The Effects of Regulating Mobile Termination Rates for Asymmetric Networks∗ Ralf Dewenter†
Justus Haucap‡
Helmut-Schmidt University
Ruhr-University of Bochum
December 2004
Abstract This paper examines mobile termination fees and their regulation when networks are asymmetric in size. It is demonstrated that with consumer ignorance about the exact termination rates (a) a mobile network’s termination rate is the higher the smaller the network’s size (as measured through its subscriber base) and (b) asymmetric regulation of only the larger operators in a market will, ceteris paribus, induce the smaller operators to increase their termination rates. The results are supported by empirical evidence using data on mobile termination rates from 48 European mobile operators from 2001 to 2003. Keywords: mobile termination, telecommunications, consumer ignorance, price regulation To be published in: European Journal of Law and Economics 20, 2005, 185-197. ∗
For most helpful comments and discussions, we thank Michael Bräuninger, Jörn Kruse, Johannes Mönius and seminar participants at the 4th ZEW-Conference on the Economics of Information and Communication Technologies at Mannheim, the International Industrial Organization Conference at Chicago, and the 15th Biennial Conference of the International Telecommunications Society (ITS) at Berlin. Furthermore, we are grateful to Tobias Hartwich for his critical review of the manuscript. Of course, the usual disclaimer applies. † Helmut-Schmidt University, University of the Federal Armed Forces Hamburg, Institute for Economic Policy, Holstenhofweg 85, D-22043 Hamburg, Germany; e-mail:
[email protected]. ‡ Ruhr-University of Bochum, Department of Economics, Universitätsstr. 150, D-44780 Bochum, Germany; e-mail:
[email protected].
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1
Introduction
While in many mobile telecommunications markets across the world competition has long been left without much regulatory intervention, recently some aspects have come under close scrutiny by regulatory authorities. Apart from mobile number portability and national and international roaming, one of the key areas under investigation are mobile termination charges (see, e.g., European Commission, 2004, Ofcom, 2004). While mobile termination rates are already regulated in some countries (such as Austria and the UK), they are not regulated in others (such as Germany or Switzerland). In some other countries again (such as the Netherlands or Spain), only the termination rates of the larger mobile operators (which are supposed to be dominant or to enjoy significant market power) are regulated. In the latter case operators are regulated in an asymmetric fashion, with some termination rates being regulated while others are set by unregulated firms. In fact, this pattern of asymmetric regulation has been discussed in most European countries and is currently applied in a number of jurisdictions. Two policy questions arise, given these different institutional frameworks governing mobile termination: First, what termination rates do emerge if prices are left unregulated? And secondly, how are these rates affected by regulation? Gans and King (2000) have addressed exactly these questions. Their finding is that mobile termination rates will be excessive due to a negative pricing externality, which results from consumer ignorance regarding prices. Consumer ignorance is a particular problem of mobile telephony as customers are often not able to identify which specific network they are calling. This is because consumers may not know which operator is associated with each particular number. As a consequence, consumers are often ignorant about the price that they actually have to pay for a mobile call if prices differ between different networks (see Gans and King, 2000; Wright, 2002). In addition, mobile number portability is likely to exacerbate this problem as mobile prefixes will no longer identify networks (see Bühler and Haucap, 2004).1 Hence, as Gans and King (2000) have pointed out consumers are likely to base their calling decisions on average prices. This will be the case if either carriers are unable to set different prices for different mobile networks anyway or if consumers cannot determine ex ante which mobile network they are actually ringing when placing a call, i.e. if callers suffer from consumer ignorance. If consumers are not aware of the correct prices and base their demand on the average price, a negative pricing externality arises as the price of one firm will not only affect its own demand, but also that of its rivals. This induces firms to increase their termination 1
While some countries (such as Finland) have tried to solve this problem through acoustic signals that identify the called network, many consumers have found these mechanisms so annoying that the acoustic signals were abandoned again.
