Predictors of mobile Internet usage in 10 African countriesEnrico Calandro, UCT/Research ICT AfricaRong Wang, USC Annenberg School of CommunicationITS Bangkok, 18-21 Nov 2012

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Mobile phones and Internet contributing to positive development outcomesPart of the research which seeks to explore the role of ICTs in contemporary social and political engagement (food protest in Mozambique 2007/2008, ethnic mobilisation in Kenya in 2009, Arab spring) The study concludes with policy implications on mobile Internet usage in African countries related to democracy, empowerment and capability development.

Research outline

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Research questions:

What are the social aspects and demographic factors influencing mobile Internet usage in selected African countries?

To what extent does belonging to specific civic affiliations such as religious, recreational or political groups impact on mobile Internet usage?

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Nationally representative ICT access and usage household and individual data10 African countries in end 2011 and beginning of 20124 Stage sampling based on census sample frame

Research methodology

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Assessing mobile Internet usageAssessing mobile Internet access and usage

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South Africa

Namibia

Botswana

Ghana

Nigeria

Tanzania

Rwanda

Uganda

Cameroon

Ethiopia 7%

15%

15%

19%

19%

23%

29%

30%

31%

51%

15+ Owners of a mobile with mobile that is capable of browsing the Internet

15+ Owning a mobile

South Africa

Botswana

Nigeria

Ghana

Namibia

Uganda

Cameroon

Tanzania

Rwanda

Ethiopia 18%

24%

36%

45%

47%

56%

60%

66%

80%

84%

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Network exposureIn smaller markets most users are innovators

South Africa

Botswana

Nigeria

Ghana

Namibia

Uganda

Cameroon

Tanzania

Rwanda

Ethiopia 18%

24%

36%

45%

47%

56%

60%

66%

80%

84% Botswana

South Africa

Namibia

Ghana

Nigeria

Cameroon

Uganda

Tanzania

Rwanda

Ethiopia 8%

14%

28%

30%

35%

39%

48%

52%

57%

63%

Mobile ownership in the close group of friends

(5 friends)

Ethiopia

Cameroon

Rwanda

Tanzania

Nigeria

Ghana

Uganda

Botswana

Namibia

South Africa 9%

10%

11%

14%

16%

25%

27%

35%

38%

59%

Mobile ownership in the close group of friends

(0 friends)Share mobile owners

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South AfricaNamibia

BotswanaNigeria

RwandaGhana

CameroonUganda

TanzaniaEthiopia 5%

5%8%8%

13%15%16%

23%24%

28%

Using the mobile to browse the Internet

What do you use yourmobile phone for?

South AfricaBotswana

NigeriaRwandaNamibiaEthiopia

GhanaUganda

TanzaniaCameroon 4%

5%6%

10%10%

12%13%

15%17%17%

Using the mobile for emailingUsing the mobile for social networkSouth Africa

BotswanaNamibiaNigeria

RwandaGhana

CameroonUganda

TanzaniaEthiopia 2%

5%7%8%

11%14%

16%17%18%

25%

Substitution effectSocial networks/Email

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NigeriaUganda

CameroonTanzania

BotswanaEthiopia

GhanaRwandaNamibia

South Africa 36%36%38%

44%47%48%50%50%54%

62%

People sharing religious beliefs

Does your use of the mobile phone increase your contact with the

following groups?

UgandaCameroon

EthiopiaRwandaNigeria

NamibiaBotswana

TanzaniaSouth Africa

Ghana 69%69%

75%75%77%78%80%81%82%

90%

Family and friends

People sharing political views

NigeriaUganda

BotswanaRwandaNamibiaTanzania

GhanaCameroon

EthiopiaSouth Africa 16%

17%19%19%21%

26%26%27%

32%43%

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Ethiopia

South Africa

Botswana

Cameroon

Rwanda

Ghana

Tanzania

Uganda

Nigeria

Namibia 46%

49%

58%

63%

75%

77%

78%

79%

84%

89%

36%

28%

22%

22%

9%

12%

8%

11%

9%

7%

I use my phone to mobilise the community

of for political events

I use my phone more for business than for social calls

South Africa

Botswana

Ethiopia

Namibia

Tanzania

Rwanda

Ghana

Cameroon

Uganda

Nigeria 38%

45%

47%

51%

55%

55%

57%

58%

70%

77%

35%

42%

38%

29%

16%

31%

30%

35%

20%

18%

Agree Disagree

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Model 1/2 specifications: Logistic regressionDependent variables: browsing the Internet through the mobile phone/

Using social media through the mobile phone(mobile phone users only)

Independent variables

expected sign

Description

Age NegativeWith age the probability of mobile Internet/social

network use decreases, younger are more inclined to learn new technologies

Male Positive Males control assets

Being a student Positive Students are more incline to learn new technologies

Highest level of schooling completed

Positive Higher level of ICT literacy

Total years of schooling

Positive The more total years of schooling, the higher level of ICT literacy

Being employed Positive Being employed, higher income

Disposable income

Positive Higher income, higher probability of owning a phoneFriday, 24 May 13

Independent variables

expected sign

Description

Religious group Positive Religious groups presence on social media

Trade union Positive Social mobilisation through social media

Cultural or political group Positive Social mobilisation through social media

Farmer association/cooperative

Negative Low level of ICT penetration between farmers

Saving club Positive Mobile Internet/Social media may be used by saving groups

Recreational group Positive Mobile Internet may help people beloning to

recreational groups to keep in touch

Model 1/2 specifications: Logistic regressionDependent variables: browsing the Internet through the mobile phone/

Using social media through the mobile phone(mobile phone users only)

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Model 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsCountry R2 Prob

> FOnly significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Country R2 Prob

> FAge Male Being a

studentHighest

level schooling

compl.

