DOES EDUCATION ENCOURAGE THE ABCS: ABSTAIN, BE FAITHFUL AND CONDOMIZE? A STUDY OF DETERMINANTS OF HIV/AIDS INFECTIONS

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1 DOES EDUCATION ENCOURAGE THE ABCS: ABSTAIN, BE FAITHFUL AND CONDOMIZE? A STUDY OF DETERMINANTS OF HIV/AIDS INFECTIONS AMONG CAPE TOWN YOUTH FROM A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy By Jenny Hai-Ling Hsu, B.B.A. Washington, D.C. April 13, 2010

2 DOES EDUCATION ENCOURAGE THE ABCS: ABSTAIN, BE FAITHFUL AND CONDOMIZE? A STUDY OF DETERMINANTS OF HIV/AIDS INFECTIONS AMONG CAPE TOWN YOUTH FROM Jenny Hai-Ling Hsu, B.B.A. Thesis Advisor: Andrew Dillon, Ph.D. ABSTRACT HIV/AIDS infections continue to increase, especially among youth in sub- Saharan Africa. South Africa s national HIV/AIDS prevention strategy targets youth to encourage behavior change, particularly through the school system. This motivates the question of this analysis: Does education affect risky sexual behavior among youth? The Cape Area Panel Study surveyed 4800 Cape Town youth ages from and included questions regarding their sexual behavior and attitudes. Risky sexual behavior is measured through condom usage, number of sexual partners and age of first sexual intercourse. This study finds education is only significant in increasing age of first sex and promoting condom usage, although by very small magnitudes. Education had no effect on the number of sexual partners. Magisterial districts and being African were found to be statistically significant in behavior change in condom usage and have large magnitudes. Given these results, the South African government can consider the importance of targeting HIV/AIDS prevention information to specific districts and population groups. ii

3 TABLE OF CONTENTS Introduction... 1 Background of HIV/AIDS in South Africa... 5 Literature Review on HIV/AIDS Prevention and Youth... 7 Conceptual Framework and Hypothesis Data and Methods Descriptive Results Regression Results Conclusion Bibliography iii

4 Introduction Despite tremendous global efforts to combat HIV/AIDS, unacceptably high levels of new HIV infections and AIDS deaths still remain. In 2008, there were an estimated 33.4 million people living with HIV worldwide and 2.7 million new HIV infections. Overall, 2 million died due to AIDS in Sub-Saharan Africa continues to bear a disproportionate share of the global burden of HIV with 35% of HIV infections and 38% of AIDS deaths in 2008 occurring in that region. In total, sub-saharan Africa is home to 67% of all people living with HIV. 1 In South Africa, 5.7 million people are infected with HIV which is 11.7% of its population. The Western Cape Province, where Cape Town is the major metropolitan area, experienced a decline in infection rates from 10.7% in 2002 to 3.8% in Although this is a significant improvement, certain groups experience higher rates of infections. These groups include women, Africans and youth. In fact, 8.7% of the country s youth ages were infected which accounts for 45% of all new HIV infections. The South African government has acknowledged the impact of the disease on its country and has detailed the national response in its HIV & AIDS and Sexually Transmitted Infections (STI) Strategic Plan for South Africa (NSP). The comprehensive strategy was prepared by the South African National AIDS Council and outlines national interventions that seek to reach the country s goals of halving new infections by 2011 and reducing the societal impact of HIV/AIDS. The plan s key 1 UNAIDS AIDS Epidemic Update. Geneva, Switzerland: UNAIDS. 2 Human Sciences Research Council South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey2008, Human Sciences Research Council. 1

5 priority is to evaluate existing interventions and develop programs aimed at behavior change for HIV prevention. 3 Treatment and prevention techniques are the two tools utilized in the fight against HIV/AIDS. However, gains in expanding treatment access cannot be sustained without greater progress in reducing the rate of new HIV infections. It is estimated that for every one person who receives treatment, two new people are infected. 4 Moreover, every dollar spent on protection is approximately 28 times more effective than antiretroviral treatments (ARTs) in reducing the burden of disease, as measured by illness and premature death. 5 Prevention efforts by the South African government target several groups including young people aged This group must be reached since they demonstrate a faster rate of transmission and they account for almost half of all new HIV infections. 6 Unfortunately, many young people still lack accurate and comprehensive information on how to avoid exposure to the virus. Schools are crucial to the dissemination of HIV/AIDS prevention knowledge and can be an efficient distribution center of prevention information to a captive audience. South Africa has mandated HIV education in all public schools and created the Life Skills program to use in conjunction with Information Education and Counseling (IEC) materials. Unfortunately, these efforts have not resulted in optimal levels of youth behavioral changes. This can be due to high rates of youth leaving school. Primary school 3 South African National AIDS Council HIV & AIDS and STI Strategic Plan for South Africa South Africa Department of Health. 4 UNAIDS AIDS Epidemic Update. Geneva, Switzerland: UNAIDS. 5 Canning, David. The economics of HIV/AIDS in low-income countries: The case for prevention. Journal of Economic Perspectives 20, no ). : Human Sciences Research Council South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey2008, Human Sciences Research Council. 2

