The document summarizes an economic analysis of competition in the Swiss fixed telephony market between 2004-2012. It presents a model to estimate the residual demand curve faced by the incumbent firm, Swisscom, taking into account supply from competitive fringe firms. The analysis finds that Swisscom behaved largely competitively over this period, with an estimated price elasticity of -0.124 for its voice traffic, indicating it did not maximize profits by restricting output. This suggests existing limited price regulation on Swisscom's fixed-to-fixed calls could potentially be lifted given the competitive market conditions. The dynamic residual demand model allows estimating both the immediate and long-term effects of various demand and cost

1. Competition and Market Strategies in the
Swiss Fixed Telephony Market
An estimation of the incumbent’s
dynamic residual demand curve
Roberto Balmer
Bundesamt für Kommunikation
Disclaimer: The views presented here are those of the author and do not reflect those of BAKOM.
International Telecommunications Society
Regional Conference Europe 2015, Madrid
25 June 2015, “Demand” session
Link to working paper

2. 2
Market and Assumptions (1/2)
The question:
• Retail fixed telephony regulation lifted in many European States
• Sufficient competition?
• Demand/supply model for Switzerland 2004-2012
The challenge:
• Highest frequency data available is usually quarterly (few data points)
• Complexity of market model strongly limited
Strong simplifications:
1) No vertical integration issues:
Identical network origination costs are assumed across competitors (access at
efficient marginal costs for access seekers and infrastructure-based operators). LRIC
prices therefore assumed to be close to marginal cost (exogenous).
2) Two-way access:
a) Regulated fixed termination rates (LRIC) are close to zero (<1€cent/min). No fixed
termination costs & revenues modeled.
b) Mobile termination rates are unregulated in Switzerland. Rate modeled as
exogenous cost shifters for fixed operators (>10€cent/min).
1. Introduction 2. Model 3. Variables 4. Conclusions

3. 3
Market and Assumptions (2/2)
3) Marginal costs
are assumed to increase with output (customer care, capacity constraints
in backhaul) in the segment under consideration
4) Fixed-mobile substitution
is assumed to be limited (mobility, prices) &
fixed divisions of fix-mobile operators act to independently
5) VoBB
is assumed to not be a substitute (QoS)
6) Market for Access
is abstracted from (inelastic demand, high penetration, no price changes).
 Fixed fee is abstracted from as not critical. No waterbed effects expected
as FTR is assumed to be zero.
 Only a average linear tariff for national fixed originated traffic is considered
(market ARPM).
1. Introduction 2. Model 3. Variables 4. Conclusions

4. 4
Traditional model assuming a particular way how firms compete:
• Dominant firm takes the reaction of the fringe firms into account when deciding on
quantity and price
• Fringe firms are assumed to be pure price takers ( )
• Fringe supply is therefore given by sum of fringe marginal cost curves.
• Incumbent Swisscom maximizes profitability with Q and P such that
MR(residual demand)=MC (Swisscom)
Fcp 
Dominant firm – Competitive Fringe (1/3)
1. Introduction 2. Model 3. Variables 4. Conclusions
DM is the market demand curve (Q is total
demand)
𝑆 𝐹 is the fringe supply curve (𝑄 𝐹 is fringe supply)
𝐷 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙 is the residual demand curve that the dominant
firm faces (𝑄 𝐷 is dominant firm demand)
𝑀𝑅 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙 is the residual marginal revenue curve the
dominant firm faces
𝐶 𝐷 is the dominant firm’s marginal costs
𝑃 is the unique market price

5. 5
Dominant firm – Competitive Fringe (2/3)
• Market demand
• Fringe supply depends on marginal costs which in turn depend on the level of output
& possible cost shifters (unique to fringe firms or common with incumbent).
• Inverse Dominant firm supply (MR (residual demand) =MC (Swisscom)):
expressed in log-linear (isoelastic) form:
Supply relation not structurally changed with λ<1 (constant)
• Dominant firm residual demand:
expressed in log-linear (isoelastic) form:
1. Introduction 2. Model 3. Variables 4. Conclusions
dominant firm
generalised, λ<1 is less
dominant behaviour

6. 6
Model equation 1 (first stage):
incumbent cost shifters include its number of staff. Instrumented prices are then used to
obtain the incumbents residual demand
Model equation 2 (second stage):
λ cannot be estimated in traditional models (need demand rotator or similar). The
particular estimates of this model allow, however,to infer a higher bound.
Dominant firm – Competitive Fringe (3/3)
1. Introduction 2. Model 3. Variables 4. Conclusions

7. 7
Variable Definition
𝑝 Price; average revenue per outgoing minute (subscription revenues excluded), deflated
by CPI (100=2006), traditional and proprietary VoIP
𝑞 𝐷 Swisscom’s fixed national outgoing calling minutes (to fixed and to mobile)
𝑥1 Income; real GDP per capita (100=2006)
𝑥2 Number of active telephony lines (PSTN)
𝑥3, 𝑥3, 𝑥4
Quarterly dummies for quarters 2, 3 and 4
𝑤 𝐹1 Number of wholesale ADSL lines sold by Swisscom
𝑤 𝐹2 Number of ULL access lines sold by Swisscom
𝑤 𝐶1
Average regional fixed voice origination prices per minute deflated by consumer price
index (CPI) (100=2006)
𝑤 𝐶2
Weighted average of mobile termination rates deflated by CPI (100=2006)
𝑤 𝐶3
Interest rates on 30 year bonds of the Swiss Confederation
𝑤 𝐶4
Exchange rate EUR/CHF
𝑤 𝐷1 Number of staff working for Swisscom
𝑤 𝐷2 Number of active retail ADSL lines (Swisscom)
Demand shifters:
Fringe supply shifters:
Common supply shifters:
Dominant firm supply shifters:
Variables (1/2)
1. Introduction 2. Model 3. Variables 4. Conclusions