2
rates to inefficiently high levels as they do not account for the effect that their own price has on the average price perception and, thereby, their rivals’ demand. This externality problem comes on top of any monopoly and associated double marginalization problems.2 If market shares are endogenous and termination rates are set prior to other prices, termination rates may even be set so high that they ”choke” off the demand for mobile termination altogether (see Gans and King, 2000, p. 323). Consequently, demand for termination services will increase with any downward regulation of termination rates. We build on this research and extend it into three directions: Firstly, we will introduce network asymmetry into the model and consider mobile networks of different sizes (in terms of their subscriber bases). While Gans and King (2000) analyze a symmetric duopoly, we will provide a model with four asymmetric mobile network operators. Also, in the model developed by Gans and King all calls originate in a single fixed network, whereas we will focus on calls between mobile operators. Secondly, we will analyze the effects of asymmetric regulation in this framework. In reality, asymmetric regulation is a common feature of many European telecommunications markets, which has been largely neglected so far. And thirdly, we will provide empirical evidence for our model. The main results of our paper are, firstly, that smaller mobile operators will charge higher termination rates than larger operators, as a small operator’s impact on the weighted average price is relatively small so that smaller operators can increase their prices significantly without a major reduction in the quantity demanded. In contrast, a large operator also has a larger impact on the weighted average price so that the firm is more constrained in its pricing policy. Secondly, asymmetric regulation of the larger operators will, ceteris paribus, induce the small operators to increase their termination rates even further. These results are supported by our empirical findings. The remainder of the paper is organized as follows. In Section 2 we introduce the model and present the key results of our analysis. In Section 3, we provide empirical evidence to test the model’s hypotheses. Finally, section 4 discusses policy implications and concludes. 2
Note that a grand merger could actually solve this particular problem as has been pointed out by Salsas and Koboldt (2004) in the context of international roaming. However, while such a merger would solve the problem of high termination rates it would also reduce competition in all other mobile markets (such as call origination and mobile subscriptions) and, therefore, most likely not be authorized by competition authorities.
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2
The Model
There are four mobile networks i = 1, 2, 3, 4, which differ in the size of their subscriber base. We assume that the four mobile networks’ market shares do not depend on the respective termination charges, i.e. consumers do not base their subscription decision on the price for being called. More precisely, we assume that the mobile networks’ market shares are already given when termination rates are set so that we can treat them as exogenous.3 Let us also assume that there are two large and two small mobile networks, which is a fairly typical market structure for many European telecommunications markets. The two large networks {i = 1, 2} have a subscriber base of x1 = x2 = xL customers, while the small networks’ subscriber base is denoted by xS with x3 = x4 = xS . We also assume that each individual subscriber has a linear inverse demand for mobileto-mobile telephone calls, which is given by q = a − bp, where p denotes the perceived price for mobile-to-mobile calls. We follow Ofcom (2004) and the European Commision (2003) and regard the market for mobile termination services as a relevant market in its own. Furthermore, we assume that the marginal cost of terminating a mobile call is negligible and that prices for mobile-to-mobile calls are effectively determined through the respective termination charges. That is, we abstract from any double mark-up problem which may result if operators were competing in linear tariffs. As is well known from the literature (see, e.g., Laffont and Tirole, 1998, or Wright, 2002), the double mark-up problem vanishes if operators set two-part tariffs consisting of a fixed (monthly) fee and a price per calling minute. Given that mobile operators usually set multi-part tariffs we abstract from potential double marginalization problems and assume that the price for a call from, say, mobile network 1 to mobile network 2, is given by p12 = t2 where t2 is the termination rate set by operator 2. If consumers have perfect knowledge and are not ignorant about a network’s identity, the price for a calling unit from mobile network i to mobile network j will simply equal the monopoly mobile termination rate that network j will set, i.e. pij = tM = a/(2b) for j = 1, 2, 3, 4 and i 6= j (that means, j denotes the terminating network). To capture the idea of consumer ignorance, we now follow Gans and King (2000) and assume that it is the average price which determines consumer demand for calls to other mobile networks. To focus on the termination market we restrict the analysis to off-net calls and ignore on-net calls which are calls that originate and terminate within the same 3
Note that this assumption is also employed by Gans and King (2000) in much of their analysis. In addition, it has proven extremely difficult to analyze termination rates with endogenous market shares, as the optimization problem is no longer supermodular (see, e.g, Bühler, 2002).
