2013 Gillwald - Interconnection 2 CRASA

2013 Gillwald - Interconnection 2 CRASA

Towards an understanding of
ICT access and use in Africa
CRASA Workshop on NGN Interconnection, Lilongwe, March 2013
Dr. Alison Gillwald
Executive Director: Research ICT Africa
Adjunct Professor - University of Cape Town,
GSB, Management of Infrastructure Reform and Regulation Programme
1
Network a
response to
the research
vacuum on the
continent in
relation to
ICT policy
and regulation
and dearth of
capacity to
respond to
redress it.
Context
!
!
!
!
!
!
Demand side indicators as policy outcomes
Market structure and the institutional
arrangements
Users response to services/applications
offered by/on networks at particular price and
quality
3
Household & Individual ICT survey
‣
Lack of data - decision relevant data for
ICT policy making and regulation
‣
PARTNERSHIP ON MEASURING ICT FOR
DEVELOPMENT: delivers all indicators
required by the Partnership for household,
individuals, and businesses
‣
COST EFFECTIVE: Using Enumerator Areas
(EA) of national census sample frames and
samples households, small business
simultaneously minimizes costs.
‣
SCOPE :Apart from delivering ICT
indicators required by international bodies
the survey delivers data and analysis for
several regulatory functions such as
pricing regulation, number portability and
universal access.
‣
LINKAGES:explains interactions between
households, individuals and informal and
small businesses on ICT access and usage.
5
South Africa
1,600
600
2,200
Clustering
Enumerator Areas (EA) national Census
Tanzania
1,200
500
1,700
None Response
Random substitution
Census sample from from NSO
Uganda
1,200
500
1,700
Sample Frame
Tunisia
1,200
500
1,700
Confidence Level
95%
Design Factor
Household
& individual
methodology
Total
15,300 ICT survey
6,200
21,500
Absolute precision
Population Proportion
Botswana
2
1
5%
5%
0.5, for maximum sample size
Minimum Sample
Size
2012 COUNTRIES
95%
768
384
Weighting
Cameroon
Four weights will be constructed, for households, individuals, small
businesses and public institutions. The weights are based on the
inverse selection probabilities1 and gross up the data to national level
when applied.
Ethiopia
Ghana
Mozambique
Namibia
Nigeria
Household weight:
HH w = DW
Individual weight:
INDw = DW
South Africa
Rwanda
Tanzania
Uganda
Sample Size
Business Weight:
Busw = DW
1
PHH * PEA
PHH
1
* PEA * PI
1
PBus * PEAI
The desired level of accuracy for the survey was set to a confidence
n
level of 95% and an absolute precision (relative margin of error) of Household Selection Probability: P =
HH
w
5%. The population proportion P was set conservatively to 0.5 which
HH EA
yields the largest sample size (Lwanga & Lemeshow, 1991). The
minimum sample size was determined by the following equation
HH EA
(Rea & Parker, 1997):
EA Selection Probability: PEA
w = m
HH STRATA
1
See UNSD (2005) page 119 for a detailed discussion on sampling weights.
2!
2011 Brief Survey Methodology
Census and RIA national ICT survey
Census Data
RIA Survey Data
2006
2011
2007
2011
