Collusion in the French mobiletelecoms market: a theoretical     and empirical analysis      Roberto Alimonti, Francesco R...
Outline of the presentation•  Overview of the European mobile telecoms  market•  Collusion in the French mobile market  • ...
Mobile market overviewDemand side•    Penetration rate      (121.9%)      •    Volumes of calls & data     •    Supply sid...
Mobile market overviewDemand side•    Penetration rate      (121.9%) •    Volumes of calls & data     •    Supply side    ...
Mobile market overviewDemand side•    Penetration rate      (121.9%) •    Volumes of calls & data     Supply side•    3-4 ...
Mobile market overviewDemand side•    Penetration rate      (121.9%) •    Volumes of calls & data     Supply side•    3-4 ...
Mobile market overviewDemand side•    Penetration rate      (121.9%) •    Volumes of calls & data      Supply side•    3-4...
Mobile market overviewDemand side•    Penetration rate      (121.9%)•    Volumes of calls & data     Supply side•    3-4 o...
Mobile market overviewDemand side•    Penetration rate     (121.9%) •    Volumes of calls & data    Supply side•    3-4 op...
Collusion in the French market (1/2)Parties and facts•    In August 2001, the Coinseil de la Concurrence (French     CA) f...
Collusion in the French market (2/2)•    Evidence of constant     mkt shares during     (and after) the cartel•    Price i...
Collusion in theory: ability (1/2)Factors that facilitate collusion•    Number       of      operators     and     concent...
Collusion in theory: ability (2/2)Factors that facilitate collusion•    Evolution of demand: slightly increased during the...
Collusion in theory: incentives•  Firms involved in the cartel have different mkt  shares•  The smallest (biggest) firm wou...
Incentive comparison (1/2)• Mkt shares before and during the cartel are                                    Cartel      Sta...
Incentive comparison (2/2)• Positiveand Stackelberg competition:collusion           difference between profits under➡  Ther...
Empirical evidence•    Time series econometric analysis, French mobile market 2000-2010•    Two models, with and without c...
Empirical evidence•    Time series econometric analysis, French mobile market 2000-2010•    Two models, with and without c...
Empirical evidence•    Time series econometric analysis, French mobile market 2000-2010•    Two models, with and without c...
Summary statistics                             Average   Average   WholeIndependent Variable
                             ...
Results                                 Coefficient           Coefficient Independent Variable
                             ...
Conclusions•  Evidence of ability and incentives to collude•  Empirical evidence of no change in revenues  due to the end ...
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Collusion in the French mobile telecomes market: a theoretical and empirical analysis

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Master project, Competition and Market Regulation 2011

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Collusion in the French mobile telecomes market: a theoretical and empirical analysis

