1. Multiple Indicator Cluster Surveys Data Dissemination and Further Analysis Workshop Access to Mass Media and Use of ICT Life Satisfaction and Tobacco and Alcohol Use MICS4 Data Dissemination and Further Analysis Workshop

2. Background Late additions to standard MICS questionnaires, now part of core questionnaires Added as a result of work on adolescents and young people Limited experience (mainly MICS) Both in Women and Men questionnaires

3. Access to Mass Media… Life Satisfaction Use of Tobacco and Alcohol Madagascar (South) WWW Belarus W – M Moldova W – M Pakistan Sindh

4. Access to Mass Media and Information and Communication Technologies

5. Background: Access to Mass Media and ICT Mass media –Newspapers –Radio –Television ICT –Computers –Internet

6. Exposure to influences outside the local community Can be used as a starting point for any mass media campaigns Indicator: All three media, once a week Table reports individual items, as well as no exposure Can be produced for men Table MT.1: Exposure to mass media Percentage of women age 15-49 years who are exposed to specific mass media on a weekly basis, Serbia 2010 Percentage of women age 15-49 who: All three media at least once a week [1] No media at least once a week Number of women age 15- 49 years Read a newspaper at least once a week Listen to the radio at least once a week Watch television at least once a week Age15-1976.378.697.963.6.5659 20-2479.479.897.565.2.3705 25-2975.473.697.358.2.9846 30-3474.269.598.057.21.0775 35-3976.173.098.358.9.7791 40-4469.469.999.352.6.3703 45-4971.965.399.049.8.0905 AreaUrban79.870.798.160.4.63155 Rural67.274.998.353.6.52230 EducationNone(.0)(52.1)(96.6)(.0) 27 Primary44.263.797.534.41.9704 Secondary76.272.399.058.4.33067 Higher86.276.997.067.3.41587 Wealth index quintiles Poorest44.666.297.035.21.9751 Second71.277.299.257.9.21175 Middle79.271.198.560.2.11134 Fourth81.772.797.562.31.01172 Richest85.772.798.364.6.11153 Total74.672.498.257.6.55385 Mass Media

7. Upper middle income country with high level of education Similar radio and newspaper access, TV universal Some age differentials Strong correlation with wealth – due to (1) newspaper reading, (2) radio Table MT.1: Exposure to mass media Percentage of women age 15-49 years who are exposed to specific mass media on a weekly basis, Serbia 2010 Percentage of women age 15-49 who: All three media at least once a week [1] No media at least once a week Number of women age 15- 49 years Read a newspaper at least once a week Listen to the radio at least once a week Watch television at least once a week Age15-1976.378.697.963.6.5659 20-2479.479.897.565.2.3705 25-2975.473.697.358.2.9846 30-3474.269.598.057.21.0775 35-3976.173.098.358.9.7791 40-4469.469.999.352.6.3703 45-4971.965.399.049.8.0905 AreaUrban79.870.798.160.4.63155 Rural67.274.998.353.6.52230 EducationNone(.0)(52.1)(96.6)(.0) 27 Primary44.263.797.534.41.9704 Secondary76.272.399.058.4.33067 Higher86.276.997.067.3.41587 Wealth index quintiles Poorest44.666.297.035.21.9751 Second71.277.299.257.9.21175 Middle79.271.198.560.2.11134 Fourth81.772.797.562.31.01172 Richest85.772.798.364.6.11153 Total74.672.498.257.6.55385 Example

8. Data Pattern varies largely by countries, with large proportions of ethnic groups, vulnerable, poor and uneducated populations without exposure Exposure by age varies by country, recent developments in education and spread of mass media NewspaperRadioTelevisionAllNone Timor Leste2236 1248 Lesotho186526929 Ukraine756798541 Philippines316685247

