Lifting the gender veil on ICT statistics in Africa · ICT Access in South Africa from Census 2012 and Research ICT Africa Household & Individual User Survey 2012 19.7% (Household)

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Lifting the gender veil on ICT statistics in AfricaAlison Gillwald & Mariama Deen-Swarray

WSIS Forum 2013: Measuring ICT and Gender

Thursday, 30 May 13

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Outline Conceptualising gender and ICTs

MethodologyWomen and incomeWomen and educationMarital statusPay phoneMobile access and useInternet access and useComputer access and useModelling gender and ICT

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RIA Household & Individual SME 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.

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Methodology

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RIA Household, Individual, SME survey 2013: Botswana, Cameroon, Ethiopia,Ghana, Kenya, Mozambique,Namibia, Nigeria, Rwanda,South Africa,Uganda, Tanzania.

Methodology

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• Step 2: EAs will be sampled for each stratum using probability proportional to size (PPS).

• Step 3: For each EA two listings will be compiled, one for house-holds and one for businesses. The listings serve as sample frame for the simple random sections.

• Step 4: 24 Households and 10 businesses will be sampled using simple random sample for each selected EA.

• Step 5: From all household members 15 years or older or visitor staying the night at the house one will be randomly selected based on simple random sampling.

HH SampleHH Sample

TotalHH Sample Business Sample

Total

Botswana 900 400 1,300

Cameroon 1,200 500 1,700

Ethiopia 1,600 600 2,200

Ghana 1,200 500 1,700

Kenya 1,200 500 1,700

Mozambique 1,200 500 1,700

Namibia 900 400 1,300

Nigeria 1,600 600 2,200

Rwanda 1,200 500 1,700

South Africa 1,600 600 2,200

Tanzania 1,200 500 1,700

Uganda 1,200 500 1,700

Tunisia 1,200 500 1,700

Total 15,300 6,200 21,500

Sample SizeThe desired level of accuracy for the survey was set to a confidence level of 95% and an absolute precision (relative margin of error) of 5%. The population proportion P was set conservatively to 0.5 which yields the largest sample size (Lwanga & Lemeshow, 1991). The minimum sample size was determined by the following equation (Rea & Parker, 1997):

n =Z

ap(1 − p)

Cp

⎛⎝⎜

⎞⎠⎟

2

=1.96 0.5(1 − 0.5)

0.05

⎛⎝⎜

⎞⎠⎟

2

= 384

Inserting the parameters for the survey yields the minimum sample size for simple random sampling. Due to the sampling method chosen for the survey the minimum sample size has to be multiplied by the design effect variable (Lwanga & Lemeshow, 1991). In the absence of empirical data from previous surveys that would have suggested a differed value, the default value of two was chosen for the design effect (UNSD, 2005). This yields then a minimum sample size of 768 per country for households and individuals. The actual sample size for countries is slightly larger than the minimum requirement to compensate for clustering effects and have a wide enough spread of EAs through out a country. For the businesses a design effect of 1 is assumed leading to a minimum sample of 384 businesses for each country.

Survey Characteris-tics

Household & Indi-viduals

Businesses

Target PopulationAll households

Individuals 15 years or older.

all businesses

Domains 1 = national level1 = national levelTabulation groups Urban, Rural NationalOversampling Urban 60% Rural 40%Urban 60% Rural 40%Clustering Enumerator Areas (EA) national Census Enumerator Areas (EA) national Census None Response Random substitutionRandom substitutionSample Frame Census sample from from NSOCensus sample from from NSOConfidence Level 95% 95%Design Factor 2 1Absolute precision 5% 5%Population Proportion 0.5, for maximum sample size0.5, for maximum sample sizeMinimum Sample Size 768 384

WeightingFour 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.

Household weight: HHw = DW1

PHH *PEA

Individual weight: INDw = DW1

PHH *PEA *PI

Business Weight: Busw = DW 1PBus *PEAI

Household Selection Probability: PHHw =n

HHEA

EA Selection Probability: PEAw = mHHEA

HHSTRATA

RESEARCH ICT AFRICA

2 2011 Brief Survey Methodology

1 See UNSD (2005) page 119 for a detailed discussion on sampling weights.

Thursday, 30 May 13

Fixed, mobile, PC, Internet penetration, TV, radioSA Census and RIA survey 2011

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Table : Summary of ICT Access in South Africa from Census 2012 and Research ICT Africa Household & Individual User Survey 2012





