Download The Determinants of Market Share for Mobile Telecommunications

International Telecommunications Society
16th European Regional Conference
Porto, Portugal
The Determinants of Market Share for Mobile
Telecommunications Operators
Nakil SUNG
University of Seoul, Department of Economics
90 Cheonnong-dong, Dongdaemun-gu
Seoul, 130-743, Korea
(E-Mail) [email protected], [email protected]
(Tel) 82-2-2210-2180
(Fax) 82-2-2210-5232
ABSTRACT
While competition in mobile markets is currently evolving, the level of market
competition differs dramatically across developed countries. Also many regulators
argue that mobile markets are still not effectively competitive. This study attempts to
examine the effects of regulatory policies on competition in the mobile markets, and
also, aims at addressing the relationship between market power and profitability. To
accomplish these objectives, I estimate a structural model of the mobile industry using a
panel of 94 mobile operators in 27 OECD member states over the years 1998-2003. The
empirical results indicate that some regulatory policies play a vital role in determining a
mobile operator’s market share. Market share has a positive impact on the level of
prices, which in turn affects profitability. That is, mobile markets are not governed by
competition based on an operator’s merit, but mainly by regulatory policies. The study
emphasizes the importance of sector-specific regulation for development of competition
in the mobile markets.
JEL Code: L11, L96
Key words: Regulation, Competition, Market Share, Mobile Telecommunications,
2
I. Introduction
Within a short period of time, mobile telephones have become a common product
with a penetration rate of more than 70 % in most developed countries. With rapid
diffusion of wireless telecommunications, mobile markets have been transformed into
the most competitive part of the telecommunications sector. The average HerfindahlHirschman Index (HHI) for mobile markets in the OECD member states dropped from
4,792 in 1998 to 3,827 in 2003. On average, an incumbent mobile operator lost market
share of around 10% to competitors for the same period. The establishment of a
competitive market structure led to lower prices, which in turn generated further
diffusion of mobile telephones and also, to greater variety and higher quality of mobile
services.
While competition in mobile markets is currently evolving, the level of market
competition differs across countries. As a matter of fact, even in developed countries,
there are substantial differences in terms of competitiveness. For example, as of 2003,
the number of mobile operators in the United Kingdom was the same as that of
Denmark. On the other hand, four mobile operators in the United Kingdom had nearly
equal market share on the basis of mobile subscriptions, while the incumbent in
Denmark (TDC Mobil) took nearly half of the domestic mobile market. Sometimes
demographically similar countries, for example Sweden and Norway, are different in
the competitive parameters set in the markets.
The number of mobile operators is limited by the scarcity of available spectrum,
which means that mobile markets are intrinsically oligopolistic. In other words, the
competitiveness of the mobile industry may be pre-determined by technological
constraints. Despite development from a state-owned regulated monopoly to full
competition, incumbent mobile operators are often the strongest market players in the
markets. The incumbent mobile operators have a significant market power until now in
many countries. As a matter of fact, mobile markets are often regarded as some of
profitable telecommunications markets. Many entrants have difficulties overcoming the
first mover advantage enjoyed by the incumbent. Moreover, it is rare to observe changes
in the operator rank in terms of market share. Many regulators argue that their mobile
markets are not effectively competitive despite rapid diffusion of mobile services and
3
several years of competition.1 The fact that regulation in the mobile markets has been
recently strengthened may reflect on the current status of competition in the markets.2
The objective of my study is twofold. First, the study attempts to examine the
effects of a country’s regulatory setting on the level of competition in the mobile
markets, especially market share of mobile operators, in order to discuss the extent of
competitive divergence across developed countries. Second, the study aims at
addressing the relationship between market power and profitability in mobile markets.
Although interaction between market structure and performance has attracted a lot of
studies, it deserves attention especially in mobile markets. The establishment of a
competitive mobile market, particularly in 3G mobile markets, is too important to be
ignored in terms of consumer welfare.3
To accomplish these objectives, I estimate a structural model of the mobile
industry using a panel data of 94 mobile operators in 27 OECD member states over the
years 1998-2003. 4 The empirical results suggest that some regulatory policies
significantly influence a mobile operator’s market share, which in turn has a positive
impact on its market power and hence its profitability. In other words, mobile markets
are not governed by competition based on an operator’s merit, but mainly by regulatory
policies. This study emphasizes the importance of sector-specific regulation for
development of competition in the mobile markets.
Current empirical literature on mobile markets focuses mainly on worldwide
diffusion of mobile telecommunications and its variation across countries. For example,
Gruber and Verboven (2001) and Gruber (2001) estimate a logistic diffusion model to
unravel the determinants of the diffusion of mobile telecommunications in the EU
(European Union) countries and central and eastern European countries, respectively.
1
For illustration, OFCOM argues that UK mobile markets are not effectively competitive although
competition is under way.
2
Until recently the telecommunications regulatory policy focused mainly on the liberalization of fixedline telephony. Foreseeing the future convergence of fixed and mobile telecommunications services,
regulators in the EU member states concentrated on the mobile industry as well. The new regulatory
framework for electronic communications is, to a great extent, concerned with the regulation of mobile
telephony (Grzybowski, 2005).
