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I would like to acknowledge helpful comments by Francesco Caramazza, Michael Nowak, and seminar participants at the IMF, as well as editorial support by Thomas Walter.
For example, the International Programs Center at the U.S. Bureau of the Census estimates the mortality rate for the adult population in Zimbabwe for the year 2002 at 3.1 percent, 2.9 percent of which is attributed to HIV/AIDS.
There are abundant reports on hospitals overcrowded owing to an increasing number of AIDS patients. The situation differs substantially across countries and regions; for Southern Africa, most reports indicate that 50-70 percent of hospital beds are occupied by patients who are HIV positive.
The epidemic affects both the supply of education (through increased mortality of teachers) and the demand for education (through lower birth rates, increased infant mortality, and falling enrollment rates). For most countries in the region, pupil-teacher ratios are projected to increase, owing to the HIV/AIDS epidemic; see also Haacker (2002).
In the absence of medical or life insurance (as is common in many African countries, especially in the informal sector), the illness and death of a breadwinner have a negative and oftern catastrophic impact on a household’s income and wealth.
Most important, through absenteeism, increased training costs, disruptions to the production process, and medical and death-related costs.
Haacker (2002) provides a broader discussion of the economic consequences of HIV/AIDS. A recent report by the International AIDS Economics Network on “State of the Art: AIDS and Economics” (see IAEN (2002)) includes survey articles on a broad range of issues.
See Kambou, Devarajan and Over (1993), who use an 11-sector CGE model for Cameroon. ING Barings (2000), Arndt and Lewis (2001), and BER (2001) apply different models to study the impact of HIV/AIDS in South Africa.
Throughout the paper, we assume that skilled and unskilled labor grow at the same rate n.
The picture regarding the socioeconomic gradient of the epidemic (i.e., how it affects different subgroups of the population) is not clear. Women are affected worse (and at a younger age); various professions (most notably sex workers and migrant workers) are at higher risk; at least in the early stages of the epidemic, HIV prevalence rates tend to be higher in urban areas. However, there is no clear pattern across countries regarding the level of skills. One recent study for South Africa (BER, 2001) suggests that, while HIV prevalence rates are similar for the unskilled and skilled, they are somewhat lower for the highly skilled (about 10 percent of the labor force). BIDPA (2000) does not differentiate between skill levels.
The International Programs Center at the U.S. Bureau of the Census, for example, estimates that life expectancy in Zimbabwe has fallen from 65 years to 39 years.
Both these approaches have serious drawbacks; however, given the scarcity of microeconomic data on households affected by HIV/AIDS in sub-Saharan Africa, they serve as useful approximations. One issue that is frequently raised in discussions is the savings behavior of people who are HIV positive (but have not developed any symptoms yet). Finding out about an HIV infection shortens an individuals life expectancy, and an optimizing agent would respond by increasing current consumption. At the same time, the news reduces this household’s expected lifetime income; if there are relatives to care for, or in anticipation of treatment costs, the agent may actually increase savings.
For an alternative setting featuring unemployed unskilled workers who do not contribute to aggregate output, see Section IV.
The assumption that the informal sector does not use skilled labor does not affect our results, provided that the share of skilled worker is higher in the formal sector, but it does simplify the formal analysis significantly.
This could, for instance, reflect efficiency wages and/or asymmetric information. As it is not clear how this wedge would change in response to the HIV/AIDS epidemic using either model, we do not attempt to endogenize λ. Note that, for λ = 1, the model encompasses the case of perfect mobility of unskilled labor.
This result reflects the fact that the impact of a change in total factor productivity (A) on the steady state capital stock is larger when the elasticity of output with respect to capital is higher.
See the appendix for a numeric example.
This substantial increase in per capita income appears to be accounted for by a decline in unemployment and an increase in aggregate demand (in per capita terms).
Aggregate HIV prevalence rates are generally estimated by fitting a demographic and epidemiological model to relatively few observations, mainly data on HIV prevalence among pregant women from from antenatal clinics. An alternative method focuses on trends in the pattern of mortality rates by age.
This result points to some implications of the HIV/AIDS epidemic with respect to the distribution of wealth. While income for those who do not save (presumably, agents with lower income) goes down, the accumulation of wealth per capita for those who do save may increase. However, given the simplistic structure of the model used here, these conclusions are largely speculative.