Market Structure, Investment, and Technical Efficiencies in Mobile Telecommunications: Elliott et al. (2024)

What this is. The paper’s core results, the structural model, and the estimation method are described here with key equations and source locators. To replicate or extend the analysis, read the full source at doi.org/10.1086/734132.

TL;DR

Elliott, Houngbonon, Ivaldi, and Scott develop a structural model of competition among mobile network operators (MNOs) in which firms simultaneously choose prices and infrastructure investment. The novel supply-side feature is an engineering-based model of data transmission: download speeds depend on spectrum allocation (via Hata path loss and Shannon-Hartley channel capacity) and network congestion (via M/M/1 queuing). Fewer firms mean each firm serves a denser user base, reducing path loss, and pools more spectrum and customers in one queue, reducing congestion waste — two scale efficiencies that offset the market-power cost. This tension is what Williamson (1968) posed theoretically for antitrust; the paper quantifies it in a calibrated equilibrium.

Estimating the demand system on French mobile data for October 2015 and simulating symmetric-firm counterfactuals, the paper finds that consumer surplus is maximized at eight firms while total surplus peaks at four. Monopoly delivers faster download speeds than a six-firm market, a reversal of intuition from models without scale economies that Spence (1975) and standard monopoly quality theory do not predict. All bilateral mergers among France’s four MNOs decrease consumer surplus even accounting for spectrum-pooling efficiencies, because the market-power effect dominates when infrastructure is fixed.

Core results

#	Result	Locator	Magnitude as reported
R1	Download speeds are monotonically decreasing in the number of firms; economies of pooling and density generate higher quality under concentration	Figure 7, p. 40	At 1 firm delivered speeds reach ~90 Mbps; at 6 firms speeds collapse near 0 Mbps; channel capacity changes little across firm counts at the commune level
R2	Consumer surplus is maximized at 8 firms; total surplus is maximized at 4 firms; the two peaks diverge because quality declines and producer surplus also falls with more firms	Figure 9, p. 43	CS peaks around 29.5 EUR/person/month above monopoly; TS peaks around 14.1 EUR/person/month above monopoly at 4 firms; producer surplus declines monotonically in firm count
R3	All 6 bilateral MNO mergers decrease consumer surplus in the short run when infrastructure is held fixed	Figure 12, p. 49	Reductions range from -0.22 EUR/person/month (Orange-Bouygues) to -1.24 EUR/person/month (Orange-SFR); merger-specific spectrum pooling is insufficient to offset market-power effects
R4	The marginal social value of spectrum is approximately five times a firm’s auction willingness to pay at the four-firm equilibrium	Figure 11, p. 46	Marginal consumer surplus from bandwidth = 1.24 EUR/person/MHz; firm marginal WTP = 0.25 EUR/person/MHz; the 2015 French 700 MHz auction price was ~0.70 EUR/person/MHz, between the two
R5	Asymmetric spectrum allocations are inefficient: symmetric allocation raises both consumer and total surplus relative to asymmetric, though asymmetric raises producer surplus	Table 6, p. 44	3-firm symmetric CS = -0.668 EUR/person; 3-firm asymmetric CS = -0.701 EUR/person (both relative to 4-firm symmetric benchmark); asymmetric TS = -0.076, symmetric TS = -0.054 EUR/person

Overall finding. Consolidation in mobile telecommunications presents a genuine trade-off between market power and scale efficiency. The welfare-maximizing market structure depends critically on the welfare criterion: antitrust policy targeting consumer surplus implies a more competitive market than policy targeting total surplus. All bilateral mergers among France’s 2015 MNOs decrease consumer surplus, and the social return to spectrum is substantially above what firms reveal through auction bids.

Theory and model

The paper builds a two-stage model: (i) a discrete-continuous consumer demand system and (ii) an engineering-based industry model of data transmission and firm competition.