3
This study focuses mainly on the analysis of second generation (2G) mobile markets because of
worldwide delay in the development of third generation (3G) mobile services. The presence of 3G mobile
operators is considered only when relevant data is available. The study provides useful suggestions for
determining the regulatory factors that accelerate a competitive 3G mobile market.
4
The panel data is unbalanced partly because each country has a different number of mobile operators
and partly because some operators did not provide services over all sample years.
4
Jang et al. (2005) use a similar logistic epidemic model to compute the diffusion rate for
29 OECD countries and Taiwan and then, conduct a conventional regression analysis to
explain variations in the rate across sample countries. These studies suggest that the
change from analog to digital technology, market competition and standardization
accelerate the diffusion of mobile subscriptions.
Grzybowski (2005) and Koski and Kretschmer (2005) adopt a simultaneous model
to examine not only the determinants of mobile diffusion but also those of mobile prices.
The two studies analyze the impact of regulatory and competition variables on prices
and demand for mobile services across the EU and 25 industrialized countries,
respectively. In particular, Koski and Kretschmer (2005) incorporate the determinants of
entry in their empirical model to address the question of non-random selection arising
from cross-country differences in the timing of the commercialization of 2G mobile
services. Although the two studies consider different regulatory variables, they find that
the regulatory policies have significant effects on the level of prices and the demand for
mobile services. To the best of my knowledge, however, there exists no econometric
study that addresses the impact of regulation on a mobile operator’s market share and
profitability.
The rest of the paper is organized as follows. Section II discusses the evolution of
competition in the mobile markets of OECD countries. In Section III, I specify the
empirical model with a discussion on the relationship between market share, market
power and profitability. In Section IV, I explain the data and the variables used in the
study and then, report the estimation results. Section V concludes the paper with a brief
discussion of policy implications.
II. Market Share and Profitability in Mobile Markets
Evolution of Competition in Mobile Markets
It is beyond doubt that competition in the mobile markets of OECD member states
is progressing. Table 1 provides the average of some important variables across all the
5
OECD countries excluding Iceland, Luxembourg and the Slovakia Republic.5 As the
average number of mobile operators in the sample countries increased from 2.88 in
1998 to 3.59 in 2003, the average HHI for mobile markets decreased from 4,792 in
1998 to 3,827 in 2003. The average market share of the largest operator, which was
often an incumbent monopolist, dropped from 57.3% in 1998 to 48.0% in 2003. In other
words, the incumbent lost market share of 9.3% to competitors for the sample period.
Competition in wireless telecommunications is expected to result in lower prices. This
expectation is confirmed by the finding that mobile prices, measured by average
revenue per minute (ARPM) in USD PPP, declined from 0.425 in 1998 to 0.240 in
2003.6
- Insert < Table 1> here -
On the other hand, the competitiveness in mobile markets varies substantially
across developed and emerging countries. Table 2 classifies sample OECD countries by
the level of competition, measured by the HHI. As of 2003, four OECD countries had
the HHI of less than 3,000. In particular, the U.S.A. has the most competitive mobile
markets among developed countries. The UK mobile market has been regarded as one
of the most competitive ones in Europe. In the UK, one of the two entrants that started
to provide commercial mobile services in early 1990s ascended to the throne on the
basis of mobile subscriptions. Most of European countries including Germany and
France belong to the second group which has the HHI of less than 4,000 and more than
3,000. The second group is usually in possession of three or four mobile operators and
5
These countries were dropped because Merill Lynch’s Global Wireless Matrix, one of the primary
sources of data in the study, does not provide relevant data on them.
6
The quality of my price data deserves some remarks. In general, it is difficult to construct mobile price
indexes comparable across countries and over time because of a variety of optional pricing plans.
Teligen’s representative basket methodology has been widely used to analyze quarterly price movements
of basic telecommunications services. However, Teligen’s definition of baskets changes over time to
consider changes in consumer’s usage pattern. Also, publicly available data is limited. Thus I use the
ARPM reported by Merill Lynch’s Global Wireless Matrix as a proxy for mobile prices. Since the ARPM
is the average price in nature, it may not completely reflect the marginal price. The ARPM, however, may
be the best available proxy for mobile prices consumers face.
6
experienced steady progress in competition. The third and fourth groups have a
considerably concentrated mobile market. The number of operators in these types of
countries is equal to two or three. For example, both New Zealand and Norway have a
duopolistic mobile market. Needless to say, it is obvious that there exist considerable
differences in the competitiveness between the first and the fourth group.
- Insert < Table 2> here -
In addition to substantial variations in the competitiveness across developed
countries, another interesting phenomenon deserves attention. While competition in the
sample countries is under way, its speed of progress is slowing down. As shown in
Table 1, all competition variables suffer from a clear-cut slowdown in their change rate.
For example, there has been nearly no change in the average number of mobile
operators and the average HHI since 2001. A decrease in the market share of the largest
operator has slowed down as well. On the other hand, mobile operators are making
steady progress in their profitability, measured by EBITDA margin.7 The average
EBITDA margin increased from 28.3% in 1998 to 35.5% in 2003.
Table 3 indicates that there is quite a gap between market players, especially a gap
between the first and second runners. Moreover, the gap persists and sometimes widens.
