Termination-Based Price Discrimination: Tariff-Mediated Network Effects and the Fat-Cat Effect +

Transcription

1 Termination-Based Price Discrimination: Tariff-Mediated Network Effects and the Fat-Cat Effect + Jörg Claussen a, Moritz Trüg b, Leon Zucchini b * a Ifo Institute Munich, Poschingerstr. 5, D Munich b Munich School of Management, Ludwig-Maximilians-University Munich, Schackstr. 4/III, D Munich Working Paper December 2011 * Acknowledgements. The authors gratefully acknowledge helpful comments from Christoph Dehne, Tobias Kretschmer, David Sauer, and participants at the TIME Colloquium at the University of Munich. + Corresponding author: l.zucchini@lmu.de

2 Termination-Based Price Discrimination: Tariff-Mediated Network Effects and the Fat-Cat Effect A B S T R A C T Mobile telecommunications operators routinely charge higher prices for off-net than on-net calls. Previous research provides two alternative propositions on whether on-net / off-net price differentials (OOD) are more attractive for large or for small operators. On the one hand studies on tariff-mediated network effects suggest that large operators use OOD to damage smaller rivals. On the other hand research on consumer behavior suggests that small operators may use OOD to attract customers with low on-net prices, trapping large operators with the Fat-cat effect. We test the relative strength of the two effects using data on tariff setting in the German market for mobile telecommunications from 2004 to We find that large operators are more likely to offer tariffs with OOD but that there is no significant difference between large and small operators in the magnitude of the differentials. Our findings support the proposition that large firms use tariff-mediated network effects as a competitive instrument, but also suggest the alternative theory may have some merit. Keywords: Telecommunications, Competition, Network effects, Customers, Pricing JEL: D22, L11, L96 1

3 1. Introduction Mobile telecommunications operators routinely charge their customers a higher price per minute for calls to subscribers on their own network (on-net) than for calls to subscribers on other networks (off-net). This termination-based price discrimination is interesting because it lowers compatibility between mobile operators networks, creating co-called tariffmediated network effects (Laffont et al., 1998). As telecommunications markets are subject to network externalities, compatibility is of key importance for competition and can strongly influence market structures and profitability (Katz and Shapiro, 1994). Understanding how firms use termination-based price discrimination is therefore important for scholars, managers and policy makers. Termination-based price discrimination is also interesting because previous research offers two alternative propositions on whether it is used by large or by small telecommunications operators. On the one hand, research on tariff-mediated network effects suggests that termination-based price discrimination is used by large network operators because it allows them to use their superior installed base to force small operators out of the market or prevent them from entering in the first place (Laffont et al., 1998; Hoernig, 2007). On the other hand, research on marketing and consumer behavior suggests that it is favored by small operators because it allows them to advertise with low on-net prices while recouping profits with high off-net prices (Haucap and Heimeshoff, 2011). Imitating this strategy is costly for large operators due to the Fat-fat effect (Fudenberg and Tirole, 1984). To our knowledge there is no systematic empirical test for whether one of these two effects is dominant, i.e. whether termination-based price discrimination is used predominantly by small or large operators. Our study addresses this gap using data on tariffs in the market for German mobile telecommunications from 2004 to We find that large operators are more likely to offer tariffs with on-net / off-net price differentials, but that the relative discount for on-net calls is not significantly different between large and small operators. Our findings suggest that tariff-mediated network effects are the dominant effect, but also conclude that the fat-cat argument may have some merit in our empirical setting. Our study contributes to research on termination-based price discrimination by empirically testing whether large or small firms are more likely to use it, allowing us to draw tentative conclusions about alternative explanations for the phenomenon. We also contribute to research on tariff-mediated network effects by providing evidence that they are taken into account for firm strategies. 2

