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)| false Booysen, Frederick le Roux, 2003, “ Poverty Dynamics and HIV/AIDS Related Morbidity and Mortality in South Africa,” paper presented at an international conference on “Empirical Evidence for the Demographic and Socio-Economic Impact of AIDS,” Health Economics and HIV/AIDS Research Division, University of KwaZulu-Natal, South Africa, March 26–28.
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Gonzalo Salinas is the corresponding author. Address: Economics Department, Oxford University, Manor Road Building, Manor Road, Oxford, OX1 3UQ, UK. Markus Haacker is in the African Department, International Monetary Fund. The authors would like to thank Robert Greener (UNAIDS) and Kirsty Mason (IMF) for their comments, and Leighton Harris for assistance with tables and figures.
HIV prevalence for the population of age 15–49 in sub-Saharan Africa ranges from about 1 percent (comparable to some European countries) to almost 40 percent.
According to Bell and others (2004), this negative impact on human capital is the result of household poverty and parent mortality impeding access to education and reflects the fact that returns to investments in human capital decline owing to higher mortality.
With the exception of Ghana, where prevalence rates are very low, and few outliers may have skewed the distribution.
We carried out all simulations in Stata/SE 8.0, reiterating the procedure fifty times for each scenario (to minimize random error in our estimates); our final estimates are the average value of the fifty outputs. DHS and Income and Expenditure household surveys are available in Stata format for all sample countries.
While our approach can equally be applied to an evolving epidemic, we assume that HIV prevalence rates remain constant at the rates obtained from the household surveys. Also, we make some simplifying assumptions regarding the underlying demographics: The population does not age, and the structure of households, apart from the changes induced by increased mortality, remains constant.
Age groups are five-year categories ranging from 15–19 up to 55–59 years old. The wealth index is an estimate of the household wealth (assets) and classifies both HIV-tested and non-HIV-tested individuals accordingly in quintiles.
In some subgroups, no individual tested HIV-positive, partly due to a small sample size (especially for Ghana and Kenya, where overall prevalence rates are low and data are highly disaggregated). In this case, we assign to these individuals the average prevalence rate of the entire population. The one exception to this rule is the case in which the individual actually corresponds to an age group not covered by the prevalence surveys (younger than 15 years or older than 60 years). If an individual is below (above) the age range tested for HIV, the individual is assigned a prevalence rate that is half of the prevalence rate of the youngest (oldest) age group sampled. This implies, for instance, that a 65-year-old individual is assigned a prevalence rate that is half of that for the age group of 55–59 years.
On the basis of the latest estimates from UNAIDS (UNAIDS, 2004), we assume the ratio between male and female HIV prevalence to be about 82 percent.
It is important to note that this procedure does allow for a clustering of HIV infections within households, most importantly because cohabitating couples may infect each other. Our procedure thus overestimates the number of households with at least one infection, and underestimates the number of households with multiple infections.
These increases are consistent with estimates of actual expenditures presented in Steinberg and others (2002) for the case of South Africa. The assumed differences between urban and rural areas reflect lower average incomes in rural areas, as well as higher costs of accessing health services.
This is similar to findings reported in Fox and others (2004) for Kenya, who find that the income falls by around 17 percent for individuals in their penultimate and last years before death from AIDS.
Note, however, that the $1-a-day line is recalculated in 1993 purchasing power parity (PPP) terms at about $1.08 a day; this recalculation is more thoroughly explained in World Bank (2005).
The 1$-a-day line is located between deciles 3 and 4 in Ghana, 2 and 3 in Kenya and Swaziland, and 4 and 5 in Zambia.