6 net enrollment in South Africa is estimated to be 88%; however, 60% of those who enroll in Grade 1 drop out before completing Grade Withdrawal from school is a time of insecurity for young people and this period is associated with the greatest increases in pregnancy and HIV infection. The potential protective effect of schools and the impact of school-based prevention programs in changing youth behavior have motivated this analysis and prompted the question: Does education affect risky sexual behavior among youth? Determinants of risky sexual behavior include age of first sexual intercourse, number of sex partners and condom use. This parallels the message of the ABC prevention campaign utilized throughout Africa: Abstain, Be Faithful and Condomize. This study finds that education is only significant in increasing age of first sex and promoting condom usage. For every additional year of education, age of first sex increases, on average, by years (2.34 months). Among youth who exhibit positive behavior change in condom usage, an additional year of education increases the probability of positive behavior change by 10.1% given all other independent variables are held at their mean values. Positive behavior change was also significantly effected by magisterial districts and being African. Given these results, the South African government can consider the importance of targeting HIV/AIDS prevention information to specific districts and population groups. Although South Africa s national strategy dedicates resources to school-based HIV prevention programs, this study only evaluates the overall effect of schooling on youth sexual behavior. It does not estimate the 7 UNICEF. South Africa Statistics. (accessed December 12, 2009). 3

7 effectiveness of school-based HIV prevention programs. Therefore, any significant results only reflect the overall protective effect of schooling on youth sexual behavior. Section II discusses the background of the HIV/AIDS epidemic in South Africa and Section III contains a review of relevant literature on HIV/AIDS prevention and youth. Section IV discusses the conceptual framework and hypothesis utilized in this study. Section V includes information on the research methods used for data analysis and the characteristics of the Cape Area Panel Study (CAPS) dataset. The descriptive statistics of key variables can be found in Section VI and results from the regression analysis are in Section VII. Finally, Section VIII includes policy implications of the research findings and concluding remarks. 4

8 Background of HIV/AIDS in South Africa The South African government has had a turbulent history in its campaign against HIV/AIDS. AIDS was diagnosed for the first time in South Africa in 1983 and by 1990 less than 1% of the population had AIDS. However, pandemic proportions of infections were reached in 1995 and by the turn of the century, 10% of the population was infected. 8 Several critical moments have undermined the government s legitimacy in their response to the disease. One such instance occurred in 2006 when health minister Manto Tshabalala-Msimang issued a statement that those infected with HIV should eat garlic and beetroot as a cure. 9 Also in 2006, Jacob Zuma, deputy president at that time, was tried for raping an HIV-positive woman and testified that he took a shower after sex to lower the risk of AIDS. However, the greatest negligence came from former President Thabo Mbeki who questioned the link between HIV and AIDS. As a result, antiretroviral treatments were not provided to AIDS patients and no government action was taken to prevent HIV-positive pregnant women from passing the virus to their children. It is estimated that more than 330,000 premature deaths in South Africa could have been averted if timely antiretroviral treatment programs were implemented. 10 Fortunately, the South African government has begun to address the severity of the epidemic through its HIV & AIDS and STI Strategic Plan for South Africa UNICEF. South Africa Statistics. (accessed December 12, 2009). 9 Chigwedere, Pride, George R. Seage, Sofia Gruskin, and Tun-Hou Lee. Estimating the lost benefits of antiretroviral drug use in South Africa. Journal of Acquired Immune Deficiency Syndromes 49, no. 4 (December, 2008). : Chigwedere, Pride, George R. Seage, Sofia Gruskin, and Tun-Hou Lee. Estimating the lost benefits of antiretroviral drug use in South Africa. Journal of Acquired Immune Deficiency Syndromes 49, no. 4 (December, 2008). :

9 (NSP), which it crafted in collaboration with international aid agencies. As previously mentioned, the NSP includes a comprehensive analysis of prevention and treatment interventions and techniques to monitor and evaluate the government s efforts. It also addresses the shortcomings of the current health system and outlines the next steps needed to be taken by the government and international community. Most recently, on World AIDS Day 2009, President Zuma announced that South Africa will treat all HIVpositive children under 1 year old and provide earlier treatment for patients infected with both the virus that causes AIDS and tuberculosis and for women who are pregnant and HIV-positive. These recent efforts by the South African government are a hopeful sign of longterm commitment to fighting the epidemic. However, the WHO country representative in South Africa recommended a more balanced approach between treatment and prevention and warned that the current rate of new infections renders it nearly impossible to sustain the provision of free treatments. Therefore, in addition to the substantial attention placed on increasing access to treatment, prevention efforts must also be addressed in equal weight. 6