Census DataCensus Data RIA Survey DataRIA 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%

Households with Internet 35.2% table : Summary of ICT Access in South Africa from Census 2012 and Research ICT Africa Household & Individual User Survey 2012

19.7% (Household)33.7% (Individual)

Cellphone Ownership 72.7% 88.9% 62.1% 84.2%

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GENDER

Exclusion

Inclusion

Country Dummy

Ethnicity/culture

Marital status

Income

Age

Education

Pay phones

Fixed phones

Mobile phones

Internet

AccessOwnership

UseAffordability/ Skills

ImpactHuman, economic and

social development

GENDER ANALYSIS CONCEPTUAL FRAMEWORK

Conceptual framework

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Analysis & Findings

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Income

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Table 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individualsTable 1 – General sample statistics of randomly selected individuals

Country

% females

Average individual income US $Average individual income US $Average individual income US $

Average income US$ pppAverage income US$ pppAverage income US$ ppp Average

age

% with a bank account% with a bank account% with a bank account

Country

% females

Average individual income US $Average individual income US $Average individual income US $

Average income US$ pppAverage income US$ pppAverage income US$ ppp Average

ageAll Male Female

Country

% females

All Male Female All Male Female

Average age

All Male Female

Botswana 59.1% 270 340 222 460 579 378 34 48.4 52.4 45.6

Cameroon 51.9% 72 94 52 145 189 104 33 10.9 10.8 10.9

Ethiopia 44.8% 27 39 12 69 101 30 34 3.7 4.3 3.0

Ghana 55.1% 87 117 63 183 244 134 34 29.4 35.5 24.5

Kenya 61.9% 85 119 64 154 214 116 28 44.5 57.6 36.4

Namibia 56.8% 194 279 130 270 387 181 40 56.3 51.1 60.3

Nigeria 46.9% 102 151 47 171 252 78 34 30.5 39.8 20.0

Rwanda 49.9% 28 36 21 57 72 42 30 16.3 17.4 15.2

South Africa

54.2% 402 617 221 595 914 328 36 58.9 62.7 55.7

Tanzania 54.4% 35 45 26 89 115 68 34 6.2 7.4 5.1

Uganda 44.0% 52 59 42 126 144 102 31 15.2 18.7 10.7

Thursday, 30 May 13

Income and Access to Bank Accounts

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South Africa

Botswana

Namibia

Nigeria

Ghana

Kenya

Cameroon

Uganda

Tanzania

Ethiopia

Rwanda 42

30

68

102

104

116

134

78

181

378

328

72

101

115

144

189

214

244

252

387

579

914

Average Monthly Individual Income US$ PPP

Male Female

South Africa

Namibia

Botswana

Kenya

Nigeria

Ghana

Rwanda

Uganda

Cameroon

Tanzania

Ethiopia 3

5.1

10.9

10.7

15.2

24.5

20

36.4

45.6

60.3

55.7

4.3

7.4

10.8

18.7

17.4

35.5

39.8

57.6

52.4

51.1

62.7

3.7

6.2

10.9

15.2

16.3

29.4

30.5

44.5

48.4

56.3

58.9

share of individuals with Bank Accounts

All Male Female

Thursday, 30 May 13

Main Activity Engaged in...

More men than women who are students across all countries and likewise for those who are employed.

More men than women reported to be self-employed across all countries except in Ghana and Kenya

More women are among the unemployed in 9 of the 11 countries.

There is a comparatively large number of women who are housewives or involved in unpaid house work.

In general women are less involved than their male counterparts in income generating activities


Education

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Table 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statisticsTable 2 – Gender disaggregated educational statistics

Highest Education: TertiaryHighest Education: TertiaryHighest Education: Tertiary Highest Education: SecondaryHighest Education: SecondaryHighest Education: Secondary Highest Education: PrimaryHighest Education: PrimaryHighest Education: Primary