4 The remainder of the paper is organized as follows. Section 2 reviews the related literature and describes the two alternative mechanisms proposed in previous research. We describe our data and research methods in Section 3 and present our results in Section 4. Section 5 concludes. 2. Tariff-Mediated Network Effects and the Fat Cat Effect 2.1 Tariff-Mediated Network Effects Telecommunications is a standard example for an industry with direct network effects, and a considerable body of research has investigated how they affect competitive behavior and welfare (Katz and Shapiro, 1994; Koski and Kretschmer, 2004). Generally, telecommunications operators charge consumers for their services using tariffs that comprise several price components like monthly fixed fees, minute prices per call, prices per text message, etc. In some of these tariffs they charge customers a lower per-minute price for calls to subscribers on their own network than for calls to subscribers on rival networks. This practice of using on-net / off-net price differentials (OOD) is known as termination-based price discrimination (Berger, 2004). There has been concern among both scholars and regulators that OOD may be used by telecommunications operators as an anticompetitive instrument (Harbord and Pagnozzi, 2010; Hoernig, 2008), and a substantial body of theoretical research on tariff-mediated network effects has broadly supported this prediction. Some studies even go so far as to call for regulation (Gerpott, 2008). The basic argument is that in telecommunications markets consumers derive utility from being able to call other consumers on their own or other networks (Katz and Shapiro, 1985). If the networks are perfectly compatible, welfare simply increases with the number of mobile consumers. However, if there are several network operators competing for market shares, they may have incentives to reduce compatibility to other operators using OOD, creating what are known as tariff-mediated network effects (Laffont et al., 1998). In markets with symmetric operators, studies have demonstrated that operators will use OOD to reduce the attractiveness of the rival networks (Jeon et al., 2004). In markets with asymmetric operators scholars have found that the problem of tariffmediated network effects is exacerbated. There, theoretical work has demonstrated that large operators can use OOD as an anticompetitive instrument to damage their smaller rivals or to forestall market entry (Jeon et al., 2004). By charging higher prices for off-net than for on-net calls, large operators can reduce the number and duration of calls received on the smaller operators networks. This enables them to leverage their larger installed base and make the 3

5 smaller rivals networks less attractive (Hoernig, 2007). Consequently, the small operators attract less customers than the large operators and the large operators grow even larger (Cabral, 2011). If termination prices are a strategic variable as well, the situation is even worse. In this case the price differential by the large operator will result in a call imbalance between the networks as more calls are placed from the small to the large operator than vice versa. This in turn will result in a net outflow of termination fees for the small operator, further reducing their profits (Hoernig, 2007). 1 In addition to using OOD as a predatory pricing mechanism against existing smaller competitors, large operators may also use them to forestall entry. Scholars have argued that if OOD are sufficiently large they make new operators with small installed bases less attractive (less incoming calls). Consequently, building up an installed base is more difficult and makes entry less attractive for potential rivals (Calzada and Valletti, 2008). This mechanism is also stronger if there is a corresponding rise in termination prices (Harbord and Pagnozzi, 2010). The theoretical results concerning tariff-mediated network effects rely on the assumption that there are significant network effects in telecommunications markets. This is supported by a number of empirical studies. In addition to direct tests of network effects (Doganoglu and Grzybowski, 2007), there are several which analyze the influence of tariff-mediated network effects on consumer choice and generally conclude they play a significant role. For example, Birke and Swann (2006) find that in British mobile telecommunications consumers choice of operator is related to the size of the operator s previous installed base, and that consumers make a disproportionate number of calls are on their own network. This strongly suggests that consumers utility from subscribing to a mobile network is influenced by its size, i.e. by network effects. Similar results are reported by Kim and Kwon (2003) and Fu (2004) for the mobile telecommunications markets in South Korea and Taiwan, respectively. In summary, research on tariff-mediated network effects suggests that network operators may use OOD as an anticompetitive instrument and that this is important for consumers choice of operator and usage patterns. Given this result we would expect to observe that larger 1 Related to the discussion on OOD is a body of research on termination prices in telecommunications markets. Termination prices (or access prices ) are the prices charged by operators to other operators for connecting incoming calls to their customers. They are related to OOD in that they influence firms marginal cost for off-net calls, but differ in that they are frequently regulated (and therefore are not a strategic variable) and are generally not visible to customers. There is a substantial literature on the competitive effects and efficient regulation of termination fees, the main conjecture being that they may be used as a collusive mechanism (Gans and King, 2001; Cambini and Valletti, 2003; Berger, 2005). However, in our empirical setting the German market for mobile telecommunications between 2004 and 2009 termination prices are set by the national regulator (Bundesnetzagentur), are not visible to customers, and we therefore focus exclusively on OOD. 4