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mobile network. Hence, we only consider the demand for calls which originate in one network and are terminated in another network, i.e. calls from network 1 to networks 2, 3 and 4, from network 2 to networks 1, 3 and 4, and so on. More specifically, we assume that the probability that an off-net call is made to one particular network depends on the size of its subscriber base relative to other networks’ subscriber bases. Hence, the probability that an off-net call from a large network is terminated on the other large network is given by xL /(xL + 2xS ). Similarly, the probability that an off-net call from a large network is terminated on a particular small network is given by xS /(xL + 2xS ). This assumption should be plausible under consumer ignorance as consumers do not differentiate between networks. If the probabilities of networks being called depend on their relative size, then consumers form average prices for off-net calls (possibly by retrospectively looking at their monthly bills) by weighting prices with these respective probabilities. Hence, the according demand for off-net calls from network 1 to network 2, 3 and 4 is given by ¶¶ µ µ xS xS xL q1 = xL (xL + 2xS ) a − b , p12 + p13 + p14 xL + 2xS xL + 2xS xL + 2xS where p12 = t2 , p13 = t3 and p14 = t4 . Hence, the demand for off-net calls from network 1 into other networks depends on the size of its subscriber base (xL ), the aggregate size of the other networks’ subscriber base (xL + 2xS ) and the weighted average termination rate charged by the three other networks. Let us also note that the quantity of calls from network 1 to network 2 is given by q12 = [xL /(xL + 2xS )]q1 , while the quantities of calls from network 1 to networks 3 and 4 are given by q13 = q14 = [xS /(xL + 2xS )]q1 with q1 = q12 + q13 + q14 . Similarly, we can express the demand for off-net calls from the three other networks. Since the two small networks have a subscriber base of size xS , making off-net calls to a total of (2xL + xS ) subscribers on the three other networks, we can write the demand for off-net calls from network 3 to other networks as ¶¶ µ µ xL xL xS q3 = xS (2xL + xS ) a − b , p31 + p32 + p34 2xL + xS 2xL + xS 2xL + xS where again p3j = tj for j = 1, 2, 4. The profit that an operator i generates from termination depends on its termination rate, ti , and the number of incoming calls from other networks. Assuming balanced calling patterns, operator 1’s profit is now given by µ ¶ xL xL xL q2 + q3 + q4 . π 1 = t1 xL + 2xS 2xL + xS 2xL + xS Similarly, operator 3’s profit (as a small operator) is given by µ ¶ xS xS xS q1 + q2 + q4 . π 3 = t3 xL + 2xS xL + 2xS 2xL + xS 5
Maximizing with respect to t and taking into account both the symmetry between the two large networks (1 and 2) and between the two small networks (3 and 4) we obtain the following best response functions 1 a 2x3L + 9x2L xS + 12xL x2S + 4x3S xS x2L + xL xS + x2S − tS , 4xL b x2L + 2xL xS + 3x2S xL x2L + 2xL xS + 3x2S 1 a 2x3S + 9xL x2S + 12x2L xS + 4x3L xL x2L + xL xS + x2S = − tL . 4xS b x2L + 2xL xS + 3x2S xS x2L + 2xL xS + 3x2S
tL = tS
(1)
Note that, even though operators do not set quantities but prices, the networks’ prices are strategic substitutes as ∂tI /∂tJ < 0. This contrasts with price setting under Bertrand competition and with vertically related markets with double marginalization problems where prices are strategic complements. As pointed out above, many European countries have either discussed or actually introduced asymmetric regulation patterns according to which only the large operators’ termination fees are regulated while smaller operators are left unregulated. In this context, the following