Households with Fixed Line
18,5%
14,5%
18,2%
18,0%
Households with Computer
15,6%
21,4%
14,8%
24,5%
Household with Radio
76,5%
67,5%
77,7%
62,3%
Households with Television
65,5%
74,5%
71,1%
78,2%
35,2%
4.8% (Household)
15.0% (Individual)
19.7% (Household)
33.7% (Individual)
88,9%
62,1%
84,2%
Households with Internet
Cellphone Ownership
72,7%
Africa’s Digital Divide
Household data analysis
General sample statistics of randomly selected individual
!""#$%&'$(
)*$+&,$"-./-*-/0&'"-.12%$"
)*$+&,$"-.12%$"345"666
34"5
♂
)''
♀
♂
)''
!"7-89"&":&.;"
&1120.8
)*$+&,$"
&,$
♀
♂
)''
♀
Botswana
59%
270
340
222
460
579
378
34
48,4
52,4
45,6
Cameroon
52%
72
94
52
145
189
104
33
10,9
10,8
10,9
Ethiopia
45%
27
39
12
69
101
30
34
3,7
4,3
3,0
Ghana
55%
87
117
63
183
244
134
34
29,4
35,5
24,5
Kenya
62%
85
119
64
44,5
57,6
36,4
Namibia
57%
194
279
130
56,3
51,1
60,3
ICT Access and Usage
270 2011
387 Survey
181
40
154
214
116
28
Nigeria
47%
102
151
47
171
252
78
34
30,5
39,8
20,0
Rwanda
50%
28
36
21
57
72
42
30
16,3
17,4
15,2
South Africa
54%
402
617
221
595
914
328
36
58,9
62,7
55,7
Tanzania
54%
35
45
26
89
115
68
34
6,2
7,4
5,1
Uganda
44%
52
59
42
126
144
102
31
15,2
18,7
10,7
!"#$%&'(&)#*+&#,-.*/0&%+1#1%2&*+333&
K;F130;$L$>F>7M$
J=;K9/0/$
E=F;<$
GHB7I/$
C/A7D7/$
C7.3B7/$
@/A3B==0$
%!#!$
%"#+$
2304/$
-./01/$
(#($
)#+$
'#'$
!,#'$
&#,$
%&#)$
)#&$
(#)$
%(#($
!"#"$
'#&$
%,#+$
3A>M=431$
*#&$
!)#($
)#)$
"%#%$
!&#,$
'#%$
%(#)$
(%#&$
*#($
('#)$
%*#%$
(*#+$
(#+$ (#%$
()#"$
%(#)$
%&#!$
"#"$
!!#&$
*+#&$
%!#*$
%&#%$
17K/DM31$/01$F0/DM3$;=$9=BN$
"*#'$
!!#($
%&#&$
%&#&$
%%#+$
!+#!$
!%#"$
'#&$
F03A>M=431$
()#+$
)#'$
%)#"$
%'#%$
B3OB31$
!)#)$
%*#*$
89/01/$
5/06/07/$
(#+$
%&#"$
?</0/$
:;<7=>7/$
F0>/71$9=BN$L$<=FK397H3$
"(#+$
%,#%$
%,#+$
!*#&$
"'#%$
K3MHP3A>M=431$
Table 2 – Gender disaggregated educational sample statistics
Highest Education: Tertiary
♂
!""
Highest Education: Secondary
♀
♂
!""
Highest Education: Primary
♀
♂
!""
♀
20,5%
21,9%
19,4%
53,9%
53,9%
54,0%
18,7%
19,3%
18,2%
Cameroon
7,4%
8,6%
6,2%
22,8%
19,2%
26,2%
30,6%
30,7%
30,6%
Ethiopia
2,1%
2,4%
1,8%
1,8%
1,3%
2,4%
13,1%
16,4%
8,9%
Ghana
10,5%
15,8%
6,2%
36,6%
38,9%
34,8%
27,3%
25,3%
28,9%
Kenya
26,2%
32,7%
22,3%
41,4%
41,1%
41,7%
27,4%
22,8%
30,2%
Namibia
7,1%
8,4%
6,1%
27,8%
24,3%
30,4%
45,2%
42,4%
47,4%
Nigeria
14,8%
19,5%
9,6%
37,8%
40,3%
34,9%
18,7%
18,1%
19,3%
Botswana
Rwanda
1,2%
1,7%
0,7%
15,3%
16,8%
13,7%
58,4%
59,4%
57,4%
South Africa
13,3%
18,0%
9,1%
65,3%
65,8%
64,8%
17,0%
13,2%
20,2%
Tanzania
1,4%
1,5%
1,2%
11,1%
14,9%
7,8%
72,0%
73,3%
70,9%
Uganda
9,1%
11,2%
6,3%
29,9%
33,3%
25,6%
44,2%
44,6%
43,7%
Percentage of households with electricity still very low
in many African countries, some even saw a decline
2007/8
2011/12
89%
73%
46,6% average
45%
18%
48%
42%
60%
65%
63%
57%
47%
19%
13%
South Africa
Ghana
Cameroon
Kenya
Botswana
Nigeria
Namibia
Tanzania
Ethiopia
5%
Rwanda
Uganda
10%
16%
13%
60%
58%
77%
Radio still main source of information
TV luxury good in several countries
Households with TV
Households with Radio
Kenya
81%
Uganda
77%
78%
Botswana
59%
Rwanda
72%
Kenya
54%
Namibia