6 operators are more likely to offer tariffs with OOD and also to offer larger OOD than their smaller rivals. This is consistent with anecdotal evidence that OOD were used as an anticompetitive instrument by the incumbent Vodafone in New Zealand (Haucap and Heimeshof, 2011). 2.2 Consumers Bounded Rationality and The Fat-Cat Effect In contrast to the predictions from research on tariff-mediated network effects, there is anecdotal evidence that in several European mobile telecommunication markets (including Germany, the UK and Austria) smaller operators were the first to introduce OOD (Haucap and Heimeshoff, 2011; Haucap et al., 2010). This would suggest that OOD may be more attractive for small operators than for large operators. One possible explanation for this surprising observation is that customers evaluation of mobile tariff options is not completely rational. Recent studies have found that customers are bad at estimating the proportion of their future on-net and off-net calls when purchasing mobile tariffs, and that this is particularly pronounced for the common case of non-linear tariffs (Lambrecht et al., 2007). This causes customers to exhibit an excess preference for flatrate tariffs (Lambrecht and Skiera, 2006). It also causes them to overestimate the savings from a particularly low on-net or off-net price, a phenomenon Haucap and Heimeshoff (2011:330) call a price differentiation bias. Haucap and Heimeshoff (2011) propose that this may make it feasible for small operators to price differentiate between on-net and off-net calls. They argue that because of the price differentiation bias small operators can use low on-net prices to attract customers while still making profits off the customers off-net calls. This strategy is possible because they have a large proportion of off-net calls due to their relatively small installed bases. It is costly for large operators to imitate because with their large networks they have a larger proportion of on-net calls. The small operators thus exploit the fact their larger rivals are caught in what Fudenberg and Tirole (1984) call the Fat-cat effect. At first glance it might seem that this line of argument omits the important concept of network effects. However, several empirical papers provide a link by arguing that it is not global but rather local network effects that influence customers purchase decisions. The key idea is that consumers utility is not equally increased by every new consumer joining the network, but rather by the decisions of a small subset like friends and family, also known as calling clubs (Gabrielsen and Vagstad, 2008). For example, Birke and Swann (2010) find that individuals choice of network operator is strongly influenced by the network choices of their local social networks (like household members or fellow students), and only to a lesser 5

7 extent by the total number of consumers on the network. Similarly, Corrocher and Zirulia (2009) find evidence that consumers take into account local networks when deciding on mobile operators, and that those consumers who are most aware of local networks also have comparatively low bills. These studies suggest that small operators offering OOD are compatible with network effects, because the network effects operate at a much more finegrained level than the total installed base and customers can self-select into the network where their preferred contacts are located. Consequently, research suggesting that small operators favor OOD is compatible with network economics. In summary, this literature argues that customers exhibit bounded rationality when choosing tariffs and that global network effects have comparatively small importance due to calling clubs. Therefore, using OOD is attractive for small operators because it allows them to attract customers with low on-net prices, a strategy that is costly to imitate by larger rivals due to the fat-cat effect. This would suggest that we should empirically observe small operators setting OOD. 2.3 Summary and Research Questions To summarize, in previous research we encounter two alternative mechanisms explaining why large or small network operators might prefer OOD. Research on tariff-mediated network effects suggests that large operators have an incentive to set large OOD to leverage their installed base, reduce the attractiveness of their smaller rivals, and forestall entry. This is indirectly supported by empirical evidence for global network effects. On the other hand, recent research on consumers bounded rationality suggests that smaller operators will use OOD because it enables them to advertise with very low on-net costs and earn revenues with off-net calls, a strategy unavailable to large operators because of the fat-cat effect. This strategy s feasibility is supported by the finding that consumers tend to overestimate the proportion of calls they make on-net as opposed to off-net. The finding that local network effects are more important than local network effects means that it does not contradict the concept of network effects. From these predictions we derive two related questions for empirical analysis. First, we ask whether large or small operators are more likely to offer tariffs with OOD. Second, we concentrate on only the tariffs with OOD and ask whether large or small operators offer larger OOD (i.e. discounts for on-net calls). It is important to note that the two effects are not mutually exclusive: potentially both are at work. In the data we observe the combined effect of the two mechanisms, and our question is therefore whether one of the two is clearly dominant. 6

8 3. Data and Estimation 3.1 Data For the empirical analysis we use data on the market for mobile telecommunications in Germany from 10/2004 to 02/2009. Data on the business-to-consumer mobile tariffs offered by each operator was gathered by teltarif.de, a price comparison website tracking all mobile tariffs in the German market, and was subjected to extensive cleaning. The most important steps of the data cleaning process are described in the appendix. In the period under observation we observe three types of companies in the market. First, there are four large mobile network operators (MNOs) that entered the market the prior to 2000 and that hold well over 50% market share (T-Mobile, Vodafone, E-Plus and O2). The MNOs are vertically integrated companies that not only sell tariffs but also own and operate infrastructure (switches, backbones, masts, etc.). They offer tariffs for all market segments from rare callers to heavy business users. Of the four MNOs, the two companies T-Mobile and Vodafone were substantially larger than the others: In 2009 they had approximately 27 million subscribers each, while E-Plus and o2 had only 9.5 and 7.4 million subscribers, respectively (German Federal Network Agency, 2009). The second group of companies comprises 67 mobile virtual network operators (MVNOs, e.g. Base or AldiMobil), the first of whom entered the market beginning in late 2004 and early In recent years they have rapidly gained market share but during the period covered by this study they were significantly smaller than the MNOs. In contrast to the MNOs they do not own or operate their own infrastructure. Instead, they buy airtime wholesale from the MNOs and use it to construct new tariffs that are sold under their own brand. Although MVNOs also offer some tariffs for business customers, they generally focus on low-usage market segments, and some of them are subsidiary brands of MNOs specifically targeting those segments (e.g. Congstar or Simyo). The differences in market focus are taken into account in the analyses by using fixed effects for four discrete market segments. The process used to define market segments is described in detail in the Appendix. The final group of companies comprises 23 tariff resellers (e.g. Mobilcom, Freenet). They operate mainly as an independent distribution platform for MNO tariffs and the majority of their offering comprises MNO tariffs sold under a joint brand. They were excluded from the analysis in this study, as including them would have artificially inflated the number of tariffs in the market, systematically favoring the MNO tariffs. 7

