Foreclosing or collusive equilibria in the roaming market: a model of Mobile Virtual Network Operator’s entry.

Carlo Capuano [1][2]

Università degli Studi di Napoli FEDERICO II

August 2005

The paper deals with the entry of Virtual Network Operators into the mobile communication sector and it proves that (static) non-cooperative foreclosing or collusive equilibria in the upstream roaming market are sustainable even if there exist more incumbents. This depends on the increase in the downstream complementarity that occurs when an incumbent contracts the entrant’s upstream roaming access. In fact, the agreement causes a stronger attitude to implement a less aggressive price competition in the downstream market and, when the incumbents are tacitly coordinated to some foreclosing access charges, the same complementarity is the weakness of a potential upstream deviator that faces a more aggressive competition in the downstream market.

In the first part of the paper we give some evidences that even if exogenous, the entries of new competitors have represented a significative increase in the diffusion speed of mobile communication service. We propose an Arellano-Bond dynamic panel data estimation on European countries from 1985 to 2003.

JEL Code: D43, L13, L14, L42, L51.

Keywords: network oligopoly, roaming, mobile, telecommunication.

Introduction.

The mobile communication markets are ones of the more dynamic in ICT sector and in the last twenty years they have been generally characterized by the highest rates of growth. As shown in figure 1, in most European countries the mobile communication services diffusion is now characterized by penetration indexes[3] not lower than 66% (France), while in some countries as Italy and Luxembourg, because of the SIM card[4] duplication phenomenon, the penetration indexes are higher than 110%!

Figure 1. The penetration indexes in percentage (% of SIM cards/ Population) in European countries in 2003. (Source of data: Eurostat).

Until 2003, the (sold) SIM cards were about 305,6 millions, when just until one year before they were only 250 millions. These evidences are extraordinary when we consider that in 1998, only five years before, we computed not more than 69 millions of SIM cards and the data showed a continental penetration index about 18,3 %. Only in the period 2002-2003 we measured an average growth not lower than 6%.

Figure 2. The penetration indexes in percentage (SIM cards/ Population in %) in European countries from 1980 to 2003. (Source of data: Eurostat).

Nowadays the mobile network operators, MNOs, are over than 50, and we compute about 120 mobile service providers, with a sector turnover in 2003 higher than 100 billions euro. In spite of the extraordinary growth and diffusion of the mobile communication services in any countries, as shown in figure 3, the national markets are still too concentrated: in 2003 the average CR2 index[5] was about 78,8 %.

Figure 3. Concentration Rate Indexes, CR1 and CR2, in European countries in 2003. (Source of data: Eurostat).

Moreover, even if in many countries more than one operator have a Significant Market Power (SMP), in 14/15 of the considered sample the leader firm is the subsidiary of the incumbent fixed operator[6].

Extracted from the 8th and the 9th European Commission Reports on Telecommunication[7][8], the mentioned data immediately show why even if the sector is very dynamic, ex ante and ex post regulation enforcements are still necessary: in the regulatory agenda we find not only the liberalization of the markets (by beauty contests, spectrum auctions or lotteries) but also the regulation of any access prices (interconnection and roaming). By the way, in each country antitrust authorities are continuously involved in abuse of single or collective dominance investigations.

The first part of the paper gives some evidences that even if exogenous, the entries of new competitors have represented a significant increase in the diffusion speed of mobile communication service. The proposed Arellano-Bond dynamic panel data estimation underlines the implication that the risk of foreclosing the market to new mobile virtual network is not out of interest from regulatory point of view. And this evidence introduces our theoretical model about static non cooperative foreclosing or collusive equilibria in the upstream roaming market.

The second part of the paper explores the Mobile Virtual Network Operators (MVNOs) entry mechanism into vertical integrated oligopolies, i.e. network oligopolies. The MVNOs are not owners of any physical infrastructures but they typically contract the roaming access with one or more incumbents to use their essential facilities. These access agreements are more and more diffused and represent what we call the “liberalization frontier”. In fact, when the presence of more vertical integrated incumbents induces an upstream price competition, then entry is always accommodated and no structural regulation is needed. Otherwise, in order to avoid foreclosing or anticompetitive equilibria a specific National Regulatory Authority’s enforcement should be required. Unfortunately, there not exist robust theoretical results about downstream entry into vertical oligopolies and, maybe as a consequence, we observe very different approaches by the National Regulatory Authorities, NRAs, with respect to the roaming access mechanism[9].