All Male Female All Male Female All Male Female

Botswana20.5% 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%

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%

Thursday, 30 May 13

Gender disaggregated Educational Statistics

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Kenya

Botswana

Nigeria

South Africa

Ghana

Uganda

Cameroon

Namibia

Ethiopia

Tanzania

Rwanda 0.7

1.2

1.8

6.1

6.2

6.3

6.2

9.1

9.6

19.4

22.3

1.7

1.5

2.4

8.4

8.6

11.2

15.8

18

19.5

21.9

32.7

1.2

1.4

2.1

7.1

7.4

9.1

10.5

13.3

14.8

20.5

26.2

share of individuals who have attained tertiary education

All Male Female

South AfricaBotswana

KenyaNigeriaGhana

UgandaNamibia

CameroonRwanda

TanzaniaEthiopia 2.4

7.8

13.7

26.2

30.4

25.6

34.8

34.9

41.7

54

64.8

1.3

14.9

16.8

19.2

24.3

33.3

38.9

40.3

41.1

53.9

65.8

1.8

11.1

15.3

22.8

27.8

29.9

36.6

37.8

41.4

53.9

65.3

share of individuals who have secondary schooling as highest level of education

All Male Female

TanzaniaRwandaNamibiaUganda

CameroonKenyaGhana

BotswanaNigeria

South AfricaEthiopia 8.9

20.219.318.2

28.930.2

30.643.747.4

57.470.9

16.413.218.119.3

25.322.8

30.744.642.4

59.473.3

13.117

18.718.727.327.430.6

44.245.2

58.472

share of individuals with primary schooling as highest level of education

All Male Female

Thursday, 30 May 13

Mobile Phone Adoption

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There has been an increase in mobile adoption from 2007/08 to 2011/12. Adoption in Ghana remained almost fixed.

Adoption is much higher among women in Botswana, Namibia and Cameroon (2011/12)

26.4%&

56.2%&

56.0%&

83.8%&

26.2%&

41.7%&

11.8%&

27.6%&

24.8%&

60.8%&

61.2%&

33.1%&

44.2%&

76.5%&

53.3%&

55.2%&

56.4%&

86.3%&

59.7%&

76.1%&

12.2%&

34.5%&

46.9%&

67.9%&

17.6%&

30.9%&

7.5%&

21.2%&

10.4%&

57.2%&

58.2%&

39.4%&

44.9%&

54.9%&

45.4%&

57.0%&

64.9%&

82.4%&

58.9%&

82.7%&

19%&

47%&

50%&

74%&

21%&

36%&

10%&

24%&

3%&

18%&

59%&

60%&

36%&

45%&

66%&

49%&

56%&

61%&

84%&

59%&

80%&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

2007/2008&

2011/2012&

Ugand a&

Kenya&Tanzan

ia&

Rwand

a&Ethiop

ia&

Ghana&Ca

mer

oon&

Nigeria&Nam

ibi

a&South&

Africa&Bo

tsw

ana&

share&of&individuals&that&own&a&mobile&phone...(16+&for&2007/2008)&Male& Female& NaQonal&

Thursday, 30 May 13

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Table 3: Monthly Expenditure on Mobile PhoneTable 3: Monthly Expenditure on Mobile PhoneTable 3: Monthly Expenditure on Mobile PhoneTable 3: Monthly Expenditure on Mobile PhoneTable 3: Monthly Expenditure on Mobile PhoneTable 3: Monthly Expenditure on Mobile PhoneTable 3: Monthly Expenditure on Mobile Phone

Monthly average mobile expenditure in US$ PPPMonthly average mobile expenditure in US$ PPPMonthly average mobile expenditure in US$ PPP Monthly average mobile expenditure in US$Monthly average mobile expenditure in US$Monthly average mobile expenditure in US$

All Male Female All Male Female

Botswana 28.58 34.65 24.91 16.83 20.40 14.67

Cameroon 20.69 23.01 18.57 10.38 11.54 9.31

Ethiopia 6.77 6.67 7.08 2.71 2.67 2.84

Ghana 20.29 21.64 19.10 9.81 10.47 9.24

Kenya 17.49 18.82 16.48 9.66 10.40 9.11

Namibia 20.91 26.07 17.08 15.07 18.79 12.31

Nigeria 20.15 23.78 14.43 12.37 14.59 8.86

Rwanda 8.27 7.79 8.89 4.08 3.84 4.38

South Africa 28.63 37.75 20.60 19.34 25.50 13.92

Tanzania 22.51 22.05 23.02 8.76 8.59 8.96

Uganda 13.08 13.95 11.27 5.40 5.76 4.65

Thursday, 30 May 13

Average Monthly Expenditure on Mobile Phone Use

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South Africa

Botswana

Namibia

Nigeria

Cameroon

Tanzania

Ghana

Kenya

Uganda

Rwanda

Ethiopia 7.08

8.89

11.27

16.48

19.10

23.02

18.57

14.43

17.08

24.91

20.60

6.67

7.79

13.95

18.82

21.64

22.05

23.01

23.78

26.07

34.65

37.75

Monthly Expenditure on Mobile Phone in US$ PPP

Male Female

Thursday, 30 May 13

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Table 4: Mobile phone use and access across 11 African countriesTable 4: Mobile phone use and access across 11 African countriesTable 4: Mobile phone use and access across 11 African countriesTable 4: Mobile phone use and access across 11 African countriesTable 4: Mobile phone use and access across 11 African countries

