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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 Determination in the Contemporary Airline Industry,” Review of Industrial Organization, 8, 1993, 419-433. [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. 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[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