The main result of this paper is proving that when not regulated, as in Italy, incumbents may have a unilateral, i.e. non-cooperative, incentive to foreclose the market to new competitors. Anticipating our results, in this paper we stress the increase in downstream complementarity that occurs when upstream an incumbent signs a roaming agreement with a new competitor: the higher the downstream traffic served by the new competitor, the higher the upstream roaming revenue. Indeed, there exists a stronger attitude to implement a less aggressive price competition in the downstream market. But, the crucial point of our analysis is that when the incumbents are tacitly coordinated to some foreclosing upstream pricing, the deviator incumbent becomes the one much more affected by downstream complementarity with the entrant. Indeed, if deviation occurs the other incumbents have an immediate instrument of punishment by setting lower downstream prices: the incentives to more aggressive strategies are just due to the fall in downstream complementarity. As a last point, we check the role played by network effects in mature market and we find out that there not exist any univocal relationships between network effects and competitive or non-competitive equilibria sustainability. Note that in oligopoly, any non-competitive equilibrium, i.e. foreclosing or collusive one, represents a clear abuse of collective dominance[10].

1 The econometric model of diffusion.

We estimate the impact of entry of new competitors on the penetration rate of the mobile communication services , measured as the number of SIM cards on the population. A similar analysis has been developed in Gruber and Verboven (2001) estimating a logistic model using panel data up to 1997. Their study has underlined the importance of ex ante regulation (liberalization of the market)} and technological progress (the transition from analogue to digital signal). Our target is testing the same results using a longer time series, up to the 2003, considering 16 European countries (Austria, Belgium, Czech Republic, Denmark, Finland, France, German, Italy, Island, Ireland, Luxembourg, Nederland, Norway, Spanish, Sweden, United Kingdom). Indeed, we estimate the following logistic specification[11]

from which

where the subscripts i,t, represent country and time period, respectively; is the unobserved individual heterogeneity and is such that [12]

When

and we obtain the following specification that we estimate.

The variable t is a time counter, GSMit , MNOit and POPit are exogenous explanatory covariates[13]: GSM is a dummy variable that assumes the unit value after the introduction of the digital signal technology, MNO is the number of mobile network operators, POP is the population. All data are collected from OECD, Eurostat and EU reports.

Our depend variable is a measure of the penetration rate. Because of network effects we assume that its level at time t is strongly correlated with its lagged value. This is the reason we use yit-1 as an endogenous variable[14]. Our idea is that entries of new competitors and the technological shock can better explain the deviation from an autoregressive process. So, we estimate our specification by Random-effects GLS regression as benchmark and we compare it with a proper GMM estimation.

Number of obs = 288 Group variable (i): country Number of groups = 16

R-sq: within = 0.9808 Obs per group: min = 18

between = 0.9826 avg = 18

overall = 0.9804 max = 18

Random effects u_i ~ Gaussian Wald chi2(4) = 14154.63

corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

y | Coef. Std. Err. z P>|z| [95% Conf. Interval]

ly | .923213 .0152037 60.72 0.000 .8934143 .9530117

tmno | .0046482 .0010116 4.60 0.000 .0026655 .0066308

gsm | .0640511 .0342051 1.87 0.061 -.0029896 .1310918

tpop | -4.59e-11 4.15e-11 -1.11 0.268 -1.27e-10 3.53e-11

_cons | .01779 .0455913 0.39 0.696 -.0715674 .1071473

sigma_u | 0 sigma_e | .18547202 rho | 0 (fraction of variance due to u_i)

Table 1: Random-effects GLS regression.