Mobile Phone (Multiple Responses)Mobile Phone (Multiple Responses)Mobile Phone (Multiple Responses)

All Male Female

Main reasons for using the mobile

phone

missed call/please call me 83.8% 85.7% 86.5%


phone

sending/receiving text 83.2% 85.2% 88.2%


phone

playing games 48.0% 46.3% 42.7%Main reasons for using the mobile

phonesending/receiving money 18.8% 27.5% 34.9%


phonebrowsing the internet 17.2% 21.5% 16.0%


phone

downloading applications 15.1% 18.2% 12.9%


phone

reading/writing emails 13.6% 16.1% 11.7%

Why individuals do not have a mobile phone

cannot afford it 80.9% 81.3% 83.7%


no electricity at home to charge 56.7% 57.7% 55.8%


phone got stolen 19.5% 21.4% 18.8%Why individuals do not have a mobile phone no coverage where I live 18.6% 19.9% 16.4%


don’t have anyone to call 19.1% 19.1% 19.3%


phone is broken 7.4% 7.5% 8.0%

Thursday, 30 May 13

Mobile phone use and access across 11 African countries

18

missed call/please call me

sending/receiving text

playing games

sending/receiving money

browsing the internet

downloading applications

reading/writing emails 11.7

12.9

16

34.9

42.7

88.2

86.5

16.1

18.2

21.5

27.5

46.3

85.2

85.7

13.6

15.1

17.2

18.8

48.0

83.2

83.8

main reasons why individuals use mobile phones

All Male Female

cannot afford it

no electricity at home to charge

phone got stolen

no coverage where I live

don’t have anyone to call

phone is broken 8

19.3

16.4

18.8

55.8

83.7

7.5

19.1

19.9

21.4

57.7

81.3

7.4

19.1

18.6

19.5

56.7

80.9

reasons why individuals do not have a phone...

All Male Female

Thursday, 30 May 13

Internet Use

The emergence of mobile internet and the wider adoption of mobile phones has contributed positively to internet use.

8.5% of those using the internet did so first on their computer whilst 7% used it first on their mobile phones.

Internet use in the countries surveyed increased to 15.5% in 2011/12 from less than 10% in 2007/8.

Internet use in all countries in general and by gender increased between 2007/8 and 2011/12;

There are more men using the internet than women in all countries, except in Cameroon and Tanzania but with very little difference.


20

3.7%%11.8%%

21.1%%35.8%%

3.4%%

6.9%%

3.9%%8.1%%

17.8%%13.1%%13.4%%16.4%%

22.8%%11.2%%

18.7%%20.4%%

40.6%%10.1%%

32.6%%

1.1%%3.1%%

11.5%%20.5%%

3.5%%

5.2%%

1.1%%3.2%%

8.5%%12.8%%14.7%%7.6%%

13.4%%7.2%%

14.2%%11.3%%

28.6%%4.0%%

26.5%%

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

2007/2008

2011/2012

Uga

nda

Keny

a Ta

nzan

ia R

wan

da E

thio

pia

Gha

na

Cam

eroo

n N

iger

ia

Nam

ibia

Sout

h Af

rica

Bots

wan

a

share&of&individuals&that&use&the&Internet...&Male% Female%

Thursday, 30 May 13

21

Table 5: Internet use and access across 11 African countriesTable 5: Internet use and access across 11 African countriesTable 5: Internet use and access across 11 African countriesTable 5: Internet use and access across 11 African countriesTable 5: Internet use and access across 11 African countries

Internet Use Internet Use Internet Use

All Male Female

Whether using the internet increases an individuals

contact with people who...

