1. Factors influencing use of mobile data services among women in Myanmar Ayesha Zainudeen, Chiranthi Rajapakse, Helani Galpaya, Suthaharan Perampalan CPRsouth 2016 This work was carried out with financial support from the UK Government's Department for International Development and the International Development Research Centre, Canada. The qualitative data collection was co- funded by USAID and Australian Aid through the GSMA Connected Women program.

2. When women in Myanmar buy phones as likely as men to buy ‘touch phones’ 1 Base: Day wage earners Source: LIRNEasia Baseline Survey (2015) “A touch phone is high class, and you can play games with it. Keypads are outdated” -Female FGD respondent, 18-28, SEC C/D, Pantanaw

3. However there is a slight gap in data service use… 2 Base: Mobile owners Source: LIRNEasia Baseline Survey (2015) What mobile owners do with their mobiles % of mobile owners

4. Past work Do gender disparities in internet access reflect other socio economic disparities? Varying evidence Odds of adopting more than voice use; found that gender was significant once other variables were controlled for (Zainudeen et al. 2011) Female gender showed a negative and significant relationship with Internet use, once other variables (income, education, age, employment status, work experience, marital status) were controlled for (Deen-Swarray et al. 2013) Gap between Internet users and nonusers was associated with income and age, but no longer with gender and race, once other variables are controlled (Rice and Katz, 2003) 3

5. Questions Is gender a barrier to mobile data service uptake in Myanmar? If yes, why? Are the reasons different in urban versus rural settings? 4

6. Data sources Quantitative – 2015 nationally representative survey of ICT use in Myanmar – 8,138 households and over 12,000 individuals – February, March 2015 – General population aged 15-65 Qualitative – 25 in-depth-interviews and 11 focus group discussions – 91 respondents (female and male) – Yangon, Pantanaw (urban/ rural) – July 2015 – Respondents aged 18 - 65 5

7. Data sources – Quantitative survey details Representative of 97% of households and 96.3% of population aged 15– 65 At 2.5% margin of error, representative of – 96.3% population aged 15-65 – 97% of total households – 91.8% of total population In all accessible areas of Myanmar – 32 townships excluded due to security concerns Stratified four stage PPS cluster sampling design used; stratification by: 1)Population size (big cities; other major cities; smaller townships) 2)Geographic region (Delta, Eastern hills, Long coast, etc.) 3)Urban/rural 6

8. Binary logistic regression used to model Mobile adoption A binary logistic regression is a way of modeling the probability of an event when the event is a binary outcome, so adoption = 1 (yes) or 0 (no). Coefficient in logistic regression cannot be directly interpreted  Odds Ratio is calculated from it. – The ‘Odds’ is directly related to (but not the same as) the probability of something happening. – Odds = probability of adoption / probability of not adopting Odds ratio implies for each unit increment of the independent variable, the odds of mobile data service changes by a percentage of (odds ratio – 1) 7

9. 8 Women in Myanmar less likely to use mobile data (29% lower odds of adopting than men) Coefficient ( β ) Odds Ratio Change in Odds Ratio due to 1 unit increase in variable p-value Gender (0=male; 1=female)-0.3420.711-28.90.002 Urban Rural Status ( 0=Urban, 1=Rural)-0.350.705-29.50.011 Primary education being the highest obtained (0=no, 1=yes)-0.2050.815-18.50.713 Secondary education being the highest obtained (0=no, 1=yes)0.7862.194119.40.144 Tertiary education being the highest obtained (0=no, 1=yes)1.8256.202520.20.001 Having television at home (0=no, 1=yes)0.0381.0383.80.819 Having Landline at home (0=no, 1=yes)0.0651.0676.70.712 Having Radio at home (0=no, 1=yes)0.2971.34534.50.034 Having Electricity at home (0=no, 1=yes)0.4141.51451.40.038 Status Married (0=Single, 1=Married)-0.5650.568-43.20.000 Employment status (0=not employed; 1=employed)-0.0480.953-4.70.695 Perceived Economic impact of mobile (scalar variable: 1-5)-0.2520.778-22.20.002 Perceived knowledge impact of mobile (scalar variable: 1-5)0.3921.47947.90.000 Perceived Social impact of mobile (scalar variable: 1-5)-0.1310.877-12.30.099 Perceived emotional impact of mobile (scalar variable: 1-5)0.2411.27227.20.004 Age classification-0.5070.602-39.80.000 No of Business Contact0.0531.0545.40.004 Monthly household expenditure (MMK)0.3541.42542.50.000 Constant-1.4091.42542.50.000 Base: Mobile owners Source: LIRNEasia Baseline Survey (2015)