1.1 Dynamic Panel Data Methodology.

We work with a pooled data set of cross-country and time-series observations (data details are given above). We use an estimation method that is suited to dynamic as the generalized method of moments (GMM) for dynamic models of panel data developed by Arellano and Bond (1991) and Arellano and Bover (1995).

The general regression equation to be estimated is the

where the subscripts i,t, represent country and time period, respectively. y is the dependent variable of interest, that is, the logistic transformation of the penetration index. X is a set of time- and country-varying explanatory variables while is the vector of coefficients to be estimated. Finally, is an unobserved country specific effect, and is the error term.

Parameter identification is achieved by assuming that future realizations of the error term do not affect current values of the explanatory variables, that the error term is serially uncorrelated, and that changes in the explanatory variables are uncorrelated with the unobserved country-specific effect. As Arellano and Bond (1991) and Arellano and Bover (1995) show, this set of assumptions generates moment conditions that allow estimation of the parameters of interest. The instruments corresponding to these moment conditions are appropriately lagged values of both levels and differences of the explanatory and dependent variables. The latter is because the model is dynamic. Since typically the moment conditions over-identify the regression model, they also allow for specification testing through a Sargan-type test. Analytically, when we assume that (scalar) and T > 3, we estimate a first different transformation .

Table (1) and table (2) respectively report the Random-effects GLS regression and the Arellano-Bond dynamic panel-data estimation[15]\. The latter is derived by imposing a two step estimation using the exogenous variables as instrumental ones. There, all the estimated coefficient are significative with a p-value lower than 5%. Even if the lagged value yit-1 explains most of the panel dynamicity, the number of operators MNO has a positive impact on the diffusion speed in both the estimations. This is not true for GSM dummy variable that only in the Arellano-Bond estimation is significative with a p-value lower than 5%. This variable explains not negligible shifts of the S-shaped diffusion function forward[16]. By the way, their final impacts are higher because of the multiplicative effects due to the autoregressive specification. Moreover, even if quite negligible because of the different scales, the population variable POP negative affects the penetration growth of the service. With respect the GMM estimation, the Sargan test for the over-identification of the restrictions is satisfied[17] and, more important, the Arellano-Bond tests for autocovariance in residuals of order 1 and 2 avoid to reject the null hypothesis[18].

Number of obs = 272 Group variable (i): country Number of groups = 16

Wald chi2(.) = . Time variable (t): time Obs per group: min = 17

avg = 17

max = 17

Two-step results

y | Coef. Std. Err. z P>|z| [95% Conf. Interval]

y | LD | .924508 .0196392 47.07 0.000 .8860159 .9630001

tmno | D1 | .0061463 .0012386 4.96 0.000 .0037187 .0085739

gsm | D1 | .0705575 .0228833 3.08 0.002 .0257071 .1154079

tpop | D1 | -2.77e-10 9.16e-11 -3.02 0.002 -4.56e-10 -9.75e-11

Sargan test of over-identifying restrictions: chi2(154) = 13.02 Prob > chi2 = 1.0000

Arellano-Bond test that average autocovariance in residuals of order 1 is 0:

H0: no autocorrelation z = -1.83 Pr > z = 0.0672

Arellano-Bond test that average autocovariance in residuals of order 2 is 0:

H0: no autocorrelation z = 0.36 Pr > z = 0.7220

Table 2: Arellano-Bond dynamic panel data estimation.

As results of our estimation we can say that the number of operator has been significative in order to explain diffusion speed of the mobile communication services in Europe. This is true even if the entry process was strictly exogenous because of a regulatory enforcements. The implication for our theoretical model is that risk of foreclosing the market to new mobile virtual network is not out of interest from regulatory point of view because of entry impact on next generation services diffusion.

2 Model Setting.

2.1 Timing and strategies.

We consider the mobile communication sector as a vertical integrated one: upstream we find the network as an essential facility[19], downstream we find the mobile communication services. The provided services are imperfect substitutes and when prices are the control variables, strategic complementarity occurs[20]. If all competitors are vertical integrated the sector is a typical network oligopoly and all operators are MNOs.

