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

WSIS Forum 2013: Measuring ICT and Gender

1 Thursday, 30 May 13

Outline Conceptualising gender and ICTs Methodology Women and income Women and education Marital status Pay phone Mobile access and use Internet access and use Computer access and use Modelling gender and ICT 2 Thursday, 30 May 13

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.

3 Thursday, 30 May 13

Methodology

4 Thursday, 30 May 13

Rwanda

1,200

500

1,700

Oversampling

South Africa

1,600

600

2,200

Clustering

Tanzania

1,200

500

1,700

None Response

Methodology

RIA Household, Individual, SME500 survey1,700 2013:Sample Botswana, Frame Uganda 1,200 Confidence Level Cameroon, Kenya, Tunisia Ethiopia,Ghana, 1,200 500Mozambique,Namibia, 1,700 Design Factor Nigeria, Africa,Uganda, Tanzania. TotalRwanda,South 15,300 6,200 21,500 Absolute precision Population Proportion Minimum Sample Size

Weighting

Four weights will be co businesses and public inverse selection probab when applied.

Thursday, 30 May 13

Household weight:

HH

Individual weight:

IND

Business Weight:

5

Busw

SA Census and RIA survey 2011 Fixed, mobile, PC, Internet penetration, TV, radio Table  :  Summary  of  ICT  Access  in  South  Africa  from  Census  2012  and  Research  ICT  Africa   Household  &  Individual  User  Survey  2012 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%

table : Summary of 19.7% (Household) ICT Access in South 33.7% (Individual) Africa from Census 2012 and Research ICT Africa Household & Individual User Survey 2012

88.9%

62.1%

Households with Internet

Cellphone Ownership Thursday, 30 May 13

72.7%

84.2%

6

Conceptual framework Country Dummy Ethnicity/culture Marital status Income Age Education Exclusion

Access Ownership

GENDER

Use Affordability/ Skills

Inclusion Pay phones Fixed phones Mobile phones Internet

Impact Human, economic and social development

GENDER ANALYSIS CONCEPTUAL FRAMEWORK

7 Thursday, 30 May 13

Analysis & Findings

8 Thursday, 30 May 13

Income Table 1 – General sample statistics of randomly selected individuals % females Country

Average individual income US $

Average income US$ ppp

All

All

Male Female

Male

Average age

Female

% with a bank account 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

9 Thursday, 30 May 13

Income and Access to Bank Accounts

Male

All

Female

Male

Average Monthly Individual Income US$ PPP South Africa

914 579

Botswana Namibia

387 252

78

Ghana

244

134

Kenya

214

Cameroon

189

Uganda Tanzania

116 104

144 102 115 68

Ethiopia

10130

Rwanda

72 42

378 181

Nigeria

328

Female

share of individuals with Bank Accounts South Africa

58.9

Namibia

56.3

Botswana

62.7 51.1

48.4

Kenya

52.4

44.5

Nigeria

30.5

Ghana

29.4

55.7

45.6

57.6 39.8 35.5

Rwanda

16.3 17.4 15.2

Uganda

15.2 18.710.7

60.3

36.4 20

24.5

Cameroon 10.910.8 10.9 Tanzania 6.27.4 5.1 Ethiopia 3.7 4.3 3

10 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

11 Thursday, 30 May 13

Education Table 2 – Gender disaggregated educational statistics Highest Education: Tertiary