10 Literature Review on HIV/AIDS Prevention and Youth The World Bank stated that education may be the only vaccine against HIV/AIDS we have. 11 Specifically, prevention efforts should target youth. The United Nations General Assembly Special Session on HIV and AIDS identified young people aged as a priority group in reducing new HIV infections. Although South Africa has invested significantly in this age group, the desired impact has yet to be met. Regardless, the South African government has resolved to continue to invest in and expand targeted evidence-based programs and services focusing on this age group since they believe young people represent the main focus for altering the course of this epidemic. 12 School-based HIV/AIDS Prevention Programs Policymakers have an opportunity to significantly improve prevention efforts by considering evidence of the effectiveness of school-based efforts and behavior change. Duflo, Dupas, Kremer and Sinei conducted a two-year randomized evaluation in Kenya to assess the relative effectiveness of three primary school-based approaches to HIV/AIDS education. These approaches include: 1) training teachers in the Kenyan Government s HIV/AIDS education curriculum; 2) encouraging students to debate the role of condoms and to write essays on how to protect themselves against HIV/AIDS; and 3) reducing the cost of education. The study stemmed from their belief that prevention 11 World Bank Committing to Results: Improving the Effectiveness of HIV/AIDS Assistance. World Bank Independent Evaluation Group. PK: ~piPK: ~theSitePK: ,00.html (accessed December 12, 2009) 12 South African National AIDS Council HIV & AIDS and STI Strategic Plan for South Africa South Africa Department of Health. 7

11 efforts should be focused on youth because the future course of the AIDS epidemic in Africa depends in large part on the behavior of the next generation. 13 They found that teaching the government s HIV curriculum had no effect on selfreported sexual activity. The number of boys reporting having ever used a condom increased by 8%, but did not significantly increase the likelihood that they report having used a condom at their last sexual encounter. Regarding marriage rates, girls attending the schools with teacher training were 6% more likely to be married to the father of their child. In contrast, the condom essays and debates increased the likelihood that boys report having used a condom at last encounter by 20%. Also, reducing the cost of education decreased dropout rates which the authors attributed to the higher opportunity cost of pregnancy and thus of unprotected sex. Girls attending schools where free uniforms were provided were 10% less likely to have started childbearing. Overall, the study s results suggest that reducing the cost of education generates effective incentives for teenagers to avoid teen pregnancy or marriage. Dupas conducted another randomized experiment to compare the effects of providing abstinence-only information versus detailed HIV risk information on sexual behavior to teenagers. This distinction is important since many sub-saharan African countries have incorporated HIV/AIDS education in their school curriculum and it has mainly been limited to risk avoidance information (e.g. promoting abstinence) instead of providing risk reduction information (e.g. teaching condom usage). The study finds that teenagers do not alter their behavior based on risk avoidance messages but rather 13 Duflo, Esther, Pascaline Dupas, Michael Kremer, and Samuel Sinei Education and HIV/AIDS prevention: Evidence from a randomized evaluation in western Kenya. World Bank Policy Research Working Paper No

12 they are responsive to information on the relative riskiness of potential partners, especially older men. Although there was an increase in reported sexual activity among teenagers who received information on relative risk, there was a 28% decrease in the incidence of childbearing within a year, suggesting an important decrease in the incidence of unprotected sex among those girls. In contrast, the abstinence programs had no impact on the incidence of childbearing. Therefore, teenagers are responsive to risk information and Dupas suggests that HIV education campaigns may achieve a wider health impact if they include both risk reduction and risk avoidance information. 14 Despite Dupas proof that promoting risk reduction may resonate more with youth, there is evidence that educators are conflicted about sex education. Ahmed, Flisher, Mathews, Mukoma and Jansen interviewed 15 educators from Western Cape high schools and found that the overwhelming majority of the observed teachers supported abstinence promotion, but they were challenged by teaching safe sex practices since it contradicted with their values and beliefs. 15 Furthermore, many teachers believe that distributing condoms at schools endorsed sexual activity among youth and would send mixed messages to students after they have also been taught abstinence. In addition to the reluctance to teach sex education courses, the confidence that educators have in teaching the subject matter also impacts youth response. Helleve, Flisher, Onya, Kaaya, Mukoma, Swai and Klepp studied teachers in Tanzania and South Africa and concluded that teacher confidence is a construct that is associated with 14 Dupas, Pascaline. May Do teenagers respond to HIV risk information? Evidence from a field experiment in Kenya. NBER Working Paper No Ahmed, Nazeema, Alan J. Flisher, Catherine Mathews, Wanjiru Mukoma, and Shahieda Jansen. HIV education in South African schools: The dilemma and conflicts of educators. Scandinavian Journal of Public Health. 37, no. 2. June :

