A recent United Nations publication states that ‘poverty increases vulnerability to HIV/AIDS’ , although in a complex fashion; the HIV burden is concentrated in the poorest regions of the world but not always among the poorest populations in these areas. HIV prevalence rates are indeed highest among South African youth living in poor urban informal settlements compared with other areas . The mechanisms by which conditions of poverty may influence sexual risk-taking behavior and thus the probability of contracting HIV and other sexually transmitted diseases are, however, complex and currently not well understood.
Researchers in public health and economics have hypothesized and less frequently tried to measure the channels from individual and community poverty to higher rates of sexual risk-taking behavior. Fenton , Gersovitz  and Sunmola  argued that inadequate information (often concentrated among the poor) acts as a barrier to adopting safer behaviors; Cohen et al.  argued that access to resources for safer sex may be costly; MacPhail and Campbell  and LeClerc-Madlala  argued that poverty directly induces sex work and informal transactional sex relationships particularly for young women, whereas Johnston and Way  provided descriptive evidence of these correlations and Luke  and Luke  used survey data to quantify this relationship. Dunkel et al.  used data from young, rural South African young men to show a strong positive correlation between higher socioeconomic status and the probability of reporting transactional sex with casual partners. At a community level, Zulu et al.  compared non-poor non-slum residents with poor slum residents in Nairobi and found significantly higher probabilities of reporting early sexual debut, more sexual partners and a lack of condom use among the poor slum dwellers. Economic inequality may also operate to increase risk-taking within communities: LeClerc-Madlala  posited that the growth of a black middle class with money has increased the emphasis on transactional sex in some South African communities.
In many of those studies, it is difficult to isolate whether economic resources matter directly for behavior or whether unobservable characteristics correlated with poverty are driving factors. In addition, as the experience of poverty is likely to have persistent effects on behavior over time, it is hard to distinguish whether current or long-term resource deprivation matters for behavior. To measure the direct effect of economic resources on behavior convincingly, we would want to assign these resources randomly to households and observe the impact on behaviors. Approaching this research design with observational data is challenging.
In this paper, we investigated whether household and community incomes and negative economic shocks predict risky behaviors of young adults. Focusing on young adults who are for the most part not yet working and who are just transitioning into sex allowed us to isolate the relationship between household level and community resources and behavior. In the absence of the random assignment of income to households or communities, we used economic shocks to capture one source of unexpected variation in household resources. Although this research design does not identify the causal effect of economic deprivation on behavior, we advanced some way towards an understanding of the relationship between economic resources and the risky sexual behavior of young adults. Surprisingly, we found little evidence that differences in household or community income or differences in economic shocks are correlated with more risk-taking behavior.
Young adults are a particularly interesting demographic group as they represent healthy cohorts whose future behavior will influence the course of the HIV epidemic. Various researchers have shown that in high seroprevalence regions, a large proportion of new infections occur during adolescence . Survey data that match detailed individual sexual behavior measures to good measures of household and community level resources are rare; panel data that enable us to see the evolution of sexual behaviors for young adults are even more unusual. We used new panel data on adolescents (aged 14–22 years) in urban Cape Town, South Africa, to measure the extent to which resources and shocks to household resources early on in their lives could predict variation in sexual behaviors. We considered the following outcome measures that correspond to the A–B–C of HIV prevention campaigns: sexual debut, annual number of sexual partners and condom use at last sex.
Data and methods
The Cape Area Panel Study (CAPS) is a representative longitudinal study of 4752 adolescents aged 14–22 years (in 2002) living in Cape Town, South Africa. The full sample was first interviewed in 2002 and again in 2005. Most data are collected directly from the young adults. We use data from the household module, basic demographic data and detailed information about sexual relationships captured in both waves. We do not use data on what young people know about HIV and AIDS. Anderson and Beutel  reported that levels of HIV and AIDS knowledge were very high in the 2002 CAPS data. These panel data allow us to look at whether sexual behavior is changing over time as well as how current behaviors are related to a range of household level variables measured earlier in the young adult's life.