observation should be interesting: Remark 1. As termination rates under consumer ignorance are strategic substitutes, any downward regulation of the large operators’ termination rates will, ceteris paribus, lead to an increase in the small operators’ termination rates (as ∂tI /∂tJ < 0). As can be easily seen by equation (1), if tL were regulated down to zero, tS would take the maximum value for all tL ≥ 0. While regulation usually takes the form of cost based price regulation (see European Commission, 2004), Remark 1 implies that the small mobile operators will increase their termination fees above the unregulated equilibrium level for any binding regulatory constraint that actually suppresses the large operators’ termination rates below the unconstrained equilibrium level. If instead mobile termination rates are left unregulated, the unregulated equilibrium termination rates can be obtained by solving the best response functions given above: 1 a 2x5S + 9xL x4S + 22x2L x3S + 31x3L x2S + 15x4L xS + 2x5L , 4xL b 2x4L + 6x3L xS + 11x2L x2S + 6xL x3S + 2x4S 1 a 2x5L + 9x4L xS + 22x3L x2S + 31x2L x3S + 15xL x4S + 2x5S = . 4xS b 2x4L + 6x3L xS + 11x2L x2S + 6xL x3S + 2x4S
tL = tS
(2)
Comparing tL and tS we can state the following result: Proposition. The small operators’ termination rate, tS , is strictly larger than the large operators’ rate, tL , ( tS > tL ) if xL > xS . Proof. See Appendix. The intuition for this result is that the small operators only have a relatively small impact on the average price, which determines demand. Hence, if a small operator 6
3.5 3
ts
2.5 2 t 1.5 1 0.5
0.99
0.87
0.81
0.75
0.69
0.63
0.57
0.51
0.45
0.39
0.33
g*
0.27
0.21
0.15
0.09
0
0.93
tM
tL
g
Figure 1: Simulated termination rates increases its termination rate the demand for off-net calls will only be reduced by a relatively small amount as the increase in the average termination price will be relatively small. In contrast a large operator has a relatively large affect on the average price so that the incentive to increase the termination rate will be lower than with a small operator. To illustrate the relationship between network size and termination rates, let us assume that we can express the small networks’ size as a fraction of the large networks’ subscriber base, i.e. xS = gxL with 0 ≤ g ≤ 1. In this case, the firms’ termination rates are given by a 2 + 15g + 31g 2 + 22g 3 + 9g 4 + 2g5 , 4b 2 + 6g + 11g 2 + 6g 3 + 2g4 a 2 + 9g + 22g 2 + 31g 3 + 15g 4 + 2g 5 = . 4bg 2 + 6g + 11g2 + 6g3 + 2g 4
tL = tS
Note that the large operators’ termination rates are increasing in g for 0 ≤ g ≤ 1 (i.e. ∂tL /∂g ≥ 0), while the small operators’ termination rates are decreasing over this range (i.e. ∂tS /∂g ≤ 0). Figure 1 depicts the termination rates given above for a = b = 1. Comparing these termination rates with the monopoly price in the absence of consumer ignorance, tM = a/(2b), we find that the small operators’ termination rate, tS , 7
will always exceed tM , while the large operators’ rate, tL , will only exceed the monopoly benchmark for g ≥ g∗ ≈ 0.29783. Otherwise, the negative pricing externality created by the small operator is so large that the large operators’ price will be constrained even below the monopoly level. In summary, as can be seen from the Proposition and Remark 1, our theoretical model suggests (a) that a network’s termination rate is the higher the smaller the network’s size (as measured through its subscriber base) and vice versa and (b) that asymmetric regulation of only the larger operators in a market will, ceteris paribus, induce the smaller operators to increase their termination rates. In the following section, we will provide some empirical evidence to test these two hypotheses.