72%
Ghana
54%
Ghana
72%
Nigeria
53%
Nigeria
70%
Botswana
66%
Cameroon
63%
Tanzania
South Africa
62%
Uganda
Cameroon
41%
34%
44%
Namibia
Tanzania
Ethiopia
ga
South Africa
41%
18%
13%
Ethiopia
10%
Rwanda
9%
Share of households with fixed-lines
2007/8
2011/12
18,2%
18,0%
South Africa
17,4%
Namibia
11,5%
11,0%
Botswana
Ethiopia
4,0%
2,6%
1,8%
Ghana
Kenya
2,3%
0,6%
1,8%
2,2%
Cameroon
Tanzania
Uganda
Rwanda
Nigeria
ga
High MTR =
active
contribution
to fixedmobile
substitution
15,0%
7,6%
0,9%
0,4%
0,3%
1,5%
0,1%
0,2%
0,3%
Fixed-lines in decline
except Botswana,
Cameroon, Uganda and
Rwanda
% of households with a working Fixed-line TELEPHONE at home
High MTR =
active
contribution
to fixedmobile
substitution
South Africa
Namibia
Senegal
Botswana
Ethiopia
Côte d'Ivoire
Burkina Faso
Benin
Ghana
Zambia*
Kenya
Cameroon
Mozambique
Tanzania
Uganda
Rwanda
18,2
17,4
11,7
11
7,6
4,8
4,7
4,6
2,6
2,4
2,3
1,8
1,7
0,9
0,3
0,1
Share of households with a
working computer
South Africa
24,5%
Botswana
15,7%
Namibia
14,7%
Kenya
12,7%
Share of households with a
working Internet connection
South Africa
Kenya
Nigeria
Ghana
8,5%
Ghana
Uganda
2,2%
Rwanda
2,0%
Tanzania
1,6%
Ethiopia
0,7%
11,5%
Botswana
Cameroon
6,6%
12,7%
Namibia
8,6%
Nigeria
19,7%
Cameroon
8,6%
3,4%
2,7%
1,3%
Uganda
0,9%
Tanzania
0,8%
Rwanda
0,7%
Ethiopia
0,5%
Less than a quarter of households have a computer and even
fewer Internet access
ga
Individual Access and Usage
15+ Own a mobile that is
capable of browsing the
Internet
15+ Own a mobile
South Africa
84%
Botswana
80%
Kenya
74%
Nigeria
66%
Ghana
60%
Namibia
56%
Uganda
47%
Cameroon
45%
Tanzania
Rwanda
Ethiopia
36%
24%
18%
South Africa
51%
Kenya
32%
Namibia
31%
Botswana
30%
Ghana
29%
Nigeria
23%
Tanzania
19%
Rwanda
19%
Uganda
15%
Cameroon
15%
Ethiopia
7%
sending cash with someone preferred way of sending money
Means of sending and receiving money that the business uses
Up to a two Mobile
line subtitle, generally
used to describesend
thecash with
Western
Post
Office
Banks
takeaway forMoney
the slide
Union etc
someone
Uganda
16%
1%
2%
17%
81%
Tanzania
14%
0%
0%
5%
93%
Rwanda
8%
0%
1%
10%
70%
Ethiopia
0%
0%
0%
5%
55%
Ghana
0%
1%
1%
12%
54%
Cameroon
0%
1%
26%
4%
75%
Nigeria
0%
0%
0%
11%
77%
Namibia
1%
25%
1%
41%
86%
Botswana
2%
16%
3%
27%
73%
Some mobile money use in East Africa
19
Internet Access & Usage
20
!"#$%&'&($)*+,*-'&.*$/0$1-%121%3&45$351-.$'6*$!-'*+-*'
!'%!!
!&#!!
!&%!!
-./01232!
8/9:.;:2!
=63>2!
D25:A:2!
F123G2!
L23@23:2!
42567..3!
<9232!
?.@25A:BC6!
D:E67:2!
H.C/9!IJ7:K2!
ME23G2!
!$#!!
!$%!!
!#!!
!"!!
$(()! $((*! $((+! $(((! &%%%! &%%$! &%%&! &%%'! &%%,! &%%#! &%%)! &%%*! &%%+! &%%(! &%$%!
2007/8
Ethiopia
1%
Tanzania
2%
Rwanda
2%
Uganda
2%
Ghana
3%
6%
8%
6%
13%
13%
9%
18%
Kenya
South Africa
ga
14%
16%
Nigeria
Botswana
Internet
use (15+)
more than
doubled
within 4
years
4%
Cameroon
Namibia
2011/12
15%
6%
26%
29%
15%
34%
Using mobile to browse the Internet
South Africa
Kenya
Namibia
Botswana
Nigeria
Rwanda
Ghana
Cameroon
Uganda
Tanzania
Ethiopia
28%
25%
24%
23%
16%
15%
13%
8%
8%
5%
5%
Using mobile for Facebook etc.