9. Further analysis/Further work A first step for understanding access and exposure; more detail possible May be followed up with more detailed data collection on the nature of exposure to fine-tune media messages – frequency, timing, type Follow up is applicable in both cases: very high, or low percentages Data quality: check with TV ownership, literacy Supplement educational level with media May be used as a valuable independent variable for analyzing outcomes

10. ICT Table MT.2: Use of computers and internet Percentage of women age 15-24 who have: Number of women age 15-24 years Ever used a computer Used a computer during the last 12 months [1] Used a computer at least once a week during the last one month Ever used the internet Used the internet during the last 12 months [2] Used the internet at least once a week during the last one month Age15-1995.994.086.589.086.979.0659 20-2492.789.077.785.483.273.1705 AreaUrban97.296.290.094.092.687.1814 Rural89.984.370.077.073.759.4549 EducationNone******6 Primary60.850.927.839.132.915.5112 Sec.96.393.682.187.785.574.5789 Higher100.098.796.199.097.994.2457 Wealth index quintiles Poorest74.265.141.651.747.328.8199 Second93.388.574.682.279.666.1300 Middle98.797.689.794.090.782.8272 Fourth98.8 94.297.096.891.0281 Richest99.999.097.199.798.796.1311 Total94.291.482.087.185.076.01364 Exposure to global influences, communication, learning opportunities Even for the less educated Only for women (or men) age 15- 24 Ever use, recent use, frequency of use of computers and internet

11. ICT Table MT.2: Use of computers and internet Percentage of women age 15-24 who have: Number of women age 15-24 years Ever used a computer Used a computer during the last 12 months [1] Used a computer at least once a week during the last one month Ever used the internet Used the internet during the last 12 months [2] Used the internet at least once a week during the last one month Age15-1995.994.086.589.086.979.0659 20-2492.789.077.785.483.273.1705 AreaUrban97.296.290.094.092.687.1814 Rural89.984.370.077.073.759.4549 EducationNone******6 Primary60.850.927.839.132.915.5112 Sec.96.393.682.187.785.574.5789 Higher100.098.796.199.097.994.2457 Wealth index quintiles Poorest74.265.141.651.747.328.8199 Second93.388.574.682.279.666.1300 Middle98.797.689.794.090.782.8272 Fourth98.8 94.297.096.891.0281 Richest99.999.097.199.798.796.1311 Total94.291.482.087.185.076.01364 Ever use should always be higher Internet use likely to be lower, but fairly close to computer use

12. ICT Table MT.2: Use of computers and internet Percentage of women age 15-24 who have: Number of women age 15-24 years Ever used a computer Used a computer during the last 12 months [1] Used a computer at least once a week during the last one month Ever used the internet Used the internet during the last 12 months [2] Used the internet at least once a week during the last one month Age15-1995.994.086.589.086.979.0659 20-2492.789.077.785.483.273.1705 AreaUrban97.296.290.094.092.687.1814 Rural89.984.370.077.073.759.4549 EducationNone******6 Primary60.850.927.839.132.915.5112 Sec.96.393.682.187.785.574.5789 Higher100.098.796.199.097.994.2457 Wealth index quintiles Poorest74.265.141.651.747.328.8199 Second93.388.574.682.279.666.1300 Middle98.797.689.794.090.782.8272 Fourth98.8 94.297.096.891.0281 Richest99.999.097.199.798.796.1311 Total94.291.482.087.185.076.01364 Under-use and over-use by children/adoles cents may both be concerns Sharp correlation with age and other background characteristics Fairly similar ever and recent use

13. Further work/Further analysis Check, analyze together with other media exposure May be followed up with collection of more detailed data – social networks, type and nature of use Questionnaire allows more detailed assessment of frequency (almost everyday, once a week, less) Further analyze, compare by gender Supplement educational level with other mass media information May be used as a valuable independent variable for analyzing outcomes