3
Empirical Evidence
3.1
Data
To test our model’s hypotheses empirically, we have assembled data on mobile termination rates and the subscriber base of 48 different mobile operators from 17 European countries.4 Data on the networks’ subscriber base has been gathered from Mobile Communications, while the termination rates have been obtained from various issues of the Cullen Report, published by Cullen International. Information on regulatory regimes has also been obtained from this source and also from various regulatory authorities. Our earliest observations are from February 2001 and our most recent one from February 2003. Hence, our data set includes regulated and unregulated termination rates. While we use monthly data in principle, there are missing observations for several months due to limited data availability.5 Therefore, we cannot conduct a panel data analysis, but have to confine our analysis to pooled estimations. The endogenous variable of our analysis is the operators’ termination rate. Since termination rates differ in their structure across countries and at times even across firms,6 we have calculated termination rates for a two minute call. We have also restricted the analysis to peak-time tariffs. As exogenous variables we have used (apart from a 4
These are the 15 EU countries plus Norway and Switzerland. In total, we have data for 13 different months, namely: February 2001, April 2001, June 2001, September 2001, November 2001, January 2002, March 2002, May 2002, July 2002, September 2002, October 2002, December 2002, and February 2003. Since we cannot observe all operators’ prices for every observation point (especially in 2001), we have less than 13 observations for some of the 48 operators. The total number of observations is 458. 6 While most countries use linear tariffs, some countries have two-part tariffs consisting of a call set-up fee and a variable per minute charge. 5
8
constant) market shares (based on subscriber numbers), the Herfindahl Index (HHI), market size (based on total subscriber numbers), a dummy variable (GSM1800) for the mobile network technology employed by an operator and two dummy variables (RC and RF ) describing the regulatory framework in place. The dummy variable describing an operator’s technology (GSM1800) is set to one if an operator excluxively uses GSM1800 MHz technologies, while it is set to zero if an operator either exclusively uses GSM900 MHz technologies or hybrid networks. This dummy varibale is introduced in order to account for potential cost differences between networks, as pure GSM1800 MHz networks are sometimes considered to be somewhat more costly (see, e.g., Ofcom, 2004). In fact, as virtually all European countries have sequentially licensed mobile operators, it is usually the smaller operators (which have entered the markets at a later stage), who have exclusively adopted the GSM1800 MHz technology. Therefore, we have included an explanatory dummy variable (GSM1800) to account for any possible cost differences. Concerning the regulatory framework the variable RC is set to one if any mobile termination rate in a specific country is regulated, while RC is zero if none of the mobile operators’ termination rates is regulated. Furthermore, the variable RF is set to one if a specific firm’s termination rate is regulated, while RF is zero if the firm’s termination rate is not regulated. Using two dummy variables is necessary because in some countries all mobile termination rates are regulated, in others only some termination rates (usually those of large operators) are regulated, and in others again none are regulated. Hence, RC = RF = 1 if all firms are regulated in a country, RC = RF = 0 if none is regulated, and RC = 1 and RF ∈ [0, 1] if some firms, but not all are regulated in a country. We have also used dummy variables indicating the respective year and country to control for eventual time trends and country-specific effects. Before we present our empirical results let us briefly provide some descriptive statistics of our variables to shed some light on price trends and regulatory practice in Europe. The (unweighted) average termination rate across all 48 operators decreases from 54.2 Eurocents in 2001 to 38.5 Eurocents in 2002 and 37.8 Eurocents in February 2003. While the maximum rate in 2001 has been 80 Eurocents and the minimum 37 Eurocents, in 2003 the maximum rate has been 54 Eurocents and the minimum 19.7 Eurocents. Over this period the regulated firms’ average termination rate has been 42.3 Eurocents, while it has been 45.3 Eurocents for unregulated firms. Looking at the different countries, the average termination rate in regulated countries has been 44.4 Eurocents, while it has only been 42.8 Eurocents in unregulated countries. This indicates that termination rates have been higher on average in regulated countries. In this context, it may be interesting to note that only 14 operators had been regulated in February 2001 while there have
9
been 26 regulated firms in February 2003. The observed firms’ average market share has been steadily around 33 percent, ranging from less than 2 per cent for the smallest operator (Italy’s Blu in February 2001) and more than 75 percent for the largest operator (Norway’s Telenor also in February 2001). Finally, market size obviously varies considerably between countries ranging from 304,000 subscribers in Luxembourg in February 2001 up to more than 57 million subscribers in Germany in February 2003. More detailed descriptive statistics can be found in Table 1 in the Appendix.