  1. 1. Collusion in the French mobiletelecoms market: a theoretical and empirical analysis Roberto Alimonti, Francesco Rieppi Master in Competition and Market Regulation Master Project
  2. 2. Outline of the presentation•  Overview of the European mobile telecoms market•  Collusion in the French mobile market •  Collusion in theory •  Empirical evidence•  Conclusions
  3. 3. Mobile market overviewDemand side•  Penetration rate (121.9%) •  Volumes of calls & data •  Supply side •  3-4 operators •  Mkt concentration (MVNOs) •  Average price (MTRs) •  Revenues (mostly from data)
  4. 4. Mobile market overviewDemand side•  Penetration rate (121.9%) •  Volumes of calls & data •  Supply side •  3-4 operators •  Mkt concentration (MVNOs) •  Average price (MTRs) •  Revenues (mostly from data)
  5. 5. Mobile market overviewDemand side•  Penetration rate (121.9%) •  Volumes of calls & data Supply side•  3-4 operators •  Mkt concentration (MVNOs) •  Average price (MTRs) •  Revenues (mostly from data)
  6. 6. Mobile market overviewDemand side•  Penetration rate (121.9%) •  Volumes of calls & data Supply side•  3-4 operators•  Mkt concentration (MVNOs) •  Average price (MTRs) •  Revenues (mostly from data)
  7. 7. Mobile market overviewDemand side•  Penetration rate (121.9%) •  Volumes of calls & data Supply side•  3-4 operators•  Mkt concentration (MVNOs)•  Average price (MTRs) •  Revenues (mostly from data)
  8. 8. Mobile market overviewDemand side•  Penetration rate (121.9%)•  Volumes of calls & data Supply side•  3-4 operators•  Mkt concentration (MVNOs)•  Average price (MTRs, flat-rates) •  Revenues (mostly from data)
  9. 9. Mobile market overviewDemand side•  Penetration rate (121.9%) •  Volumes of calls & data Supply side•  3-4 operators•  Mkt concentration (MVNOs)•  Average price (MTRs, flat-rates)•  Revenues (mostly from data)
  10. 10. Collusion in the French market (1/2)Parties and facts•  In August 2001, the Coinseil de la Concurrence (French CA) found an hardcore cartel in the national mobile market•  Parties: Orange, SFR and Bouygues Telecom •  1997-2003: proof of exchange of strategic information (new subscriptions and cancellations of previous month) •  2000-2002: agreement to stabilize mkt shares ➡  Information exchanged used as a monitor device
  11. 11. Collusion in the French market (2/2)•  Evidence of constant mkt shares during (and after) the cartel•  Price increase & adoption of particular commercial strategies (i.e. billing per 30-seconds after first min of call)
  12. 12. Collusion in theory: ability (1/2)Factors that facilitate collusion•  Number of operators and concentration: only 3 firms from 1996; high HHI (MVNOs only have 7.5% mkt shares) coordination and monitoring is easier•  Presence of barriers to entry •  High fixed and sunk costs •  Spectrum allocation (not frequent) •  Network effects (customer base) •  Switching costs more easy to collude
  13. 13. Collusion in theory: ability (2/2)Factors that facilitate collusion•  Evolution of demand: slightly increased during the cartel deviation is less profitable; more easy to monitor•  New subscribers, calls and data consumption is very frequent and regular easier to monitor and retaliate•  Multi-mkts contacts: operators are present both on mobile and fixed segment, in several national mkts. •  Moreover, Orange and SFR (Vodafone) have symmetrical mkt shares at an aggregate level•  Countervailing buyer power: low in retail mkt, but higher in wholesale mkt (i.e. Tele2 Mobile, Carrefour, Auchan, Virgin and Universal Music) more difficult to collude
  14. 14. Collusion in theory: incentives•  Firms involved in the cartel have different mkt shares•  The smallest (biggest) firm would not have incentives to collude with Cournot competition and equal (unequal) costs•  Collusion can be explained with a Stackelberg model
  15. 15. Incentive comparison (1/2)• Mkt shares before and during the cartel are Cartel Stackelberg Mkt shares Costs Profits
  16. 16. Incentive comparison (2/2)• Positiveand Stackelberg competition:collusion difference between profits under➡  Therefore, all the firms have incentives tocollude
  17. 17. Empirical evidence•  Time series econometric analysis, French mobile market 2000-2010•  Two models, with and without costs•  Dependent variable: monthly revenues per subscription (no internet revenues)•  Independent variables: competition and mobile sector variables, costs
  18. 18. Empirical evidence•  Time series econometric analysis, French mobile market 2000-2010•  Two models, with and without costs•  Dependent variable: monthly revenues per subscription (no internet revenues)•  Independent variables: competition and mobile sector variables, costs
  19. 19. Empirical evidence•  Time series econometric analysis, French mobile market 2000-2010•  Two models, with and without costs•  Dependent variable: monthly revenues per subscription (no internet revenues)•  Independent variables: competition and mobile sector variables, costs
  20. 20. Summary statistics Average Average WholeIndependent Variable
 2000
 2010
 Period
 Monthly revenues per 26.63
 19.5
 23.82
 subscriber (MREV)
Monthly voice volume per 116
 136
 137
 subscriber (VOC)
Monthly SMS volume per 5
 137
 38
 subscriber (SMS)
 Herfindahl index (HHI)
 3813
 3252
 3634
 Termination rate (TR)
 0.296
 0.026
 0.14
 Cost of labour (LAB)
 81.5 
 87.7
 83.97
 Cost of energy (ENE)
 0.0553
 0.0552
 0.0512

  21. 21. Results Coefficient Coefficient Independent Variable
 (Standard Error)
 (Standard Error)
Monthly voice volume per 0.071** (0.019)
 0.067** (0.020)
 subscriber (VOC)
Monthly SMS volume per -0.002 (0.006)
 -0.003 (0.010)
 subscriber (SMS)
 Herfindahl index (HHI)
 0.005*** (0.001)
 0.005*** (0.001)
 Termination rate (TR)
 20.201*** (4.059)
 21.92*** (4.777)
 Cartel (CRT)
 -0.306 (0.555)
 -0.325 (0.562)
 Financial crisis (FIN) 0.105 (0.261)
 0.082 (0.256)
 Cost of labour (LAB)
 -
 0.081 (0.204)
 Cost of energy (ENE)
 -
 -36.036 (31.207)
 Constant 
 -8.059 (5.033)
 -11.506 (16.716)
Number of observations
 44
 44

  22. 22. Conclusions•  Evidence of ability and incentives to collude•  Empirical evidence of no change in revenues due to the end of the cartel•  Likely effectiveness of the cartel before 2003 suggest tacit collusion was the outcome after that year•  Research based on international comparison suggested to check the effects of tacit collusion

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