Demand. Consumer $i$‘s indirect utility for phone plan $j$ in municipality $m$, at chosen data consumption $x$, is (equation 1, p. 15):

u_{jm}(x;\, \vartheta_i,\varepsilon_{ij},\theta_{pi}) = w_j(x,Q_{f(j),m},\vartheta_i) + \theta_v v_j - \theta_{pi} P_j + \xi_{jm} + \varepsilon_{ij} \tag{1}

where $w_j(\cdot)$ is the utility of data consumption, $\theta_v$ is the voice-quality valuation, $\theta_{pi}$ is a price sensitivity parameter that decreases in household income, $\xi_{jm}$ is an unobserved product-market demand shock, and $\varepsilon_{ij}$ follows a nested logit structure (two nests: outside good vs. all mobile plans). The data consumption utility is (equation 2, p. 16):

w_j(x,Q,\vartheta_i) = \vartheta_i \log(1+x) - c_j(x,Q) \tag{2}

where $\vartheta_i \sim \mathrm{Exp}(\theta_{di})$ is the consumer’s data valuation shock and $c_j(x,Q)$ is the time cost of downloading $x$ GB at speed $Q$. Below the data limit $\bar{d}_j$ the cost is linear in $x/Q$; above $\bar{d}_j$ the throttled speed $Q^L = 128$ Kbps applies (equation 3, p. 17). Optimal data consumption is pinned down by the first-order condition, which yields a closed-form for $x^*(P_j,Q,\vartheta_i,v_j)$.

Download speed from engineering. Channel capacity (maximum average speed absent congestion) is derived from hexagonal cell geometry and the Hata path loss model (equation 14, p. 26):

\bar{Q}_{fm}(R_{fm},B_{fm}) = \frac{B_{fm}\, A(R_{fm})}{\displaystyle\int_{\ell\in\mathcal{L}(R_{fm})} q_{m\ell}^{-1}\,d\ell} \tag{14}

where $R_{fm}$ is the cell radius (the firm’s infrastructure choice), $B_{fm}$ is spectrum bandwidth, $A(R_{fm}) = \frac{3\sqrt{3}}{2}R_{fm}^2$ is the hexagonal cell area, and $q_{m\ell} = \gamma_m \log_2(1+SINR_\ell)$ is the location-specific data rate in bits per second per Hz via the Shannon-Hartley theorem. The signal-to-noise-and-interference ratio $SINR_\ell$ follows the Hata model with estimated path loss exponent 3.522.

Actual delivered speeds also depend on congestion. Following an M/M/1 queuing model (equation 17, p. 28):

Q_{fm} = \bar{Q}_{fm} - Q_{fm}^D \tag{17}

where $Q_{fm}^D$ is the Poisson arrival rate of download requests from consumers on operator $f$‘s network in municipality $m$. This yields two sources of scale efficiency from consolidation: economies of density (fewer firms means each serves a denser population per cell, shortening the average distance and reducing path loss) and economies of pooling (combined spectrum and customer base in a single queue cuts queuing waste).

Firm competition. Each operator unilaterally and simultaneously sets a national price vector $\mathbf{P}_f$ and a vector of cell radii $\mathbf{R}_f$ (one per municipality) to maximize profits (equation 24, p. 34):

\Pi_f(\mathbf{P},\mathbf{R},\mathbf{B}) = \sum_m \Pi_{mf}(\mathbf{P},\mathbf{R}_m,\mathbf{B}_m) - \sum_m C_{fm}(R_{fm},B_{fm}) \tag{24}

with infrastructure cost proportional to both cell area covered and bandwidth operated per base station (equation 22, p. 33):

C_{fm}(R_{fm},B_{fm}) = c_{fm}^s\,\frac{A_m}{A(R_{fm})}\,B_{fm} \tag{22}

The Nash equilibrium prices $\mathbf{P}^$ and cell radii $\mathbf{R}^$ satisfy first-order conditions for both price and infrastructure simultaneously; the paper solves these jointly using numerical methods across 589 municipalities.

Counterfactual equilibria are computed on a single representative commune (population-weighted mean density 2,792 people/km$^2$) with $n$ symmetric firms each holding $B_0/n$ MHz of spectrum (Section 6.1, pp. 40-43). Merger counterfactuals add a second and third representative commune (to capture all population density categories) and combine the merging MNOs’ bandwidth allocations while keeping infrastructure fixed in the short run (Section 6.3, pp. 47-49).

Method

Demand estimation. The paper follows Berry, Levinsohn and Pakes (1995) extended to a discrete-continuous demand system. Market shares observed at two levels of aggregation (plan-level for Orange, firm-level for other MNOs) require a modified contraction mapping in which the inner loop solves for $\boldsymbol{\xi}(\theta)$ that simultaneously matches both data sources (Appendix B, equations B.1-B.4). The outer loop searches over demand parameters $\theta = (\theta_{pi},\theta_{di},\theta_c,\theta_v,\sigma)$ minimizing a two-stage efficient GMM objective.