9 3.2 Initial Descriptive Results After cleaning we observe a total of 969 tariffs. The first column in Table 1 shows the number of tariffs offered by the MVNOs and the larger and smaller MNOs. The second and third columns in Table 1 give the absolute and relative number of tariffs with a non-zero OOD for the different types of operators. The data strongly suggest a positive correlation between the relative number of tariffs with OOD and operator size. Only 14% of MVNO tariffs have non-zero OOD, for small MNOs it is 39% and for large MNOs it is 63%. This would support theories proposing tariff-mediated network effects as the central motivation for OOD. This result is borne out in the logistic regressions reported below INSERT TABLE 1 AROUND HERE The fourth column in Table 1 shows the average discount offered customers for on-net calls compared to the off-net minute price, for the 388 tariffs which had a positive OOD (those in the second column of Table 1). Here the effect is also suggestive, albeit exactly the other way around: When they use OOD, MVNOs offer an average on-net discount of 80%, for small MNOs it is 61% and for large MNOs only 55%. This would seem to support the notion that small operators use larger OOD to attract customers. It also seems intuitive that small operators should have larger discounts: if they are using OOD as a strategic marketing tool it seems reasonable that they would want to make the on-net prices noticeably low, increasing the difference to off-net prices. However, this result is not significant in the regressions reported below. The following section describes the measures and the econometric specifications with which we formally test the two effects described above. To ensure that our results are indeed due to differences in network size and not differences in business models (e.g. degree of vertical integration), we consider differences between MVNOs and MNOs, but also between large and small MNOs (which have the same business model) and finally between all three groups. 3.3 Measurement and Estimation To test our first empirical question we estimated the probability that a tariff has a non-zero OOD depending on whether it was offered by a large or small operator by fitting a logit model: ( ) 8

10 On the left hand side ( ) is the logit link function and is the probability that a tariff had a different minute price for on-net and off-net calls. Note that this always meant the offnet price was higher, as we did not observe tariffs with higher on-net than off-net prices. For each tariff, this is denoted by the dummy variable, which takes the value 1 if there is a difference between on-net and off-net calls and 0 otherwise. On the right hand side our first independent variable is a dummy which takes the value 1 if a tariff was offered by an MNO and zero otherwise. indicates whether the operator offering tariff was one of the two large MNOs (1 if yes, 0 otherwise). is a vector of the control variables that are described below, and denotes the idiosyncratic error term. All models use robust standard errors that are clustered at the firm level. We address our second question by modeling the relative difference between on-net and off-net prices, conditional on the fact that the two are in fact different (i.e. there is a non-zero OOD). With notation as above we estimate: The dependent variable is the relative difference between the on-net and off-net prices: ( ) where and are the minute prices in Euro cents of each tariff for on-net and off-net calls, respectively. It can be interpreted as the discount in percent that is granted customers for placing calls on their own operator s network rather than calling consumers on other networks. We observed a number of tariffs where, but in all of these cases as well, so the tariffs were not in the choice set the second regression because the price differential was zero anyway. To ensure our results are not biased we included the control variables Free Minutes, Base Fee, and fixed effects dummies for market segments and years. The dummy variable Free Minutes takes on the value 1 if free minutes are offered with a certain tariff and 0 otherwise. For these tariffs, customers are allotted a certain contingent of free minutes which are not directly charged; usually the charge is bundled with the monthly base fee. Due to customers tendency to overestimate the value of free minutes we expected the value of OOD for strategic marketing purposes may decrease if free minutes are included in the tariff package here we assume decreasing marginal returns of additional tariff components. The variable Base Fee is 1 if a monthly base fee is charged and 0 otherwise. A monthly base fee was expected to lead to lower minute prices for off-net as well as on-net calls, and hence reduce the difference between the two. We are very aware that these two control variables are endogenous to tariff setting and therefore to OOD. Consequently, we estimate specifications 9