14. Life Satisfaction (Subjective Well-Being)

15. Background: Subjective Well-Being Subjective perceptions of well-being play an important role, autonomously from objective conditions, such as income, health Can help create a fuller picture of well-being Life satisfaction: summation of evaluation regarding a person’s life as a whole Happiness – a fleeting, transient condition that can be affected by numerous current factors (weather, recent incident) Life satisfaction and happiness are sometimes used interchangeably Perceptions of a better life is also an important correlate of both life satisfaction and happiness

16. Background: Subjective Well-Being All of these (life satisfaction, happiness and perceptions of a better life) complete a large portion of subjective well-being Life satisfaction included in Human Development Report in 2010 –Reports on a scale out of 10, on job, health, standard of living, purposeful life, treatment with respect, social support network

17. Subjective well-being: Life satisfaction Table SW.1: Domains of life satisfaction Percentage of women age 15-24 years who are very or somewhat satisfied in selected domains, Country, Year Percentage of women age 15-24 who are very or somewhat satisfied with selected domains: Percentage of women age 15-24 who: Number of women age 15-24 years Family life Friend- shipsSchool Current jobHealth Living envi- ronment Treatment by others The way they look Current income Are not currently attendin g school Do not have a job Do not have any income Age 15-19 20-24 Region Residence Marital Status Education Wealth index quintile Religion/Language/Ethnicity of household head Total Individual items reported – very or somewhat satisfied Percentage who are not attending school, who do not have a job, and who do not income separately reported

18. Life satisfaction and happiness Table SW.2: Life satisfaction and happiness Percentage of women with life satisfaction 1 Average life satisfaction score Missing / Cannot be calculated Women with life satisfaction who are very or somewhat satisfied with their income No income / Cannot be calculated Percentage who are very or somewhat happy 2 Number of women age 15-24 years Age 15-19 20-24 Region Residence Marital Status Education Wealth index quintile Religion/Language/Ethnicity of household head Total 1 MICS Indicator SW.1 2 MICS Indicator SW.2 Indicator: Women who are very or somewhat satisfied with family life, friendships, school, current job, health, where they live, how they are treated by others, and how they look

19. Life satisfaction and happiness Table SW.2: Life satisfaction and happiness Percentage of women with life satisfaction 1 Average life satisfaction score Missing / Cannot be calculated Women with life satisfaction who are very or somewhat satisfied with their income No income / Cannot be calculated Percentage who are very or somewhat happy 2 Number of women age 15-24 years Age 15-19 20-24 Region Residence Marital Status Education Wealth index quintile Religion/Language/Ethnicity of household head Total 1 MICS Indicator SW.1 2 MICS Indicator SW.2 Average score = mean of responses to domains included in calculation of indicator Not calculated if missing values for more than half of domains

20. Life satisfaction and happiness Table SW.2: Life satisfaction and happiness Percentage of women with life satisfaction 1 Average life satisfaction score Missing / Cannot be calculated Women with life satisfaction who are very or somewhat satisfied with their income No income / Cannot be calculated Percentage who are very or somewhat happy 2 Number of women age 15-24 years Age 15-19 20-24 Region Residence Marital Status Education Wealth index quintile Religion/Language/Ethnicity of household head Total 1 MICS Indicator SW.1 2 MICS Indicator SW.2 Correlates with perception of “ultimate well-being” (income), as well as happiness

21. Perceptions of a better life Table SW.3: Perception of a better life Percentage of women age 15-24 years who think that their lives improved during the last one year and who expect that their lives will get better after one year, Country, Year Percentage of women who think that their life Number of women age 15- 24 years Improved during the last one year Will get better after one yearBoth 1 Age 15-19 20-24 Region Residence Marital Status Education Wealth index quintile Religion/Language/Ethnicity of household head Total 1 MICS indicator SW.3 Hopelessness prevalent among youth in middle income countries The relationship between perceptions of improvement during last year and hope for the next year

