With mobile phones now integral to modern life, the actions of network operators – and the regulators who oversee them – have far-reaching consequences. How do mobile mergers impact prices, investment, and consumers? How should regulators manage the radio frequencies that underpin mobile communication? These questions are addressed in a new study by Marc Ivaldi, researcher at TSE Competition Policy & Regulation Center. His findings suggest that regulators must tread carefully when weighing the costs and benefits of consolidation in the mobile sector.
Why does regulation of the airwaves play such a crucial role in this industry?
Market structure in the mobile sector is heavily influenced by antitrust policy and regulation of spectrum holdings, which are the radio frequencies on which each firm has the right to operate. Spectrum is a firm’s lifeblood, so the way it is distributed between operators has a huge impact on competition. The US Federal Communications Commission (FCC) noted in 2010 that insufficient spectrum allocation to mobile operators risks higher prices and reductions in service quality, competitiveness, demand, and innovation.
Recently, network operators in many countries have sought to merge their spectrum holdings, sparking debates about the optimal number of mobile operators that balances competition, quality of service, and sustainability. There are anecdotal reports that network operators in France and elsewhere have avoided proposing four-to-three mergers for fear that they will be blocked by antitrust authorities.
What might explain regulators’ mixed response toward mobile mergers?
Consolidation increases market power, which can impact prices. However, in mobile telecommunications, it can also lead to more efficient data transmission due to economies of scale.
Economies of density result from the loss of power by electromagnetic waves as they travel. This means that a network operator can serve densely populated urban areas more efficiently – offering higher download speeds, or lower costs – than remote rural areas. As the population density served by each firm is inversely proportional to the number of operators, service quality is likely to improve with fewer firms.
Economies of pooling arise in managing the queues formed when many users make data requests at the same time. Due to limited bandwidth, this results in slower downloads. By pooling their customers and spectrum, merging firms can handle congestion more efficiently, increasing download speeds.
How does your paper attempt to add to the debate?
Our research draws on tools from empirical industrial organization and telecoms engineering to understand the impact of market power and scale efficiencies. We first build a model that links firms’ pricing and investment decisions to market structure. Consumers choose a mobile phone plan, as well as how much data to consume, given the download speeds that serve as our measure of quality of service. Now, thanks to our engineering model of data transmissions, download speeds emerge from firms’ and consumers’ decisions. By changing the number of operators and allocation of spectrum, we can then simulate merger impacts such as the trade-off between market power and economies of scale.
We estimate our model based on the French market in 2015, relying on a dataset that includes information about subscriptions and data consumption for Orange Mobile. We also incorporate download speeds from Ookla, detailed data on mobile network infrastructure from the radio frequency regulator (ANFR), income distribution data from the French statistical office (INSEE), and the prices and characteristics of all contracts available from other network operators.
Why are download speeds so difficult to model?
As increased user traffic clogs up available bandwidth, download speeds are determined by both consumers’ data consumption and firms’ investments. Even without considering congestion, there is no straightforward mapping from firms’ investments to data transmission rates. This is because data transmission depends on the spectrum holding and the distance of transmission.
What do your results tell you about how mergers affect consumers?
Our simulations reveal a clear trade-off: Consolidation leads to faster downloads but higher prices. The improved service is mainly due to merging firms pooling their spectrum and customers, rather than from serving denser populations.
Focusing on symmetric firms, we find that consumer surplus – the current barometer for antitrust policy – is maximized at eight firms. However, consumers do not agree on the optimal number. Low-income consumers prefer more firms because they are less willing to pay for faster downloads than high-income consumers.
Note, however, that total welfare that comprises consumer surplus and operators’ profits is maximized at four firms, which is the present industrial structure in France and in many other countries in the world.
What about the impact of spectrum allocation?
Our research highlights the importance of using a structural model to quantify the social value of allocating more spectrum, which can be a crucial guide for regulators. We find this value to be about five times greater than an individual firm’s willingness to pay for a marginal unit of spectrum. While auctions may reveal network operators’ willingness to pay for spectrum, this may grossly underestimate its social value.
What are the policy implications?
For regulators such as France’s ARCEP and ANFR, a key lesson is that merger control must be closely coordinated with spectrum management. Approving a merger without addressing spectrum allocation risks leaving consumers worse off. Conversely, even a highly concentrated market may deliver desirable outcomes if spectrum policies are carefully designed.
When allocating mobile bandwidth, regulators must consider the impact on social welfare. This requires an understanding of both the marginal social value of spectrum and its opportunity cost. Our model quantifies the former; but quantifying the value of spectrum for other purposes is beyond the scope of our paper as it calls for a model of other industries.
KEY TAKEAWAYS
• Informed decisions – Regulators of mobile mergers need to consider scale efficiencies and market power.
• Mobile merger tradeoff – Fewer firms means higher prices but better service.
• Income matters – Low-income consumers favor more competitors; high-income consumers prefer faster service.
• Spectrum is undervalued – The social value significantly exceeds firms’ willingness to pay.
• Integrated policy – Merger reviews and spectrum allocation must be carefully coordinated to protect consumers.
Our simulations reveal a clear trade-off: Consolidation leads to faster downloads but higher prices. The improved service is mainly due to merging firms pooling their spectrum and customers, rather than from serving denser populations.