11 omitting them: as both specifications yielded qualitatively similar results we are confident that our results are not biased. We controled for the possible effects of differences in market segments by using market fixed-effects. Although tariffs are obviously targeted at very different customers segments, e.g. occasional callers and heavy usage business customers, the dataset initially lacked a classification of market segments. We therefore allocated each tariff to one of four unique market segments (subsequently referred to simply as markets ) using standardized usage baskets (German Federal Statistical Agency, 2006). The markets reflect four different customer types: rare, low, average and heavy users. The allocation of tariffs to markets was performed with a standardized algorithm, checked by hand, and subjected to extensive robustness tests. The allocation procedure is described in detail in the appendix. Fixed-effects dummies are used for the low, average, and heavy usage markets, with the rare users as the base category. Finally, the regressions include year fixed effects for 2004 to Results Descriptive statistics and correlations for the dependent, independent and control variables are reported in Table 2. The correlations between the independent variables are highly significant and there are indications of high multicollinearity. However, omitting the year dummies reduced multicollinearity to acceptable levels (the condition number of variancecovariance matrix is ) and robustness tests for our regressions omitting year dummies yield similar results to our preferred specifications. We conclude that multicollinearity is not a serious concern INSERT TABLE 2 AROUND HERE Table 3 reports the results of the logit regression models estimating the probability a tariff has a non-zero OOD. Although we cannot directly interpret the magnitude of the coefficients, the sign and significance levels are the same as those of the marginal effects of the covariates on our independent variable. In models (1a) and (1b) we compare the probability of tariffs having OOD for MNOs against the base category MVNOs. The coefficients of the MNO dummy are positive and significant. Thus, MNO tariffs have a significantly higher probability of having OOD than MVNOs. Models (2a) and (2b) in Table 3 report the results for regressions comparing large MNOs with the base category small MNOs (note that tariffs by MVNOs are excluded for this 10

12 analysis). The coefficients for the Large MNO dummy are positive and significant, indicating that large MNOs are more likely to offer tariffs with OOD. This result is important, as it provides strong evidence that the observed result is indeed due to operator size and not to differences in business model as described above the MNOs have similar business models. In models (3a) and (3b) we compare all three groups of firms. The coefficient for large MNOs is significant and positive in both models, but the MNO coefficient is not significant in our preferred specification (3a). We cautiously interpret this as further supporting the finding that large operators are more likely to offer tariffs with OOD than small operators. For the control variables the results are mixed. For the free minutes dummy the coefficients sign and significance vary across models. For the base fee dummy the results are as expected: tariffs with a base fee are less likely to have a non-zero OOD. The results for the market dummies are also encouraging: compared to the base category Rare Users the coefficients for each market are positive and in most cases significant. This indicates that tariffs for heavier users are more likely to have a non-zero OOD, which is consistent with lower tariffs having simpler structures. Taken together, the models provide strong evidence that large operators are more likely to offer tariffs with OOD than small operators, and that this effect is indeed due to size and not differences in strategy or business model. This suggests that of the two arguments discussed above, tariff-mediated network effects are the dominant effect INSERT TABLES 3 AND 4 AROUND HERE Table 4 reports the results from our second empirical question: considering only tariffs that have non-zero OOD, do large or small operators offer larger discounts for on-net calls? The results there are less clear than in the logit models: Most of the coefficients for the MNO and Large MNO dummies are not significant. However, in our preferred regressions (1a), (2a) and (3a) they are consistently negative, and for regression (1a) the sign for the MNO dummy is significant at the 5% level. Although these results are not significantly different from zero, we make two observations based on them. First, we note that the clear correlation of OOD with size that we observed in the first set of regressions in Table 3 and that favors the tariffmediated network effects argument is absent here. This suggests that there may be a second effect balancing larger operators preference for OOD. We also cautiously note that it appears from the negative coefficient signs that smaller operators have larger OOD, albeit in most specifications not significantly so. Furthermore, the positive coefficients in models (2b) and 11

