1. CONFERENCE OF TOBACCO CONTROL SCHOOL OF ECONOMICS, UNIVERSITY OF CAPE TOWN 16-18 JULY 2014 Socio-economic determinants of tobacco use in the Southern African Customs Union Socio-economic determinants of tobacco use in the Southern African Customs Union Linda Nyabongo presented by Corne van Walbeek

2. Background to the study Determinants of smoking prevalence is fairly well understood in many (mainly developed) countries Studies about smoking prevalence in Africa has lagged behind Pampel (2008) investigated socio-economic determinants of smoking in many African countries, using a general, undifferentiated model This study adds to the literature by looking at countries in a region with similar price and tax regimes

3. The data CountryWomen in DHS Women in final sample (age 15-49) Men in DHS Men in final sample (age 15-49) Lesotho (2009) 7624 (age 15-49) 76213317 (age 15-59) 2988 Namibia (2006-7) 9804 (age 15-49) 97793915 (age 15-49) 3899 Swaziland (2006-7) 4987 (age 15-49) 49774156 (age 15-49) 4149 South Africa (2008) Not declared 6499Not declared 4649

4. The descriptive statistics (an example of Lesotho) Men (n = 2988) Women (n = 7621) n Weighted % n Weighted % Age 15-19 838 27.8 1,840 23.4 20-24 631 21.1 1,555 20.4 25-29 462 15.4 1,203 16.3 30-34 370 13.2 960 12.9 35-39 282 9.7 755 10.0 40-44 204 6.5 663 8.6 45-49 201 6.4 645 8.4 Residence Rural 2313 71.9 5,646 66.3 Urban 675 28.1 1,975 33.7 Education No school 393 11.2 114 1.1 Primary 1494 48.8 3,863 46.6 Secondary 955 34.1 3,276 46.4 Post-Secondary 146 5.9 368 5.8 Occupation Not Working 936 31.8 4,285 54.9 Agriculture 1143 34.8 951 9.9 Service-Manual 662 24.2 1,367 21.2 Non-Manual 247 9.2 1,018 14.0 Religion Other 348 10.2 583 7.0 Catholic 1217 42.4 3,217 42.6 Protestant 11423 47.4 3,821 50.2

5. Tobacco use prevalence amongst males

6. Tobacco use prevalence amongst females

7. Cigarette smoking prevalence by males by age

8. Pipe smoking prevalence amongst males by age

9. Cigarette smoking prevalence by females by age

10. Snuff use prevalence by females by age

11. Cigarette smoking prevalence by education, males

12. Pipe smoking prevalence by education, males

13. Cigarette smoking prevalence by education, females

14. Snuff use prevalence by education, females

15. The empirical model Logistic regression Results are presented in the form of odds ratios  Odds ratio > 1: more likely to consume tobacco than base category  Odds ratio < 1: less likely to consume tobacco than base category P(Tob = 1) = f(Age, Residence, Education, Occupation, Religion)

16. Example of regression output

17. Another example: females in Namibia

18. Combined regression output for cigarettes, males

19. Combined regression output for cigarettes, females

20. Summary of main findings Cigarette smoking more likely in the urban areas amongst females Pipe smoking, chewing tobacco and snuff more concentrated in rural areas for both males and females Generally, as education increases, prevalence of tobacco use decreases  Exceptions: cigarette smoking among females No clear relationship between occupation and smoking prevalence

21. Limitations of the study Age restricted to 15-49 Price not included in the analysis Ethnicity not asked in DHS (although this is only really relevant in Namibia)

22. Implications and conclusion Negative relationship between SES and prevalence of tobacco use Many studies (not this one) have shown that people with lower SES are more price responsive An increase in the price of tobacco products will be more effective in reducing tobacco use amongst people with lower SES and will thus decrease inequalities in tobacco use