Measuring digital inequality in SAAlison Gillwald (PhD)

Executive Director: Research ICT Africa

Data enquiries:Onkokame Mothobi (PhD) omothobi@researchictafrica.net

WEF Internet for All Data Working Group 13 February 2018, Johannesburg

Sampling Sampling is based on Census sample frames. Census divides a country in Enumerator Areas (EAs) with roughly a household density of 200

2

National Census Sample Frame or Census

Split enumerator areas (EAs) into rural EAs

Separate PPS sampling of EAs for rural and

Listing of all households (HH) and all

Simple Random Sampling of X

households from

Simple Random Sampling of Y

businesses from Simple Random Sampling of 1

SDG - ICT targets

‣ 7 ICT indicators covering 6 targets under Goals 4, 5, 9, and 17 • Target 4a: Proportion of schools with access to the Internet for pedagogical purposes • Target 4a: Proportion of schools with access to computers for pedagogical purposes • Target 4.4: Proportion of youth/adults with ICT skills, by type of skills • Target 5b: Proportion of individuals who own a mobile telephone, by sex (ITU) • Target 9c: Percentage of the population covered by a mobile network, broken down by technology

(ITU) • Target 17.6: Fixed Internet broadband subscriptions, broken down by speed (ITU) • Target 17.8: Proportion of individuals using the Internet (ITU)3

Most popular include the World Economic Forum’s Network Readiness Index (NRI), the ITU’s ICT Development Index (IDI), the Alliance for Affordable Internet’s Affordability Drivers Index (ADI), GSMA’s Mobile Connectivity Index and the Inclusive Internet Index (3i) from the Economist Intelligence Unit/ Facebook.

Indices - common problems

‣ Summing up indicators that explain the same factor (muti-colinearity)

‣ Summing up indicators that are all highly correlated to GDP per capita

‣ Measuring affordability as prices expressed as % of GDP per capita, masking inequality (SA one of highest GINI co-efficients in the world)

‣ Using first world indicators such as fixed- line penetration and wired broadband

4

Comparing rankings

Source: Esselaar S, Gillwald A & Stork C (2017) Analysis Instead of Summation: Why Indices Are Not Enough for ICT Policy and Regulation https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3043719

5

Table 1: Comparing rankings against

selected ICT indicators

Rankings ICT Indicators

ADI 3i IDI NRI MCI1 GB

prepaid data USD

Active SIM cards per

100

Fixed-line per 100

Nigeria 13 45 137 119 98 3,20 83 0,10

Kenya 30 51 129 86 105 5,00 82 0,19

Ghana 26 49 112 102 96 2,46 128 1,01

Namibia 31 NA 120 99 NA 5,89 99 7,42

Brazil 10 18 63 72 56 8,48 124 21,01

Sources A4AI 2017

EIU 2017

ITU 2016

WEF 2016

GSMA 2016

RIS / RIA Q4 2016

ITU 2016

ITU 2016

Sample

6

Table 1: SA EA sampling Rural Urban Total

Sample of households 720 1080 1800

HHs per EA 24 24 24

Sampled EAs 30 45 75

Two sampling frames were available for South Africa based on the 2011 census: Enumerator Areas (EAs) and Small Layers (SAL)

Out of 90, 425 EAs, a total of 75 EAs were sampled using SRS 47 urban areas and 32 rural areas were sampled ( 60% urban : 40% rural split)

The target sample of 1800 was spilt in to 60% urban and 40% rural, yielding a target of 720 rural and 1080 urban households

Household and individual weights were then constructed based on the census and world data on urban and rural, male and female proportions

Individual sample structure

7

Male Female Urban RuralRIA, 2017 45% 55% 65% 36%Census, 2011 49% 51% 60% 40%Student 18% 18% 19% 17%Unpaid housework 1% 13% 11% 6%Retired 10% 11% 9% 12%Unemployed, active 22% 24% 28% 20%Unemployed, not active 7% 9% 12% 6%Disabled, not active 1% 1% 1% 1%Employed 33% 21% 16% 32%Self employed 8% 3% 3% 6%

Household Indicators

8

Perc

enta

ges

0

20

40

60

80

Fixed line Computer Radio TV Internet

11

7967

87 20

7862

261835

7568

2115

2011/Census 2012/RIA 2017/RIA

Household access to electricity

9

National Urban RuralGhana 85% 96% 67%Kenya 42% 86% 19%

Mozambique 24% 46% 10%Nigeria 64% 84% 44%Rwanda 29% 60% 20%

South Africa 89% 90% 86%Tanzania 33% 61% 19%

By international standard South Africa is highly electrified and the disparity between urban and rural is insignificant. Ghana’s urban areas are more electrified than South Africa with one but rural areas in SA are more developed than Ghana.