22. Perceptions of a better life

23. Further analysis Correlate with measures of objective well-being: income, education, wealth Identify groups and compare life satisfaction: Does vulnerability correlate with life satisfaction? Correlate happiness and perceptions with tobacco and alcohol abuse Use as dependent variables: Determinants of satisfaction, happiness, and hope

24. Use of tobacco and alcohol

25. Background: Use of tobacco and alcohol Tobacco: known risk factor for cardiovascular disease, lung and other cancers Alcohol abuse is a serious problem in many countries. Associated with: –increased risk of accidents, cirrhosis, hypertension, psychological illnesses, and congenital malformations. Aggravates risk of family problems

26. Background: Use of tobacco and alcohol Information collected: –Ever and current use of cigarettes and the age at which cigarette smoking first started –Ever and current use of smoked and smokeless tobacco products intensity –The intensity of use, of cigarettes, and smoked and smokeless tobacco products intensity –Ever and current use of alcohol, and intensity of use Both in women and men’s questionnaires

27. Background: Use of tobacco Any intensity of tobacco use is considered a (potential) health problem (Indicator TA.1) Intensity is directly correlated with poor health outcomes Early initiation increases length of exposure

28. Tobacco use Table TA.1: Current and ever use of tobacco Percent distribution of women age 15-49 years by pattern of use of tobacco, Country, Year Never smoked cigarettes or used other tobacco products Ever users Used tobacco products on one or more days during the last one month Number of women age 15-49 years Only cigarettes Cigarettes and other tobacco products Only other tobacco products Any tobacco product Only cigarettes Cigarettes and other tobacco products Only other tobacco products tobacco Any tobacco product 1 Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Region Region 1 Region 2 Region 3 Region 4 Region 5 Area Urban Rural Education None Primary Secondary Higher Maternity status Pregnant Breastfeeding (not pregnant) Neither Wealth index quintile Poorest Second Middle Fourth Richest Religion/Language/Ethnicity of household head Group 1 Group 2 Group 3 Total 1 MICS indicator TA.1

29. Tobacco use Trends in initiation can be estimated from age cohorts Table TA.2: Age at first use of cigarettes and frequency of use Percentage of women age 15-49 years who smoked a whole cigarette before age 15, and percentage distribution of current smokers by the number of cigarettes smoked in the last 24 hours, Country, Year cigarette Percentage of women who smoked a whole cigarette before age 15 1 Number of women age 15-49 years Number of cigarettes in the last 24 hours Number of women age 15- 49 years who are current cigarette smokers Less than 55-910-1920+Total Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Region Region 1 Region 2 Region 3 Region 4 Region 5 Area Urban Rural Education None Primary Secondary Higher Maternity status Pregnant Breastfeeding (not pregnant) Neither Wealth index quintile Poorest Second Middle Fourth Richest Religion/Language/Ethnicity of household head Group 1 Group 2 Group 3 Total 1 MICS indicator TA.2

30. Alcohol use Table TA.3: Use of alcohol Percentage of women age 15-49 who have never had one drink of alcohol, percentage who first had one drink of alcohol before age 15, and percentage of women who have had at least one drink of alcohol on one or more days during the last one month, Country, Year Percentage of women who: Number of women age 15-49 years Never had one drink of alcohol Had at least one drink of alcohol before age 15 1 Had at least one drink of alcohol on one or more days during the last one month 2 Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Region Region 1 Region 2 Region 3 Region 4 Region 5 Area Urban Rural Education None Primary Secondary Higher Wealth index quintile Poorest Second Middle Fourth Richest Religion/Language/Ethnicity of household head Group 1 Group 2 Group 3 Total 1 MICS indicator TA.3 2 MICS indicator TA.4

31. Further analysis Joint analysis of tobacco and alcohol use Correlate with measures of life satisfaction, happiness, perceptions of better life Comparisons of ever and current use of tobacco use may be indicative of reversing trends Sex differentials by social/economic groups Children and mother’s smoking habits Any correlation of alcohol with domestic violence?