How Does Educational Attainment Affect the Risk of Being Infected by HIV/AIDS? Evidence from a General Population Cohort in Rural Uganda.

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How Does Educational Attainment Affect the Risk of Being Infected by HIV/AIDS? Evidence from a General Population Cohort in Rural Uganda. Damien de Walque + The University of Chicago September 2002 I am
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How Does Educational Attainment Affect the Risk of Being Infected by HIV/AIDS? Evidence from a General Population Cohort in Rural Uganda. Damien de Walque + The University of Chicago September 2002 I am very grateful to Dr June Busingye, George Katongole, Jessica Nakiyingi, Julie Pickering, Anthony Ruberantwari and Professor James A.G. Whitworth from the Medical Research Council (UK)/Department for International Development (UK)/Uganda Virus Research Institute Programme on AIDS in Uganda for their hospitality in Uganda, for giving me access to the data set of the General Population Cohort and helping me with numerous advices and comments on the data sets and on the general situation of the HIV/AIDS epidemic in Uganda. I thank Gary Becker, Hoyt Bleakley, Raphael De Coninck, Mark Duggan, Michael Greenstone, Patrick Heuveline, Fabian Lange, Steven Levitt, Casey Mulligan, Kevin Murphy, Tomas Philipson, Chris Rohlfs, James Whitworth and participants of the micro-lunch at the University of Chicago for helpful comments and discussions. Financial support from the Flora and William Hewlett Foundation Fellowship and the Esther and T.W. Schultz Endowment Dissertation Fellowship is gratefully acknowledged. Please do not quote without the author s permission. + Abstract Rates of return to education, as traditionally calculated, only account for labor market earnings. It is thought, however, that education may increase people's life expectancy. This paper tests this hypothesis in the context of the HIV/AIDS epidemic in Africa. Using data from the General Population Cohort of the Medical Research Council Programme on AIDS in Uganda, it shows that over the last decade there has been a substantial evolution in the HIV prevalence/education gradient among the population in rural Uganda surveyed. Early in the epidemic, in 1990, there was no robust relation between HIV/AIDS and education. In 2000, after more than a decade of information efforts about the dangers of the epidemic, for individuals who started their sexual life after the start of the prevention campaigns, the higher the education level, the lower the risk of being HIV positive. This result is new in Africa, as previous studies of the HIV/education relation have generally concluded that there was either a positive or no association between HIV infection and schooling levels. I estimate that more educated individuals have reacted much stronger to the arrival of the information about the epidemic than individuals with less education. Results on HIV incidence in a duration framework confirm this finding. The analysis of sexual behaviors reinforces it: in particular, condom use is associated positively with schooling levels. This suggests that there might be substantial additional, non labor-market, returns to schooling associated with the effect of education on health. I find that the effect of education in reducing HIV/AIDS infection adds between 0.5 and 3.5 percentage points to the standard estimates of the returns to schooling. 1 Introduction The HIV/AIDS epidemic is probably the greatest challenge facing Africa. According to UNAIDS [46], in 2001, 28.1 million people were infected by HIV/AIDS in Africa (this represents 70% of the worldwide total), 2.3 million died from the virus and 3.4 million became newly infected. Figure 1 shows the evolution of the epidemic in selected African countries between 1997 and In Uganda, the percentage of infected adults in 2001 was 5.0% -down from 14% in the early 90 s- and the forecasted reduction in life-expectancy is from 54 to 43 years [48]. The economic profession has mainly addressed this epidemic by looking at its demographic and economic consequences [6] [17] [7] [27] [1] or by looking at the issue of the affordability of the treatment and the incentives to create a vaccine [25]. This paper focuses on an economic analysis [39] of the behaviors leading to HIV/AIDS infection, with a special emphasis on the impact of education. In particular, it investigates the relation between educational attainment and the risk of being infected by HIV/AIDS. How does educational attainment affect the risk and the probability to be infected by the HIV/AIDS epidemic? The general empirical observation that educated and rich people are usually healthier and the complementarity between health and education predicted by economic theory would suggest that more educated people would be less affected by a disease perceived as facilitated by ignorance and poverty. The relationship between HIV prevalence and education has been studied in the epidemiological and medical literature [5] [15] [18] [19] [23] [49]. A recent review