13 (3b) may be due to the endogeneity of the fixed fee and base fee control dummies (note that the market dummies also take on unexpected signs in theses regressions). We interpret this as (very) tentative evidence that the fat-cat effect may also have some merit. In summary, we observe strong evidence that large operators are more likely to offer tariffs with non-zero OOD than their smaller rivals. In contrast, the magnitude of the discounts offered generally does not differ significantly between operators, although the fairly consistent negative signs seem to suggest that the fat-cat effect may be at work, perhaps cancelled out by the tariff-mediated network effects. 5. Conclusions Termination-based price discrimination is a pervasive phenomenon in telecommunications markets. Although there is widespread agreement that it is used as a competitive instrument, there have been different proposals as to whether it is more attractive for small or large operators and to the mechanisms driving these preferences. In this paper we described and tested two main propositions for termination-based price discrimination. On the one hand, research on tariff-mediated network effects suggests that large operators will favor OOD to leverage their larger installed base and decrease the attractiveness of smaller rivals. On the other hand, anecdotal evidence and recent research on consumer behavior suggests that small operators may favor OOD to attract customers: they advertise with low on-net prices while keeping off-net prices high. This marketing strategy is costly for their larger rivals to imitate as low on-net prices would cause much larger cannibalization on their larger networks the so-called fat-cat effect. We use data on tariff setting in German mobile telecommunications between 2004 and 2009 to determine which of these mechanisms is dominant. We find that large operators are more likely to charge OOD, supporting the hypothesis that tariff-mediated network effects are a driver of termination-based price discrimination. However, we also find that the magnitude of OOD offered does not differ between larger and smaller operators. There are even some suggestions (albeit not significant) that small operators may offer higher differentials, which suggests that the argument in favor of customer acquisition strategies as a driver of termination-base price discrimination may have some merit. We contribute to research on termination-based price discrimination network effects by providing evidence that tariff-mediated network effects are not only taken into account by customers (Haucap and Heimeshoff, 2011), but are also used as a strategic parameter by firms. Furthermore, we demonstrate that although there may be some merit to the argument 12

14 that small operators use termination-based price discrimination as a strategic marketing tool, the effect of tariff-mediated networks is dominant. Our study has limitations that suggest avenues for future research. First, we do not observe the causal mechanisms proposed by previous theoretical work directly, but only their result. Future work directly capturing these effects might provide a more detailed picture of the motivation for termination-based price discrimination. Second, we do not observe the number of consumers for each tariff. More detailed data on sales would allow us to directly study the link between termination-based price discrimination, consumer choices and firm profitability. It would also allow us to gain a more comprehensive understanding of the interaction between operators installed bases and their competitive behavior. Finally, our data is limited to a turbulent period in the mobile telecommunications industry in Germany. A cross-validation of our results in other countries and industry life-cycle phases would be beneficial. The telecommunications industry is a pervasive part of our daily lives and it continues to evolve rapidly. It is also highly advanced in terms of non-linear pricing strategies. This makes it an excellent testing ground for theories of network economics, firm strategy, and bounded consumer rationality, and we look forward to future contributions on these issues. 13

15 6. References Berger U Access Charges in the Presence of Call Externalities. The B.E. Journal of Economic Analysis & Policy 3(1) Berger U Bill-and-Keep vs. Cost-based Access Pricing Revisited. Economics Letters 86(1) 2005: Birke D, Swann G Network effects and the choice of mobile phone operator. Journal of Evolutionary Economics : Birke D, Swann GMP Network Effects, Network Structure and Consumer Interaction in Mobile Telecommunications in Europe and Asia. Journal of Economic Behavior & Organization 76(2) 2010: Cabral L Dynamic Price Competition with Network Effects. The Review of Economic Studies 78(1) 2011: Calzada J, Valletti TM Network Competition and Entry Deterrence*. The Economic Journal 118(531) 2008: Cambini C, Valletti TM Network competition with price discrimination: bill-andkeep is not so bad after all. Economics Letters 81(2) 2003: Corrocher N, Zirulia L Me and You and Everyone We Know: An Empirical Analysis of Local Network Effects in Mobile Communications. Telecommunications Policy 33(33) 2009: Doganoglu T, Grzybowski L Estimating Network Effects in Mobile Telephony in Germany. Information Economics and Policy 19(1) 2007: Fu WW Termination-Discriminatory Pricing, Subscriber Bandwagons, and Network Traffic Patterns: the Taiwanese Mobile Phone Market. Telecommunications Policy 28(1) 2004: Fudenberg D, Tirole J The Fat-Cat Effect, The Puppy-Dog Ploy, and the Lean and Hungry Look. American Economic Review 74(2) 1984: Gabrielsen TS, Vagstad S Why is on-net traffic cheaper than off-net traffic? Access markup as a collusive device. European Economic Review 52(1) 2008: Gans JS, King SP Using 'bill and keep' Interconnect Arrangements to Soften Network Competition. Economics Letters 71(3) 2001: German Federal Statistical Agency Recalculating the Consumer Price Index for Telecommunications Services on Base 2000: Methodological Description. [16 March 2011]. 14

