 0.763
TE 0.835 0.664 0.560 0.784 1.000 0.737 0.763
TGR 1.000 0.996 0.996 1.000 1.000 1.000 0.999
NTT
DoCoMo
TE* 1.000 1.000 0.898 0.795 0.719 0.810 0.870
TE 1.000 1.000 0.898 0.795 0.719 0.810 0.870
TGR 1.000 1.000 1.000 1.000 1.000 1.000 1.000
KDDI
TE* 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TE 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TGR 1.000 1.000 1.000 1.000 1.000 1.000 1.000
SKT
TE* 1.000 0.961 0.955 1.000 0.924 0.797 0.940
TE 1.000 0.961 0.955 1.000 0.936 0.912 0.961
TGR 1.000 1.000 1.000 1.000 0.987 0.874 0.977
KTF
TE* 0.966 1.000 1.000 1.000 1.000 1.000 0.994
TE 0.972 1.000 1.000 1.000 1.000 1.000 0.995
TGR 0.993 1.000 1.000 1.000 1.000 1.000 0.999
Celcom
TE* 0.466 0.510 0.640 0.568 0.678 0.814 0.613
TE 0.520 0.654 0.816 0.700 0.700 0.814 0.701
TGR 0.895 0.781 0.785 0.811 0.969 1.000 0.874
Telecom
New
Zealand
TE* 0.770 0.812 0.768 0.723 0.705 0.644 0.737
TE 0.770 0.812 0.768 0.723 0.705 0.644 0.737
TGR 1.000 1.000 1.000 1.000 1.000 1.000 1.000
SingTel
TE* 1.000 0.912 1.000 1.000 1.000 0.996 0.985
TE 1.000 1.000 1.000 1.000 1.000 0.996 0.999
TGR 1.000 0.912 1.000 1.000 1.000 1.000 0.985
CHT
TE* 0.977 1.000 1.000 0.801 1.000 1.000 0.963
TE 1.000 1.000 1.000 0.909 1.000 1.000 0.985
TGR 0.977 1.000 1.000 0.881 1.000 1.000 0.976
TMB
TE* 1.000 1.000 1.000 0.947 1.000 1.000 0.991
TE 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TGR 1.000 1.000 1.000 0.947 1.000 1.000 0.991
AIS
TE* 0.904 0.791 0.786 0.689 0.843 1.000 0.836
TE 1.000 1.000 1.000 0.900 0.897 1.000 0.966
TGR 0.904 0.791 0.786 0.766 0.940 1.000 0.865
DTAC
TE* 0.717 0.751 0.684 0.729 0.782 0.907 0.762
TE 0.894 1.000 0.988 0.985 0.919 0.987 0.962
TGR 0.802 0.751 0.692 0.741 0.851 0.920 0.793
MTS
TE* 0.628 0.748 0.814 0.873 0.967 0.988 0.836
TE 0.795 1.000 1.000 1.000 1.000 1.000 0.966
TGR 0.790 0.748 0.814 0.873 0.967 0.988 0.863
Vimpelcom
TE* 0.779 0.705 0.704 0.728 0.722 0.702 0.723
TE 1.000 1.000 1.000 1.000 0.929 0.722 0.942
TGR 0.779 0.705 0.704 0.728 0.778 0.972 0.778
Verizon
Wireless
TE* 0.381 0.405 0.431 0.510 0.576 1.000 0.551
TE 0.383 0.415 0.431 0.510 0.576 1.000 0.553
TGR 0.995 0.975 1.000 1.000 1.000 1.000 0.995
AT&T
Mobility
TE* 0.610 0.579 0.559 0.580 0.560 0.615 0.584
TE 0.610 0.627 0.559 0.580 0.560 0.615 0.592
TGR 1.000 0.923 1.000 1.000 1.000 1.000 0.987
Average
TE* 0.816 0.798 0.796 0.791 0.822 0.852
---------TE 0.858 0.870 0.862 0.856 0.847 0.864
TGR 0.952 0.920 0.932 0.930 0.972 0.986
Notes: TE*=efficiencies estimated with respect to the meta-frontier; TE=efficiencies estimated with respect to the group
frontier; TGR=meta-technology gap ratio.