13 successful implementation of HIV/AIDS education. 16 They found that teacher confidence depended on self-efficacy and positive outcome expectancy. The authors defined self-efficacy as a teacher s perception of their own ability to use certain teaching methods, such as role play. Positive outcome expectancy was defined as whether the teacher perceived they could influence the behavior of their students. Regardless of educator competency and comfort in teaching sex education, it has been suggested that schools in general have a protective effect on youth. McGrath et al. found school attendance to be significantly associated with a delay in first sexual intercourse for both sexes in South Africa. 17 The study reports that girls who were attending school had sex for the first time when they were, on average, 17.3 years of age while girls who were not attending school had sex for the first time when they were, on average, 16.4 years of age. For boys attending school, the average age of first intercourse was 19.5 years of age while boys not attending school reported an average age of first intercourse of 18.5 years of age. Behavior Change and Perceived Risk Behavior change results from a variety of factors. Oster assessed the likelihood of sexual behavior change through the measurement of opportunity costs. Consistent with previous findings, Oster found that high-risk groups in Africa have less behavior change in response to HIV rates compared to higher response rates from gay men in the U.S. The utility cost of sex for African women decreases on average by and by for 16 Helleve, Arnfinn, Alan J. Flisher, Hans Onya, Sylvia Kaaya, Wanjiru Mukoma, Caroline Swai, and Knut-Inge Klepp. Teachers' confidence in teaching HIV/AIDS and sexuality in South African and Tanzanian schools. Scandinavian Journal of Public Health 37, no. 2 (June 1, 2009). : McGrath, N., M. Nyirenda, V. Hosegood, and M-L Newell. Age at first sex in rural South Africa. Sexually Transmitted Infections 85, no. Suppl 1 (April, 2009). : i49-i55. 10

14 African men. On contrast, the utility cost of sex for gay men in the U.S. decreases by The study concluded that behavioral response is larger among people who are wealthier and those with more future years of life expected. 18 Behavior change and risk perception also varies between genders. Barden- O Fallon et al. found that knowledge of HIV/AIDS did not necessarily translate into perceived risk among those observed in rural Malawi. Even though women reported lower levels of knowledge than men, there was more of an association with individual risk perceptions. 19 Moreover, Anderson et al. used data from Waves 1 and 3 of CAPS and found females but not males associated sexual experience with higher perceived risk. Also, among females, knowing someone with AIDS was associated with delay in first sexual intercourse and with an elevated perceived HIV risk. Results suggest HIV/AIDS education and prevention programs should more carefully consider how gender and race may intersect to influence risk perceptions and risk behaviors. 20 The literature above describes the impact of school-based HIV prevention efforts and perceived risk on behavior change. Since the CAPS dataset does not indicate if the youth respondent has been exposed to school-based HIV prevention programs, this study will not distinguish between the effects of these programs and the protective effect of schooling on sexual behavior. Instead, this analysis will build upon the existing body of work by evaluating if schools possess the hypothesized protective effect on youth where 18 Oster, Emily F. April HIV and sexual behavior change: Why not Africa? NBER Working Paper No. W Barden-O'Fallon, Janine L., Joseph degraft-johnson, Thomas Bisika, Sara Sulzbach, Aimee Benson, and Amy O. Tsui. Factors associated with HIV/AIDS knowledge and risk perception in rural Malawi. AIDS and Behavior 8, no. 2 June : (accessed October 23, 2009). 20 Anderson, Kermyt G., Ann M. Beutel, and Brendan Maughan-Brown. HIV risk perceptions and first sexual intercourse among youth in Cape Town, South Africa. International Family Planning Perspectives 33, no. 3 (09, 2007). :

15 mere school attendance brings about less risky sexual behavior regardless of uptake of prevention information. 12

16 Conceptual Framework and Hypothesis This analysis hypothesizes that youth who have attained more years of education are less likely to participate in risky sexual behavior. This belief is based on the importance of schools in providing 1) the information they receive from school-based HIV prevention programs and; 2) the overall protective effect of schools. Figure 1 presents the relationship between the observable and unobservable factors and their relationship to participation in risky sexual behavior. Figure 1. Determinants of Risky Sexual Behavior Contextual and Community Factors Observable Characteristics Availability of HIV/AIDS resources (eg. condoms, health prevention education, HIV/AIDS testing) Unobservable Characteristics Observable Characteristics Household and Individual Level Factors Unobservable Characteristics Income Parent and peer attitudes towards sex Schooling Level of risk aversion Knowing someone with HIV/AIDS Measurement of opportunity cost and belief in future potential Gender, Race, Age Gender and Racial Inequality HIV/AIDS stigma HIV/AIDS Prevention Knowledge Risky Sexual Behavior Contextual and Community Factors An observable community characteristic that impacts an individual s sexual behavior is the distribution of and access to prevention and treatment resources. Although the South African government has mandated HIV prevention education in all public schools, the quality of the education varies by school. Teachers may not actually teach 13