Highest Education: Secondary

Highest Education: Primary

All

Male

Female

All

Male

Female

All

Male

Female

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%

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%

Botswana

12 Thursday, 30 May 13

Gender disaggregated Educational Statistics

All

Male

All

Female

Male

Female

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

share of individuals who have attained tertiary education

South Africa

65.3

Botswana Kenya

26.2

Botswana Nigeria

32.7

20.5

21.9

14.8

19.5

22.3 19.4

9.6

13.3

18

9.1

53.9

53.9

Kenya

41.4

Nigeria

37.8

40.3

Ghana

36.6

38.9

Uganda

29.9

Namibia

27.8

Cameroon South Africa

65.8

Rwanda

41.1

33.3 24.3

64.8 54

41.7 34.9 34.8

25.6 30.4

22.8 19.2 26.2 15.3 16.813.7

Tanzania 11.114.97.8 Ghana

10.5

Uganda

9.1

15.8 11.2

6.3

Cameroon

7.4

8.6

6.2

Namibia

7.1

8.4

6.1

Ethiopia 2.1 2.4 1.8 Tanzania 1.4 1.5 1.2 Rwanda 1.2 1.7 0.7

6.2

Ethiopia 1.8 1.3 2.4 All

Male

Female

share of individuals with primary schooling as highest level of education

Tanzania 72 73.3 70.9 Rwanda 58.4 59.4 57.4 Namibia 45.2 42.4 47.4 Uganda 44.2 44.6 43.7 Cameroon 30.6 30.7 30.6 Kenya 27.4 22.8 30.2 Ghana 27.3 25.3 28.9 Botswana 18.719.3 18.2 Nigeria 18.718.1 19.3 South Africa 1713.2 20.2 Ethiopia 13.1 16.4 8.9

13 Thursday, 30 May 13

Mobile Phone Adoption share&of&individuals&that&own&a&mobile&phone...(16+&for&2007/2008)& Ugand Tanzan Rwand Ethiopi Camer Namibi South& Botsw a& Kenya& ia& a& a& Ghana& oon& Nigeria& a& Africa& ana&

Male&

Female&

NaQonal&

76.1%& 82.7%& 2011/2012& 59.7%& 58.9%& 59%& 2007/2008& 86.3%& 82.4%& 2011/2012& 56.4%& 64.9%& 61%& 2007/2008& 55.2%& 57.0%& 56%& 2011/2012& 53.3%& 45.4%& 49%& 2007/2008& 76.5%& 54.9%& 66%& 2011/2012& 2007/2008& 44.2%& 44.9%& 45%& 2011/2012& 33.1%& 39.4%& 36%& 2007/2008& 61.2%& 58.2%& 60%& 2011/2012& 60.8%& 57.2%& 59%& 2007/2008& 2011/2012& 24.8%& 10.4%& 18%& 3%& 2007/2008& 27.6%& 21.2%& 24%& 2011/2012& 7.5%& 10%& 2007/2008& 11.8%& 41.7%& 30.9%& 36%& 2011/2012& 26.2%& 17.6%& 21%& 2007/2008& 83.8%& 67.9%& 2011/2012& 56.0%& 46.9%& 50%& 2007/2008& 56.2%& 34.5%& 47%& 2011/2012& 26.4%& 12.2%& 19%& 2007/2008&

80%& 84%&

74%&

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) 14 Thursday, 30 May 13

Table 3: Monthly Expenditure on Mobile Phone

Monthly average mobile expenditure in US$ PPP All

Male

Female

Monthly average mobile expenditure in US$ 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

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

Ethiopia

15 Thursday, 30 May 13

Average Monthly Expenditure on Mobile Phone Use

Male

Female

Monthly Expenditure on Mobile Phone in US$ PPP

37.75

South Africa

34.65

Botswana

Nigeria

23.78

Cameroon

23.01

Tanzania

22.05

Ghana

21.64

17.08 14.43 18.57 23.02 19.10

18.82

Kenya

13.95

Uganda

Ethiopia

24.91

26.07

Namibia

Rwanda

20.60

7.79 6.67

16.48 11.27

8.89 7.08

16 Thursday, 30 May 13

Table 4: Mobile phone use and access across 11 African countries Mobile Phone (Multiple Responses) All

Main reasons for using the mobile phone

Why individuals do not have a mobile phone

Male

Female

missed call/please call me

83.8%

85.7%

86.5%

sending/receiving text

83.2%

85.2%

88.2%

playing games

48.0%

46.3%

42.7%

sending/receiving money

18.8%

27.5%

34.9%

browsing the internet

17.2%

21.5%

16.0%

downloading applications

15.1%

18.2%

12.9%

reading/writing emails

13.6%

16.1%

11.7%

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%

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%

17 Thursday, 30 May 13

Mobile phone use and access across 11 African countries

All

Male

Female

All

83.8

85.7

86.5

sending/receiving text

83.2

85.2

88.2

playing games

48.0

46.3

Female

reasons why individuals do not have a phone...

