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Mobile Telephony in Emerging Markets: The Importance of Dual-SIM Phones

  • Kjetil Andersson ORCID logo EMAIL logo and Daniel Göller
Published/Copyright: May 18, 2021

Abstract

A substantial share of customers in emerging markets use dual-SIM phones and subscribe to two mobile networks. A primary motive for so called multi-simming is to take advantage of cheap on-net services from both networks. In our modelling effort, we augment the seminal model of competing telephone networks á la Laffont, Rey and Tirole (1998b) by a segment of flexible price hunters that may choose to multi-sim. According to our findings, in equilibrium, the networks set a high off-net price in the linear tariffs to achieve segmentation. This induces the price hunters to multi-sim. We show that increased deployment of dual-SIM phones may induce a mixing equilibrium with high expected on-net prices. Thus, somewhat paradoxically, deployment of a technology that increases substitutability, and thereby competition, may end up raising prices.

JEL Classification: D43; L13; L96

Corresponding author: Kjetil Andersson, University of Agder, School of Business and Law, Department of Economics and Finance, Universitetsveien 19, N-4630Kristiansand, Norway, E-mail:

Funding source: Telenor

Award Identifier / Grant number: Telenor Research under DCVX20 RE

Acknowledgments

This paper replaces “Mobile telephony in emerging markets: the importance of multi-simming customers”. We would like to thank Bjørn Hansen, Sjaak Hurkens, Steffen Hoernig, Jochen Jungeilges, Espen Moen, Trygve Kastberg Nilssen and the participants of the 42nd EARIE Annual Conference for helpful discussions on earlier drafts of this paper. Andersson is grateful for the support of Telenor Research under DCVX20 RE.

Appendix

Implications of Assumption 1

The purpose of this appendix is to discuss the implications of our assumption that the H-types do not multisim. This assumption can be justified by the H-types having a significant hassle cost h of using two sims and/or high transportation costs t. If h is high and both types of customers have the same hassle cost, also the L-types do not multisim and we are back in the world in which all users have just one subscription from one network. If h = 0, and t takes an intermediate value, some but not all of the H-types multisim. The intrinsic utility of being connected, v0, is high enough that every H-types wants to be connected to at least one network. However, it is more preferable for an H-type in the center of the Hotelling line to sign up to a second network, compared to an H-type close to the extremes. For instance, an H-type located at x = 0 would incur additional travel costs of t and an H-type located at x = 0.5 would just incur 0.5t. Consequently, depending on t, more or less H-types close to the center would multi-sim, whereas the remaining would use only one network. This is an intriguing case to be analyzed in future research. When handt are low, one may intuitively think all customers multisim. However this is not correct. Given that all other customers multisim, an H-type can reach all others on-net (and off-net) and hence has an incentive to use only one network to save transportation cost.[13] As a consequence, some H-types would always single-sim, presumably those close to the extremes of the Hotelling line.

Would it still make sense for the networks to use the off-net prices in the linear tariffs as a segmentation device when some of the H-types use two tariffs? This depends on how many H-types multisim, given p̄ij is high. If few, we are back in the world of our model. If many, then these customers could arbitrage away off-net/on-net price differentials. Moreover, there would be an upper bound on the equilibrium subscription fee in the two-part tariff as otherwise more H-types would start multi-simming on the linear tariffs. Presumably, the networks would abandon our pricing strategy in that case. Hence, a prediction of our model would then be that price discriminating contracts in emerging markets would become less prevalent as both the hassle of having and using more than one tariff decreases. What happens to networks’ profits as first a few and then more and more H-types start multi-simming? As long as the networks use our pricing strategy, we conjecture that networks’ profits would decrease in the beginning. In our model, the networks have a monopoly on the calls from the L-types to their own H-types, increasing the on-net prices in the linear tariffs. When the L-types can reach more and more H-types on-net, this effect diminishes.

Shubik–Levitan call utility

The point of this subsection is to demonstrate a quadratic Shubik–Levitan type utility function that satisfies (1) and (2). Recall that a multi-simmer makes two types of calls: calls to single-simmers and calls to other multi-simmers. Suppose that a multi-simmer’s utility of a call to a singlesimmer at network i is given by bqq2/2, where b is a positive constant so that the indirect utility amounts to v(p̄1)=(bp̄1)2/2 and the call demand is q(p̄1)=bp̄1. Moreover, suppose that for calls to other multi-simmers the L-types have a Shubik–Levitan type utility function, or more precisely u(q1,q2)=b(q1+q2)(z(q1+q2)2+2(1z)(q12+q22))/2. Here, q1 and q2 are the calls made with SIM 1 and SIM 2, respectively, and z is a constant satisfying 0 ≤ z < 1. The parameter z reflects to which degree an L-type considers the two SIMs as substitutes: calling with SIM 1 and SIM 2 are independent when z = 0, and perfect substitutes when z → 1. Indirect utility is given by Maxq1,q2{u(q1,q2)p̄1q1p̄2q2}, which after some manipulation can be written as

vm(p̄1,p̄2,z)=2b2+p̄12+p̄222b(p̄1+p̄2)z2(2bp̄1p̄2)24(1z).