17 the prescribed curricula and if it is actually taught, they may not affect knowledge, attitudes and behavior. Additionally, the location and resources of health centers can impact access to testing for HIV or other STDS and an individual s incentive to know one s disease status. Unobservable characteristics include gender disparities which still occur in South Africa. When women lack bargaining power with sexual partners, this impacts the use of contraceptives and protection against disease. Additionally, racial inequality still contributes to economic and health disparities by race even though apartheid laws were repealed almost two decades ago. This is particularly true regarding access to quality of schools and education for low-income Africans. It is also difficult to quantify the effect of community leaders and strong community institutions on the behavior of youth. Household and Individual Factors Observable individual characteristics that determine risky sexual behavior include age, gender and race. Youth who initiate sex at an early age have more years of their lives at risk of HIV infection and engaging in first sex is the entry point for subsequent risky behavior. Among South Africans aged 15-24, more than half have had sex by age Furthermore, the studies discussed in the literature review quantify the differences in condom usage, age of first sex and number of sexual partners depending on race and gender. 21 Anderson, Kermyt G., Ann M. Beutel, and Brendan Maughan-Brown. HIV risk perceptions and first sexual intercourse among youth in Cape Town, South Africa. International Family Planning Perspectives 33, no. 3 (09, 2007). :

18 Although educational attainment, school enrollment and socioeconomic status can be observed, it is difficult to separate out the effects of each. In particular, it is difficult to identify whether it is income or education that may improve knowledge of the disease or impact the behavioral response of the individual. For example, Oster s research shows how a person s perceived opportunity cost and future utility influences their decision to participate in risky sexual behavior. The unobservable characteristic of how much an individual views HIV infection as a stigma also determines their sexual behavior. Even if the youth obtains prevention knowledge, they may still downplay their personal risk since HIV and AIDS are highly stigmatized in South Africa. Acknowledging one s own risk admits the possibility of being part of a stigmatized group. Moreover, young people who know someone living with HIV/AIDS or who died of the disease may not participate in the same risky behavior because they have seen the impact of the disease. However, the reverse may also occur. People who participate in risky behavior may surround themselves with those who make the same risky decisions. Furthermore, an individual s assessment of risk depends on their level of risk aversion and the perspectives of sexual behavior of the youth s parents, which are also unobservable characteristics. 15

19 Data and Methods Data: Population and Survey Characteristics This analysis derives its data from the Cape Area Panel Study (CAPS) conducted by the University of Michigan, University of Cape Town and Princeton University from CAPS is a longitudinal study of the lives of 4800 youths and young adults ages in metropolitan Cape Town, South Africa. The CAPS sample was stratified on Cape Town s three major population groups: African, colored and white. The survey questions were constructed to paint a comprehensive picture of the respondent s life over four years. Question topics included health, education, and household characteristics and detailed information was collected on sexual behavior, attitude and knowledge. These characteristics of the CAPS Study make it well-suited for understanding the effects of schooling on risky sexual behavior. Table 1. Response Rate across Wave Wave 1 Wave 2 Wave 3 Wave 4 Total completed young adult interviews 4,752 3,927 3,531 3,439 Young adult response rate (out of 4752) 89.60% 82.60% 74.30% 72.40% Inevitably, attrition occurred across the four waves as the survey progressed. Table 1 displays the response rates across all four waves. Response rates were calculated as a percentage of the successful Wave 1 interviews. This study will include the entire population of young people age in the CAPS data who were successfully interviewed in all four waves. The reason for including the whole sample is to maintain the highest level of variation possible in gender, race, education, household income and sexual behavior. Regarding bias attributed to attrition, the highest non-response rate occurs among whites. In Wave 4, 74% of the Wave 1 African youth and 80% of the 16

20 Wave 1 colored youth participated. However, only 42% of young white adults from Wave 1 participated in Wave 4. Additionally, in all three waves, higher non-response occurs among older youth, those not enrolled in school and those born outside of Cape Town. These characteristics of the data will be factored into the analysis of the results. Furthermore, measurement error is a concern when relying on self-reported data on sexual behavior. Baird, McIntosh and Özler state that there are two kinds of errors: 1) an average amount of misreporting that can occur in the entire sample which can lead to attenuation bias and 2) differential reporting where one group is more likely to misreport than another group (e.g. males vs. females). The first error may result in higher standard errors, but does not bias the estimates especially if the sample sizes are large enough to have the statistical power to detect the reasonable impact of the treatment. However, if significant misreporting occurs, this may lead to the inaccurate finding of no impact. The second error is more problematic and can bias the estimates because these differences in self-reporting outcomes would lead to coefficients that over or underestimate the impact of the treatment. 22 For example, the issues of stigma, participation bias, difficulties with memory and desire to appease the interviewer may lead to a lower reported number of sexual partners, higher condom usage and later age of first sex than compared to actual behavior. Research Methods: Analysis Plan This assessment will evaluate the effects of schooling on risky sexual behavior. Condom usage, number of sexual partners and age of first sexual intercourse will be used 22 Baird, Sarah, Craig McIntosh, and Berk Özler. Testing the reliability of self-reported data on schooling and sexual behavior. December 13, surement_paper_ pdf (accessed March 30, 2010) 17

