Abstract
Demographic and Health Survey data from nine African countries make it clear that HIV/AIDS prevention knowledge has been increasing. Still, in many cases, fewer than half of adult respondents can identify specific prevention behaviors. Knowledge is lowest in rural areas and among women. HIV testing generally remains rare but is highly variable across countries, likely reflecting differences in the supply of testing services. In most cases, schooling and wealth impacts on prevention knowledge have either been stable or have increased; hence, in the majority of contexts, initial disparities in knowledge by education and wealth levels have persisted or widened.
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Notes
Gersovitz (2001) is a partial exception in that he uses artificial cohort analysis from repeated cross-sections to assess changes in a limited number of behaviors (e.g., age at first intercourse) over time.
It obviously would also be of interest to gauge changes over time in risk behaviors such as number of sexual partners or use of condoms. This is examined in a companion paper, Glick and Sahn (2005).
Greater investment in education may be a reflection of a lower discount rate, which again would incline those with an education to seek information and change behaviors to insure their longevity.
The word ‘may’ is important, as people who have long engaged in high risk behavior may feel strongly that they have already been infected, hence (unless they are altruistic and seek to prevent infecting others) may see little benefit to testing or learning more about HIV prevention.
Or, information eventually may become so widely disseminated that its cost is essentially driven to zero for everyone, which would eliminate any advantage in information access to being educated.
Prevalence estimates are taken from UNAIDS reports, various years. We report data for the closest year available to the most recent DHS round used in each country.
In some of the surveys, a “safe sex” is allowed as a response; the individual is then asked what she means by this, again without being prompted with possible answers.
What is more important is that the questions be posed consistently over time. In the majority of our countries, the questions were essentially identical in both survey years, but in Burkina Faso, Nigeria, and Zambia there were slight differences over time in the “limit partners” categorizations. We note these below when presenting our results.
The results will not be unbiased if the unobservables enter nonlinearly, that is, if they interact with included individual level covariates—for example, if the response to the presence of a local program for dispensing HIV information depends on wealth or education. As the earlier discussion makes clear, such interactions cannot be ruled out and this should be kept in mind in evaluating the estimates.
Note that because the surveys are repeated cross-sections, not panels, we are unable to estimate the determinants of changes in an individual’s knowledge or testing behavior over time.
For Nigeria, and for men in Zambia, the shares identifying limiting the number of partners as a prevention behavior actually appears to have declined over time. Some caution is called for here, however, the questions on limiting the number of partners change slightly between surveys for these two countries, most notably in that the earlier years alone allow a “have safe sex” response with a follow-up question for what this means.
Uganda is a very interesting example because the country has famously managed to turn the tide on the epidemic. Incidence and prevalence are thought to have begun falling before 1995, the year of our first survey—yet as seen in the table in that year, the shares of women able to identify prevention behaviors were 50% or lower for each such behavior. However, prevalence fell in part due to mortality among those with AIDS and likely also among delayed or reduced sexual activity specifically among the young (see Parkhurst 2002; Konde-Lule 1995), both of which are not incompatible with the population HIV knowledge means from the DHS.
The relatively low numbers in many of the countries of individuals identifying avoiding sexual relations are somewhat surprising. It is possible that despite careful wording of the question in the DHS, respondents personalize the question and do not think of abstinence as a viable means of prevention because it is not a practical option for them. Or, they may not consider abstinence as a behavior distinct from limiting the number of partners.
Note the comparisons of changes across age groups is not a longitudinal cohort analysis: we are not considering how knowledge has changed among, say, individuals who were 15–25 at the time of the first survey (this could, however, be accomplished by constructing synthetic cohorts). What the comparisons we report show is whether, for example, 15- to 25-year-olds know more, now, than 15- to 25-years-olds knew before and how this change compares with other age groups.
Our concern is not so much with misreporting by respondents but with whether those interviewed are truly representative of the urban population.
With respect to the gender gap in testing experience, one can hypothesize more speculatively that the implicit costs of testing are higher for women (see Glick 2005). They probably have more to lose in terms of the stability of their partnerships from testing, especially if testing positive (and if observed or discovered by their spouses) or from stigma generally. If they are less mobile, it may be harder for them to find ways to test discretely. The fact that the reported desire to be tested is similar for men and women while actual testing behavior differs lends some credence to this idea.
The complete set of probit results can be obtained from the authors.
These calculations include all subsamples, not just cases with statistically significant impacts.
Indeed, there was also a positive association of education and condom use. De Walque explains these results as showing that the information provided in the prevention campaigns was more easily absorbed by the educated—i.e., that schooling and information are complements in the production function for HIV knowledge—rather than that the uneducated in the study villages had less access to the information.
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We would like to acknowledge the helpful comments of two anonymous referees.
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Glick, P., Sahn, D.E. Changes in HIV/AIDS knowledge and testing behavior in Africa: how much and for whom?. J Popul Econ 20, 383–422 (2007). https://doi.org/10.1007/s00148-006-0085-8
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DOI: https://doi.org/10.1007/s00148-006-0085-8