Unbundling the Incumbent and Entry into Fibre

Transcription

1 Unbundling the Incumbent and Entry into Fibre Evidence from France Marc Bourreau, Maude Hasbi and Lukasz Grzybowski 11th International Conference on Competition and Regulation Rhodos, 1 July 2016

2 Motivation According to the Digital Agenda for Europe by 2020: All EU households have access to at least 30 Mbit/s connection, At least 50% of EU households have access to 100Mbit/s connection. The European Commission has estimated that it would cost between 181 and 268 billion euros to achieve this target. High speed broadband based on fibre technology faces competition from old broadband technologies: xdsl, cable, VDSL.

3 Motivation In Europe, the regulation of wholesale access to the incumbent s copper local network via local loop unbundling (LLU) generated a heated debate: On the one hand, LLU may facilitate entry of alternative operators who first gain experience and build customers base, and then deploy own high speed networks. On the other hand, LLU may undermine incentives to invest on high speed broadband for both incumbent and entrant operators.

4 Question: impact of LLU on fibre deployment We analyze the case of France: LLU regulation in place since the early 2000s Significant entry of LLU operators Cable network coverage by Numericable: 30% of population Fibre deployed by the incumbent and two entrants: Orange, SFR and Free We estimate a model of fibre and LLU entry into local markets in France using panel data on 36,080 municipalities in Metropolitan France in years What are the determinants of entry with fibre and LLU technologies? What is the impact of LLU regulation on fibre investment?

5 Literature review Theory: ambiguous impact of access regulation on incentives to invest in new generation access networks (NGANs): Inderst and Peitz (2012) and Bourreau, Cambini and Doǧan (2014). Only few empirical studies on investments in NGANs: Bacache, Bourreau and Gaudin (2014): 15 EU countries in , no evidence for the transition from LLU to NGANs. Briglaueur (2015): 27 EU countries in , regulation has negative impact on investment. Minamihashi (2012): municipality-level data for Japan in , negative impact of LLU on entrants investment. Fabritz and Falck (2013): LE areas in the UK in , deregulation has a positive effect on the roll-out of fibre. Mature literature on entry (Bresnahan and Reiss, 2007): Xiao and Orazem (2011), entry of LLU operators in U.S. Nardotto et al. (2015), entry of LLU operators in the UK.

6 Deployment of fibre and cable in France Situation on 30/09/2015

7 Entry of fibre and main LLU operators in municipalities in France Fibre Year Orange SFR Free Total LLU Year Numericable SFR Free Total ,857 6,395 8,473 12, ,997 7,727 10,706 15, ,041 9,570 12,870 17, ,929 13,001 16,077 20, ,937 14,119 19,463 23,179 Out of 36,080 municipalities.

8 Model of entry: profit function Following Xiao and Orazem (2011) and Nardotto, Valletti and Verboven (2015), operator profits in market i in period t: π n it = π n it + ɛ it α t lns it + X it β + Z it γ t µ n I (N it = n) + η i + ɛ it S it is market size represented by the number of households locally. X it is set of variables representing competing broadband technologies: LLU, Cable, VDSL. Z it is set of socioeconomic attributes of municipalities: density of population, share of flats, income and unemployment rate. µ n are negative fixed profit effects from the n th firm. ν i are market-specific unobserved effects. ɛ it i.i.d. normally distributed error term.

9 Model of entry: observing n firms in market i at time t One or more firms have entered and there were fewer than n firms at time t 1, or N mt > N mt 1. Case 1, net entry: N it > N it 1 if π n it SC and π n+1 it No firm has entered or exited a market with n firms, or N mt = N mt 1. Case 2, inaction: N it = N it 1 if π n it 0 and π n+1 it < SC < SC One or more firms have exited and there were more than n firms at time t 1, or N mt < N mt 1. Case 3, net exit: N it < N it 1 if π n it 0 and π n+1 it < 0 We do not observe Case 3 for fibre.

10 Entry/exit transition matrix for fibre fibre t / fibre t , Number of municipalities with {0,1,2,3} fibre operators in periods t and t 1.