14. Table 1 (b) Summary of TEs and TGRs of the APEC mobile operators – Group of low
penetration rate
DMU 2003 2004 2005 2006 2007 2008 Average
Telkomsel
TE* 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TE 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TGR 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Indosat
TE* 0.526 0.513 0.548 0.631 0.658 0.731 0.601
TE 0.531 0.513 0.548 0.631 0.658 0.731 0.602
TGR 0.990 1.000 1.000 1.000 1.000 1.000 0.998
China Mobile
TE* 0.805 0.625 0.661 0.675 0.637 0.560 0.661
TE 0.846 0.712 0.737 0.773 0.724 0.797 0.765
TGR 0.952 0.878 0.896 0.874 0.879 0.702 0.864
China Unicom
TE* 1.000 1.000 1.000 1.000 1.000 0.522 0.920
TE 1.000 1.000 1.000 1.000 1.000 0.662 0.944
TGR 1.000 1.000 1.000 1.000 1.000 0.787 0.965
Smart
Communication
TE* 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TE 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TGR 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Globe Telecom
TE* 0.770 0.818 0.817 0.922 1.000 1.000 0.888
TE 0.774 0.818 0.817 0.922 1.000 1.000 0.889
TGR 0.994 1.000 1.000 1.000 1.000 1.000 0.999
Rogers Wireless
TE* 0.584 0.525 0.513 0.549 0.586 0.653 0.568
TE 1.000 0.777 0.866 0.839 0.905 1.000 0.898
TGR 0.584 0.676 0.593 0.655 0.647 0.653 0.635
Bell Wireless
TE* 0.454 0.438 0.626 0.601 0.662 0.780 0.594
TE 0.766 0.651 1.000 0.935 1.000 1.000 0.892
TGR 0.593 0.672 0.626 0.642 0.662 0.780 0.663
Telcel
TE* 0.816 0.854 0.795 0.998 1.000 0.809 0.879
TE 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TGR 0.816 0.854 0.795 0.998 1.000 0.809 0.879
Movistar
TE* 0.600 0.569 0.571 0.631 0.948 0.801 0.687
TE 0.621 0.605 0.642 0.646 0.953 0.901 0.728
TGR 0.966 0.940 0.890 0.977 0.995 0.888 0.943
Average
TE* 0.755 0.734 0.753 0.801 0.849 0.785
------TE 0.854 0.808 0.861 0.875 0.924 0.909
TGR 0.889 0.902 0.880 0.915 0.918 0.862
Notes: TE*=efficiencies estimated with respect to the meta-frontier; TE=efficiencies estimated with respect to the group frontier;
TGR=meta-technology gap ratio.
5. Concluding Remarks
The existing efficiency studies on telecommunications industry mainly analyzed fixed-line
operators or integrated operators (see, for example, Lee, Park and Oh, 2000; Uri, 2000 and 2002;
Facanha and Resende, 2004; Tsai, Chen and Tzeng, 2006), but few focused on mobile operators or
mobile sector of integrated operators. This study has analyzed the relative efficiency and technology
gap of 28 mobile operators in APEC over the time period of 2003 to 2008 using the methodologies
of DEA and Meta-frontier. This study provides two contributions to the existing literature. First, to
my best knowledge, this study is one of the first to apply the Meta-frontier approach to analyze the
technology gaps of operators in telecommunications industry. The use of the Meta-frontier is able to
accommodate the potential variation of available technology across groups and to obtain
comparable technical efficiencies for the groups in APEC’s mobile markets.

15. Second, there are sufficient DMUs used in this cross-country and cross-period study, i.e., 28
APEC mobile operators for the research period of six years, as compared to the related studies. A
large number of 168 DMUs used in the analysis are to provide the results with higher discriminating
power.5
The objects of telecommunications studies on efficiency measurement can approximately
be divided into two categories: (1) single operator for a period of time and/or its regional operating
centers at a particular time when experiencing different types of regulations, business reform and
liberalization (Sueyoshi, 1998; Giokas and Pentzaropoulos, 2000); (2) multiple operators at a
particular time and/or for a period of time when comparing them from international perspective or
overall telecommunications industry of countries (Tsai, Chen and Tzeng, 2006; Lam and Shiu, 2008;
Sastry, 2009; Yang and Chang, 2009). There are some limitations within this literature. The former
one did not compare the object with other competing operators, and the latter one did not consider
factors such as national development, mobile communication technology and application, market
size, cultural, and usage habit of mobile services. Further, some studies compared mobile operators
with integrated service operators (Tsai, Chen and Tzeng, 2006), and thus, the results might have
possible bias.