Household access to electricity vs Mobile and Internet penetration - individual level

10

Electricity Mobile InternetGhana 85% 78% 27%Kenya 42% 87% 27%Mozambique 24% 40% 9%Nigeria 64% 65% 29%Rwanda 29% 46% 7%South Africa 89% 85% 48%Tanzania 33% 59% 14%

More developed countries have accelerated adoption rates than low income countries

Household access and use of the Internet

11

Smartphone effect

ADSL

Mobile phone

USB Dongle/MIFI

Fiber

Percentages0 10 20 30 40

2,1

34,71

36

36,5

12

Household reasons for not using the Internet

0

5

10

15

20

25

30Costofequipmenttoohigh

Costofservicetoohigh

DonotneedtheInternet

HaveaccesstotheInternetelsewhere

Internetnotavailable inthearea

Donotknowhowtouseit

Privacyorsecurityconcerns

Individual Adoption of ICT

13

National Male FemaleNo bank account 40% 37% 42%

Yes, my own 57% 60% 54%I use someone 3% 2% 4%

computer/laptop 14% 17% 12%Mobile phone 84% 84% 85%Basic Phone 36% 33% 39%

Feature Phone 8% 7% 10%Smartphone 56% 60% 52%

There is a slight gender gap in the adoption of computers and smartphones. In South Africa the adoption of smartphones has reached the critical mass and the

gap between male and females is diminishing

Comparison of adoption patterns between 2012 and 2017

14

Perc

enta

ges

0

25

50

75

100

Mobile phone Ownership Prepaid Smartphone More than 1 active SIM card

19,4

55,5

91,383,2

14,2

51

87,584,2

2012 2017

Smartphone penetration increasing while mobile phone adoption is saturated. Shifts only observed in the type of mobile phone with demand for smartphone

increasing.

Adoption of ICT - urban rural divide

15

National Urban Rural

Basic Phone 36% 25% 39%

Feature Phone 8% 7% 8%

Smartphone 56% 54% 33%

Adoption of ICT by age group

16

15-24 25-54 55-64 65+Sample structure 26% 53% 12% 9%

Basic Phone 14% 31% 51% 45%

Feature Phone 9% 6% 8% 6%

Smartphone 56% 51% 28% 19%

Internet 78% 72% 58% 37%

Smartphone driving Internet adoption

17

Yes With difficulty Not at allRead

National 78% 16% 6%Mobile phone 88% 74% 57%

Internet 73% 50% 0%Write

National 77% 16% 6%Mobile phone 88% 77% 55%

Internet 73% 16% 0%There is a slight gender gap in the adoption of smartphones in South Africa the adoption of smartphone has reached the critical mass/tipping point and the gap

between male and females is diminishing

Literacy rate and adoption of mobile phones and Internet

Device used to access the Internet for the first time

18

Perc

enta

ges

0

10

20

30

40

Male Female Urban Rural

0,41,20,816,1

16,5

10,915,1

30

39,434,6

37,9

Mobile phone Computer Tablet

Employment status, adoption and affordability

19

Mobile phone Internet Internet

expensiveStudent 79% 76% 56%Unpaid housework 84% 77% 59%Retired 76% 37% 41%Unemployed, active 84% 64% 44%Unemployed, not active 76% 50% 47%

Disabled, not active 70% 36% 36%Employed 91% 83% 43%Self employed 91% 77% 37%

Unemployment, Adoption and affordability

20

Price

(ZAR

)

0

50

100

150

200

2014Q2 2014Q4 2015Q2 2015Q4 2016Q2 2016Q4 2017Q2 2017Q4

Cell C MTN Vodacom Telkom Virgin Mobile

Source: RIA African Mobile Pricing (RAMP) Index

Price of 1G data

21

Data prices in South Africa are high and remain unaffordable to the poor. The reason behind the 50% of the poor that are still not connected.

Egypt

Ghana

Uganda

Tunisia

Nigeria

0 0,7 1,4 2,1 2,8

2,8

2,8

2,27

2,24

1,69

Source: RIA African Mobile Pricing (RAMP) Index

Price, Internet penetration

22

0

12,5

25

37,5

50

South Africa Nigeria Kenya Ghana Tanzania Mozambique Rwanda

Internet 1GB Price

79

14

272729

48

2,392,642,252,272,942,87,267,26

2,8 2,94 2,27 2,25 2,64 2,39

Saving mechanisms

23

Special data promotions

Public WiFi

Home Internet

None

0 12,5 25 37,5 50

7,67

21,67

31,09

49,72

Majority of South African residents access Internet on a mobile phone daily. The study shows that most South African Internet users use special data packages (49.72%) and Public WiFi (31%) as a saving mechanism.

