1. Who Gets AIDS and How? Education and Sexual Behaviors in Burkina Faso, Cameroon, Ghana, Kenya and Tanzania Damien de Walque The World Bank Development Research Group IAEN Cuernavaca, August 1, 2008

2. Motivation and data Determinants of HIV infection and behaviors are still not well understood. Determinants of HIV infection and behaviors are still not well understood. Only recently that we have access to nationally representative surveys including HIV testing and behaviors: the new wave of Demographic and Health Surveys (DHS) Only recently that we have access to nationally representative surveys including HIV testing and behaviors: the new wave of Demographic and Health Surveys (DHS) Countries can now have a representative picture of their epidemic (leading to substantial revisions of their estimates of HIV prevalence) Countries can now have a representative picture of their epidemic (leading to substantial revisions of their estimates of HIV prevalence)

3. DHS surveys : advantages Nationally representative and large samples Nationally representative and large samples Variables defined similarly across countries: Are results comparable and can they be generalized? Variables defined similarly across countries: Are results comparable and can they be generalized? Biomedical record (HIV test) can be merged with socio-economic variables Biomedical record (HIV test) can be merged with socio-economic variables Biomedical record (HIV test) can be merged with behavior variables (and check inconsistencies) Biomedical record (HIV test) can be merged with behavior variables (and check inconsistencies) Available on the web! Available on the web!

4. DHS Surveys: disadvantages Cross-sections: Because of mortality and treatment, HIV prevalence is not the best indicator of the current state of the epidemic. HIV incidence would be preferred. Cross-sections: Because of mortality and treatment, HIV prevalence is not the best indicator of the current state of the epidemic. HIV incidence would be preferred. Representative at the national level, but then difficult to focus on high-risk groups. Representative at the national level, but then difficult to focus on high-risk groups. Behavior is self-reported. Leads to inconsistencies as reported by Gersovitz (2005). Behavior is self-reported. Leads to inconsistencies as reported by Gersovitz (2005).

5. 5 first DHS with HIV testing Burkina Faso (2003) Burkina Faso (2003) Cameroon (2004) Cameroon (2004) Ghana (2003) Ghana (2003) Kenya (2003) Kenya (2003) Tanzania (2003-2004) Tanzania (2003-2004) Adult HIV Prevalence Males: 1.9% Females: 1.8% Males: 3.9% Females: 6.6% Males: 1.6% Females: 2.7% Males: 4.6% Females: 8.7% Males: 6.2% Females: 7.6%

6. HIV status regressions Dependent variable: HIV test result: Dependent variable: HIV test result: 0= HIV negative ; 1 = HIV positive Probit estimates: marginal effects are reported Probit estimates: marginal effects are reported Separate regressions for males and females and then pooled regressions. Separate regressions for males and females and then pooled regressions. Controls: 5 year age group dummies, regional and ethnicity (not in Tanzania) dummies, wealth quintiles, religion, education, urban, marital status, male circumcision, female genital mutilation. Controls: 5 year age group dummies, regional and ethnicity (not in Tanzania) dummies, wealth quintiles, religion, education, urban, marital status, male circumcision, female genital mutilation. More detailed analysis of the effect of education More detailed analysis of the effect of education

7. Endogeneity? I am not entering behaviors and attitudes as independent variables because of endogeneity and reverse causality issues. Ex: Condom use I am not entering behaviors and attitudes as independent variables because of endogeneity and reverse causality issues. Ex: Condom use Still, many controls like marital status, education and wealth are potentially endogenous. No instruments or panel to verify causality. Still, many controls like marital status, education and wealth are potentially endogenous. No instruments or panel to verify causality. Be cautious about causality, but still results are relevant in order to target prevention. Be cautious about causality, but still results are relevant in order to target prevention.

9. Coefficients on years of education (dependent variable: HIV status) MalesFemales Burkina Faso 0.0001 [0.0005] -0.0010 [0.0007] Cameroon -0.0006 [0.0007] -0.0011 [0.0011] Ghana -0.0000 [0.0003] 0.0001 [0.0005] Kenya -0.0005 [0.0006] -0.0007 [0.0014] Tanzania -0.0008 [0.0012] -0.0002 [0.0011]

10. Generalizable results Wealth and HIV: positive relationship (3 for females and once for males), confirms simple tabulations. Wealth and HIV: positive relationship (3 for females and once for males), confirms simple tabulations. Education and HIV status: no robust relationship: Education and HIV status: no robust relationship: - despite indication of positive association in simple tabulations - despite indication of positive association in simple tabulations - does not confirm results from De Walque (2007): negative gradient for young females in rural Uganda. - does not confirm results from De Walque (2007): negative gradient for young females in rural Uganda.

11. Pooled regressions OK for education as coefficients on interaction country*education are not significantly different across countries in a pooled regression where all variables are interacted with country dummy. OK for education as coefficients on interaction country*education are not significantly different across countries in a pooled regression where all variables are interacted with country dummy. Data weighted by: Data weighted by: sample weight*country population/sample size sample weight*country population/sample size Asset specific dummies rather than wealth quintiles Asset specific dummies rather than wealth quintiles

12. Coefficients on education on HIV status With Tanzania Without Tanzania Males all ages -0.0002 -0.0002[0.0003]-0.0002[0.0003] Males 15-29 -0.0004[0.0003]-0.0004*[0.00025] Females all ages -0.0001[0.0004]-0.0002[0.0004] Females 15-29 -0.0007[0.0005]-0.0009[0.00058]

13. No effect of education? Not in country-specific regressions, not in pooled regressions (but negative and “close” to significant for young 15-29). Not in country-specific regressions, not in pooled regressions (but negative and “close” to significant for young 15-29). BUT, Education is very strong predictor of behavior BUT, Education is very strong predictor of behavior positively related with: condom use, VCT, discussion with spouse and knowledge positively related with: condom use, VCT, discussion with spouse and knowledge tends to be negatively related with fidelity, abstinence and virginity tends to be negatively related with fidelity, abstinence and virginity Goes in opposite directions and might explain the absence of association with HIV status Goes in opposite directions and might explain the absence of association with HIV status

14. Effect of education on behaviors (pooled regressions: caveat coefficient differ significantly across countries) Dependent variables Condom last sex if with spouse Condom last sex if not with spouse Non marital sex last 12 months Males all ages 0.0026***[0.0065]0.0250***[0.0032]0.0041***[0.0012] Males15-290.0059***[0.0016]0.0271***[0.0034]0.0024[0.0034] Females all ages 0.0023***[0.0042]0.0231***[0.0038]0.0009***[0.0002] Females15-290.0028***[0.0007]0.0269***[0.0046]0.0023***[0.0005]