3.2
Empirical Results
Table 2 reports estimation results of price levels and logarithms of the price (in that case also using logarithms of the market size). In order to determine an adequate functional form we have applied Ramsey’s (1969) RESET tests for omitted variables. As can be observed from Table 2, linear specifications are found to be adequate. In the regressions we have also calculated Moulton (1990) corrected t-statistics in order to correct for potential biases that arise if aggregated variables are used to measure effects on micro units. Since the assumption of independent disturbances is usually violated with aggregated exogenous variables, using ordinary least squares can lead to standard errors that are seriously biased downwards. Hence, it is important to bear in mind the data’s group structure as suggested by Moulton (1990).7 The analysis reveals that technology has a positive and statistically significant impact on termination rates, at least at the 10% level of significance. Firms using exclusively the GSM1800 technology tend, therefore, to have higher termination rates by about 2.6 Eurocents on average for a two minute peak-time call. Operators’ market shares tend to have a statistically significant impact on their termination rates with the sign as predicted by our model, i.e. smaller operators tend to have significantly higher mobile termination rates. In light of our hypothesis, this may indeed indicate that especially the smaller operators can exploit consumers’ ignorance and set relatively high termination rates as they only have small effects on average prices. 7
Furthermore, we have applied Hausman-Wu test to analyze a possible endogeneity of market structure. Even though we have assumed market structure to be exogenous in the theoretical part of this paper, endogeneity could of course be an empirical problem. In none of the specifications the hypotheses of exogenous market structure could have been rejected.
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Model I levels
II levels
III logs
IV logs
Constant
22.9849 (8.81)
22.0891 (6.81)
2.6537 (0.94)
2.6411 (0.95)
GSM1800
2.6140 (1.75)
2.6082 (1.73)
0.0641 (1.85)
0.0640 (1.84)
-11.6626 (-4.16)
-11.7048 (-4.14)
-0.3249 (-3.82)
-0.3250 (-2.78)
-
2.0621 (0.58)
-
0.0033 (0.04)
1.76e-07 (0.79)
1.91e-07 (0.86)
0.0405 (0.23)
0.0412 (0.24)
RC
8.7075 (5.78)
8.7084 (5.74)
0.1613 (3.40)
0.1613 (3.40)
RF
-2.9304 (-2.29)
-2.9190 (-2.26)
-0.0290 (-0.64)
-0.0289 (-0.64)
Yes Yes
Yes Yes
Yes Yes
Yes Yes
0.87 302.30 4.82 458
0.87 334.97 6.01 458
0.85 306.01 41.52 458
0.85 302.20 45.19 458
Variable
Market Share
HHI
Market Size
Year Dummies Country Dummies adj. R2 F-statistic RESET Nobs
Table 2: Linear and log-linear models Heteroscedasticity robust t-statistics are given in parenthesis, heteroscedasticity robust t-statistics using Moulton correction in brackets.
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In contrast, the Herfindahl Index does not appear to be statistically significant for explaining termination rates. Hence, market concentration is apparently less of an issue for the determination of termination rates than an operator’s relative size or, more precisely, its relative smallness. Admittedly, we would expect that concentration had a significant impact on termination rates as well, since under consumer ignorance large asymmetries should lead the small operators to charge higher termination rates as Figure 1 illustrates. However, market shares and concentration are usually highly correlated, which may reduce the explanatory power of HHI in combination with market shares due to their collinearity. While market size is not statistically significant, we find statistically significant effects for the regulatory framework. On the one hand, and not surprisingly, firm-specific regulation tends to lower the regulated firm’s termination rate. Regulated firms termination charges are lower by about 3 Eurocents for a two minute peak—time call. On the other hand, termination rates in regulated countries tend to be higher overall by about 8.7 Eurocents for a two minute peak—time call. Remember that while RF is a dummy variable set equal to one (and otherwise zero) for regulated firms, independent of the regulation of their competitors, RC always equals one if at least one firm in the same country is regulated. The estimated coefficient of RF therefore measures the average difference between regulated firms’ termination rates and average termination rates overall. The coefficient of RC, in contrast, estimates the difference between average termination rates in regulated and unregulated countries. The combination of both variables (RC-RF ) thus calculates the markup in average termination rates for unregulated firms whose competitors are regulated. Hence unregulated firms’ termination rates in regulated countries are higher by about 8.7-(-2.9)=11.6 Eurocents for a two minute peak—time call. Overall, the above results are consistent with our hypothesis formulated in Remark 1, i.e. that downward regulation of competitors’ termination rates leads, ceteris paribus, to an increase in the unregulated firms’ termination rate, as termination rates are strategic substitutes if consumers are ignorant.
