Measuring digital inequality in SA

Transcription

1 Measuring digital inequality in SA Alison Gillwald (PhD) Executive Director: Research ICT Africa Data enquiries:onkokame Mothobi (PhD) WEF Internet for All Data Working Group 13 February 2018, Johannesburg

2 Sampling Sampling is based on Census sample frames. Census divides a country in Enumerator Areas (EAs) with roughly a household density of 200 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 1 2 Simple Random Sampling of Y businesses from

3 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 3 (ITU)

4 Indices - common problems 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. 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

5 Comparing rankings Table 1: Comparing rankings against selected ICT indicators Rankings ADI 3i IDI NRI MCI 1 GB prepaid data USD ICT Indicators Active SIM cards per 100 Fixed-line per 100 Nigeria , ,10 Kenya , ,19 Ghana , ,01 Namibia 31 NA NA 5, ,42 Brazil , ,01 Sources A4AI 2017 EIU 2017 ITU 2016 WEF 2016 GSMA 2016 RIS / RIA Q ITU 2016 ITU 2016 Source: Esselaar S, Gillwald A & Stork C (2017) Analysis Instead of Summation: Why Indices Are Not Enough for ICT Policy and Regulation 5

6 Sample 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 Table 1: SA EA sampling Rural Urban Total Sample of households HHs per EA Sampled EAs

7 Individual sample structure Male Female Urban Rural RIA, % 55% 65% 36% Census, % 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% 7

8 Household Indicators 80 Percentages Fixed line Computer Radio TV Internet 2011/Census 2012/RIA 2017/RIA 8

9 Household access to electricity National Urban Rural Ghana 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. 9

10 Household access to electricity vs Mobile and Internet penetration - individual level Electricity Mobile Internet Ghana 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 10

11 Household access and use of the Internet Smartphone effect ADSL Mobile phone USB Dongle/MIFI 36, ,71 Fiber 2, Percentages 11

12 Household reasons for not using the Internet Cost of equipment too high Privacy or security concerns Cost of service too high Do not know how to use it Do not need the Internet Internet not available in the area Have access to the Internet elsewhere 12

13 Individual Adoption of ICT National Male Female No 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 13

14 Comparison of adoption patterns between 2012 and ,2 83,2 87,5 91,3 75 Percentages ,5 14,2 19,4 0 Mobile phone Ownership Prepaid Smartphone More than 1 active SIM card Smartphone penetration increasing while mobile phone adoption is saturated. Shifts only observed in the type of mobile phone with demand for smartphone increasing. 14

15 Adoption of ICT - urban rural divide National Urban Rural Basic Phone 36% 25% 39% Feature Phone 8% 7% 8% Smartphone 56% 54% 33% 15

16 Adoption of ICT by age group 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 16

17 Literacy rate and adoption of mobile phones and Internet Yes With difficulty Not at all Read 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 17

18 Device used to access the Internet for the first time ,9 39,4 34,6 30 Percentages ,1 10,9 16,5 6, ,8 1,2 Male Female Urban Rural 0,4 Mobile phone Computer Tablet 18

19 Employment status, adoption and affordability Mobile phone Internet Internet expensive Student 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% 19

20 Unemployment, Adoption and affordability Price (ZAR) Q2 2014Q4 2015Q2 2015Q4 2016Q2 2016Q4 2017Q2 2017Q4 Cell C MTN Vodacom Telkom Virgin Mobile Source: RIA African Mobile Pricing (RAMP) Index 20

21 Price of 1G data Egypt 1,69 Ghana Uganda 2,24 2,27 Tunisia Nigeria 2,8 2,8 0 0,7 1,4 2,1 2,8 Source: RIA African Mobile Pricing (RAMP) Index 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. 21

22 Price, Internet penetration , ,5 0 7, ,8 2,94 2,27 2,25 2,64 2,39 South Africa Nigeria Kenya Ghana Tanzania Mozambique Rwanda 14 Internet 22 1GB Price

23 Saving mechanisms Special data promotions 49,72 Public WiFi 31,09 Home Internet 21,67 None 7, , ,5 50 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. 23

24 Activities on the Internet 10% 18% 37% 10% 10% 14% Social Media Game apps News Educational Search tools Weather 24

25 Activities on the Internet Urban Rural Urban Rural Educational 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 4,9% 8% 7% 8% services Job searching 26,1% 26,1% 26,2% Online banking 3,8% 16,8% 20,8% 5,5% 32% 22% Internet in South Africa is mostly used for social networking 25

26 Reasons for not using the Internet National Male Female No 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% 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 26

27 Reasons for no mobile phone I 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% 0 1% How many active SIM cards do you have 1 94% 2+ 5% Do you plan to buy a phone in the next 6 months 39% 27

28 Mobile money vs Bank account ownership Mobile money Bank account Ghana 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 28

29 Mobile money vs bank account ownership Airtime top up 48,49 Bill payments 22,58 Salary payments Insurance payments Receive pension 3,89 7,67 12, , ,5 50 Majority of those who subscribed to mobile money use it to purchase airtime voucher 29

30 Reasons for not using mobile money I do not trust mobile money Do not have a mobile phone No one to send money to or receive money from Mobile money is too complicated Mobile money is expensive Most people do not use mobile money in SA because there is no one to send to (and though large numbers underbanked, banked. 30

31 social media use and devices used for access 58% 42% Most people use smartphones to access their social media 90 Social media No 80,4 67,5 Percentages 45 22,5 0 10,5 10,8 3,7 Computer Feature phone Smartphone Tablet 31

32 Information shared on social media / Privacy issues 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% 32

33 Social media activities Chatting 100 Marketing products/services Making proffessional and business contacts Following government pages Online calls Sharing video/pictures/music making new friends Getting opinion/sharing experience Reading news looking for educational content Playing games Social media is mostly used for chatting with friends and making online calls 33

34 Microwork 50 10% 20% Flexibility Fill the gaps in flactuations of income to gain experience July 42,55 37,5 34% Percentages 25 12,5 4,34 5,37 37% 7,37 9,32 11,25 12,68 16,07 0 Ride hailing Shopping for delivery Online tasks Cleaning and laundry Male Males are more likely to do online jobs than females except for shopping and ride hailing 34 Female

35 Quality of service 35

36 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

37 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 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.

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39 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 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.