main reasons why individuals use mobile phones

missed call/please call me

Male

cannot afford it

no electricity at home to charge

80.9

56.7

57.7

81.3

83.7

55.8

42.7 phone got stolen 19.5 21.4 18.8

sending/receiving money 18.8 27.5 34.9 no coverage where I live 18.6 19.9 16.4 browsing the internet 17.2 21.5 16 don’t have anyone to call 19.1 19.1 19.3 downloading applications15.1 18.2 12.9

reading/writing emails13.6 16.1 11.7

phone is broken7.47.58

18 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.

19 Thursday, 30 May 13

share&of&individuals&that&use&the&Internet...& Cameroo South n Uganda Kenya Tanzania Rwanda Ethiopia Ghana Nigeria Namibia Africa Botswana

Male%

32.6%%

2011/2012

10.1%%

2007/2008

Female%

26.5%%

4.0%% 40.6%%

2011/2012

28.6%%

20.4%% 11.3%% 18.7%% 14.2%% 11.2%% 7.2%% 22.8%% 13.4%% 16.4%% 7.6%% 13.4%% 14.7%% 13.1%% 12.8%% 17.8%% 8.5%% 8.1%% 3.2%% 3.9%% 1.1%%

2007/2008 2011/2012 2007/2008 2011/2012 2007/2008 2011/2012 2007/2008 2011/2012 2007/2008 2011/2012 2007/2008

6.9%%

2011/2012

5.2%%

2007/2008

3.4%% 3.5%%

2011/2012 2007/2008

35.8%%

2011/2012 2007/2008 2011/2012 2007/2008

21.1%% 11.8%% 3.1%% 3.7%% 1.1%%

20.5%% 11.5%%

20 Thursday, 30 May 13

Table 5: Internet use and access across 11 African countries Internet Use All

Whether using the internet increases an individuals contact with people who...

Why individuals do not use the Internet (multiple responses)

Male

Female

share same hobbies/recreational activities

59.6%

59.1%

60.4%

share same political views

30.8%

37.7%

20.1%

share religious beliefs

47.1%

46.5%

47.9%

are family and friends

69.9%

71.6%

67.4%

are colleagues

58.1%

58.3%

57.9%

don’t know how to use it

68.7%

66.8%

70.5%

no computer/internet connection

65.2%

65.4%

65.1%

don’t know what the Internet is

64.6%

60.7%

68.5%

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%

21 Thursday, 30 May 13

Internet use and access across 11 African Countries All

Male

Female

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

share same hobbies/recreational activities

share same political views

share religious beliefs

59.6

59.1

All

Male

why individuals do not use the internet (multiple responses)

don’t know how to use it

60.4

66.8

65.2

65.4

don’t know what the Internet is

64.6

60.7

70.5

65.1

30.8 37.7 20.1

47.1

46.5

69.9

58.1

68.5

47.9

71.6

58.3

54.6

53

56

67.4 no interest/not useful

are colleagues

68.7

no computer/internet connection

too expensive

are family and friends

Female

57.9

38.5 38.3 38.8

too slow, limited bandwidth13.415.811.2

22 Thursday, 30 May 13

Computer Use Table 6 Computer use and ownership Share of individuals (15 or older) that use a Computer Country

All

Male

Female

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

Male

Female

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

23

Computer use and ownership All

Male

Female

All

Male

share of individuals (15+) that use a computer South Africa

29.1

Kenya

21.2

Cameroon

29.3

15.1

Namibia Nigeria

36.2

15.6

13 7.5

Ghana

15.9 11.2

10

14.2

23.1 16.2

14.6 10.8

share of computer users that own a personal desktop Ghana

48

39.8

Rwanda

45.3

South Africa

44.4

42.8

Kenya

35.7

Uganda

33.8

31.7

Namibia

30.8

39.8

Cameroon

30.2

Tanzania

18.6

34.4

37.1 37.7 22.7

35.2 24.2

46.4

25.3

14.8

Nigeria 12.212.4 11.7

3.3

Ethiopia 12.110.7 13.8

6.6 Male

4.8 5.6 3.7

Rwanda 3.52.5 4.5 Ethiopia

62.5

14.6 62.4

All

Uganda

Female

22 2

Tanzania 1.9 1.72

share of computer users that own a personal laptop

Nigeria

58.6

Namibia

57.6

Tanzania

43.2

Ghana

41.1

South Africa Kenya

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.