In the Meta-frontier results, the operators in the group of high penetration rate had higher
technical efficiencies and smaller technology gap on average than those in the group of low
penetration rate, implying that the operators in the high penetration rate group had higher technical
efficiencies and their technologies were closer to the metafrontier as compared with those in the low
penetration rate group. Further, Roger Wireless and Bell Wireless in the low penetration rate group
are the two operators which had the largest technology gap among all APEC mobile operators. This
indicates that the technologies of these two operators were farther away from the metafrontier.
Similar results can be found in Battese, Rao and O’Donnell (2004) in which garment firms in East
Java of Indonesia had the highest mean technical efficiency relative to their regional stochastic
frontier, but they tended to be farthest from the potential outputs defined by the metafrontier
function. O’Donnell, Rao and Battese (2008) also found that the technical efficiency of South
Africa was quite high when measured with respect to the African frontier but low when measured
against the metafrontier. This difference implies a low TGR in DEA-Metafrontier approach.
This study is subject to some limitations. First, the operators in this study are compared cross
countries and cross periods, and the data inaccuracy of the variables is affected by price variation.
But, in this study, the monetary variables of total assets, capital expenditures and total revenues, are
not adjusted to eliminate the effect of inflations by using each country's gross domestic product
deflator or consumer price index. It should be fixed in the future. Second, due to the limited
availability of the data for some integrated operators, in which revenues are only reported in the
aggregate amounts of both fixed-line and mobile businesses, a ratio of mobile revenues to total
5
For example, an important experienced rule of thumb when using DEA, is that the number of DMUs is at least twice
the sum of the number of inputs and that number plus outputs. Otherwise, the model may produce numerous relatively
efficient units and decrease discriminating power.

16. telecommunications revenues is used calculated the input values of these operators in this study.
Similarly, it may lead to some bias on the value of the variables.
There are some possible directions for future researches. It would be interesting to consider the
impacts of technology, research and development (R&D) and equipment inputs on operator’s
performance. For example, number of R&D employees, percentage of digitalized switchboards and
quantity of 2G/3G/3.5G base stations can be used as input variables to reflect the status of technical
progress and technical efficiency of an operator. It would be also good to divide total revenues into
revenues of different services by using 2G revenue, 3G revenue, 3.5G revenue and value-added
services as the output variables in the measurement of efficiency. These revenues better reflect the
technology-variation characteristics of a mobile operator and, in particular, the development of
telecommunications market. However, this will depend on the availability of data. In reality, not all
mobile operators publish their operating data in details because these data of operation and cost are
considered as the inner information in businesses. Second, the study can be extended to investigate
the impacts of DMUs’ environmental variables on their metafrontier technical efficiency by Tobit
regression. Possible influential factors related to a mobile operator’s efficiency includes service
quality, competition degree, 3G network type, ratio of 3G subscribers, ratio of data service revenues,
geographical territory. This is a two-stage approach in which an efficiency measurement is
evaluated in the first stage and the influential factors of the efficiency are investigated in the second
stage.
References
1. AIS (2009), “Annual reports 2003-2008”, available at: http://investor.ais.co.th/
AricleListAISIRNews.aspx?mid=77
2. America Movil (2009), “Annual reports 2003-2008”, available at: http://www.
americamovil.com/
3. American Telephone & Telegraph (AT&T) (2009), “Annual reports 2003-2008”, available at:
http://www.att.com/gen/ investor-relations?pid=5691
4. Asia-Pacific Economic Cooperation (APEC) (2008), available at: http://www.apec.org/
5. Battese, G.E., Rao, D.S.P., and O’Donnell, C.J. (2004), “A metafrontier production function for
estimation of technical efficiencies and technology gaps for firms operating under different
technologies”, Journal of Productivity Analysis, Vol. 21, No. 1, pp. 91-103.
6. Bell Canada Enterprise (2009), “Annual reports 2003-2008”, available at:
http://www.bce.ca/en/investors/financialperformance/annualreporting
7. Bos, J.W.B. and Schmiedel, H. (2007), “Is there a single frontier in a single European banking

17. market?”, Journal of Banking & Finance, Vol. 31, No. 7, pp. 2081-2102.