21 to measure risky sexual behavior in five regression models. The control variables on the individual level (β j X i ) include gender, age, population group and marital status. The control variables on the household level (β j X h ) include household size and magisterial district. Regression Model 1: OLS of Wave 3 for age at first sex Age at first sex ih = β 0 + β 1 X ih + β 2 education i + u ih Since the age at first sex does not change between waves, an OLS model is appropriate to measure the impact of education on age at first sex. If a youth responded to this question in Wave 1, the response is carried over to Wave 3. The coefficient in the model is interpreted as follows: an increase in education of one year is predicted to increase age at first sex by β 2. The OLS model allows for easier interpretation of the coefficients than the other models. However, the concerns of omitted variable bias cannot be addressed as well with the OLS model as the fixed effects model below. Regression Model 2: Fixed Effects Model for number of sexual partners # of sexual partners iht = β 0 + β 1 X iht t+1 + β 2 education it t+1 +β 3 WAVE t + β 4 INDIV i + u iht WAVE = dummy for each wave INDIV= dummy for each youth observation The unobservable characteristics mentioned in the conceptual framework motivate the use of a fixed effects approach to control for characteristics that may be correlated with other independent variables. Although this model controls for the fixed effects related to age, gender and population group, it assumes that the omitted variable is fixed and it does not account for the omitted variable bias particularly due to time varying 18

22 characteristics such as attitude. Additional limitations include an increase in standard errors since there is less variance in the observations and it is possible that the smaller fraction of changes over time may not be random. In this model, the coefficient on education is interpreted as an increase in education of one year results, on average, in a decrease in the number of sexual partners by β 2. Regression Model 3: Probit model for positive behavior change in condom usage during last intercourse (full sample) condom usage ih = β 0 + β 1 X iht + β 2 education it + u it A new variable was derived from the original CAPS dataset that captures positive behavior change, meaning a youth who did not use a condom in Wave 1 and did use a condom in Wave 3. Therefore, the coefficients will measure the predicted probability of positive behavior change in condom usage, not the predicted probability of using a condom. The probit model is appropriate for estimating the binary outcome variable of condom usage during last intercourse. It is preferred over the linear probability model because the LPM assumes that the expected value of the dependent variable is a linear combination of a set of independent variables. Instead, the probit model resolves the deficiencies of the LPM model by adopting an S-shaped curve rather than the straight line of the LPM. Therefore, the probit model allows the effect of the independent variables to more accurately represent minute changes that lead to different predicted values of the dependent variable. However, the probit model is not as easy to interpret since the coefficients cannot be interpreted directly and the estimates must be computed. For this 19

23 model, the coefficients are interpreted to mean that given all other independent variables are held at their mean values, an extra year of education changes the probability of positive behavior change in condom usage by β 2 %. Regression Model 4: Probit model for positive behavior change in condom usage during last intercourse (sub-sample of only those that exhibited positive behavior change) condom usage ih = β 0 + β 1 X iht + β 2 education it + u it This probit model is similar to Model 4; however, it includes only the sample of youth that exhibited positive behavior change. This allows the estimates on the explanatory variables to be useful in determining the individual and household characteristics that are related to positive behavior change. The coefficients are interpreted to mean that given all other independent variables are held at their mean values, an extra year of education changes the probability of positive behavior change in condom usage by β 2 % among the sub-sample of youth who exhibited positive behavior change. 20

24 Descriptive Results Table 2 displays the dependent variables used to measure change in sexual behavior among Cape Town youth from Wave 1 (2002) to Wave 3 (2005). 23 For condom usage, the average number of youth using condoms during last intercourse increased from 0.59 in Wave 1 to 0.67 in Wave 3. Also, the number of sexual partners decreased from 1.70 in Wave 1 to 1.16 in Wave 3. It is important to note that the standard deviation for the number of sexual partners in Wave 1 is particularly high due to more respondents reporting a higher number of partners. The percentage of respondents in Wave 1 with four or more partners is 8.02% while in Wave 3 the percentage of respondents with four or more partners is 4.66%. The age of a youth s first sexual experience also slightly increased from years of age to years of age. These three outcome variables all show positive changes in sexual behavior; however, the natural increase in age from Wave 1 to 3 may influence the increase of the age of first sex. T-tests were conducted to evaluate the statistical significance of the differences between the waves. The differences in the mean condom usage and the number of sexual partners are statistically significant with a p-value less than However, the difference in the age of first sex between the two waves is not statistically significant. 23 Wave 2 is not used because the survey did not include the same questions regarding risky sexual behavior. Wave 4 is not used because the outcome variable condom usage was not coded as a binary variable; only yes responses were included and no responses were not included. 21

25 Table 2: Sexual Behavior Among Youth Condom used during last sex 0=no 1=yes # of sexual partners W1 Mean W3 Mean Change b/t Waves *** (2.07) (0.64) -0.54*** Age of first sex (1.65) (1.74) Number of observations=536, ***p-value<0.01 Table 3 displays the individual and household characteristics of the youth who were interviewed in both Waves 1 and 3. There are slightly more females than males and the average age was years in Wave 1 and in Wave 3, which correctly illustrates the natural increase in age from the three years that passed between the interviews. The average years of education completed was 8.93 years in Wave 1 and years in Wave 3. Unfortunately, this shows that the years of education did not increase in line with the expected increase of three additional years of schooling that should have occurred between waves. The level of household savings stayed relatively the same with around 61% of the households possessing at least some level of savings in both waves. Total household income was not used to measure the wealth of a household due to a high level of non-response in the sample. Therefore, household savings was used as an alternative measurement of household wealth. Household size also remained similar with an average of 5 to 6 members per household in both waves. The standard deviations in both waves are particularly high due to a distribution of household sizes that are skewed to the right. T-tests were conducted for the individual and household characteristics. Only age and education had p-values that were less than meaning that the null hypothesis is 22