In order to generate an approximately equal sample of African and coloured individuals, African youth were oversampled. See Lam et al.  for details of sampling methodology, initial non-response and attrition. Completed interviews for 2151 Africans, 1980 coloureds and 621 whites and other races were captured in 2002. Once weights adjusting for survey design and wave 1 non-response were applied, Africans represented 15% of this wave 1 sample, coloureds represented 59% and the remaining races constituted 26% of the sample. In the 2005 wave 3324 of the initial 4752 sample were re-interviewed, of which 2993 were African and coloured individuals (27.8% attrition rate).
As initial non-response and attrition between waves were very high for the small sample of white youth, we excluded them from our analysis. Fifty-three per cent of white individuals were successfully followed in 2005, whereas attrition among coloured and African subsamples was substantially lower (21 and 36%, respectively). All of our reported results are weighted with sampling weights correcting for sample design and first wave non-response. Weighting for attrition between 2002 and 2005 does not change any results substantively (results not reported). Our final matched subsample consists of 1410 African youth and 1583 coloured youth.
For each individual in our analysis subsample, we used sexual behavior information provided by the respondent in 2002 and 2005. To examine changes in average behavior over time, we investigated three reported behaviors for the group aged 17–22 years: whether the young adult has ever had sex, whether the young adult used a condom at last sex and whether the young adult had more than one sexual partner in the 12 months before each survey.
We are cautious about the reliability of reported sexual behavior data. Misreporting is more likely when questions are more sensitive [16–19]. CAPS questions and survey protocols were carefully constructed to try to minimize the biases in these sensitive questions. In both years, respondents were questioned without the presence of any other family members as far as possible. For the 2005 survey, respondents could choose to fill out their responses directly regarding each of their 10 most recent partnerships instead of having the interviewer fill in the information. Fourteen per cent of applicants chose to self-report. Comparing those who did with those who did not respond themselves, there was no systematic difference in the number of sex partners reported in 2005.
Restricting to the same set of ages (17–22 years) in 2002 and 2005 allowed us to compare average behavior for this group over time. To examine how 2002 individual, household and community-level variables were correlated with behavior, we investigated these three sexual behavior variables measured in 2005. The variables used to predict individual behaviors within the probit model included: age in 2002; sex; education; race; literacy and numeracy test scores; per capita household income in 2002; the presence of parents at home in 2002; and the proportion of households in the community below the poverty line in the 2001 census. We also used information on negative economic shocks experienced at the household level between 2002 and 2005. A negative shock is defined as having occurred if the household experienced a death, job loss, loss of a grant or loss of support from outside the household, and if the household respondent reported that the shock had a moderate to severe financial impact on the household.
The same literacy and numeracy test was administered to each young adult in 2002 regardless of age or education level. Although the test is not age-appropriate for the entire sample, we included a full set of age dummies in the probit models to take out mean differences across age groups in test performance. Therefore, if 22-year-old youth scored consistently above average on the test simply because they were older and had more schooling, this effect was absorbed in the 22-year-old dummy.
We compared the proportion of each sex, race and age group reporting each type of behavior in each wave for ages 17–22 years. The change in these proportions between 2002 and 2005 gave us some insight into overall trends in behaviors. To measure the association between individual-level demographic data, previous income shocks, household and community resources and current sexual behavior, we estimated probit models for each of the three binary outcome variables separately for young women and young men. The results are reported as the marginal change in the probability of a particular behavior associated with a unit increase in each explanatory variable.
In the probit models we restricted the sample to ages 14–18 years in 2002 for the sexual debut and condom use outcomes, but included all ages 14–22 years in 2002 when modeling multiple partners. There are two reasons for this. First, a large proportion of those aged 19 years and older had already sexually debuted so there was little variation contributed by those individuals. Second, although multiple partnerships reflected relatively unsafe behavior at all ages, not using a condom at last sex was not unambiguously risky, especially in cases in which older individuals were married or in longer-term monogamous relationships. The results are presented as marginal effects and robust standard errors are clustered at the household level because up to three young adults were interviewed per household.