8. 8
Econometrical problems
• Variables p and q decline over time (non-stationarity)
• Errors are serially correlated
Solution
To avoid spurious regression results: ARDL(1,1)
• Sufficient level of cointegration
• No serial correlation
Variables (2/2)
1. Introduction 2. Model 3. Variables 4. Conclusions

9. 9
• ARDL(1, 1) implies that a change in an independent variable has a direct same period effect
(coefficient; „impact multiplier“) and longer and long term effects („long term multiplier“). A
dynamic residual demand function of the incumbent is therefore estimated:
• The long term multiplier can be interpreted as long term cumulative elasticities (as a log linear
function is used).
• Multiplier effects from t to t+k:
-0.300
-0.200
-0.100
0.000
0.100
0.200
0.300
0.400
q t+1 quarter t+2 quarter t+3 quarter t+4
Results (1/2)
exchange rate
mobile termination
ADSL wholesale lines
Price
Regulated origination rates
Interest rates
Long term effects on Swisscom‘s residual demand:
- Price (-0.124)
Demand shifters
- Income elasticity (1.5)
- PSTN lines (2.4)
Fringe cost shifters
-ULL lines (-0.002)
-ADSL wholesale lines (-0.158)
Common cost shifters
-Mobile termination rates (0.141)
-Interest rate (-0.035)
-Exchange rate (0.473)
-Origination price (-0.051)
ULL lines
1. Introduction 2. Model 3. Variables 4. Conclusions

10. 10
• Most importantly price elasticity for Swisscom voice traffic is shown
to be very low (-0.124)
• 10% market price increase corresponds to a decline in Swisscom traffic by 1.24% (long
term).
• Such a low elasticity is incompatible with a profit maximising dominant firm (λ=1) as
MR is negative (MC positive).
• When λ<0.12, MR is positive again (conduct is compatible with profit maximisation)
Conclusion:
• Incumbent Swisscom acted largely competitively (λ=0 is price taking)
• Todays very limited price regulation (Swisscom F2F calls) could be lifted.
Variable
ARDL (1,1) estimates
Coefficient
estimate
Robust Std.
err.
P>| t |
p -0.044 0.044 0.321
L1 -0.102 0.049 0.038
LR -0.124
Results (2/2)
1. Introduction 2. Model 3. Variables 4. Conclusions

11. 11
Questions?
Dr. Roberto Balmer
Bundesamt für Kommunikation
TC / Sektion Ökonomie
Zukunftstr. 44
2501 Biel
Switzerland
Tel. +41 32 327 56 43
roberto.balmer@bakom.admin.ch
linkedin.com/in/RobertoBalmer
slideshare.net/RobertoBalmer
amazon.com/author/roberto.balmer
ssrn.com/author=572707
1. Introduction 2. Model 3. Variables 4. Conclusions

12. 12
Backup
1. Introduction 2. Model 3. Variables 4. Conclusions

13. 13
Structure – Conduct – Performance
Source: Davis, Quantitive Techniques for Competition and Antitrust Analysis, Chap. 6
• The pre-Game Theory IO world was dominated by the SCP paradigm (50s)
• Causality between market structure, competition and performance (prices, profits, welfare)
• Bain (1950): Regresses profit on market structure (HHI) finding positive coefficients; interprets
as direct causality.
• SCP is obsolete. Conduct may depend on numerous factors.
• Example: A monopolist may non act like a monopolist but in the extreme case like a firm
under perfect competition in case of low barriers to entry.
• Structural parameters continue to play an important role in market analyses as indicators for
competition (e.g. number of firms, market shares, HHI). But challenge is to measure conduct.
1. Introduction 2. Model 3. Variables 4. Conclusions

14. 14
New Empirical Industrial Organization
• SCP is «old IO», «NEIO» is coherent with game theory; Example
• Structural demand/supply model can allow to directly measure the level of
competition / conduct with sufficient data on prices, volumes, etc.
• Inverse demand: (1)
• Marginalkostenfunktion
• Supply; F.O.C. of profit maximisation problem. Parameter lamda can nest PC,
Cournot or Cartel:
F.O.C.
• Lamda=«conduct»
• Therefore supply relation: (2) where
Quelle: Bresnahan 1982. “The oligopoly solution concept is identified”
1. Introduction 2. Model 3. Variables 4. Conclusions

15. 15
• Simultaneous equations (1) und (2)
(1)
(2)
• Identification as usual: An equation is identified if at least one variables is excluded of it. I.e.
if a variable only shifts demand (e.g. income), then supply is identified and vice versa (e.g.
input prices).
• In this case parameters can be estimated (including demand elasticities etc.). BUT:
Estimation of gamma does not allow any inference on Lamda (conduct/level of competition).
The Solution: Rotation
1) When Beta1 is known (i.e. the cost function), Gamma can be calculated. This is also the
case when MC are assumed to be constant (Beta1=0).
2) If next to cost and demand „shifters“ and demand „rotators“ (Z) are available (i.e. Variables
that change both the level as well as the slope of the demand function), estimation of Gamma
becomes possible in any case.
New Demand (1’)
New Supply includes now
interaction term (2’)
-Tests can now tell which form of competition / conduct best explains the data. E,g, econometric
test for Lamda=0 becomes possible (perfect competition)
- Such regressions are still rare in NRA/NCA practice, but approach is promising
New Empirical Industrial Organization
1. Introduction 2. Model 3. Variables 4. Conclusions