share same hobbies/recreational activities 59.6% 59.1% 60.4%



share same political views 30.8% 37.7% 20.1%Whether using

the internet increases an individuals


share religious beliefs 47.1% 46.5% 47.9%


contact with people who... are family and friends 69.9% 71.6% 67.4%



are colleagues 58.1% 58.3% 57.9%

Why individuals do not use the

Internet (multiple responses)

don’t know how to use it 68.7% 66.8% 70.5%



no computer/internet connection 65.2% 65.4% 65.1%Why individuals do not use the


don’t know what the Internet is 64.6% 60.7% 68.5%Why individuals do not use the

Internet (multiple responses) too expensive 54.6% 53.0% 56.0%



no interest/not useful 38.5% 38.3% 38.8%



too slow, limited bandwidth 13.4% 15.8% 11.2%

Thursday, 30 May 13

Internet use and access across 11 African Countries

22

share same hobbies/recreational activities

share same political views

share religious beliefs

are family and friends

are colleagues 57.9

67.4

47.9

20.1

60.4

58.3

71.6

46.5

37.7

59.1

58.1

69.9

47.1

30.8

59.6

share of individuals who reported that using the internet increase contact with people who....

All Male Female

don’t know how to use it

no computer/internet connection

don’t know what the Internet is

too expensive

no interest/not useful

too slow, limited bandwidth 11.2

38.8

56

68.5

65.1

70.5

15.8

38.3

53

60.7

65.4

66.8

13.4

38.5

54.6

64.6

65.2

68.7

why individuals do not use the internet (multiple responses)

All Male Female

Thursday, 30 May 13

Computer Use

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Table 6 Computer use and ownershipTable 6 Computer use and ownershipTable 6 Computer use and ownershipTable 6 Computer use and ownershipTable 6 Computer use and ownershipTable 6 Computer use and ownershipTable 6 Computer use and ownershipTable 6 Computer use and ownershipTable 6 Computer use and ownershipTable 6 Computer use and ownership

Country

Share of individuals (15 or older) that use a ComputerShare of individuals (15 or older) that use a ComputerShare of individuals (15 or older) that use a Computer

Share of computer users (15+) that own a personal desktop



Share of computer users (15+) that own a personal laptop



Country All Male Female All Male Female All Male Female

Cameroon 15.1% 15.6% 14.6% 30.2% 35.2% 25.3% 13.2% 21.2% 5.2%

Ethiopia 2.0% 2.0% 2.0% 12.1% 10.7% 13.8% 15.7% 18.7% 11.8%

Ghana 10.0% 14.2% 6.6% 48.0% 39.8% 62.5% 41.1% 55.1% 16.3%

Kenya 21.2% 29.3% 16.2% 35.7% 34.4% 37.1% 23.8% 25.7% 21.7%

Namibia 13.0% 15.9% 10.8% 30.8% 39.8% 22.7% 57.6% 58.5% 56.6%

Nigeria 7.5% 11.2% 3.3% 12.2% 12.4% 11.7% 58.6% 65.1% 33.9%

Rwanda 3.5% 2.5% 4.5% 45.3% 14.6% 62.4% 7.8% 16.5% 3.0%

South Africa 29.1% 36.2% 23.1% 44.4% 42.8% 46.4% 34.6% 39.4% 28.8%

Tanzania 1.9% 1.7% 2.0% 18.6% 24.2% 14.8% 43.2% 77.1% 20.1%

Uganda 4.8% 5.6% 3.7% 33.8% 31.7% 37.7% 19.0% 19.3% 18.5%

Computer use is still relatively low across African countries. The RIA 2011/12 results show that computer use among individuals is above 10% in only 4 of the countries surveyed.

Only in South Africa is computer use close to 30% and in Kenya it is slightly above 20%.

There are more men than women making use of computers in all countries with the exception of Ethiopia (at par), Tanzania and Rwanda (slightly more women); the gender gap much wider in Kenya and South Africa.

