Dr. Christoph Stork

1. Measuring ICT Sector
Performance
Dr. Christoph Stork

2. Research ICT Africa
Year Project
African
Countries
2003
2004
2005
2006
2007
2009
ICT Sector Performance Review 7
Household e-Access & e-Usage Survey 10
SME e-Access & e-Usage Survey 15
ICT Sector Performance Review 17
Household e-Access & e-Usage Survey 17
ICT Sector Performance Review 18

3. ICT Sector Performance

4. Tools
Supply Side:
Annual reports from Operators
OECD Price Baskets
National Accounts
Demand Side
User Surveys (Household, SMEs, Institutions)
Telecom Regulatory Environment Survey (TRE)

5. Impact of regulatory interventions
Demand Side Supply Side
Access
UsageUsage
Affordability
Operator
Performance
Operator
Performance
Operator
Performance
Operator
Performance
Economy
Ownership
(rural urban, gender, income)
Subscribers
Expenditure (total, share of
disposable income)
Average revenue per
user (ARPU)
No of SMS send and minutes
called
Minutes of Use (MOU)
Price elasticity
WTP
OECD Price baskets
ARPU/MOU
Investment
EBITDA Margin
Return on Equity
Revenue
Business Survey GDP
contribution and employment
National accounts GDP
contribution and
employment

6. Tool: Surveys
Untapped demand: WTP of non-users
Income elasticity of demand of users
Multiple SIM card ownership
Internet adoption: with focus on mobile internet
Mobile money transfer adoption and m-banking
Employment generation and GDP contribution of
SMEs and informal operators
ICT access function of public institution

7. Nationally Representative Surveys

8. 2007/2008
Current
market in
US$
million
Monthly
untapped
market in US
$ million
Untapped
market as
percentage of
current
market
Nigeria*
South Africa
Namibia
Mozambique
Botswana
Kenya
Zambia*
Senegal
Ghana
Côte d'Ivoire
Cameroon
Tanzania
Benin
Burkina Faso
Ethiopia
686.54 65.25 9.5%
320.49 36.27 11.3%
7.14 1.35 19%
30.47 6.7 22%
6.67 1.47 22.1%
112.11 25.69 22.9%
25.96 8.2 31.6%
27.54 11.33 41.2%
78.23 38.4 49.1%
63.13 31.44 49.8%
21.29 13.14 61.7%
30.79 21.42 69.6%
11.38 8.26 72.6%
10.77 13.71 127.3%
5.29 25.68 485.7%
Willingness and ability
to spend on
communication of
none-users exceeds
current market in some
countries
AFRICANS are price
sensitive and will talk
more if prices are
lowered
Mobile Expenditure and WTP

9. Namibia’s digital divide at a
household level (RIA 2004 & 2007 surveys)
! ! ! !
! ! ! !
! ! ! !
! ! ! !
! ! ! !
! ! ! !
Households
with
working...
NamibiaNamibiaNamibia UrbanUrbanUrban RuralRuralRural
Urban Rural
Difference
Urban Rural
Difference
Urban Rural
Difference
Households
with
working... 2004 2007 2004 2007 2004 2007 2004 2007
Electricity 34% 47% 78% 81% 6% 24% 72% 57%
Fixed-line 13% 17% 31% 32% 2% 8% 29% 24%
Radio 77% 73% 88% 82% 70% 66% 18% 16%
TV 31% 38% 71% 72% 5% 15% 66% 57%
Computer 5% 11% 11% 23% 0.4% 4% 11% 19%
Internet 2% 3% 4% 7% 0.0% 1% 4% 6%

10. Tool: OECD Mobile Basket
Methodology
OECD Basket
Methodology
Comparing
Countries
Comparing
Operators
Comparing
Products

11. Comparing Countries
Comparing cheapest product available from
dominant operators
cheapest operator
most expensive operator
Comparing the difference between
cheapest in country - cheapest from dominant operators
cheapest in country - cheapest from most expensive
operator