article [21] concluded that in Africa, several studies have shown that higher education levels were associated with a greater risk of infection. studies, however, are in urban settings and based on data taken earlier in the epidemic. Most of these This positive association between education and HIV infection has been seen as puzzling and as a cause of concern from a policy perspective. However, there is evidence that among younger cohorts this positive association has disappeared. In Uganda, this pattern - the positive gradient between education and HIV and the subsequent flattening of it - has been documented among pregnant women visiting ante-natal care clinics in the city of 1 Fort Portal [23]. In previous studies, I have analyzed this pattern from an economic perspective, using the surveillance data from Fort Portal [11] [10]. The very strong correlation between education levels and health outcomes, even after controlling for income, has been recognized as a robust empirical observation in the social sciences and economic literature [9] [14] [26]. The decision to engage in risky sexual behavior in the presence of the HIV/AIDS epidemic is a choice that directly affects the health status and ultimately the mortality of the individuals. It, therefore, provide an interesting opportunity to investigate how education, by influencing behaviors, affects health outcomes. Unprotected sex was not initially perceived as dangerous in Uganda. At the end of the 80 s and early in the 90 s the information about the HIV/AIDS epidemic gradually came in and revealed the risk associated with the multiplicity of partners and unprotected sex. Variations over time in HIV prevalence across education groups might therefore be very informative about the way individuals reacted to that information and how education is instrumental in accessing and processing this information. Using data from the General Population Cohort (GPC) of the Medical Research Council (MRC) Programme on AIDS in Uganda, this paper shows that over the last decade there has been a substantial evolution in the HIV prevalence/education gradient among the population in rural Uganda surveyed by the GPC. Early in the epidemic, in 1990, a preliminary inspection indicates that education increases the risk of being infected by HIV. However, careful analysis of the data reveals that this positive correlation is in large part due to an age or cohort effect: young individuals are more likely to have some secondary education and, at the same time, they are more likely to be HIV positive. In 2000, after more than a decade of information and prevention efforts about the dangers of the epidemic, the pattern is very different: for individuals who started their sexual life before the start of the prevention campaigns, education is associated with an increased risk of HIV infection. On the contrary, for individuals who started their sexual life after the start of the prevention campaigns, the higher the education 2 level, the lower the risk of being HIV positive. The present paper details and analyzes this reversal of the education/hiv infection gradient illustrated in Figure 2. This seems to be one of the first study reporting robust evidence that for young cohorts in Africa 1 there is a negative gradient between education and HIV infection [21]. By comparing individuals who had no access to information about the epidemic during the portion of their sexual life covered at the time of the survey - individuals aged 18 to 29 in with individuals for whom information was accessible from the start of their sexual life - individuals aged 18 to 29 in 2000, this paper also estimates that more educated individuals have reacted much stronger to the arrival of the information about the epidemic than individuals with less education. As illustrated in Figure 3, the HIV prevalence for young individuals with no education or primary education decreased by 6 percentage points, while it decreased by 12 percentage points for individuals who went to secondary school. Results from a study of HIV incidence in a duration framework confirm this finding. Moreover, this paper documents one of the mechanisms leading to this result by showing, in a different data set, the 1995 Demographic and Health Survey for Uganda, that changes in sexual behavior, in particular condom use, have been more widespread among educated individuals. The policy implications of this finding are important as they suggest that investing in education is not only beneficial for the labor market outcomes of individuals, but also helps them to make decisions that improve their health and longevity. This is especially true in an environment where most individuals are not taking part in the formal economy and where, therefore, labor markets outcome are more difficult to assess. Rates of return to education, as traditionally calculated in the economics literature, only account for labor market earnings. Given the large impact on mortality of HIV/AIDS, by showing that educational achievement has an important role in the prevention of the epidemic, this paper suggests that traditional 1 Out of 27 studies reviewed, only one, Fontanet et al., 2000, among sugar estate workers in Ethiopia, reported a significantly negative association between HIV infection and education. A negative association between HIV infection and education has also been reported among military conscripts in Thailand (see Hargreaves et al., 2002 [21] for a review). 