26 rejected and the differences in means between the waves for age and education are statistically different from each other. The differences in means for gender and household size had p-values greater than 0.05 and therefore not statistically significant differences. Table 3: Individual and Household Characteristics W1 Mean W3 Mean Change b/t Waves Gender =female 1=male Age (1.98) (2.04) 2.98*** Education 8.93 (2.1) Household Size 5.9 (2.7) (1.98) 5.64 (2.71) 1.31*** Number of observations=536, ***p-value<0.01 The positive behavior change in condom usage from Table 1 is also reflected in Table 4 where positive behavior change is defined as a youth who did not use a condom in Wave 1 and used a condom in Wave % of respondents exhibited positive behavior change while 13.06% of respondents used a condom in Wave 1 and did not use a condom in Wave 3. The majority of the respondents at 66.23% did not change their behavior between the waves. Table 4: Behavior Change in Condom Usage from W1 to W3 Negative Behavior Change 13.06% Positive Behavior Change 20.71% No Behavior Change 66.23% Number of observations=536 23

27 Condom usage also varies among males and females. Table 5 shows that in both Waves 1 and 3 more females do not use condoms than males, but this could be due to a perception that condom usage is controlled by the male partner and the use of other contraceptives by females. However, there is an increase in females using condoms during last intercourse between Wave 1 at 27.24% and Wave 3 and 34.89%. Table 5: Condom Usage by Gender Gender Did not use condom Used condom W1 Female 30.04% 27.24% W1 Male 11.01% 31.72% W3 Female 22.39% 34.89% W3 Male 11.01% 31.72% Number of observations=536 Table 6 shows more youth used condoms during last intercourse in every age group in both waves. In Wave 1, the greatest variation in condom usage occurs in the age group with condom usage being 12.5 percentage points higher than non-condom usage. In Wave 3, large differences occur in the age group (12.32 percentage points higher than non-usage) and the age group (16.42 percentage points higher than non-usage). Table 6: Condom Usage by Age Age Did not use condom Use condom WAVE years 4.10% 5.97% years 13.06% 25.56% years 23.88% 27.43% WAVE years 0.93% 2.80% years 8.58% 20.90% years 16.79% 33.21% years 7.09% 9.70% Number of observations=536 24

28 The results from Table 7 show that in both Waves 1 and 3 men, on average, have more sexual partners than females. However, this difference decreases in Wave 3 with men having an average of 0.34 more partners than females compared to Wave 1 where men had an average of 1.27 more partners than females. As mentioned in the earlier, measurement error is a concern when evaluating the number of sexual partners as some respondents may have difficulty recalling the specific number of partners, especially those with higher numbers of partners. Moreover, some youth may feel uncomfortable disclosing this information even if the interviewer affirms the confidentiality of their survey responses. Given this concern, it is hypothesized that the average number of sexual partners may be higher than reported. Table 7: Number of Sexual Partners by Gender and Wave Gender W1 Male W1 Female W3 Male W3 Female Average Number of Sexual Partners WAVE (2.89) 1.16 (0.77) WAVE (0.80) 1.01 (0.44) Number of observations=536 25

29 Regression Results Effect of education on age at first sex The outcome variable of age at first sex is not compared across waves since age at first sex can only be answered once per respondent. For example, a youth interviewed in Wave 1 who responds that the age of first sex is 16 would also have the same response of 16 in Wave 3. Therefore, only Wave 3 responses are used in an OLS regression. The results of this model are summarized in Table 8 below. The baseline category represents youth who are female, single and White from households with no savings living in the Bellville magisterial district. As expected, the coefficient of the primary independent variable of interest, years of education completed, is significant at the 1% level. Moreover, the positive relationship between education and age of first sex is also aligned with expectations. Therefore, it can be concluded that, on average, completing an additional year of education increases the age of first sex by years (2.34 months). Although the coefficient on education is highly significant, its magnitude is relatively small. Mitchell s Plain is also significant at the 1% level with a negative relationship between living in Mitchell s Plain and the age of first sex. Thus, on average, the age of first sex of a youth who resides in Mitchell s Plain has sex 0.82 years (9.84 months) earlier than a youth who lives in Bellville. This confirms the importance of including the magistrate dummies to increase the precision of the point estimates and capture potential unobservable characteristics across magistrates. 26