Female

Uganda Ethiopia Cameroon

55.1

19

39.4 25.7

33.9

58.5

56.6

77.1

34.6 23.8

65.1

20.1 16.3

28.8

21.7

19.3 18.5

15.7 18.7 11.8 13.2 21.2 5.2

Rwanda 7.8 16.5 3

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

24

Table 7: Location and main use of computers across 10 African countries Computer (Multiple responses) All

Where do you use a computer...

What do you use your computer for...

Male

Female

work

40.1%

44.4%

33.2%

school/university

33.0%

33.9%

31.6%

library

8.7%

8.2%

9.6%

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%

writing letters, editing documents

75.3%

78.1%

70.7%

calculations using spreadsheets

53.4%

55.1%

50.8%

browsing the internet

72.9%

72.9%

72.9%

programming

40.9%

45.2%

34.2%

remixing content found online

37.7%

41.2%

32.1%

playing games

63.5%

64.4%

62.0% 25

Thursday, 30 May 13

Location and main use of computers (multiple responses)

All

Male

Female

All

where individuals make use of computers...

at home

internet cafe

60.1

61.6

49.3

work

40.1

at a friend’s place

36.6

school/university

33

library 8.78.2 9.6

52.1

44.4

41.5

33.9

44.9

33.2

28.9

31.6

57.7

Male

Female

what individuals use their computers for.....

writing letters, editing documents

75.3

browsing the internet

72.9

playing games

calculations using spreadsheets

78.1

72.9

63.5

53.4

programming

40.9

remixing content found online

37.7

70.7

64.4

55.1

45.2

72.9

62

50.8

34.2

41.2 32.1

26 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 Phones Public Pay Phones Male

How often do you use a public phone

Type of public phone use most

Main reasons for using a public pay phone

What makes you use a particular community/public pay phone

Female

Use of a pay phone in the last 3 month

18.8%

18.6%

More than once a day

6.8%

8.0%

Everyday or almost everyday

13.1%

13.3%

At least once a week

41.6%

40.8%

At least once a month

24.7%

26.9%

Less thank once a month

13.8%

11.0%

Telephone booth (fixed line operator)

16.7%

15.6%

Telephone kiosk, umbrella operator

82.0%

83.9%

do not have a fixed line phone at home

8.4%

8.8%

do not have a mobile phone

23.0%

22.0%

use it because it is cheaper

45.7%

49.1%

easier than having to recharge airtime mobile

13.4%

13.9%

difficulties charging the battery of mobile

6.9%

3.9%

Price of calls

55.9%

58.9%

Convenience (e.g. close to my house)

36.4%

34.7%

Security

3.7%

3.0%

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Access to and use of Public Pay Phones Male

Female

Male

Female

type of public phone mostly used...

frequency with which individuals use a public phone...

41.6

At least once a week

24.7

At least once a month

82.0

83.9

15.6

13.1 13.3

More than once a day 6.8 8

Telephone booth (fixed line operator)

Telephone kiosk, umbrella operator

Male

Female main reasons why individuals use public pay phones...

it is cheaper

45.7

Male

Female

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

49.1 price of calls

no mobile phone

easier than to recharge airtime on mobile

no fixed line phone at home

26.9

13.8 11

Less than once a month

Everyday or almost everyday

16.7

40.8

23.0

55.9

58.9

22.0

13.4 13.9

convenience (proximity)

36.4

34.7

8.4 8.8 security 3.73.0

difficulting charge mobile phone battery 6.9 3.9

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Empirical Findings Income & Education 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;

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Empirical Findings Mobile Adoption 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.

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Internet & Computer Use Being 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. 32 Thursday, 30 May 13

Conclusion

This 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. 33

Thursday, 30 May 13

This research is made possible with the support of the IDRC

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