Hence, the demand for calls with the two SIMs is

q1(p̄1,p̄2,z)=vm(p̄1,p̄2,z)p̄1=bp̄12+z4(1z)(p̄2p̄1),and
q2(p̄1,p̄2,z)=vm(p̄1,p̄2,z)p̄2=bp̄22+z4(1z)(p̄1p̄2).

Inserting symmetric prices in the above functions, we see directly that (1) and (2) are satisfied for all z ∈ [0, 1).

Proof of Proposition 2

Proof

Consider network 1. The proposition states that if α is sufficiently close to one or z is not too high, it is a best reply for network 1 to charge p̄on*, given that network 2 also charges p̄on*. After maximizing (9) with respect to p̄1, plugging in p̄on* for both p̄1 and p̄2, using n1 = n2 = 1/2, and some reorganization of terms, we obtain (10).[14] Note that to set p̄1=p̄on* is only a local maximum of network 1’s profit function, given that network 2 also charges p̄on*. There may exist a second local maximum, which may emerge when network 1 sets a very high price as a best reply to p̄2=p̄on*. In that case, q1(p̄1,p̄on*,z)=0 and hence q1(p̄1,p̄on*,z)/p̄1=0. Less formally, this means that all L-types use network 2’s tariff to call one another. However, by setting a high on-net price, network 1 capitalizes on the calls from the L to the H-types, where it has a monopoly. The second local maximum (best reply candidate) is found by maximizing π1 under the constraints that q1(p̄1,p̄2,z)=0 and p̄2=p̄on*. After reorganizing terms, the optimal “monopoly” price p̄onM is given by

p̄onM2cp̄onM=q(p̄onM)p̄onMq(p̄onM)>p̄on*2cp̄on*.

Note, however, that to set p̄2=p̄on* cannot be a best reply for network 2, given that network 1 charges p̄1=p̄onM. In that case, network 2 could increase its on-net price by a small amount to increase the profit it derives from the calls from L-types to L-types, hence increasing its total profit. Likewise, p̄1=p̄onM cannot be a best reply to p̄2*=p̄onM, because a small decrease in price would yet again increase network 1’s profit. Consequently, a mixing equilibrium candidate emerges in which the networks choose their prices from a probability distribution with support [p̄on*,p̄onM]. Let us now derive when it is not profitable to deviate to p̄onM, given the other network charges p̄on*. It is important to realize p̄onM affects the deviating network’s optimal and that market shares are non-symmetric after the deviation. Profit under the deviation is given by

π1(p̄onM,p̄on*)=n1M[r1Mf+α(1n1M)(ac)q(a+c)]+n1M[(1α)λ(p̄onM2c)q(ponM)]λf,

where r1M<r1* and n1M>1/2 are the deviating network’s subscription fee and market share, respectively.[15] The rankings follow from the fact that by charging p̄2M, the network increases its profit from the calls from the L-types to its own H-types. Consequently, it has an incentive to reduce its subscription fee to attract more H-types, n2M>1/2. Charging p̄onM is not profitable when

π1(p̄on*,p̄on*)π1(p̄onM,p̄on*)0,

or equivalently

(21)12α(ac)(12n1M)2q(a+c)+f(2n1M1)+r1*2n1Mr1M+λ(1α)((p̄on*2c)q(p̄on*)2n1M(p̄onM2c)q(p̄onM))+αλ(p̄on*2c)q1(p̄on*,p̄on*,z)0.

Depending on the parameter constellation, deviating to p̄onM may or may not be profitable. When α is sufficiently close to one, deviating to the monopoly price is disadvantageous. To see this, recall that the deviating network forgoes all profit from the calls between the L-types αλ2(p̄on*2c)q1(p̄on*,p̄on*,z), but gets increased profit from the calls between the L-types and its own H-types,

λ(1α)((p̄on*2c)q(p̄on*)+2n1M(p̄onM2c)q(p̄onM)).