3.3
Discussion
In summary, our empirical analysis tends to support the hypotheses derived from our theoretical model. Firstly, smaller mobile operators tend to have higher termination rates than their larger competitors. Secondly, downward regulation of the large operators’ rates tends to have a positive effect on the termination rates of unregulated operators. At this point, some general remarks about the desirability of regulating mobile termination rates may be adequate. First of all, it is important to emphasize that customer 12
ignorance by itself is not a justification for economic regulation.8 In fact for the consumer ignorance problem remedies other than price regulation (such as an automated price information) may already solve the problem. This would not require Government intervention, as market mechanisms may solve these information problems. However, as long as consumer ignorance plays a role, our paper offers one potential explanation for the apparently counter-intuitive observation that smaller operators charge higher prices.9 With respect to the more general question whether mobile termination rates should be regulated at all, one should note that there are a number of strong arguments against their regulation: First of all, it is not only necessary, but even efficient that some prices exceed marginal costs in an industry characterized by significant sunk and common costs. If mobile termination rates exceed marginal costs because of Ramsey-type pricing patterns, there is little reason for regulatory intervention (see, e.g., Kruse, 2003, or Koboldt, 2003). And secondly, profits from high termination fees may be used to subsidize mobile handsets, thereby allowing a faster diffusion of mobile telephony in general and new mobile services (such as UMTS) in particular (see Wright, 2002). While these arguments have to be weighed against arguments in favor of regulation (such as some potentially inefficient substitution between fixed-line and mobile telephony), we concur with Crandall and Sidak (2004) that overall there are convincing arguments against the regulation of mobile termination fees. In addition to the general concerns about the regulation of mobile termination rates, this paper has also demonstrated that there are significant costs associated with an asymmetric regulation of mobile termination rates, which is sometimes seen as some ”lighter” form of regulation and, therefore, applied in a number of European countries. As we have shown asymmetric regulation of only the larger operators leads smaller operators to increase their termination fees even further if consumers are ignorant about operator-specific termination charges. Hence, we would issue a warning against this supposedly ”lighter” form of regulation. 8
We are grateful to one referee to point this out. As one referee pointed out Ramsey-like pricing patterns in market characterized by economies of scale and scope may offer an alternative explanation. As cost differences do not suffice to explain the different pricing behavior of large and small firms, this would require very different elasticities for calls to small and to large networks. While this may, in theory, be possible, there is currently no empirical evidence to support the view that calls to smaller mobile networks have different elasticities than calls to larger networks. 9
13
4
Conclusions
In this paper, a simple theoretical model has been developed to show (a) that a mobile network’s termination rate is the higher the smaller the network’s size (as measured through its subscriber base) and vice versa and (b) that asymmetric regulation of only the larger operators in a market will, ceteris paribus, induce the smaller operators to increase their termination rates. These theoretical results are based on the notion that consumer ignorance induces pricing externalities. In fact, empirical evidence from 48 European mobile operators supports these hypotheses. In all our regressions market share has a statistically significant and negative impact on firms’ termination rates, as predicted by the model. Furthermore, unregulated firms in regulated markets tend to have higher termination rates than firms in unregulated markets. We believe that these findings may be helpful for regulatory authorities that analyze mobile termination rates and their regulation. While we hold the view that there are, quite generally, strong reasons for not regulating mobile termination rates (see, e.g., Kruse, 2003; Crandall and Sidak, 2004), we do not want to repeat these general arguments here. Instead our paper complements these arguments, as we have shown that an asymmetric regulation pattern (where only the larger mobile operators’ termination rates are regulated) is likely to carry perverse incentives for smaller operators to increase their termination rates. One should keep in mind, however, that we have adopted a very simple model of termination rate setting. In particular, we have abstracted from the challenging issue of endogenous market shares and ignored the possibility of further entry into mobile telecommunications markets. Future research into these directions might prove to be instructive for theorists and practitioners alike.