3 measures of the returns to education might be too low and estimates that, by introducing the effect of education on health, the addition might be between 0.5 and 3.5 percentage points The paper is structured as follows. Section 2 gives a short background on the HIV/AIDS epidemic in Africa. Section 3 presents theoretical models where choices in sexual behavior are modelized when confronted with the HIV/AIDS epidemic. Section 4 exposes the empirical analysis of the data from the General Population Cohort of the MRC Study. Section 5 covers the analysis of sexual behavior. Section 6 proposes a framework to estimate the additional returns to education due to the negative relation between HIV/AIDS and schooling. Section 7 concludes. 4 2 Some important facts about the HIV/AIDS epidemic in Africa In Africa, the transmission of the HIV epidemic occurs overwhelmingly through heterosexual intercourses. There is a long interval between HIV infection -seroconversion- and the actual development of AIDS. Using the data from the Natural History Cohort, a nested cohort study within the GPC, it has been estimated that the median time from seroconversion to AIDS was 9.4 years and from AIDS to death was 9.2 months [33] [31]. This median survival time is similar to what was found in industrialized countries before the introduction of antiretroviral therapies. For the vast majority of HIV/AIDS patients in Africa, however, and certainly for the periods considered in this paper, antiretroviral therapies were not available nor affordable. The long interval between seroconversion and AIDS has several implications that should be kept in mind while studying the dynamics of the HIV/AIDS epidemic. The firstimplicationisthatmosthivpositive individuals appear healthy. In an environment where voluntary testing is limited 2, this means that most people ignore their own HIV status as well as the status of their sexual partners. Another implication of this long interval is that any prevalence estimates reflects behavior which is on average 5 years old. This is an important fact to realize when trying to determine in how far some prevalence rates could have been influenced by information campaigns. For example, prevalence rates in reflect behaviors that are on average 5 years old, while the first prevention campaigns were launched in 1986 at a national level and were, at a local level, reinforced by the establishment of the MRC Programme in 1989 in the surveyed area. It is therefore reasonable to advance that a substantial component of the prevalence in is the consequence of sexual behaviors that predated the availability of the information about the epidemic. A final implication of the long interval between seroconversion and AIDS is that the level of the epidemic can be seen as a stock/flow issue. Theprevalenceratemeasuresthestock of seropositive individuals which is affected both by the incidence - the flow of individuals who seroconverted- and the mortality rates. In the 2 The Uganda Demographic Health Survey estimates that 8.4% of females and 12% of males have been tested for the AIDS virus [43]. 5 absence of migration, the relation between the prevalence of HIV and the incidence and the mortality can be summarized as such: H =(1 H)λ H Hλ D (1) H is the change in the prevalence rate at each period, typically one year. H is the HIV prevalence rate, λ H is the incidence rate, i.e. the rate at which HIV negative individuals become HIV positive and λ D is the mortality rate from AIDS for HIV positive individuals. This paper mainly analyzes HIV prevalence. However, it proposes also a survival analysis based on the few (133) incident cases that can be retraced from the information available at Rounds 1, 7, 8, 9 and 11. The GPC is the first study to report a significant decline in adult HIV incidence in rural Africa [29] [51]. It is also important to realize that, in light of the fact that the median interval between infection and death is around 10 years, HIV prevalence at young ages is only marginally affected by mortality. The first AIDS case in Uganda was diagnosed in The HIV virus was probably introduced late in the 1970 s or early in the 1980 s. [43] [42]. The Ugandan government was the first in Africa to recognize the extent of the epidemic [33]. As early as 1986, the government set up a national AIDS Control Programme which has been in charge of disseminating the information about the HIV/AIDS epidemic. From a peak of 14% in the early 90 s, the prevalence rate in the adult population has now been brought down to an estimated 5% [48]. Several studies have attributed this decline to a change in behavior associated with strong prevention campaigns. While, as documented in Figure 1, the epidemic is still on the rise in most of Africa, especially in southern Africa, with a prevalence rate as high as 38.5% [47] in the adult population of Botswana, Uganda appears as one of the few countries that have been successful in fighting the epidemic. Uganda has a population of 23 millions of which 15% is urban. In 1999, Uganda had a GDP per capita of 1,167 US $ in purchasing power parity [50]. 