Activities on the Internet

24

18%

10%

10%

10% 14%

37%

Social Media Game apps News EducationalSearch tools Weather

Activities on the Internet

25

Internet in South Africa is mostly used for social networking

2012 2017 Urban Rural Urban RuralEducational 9,8% 44,0% 44,7% 41,9% 45% 43%

Social networking 51,7% 73,2% 75,1% 67,7% 71% 76%Work related 26,9% 30,4% 17,0%

Shopping 3,2% 10,4% 12,8% 3,5%Government

services4,9% 8% 7% 8%

Job searching 26,1% 26,1% 26,2%Online banking 3,8% 16,8% 20,8% 5,5% 32% 22%

Reasons for not using the Internet

26

36% of those who do not use the Internet, stated that lack of devices as the main inhibitor to using the Internet. Affordability is also an issue with more females stating that Internet is more expensive

National Male FemaleNo access device 36% 38% 35%

No interest 38% 15% 16%Dont know how to use it 9% 12% 7%

Too expensive 15% 11% 18%Spouse or parents dont allow 5% 1% 8%

Reasons for no mobile phone

27

2012 2017I cannot afford it 63% 43%

No mobile coverage 2% 1%No electricity 9% 1%

Privacy concern 12%

National

Did you use mobile phone in the past three months 38%

How many active SIM cards do you have

0 1%

1 94%

2+ 5%

Do you plan to buy a phone in the next 6 months 39%

Mobile money vs Bank account ownership

28

Mobile money Bank accountGhana 54% 27%Kenya 83% 34%

Mozambique 11%Nigeria 3% 34%Rwanda 26% 28%

South Africa 8% 57%Tanzania 45% 11%

Mobile money is common in countries where majority of residents are unbanked. South Africa use more banking facilities than in other countries

Mobile money vs bank account ownership

29

Majority of those who subscribed to mobile money use it to purchase airtime voucher

Airtime top up

Bill payments

Salary payments

Insurance payments

Receive pension

0 12,5 25 37,5 50

3,89

7,67

12,14

22,58

48,49

Reasons for not using mobile money

30

Most people do not use mobile money in SA because there is no one to send to (and though large numbers underbanked, banked.

0510152025303540

Donothaveamobilephone

Noonetosendmoneytoorreceivemoney

from

Mobilemoneyisexpensive

Mobilemoneyistoocomplicated

Idonottrustmobilemoney

social media use and devices used for access

31

Most people use smartphones to access their social media

58%42%

Social media No

Perc

enta

ges

0

22,5

45

67,5

90

Computer Feature phone Smartphone Tablet

3,7

80,4

10,810,5

Information shared on social media / Privacy issues

32

National Male Female

Real name 73,3% 72,0% 74,4%

Gender 62,7% 62,1% 63,2%

Marital status 56,1% 57,1% 55,1%

Mobile number 46,3% 45,7% 46,9%

Pictures and video of family members 36,5% 38,0% 35,1%

Religion 15,0% 53,2% 52,9%

Political view 28,1% 24,4% 31,4%

Sexual orientation 11,1% 13,6% 8,9%

Social media activities

33

Social media is mostly used for chatting with friends and making online calls

0

10

20

30

40

50

60

70

80

90

100Chatting

Onlinecalls

Sharingvideo/pictures/music

makingnewfriends

Readingnews

Playinggameslookingforeducationalcontent

Gettingopinion/sharingexperience

Makingproffessionalandbusinesscontacts

Marketingproducts/services

Followinggovernmentpages

Microwork

34

Males are more likely to do online jobs than females except for shopping and ride hailing

Perc

enta

ges

0

12,5

25

37,5

50

Ride hailing Shopping for delivery Online tasks Cleaning and laundry

16,0711,259,32

5,37

12,68

42,55

7,374,34

Male Female

10%

34%

37%

20%

FlexibilityFill the gaps in flactuations of income to gain experienceJuly

Quality of service

35

Vodacom performs better in both measures followed by MTN. Telkom performance, however, is worse in the two measures. This provides some explanation for why consumers are not migrating to the cheapest Telkom services.

Data source: SpeedCheckers, 2018

Using the value for money index, constructed from 1GB data prices, download and upload speed, shows between 2014Q1 and 2015Q3 the two dominant operators Vodacom and MTN offered high quality. In the same period Telkom’s quality was the lowest. However, since 2016Q1, it seems that smaller operators improved their quality (reflected in increased investments) catching up with dominant operators in 2 0 1 6 Q 2 . Vo d a c o m S A’s h i g h p r i c e s a re accompanied by higher Internet speeds compared to MTN SA and Cell C, which are performing more poorly on the Value for Money Index, based on average download/upload speed (in Mbps) divided by 1GB basket costs.

Latency- The previous figures show the logarithmic latency to Measurement Lab servers around the world from South Africa as measured from our MySpeed Test probes since 2015.

- In the first figure, between 2015 and 2017, on average, all operators had relatively high latency levels, with MTN and Telkom mobile having the highest levels of variability.

- When we analyse latency on different years, in 2015 and 2017, both MTN and Vodacom had very variable latency levels, although they also had the lowest levels of latency.