10. 9 Base: Mobile owners Source: LIRNEasia Baseline Survey (2015 )

11. Why is gender a barrier to mobile data service uptake? 10 Transcripts analysed used n vivo

12. Why is gender a barrier to mobile data service uptake? Affordability of usage charges “Women also have to take care of the family. Instead of using phone charges, they have to use it for food.” Female, working non owner, 36, SEC D/E, Yangon 11

13. Why is gender a barrier to mobile data service uptake? Low digital skills a problem Women often not present at time of purchase of phone. Miss out on initial introduction Mostly use pre installed apps. Dependent on others to learn skills “I have no clue about the mobile phone. Even if they [phone shops] explain I don’t know anything and when I ask a question they might think ‘what are you talking about?’…so I stay away from the mobile shop.” Female, working female owner, 25, SEC C, Yangon “I do [learn from others], but to learn from my elder brother is difficult because he is too busy and comes home late, and to learn from my sister is also not possible as she is occupied with her studies so I don’t want to bother them.” Female working owner, 25, SEC C, Yangon 12

14. Why is gender a barrier to mobile data service uptake? 13 Lack of comfort with device Many female respondents worry that they may ‘damage’ the phone by experimenting with it “ Boys are more curious and they can fiddle with the mobile, so they know better than girls…When you see phone repair shops, it’s very rare to see a lady technician, there are only male technicians.” Female non working owner, 19, SEC C, Yangon “We don’t fiddle with it a lot because we are concerned that we would damage it. We don’t want to waste money. I can’t afford a new one.” FGD, female owners, 18–29, working, SEC B-C, Yangon

15. Why is gender a barrier to mobile data service uptake? Limited awareness on use of data services Seen among both male and female respondents Mostly aware of social networking, chat applications News equated with Facebook newsfeed “We have all heard of ‘Internet’ but don’t know what it is. It’s installed in the phone. You can see the other side’s face when you call. You can make friends – boyfriend and girlfriend.” FGD, female non-owners 46–65, non-working, SEC D/E, rural “Younger people have internet, they upload pictures and talk to friends. I had a look when somebody used it.” Female non-owner, 35, working, SEC D/E, rural 14

16. Why is gender a barrier to mobile data service uptake? Limited awareness on use of data Rural respondents had more restricted concept of uses when compared to urban Few urban respondents used Google, dictionary apps but these were a minority 15

17. Why is gender a barrier to mobile data service uptake? Perceptions about negative implications of internet use More among rural respondents ‘Heard’ from others Linked to the lesser use of Internet Perceptions reduce once women started using data services themselves Lack of knowledge on internet safety Low awareness on how to control privacy settings “Both my parents and friends [disapprove]. At the beginning, I was using Internet for about one month. But all discouraged me of using Internet so gradually I stopped and now I am using only for calling.” FGD, working female mobile owners, SEC C/ D, 18-25, rural ) “Those under 18 shouldn’t use the internet. They are still immature. They could fall in love, meet outside, and get abused. But if they are older it is not a problem.” FGD, female owners, 18–29, working, SEC C/D, rural “I heard about inappropriate photos on the internet.” FGD, female non-owners, 18-29, non-working, SEC C/D, Yangon 16

18. Policy implications and recommendations 17

19. Policy implications and recommendations Reduce costs of data services “I spent too much money for the internet so I had no credit for calling anymore.” FGD female owner, 18-29, working, SEC D/E, rural Cheaper plans, micro-top-ups Provide content that is relevant to women e.g. Dictionary apps, apps to learn English, prayer apps (among older women), and weather-related information. 18

20. Policy implications and recommendations Improve digital skills “Moderator: “For people like yourselves to be more friendly with Internet, what should we do?” Respondent: “Why don’t you show on TV how to use Internet, how to download applications and how to open a Facebook account?” Female working user, 25 years, SEC C, Yangon Teach basic digital skills Include education on privacy and security settings Video tutorials Operator’s role; provide incentives to agents Encourage tertiary education 19

21. Thank you. 20