For example, as of 2003, the difference in the EBITDA margin between the first and
second largest operators was 7% in 1998 and 6.1% in 2003. A gap in the churn rate
between the two operators was 0.2-0.3% in late 1990s, but 0.5% in 2003. In other words,
an incumbent operator still enjoys a strong first mover advantage, which contributes to
slowdown in its declining market share. All these findings are not consistent with the
prospect that mobile markets are becoming effectively competitive.
7
EBITDA margin is defined as the sum of operating income and depreciation divided by the total
amount of sales. Thus in the exact sense, the EBITDA margin of an operator measures its cash flow or
deep pocket. Because of a lack of appropriate data, I use the EBITDA margin as a proxy for both
profitability and cash flow.
7
- Insert < Table 3> here -
Market Share, Price Levels and Profitability
The cross-operator variation in market share as well as the cross-country variation
in market concentration can be explained by the difference in the regulatory policies.
Obviously the year of entry for a mobile operator has a significant impact on its market
share. Figure 1 shows the relationship of entry year to market share and profitability. In
Figure 1, the two peaks in the number of operators entering mobile markets can be
identified. The first peak took place over the period 1984-1986, while the second peak
came into being over the period 1993-1999. The former corresponds to the introduction
of analog 1G mobile services, while latter to the commercialization of the digital 2G
mobile system and hence, to the period when regulators in all sample countries gave a
license to new competitors. As shown in Figure 1, the mobile operators which started to
provide mobile services in the 1980s experienced similar market share and profitability
as of 2003. On the other hand, the operators which entered into the mobile markets in
the 1990s had less market share and a lower EBITDA margin than the incumbents.
Moreover, the later the operator entered into the markets, the less its market share was
and the lower its profitability was.
-
Insert < Figure 1> here –
Figure 2 shows the relationship of market share to average revenue per unit
(ARPU) and ARPM.8 According to Figure 2, it appears that an operator’s market share
has no relationship with its ARPU but is slightly positively related with its ARPM.9 In
other words, in the OECD mobile markets, higher market share implies greater market
power and hence higher prices, but the relationship of market share to prices may be
8
ARPU and ARPM are calculated by dividing total revenues by the total number of subscribers and
total minutes of use, respectively. Thus ARPU is equal to ARPM times minutes of use per subscriber
(MOU).
9
The correlation coefficient between market share and ARPU (ARPM) is -0.047 (0.140).
8
relatively weak. Also, whether higher market share is associated with more or fewer
minutes of use per subscriber is not unambiguous. A mobile operator’s market share,
however, has a positive relationship with its profitability.10 As shown in Figure 3, a
higher market share leads to a higher EBITDA margin. Also, this finding is true at the
country level. Market concentration in a country is positively associated with the
EBITDA margin.
- Insert < Figure 2> and < Figure 3> here –
III. Empirical Models
Structure-Behavior-Performance Relationship
In order to accomplish the objective of the paper, I use a simultaneous equation
approach on the basis of the structure-behavior-performance paradigm. The structurebehavior-performance relationship is one of the most frequently attacked topics in
empirical industrial organization. As a matter of fact, there have been numerous
empirical studies of the relations among various measures of market structure, conduct,
and performance, especially using industry data.11 On the other hand, the firm-level
relationship between market share and performance has not received attention
commensurate with its importance.12 Although several researchers paid attention to
stability in market share and firm ranks, data limitations have kept the empirical
literature on the subject thin (Schmalensee, 1989).
In particular, the number of studies on the determinants of market share and its
interaction with either behavior or performance is relatively small in empirical industrial
organization. Among these few studies, Nakos (1993) estimates a simultaneous
10
The correlation coefficient between market share (HHI) and EBITDA margin is 0.685 (0.329).
Refer to Schmalensee (1989) for a useful survey of inter-industry study on the structure-behaviorperformance relations. Schmalensee (1989) argued that inter-industry cross-section studies are limited by
serious problems of interpretation and measurement and suggested the use of panel data and industryspecific data.
12
Either identifying the determinants of market share or forecasting future market share is an important
research subject in marketing.
11
9
equations system for market share, R&D, advertising, and profitability. Using the data
on the leading industrial firms in Japan, he indicates that market share and demand
growth have positive effects on profitability, and an increase in the stock of goodwill
results in increases in market share as well as profitability. Abramowitz and Brown
(1993) specify a structural equation for airline route share and prices in order to
examine airline pricing and market structure determination for the U.S. domestic
airport-pair routes. Their major finding is that airport dominance as a source of market
power leads to the non-contestability of airline routes and also, higher market shares are
associated with higher prices. Alexander et al (1995) find that R&D productivity has a
positive effect on global market share for twenty six international pharmaceutical firms.
They use the predicted values for productivity measures as instrumental variables in the
market share equations in order to avoid the simultaneous bias problem. Bhattacharya
(2002) consider market share as one of the factors affecting profitability to prove that
efficiency plays a vital role in explaining profits rather than market power. His data
comes from Australian manufacturing industries. Mixon and Hsing (1997), to the best
of knowledge, provide the only study on market share in telecommunications.13 They
suggest that in the long run, market share is mainly determined by long-distance rates
among major carriers and government regulation. In particular, price-cap regulation had
reduced AT&T’s market share and increased competition in the long-distance market.
