termination rate debate in africa · mobile penetration rates and mobile retail prices in a country...
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Termination Rate Debate in Africa
Dr. Christoph Stork
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Termination Rates: Monopoly = Cost based
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Dominant Operators will argueThey use termination revenue to subsidise access and usage (Two sided market or waterbed effect argument)
If MTRs are lowered:Retail prices will increase
There will be less subscribers
Operators will invest less
However, the opposite is the caseIncreased competition leads to lower retail prices and more subscribers
Operators have to invest more to stay competitive
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Mobile Termination Rates 2009 in US cents
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Mobile Termination Rates 2009 in US cents
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Two-sided Market and Waterbed Effect
Fail to predict market outcomes correctly
Cannot be empirically observed following termination rate cuts
Retail and wholesale prices are not interdependent for several reasons:
Termination rates are not prices but contractual arrangements that rarely changes
Operators cannot increase MTR because its market share increased
MTRs are wholesale costs and wholesale revenue
Operators have choices : MTR reductions can be passed on or not
Operators can set only their own retail prices and not those of other operators: off-net prices will influence how many calls are being received
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Pro Waterbed EffectsAn argument put forward is that the waterbed effects exists but it is masked by other developments such as increased competition and decreasing unit costs and can hence not be observed with the naked eye
Why should policymakers pay attention to the waterbed effects if so small that one needs advanced econometric techniques to find it?
Questionable evidence from panel data studies
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1) Constructing data sets with enough data points to account is impossible
Mobile penetration rates and mobile retail prices in a country depend on many factors:
Number of fixed and mobile operators
sequence of market entry
technologies deployed, market share of operators
user profiles of subscribers
brand loyalty
contractual lock-ins and club effects
price elasticity of demand
income elasticity of demand
levels of disposable income
business models used by operators
penetration of substitute technologies like fixed-line and cable TV
past regulatory interventions and their sequence
regulatory strategies
communication laws and policies and many other social and economic factors.
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2) Omitted variables may render models invalid
: An example is the paper by Sandbach and Hooft (2009), which tries to estimate the impact of telecommunication policies on mobile penetration and usage without including prices in its models. Including prices, which are undoubtedly significant factors in explaining access and usage in economic theory, could lead to changes in significance levels and coefficients, or even signs of coefficients. Data sets constructed for panel studies are unlikely to capture even the most important variables.
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3) Retail prices used for modelling only prices of dominant operators
Several studies attempt to show that if termination rates are being reduced retail prices will increase using OECD basket methodology (e.g. CEG 2009 and Genakos & Valletti 2009)
OECD price baskets methodology only captures the retail prices of dominant operators (together 50% market share)
Including smaller operators would indicate price changes following regulatory interventions better
Dominant operators are less likely to change retail prices than new entrants
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GhanaTanzania
KenyaNigeria
Ethiopia*Rwanda
BeninBotswana
TunisiaNamibiaSenegalUgandaZambia
Côte d’IvoireMozambique
South AfricaCameroon
Burkina Faso 11.04
8.59
7.64
7.45
7
6.57
6.33
6.12
5.06
5.06
5.04
4.92
3.74
3.74
3.63
3.35
2.93
2.29 GhanaTanzania
KenyaNigeria
Ethiopia*Rwanda
BeninBotswana
TunisiaNamibiaSenegalUgandaZambia
Côte d’IvoireMozambique
South AfricaCameroon
Burkina Faso 12.54
9.3
7.64
7.45
8.15
6.6
6.95
6.12
8.96
5.06
5.04
7.5
6.87
3.74
7.76
5.93
7.26
3.04
Cheapest operator Dominant operator
GhanaTanzania
KenyaNigeria
Ethiopia*Rwanda
BeninBotswana
TunisiaNamibiaSenegalUgandaZambia
Côte d’IvoireMozambique
South AfricaCameroon
Burkina Faso 12.54
9.3
10.36
8.32
9.54
8.18
7.04
7.52
8.96
5.36
6.66
8.81
6.87
3.74
7.76
5.93
7.26
3.15
Most expensive operator
Cheapest Prepaid product in country in US$Feb 2010
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3) Lower EBITDA margins following MTR cuts are not proof for waterbed effect
To be expected for some operators since lower termination rates increase competition and lead to lower, not higher, retail prices, though traffic may increase as a result
Operators that were shielded by high termination rates from competition could decline under competitive pressure if business models are not being adjusted
Dominant operators may have lower EBITDA margins others may have higher as a result of termination rate cuts
Lower EBITDA margins of operators from one country compared to EBITDA margins of operators from another country based on the level of MTR is no proof for waterbed effect but for profit form anti-competitive practices
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Alternative 1: Panel data model based on operators not countries
Incorporate all operators of a country
Increase the data available by a factor of 3 or 4
allows to include significant explanatory variables such as market share and year of market entry
The waterbed effect is a hypothesis about the pricing strategies of operators and as such need to be tested at the operator level
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Alternative 2: Case Studies
A less econometrically sophisticated but more plausible: Did Vodafone UK increase its retail prices after any MTR reduction in the UK?