Thursday, 30 May 13

Computer use and ownership

24

South Africa

Kenya

Cameroon

Namibia

Nigeria

Ghana

Uganda

Rwanda

Ethiopia

Tanzania 2

2

4.5

3.7

6.6

3.3

10.8

14.6

16.2

23.1

1.7

2

2.5

5.6

14.2

11.2

15.9

15.6

29.3

36.2

1.9

2

3.5

4.8

10

7.5

13

15.1

21.2

29.1

share of individuals (15+) that use a computer

All Male Female

Ghana

Rwanda

South Africa

Kenya

Uganda

Namibia

Cameroon

Tanzania

Nigeria

Ethiopia 13.8

11.7

14.8

25.3

22.7

37.7

37.1

46.4

62.4

62.5

10.7

12.4

24.2

35.2

39.8

31.7

34.4

42.8

14.6

39.8

12.1

12.2

18.6

30.2

30.8

33.8

35.7

44.4

45.3

48

share of computer users that own a personal desktop

All Male Female

NigeriaNamibia

TanzaniaGhana

South AfricaKenya

UgandaEthiopia

CameroonRwanda 3

5.2

11.8

18.5

21.7

28.8

16.3

20.1

56.6

33.9

16.5

21.2

18.7

19.3

25.7

39.4

55.1

77.1

58.5

65.1

7.8

13.2

15.7

19

23.8

34.6

41.1

43.2

57.6

58.6

share of computer users that own a personal laptop

All Male Female

Computer use is still relatively low across African countries. The RIA 2011/12 results show that computer use among individuals is above 10% in only 4 of the countries surveyed.

Only in South Africa is computer use close to 30% and in Kenya it is slightly above 20%.

There are more men than women making use of computers in all countries with the exception of Ethiopia (at par), Tanzania and Rwanda (slightly more women); the gender gap much wider in Kenya and South Africa.

Thursday, 30 May 13

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Table 7: Location and main use of computers across 10 African countriesTable 7: Location and main use of computers across 10 African countriesTable 7: Location and main use of computers across 10 African countriesTable 7: Location and main use of computers across 10 African countriesTable 7: Location and main use of computers across 10 African countries

Computer (Multiple responses)Computer (Multiple responses)Computer (Multiple responses)

All Male Female

Where do you use a computer...

work 40.1% 44.4% 33.2%


school/university 33.0% 33.9% 31.6%


library 8.7% 8.2% 9.6%Where do you use a computer... at home 60.1% 61.6% 57.7%


internet cafe 49.3% 52.1% 44.9%


at a friends place 36.6% 41.5% 28.9%

What do you use your computer

for...

writing letters, editing documents 75.3% 78.1% 70.7%


for...

calculations using spreadsheets 53.4% 55.1% 50.8%


for...

browsing the internet 72.9% 72.9% 72.9%What do you use

your computer for... programming 40.9% 45.2% 34.2%


for...

remixing content found online 37.7% 41.2% 32.1%


for...

playing games 63.5% 64.4% 62.0%

Thursday, 30 May 13

Location and main use of computers (multiple responses)

26

at home

internet cafe

work

at a friend’s place

school/university

library 9.6

31.6

28.9

33.2

44.9

57.7

8.2

33.9

41.5

44.4

52.1

61.6

8.7

33

36.6

40.1

49.3

60.1

where individuals make use of computers...

All Male Female

writing letters, editing documents

browsing the internet

playing games

calculations using spreadsheets

programming

remixing content found online 32.1

34.2

50.8

62

72.9

70.7

41.2

45.2

55.1

64.4

72.9

78.1

37.7

40.9

53.4

63.5

72.9

75.3

what individuals use their computers for.....

All Male Female

Thursday, 30 May 13

Public Pay Phones

Some individuals still make use of public pay phones. The results do not show a significant difference in the use of public phones by gender.

The telephone kiosk or umbrella operator appear to have replaced the use of the formal telephone booths. The results show that slightly more women are using the telephone kiosk/umbrella operators to make calls.

The issue of affordability is shown as the main reason why public pay phones/community phones are still being used. More women than men claim that they use public pay phones because it is cheaper.


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Table 8: Use of Pay PhonesTable 8: Use of Pay PhonesTable 8: Use of Pay PhonesTable 8: Use of Pay Phones

Public Pay PhonesPublic Pay Phones

Male Female

Use of a pay phone in the last 3 monthUse of a pay phone in the last 3 month 18.8% 18.6%

How often do you use a public phone

More than once a day 6.8% 8.0%


Everyday or almost everyday 13.1% 13.3%How often do you use a

public phone At least once a week 41.6% 40.8%How often do you use a

public phone

At least once a month 24.7% 26.9%


Less thank once a month 13.8% 11.0%

Type of public phone use most

Telephone booth (fixed line operator) 16.7% 15.6%Type of public phone use

most Telephone kiosk, umbrella operator 82.0% 83.9%

Main reasons for using a public pay phone


do not have a fixed line phone at home 8.4% 8.8%


do not have a mobile phone 23.0% 22.0%Main reasons for using a

public pay phone use it because it is cheaper 45.7% 49.1%Main reasons for using a

public pay phone

easier than having to recharge airtime mobile 13.4% 13.9%


difficulties charging the battery of mobile 6.9% 3.9%

What makes you use a particular community/public

pay phone

Price of calls 55.9% 58.9%

What makes you use a particular community/public

pay phone

Convenience (e.g. close to my house) 36.4% 34.7%What makes you use a particular community/public

pay phone Security 3.7% 3.0%

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Access to and use of Public Pay Phones