In our model we consider two incumbent MNOs, indexed by , owners of independent networks, and we analyze the entry of a potential competitor, indexed by : the entry process requires either building a new independent facility or contracting roaming access agreements with one or more MNOs. The roaming agreement is an access contract to the upstream facility that lets the new competitor transport its communication traffic by the MNO’s network.

UPSTREAM ENTRY DOWNSTREAM

COMPETITION DECISIONS COMPETITION

t=1 t=2 t=3

I1 chooses x1 I3 decides e | x1, x2 I1 chooses p1 | e ,r, x1, x2

I2 chooses x2 I3 decides r | x1, x2 I2 chooses p2 | e ,r, x1, x2

[ I3 chooses p3 | e ,r, x1, x2]

Figure 4. Timing of the model.

Even if we consider more decisional stages, the model is characterized by complete but imperfect information. As shown in figure 4, the timing is the following: at time the incumbents simultaneously and non-cooperatively choose their roaming price, . Observed the roaming charges, at time the potential competitor decides if entering or not the market, , where the null value means no entry. If , at the same time the entrant either builds a new network[21] or contracts one or more roaming access agreements, , where the unit value labels the roaming agreement case. At time , all operators know the history of the market. Different scenarios can occur: the new competitor does not enter the market (), the new competitor enters the market by contracting a roaming access (), the new competitor builds an independent network, (but ). In any scenario, the operators, i.e. the incumbents and the third competitor if entered, play a Bertrand game in the downstream market, i.e. the control variables are the downstream price for the mobile communication services, . Each operator simultaneously plays in order to maximizing its own static profit function. For simplicity we assume that all expenditures and revenues occur at time , when we attribute the payoffs of our one-shot multistage game.

2.2 Roaming contract Vs. Independent Network.

In our model we assume that the incumbents, , simultaneously play in a non-cooperative way. Summing up, at time , each one chooses the own proposed roaming price, respectively x1 and x2. The roaming agreement provides the access to the network as a homogenous intermediate service. At time , if more profitable than building an independent network, the entrant contracts a roaming access with one or both incumbents. In particular, the entrant contracts with the incumbent that charges the lower roaming price: we assume that when the incumbents propose the same roaming price, , they equally share the traffic of the new competitor. Then the following entrant traffic allocation rule holds. For and ,

/ [1]

where i is the percentage of traffic served by the incumbent i.

Differently, building a new network can support the entry: the new structure is immediately operative. At time , roaming prices were announced and the entrant already decided if entering the market, if contracting a roaming access or building an own network. Then, the profit function of each competitor is the following. For ,

/ [2]
/ [3]

with, for ,

/ [4]

and

/ [5]

where is the amount of traffic served in equilibrium by operator i, is the charged downstream price, is the vector of the different-to-i operators’ prices. Then, is the lower roaming price offered at time . The marginal cost is assumed to be constant and it is the same for all operators. Finally, Ciis the fix cost paid by the operator , when the third competitor enters the market, whileis the increase in the fix cost paid by the entrant that builds an independent network. Note that as a negative externality the third operator entry causes an increase in the fix cost of the incumbents. In fact, because of band sharing, interconnections mechanisms and new SIM signals management, a specific upkeep of the network is required.

2.3 Roaming contract and downstream price complementarity.

Proposition 1.Contracting an upstream roaming access increases the effect of the downstream strategic complementarity.

Proof. Consider the case in which the entrant contracts a roaming access, , with at least the incumbent i. Starting from the profit functions [2] and [3], at time t=3, we derive the following partial[22] and mixed derivatives.

/ [6]
/ [7]
/ [8 ]
/ [9]

Note that when services are substitutes, i.e. , [8] and [9] verify strategic complementarity among entrant and incumbents. Now, we want to check if the downstream prices are increasing functions of the equilibrium roaming price x. Then, total differentiating [6] and [7] we obtain [10] and [11], and by Dini’s Theorem [12] and [13].

/ [10]
/ [11]
/ [12]
/ [13]

Given [8]-[9], and [12]-[13], it immediately follows that the higher the upstream roaming price, the higher the downstream prices due to the strategic complementarity.■