In Table 1, we present summary statistics separately by race to highlight the vast differences in living environments of African and coloured youth. Except for age, all of these differences are statistically significant across race groups. Both groups were disadvantaged under apartheid, but coloured individuals were generally able to access better educational and work opportunities in Cape Town than Africans. Mean schooling was approximately ninth grade, although Africans had on average half a year less schooling than coloureds. Africans also exhibited poorer performance on the literacy and numeracy test. Coloured youth were significantly more likely to live with their biological mothers (82% compared with 64% for Africans) and fathers (54% compared with 35% for Africans). Coloured households had a higher mean log per capita income compared with African households. On average, youth lived in communities in which 25% of households were below the 2001 poverty line, but this percentage was substantially higher for Africans (45%). Eighteen per cent of these young adults lived in households experiencing a serious economic shock between 2002 and 2005. Whereas shocks were observed in households in all income quintiles, they were somewhat more prevalent in the poorest quintiles (results not shown). Almost one in five African youth lived in a household that experienced an economic shock between 2002 and 2005. Across all variables, African youth lived in significantly poorer households and communities.
Table 2 shows the percentage of each race, sex and 2-year age group reporting each of three sexual behaviors in 2002 and 2005. Note that for Table 2 we did not follow the same individuals over time, but simply looked at the cross-section of respondents in a given age group in each wave. As the original sample of 14–22 year olds was 17–25 years of age in 2005, we looked at the ages from 17 to 22 years, the ages that overlap in the two waves. The first panel shows the percentage reporting having ever had sex at the time of the 2002 or 2005 interview. The overall pattern is an increase in sexual activity between 2002 and 2005. Across all groups, young adults aged 17 to 22 are more likely to report sexual debut in the later period. African girls aged 17 to 18 report the largest increases in sexual debut: 60% of this group reported ever having sex in 2002, compared with 72% in 2005. At the same time, parts B and C of Table 2 indicate significant increases in safer sex practices. Condom use at last sex for male and female Africans is significantly higher in 2005 than in 2002 across all age groups, except young men aged 17 and 18 years. For African young women, these increases are very large, approximately 20% or higher for each age group. There is also some evidence of increased condom use among coloured young women, although both the initial level and the increase between waves is smaller for coloured young women than for African young women.
The changes in condom use between 2002 and 2005 are shown graphically in Figure 1, using single years of age from 17 to 22. Reported condom use by African young women increased at every age between 2002 and 2005. The proportion of 17-year-old African young women who reported using a condom at last sex rose from 50% in 2002 to 82% in 2005. In contrast, coloured young women consistently reported lower rates of condom use than African young women at every age, and there is less evidence of an increase in condom use over time for young coloured young women. Higher rates of marriage at young ages cannot explain this significantly lower rate of condom use among coloured young women, because only 4–5% of African and coloured young women were married by ages 17–22 years in 2002. In 2005, only 3.4% of coloured young women and 1.6% of African young women aged 17–22 years were married. Part C of Table 2 shows the changing prevalence of multiple sexual partners by age, race and sex.
There is a fairly consistent pattern of decreasing unsafe sexual behavior for all groups. Among African young women, 22% of 17–22 year olds reported having multiple sexual partners (not necessarily concurrently) in the past 12 months in 2002, compared with 11% in 2005. For African young men, this decrease was even larger: 55% of African young men reported multiple partners in 2002, falling to 37% in 2005. Coloured young men also showed a decline in the incidence of multiple partnerships across all age groups.
Table 3 presents probit results analysing the determinants of sexual debut between 2002 and 2005, condom use at most recent sex in 2005, and multiple partners in the past year in 2005. For condom use and multiple partner outcomes, we included a dummy variable for whether sexual debut had occurred by 2002, to capture differences in behavior between those who made an early versus a late sexual debut.