These studies have something in common. First, most of them use a simultaneous
equation model or adopt an instrumental variable estimation method except for Mixon
and Hsing (1997). It is a matter of course because market share affects and also is
affected by profitability. The two-way causal relationship between market share and
performance is widely accepted in the current structure-behavior-performance literature.
Second, they use intra-industry firm data. When a study focuses on market share, the
use of firm-level data is inevitable. Finally, in general, they provide evidence of a
positive role of market share in increasing prices and profits and also, indicate that some
13
Mixon and Hsing (1997) examine the factors affecting the market share of the dominant firm in the
U.S. long-distance telecommunications, AT&T. Their sample contains annual data of long-distance rates
(interstate message toll telephone daytime rates for five minute calls) between New York City and six
other metropolitan areas for the period 1984-1994.
10
behavior variables have effects on market share. My study inherits these common
features from the previous studies. I presume that a mobile operator’s market share
affects its price levels, which in turn affects profitability, while profitability plays a role
in changing market share.
Basic Estimation Model
I specify a simultaneous equation for market share, prices, and profitability in this
sub-section. A mobile operator’s market share is expected to be affected by the presence
or absence of the first mover advantages, the extent of marketing efforts, the number of
competitors, and a gap in efficiency. I measure the first mover advantages by two
indexes: one is whether an operator is a former incumbent monopolist or not (INCUM)
and the other is an operator’s age (AGE). INCUM gets value one if the operator is an
incumbent monopolist and zero otherwise. AGE is years passed after the operator’s
entry into mobile markets.
The degree of marketing efforts is proxied by the EBITDA margin, which is the
commonly used measure of the amount of cash owned by a firm. I include only facilitybased competitors in the number of mobile operators in a country (NOOP) because of a
lack of data on resellers and mobile virtual network operators (MVNOs). Assuming that
all operators have the same state of technology, I consider only the difference in types
of frequency band (FREQ) as a factor determining the efficiency of a mobile operator. It
is well-known that the frequency band of 800MHz is superior to that of 1800MHz (PCS
band) in both the quality and cost aspects.14 FREQ is equal to zero if an operator owns
and uses only the PCS frequency band, and one otherwise.
Using i to index the operator, j to index the country, and t to index the calendar
year, the complete specification of the market share equation is:
MS ijt = α 0j + α 1 EBITDAijt + α 2 INCUM ijt + α 3 AGEijt + α 4 NOOPjt + α 5 FREQijt . (1)
14
Different spectrums have different production costs of mobile services because they have different
properties. In particular, some regulators argue that the use of 800MHz would result in lower cost for
mobile operators than those operators using the 1.9GHz spectrum. For example, refer to Oftel (2001).
11
where α 0j is a country-specific intercept measuring unobserved country specific
effects.
I suppose that a mobile operator’s profitability is determined by its market power
directly through value creating and indirectly through higher prices. The first route is
captured by the operators’ market share (MS) and its degree of globalization (GLOB),
while the second route is gauged by the ARPM. GLOB equals one if an operator’s
majority share is owned by three global mobile carriers such as Vodafone, DT T-Mobile,
and FT Orange. My measure of profitability, EBITDA margin, includes accumulated
depreciation as well as operating income. To control for variation in the amount of
depreciation across countries and operators, I insert the two variables, an operator’s
operating years (AGE) and a density of cellular telephone per 100 inhabitants in a
country (CELDEN). Similarly to the market share equation, the profit equation is
specified as follows:
EBITDAijt = β 0 + β1 MS ijt + β 2 ARPM ijt + β 3 GLOBijt + β 4 AGEijt + β 5 CELDEN jt
(2)
Here country-specific fixed constants are added as well.
Finally, a mobile operator’s prices are supposed to be affected by its market power,
the competitive environment surrounding the operator, and the costs of service
provision. As mentioned before, I presume that the ARPM is a reasonable proxy for the
mobile prices. The market power is measured by market share (MS), while the degree of
competition in a country is represented by the number of competitors (NOOP) and the
presence of mobile number portability (MNP). An operator-year observation for MNP
gets value one if the portability of the mobile telephone number is available and zero
otherwise. It is difficult or sometimes impossible to measure an operator’s marginal cost
of producing and delivering mobile services mainly because of a lack of data. I
incorporate the three variables into the price equation to proxy the marginal cost: the
number of mobile subscriptions to the operator (SUBS), the population density of a
country (POPDEN), and type of frequency band (FREQ).15 The first two variables
15
It may be desirable to consider determinants of the marginal costs of providing mobile services, for
12
stand for economies of scale and density the operator experiences, while the last
variable represents the difference in the inherent cost condition. Accordingly, my price
equation is the following:
ARPM ijt = γ 0j + γ 1 MS ijt + γ 2 NOOPjt + γ 3 MNPjt + γ 4 SUBS ijt +
γ 5 FREQijt + γ 6 POPDEN jt
(3)
Here country-specific intercepts are added as before.
Estimation Method
There exist three endogenous variables and nine exogenous variables in the
system of equations (1)-(3). Since the number of exogenous variables excluded from
each equation is as large as the number of endogenous variables included in the
equation, all the equations are identified. I estimate the system of equations (1)-(3)
using the two-stage least-squares (2SLS) estimation method with country dummy
variables. To check robustness, results obtained from applying the three-stage leastsquares (3SLS) method without country dummy variables are reported as well.