Total years of school

Being employ

ed

Disposable

income

Botswana 0.41 0.000 +++ +++Cameroon 0.19 0.000 - --Ethiopia 0.44 0.000 --- +++ + +++Ghana 0.46 0.000 --- +++ +++Namibia 0.45 0.000 --- +++ +++Nigeria 0.37 0.000 --- +++ +++ +++ +++Rwanda 0.51 0.000 +++ ++South Africa

0.37 0.000 --- +++ ++ +++ +++

Tanzania 0.32 0.000 +++ ++Uganda 0.22 0.000 ++Friday, 24 May 13

Model 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsModel 1: Logistic regression resultsCountry R2 Prob >

FOnly significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Country R2 Prob >

F

Religious group

Trade union

Cultural or political group

farmers associat/

coop.

Saving club

Recreational

groups

Botswana 0.41 0.000 ++ +++ +++Cameroon 0.19 0.000 +++ ++Ethiopia 0.44 0.000 ++ +Ghana 0.46 0.000 + +++Namibia 0.45 0.000 +++ - +++Nigeria 0.37 0.000 +++Rwanda 0.51 0.000 -- +++South Africa

0.37 0.000 - +++

Tanzania 0.32 0.000 ++Uganda 0.22 0.000 +++Friday, 24 May 13

Model 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsCountry R2 Prob

> FOnly significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Only significant coefficients are being displayed: alpha

level of .05Country R2 Prob

> FAge Gender

(M = 1,F = 0)

Being a student

Highest level

schooling compl.

Total years of school

Being emplo

yed

Disposable

income

Botswana 0.43 0.000 -- +++ +++ ++Cameroon 0.26 0.000 -- +

Ethiopia 0.43 0.000 --- + +++Ghana 0.45 0.000 --- +++ +++Namibia 0.45 0.000 --- +++ +++Nigeria 0.35 0.000 -- +++ ++ +++Rwanda 0.60 0.000 ++ +++ +++South Africa

0.38 0.000 --- +++ +++ +++

Tanzania 0.46 0.000 +++Uganda 0.45 0.000 --- ++ +++Friday, 24 May 13

Model 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsModel 2: Logistic regression resultsCountry R2 Prob >

FOnly significant coefficients are being displayed: * =

significant at 0.1 level or above, ** = significant at 0.05 level, *** = significant at 0.01 level

Only significant coefficients are being displayed: * = significant at 0.1 level or above, ** = significant at 0.05

level, *** = significant at 0.01 level

Only significant coefficients are being displayed: * = significant at 0.1 level or above, ** = significant at 0.05

level, *** = significant at 0.01 level

Only significant coefficients are being displayed: * = significant at 0.1 level or above, ** = significant at 0.05

level, *** = significant at 0.01 level

Only significant coefficients are being displayed: * = significant at 0.1 level or above, ** = significant at 0.05

level, *** = significant at 0.01 level

Only significant coefficients are being displayed: * = significant at 0.1 level or above, ** = significant at 0.05

level, *** = significant at 0.01 level

Country R2 Prob > F

Religious group

Trade union

Cultural or

political group

farmers associat/

coop

Saving club

Recreational

groups

Botswana 0.43 0.000 +++ +++Cameroon 0.26 0.000 -- ++ +++

Ethiopia 0.43 0.000 -- +Ghana 0.45 0.000 - +++Namibia 0.45 0.000 +++Nigeria 0.35 0.000 +++ +++Rwanda 0.60 0.000 +++ -- -- +++South Africa

0.38 0.000 -- ++

Tanzania 0.45 0.000 ++Uganda 0.45 0.000 +++Friday, 24 May 13

Conclusions (1/2)The mobile is closing the voice and the data gap in AfricaSocial media through mobile phone reduces the cost of communicationUsers get connected to social media through the mobile phone to keep in touch with family and friends or for entertainmentMobile phone usage increases contacts with family & friends, but

no significant evidence to increase contacts with political groupsno significant evidence of using the phone for mobilising the community or for political events

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Conclusions (2/2)Very little evidence was found of using mobile media through the phone for social mobilisation or to keep in touch with political organisationsTo be a student and highest level of education achieved were found to be the most significant predictors, together with being male and with a higher disposable income in four countries (Nigeria, Rwanda, SA, Tanzania)Be associated to a recreational network was the only significant positive predictor regarding social affiliations

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RecommendationsReform strategies to increase competition will lower prices and lead to better and faster access:

Spectrum re-farming to issue LTE spectrumIssue more and converged licences (Ethiopia)Require reselling of fixed-broadband (ADSL), for it to become cheaper and to compete with mobile broadband

Censorship of the Internet in countries where the Internet take up is slow or constrained by high prices of broadband and data may impact on the development of the mobile Internet sector

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Thank youwww.researchICTafrica.netUniversity of Cape Town,

Graduate School of Business

USC Annenberg School of Communication

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