30 Table 8: Effect of education on age at first sex (OLS) Independent variables: Years of education completed (0.05)*** Male (0.20) Marital status (1= married) (0.30) Household Size (0.03) Age (0.04) African (0.31) Colored (0.24) Kuils Rivier District (0.47) Mitchell s Plain District (0.21)*** Wynberg District (0.43) Simonstown District (0.47) Cape District (0.49) Goodwood District (0.41) Notes: 366 observations; robust standard errors in parentheses; *** significant at 1%. This model was computed using the STATA software package, using the command regress and using the weight weightyr which adjusts for individual non-response, the intentional oversampling of African and white households and the intentional differential sampling of households with and without young adult household members. 27

31 Effect of education on a youth s number of sexual partners A fixed effects model was used to measure the effect of education on the changes within the respondents between Waves 1 and 3 regarding their number of sexual partners. Table 9 displays the results of this model and characteristics fixed over time are not included, such as gender and race. Additionally, some concerns of bias resulting from omitted variables occurring across waves are addressed through using a fixed effects model. Education is insignificant, but age is significant at the 1% level where an increase in age of one year results, on average, in a decrease in the number of sexual partners by 0.137, or a decrease of 1.37 partners for 10 years of age. Table 9: Effect of education on number of sexual partners (Fixed effects model) Independent variables: Years of education completed (0.10) Marital status (1= married) (0.31) Household Size (0.05) Age (0.04)*** African Colored (0.15) Notes: 1072 observations; 536 groups; robust standard errors in parentheses; *** significant at 1%. This fixed effects model was computed using the STATA software package, using the command xtreg. The following control variables were used, but not included in the table: marital status and household size. Gender and race dummies were dropped due to lack of change between the waves. 28

32 Effect of education on condom usage during last intercourse In order to estimate the effect of education on condom usage during last intercourse, a probit model was used to evaluate the probability of positive behavior change, where the youth uses a condom in Wave 3 while not using a condom in Wave 1. Table 10 displays the results where the only significant variables are male at the 1% level and Goodwood District at the 10% level. Therefore, being a male has a negative relationship with the behavior change of using a condom in Wave 3 compared to not using a condom in Wave 1. This means that given all other independent variables are held at their mean values, a male youth is 8.3% less likely than a female to exhibit positive behavior change in condom usage. Additionally, a youth living in the magisterial district of Goodwood is 14.4% less likely than a youth living in Bellville to demonstrate positive behavior change in condom usage when all other independent variables are held at their mean values. Education is not a statistically significant factor in impacting positive behavior change nor is the impact of large magnitude. 29

33 Table 10: Effect of education on condom usage (Probit Model) Independent variables: Years of education completed (0.01) Male (0.02)*** Marital status (1= married) (0.04) Household Size (0.01) Age (0.01) African (0.04) Colored (0.04) Kuils Rivier District (0.09) Mitchell s Plain District (0.10) Wynberg District (0.09) Simonstown District (0.13) Cape District (0.17) Goodwood District (0.06)* Notes: 1072 observations; standard errors in parentheses; * significant at 10%; *** significant at 1%. This model was computed using the STATA software package using the command dprobit, which automatically computes change in predicted probabilities given a marginal change in the independent variable, given that all other variables are equal to their mean values. 30

34 The estimates in Table 10 used a sample of youth who exhibited both positive and negative behavior change. In Table 11, a sub-sample (n=222) of only those respondents who demonstrated positive behavior change were used in a probit regression to model the characteristics that influence the probability of condom usage during last intercourse in Wave 3 when no condom was used in Wave 1. Therefore, these results should be interpreted to reflect the characteristics of only this sub-sample. The biggest change in the makeup of the individual characteristics of the sub-sample is a large decrease in the number of males in the sub-sample. Among the entire sample, there are 458 males (42%) while there are only 72 males (32%) in the sub-sample. However, male is not significant in the model and of small magnitude. Education is significant at the 1% level where among those who exhibited positive behavior change, an additional year of education increases the probability of positive behavior change by 10.1% given all other independent variables are held at their mean values. Age is also significant at the 1% level where an additional year of age increases the probability of positive behavior change by 16.6% given all other independent variables are held at their mean values. Additionally, the race dummy African is significant at the 1% level. Therefore, given all other independent variables are held at their mean values, being an African youth is 48.8% less likely to exhibit positive behavior change when compared to a White youth. In sum, out of those who demonstrated positive behavior change, the individual and household characteristics that provoked positive behavior change were education, age and African. There was no overlap of significant variables between the models using the entire sample and sub-sample. 31

35 Table 11: Effect of education on condom usage (Probit Model of sub-sample) Independent variables: Years of education completed (0.02)*** Male (0.10) Marital status (1= married) (0.19) Household Size (0.02) Age (0.02)*** African (0.10)*** Colored (0.13) Kuils Rivier District (0.43) Mitchell s Plain District (0.33) Wynberg District (0.31) Simonstown District (0.22) Cape District (0.18) Goodwood District (0.39) Notes: 222 observations are in a sub-sample of youth exhibiting positive behavior change; standard errors in parentheses; *** significant at 1%. This model was computed using STATA software and the command dprobit, which automatically computes change in predicted probabilities given a marginal change in the independent variable, given that all other variables are equal to their mean values. 32