6 3 A theoretical model of sexual behavior in the presence of the HIV/AIDS epidemic The question of whether the effect of education is causal is central in the economics literature on the interaction between health and education. Theoretical explanations for this correlation can be classified into three broad categories. One explanation stresses that education is an investment [3]. Education will deliver an higher income, an higher consumption level in the future, raising the value of staying alive. More educated individuals are healthier because their investment in the future give them the right incentives to protect their health. Another explanation, based on education entering as a factor in the health production function [20] [22], emphasizes that education improves the access to health related information and helps processing that information to make the healthy decision. A third view [12] [14] claims that the observed correlation between health and education is mainly due to third factors - unobservables, like the discount factor or the ability - that causes the same individuals both to study longer and to take greater care of their health. This section presents three variations of a model that sketches choices in sexual behavior during the HIV/AIDS epidemic. These three explanations are not mutually exclusive, however, and it is very likely that they all contribute to the explanation of the positive correlation between health and education. However, for the purpose of clarity, I have separated the exposition in three steps. 3.1 Baseline model: in the absence of information about the HIV/AIDS epidemic The model presented here is very simplified but could be easily extended. For the sake of simplicity, it is a two periods model. At each period, individuals derive utility from consumption goods (c t ) and the number of sexual partners (n t )theyhaveineachperiod,witht =1, 2. 7 For convenience, I make the following assumptions, which could be relaxed without changing the main results : the agents choose only the number of their sexual partners and they have the same number of sexual intercourses with each partner. The period utility function is separable in consumption and in the number of sexual partners. U(c, n) =u(c)+v(n) (2) where u(.) and v(.) are both increasing and concave in their respective arguments. Agents live two periods provided they survive from the first to the second. Their survival is determined by a survival probability Q where 0 Q 1. In the baseline model, I assume that agents are not aware of the risks associated with the HIV/AIDS epidemic. Therefore, they take Q, the average survival probability in the population, as given and do not consider the effects of HIV exposure on survival when deciding about their sexual behavior. In order to introduce variation in the level of education, I introduce two type of agents : these with low human capital K L and those with high human capital K H. The wage W (K i ) is an increasing function of the level of human capital K i with i = L, H. The price of consumption goods is normalized to one in both periods. The individuals have the opportunity to protect their sexual intercourses, for example, by using condoms. π 1 denotes the proportion of protected sexual intercourses in the first period. The price of protection (price of condoms, of HIV testing, control of partner s fidelity) of sexual intercourse is defined as p π. The price, p n (market price in case of prostitution, gifts, dowry or shadow cost in case of non commercial sex) of a partner is identical for each of them. The asset level of the individual is defined by A. I assume a perfect annuity market so that assets A in period 1 carried to the next period become A(1+r) Q,wherer is the interest rate. For convenience, I will let the discount factor, β = 1 1+r. 8 Agents choose for each period their level of consumption (c 1,c 2 ), their number of sexual partners (n 1,n 2 ) and the proportions (π 1, π 2 ) of protected sexual intercourse. The inter-temporal problem they are facing is therefore characterized by: MAXc 1,c 2,n 1, n 2, π 1, π 2 u(c 1 )+v(n 1 )+βq[u(c 2 )+v(n 2 )] (3) subject to the following budget constraint: [λ] c 1 +(p n + p π π 1 )n 1 + Q 1+r [c 2 +(p n + p π π 2 )n 2 ]=W(K i )(1 + Q 1+r ) (4) with i = L, H. and subject to [θ] n 1 0 [ψ] π 1 0 [φ] π 1 1 The first order conditions for a maximum for this problem are very standard: [c 1 ]: u 0 (c 1 )=λ (5) [c 2 ]: u 0 (c 2 )=λ (6) 9 [π 1 ]:ψ = φ + λp π n 1 (7) [n 1 ]:v 0 (n 1 )+θ = λ(p n + p π π 1 ) (8) From equation (7), it follows that, since λp π n 1 0 (unless n 1 =0,inwhichcaseπ 1 i
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