One of important issues to discuss is whether the regulatory variables in this study
might be considered as endogenous. There may be a possibility that mobile operators
may influence regulator to manipulate telecommunications policy such as licensing and
frequency policies. On the other hand, although the possibility cannot be ignored, it is
more reasonable to suppose that most regulatory and competition variables in the study
are beyond the operators’ ability. Thus, following previous empirical studies on the
telecommunications industry including Grzybowski (2005) and Koski and Kretschmer
(2005), I assume that the regulatory variables in the study are uncorrelated with
unobservable shock in the three equations.
example input prices, because of potential differences in the production costs between mobile operators
and countries. Appropriate measures of input prices, however, are difficult to find. Grzybowski (2005)
finds that his rather crude measures of labor and capital costs have few effects on mobile demand and
prices. Thus, instead of incorporating input prices, I attempt to consider only major differences in the
production condition between countries and between operators; country-specific economies of density
and operator-specific economies of scale. Other variation across countries may be captured by countryspecific intercept terms.
13
IV. Empirical Results
Data and Variables
The primary sources of data used in this analysis are Merill Lynch’s Global
Wireless Matrix (GWM) and ITU’s World Telecommunications Indicators (WTI).
Operator data are collected mainly from the GWM, while country data are collected
from the WTI. I obtain data on country-specific regulatory polices from various sources
including s consulting firm’s reports, a regulator’s documents etc. Time span
encompassed by the study is 1998-2003, which corresponds to the period of rapid
growth and maturity in the development of mobile telephony.
Table 4 reports some descriptive statistics for all dependent and independent
variables. Since some operators entered into the market during the sample period, the
number of mobile operators is 94, but the number of observations is 538. One of major
problems with the data is that there are many missing values for the two important
variables, EBITDA and ARPM. According to Table 4, the number of valid observations
for EBITDA and ARPM is 399 and 343, respectively. Thus I carry out complementary
regressions after excluding data on ARPM and EBITDA.
-
Insert < Table 4> here –
Main Results
Tables 5 and 6 report main empirical results obtained by applying 2SLS with
country dummy variables and 3SLS without them to the above panel data. Since the
hypothesis of an insignificant fixed effect can be rejected with a high level of
significance, my discussion focuses mainly on Table 5. Also, results in both tables are
quite similar although some coefficients are statistically significant in one table but
insignificant in the other table. Thus Table 6 will be touched on only if necessary.
14
-
Insert < Table 5> and < Table 6 > here –
Table 5 suggests that all coefficients in the three equations are statistically
significant except for FREQ in the market share equation and MS in the profit equation.
As expected a priori, the three dependent variables have impacts on each other. The
higher an operator’s profitability is, or the deeper the operator’s pockets are, the higher
the operator’s market share is. Higher market share leads to higher ARPM in the price
equation which in turn generates higher profits in the profit equation. The insignificant
coefficient of MS in the profit equation implies that market power affects profitability
mainly through its impact on prices.
One of the principal findings is that regulatory policies significantly contributed to
an increase or a decrease in the dependent variables.16 That is, the regulatory policies
have significant effects on the market share, the level of prices and the profits for
mobile telecommunications operators across the OECD member states. My results
indicate that the market share of a former incumbent monopolist is higher than that of
other competitors by 7.5%, with other things being equal. The coefficient of an
operator’s age (AGE) is 0.009 and 0.007 in the market share and price equation,
respectively. It implies that a one year increase in the age of an operator leads to an
increase of 0.9% (0 0.7%) in the market share (EBITDA margin), respectively. This
finding confirms the claim in Section 2 that the year of entry for a mobile operator may
play a vital role in determining its market share and profitability.17 The parameter
estimates of the number of competitors (NOOP) are consistent with theoretical
16
This is consistent with Grzybowski (2005)’s argument. Grzybowski (2005), however, considers
different regulatory variables; the liberalization of fixed telephony, regulation of interconnection, the
presence of airtime resellers. His key findings are that (1) the liberalization of fixed telephony has a
negative impact on prices and a positive impact on the demand. (2) Regulation of interconnection charges
through the designation of mobile operators with significant market power increases the demand for
mobile telephony but exhibits no significant impact on prices. (3) The presence of airtime resellers does
not lower the prices or increase demand.
17
This is consistent with Mueller (1986)’s finding that leading firms’ market shares tend to persist over
long periods.
15
expectation. It appears that an operator’s market share and prices decreases with the
number of competitors.
The coefficients of type of frequency band (FREQ) are not always statistically
significant. When they are statistically significant, their sign is positive in both the
market share and price equations. That is, an operator with a frequency band of
800MHz or a dual band tends to experience both greater market share and higher prices
than others. This interpretation, however, should be considered with caution because the
parameter estimate is statistically insignificant in the market share equation of Table 5.