Three main points emerge from these results. First, African and coloured behavior is statistically significantly different on all outcomes except for male sexual debut in 2005. These differences are large but do not consistently reflect more risky behavior on the part of African youth. Compared with coloured young women, African young women had a 33.6% higher probability of sexual debut and an 8.4% higher prevalence of multiple partners, controlling for the other variables included in the probits. At the same time, African young women had a 52.6% higher probability of using a condom at last sex. As the sample changed across outcome variables, the group of girls in column (1) is a subset of the girls in column (3). Second, higher levels of education are associated with more unsafe behavior for young women and young men. Controlling for age and the other variables in Table 3, those with more schooling were more likely to have had sex and more likely to report multiple partners. Higher scores on the 2002 literacy and numeracy test were associated with a statistically significant lower probability of sexual debut and a lower likelihood of multiple partnerships for both sexes. A young adult with one standard deviation higher score on the test had a 5% lower probability of sexual debut between 2002 and 2005.
A third point relates to the set of economic variables. Per capita household income has a small and statistically significant negative correlation with the probability of female sexual debut. A 10% increase in 2002 income was associated with a 0.6% decline in the probability of sexual debut between 2002 and 2005. The estimated marginal effect of income on sexual debut was also negative for young men but not statistically significant at conventional levels. Young men were less likely to report condom use at last sex if they lived in poorer communities: for a 10% increase in community poverty rate, this was a 5% reduction in the probability of condom use at last sex. Young women were 0.04% more likely to report multiple partners if they lived in a household experiencing an economic shock. We tested the joint significance of all of the economic variables (household per capita income, household shock and community poverty) and could not reject the possibility that the coefficients were jointly zero in each regression.
Three main findings emerge from our analysis of the panel data of 2993 Cape Town youth. First, for young people aged 17–22 years, we documented large and statistically significant increases in the probability of sexual debut for young women of both races, increased condom use at last sex for African young women, African young men, and coloured young men, as well as significant reductions in the reporting of multiple sexual partnerships. Changes in household or community-level economic resources are unlikely to explain these behavioral changes, both because we estimate relatively small effects of income on sexual behavior and because there are only small improvements in economic conditions over this period. It is also unlikely that these differences arise from a change in social desirability pressure towards answering sensitive questions in particular ways; these young adults are reporting increases in risky behavior (sexual debut) at the same time as increases in protective behaviors (more condom use, fewer multiple partnerships).
Our main interest in this paper related to whether household or community poverty variables could predict risky behavior of young adults, and, in particular, whether sexual behavior is affected by unexpected income shocks. After controlling for detailed individual and family background variables, however, we found that little of the variation in sexual behavior in 2005 was predicted by economic variables. When household income and economic shocks are significant, their marginal effects are relatively small compared with other variables. Our second finding is thus that for the sample of young adults in urban Cape Town, there is little evidence that community or household-level income or income shocks are the main factors influencing risky behavior.
Taking our first two findings together, it appears that at least in Cape Town there are significant increases in condom use and decreases in the number of sexual partners. Changes that cannot be explained by individual behavioral change appear to be taking place.
The third main finding relates to the role of education in predicting sexual risk behaviors. We did not see a protective impact of grade attainment per se, although we did find that test scores were positively correlated with safer sexual behaviors. Surprisingly, we found a significant positive association of schooling with sexual debut for young women and with multiple sexual partners for both young men and young women, controlling for age, household income, and other variables. One interpretation of our results is that the test score variable captures some of the differences in knowledge or ability that education usually measures, therefore, youth with higher numeracy and literacy skills are less likely to report risky behaviors. We speculate that the unexpected positive association between schooling and risky behaviors may be a result of the impact of peers within the school system. There is a great deal of grade repetition in South Africa , with a wide mix of ages in any given grade. A 17 year old in grade 11 interacts with a much more sexually active group of peers than a 17 year old in grade 8. Further research will be required to understand why years of education may not have the protective effect that is usually hypothesized.
This paper was written for the workshop ‘A Symposium for investigating the empirical evidence for understanding vulnerability and the associations between poverty, HIV infection and AIDS impact’. The authors would like to thank USAID and the Health Economics and HIV/AIDS Research Division (HEARD) for financial support during the preparation of the paper and attendance at the workshop. The data used in this paper are publicly accessible at www.caps.uct.ac.za.
Sponsorship: This work was supported by the US National Institute of Child Health and Human Development (R01HD39788 and R01HD045581), the Fogarty International Center of the US National Institutes of Health (D43TW000657), and the Andrew W. Mellon Foundation.
Conflicts of interest: None.
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