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What Drives the HIV/AIDS Epidemic in Sub-Saharan Africa?

Par : Type de matériel : TexteTexteLangue : français Détails de publication : 2009. Sujet(s) : Ressources en ligne : Abrégé : This paper breaks down the impact of the determinants of the evolution of the HIV/AIDS epidemic in Sub-Saharan Africa from 1997 to 2005. We classify the set of determinants into socio-economic, epidemiological, and sociologic and cultural factors, and attempt to assess the importance of each factor on the spread of the epidemic across the continent. Using panel data from 42 African countries during the period 1997–2005, we examine the link between the three categories of determinants and the HIV/AIDS epidemic. After a logit transformation of the HIV prevalence rate among the adult population (15–49), we estimate our equation with a random effects model, including a regional dummy in order to capture sub-regional differences in the spread of the epidemic. Then we estimate the same equation using the prevalence rate among young members of the population (15–24). Finally, we repeat the equation using the generalized method of moments (GMM) to account for dynamic effects in our model. Our results are robust and our analysis shows that epidemiological and socio-cultural variables have a greater impact than socio-economic ones. More importantly, within the group of socio-economic variables, education, women’s economic independence, and income inequality appear very important in determining the spread of the epidemic.
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This paper breaks down the impact of the determinants of the evolution of the HIV/AIDS epidemic in Sub-Saharan Africa from 1997 to 2005. We classify the set of determinants into socio-economic, epidemiological, and sociologic and cultural factors, and attempt to assess the importance of each factor on the spread of the epidemic across the continent. Using panel data from 42 African countries during the period 1997–2005, we examine the link between the three categories of determinants and the HIV/AIDS epidemic. After a logit transformation of the HIV prevalence rate among the adult population (15–49), we estimate our equation with a random effects model, including a regional dummy in order to capture sub-regional differences in the spread of the epidemic. Then we estimate the same equation using the prevalence rate among young members of the population (15–24). Finally, we repeat the equation using the generalized method of moments (GMM) to account for dynamic effects in our model. Our results are robust and our analysis shows that epidemiological and socio-cultural variables have a greater impact than socio-economic ones. More importantly, within the group of socio-economic variables, education, women’s economic independence, and income inequality appear very important in determining the spread of the epidemic.

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