A dummy variable representing a global operator (GLOB) has a parameter estimate of
0.034 in the profit equation which is statistically significant at the 5% level. It indicates
that an operator associated with the three global carriers tends to enjoy a higher
EBITDA margin than others. The estimated difference in the market share between two
types of operators is 3.4%, with other things being equal. It appears that the introduction
of mobile number portability (MNP) results in a decrease in mobile prices. It is
consistent with theoretical expectation that mobile number portability reduces switching
costs and hence leads to fierce price competition.18
The coefficient of an operator’s mobile subscriptions (SUBS) has a negative sign
in the price equation, which implies that economies of scale may play a role in reducing
the costs of service provision. Cellular density (CELDEN) has a parameter estimate
with a positive sign in the profit equation. Thus it may be a tendency for an operator’s
profits or maybe an operator’s accumulated cash to increase with diffusion of mobile
subscriptions. The coefficient of population density (POPDEN) in the price equation
has a negative sign, which indicates that the costs of providing mobile services in more
densely populated regions may be lower.
Complementary Results
Since both EBITDA and ARPM have many missing observations, I carry out
complementary regression analyses after dropping either ARPM or both variables. In
the single equation model of Table 7, the results from relating some regulatory variables
18
It is widely accepted that the implementation of mobile number portability would have a negative
impact on prices through its effects on consumer switching costs. Grzybowski (2005) also finds that
mobile number portability turns out to have a significant negative impact on mobile prices.
16
only with market share are presented. The simultaneous equation model is the same as
before except that the price equation is deleted.
In the single equation model, all coefficients are statistically significant at the 1%
level and consistent with a priori expectation. An operator’s market share is higher than
others, if it is a former incumbent monopolist or a global carrier or an owner of an
800MHz frequency band, and as it enters into the markets earlier. Also, the operator’s
market share decreases with the number of competitors. The simultaneous equation
model reports the same results for the market share equation as before, but quite
different results for the profit equation. Some parameter estimates in the profit equation
become statistically insignificant. It may happen because of the absence of the price
equation. According to the results, an operator’s profits increase as its age goes up and
when it is associated with a global carrier.
-
Insert < Table 6> and < Table 7 > here –
V. Conclusions
I estimate a structural form model of mobile operators using panel data for the
period 1998-2003. In this study, I confirm the interaction between market share, prices
and profits in the OECD member states. Higher market share or stronger market power
leads to higher mobile prices, higher mobile prices to higher profitability, and higher
profitability to higher market share again. Also, the study provides strong evidence that
regulatory policies have a significant effect on market share, prices, and profitability of
mobile operators.
These empirical findings provide several public policy implications. First, the fact
that market share affects prices means that the mobile market is not effectively
competitive. In effectively competitive markets, long-run mobile prices are expected to
be determined by the average costs, and not affected by market share. Thus regulation
on mobile markets, especially regulation on the operator with significant market power
(SMP), need to be maintained. Only if the markets turns out to be sufficiently
17
competitive, ex post competition policy would replace traditional ex ante sector specific
regulation.
Second, the results indicate that a former incumbent monopolist enjoys a strong
first mover advantage until now. Moreover, profitability may reinforce market power.
Accordingly, the incumbents in the OECD member states may keep its leadership in the
markets in the near future and also, their market share may be stable from now on after
a steady drop due to entry of competitors. Thus regulators should pay attention to
preventing the incumbent’s market power in 2G markets form transferring to 3G
markets.
Finally, the role of regulatory policies in promoting competition should be
emphasized again. For example, the number of competitors and the implementation of
mobile number portability are proven to promote competition or to reduce prices. In
particular, the appropriate number of competitors may be crucial in maintaining or
developing competitive mobile markets. Recently consolidation between major mobile
operators is under consideration in developed countries, especially in the U.S.A.
Competition authorities should be cautious about merger policy because a sharp
reduction in the number of competitors may jeopardize consumer welfare.
References
[1] Abramowitz, Amy D., and Brown, Stephen M., “Market Share and Price
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[2] Alexander, Donald L., Flynn, Joseph E., and Linkins, Linda A., “Innovation, R&D
Productivity, and Global Market Share in the Pharmaceutical Industry,” Review of
Industrial Organization, 10, 1995, 197-207.
[3] Bresnahan, Timothy F., “Empirical Studies of Industries with Market Power,” in
Schmalensee, Richard, and Willig, Robert D., eds., Handbook of Industrial
Organization, North-Holland: Amsterdam, 1989.
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[4] Gruber, Harald, “Competition and Innovation: The Diffusion of Mobile
Telecommunications in Central and Eastern Europe,” Information Economics and
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[5]
Gruber,
Harald,
and
Verboven,
Frank,
“The
Diffusion
of
Mobile
Telecommunications Services in the European Union,” European Economic Review,
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[7] Iimi, Atsushi, “Estimating Demand for Cellular Phone Services in Japan,”
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[8] Jang, Show-Ling, Dai, Shau-Chi, and Sung, Simona, “The Pattern and Externality
Effect of Diffusion of Mobile Telecommunications: The Case of the OECD and
Taiwan,” Information Economics and Policy, 17, 2005, 133-148.
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Strategies and the Diffusion of Mobile Telephony,” Review of Industrial
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[10] Mixon Jr., Franklin G., and Hsing, Yu, “The Determinants of Market Share for the
‘Dominant Firm’ in Telecommunications,” Information Economics and Policy, 9,
1997, 309-318.
[11] Mueller, D., Profits in the Long Run, Cambridge University Press: Cambridge,
1986.