12. Ghana
Tanzania
Kenya
Nigeria
Ethiopia*
Rwanda
Benin
Botswana
Tunisia
Namibia
Senegal
Uganda
Zambia
Côte d’Ivoire
Mozambique
South Africa
Cameroon
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 Ghana
Tanzania
Kenya
Nigeria
Ethiopia*
Rwanda
Benin
Botswana
Tunisia
Namibia
Senegal
Uganda
Zambia
Côte d’Ivoire
Mozambique
South Africa
Cameroon
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
Ghana
Tanzania
Kenya
Nigeria
Ethiopia*
Rwanda
Benin
Botswana
Tunisia
Namibia
Senegal
Uganda
Zambia
Côte d’Ivoire
Mozambique
South Africa
Cameroon
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$ Low OECD user basket

13. Example:
Towards Cost based
Mobile Termination Rates
Namibia, Kenya, South Africa

14. Cost based Mobile
Termination rates
Economics:
Increased
Competition
Lower Retail Prices
More Investment
Better Sector
Performance
Dominant
Operators Argue:
Higher retail prices
Lower Profits
Less investment

15. Cheapest product available of
incumbent (MTC) in Namibia
Low User Medium User High User
106
3636
146
5050
179
119
79
296
174
83
N$/ZAR
Sep 2005 Dec 2008 May 2010 May 2010 (2005 prices)

16. Performance of incumbent
mobile operator in Namibia: MTC
2005 2006 2007 2008 2009
Subscribers 403,743 555,501 743,509 1,008,658 1,283,530
EBITDA Margin 61% 60.2% 52.2% 50.9% 53.8%
After tax Profit million N$ 292.9 337.2 339.6 356.2 387.5
Dividend paid in million N$ 110 80 245 221 370
Base Stations
250
(2004)
763
Investments announced into 4G LTE and WACS (N$400 million)

17. Regulation of Market Entry
Scarce Resources
Interconnection & facilities leasing
Tariff Regulation
Regulation of Anti-Competitive Practices
Universal Service Obligation (USO)
QoS Regulation
Average
-2 -1 0 1 2
-0.5
-0.3
-0.6
-0.6
-0.7
-0.2
-0.3
-0.2
-1.2
-1.1
-1.7
-1.4
-1.1
-0.8
-0.9
2006 2009
Telecommunication Regulatory Environment (TRE)

18. Example Kenya
August 2010: Most innovate
Interconnection ruling in Africa:
Cost based termination rates (pure LRIC)
Off-net=On-Net prices for Dominate operator
Fair commercial agreement on SMS and
money transfer interconnection or else...

19. Safaricom Zain Orange
52.6
67
11.3
54.2
66.8
9.9
55.2
66.6
8.9
OECD Low OECD Medium OECD High
Immediate price drop in %

20. Lowercross-net
tariffshitVodacom
Operator'sreuenttetakesRB00mknock
THABISOMOCHIKO
InformationTechnologyEditor
THEvoluntaryreductionin inter-
connectlonrates to 89c from
R1,25in MarchhaswipedR800m
offVodacom'srevenuefor the si,x
monthsto September.
Theterminationrates- which
operatorspaytocarryeachother's
peak in March 2012,a process
calleda glidepath.
Chief financial officer Rob
ShutersaidVodacomwillcontinue
looking for ways to offset the
expectedreductionin earnings.
Vodacom'sservicesrevenue
rose4,4o/oto R26,09bnwhiletotal
revenuerose 5,17oto R29,5bn.
However,a 41,1o/ogrowthin data
Q5ltt / Z"'io (Svt
?Il]?"Tyfryg,!9.aaco1CEO.P|eterUysspeaksatVoi-a.ini'r-t.ioq'rrrtersinMidra
afthoughtheterminationrateisaffectingearnings,retrenchmentsarenotontheia;;; ;l;i
al
;;if=B
;+
Example South Africa

21. Vodacom Interim Results
Vodacom South
Africa
September
2009
September
2010
Subscribers in million
ARPU ZAR
MOU
APRU/MOU
Operating Profit Rm
Revenue Rm
28.2 23.87
125 162
78 105
1.6 1.54
6,841 7,170
24,371 25,697

22. Conclusion
Assessing sector performance requires a set of
tools
Combination of demand side and supply side
data is crucial to:
Measure regulatory impact
Monitor policy objectives
Protect consumer interests
Provide policy advice