16 German Federal Network Agency Jahresbericht 2009: Annual Report of the German Federal Network Agency [16 March 2011]. Gerpott TJ Termination-Discriminatory Pricing in European Mobile Telecommunications Markets. International Journal of Mobile Communications 6(5) 2008: Harbord D, Pagnozzi M Network-Based Price Discrimination and 'Bill-and-Keep' vs. 'Cost-Based' Regulation of Mobile Termination Rates. Review of Network Economics 9(1) 2010: Haucap J, Heimeshoff U Consumer behavior towards on-net/off-net price differentiation. Telecommunications Policy 35(4) 2011: Haucap J, Heimeshoff U, Stühmeier T Wettbewerb im Deutschen Mobilfunkmarkt: Ordnungspolitische Perspektiven Nr. 4. Hoernig S On-net and off-net pricing on asymmetric telecommunications networks. Information Economics and Policy 19(2) 2007: Hoernig S Tariff-Mediated Network Externalities: Is Regulatory Intervention Any Good? CEPR Discussion Paper (6866) Jeon D, Laffont J, Tirole J On the "Receiver-Pays" Principle. The RAND Journal of Economics 35(1) 2004: Katz ML, Shapiro C Network Externalities, Competition, and Compatibility. American Economic Review 75(3) 1985: Katz ML, Shapiro C Systems Competition and Network Effects. The Journal of Economic Perspectives 8(2) 1994: Kim H, Kwon N The Advantage of Network Size in Acquiring New Subscribers: a Conditional Logit Analysis of the Korean Mobile Telephony Market. Information Economics and Policy 15(1) 2003: Koski H, Kretschmer T Survey on Competing in Network Industries: Firm Strategies, Market Outcomes, and Policy Implications. Journal of Industry, Competition and Trade : Laffont J, Rey P, Tirole J Network Competition: II. Price Discrimination. The RAND Journal of Economics 29(1) 1998: Lambrecht A, Seim K, Skiera B Does Uncertainty Matter? Consumer Behaviour Under Three-Part Tariffs. Marketing Science 26(5) 2007:

17 Lambrecht A, Skiera B Paying Too Much and Being Happy About It: Existence, Causes, and Consequences of Tariff-Choice Biases. Journal of Marketing Research 43(2) 2006:

18 7. APPENDIX A TABLES AND FIGURES Table 1: Tariffs by Operator Type ( ) Tariffs Tariffs % with with OOD OOD Avg. discount 1 MVNOs % 80% All MNOs % 55% Small MNOs % 61% Large MNOs % 55% Total % 53% 1 Average difference between on-net and off-net price as percent of off-net price, only tariffs with positive OOD Table 3: Logit Regressions for Existence of OOD (1a) (1b) (2a) (2b) (3a) (3b) MVNO vs. MNO Small vs. Large MNOs MVNO vs. Small and Large MNOs Dependent variable: existence of on-net discount MNO 1.491** 2.225*** *** (0.674) (0.554) (0.578) (0.461) Large MNO 1.411* 1.871** 1.365** 1.817*** (0.765) (0.806) (0.671) (0.682) Free Minutes (dummy) 1.026** (0.521) (0.700) (0.473) Base Fee (dummy) *** *** *** (0.463) (0.707) (0.548) Market: Low 1.551*** 1.114*** 1.307** 2.476*** 1.807*** 1.704*** (0.420) (0.372) (0.584) (0.557) (0.475) (0.539) Market: Average 0.996* 0.855** *** 1.176** 1.389** (0.530) (0.388) (0.697) (0.729) (0.579) (0.631) Market: Heavy 1.083* 0.711** ** 1.383* 1.365** (0.642) (0.347) (0.874) (0.818) (0.745) (0.691) Constant ** *** ** *** (1.310) (1.156) (1.714) (1.260) (1.188) (0.962) Year Dummies yes yes yes yes yes yes N R Base category for market: Rare * p<0.1, ** p<0.05, *** p<

19 Table 2: Descriptive Statistics Variable Obs Mean SD Min Max (1) (2) (3) (4) (6) (5) (7) (8) 1 OOD (Dummy) Rel. Difference * 1 3 MNO * * 1 4 Large MNO * * * 1 6 Free Minuts (Dummy) * * * * 1 5 Base Fee (Dumy) * * * * * 1 7 Market: Low * * * * * 1 8 Market: Avg * * * * 1 9 Market: Heavy * * * * Note: Correlations marked with an (*) are significant at the 5% level 18