29

At least once a week

At least once a month

Less than once a month

Everyday or almost everyday

More than once a day 8

13.3

11

26.9

40.8

6.8

13.1

13.8

24.7

41.6

frequency with which individuals use a public phone...

Male Female

Telephone booth (fixed line operator) Telephone kiosk, umbrella operator

83.9

15.6

82.0

16.7

type of public phone mostly used...

Male Female

it is cheaper

no mobile phone

easier than to recharge airtime on mobile

no fixed line phone at home

difficulting charge mobile phone battery 3.9

8.8

13.9

22.0

49.1

6.9

8.4

13.4

23.0

45.7

main reasons why individuals use public pay phones...

Male Female

price of calls

convenience (proximity)

security 3.0

34.7

58.9

3.7

36.4

55.9

reasons for using a particular community/public pay phone...

Male Female

Thursday, 30 May 13

Income & EducationEmpirical Findings

Being female, location and being single have a negative impact on income in most countries.

The age variable, the number of years of formal education, the years of work experience, mobile ownership and using the internet are positively related to income.

Country analysis - the gender variable has a negative correlation to income in South Africa but not highly significant relationship.

The urban-rural divide in income is not significant in Cameroon and South Africa.

Being a woman has a negative causal relationship to education.

In Namibia, South Africa and Botswana being a woman shows a positive correlation to income.

Household income, having a mobile phone and using the internet are positive determinants of level of education.

The findings show that an individual who is single has a better chance of gaining higher education in comparison to one who is married in Uganda and Cameroon;


Mobile AdoptionEmpirical Findings

Being a woman is negatively correlated to mobile phone ownership but shows no causal relationship.

There is only a significant relationship in Ethiopia and Rwanda.

In South Africa and Botswana, being a woman has a positive and significant relationship to mobile adoption.

Income and education variables are found to have a positive and significant relationship to mobile adoption across all countries.

Being female and married shows a negative causal relationship in comparison to being male and married only in Ethiopia and Ghana.


Internet & Computer UseBeing a woman had a negative effect on Internet use. In Ethiopia, Ghana and Nigeria this indicated a causal relationship (high significant).

Income and education show a positive causal effect on internet use. These variables have the same impact across all countries, though income shows no causal effect on internet use in Ghana and Ethiopia.

Ghana is considered to have one of the stronger economies in Africa, and one of the most dynamic mobile markets and yet it lags behind South Africa, Botswana, Kenya, Nigeria, Namibia and Cameroon in terms of internet use.

Being a student increases the probability of using the internet.

In Namibia, being female and married show a positive causal relationship to internet use.

Being a woman, location and age had a negative causal effect on computer use. This is the case in South Africa, Nigeria and Kenya.

Income, years of formal education, being a student, being employed and being female and married showed a positive causal effect on the use of computers.


ConclusionThis sex-disaggregated overview indicates that women and men are not equally able to access and use ICTs. Women generally have less access to ICTs and use them sub-optimally and this increases as the technologies and services become more sophisticated and expensive.The study confirms in the adoption models that education and income have a positive impact on ownership and use of ICTs. The gender disparities found in income and education, indicate they are key contributors if inclusion is to be achieved. The positive and causal relationship between education and income further points to the importance and need for ensuring equity in education. Income was not a significant factor of internet adoption and use in Ghana and Ethiopia.Internet access seems to be wide spread in learning institutions, but women have less access to higher education where Internet provisioning is more available..Women use public phones mainly because of affordability issues. The points of policy intervention therefore need to focus on far more fundamental intergenerational issues of education and income equity than localised ICT aggregated access points.


34

This research is made possible with the support of the IDRC

Thursday, 30 May 13

Documents

Lifting the gender veil on ICT statistics in Africa · ICT Access in South Africa from Census 2012 and Research ICT Africa Household & Individual User Survey 2012 19.7% (Household)