8. Beeline (2009), “Annual reports 2003-2008”, available at: http://www.vimpelcom.
com/investor/reports.wbp
9. Business Monitor International Ltd. (2009), “United States telecommunications report Q3
2009”, available at: http://www.businessmonitor.com/
10. China Mobile (2009), “Annual reports 2003-2008”, available at: http://www.
chinamobileltd.com/ir.php?menu=3
11. China Unicom (2009), “Annual reports 2003-2008”, available at: http://www.
chinaunicom.com.hk/tc/investor/ir_report.html
12. Chunghwa Telecom (CHT) (2009), “Annual reports 2003-2008”, available at:
http://www.cht.com.tw/
13. CSL (2009), “Annual reports 2003-2008”, available at: http://telstra.com/index.
jsp?SMIDENTITY=NO
14. DeCarlo, S. (2009). “The world's biggest public companies”, Forbes Asia, Vol. 5, No. 7, April
27, pp. 34-35, available at: http://www.forbes.com/lists/2009/18/global-09_
The-Global-2000_Rank.html
15. Facanha, L.O. and Resende, M. (2004), “Price cap regulation, incentives and quality: The case
of Brazilian telecommunications”, International Journal of Production Economics, Vol. 92, No.
2, pp. 133-144.
16. Farrell, M.J. (1957), “The measurement of productive efficiency”, Journal of the Royal
Statistical Society, Vol. 120, No. 3, pp. 253-290.
17. Giokas, D.I. and Pentzaropoulos, G.C. (2000), “Evaluation productivity efficiency in
telecommunications: Evidence from Greece”, Telecommunications Policy, Vol. 24, No. 8-9, pp.
781-794.
18. Globe Telecom (2009), “Annual reports 2003-2008”, available at: http://site.globe.com.
ph/about_globe/about_us/investor_relations/reports?sid=S3uSN8uxpRYAABxH2JYAAACce
19. Indian Council for Research on International Economic Relations (ICRIER) (2009), “India: The
impact of mobile phones”, available at: http://www.icrier.org/page.asp?
MenuID=5&SubCatId=174&SubSubCatId=663
20. Indosat (2009), “Annual reports 2003-2008”, available at: http://www.indosat.com/
Investor_Relations

18. 21. Internation Telecommunication Union (ITU) (2009), “Mobile celluar subscriptions 2003-2008”,
available at: http://www.itu.int/ITU-D/ict/index.html
22. Kokusai Denshin Denwa Inc (KDDI) (2009), “Annual reports 2003-2008”, available at:
http://www.kddi.com/English/corporate/ir/library/annual_report/ index.html
23. Korea Telecom Freetel (KTF) (2009), “Annual reports 2003-2008”, available at:
http://www.kt.com/eng/index.jsp
24. Lam, P.L. and Shiu, A. (2008), “Productivity analysis of the telecommunications sector in
China”, Telecommunications Policy, Vol. 32, No. 8, pp. 559-571.
25. Lee, Y.Y., Park, Y.T. and Oh, H.S. (2000), “The impact of competition on the efficiency of
public enterprise: The case of Korea Telecom”, Asia Pacific Journal of Management, Vol. 17,
No. 3, pp. 423-442.
26. LG Telecom (2009), “Annual reports 2003-2008”, available at: http://www.lgtelecom. com/
27. Liao, C.H., and González, B.D. (2009), “Comparing operational efficiency of mobile operators
in Brazil, Russia, India and China”, China and World Economy, Vol. 17, No. 5, pp. 104-120.
28. Market Intelligence Center (MIC) (2008), “The analysis of mobile communication market in
Taiwan, China, Japan and Korea in first quarter of 2008”, available at:
http://mic.iii.org.tw/intelligence/.
29. Market Intelligence Center (MIC) (2009), “The prediction of worldwide mobile subscribers of
2006-2013”, available at: http://mic.iii.org.tw/intelligence/.
30. Mazumdar, M. and Rajeev, M. (2009), “Comparing the efficiency and productivity of the Indian
pharmaceutical firms: A Malmquist-Meta-frontier approach”, International Journal of Business
and Economics, Vol. 8, No. 2, pp. 159-181.
31. Megafon (2009), “Annual reports 2003-2008”, available at: http://eng.megafon.ru/
company/invest/statements/
32. Nippon Telegraph and Telephone DoCoMo (NTT DoCoMo) (2009), “Annual reports
2003-2008”, available at: http://www.nttdocomo.co.jp/english/corporate/ir/library/
annual/index.html
33. Mobile TeleSystems (MTS) (2009), “Annual reports 2003-2008”, available at:
http://www.mtsgsm.com/ resources/annual_reports/
34. O’Donnell, C.J., Rao, D.S.P., and Battese, G.E. (2008), “Metafrontier frameworks for the study
of firm-level efficiencies and technology ratios”, Empirical Economics, Vol. 34, No. 2, pp.
