with primary education and by 12 percentage points for those with secondary education<sup>12</sup> . This estimation gives a measure of the interaction between the effect of the arrival of the information about the HIV/AIDS epidemic and the effect of education, showing that the more educated the individuals–in particular, for individuals with some secondary education–the more strongly their prevalence rate decreased after the arrival of that information.

Table 1, by analyzing the relation between education and HIV prevalence in a regression framework con fi rms the point made in fi gure 1. The sample has been divided in two age groups: 18—29 and 30 and above. As explained, at round11,<sup>13</sup> this distinction can be interpreted as, on the one hand, the group that started their sexual life after the beginning of the prevention campaigns, and, on the other hand, the group that initiated their sexual activity before the information was available.

At round 1, there is no signi fi cant association between HIV and schooling.<sup>14</sup> For the younger group, a higher educational achievement leads to a decrease in the probability to be infected by HIV. The point estimates for the marginal effects imply that, compared to the baseline of no education, having some primary education decreases the risk by 5.1 percentage points and some secondary education, by 8.8 percentage points. The statistical signi fi cance of the coefficient for primary education is, however, not very high. For the group of individuals older than 29, the sign on the coefficients on the education variables is positive (even though signi fi cance is not very high), in contrast with the results for the younger group. It should be kept in mind, however, that any measure of HIV prevalence for individuals older than 30 is bound to be affected by the "survivor"

> 12 The differences across education categories among individuals aged 18 to 29 are not signi fi cant at round 1, while they are at round 11: HIV prevalence is 10.81 percent (con fi dence interval 5.71-18.12) for individuals with no education, 7.81 percent (CI 6.25-9.61) for individuals with at least primary education and 3.23 percent (CI 1.82 - 5.28) for individuals with at least some secondary education. Another cut of the data at round 11 shows that the completion of primary education seems important: no education, 10.81 percent (CI 5.71-18.12), some primary education but not completed, 9.4 percent (CI 7.33-11.83), completed primary, 4.62 percent (CI 2.66-7.40), at least some secondary 3.33 percent (CI 1.81-5.71) and above secondary 2.53 percent (CI 0.30-8.84). Table 2 proposes different ways to measure schooling levels.

> 13 I have chosen to present the analysis at round 11 rather than round 12, because the education variable was not measured at round 12.

> 14 A recent review of the epidemiological literature (Hargreaves and Glynn 2002) concluded that, in Africa, several studies had shown that higher education levels were associated with a greater risk of infection. In the data set of the GPC, there is a positive correlation between raw means at round 1. However, this is explained by 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. With the introduction of age dummies in the regressions at round 1, the positive association between HIV infection and schooling disappears.

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