South Africa
Kenya
Botswana
Namibia
Nigeria
Rwanda
Ghana
Cameroon
Uganda
Tanzania
Ethiopia
How is this different
from ICT access and
usage in mature
economies?
25%
25%
18%
17%
16%
14%
11%
8%
7%
5%
2%
Using mobile for emailing
South Africa
Kenya
Botswana
Namibia
Nigeria
Rwanda
Ghana
Cameroon
Uganda
Tanzania
Ethiopia
17%
20%
17%
12%
15%
13%
10%
4%
6%
5%
10%
Daily Internet use increased dramatically in past
4 years
ga
South Africa
56%
64%
Kenya
41%
53%
Namibia
35%
59%
Ghana
32%
Botswana
31%
Tanzania
19%
Ethiopia
15%
Uganda
15%
Nigeria
13%
Rwanda
11%
Cameroon
11%
43%
55%
52%
47%
28%
34%
57%
19%
2007/8
2011/12
Internet access:
2007/08 VS 2011/12
2007/8
Ethiopia
0,7%
2,7%
Tanzania
2,2%
3,5%
Rwanda
2,0%
Uganda
Ghana
2011/12
Internet access double in
three years
6,0%
2,4%
7,9%
5,6%
12,7%
13,0%
14,1%
Cameroon
8,8%
Namibia
16,2%
Nigeria
18,4%
15,0%
Kenya
Botswana
South Africa
ga
26,3%
5,8%
29,0%
15,0%
33,7%
Frequency of Internet daily use:
2007/08 VS 2011/12
2007/8
2011/12
56%
64%
South Africa
Kenya
41%
Namibia
32%
Botswana
31%
15%
Uganda
15%
55%
52%
47%
28%
13%
Rwanda
11%
Cameroon
11%
59%
43%
19%
Ethiopia
Nigeria
ga
35%
Ghana
Tanzania
53%
34%
57%
19%
Where was the Internet used first?
Computer
ga
Mobile phone
Cameroon
82,1%
17,9%
Rwanda
70,8%
29,2%
Botswana
70,6%
29,4%
Ghana
70,5%
29,5%
Kenya
68,9%
31,1%
South Africa
65,1%
34,9%
Namibia
50,1%
49,9%
Tanzania
45,8%
54,2%
Nigeria
45,2%
54,8%
Ethiopia
33,3%
66,7%
Uganda
28,2%
71,8%
Where the Internet was used in past 12 months
Mobile phone
Work
Place of education
Internet cafe
74%
36%
61%
64%
71%
71%
Botswana
South Africa
Rwanda
20%
39%
42%
36%
21%
17%
48%
31%
55%
29%
75%
75%
78%
81%
81%
87%
Namibia
45%
45%
Uganda
52%
21%
23%
51%
Ethiopia
24%
72%
Kenya
31%
Ghana
Cameroon
ga
33%
63%
51%
35%
20%
10%
30%
32%
50%
Nigeria
51%
80%
58%
Tanzania
85%
Internet Access Models
Old Internet
New Internet
Hardware
Computer / Laptop
Mobile
Billing
Postpaid (monthly Internet
subscription)
Prepaid
Skills
requirement
High
(Windows + Internet explorer +
Viruses)
Low
Electricity
electricity mostly required at
location of Internet use
no required at home
Location
Work, school, Internet cafe
Anywhere
Implications for interconnection
!"#$%&'"()$(%*)&%+(#$,%-.'%!)/#0'%1.)$'222%
L/:0;/%
J0;/%
&"#($%
&(#)$%
J2<</1%K0;;5H;/0</%K0;;%J/%%
&&#'$%
&(#'$%
E/3123457/@/2F234%B/IB%%
*'#)$%
*"#+$%
H;0G234%40:/<%
+*#,$%
E/3123457/@/2F234%:>3/G%
A7>6<234%BC/%D3B/73/B%
=>63;>01/1%0??;2@08>3<%
./012345672834%9:02;<%
')#($%
!"#-$%
'!#($%
!'#,$%
!&#'$%
!!#)$%
!"#!$%
!"#$%&'(&$%#!')!&*"+&,)-,.,-/#0!&-'&)'1&"#.%&#&2'3,0%&4"')%555&
I4E=@4%
.=@4%
*+"!$%
F=332<%=G28H%5<%
*&"+$%
##"*$%
A2%4@4?<85?5</%=<%12E4%<2%?1=8B4%%
#!"!$%
&*"*$%
./%01234%B2<%6<2@43%
(&")$%
&-")$%
A2%?2>48=B4%C1484%D%@5>4%
&'"'$%
&'"+$%
:23;<%1=>4%=3/234%<2%?=@@%
./%01234%56%782943%
&'"&$%
*",$%
!"#$%
Conclusions
‣ The mobile is closing the voice and the data gap in
Africa
‣ First wave of Internet access through PCs and fixedline /modem dial-up. Mostly through work, school or
public access (Internet cafes)
‣ Second wave is through mobile phones
- Easier to use
- Cheaper equipment compared to computers
- Prepaid (modem dial-up was postpaid)
- No electricity at home needed
‣ Internet enabled mobile phones, low bandwidth
applications, and social networking are the key drivers
‣ Mobile Internet reduces the cost of communication:
Facebook Zero, whatsapp, Mixit
32
‣
International bandwidth no longer a problem... electricity and backhaul national networks are....