231-255.

19. 35. Pentzaropoulos, G.C. and Giokas, D.I. (2002), “Comparing the operational efficiency of the
main European telecommunications organizations: A quantitative analysis”,
Telecommunications Policy, Vol. 26, No. 11, pp. 595-606.
36. Philippine Long Distance Telephone (PLDT) (2009), “Annual reports 2003-2008”, available at:
http://www.pldt.com.ph/investor/Pages/AnnualReport.aspx
37. PMR (2008), “Russia’s mobile telephony market still on course for steady growth”, available at:
http://www.pmrpublications.com/
38. Rogers Wireless (2009), “Annual reports 2003-2008”, available at: http://www.rogers.
com/web/Rogers.portal?_nfpb=true&_windowLabel=investor_1_1&investor_1_1_actionOverri
de=%252Fportlets%252Fconsumer%252Finvestor%252FshowLandingPageAction&_pageLabe
l=IR_LANDING
39. Sastry, P. (2009), “Identifying leaders and laggards -A method and application to US local
telephone companies”, Telecommunications Policy, Vol. 33, No. 3-4, pp. 146-163.
40. SinTel (2009), “Annual reports 2003-2008”, available at: http://info.singtel.com/node/ 1788
41. SK Telecom (2009), “Annual reports 2003-2008”, available at: http://www. sktelecom.com/eng/
42. Softbank Mobile (2009), “Annual reports 2003-2008”, available at: http://www.
softbankmobile.co.jp/en/info/finance/report/index.html
43. Sprint Nextel (2009), “Annual reports 2003-2008”, available at: http://investors.
sprint.com/phoenix.zhtml?c=127149&p=irol-reportsannual
44. Sueyoshi, T. (1998), “Privatization of Nippon Telegraph and Telephone: Was it a good
decision?”, European Journal of Operational Research, Vol. 107, No. 1, pp. 45-61.
45. Telekom Malaysia (2009), “Annual reports 2003-2008”, available at: http://www.
tm.com.my/about-tm/investor-relations/annual-reports/Pages/AnnualReport.aspx
46. Telecommunications Carriers Association (TCA) (2009), available at: http://www.
tca.or.jp/english/
47. Telecom New Zealand (2009), “Annual reports 2003-2008”, available at: http://www.
telecom.co.nz/homepage
48. Telefonica (2009), “Annual reports 2003-2008”, available at: http://www.telefonica.
com/en/about_telefonica/html/publicaciones/informesanuales.shtml
49. Telekomsel (2009), “Annual reports 2003-2008”, available at: http://www.telkomsel.
com/web/annual_report

20. 50. Telus Wireless (2009), “Annual reports 2003-2008”, available at: http://about.telus. com/
51. Taiwan Mobile (TMB) (2009), “Annual reports 2003-2008”, available at: http://www.
taiwanmobile.com/
52. Telstra (2009), “Annual reports 2003-2008”, available at: http://www.telstra.com.au/
53. The World Bank (2009), “World development report 2009”, available at:
http://www.worldbank.org/
54. Total Access Communication (DTAC) (2009), “Annual reports 2003-2008”, available at:
http://www.dtac.co.th/tha/ir /index_en.html
55. Tsai, H.C., Chen, C.M., and Tzeng, G.H. (2006), “The comparative productivity efficiency for
global telecoms”, International Journal of Production Economics, Vol. 103, No. 2, pp. 509-526.
56. Uri, N.D. (2000), “Measuring productivity change in telecommunications”, Telecommunications
Policy, Vol. 24, No. 5, pp. 439-452.
57. Uri, N.D. (2001), “Measuring the impact of price caps on productive efficiency in
telecommunications in the United States,” The Engineering Economist, Vol. 46, No. 2, pp.
81-115.
58. Uri, N.D. (2002), “The measurement of the change in productivity in telecommunications”,
Telecommunications Systems, Vol. 20, No. 3, pp. 177-194.
59. Verizon Wireless (2009), “Annual reports 2003-2008”, available at: http://investor.
verizon.com/financial/quarterly/index.aspx
60. Vimpelcom (2009), “Annual reports 2003-2008”, available at: http://www.vimpelcom.
com/investor/reports.wbp
61. Yang, H.H. and Chang, C.Y. (2009), “Using DEA window analysis to measure efficiencies of
Taiwan’s integrated telecommunication firms”, Telecommunications Policy, Vol. 33, No. 1-2,
pp. 98-108.