11 Entry/exit transition matrix for LLU LLU t / LLU t , ,806 17, ,113 8,188 28, , , , Number of municipalities with {0,1,2,3,4,5,6,7} LLU operators in periods t and t 1.

12 Model of entry: probability function The probability of observing N it = n entrants in market i at time t: Pr(N it = n) = Φ(π n it SC I + it ) Φ(πn+1 it SC (I + it + I 0 it)) where Φ(.) denotes the cumulative normal distribution function, and I + it I (N it > N it 1 ) are indicator variables to denote whether entry increased (+) or remained constant (0). The parameter vector θ = [α t, β, γ t, µ n, η, σ η ] is estimated by maximizing the following log-likelihood function: LL(θ) = M T N y it ln(pr(n it = n) θ) i=1 t=1 n=1 where y it takes value of 1 if N it = n and zero otherwise.

13 Estimation results: fibre Trend *** *** *** *** *** *** *** *** (0.215) (0.197) (0.206) (0.215) (0.314) (0.319) (0.367) (0.400) Loghh *** *** (0.051) (0.218) Logdensity *** *** *** *** *** *** *** *** (0.164) (0.203) (0.182) (0.173) (0.170) (0.330) (0.296) (0.282) (0.281) (0.280) Logincome *** *** (0.202) (0.866) Share flat *** *** (0.231) (0.915) Unempl (0.020) (0.075) ULL entrants *** *** (0.041) (0.121) VDSL *** *** (0.104) (0.191) Cable *** *** (0.083) (0.208) Cut *** *** (2.140) (9.239) Cut *** *** (2.141) (9.253) Cut *** *** (2.150) (9.328) Sigma *** (2.174) Observations 180, ,330 LL

14 Estimation Results: LLU Model I y2011 y2012 y2013 y2014 Model II y2012 y2013 y2014 y2014 Trend *** *** *** *** *** *** *** *** (0.053) (0.052) (0.052) (0.051) (0.076) (0.079) (0.084) (0.086) Loghh *** *** (0.007) (0.044) Loghhdensity *** ** *** *** *** *** *** (0.014) (0.014) (0.014) (0.014) (0.013) (0.052) (0.020) (0.021) (0.021) (0.021) Logincome *** *** (0.041) (0.255) Share flat *** *** (0.046) (0.287) Unempl *** *** (0.003) (0.015) Cut *** *** (0.417) (2.607) Cut *** *** (0.418) (2.610) Cut *** *** (0.419) (2.619) Cut *** *** (0.419) (2.626) Cut *** *** (0.420) (2.638) Cut *** *** (0.426) (2.651) Sigma *** (0.476) Observations 180, ,330 LL

15 Estimation results Positive impact of market size and population density on fibre and LLU entry. Over time, it becomes more profitable to deploy fibre in smaller and less densely populated areas (smaller for LLU). More entry in areas with a higher share of flats (lower cost). More entry in richer areas (higher demand). Fibre entry influenced by the presence of competing technologies: Positive impact of the number of LLU operators (quality differentiation). Negative impact of VDSL (supply substitution). Positive impact of upgraded cable (defense strategy intense marketing by fibre operators).

16 Fibre entry thresholds status quo with LLU Year Entrant 1 Entrant 2 Entrant , , , ,562 79, , ,328 72, , ,690 59, , ,785 48, ,226 without LLU Year Entrant 1 Entrant 2 Entrant , , , ,222 86, , ,986 81, , ,394 68, , ,982 57, ,053

17 LLU entry thresholds Year Entrant 1 Entrant 2 Entrant 3 Entrant 4 Entrant 5 Entrant ,112 8,759 24, , , ,494 18, , , ,005 13,765 92, , ,529 9,702 65, , ,948 8,330 58, ,253

18 Conclusions Fibre and LLU entry is determined by: Demand factors: market size, income, unemployment Cost factors: density of population Fibre entry is influenced by the presence of competing technologies: Positive impact of the number of LLU operators Negative impact of VDSL Positive impact of cable Entry thresholds for fibre and LLU vary substantially across markets: Average thresholds decrease over time Thresholds for LLU are much lower than for fibre Many areas are and will remain unprofitable for fibre deployment