HH connected to main electricity grid
South Africa
89%
Ghana
73%
HH with landline
South Africa
18,0%
Botswana
15,0%
Cameroon
65%
Namibia
Botswana
60%
Ethiopia
Kenya
60%
Cameroon
2,2%
Nigeria
58%
Ghana
1,8%
Namibia
11,5%
4,0%
Uganda
1,5%
19%
Kenya
0,6%
Ethiopia
18%
Tanzania
0,4%
Rwanda
16%
Nigeria
0,3%
Uganda
13%
Rwanda
0,2%
Tanzania
Country
ISP
Technology
Product
name
India
MTNL
ADSL
TriB 49
2 Mbps
200 MB
Sri Lanka
SLT
ADSL
Entrée
2 Mbps
2.5 GB
Mexico
Cablevision
Cable
Intense 3.0
Mbps
3 Mbps
South Africa
MWEB
ADSL
Capped ADSL
384 Kbps
17,55
Modem cost from ZAR 369 onwards, excludes voice line rentals.
Kenya
TelkomKenya
ADSL
Surf and Talk
256 Kbps
34,99
The cost of Livebox+Panasonic Handset is taken as the modem cost
Fibre Ringo
1 Mbps
47,29
XAF95000 is the charge for equipment and installation cost.
64 Kbps
90
Cameroon
Ringo
Fibre
Uganda
Uganda Telecom
ADSL
Downstream Usage Monthly
bandwidth
cap cost (US$)
42%
0,88
3,78
10,56
1 GB
Notes
Speed reduces to 512Kbps after exceeding the usage cap + Additional charge
INR1.00 per MB after exceeding usage cap
Installation charge for the internet only package is taken as the connection
charge
iOS
34
Figure 6: Conceptual framework for business modeling
Apple relies on its own hardware and operating system as well as its own application store. A credit card is required to
register for iTunes, which disqualifies this configuration for the BoP. The price of an iPhone includes the operating system.
Operating upgrades are free, unlike those for computers and laptops. Apple takes a 30 percent cut of applications sold
in its App Store.
Google has a different combination. It offers an open-source operating system, leaving hardware development,
production and sales to companies like HTC and Samsung. Some apps can be downloaded simply by registering (i.e.,
without requiring a credit card as iTunes does), which makes the Google configuration a potential candidate for the BoP.
Some applications require payment through Google wallet, which can be loaded via credit card but also may be
loaded by other forms of payment. Google has several revenue sources: paid applications, advertising and in-app
purchases (Google charges app developers 30 percent of the revenues from in-app purchases, like Apple).
Facebook, like Mxit, has a platform that sits on top of operating systems such as iOS and Android. Unlike Mxit, however,
Facebook is only available on computers and smart phones. (Facebook does have a product called Facebook Zero that
works on feature phones, but this scaled-down version of Facebook does not offer app purchases). In South Africa,
Facebook allows app purchases by charging a user’s prepaid airtime account. This is available with Cell C, Vodacom
and MTN.
The Mxit model is also different in that it is available across all hardware and operating system combinations, from basic
phones (via Unstructured Supplementary Service Data, or USSD) through smart phones (via an Android and an iOS
App). Mxit also offers its own currency called Moola, which users can purchase with airtime or through their accounts
with First National Bank or Standard Bank. Though Mxit has more users at the BoP at present, Facebook is relatively
popular among the BoP, and many who cannot access it aspire to do so.
19
Up to a two line subtitle, generally used to describe the
takeaway for the slide
See www.researchICTafrica.net
This research is made possible with generous
support of
35