[12] Nakao, Takeo, “Market Share, Advertising, R&D, and Profitability: An Empirical
Analysis of Leading Industrial Firms in Japan,” Review of Industrial Organization,
8, 1993, 315-328.
[13] Nunn, Dana, and Sarvary, Miklos, “Pricing Practice and Firms’ Market Power in
International Cellular Markets: An Empirical Study,” International Journal of
Research in Marketing, 21, 2004, 377-395.
[14] Oftel, Effective Competition Review: Mobile, 2001 (Sept/26/2001).
[15] Schmalensee, Richard, “Inter-Industry Studies of Structure and Performance,” in
Schmalensee, Richard, and Willig, Robert D., eds., Handbook of Industrial
Organization, North-Holland: Amsterdam, 1989.
19
< Table 1 > Competition indexes in the OECD mobile markets
1998
1999
2000
2001
2002
Cellular density per
100 inhabitants
HHI
Number of operators
Market share of the
largest operator
ARPM (USD PPP)
EBITDA Margin
2003
24.3
37.7
(13.3)
53.3
(15.7)
64.2
(10.9)
70.1
(5.9)
76.1
(6.1)
4,792
4,313
(-479)
4,039
(-273)
3,879
(-160)
3,900
(21)
3,827
(-73)
2.88
3.15
(0.26)
3.41
(0.26)
3.56
(0.15)
3.56
(0.00)
3.59
(0.04)
57.3%
53.3%
(-4.0%)
50.5%
(-2.8%)
49.1%
(-1.4%)
48.9%
(-0.2%)
48.0%
(-0.9%)
0.425*
0.323
(-0.102)
0.291
(-0.031)
0.273
(-0.019)
0.258
(-0.014)
0.240
(-0.018)
28.3%*
26.1%*
(-2.1%)
27.0%
(0.8%)
31.5%
(4.6%)
34.4%
(2.9%)
35.5%
(1.1%)
Source: Merill Lynch, Global Wireless Matrix 4Q03, 2004
Note: The number in parenthesis stands for change in the corresponding variable from
the previous year to the current year. HHI refers to the Herfindahl-Hirschman Index
and ARPM to average revenue per minute. * implies that the corresponding
statistics have more than one-third missing observations. Thus careful consideration
should be given to interpretation of those numbers.
< Table 2 > Classification of OECD countries by competitiveness (as of 2003)
HHI < 3,000
USA (1,625), UK (2,502), Netherlands (2,538), Canada
(2,878)
3,000 ≤ HHI < 4,000
Austria (3,162), Greece (3,210), Germany (3,375), Poland
(3,346), Denmark (3,486), Australia (3,548), Portugal
(3,624), Italy (3,775), Czech (3,786), Hungary (3,794),
Sweden (3,795), Spain (3,864), France (3,882)
4,000 ≤ HHI < 5,000
Japan (4,025), Belgium (4,058), Finland (4,122), Korea
(4,182), Switzerland (4,568), Ireland (4,650)
5,000 ≤ HHI
New Zealand (5,032), Turkey (5,041), Norway (5,512),
Mexico (5,953)
Source: Merill Lynch, Global Wireless Matrix 4Q03, 2004
Note: The number in parenthesis refers to the HHI for a corresponding country
20
< Table 3 > Mobile operator’s characteristics by operator ranks
Monthly
Monthly
Market
EBITDA
Churn
Rate
ARPU
ARPM
Share
Margin
(USD PPP) (USD PPP)
st
1
Operator
2nd
Operator
rd
3
Operator
1998
57.3%
1.7%*
59.4*
0.444*
37.0%*
1999
53.3%
1.7%*
47.1
0.326
35.4%*
2000
50.5%
1.6%
40.8
0.276
36.2%
2001
49.1%
1.5%
38.6
0.267
38.7%
2002
48.9%
1.7%
38.3
0.250
40.4%
2003
48.0%
1.6%
39.1
0.223
41.7%
1998
32.8%
1.9%*
55.6*
0.490*
30.0%*
1999
32.1%
2.0%*
44.1
0.318*
26.9%*
2000
31.2%
2.1%*
39.2
0.262*
27.0%
2001
30.4%
2.3%*
38.3
0.267
33.6%
2002
30.4%
2.1%
38.4
0.252
33.6%
2003
29.9%
2.1%
38.3
0.224
35.6%
1998
14.6%*
2.2%*
58.6*
0.436*
19.8%*
1999
13.8%
2.0%*
47.3*
0.332*
18.5%*
2000
15.6%
2.2%*
38.8
0.271*
15.2%*
2001
15.9%
2.3%*
36.9
0.256*
21.5%*
2002
16.5%
2.1%*
35.8
0.260*
22.7%*
2003
17.7%
2.2%*
36.3
0.244*
25.7%*
Source: Merill Lynch, Global Wireless Matrix 4Q03, 2004
Note: * implies that the corresponding statistics have more than 180 missing cases,
which correspond to one-third of all observations. Thus careful consideration should
be given to the interpretation of those numbers.
21
< Figure 1 > Entry year, EBITDA margin and market share
50%
16
45%
14
40%
12
35%
30%
10
25%
8
20%
6
15%
4
10%
5%
2
0%
0
19791983
19841986
19871989
Market Share
19901992
19931995
19961997
EBITDA Margin
19981999
20002001
20022003
No of Operators
Source: Merill Lynch, Global Wireless Matrix 4Q03, 2004
Note: The data for EBITDA Margin and market share is presented as of 2003.
