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

Par : Type de matériel : TexteTexteLangue : français Détails de publication : 2010. Sujet(s) : Ressources en ligne : Abrégé : This paper decomposes the impact of the determinants of the evolution of HIV/AIDS pandemic in sub-Saharan Africa from 1997 to 2005. We classify our set of determinants into socioeconomic, epidemiological, sociologic and cultural and we try to assess the importance of each group of determinants in the spread of the epidemic across the continent. Using a panel data of 42 African countries from the 1997-2005 period, we examine the link between the three categories of determinants and HIV/AIDS epidemic. After a logit transformation of HIV prevalence rate among adult population (15-49), we estimate our equation with the random effects model and we include a regional dummy in order to capture sub-regional differences in the spread of the epidemic. Then we estimate the same equation using prevalence rate among young population (15-24). Finally, we estimate the same equation with the generalized method of moments (GMM) to accounts for dynamic effects in our model. Our results are robust and our analysis shows that epidemiological and sociocultural variables have a greater impact than socioeconomic ones. More important, within the group of socioeconomic variables, education and women’s economic independence and income inequality appear very important in determining the spread of the pandemic.JEL Classification: I10, I19, 015.
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This paper decomposes the impact of the determinants of the evolution of HIV/AIDS pandemic in sub-Saharan Africa from 1997 to 2005. We classify our set of determinants into socioeconomic, epidemiological, sociologic and cultural and we try to assess the importance of each group of determinants in the spread of the epidemic across the continent. Using a panel data of 42 African countries from the 1997-2005 period, we examine the link between the three categories of determinants and HIV/AIDS epidemic. After a logit transformation of HIV prevalence rate among adult population (15-49), we estimate our equation with the random effects model and we include a regional dummy in order to capture sub-regional differences in the spread of the epidemic. Then we estimate the same equation using prevalence rate among young population (15-24). Finally, we estimate the same equation with the generalized method of moments (GMM) to accounts for dynamic effects in our model. Our results are robust and our analysis shows that epidemiological and sociocultural variables have a greater impact than socioeconomic ones. More important, within the group of socioeconomic variables, education and women’s economic independence and income inequality appear very important in determining the spread of the pandemic.JEL Classification: I10, I19, 015.

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