This profit increase approaches zero as α approaches one. Charging p̄onM instead of p̄on* can decrease or increase profit from the H-type segment. Termination revenues, 14[α(ac)(12n1M)2q(a+c)], decrease as they are maximized when the network’s have equal market share, and we have n1M>1/2. The terms f(2n1M1)+r1*2n1Mr1M represent increased sign-up costs plus/minus the difference in subscription revenues. As α approaches one, r1M approaches r1* and n1M approaches 1/2. Hence, f(2n1M1)+r1*2n1Mr1M approaches zero and it is not profitable to charge p̄onM. When substitutability, or equivalently (q1(p̄on*,p̄on*,z)/p̄1)) in the denominator of the Lerner condition (10), is large in a negative sense, p̄on* is close to 2c. Here, profit in the L-type segment is close to zero and deviating to the monopoly price is profitable. Conversely, p̄on* is equal to p̄onM when call demand is independent.[16]

So far we have considered extremes, but that does not mean our equilibrium does not exist otherwise. If we insert the Shubik–Levitan demand functions specified above in (21) we can give some indications on the parameter space where a pure strategy equilibrium (pse) exists.[17] For simplicity, we have done all simulations for a = c and f = 0. Note that a > c will increase the parameter space where a pse exists since a network’s termination rate is maximized for equal market shares. The extremes are easily verified: if α = 1, a pse exists for all values of z ∈ [0, 1⟩, and when α < 1, a pse does not exist when z → 1. When we look at intermediate case other parameters come in to play. It can be shown that when (b − 2c)2/2 < t < 9, a pse exists for all λ > 0, α ∈ [1/2, 1⟩, and all z ∈ [0, 0.94⟩. Thus, in general, substitution (z) must be quite high for the pse to break down. Moreover, (b − 2c)2/2 < t < 9 is only a sufficient condition for a pse to exist in this {λ, α, z} space: in general it is not necessary when we evaluate (21) for specific values of λ, α,and z. Whether a pse exists for even higher values of z, i.e for z ∈ [0.94, zUP⟩ where 0.94 < zUP < 1 depends on α and λ. In general we can assert the following: given the previous sufficient condition, a pse will exist for zUP arbitrarely close to 1, (i) for all λ > 0 and all α ∈ [αLOW, 1⟩, where 1/2 < αLOW < 1, if αLOW is sufficiently close 1, or (ii) for all λ > λMIN and all α ∈ [1/2, 1⟩ if λMIN is sufficently large. For example: if zUP = 0.98 and αLOW = 0.8 then a pse exists if λ > 1.25, i.e. λMIN = 1.25. The intuition for the minimum λ requirement is that it secures that the network looses revenues from L-types calling each other when it deviates to p̄onM.

Proof of Proposition 3

Proof

Consider network 1. Given that the networks set the calling prices in the two-part tariffs equal to perceived marginal cost, the profit of 1 can be written as

π1=λπ1L+n1π1H,

where

π1L=f+(n1(1α)q(p̄1)+αλq1(p̄1,p̄2))(p̄12c),andπ1H=r1f+α(1n1)qH(a+c)(ac),

where π1L and π1H are the profits made on an L-type and an H-type, respectively. Let us insert the right-hand side of (8) for n1 and maximize the above equation with respect to r1. Then, after some manipulation, we obtain the following best response function:

(22)r1=[tα(vH(2c)vH(a+c))][f+t(1α)λ(p̄12c)q(p̄1)α(vH(2c)vH(a+c))]α(ac)qH[a+c]+2tα(vH(2c)vH(a+c))
(23)+r2t+α[(ac)qH(a+c)vH(2c)+vH(a+c)]α(ac)qH(a+c)+2tα(vH(2c)vH(a+c)).

The best response is upward sloping when ac since stability in the H-type segment requires tα(vH(2c) − vH(a + c)) > 0. Doing the same excersice for network 2 and solving the networks’ best responses yield the equilibrium subscription fees

r1*=r2*=f+t(1α)λ(p̄on*2c)q(p̄on*)α(vH(2c)vH(a+c)),

where p̄1=p̄2=p̄on* is the equilibrium on-net price in the linear tariffs. □

Proof of p̄on*z<0, (17)

Proof

Equation (17) states that p̄on*z<0. Consider network 1. Maximizing (9) with respect to p̄1 using the demand function in (2) gives the first order condition

12(1α)q(p̄1)+αλq1(p̄1,p̄2,z)+(p̄12c)12(1α)q(p̄1)+αλq1(p̄1,p̄2,z)p̄1=0.

When we totally differentiate this holding p̄2,α and λ fixed, and rearrange, we get

dp̄1dz=αλ[q1(p̄1,p̄2,z)/z+(p̄12c)2q1(p̄1,p̄2,z)p̄1z](1α)q(p̄1)+2αλq1(p̄1,p̄2,z)p̄1+(p̄12c)12(1α)q(p̄1)+αλ2q1(p̄1,p̄2,z)p̄1p̄1<0.

The terms inside the outside square brackets in the deninator is the second order condition of the initial maximization problem – which is negative. Hence the denominator is positive. Evaluated at symmetric prices, the first term in the numerator is zero due to (1). The second term is positive due to (2), hence dp̄1dz<0.By symmetry, it must also hold thatdp̄2dz<0. It follows that in a symmetric equilibrium where p̄1=p̄2=p̄on*, we must have p̄on*z<0. □

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Received: 2020-08-12
Accepted: 2021-04-26
Published Online: 2021-05-18
Published in Print: 2021-06-26

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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