References Bühler, S. (2002). “Network Competition and Supermodularity - A Note.” Discussion Paper 0302, University of Zurich. Bühler, S. & Haucap, J. (2004). “Mobile Number Portability.” Journal of Industry, Competition and Trade. 4, 223-238. Crandall, R.W. & Sidak, G. (2004). “Should Regulators Set Rates to Terminate Calls on Mobile Networks?” Yale Journal on Regulation. 21, 261-314. European Commission. (2003). Recommendation on Relevant Product and Service Markets Within the Electronic Communications Sector, COM(2003) 497. Brussels: 14
February 2003. European Commission. (2004). Tenth Report from the Commission on the Implementation of the Telecommunications Regulatory Package, COM(2004) 759. Brussels: December 2004. Gans, J.S. & King, S.P. (2000). “Mobile Network Competition, Customer Ignorance and Fixed-to-Mobile Call Prices.” Information Economics and Policy. 12, 301327. Koboldt, C. (2003). “A Perfect Miss: Cost-Based Regulation of Mobile Termination Charges.” In J. Kruse & J. Haucap (eds.), Mobilfunk zwischen Wettbewerb und Regulierung. München: Fischer Verlag, pp. 89-109. Kruse, J. (2003). “Regulierung der Terminierungsentgelte der deutschen Mobilfunknetze?” Wirtschaftsdienst. 83, 203-208. Laffont, J.J., Rey, P. & Tirole, J. (1998). “Network Competition I: Overview and Nondiscriminatory Pricing.” RAND Journal of Econmics. 29, 1-37. Laffont, J.J. & Tirole, J. (2000). Competition in Telecommunications. Cambridge, MA: MIT Press. Moulton, B.R. (1990). “An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units.” Review of Economics and Statistics. 72, 334-338. Ofcom (2004), Wholesale Mobile Voice Call Termination. London: June 2004. Available at http://www.ofcom.org.uk/consultations/past/wmvct/wmvct.pdf. Ramsey J.B. (1969). “Tests for Specification Error in Classical Linear Least Squares Regression Analysis.” Journal of the Royal Statistical Society Series B. 31, 350371. Salsas, R. & Koboldt, C. (2004). “Roaming Free? Roaming Network Selection and Inter-Operator Tariffs.” Information Economics and Policy. 16, 497-517. Wright, J. (2002). “Access Pricing Under Competition: An Application to Cellular Networks.” Journal of Industrial Economics. 50, 289-315.
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Appendix Proof of the Proposition. We have to compare tL and tS as given in (2). Obviously, tL < tS iff tL − tS < 0, which can also be written as 1 8x8S + 30x7S xL + 43x6S x2L + 35x3L x5S − 35x5L x3S − 43x6L x2S − 30xS x7L − 8x8L < 0. a 2 xL b (16x6S + 105x4L x2S + 48x5S xL + 105x4S x2L + 148x3S x3L + 16x6L + 48x5L xS ) xS The sign of the left-hand side of the inequality above is determined by the sign of 8(x8S − x8L ) + 30(x7S xL − xS x7L ) + 43(x6S x2L − x6L x2S ) + 35(x3L x5S − x5L x3S ). Close inspection reveals that this term is always negative for xS < xL , so that tL < tS always holds for xS < xL .
Table 1: Summary statistics Year Price Market Share 2001 Mean 54.15 0.3213 Range 43.00 0.6005 Min 37.00 0.0239 Max 80.00 0.6244 S.D. 9.10 0.1580 2002 Mean 38.48 0.3194 Range 40.48 0.5518 Min 19.70 0.0404 Max 60.18 0.5923 S.D. 9.47 0.1530 2003 Mean 37.80 0.3161 Range 34.30 0.5110 Min 19.70 0.0456 Max 54.00 0.5567 S.D. 8.97 0.1537 Total Mean 43.78 0.3199 Range 60.30 0.6005 Min 19.70 0.0239 Max 80.00 0.6244 S.D 11.93 0.1544
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HHI 0.3872 0.5555 0.0722 0.6278 0.1004 0.3731 0.5160 0.0555 0.5715 0.0915 0.3801 0.7037 0.0624 0.7661 0.1181 0.3791 0.7106 0.0555 0.7661 0.0972