Price variation in the October 2015 cross-section is insufficient to separately identify demand elasticities (prices were stable 2013-2015). The calibration approach from Bourreau, Sun and Verboven (2021) is used: the own-price elasticity of Orange (-2.36) and the diversion ratio to the outside good (0.036) are imposed as additional moment conditions (equations 11-12, p. 23), anchoring the demand system. The income heterogeneity parameter $\theta_{dz}$ is identified from the covariance of municipality median income with average data consumption. Download speed is instrumented by log population density to correct for measurement error and congestion endogeneity.

Cost estimation. Variable costs per user $c_j^u$ are recovered from the pricing first-order condition (equation 26, p. 35) evaluated at observed equilibrium prices and estimated demand. Infrastructure costs per base station per MHz $c_{fm}^s$ are recovered from the infrastructure first-order condition (equation 27, p. 35), obtained by numerically differentiating the marginal operating income with respect to cell radius $R_{fm}$ and equating it to the marginal cost implied by equation 22.

Identification summary. Demand parameters: identified by price variation across plans, consumption-income covariance (income heterogeneity), download speed variation across municipalities (speed coefficient), and calibrated elasticity and diversion moments. Supply cost parameters: identified from firms’ optimal pricing and investment decisions, exploiting cross-municipality variation in population density and land area.

Empirical specifications

Demand GMM moments. Nine moment conditions (p. 23): the price elasticity calibration (equation 11), the diversion ratio calibration (equation 12), mean data consumption across markets, the covariance of Orange demand shocks with median income, the covariance of Orange demand shocks with log population density, the demand-shock covariance with data limit indicators, the demand-shock covariance with voice plan indicator, and two cross-plan mean-consumption moments.

Sample. Cross-section of 589 French communes with population above 10,000 (covering 43.5% of France’s total population). Data period: October 2015 for customer shares and consumption; Q2 2016 for Ookla speed measurements. The commune is the unit of observation for the demand estimation; the representative commune is the unit for counterfactual simulations.

Counterfactual design. Symmetric counterfactuals: $n \in {1, 2, 3, 4, 5, 6, 7, 8}$ firms each with $B_0/n$ MHz. Asymmetric counterfactual (3 firms): replicates 2011 French spectrum allocation (ANFR frequencies). Merger counterfactuals: 6 pairs from France’s 4 MNOs (Orange, SFR, Bouygues, Free); three representative communes weighted by France’s population distribution within each density class.

Datasets used

Dataset	Role	Access	Wiki page
Orange Mobile customer database (proprietary)	Plan-level market shares and average data consumption per municipality, October 2015	proprietary-confidential	No page yet
Ookla Speedtest data (proprietary)	Over 1 million measured download speeds per operator per municipality, France Q2 2016	proprietary-confidential	No page yet
ANFR base station database	Locations of all mobile antennas and frequencies per operator per municipality	public	No page yet
GSMA Intelligence	National-level market shares for SFR, Bouygues Telecom, Free Mobile	licensed-commercial	No page yet
INSEE 2011 population census	Income deciles per municipality; municipality land area	public	No page yet
OSIRIS (Orange internal traffic data)	Total data traffic per network cell, used to calibrate Poisson demand arrival rates for the queuing model	proprietary-confidential	No page yet

The most restrictive source (Orange subscriber database, OSIRIS, Ookla) makes this a proprietary-confidential paper that cannot be replicated without access to those proprietary datasets. The estimation code at https://github.com/jonathantelliott/mobile-telecommunications is fully available.

When to read the full paper

Use the original if you are: building a structural model of network infrastructure competition; quantifying welfare trade-offs in mobile market consolidation or spectrum auction design; studying how engineering-based scale economies interact with product market competition; or applying a discrete-continuous demand system to a market with heterogeneous quality. The online Internet Appendix contains technical derivations of the Hata path loss formula, the modified BLP contraction mapping, the network-sharing deviation from equilibrium, and robustness checks for alternative cost specifications.

Attribution and rights

Source: peer-reviewed, Journal of Political Economy 133(5), May 2025, pp. 1401-1459. Paywalled; no open-access license found in Crossref metadata. This distillation was extracted by an LLM on 2026-06-26 and is not human-verified or independently reproduced. Only the content of this page may be used; the underlying article requires a library subscription or individual purchase.

Elliott, Jonathan T., Georges V. Houngbonon, Marc Ivaldi, and Paul T. Scott. “Market Structure, Investment, and Technical Efficiencies in Mobile Telecommunications.” Journal of Political Economy 133, no. 5 (May 2025): 1401-1459. https://doi.org/10.1086/734132.