22
< Figure 2 > Relationship of market share to ARPU and ARPM (as of 2003)
(1) Market share and ARPU
(2) Market share and ARPM
80
0.60
0.50
60
ARPM (USD PPP)
ARPU (USD PPP)
70
50
40
30
20
0.30
0.20
0.10
10
0
0.00
0.40
0.20
0.40
0.60
0.00
0.00
0.80
0.20
Market Share
0.40
0.60
0.80
Market Share
Source: Merill Lynch, Global Wireless Matrix 4Q03, 2004
< Figure 3 > Relationship of market share and HHI to EBITA margin (as of 2003)
(2) HHI and EBITDA margin
60%
50%
50%
40%
EBITDA Margin
EBITDA Margin
(1) Market share and EBITDA margin
40%
30%
20%
30%
20%
10%
10%
0%
0%
0%
20%
40%
60%
0
80%
2,000
4,000
HHI
Market Share
Source: Merill Lynch, Global Wireless Matrix 4Q03, 2004
23
6,000
8,000
< Table 4 > Descriptive statistics
Name
Description
Cases
Mean
Standard
deviation
Dependent variables
MS
Operator’s market share
538
29.6%
18.7%
EBITDA
Operator’s EBITDA margin
399
20.5%
55.5%
ARPM
Operator’s ARPM (USD PPP)
343
0.272
0.142
538
0.314
0.465
Operator-specific independent variables
INCUM
Dummy standing for whether an operator is
a former (incumbent) monopoly
AGE
Operator’s age (years passed after the entry)
521
8.69
6.02
FREQ
Dummy standing for whether an operator
499
0.780
0.415
538
0.320
0.467
538
5,498
7,180
uses a band of 800MHz or a dual band
GLOB
Dummy standing for either a global or a
nation operator
SUBS
Operator’s cellular subscribers (thousand)
Country-specific independent variables
NOOP
Number of operators in a country
538
3.63
1.01
MNP
Dummy for implementation of number
538
0.303
0.460
538
55.2
24.4
538
138.1
127.5.
portability in mobile markets
CELDEN
Cellular subscribers per 100 persons in a
country
POPDEN
Population density of a country (persons per
squared Km)
24
< Table 5 > Estimation results: 2SLS with group dummy variables
Dep. Var.
MS
Para. Est.
EBITDA
P-value
MS
EBITDA
0.563
*
Para. Est.
P-value
Para. Est.
P-value
0.183
0.282
0.148*
0.003
0.846**
0.026
0.007***
0.054
0.040*
0.000
-0.000*
0.000
-0.047*
0.000
-0.038*
0.001
*
0.001
0.001
ARPM
0.075*
0.000
AGE
*
0.009
0.000
FREQ
-0.008
0.638
INCUM
**
GLOB
0.034
0.038
SUBS
-0.058*
NOOP
ARPM
0.000
*
CELDEN
0.003
0.008
MNP
POPDEN
-0.005
2
Adjusted R
0.799
0.523
0.778
Note: The number of observations is 266. *, **, *** imply statistical significance at
1%, 5%, and 10% level, respectively.
25
< Table 6 > Estimation results: 3SLS without group dummy variables
Dep. Var.
MS
Para. Est.
Constant
0.387
*
EBITDA
P-value
0.000
MS
EBITDA
0.040
AGE
0.070*
0.000
*
0.000
***
0.088
0.012
FREQ
P-value
Para. Est.
0.035
GLOB
0.000
-0.122
0.207
0.416
-0.111
0.711
-0.072
0.346
0.838**
0.042
0.011***
0.068
0.055*
0.006
0.019
0.416
SUBS
-0.000
NOOP
-0.071
*
0.000
*
CELDEN
0.002
MNP
POPDEN
2
Adjusted R
P-value
*
0.767
ARPM
INCUM
Para. Est.
ARPM
0.636
0.012
0.171
-0.042
*
0.000
-0.032*
0.005
0.000
0.520
0.000
0.778
Note: The number of observations is 266. *, **, *** imply statistical significance at
1%, 5%, and 10% level, respectively.
26
< Table 7 > Complimentary estimation results
Model
Single Equation
Dep. Var.
MS
Para. Est.
Simultaneous Equation
MS
Para. Est.
P-value
-0.025
0.918
0.013*
0.007
0.045*
0.000
0.000
0.195
-0.021
0.248
CELDEN
0.000
0.788
MNP
0.000
0.993
Constant
0.385
*
P-value
Para. Est.
EBITDA
P-value
0.000
MS
EBITDA
0.342
***
0.096*
0.055
0.000
0.086*
0.000
*
0.000
0.010
*
0.000
FREQ
*
0.041
0.001
0.018
0.190
GLOB
0.040*
0.000
INCUM
AGE
0.014
SUBS
NOOP
-0.079
*
0.000
*
-0.062
0.000
POPDEN
Cases
488
2
Adjusted R
326
0.699
0.818
0.778
Note: *, **, *** imply statistical significance at 1%, 5%, and 10% level,
respectively.
27