And how did the smaller operators or the net-interconnect-payers react?
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Kenya
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MTR
Mar 2007 Mar 2008 Mar 2009 July 2010 July 2011 July 2012 July 2013
1.201.401.752.70
5.376.40
7.60
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Retail Prices
Jan-10 Sep-10 Jan-11
1.962.24
0
1.901.91
4.12
1.14
2.09
6.15
2.46
6.70
7.75
Safaricom* Airtel Orange Yu
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Safaricom’s key performance indicators for financial years ending in March
2007 2008 2009 2010
Revenue billion KES 47.45 61.37 70.48 83.96
Subscribers in million 6.10 10.23 13.36 15.79
EBITDA Margin 51.7% 45.9% 39.6% 43.6%
After-tax profit in billion KES 12 13.85 10.54 15.15
Dividend paid in billion KES 3 2 4 8
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Namibia
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Termination Rates US cents
Jan 2009 July 2009 Jan 2010 July 2010 Jan 2011
4.105.50
6.808.20
9.00
4.105.50
6.808.20
14.40
MTR FTR
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Retail Prices of MTC in US$ for OECD (2006) baskets
Low User Medium User High User
9.24.84.8
13.26.86.8
19.9
6.86.8
24.416.2
10.8
40.4
23.7
11.3
Sep-05 Dec-08 May-10 Mar-11Mar-11 (2005 prices)
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MTC key performance indicators
2005 2006 2007 2008 2009 2010
Subscribers in million 0.40 0.56 0.74 1.00 1.28 1.53
EBITDA Margin 61% 60.2% 52.2% 50.9% 53.8% 55.8%
After-tax profit millionUS$ 39.90 45.94 46.27 48.53 52.79 54.10
Dividend paid in million US$ 14.99 10.90 33.38 30.11 50.41 52.26
Dividend payment as share of after tax profit 37.6% 23.7% 72.1% 62.0% 95.5% 96.6%
Tax payments in million US$ 19.96 23.35 24.11 24.62 27.10 25.5
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MTNNigeria, South Africa, Botswana
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!"#$%&'""$&#Large reduction in MTR in Nigeria and RSA
• MTR changes in Nigeria and RSA
• RSA peak rate dropped from ZAR 1.25 to
Large reduction in MTR in Nigeria and RSA
(%')*+,"#$%&'""$&#(ZAR bn)
p ppZAR 0.89
• Nigeria peak rate dropped from 11.4 Naira to 8.2 Naira
-../ 0/12
3'4# 5$6$")$
70810• Interconnect revenue decreased 13%
• RSA revenue decreased 10%
• Nigeria revenue decreased 25% in local currency2 8 9:%;,"+< currency
• Net interconnect margin increased from 28% to 32%
• Higher on net traffic offset some of the
218 9:%;,"+<
-=<
• Higher on-net traffic offset some of the interconnect rate decline
• RSA prepaid on-net improved 7pts to 61%
• Nigeria total on-net increased by 4pts to 83%
-.0. 0>1.70012
• Nigeria total on net increased by 4pts to 83%
212 9:%;,"+<
?-<?-<
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High User OECD usage baskets in US$
MTN South Africa Mascom MTN Nigeria
36.544.4
68.4
36.544.4
68.457.9
44.4
68.460.7
46.6
68.4
US
cent
s
Feb 2010 Jan 2011 Feb 2011 Mar 2011
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ConclusionNo Waterbed Effect in Namibia, Kenya, Botswana, South Africa or Nigeria
Two-sided market argument can clearly be rejected
Retail prices decrease after termination rate cuts
Operators pursue different pricing strategies
Cost based termination rates lead to more competition:more subscribers
more traffic
more investment
bigger pie of revenues to share among operators