20 Table 4: OLS Regressions for Relative Size of Discount (1a) (1b) (2a) (2b) (3a) (3b) MVNO vs. MNO Small vs. Large MNOs MVNO vs. Small and Large MNOs Dependent variable: relative size of on-net discount (for tariffs with on-net discount) MNO ** (0.0759) (0.0846) (0.128) (0.131) Large MNO (0.100) (0.111) (0.0916) (0.101) Free Minutes (dummy) ** * (0.0781) (0.105) (0.0887) Base Fee (dummy) *** *** *** (0.0432) (0.0187) (0.0167) Market: Low ** ** (0.0858) (0.0732) (0.0592) (0.0817) (0.0750) (0.0920) Market: Average ** ** ** 0.162** (0.0666) (0.0490) (0.0473) (0.0575) (0.0612) (0.0657) Market: Heavy *** ** *** (0.0915) (0.0776) (0.0366) (0.0722) (0.0777) (0.0921) Constant 0.800*** 0.665*** 0.691*** 0.562*** 0.769*** 0.688*** (0.109) (0.141) (0.0473) (0.0686) (0.142) (0.163) Year Dummies yes yes yes yes yes yes N R Base category for market: Rare * p<0.1, ** p<0.05, *** p<

21 8. APPENDIX B SUPPLEMENTARY MATERIAL 8.1 Allocation of Tariffs to Markets Initially, the dataset lacked a segmentation of the tariff market although tariffs were obviously targeted at very different customers segments, e.g. occasional callers and heavy usage business customers. Treating all tariffs as competing equally for the same customers could skew our results, so we allocated each tariff to a unique market segment (subsequently referred to simply as market ). This is achieved using three standard usage baskets defined by the German Federal Statistical Agency (2006) for rare, low and average cellular phone users. No basket was available for business users, so we defined a heavy user basket as an inflated version of the average user basket. The baskets are provided in Table B1. Table B1: Usage baskets Usage in minutes (monthly values calculated with BNA baskets of 2000) Assumption: Call duration constant over different types Unit Rare users Low-level users Average users Heavy users* Total duration of calls min o/w to own network min o/w to other network min o/w to fixed-line SMS min # * Note: No basket was available for heavy users, therefore two assumptions were made: - Call distribution is assumed to be identical to average users. - Total call duration is assumed to be 600 min (30min x 20 working days) Each tariff was allocated to a unique basket ( market ) with the following procedure: 1. Calculate the monthly cost of each tariff for each of the four user baskets using the tariff components and the usage pattern for the baskets. 2. Using all tariffs, identify the lower 10%-ile in terms of cost for each user basket by month. 3. Calculate the relative distance of each tariff s cost to the lower cost percentile for each user basket in the month it was introduced. 4. Allocate each tariff to the basket where the relative distance is smallest. In calculating the monthly cost we make two simplifying assumptions. First, we take assume calls are distributed evenly over the day. In the period covered by our dataset a decreasing number of tariffs differentiate between different times of day; where different rates 20

22 were offered we used the average cost. Second, if off-net prices varied for different operators we used the average off-net price. This affected a negligible percentage of the tariffs in the sample. The results of the tariff allocation for average monthly user bills are provided in Table B2. A potential weakness of the market allocation procedure is that the user baskets were only available for In the time between then and the end of our dataset there may have been changes in usage patterns arising endogenously from increasing diffusion and falling prices. Table B2: Tariff Allocation Results of Tariff Allocation to Markets Observations and monthly user bill in EUR Market Obs. Mean SD Min Max Rare Low Average Heavy Total The allocation was robust towards a wide range of inflation factors for the heavy user basket and towards using fiscal quarters rather than months to define the lower cost quartile. Results were also robust towards using lower percentiles between 5% and 15%. The 10%-ile level was chosen because it yielded the most plausible allocation based on a qualitative investigation of tariff names. The lists allocating the tariffs to the markets are available on request from the authors. 8.2 Data Cleansing The data gathered by Teltarif was subjected to extensive cleansing at the operator and tariff levels. At the operator level non-mobile operators were excluded from the dataset, e.g. a data-service company offering tariffs as an add-on for companies implementing software solutions. Three operators who had only a few (<3) tariffs and for whom no information could be found on the internet were also dropped from the dataset. Finally, 23 tariff resellers were dropped, as discussed in detail in the paper. At the tariff level the following tariffs were dropped to avoid distortions in price calculations and the calculated number of new tariffs: Bundles whose price covered both a mobile tariff and a broadband access, bundles including both mobile and fixed-line tariffs, add-on options for tariffs (such as Extra 100 free SMS ), and pure data tariffs for mobile 21

23 devices (without calling functionality). Furthermore, we eliminated duplicate tariffs, i.e. identical tariffs offered by the same operator over the same time period under a different name, as these did not pose a substantially new offering for customers. Note that tariffs are also influenced by firms strategy of attracting new customers by subsidizing mobile handsets and subsequently recovering their investment over the duration of the tariff contract. While we cannot control for this effect due to a lack of data on handset subsidies, we see no reason why this should necessarily influence the on-net / off-net price differential. 22