Cover Image
close this bookTurbulence or Orderly Change? Teacher Supply and Demand in South Africa - Current Status, Future Needs and the Impact of HIV/AIDS (CIE, 2000, 36 p.)
View the document(introduction...)
View the documentMulti-Site Teacher Education Research Project (MUSTER)
View the documentList of Acronyms and Abbreviations
View the document1. Overview
View the document2. Background and Introduction
View the document3. The Demographic Characteristics of Teachers
View the document4. Incomes of Teachers and Non-teachers
View the document5. Teacher Turnover - The Dynamics of Teaching Employment
View the document6. Forecasting Basic Numbers
View the document7. Regionality and Micro-Regionality of the HIV/AIDS Epidemic
View the document8. Concluding Remarks
View the documentAnnex

7. Regionality and Micro-Regionality of the HIV/AIDS Epidemic

A final set of observations concern a simple but important point: that the HIV/AIDS epidemic strikes in a highly selective manner. This implies that the administrative measures used to confront the epidemic will need to be localised or, alternatively, much more directive.

Figure 10 shows HIV prevalence rates at 30% in KwaZulu Natal hospitals in 1998. The hospitals’ names have been omitted to protect anonymity and to draw attention to the numbers themselves rather than to the hospitals. A certain degree of homogeneity has been ensured by focusing on Provincially managed hospitals, excluding clinics, private hospitals, etc.


Figure 10: HIV Prevalence Rates in KZN Public Hospitals

Source: Personal communication, Daniel Wilson, EduAction, Durban. Original data: Medical Research Council, Dept of Health

These prevalence rates may or may not reflect likely prevalence rates amongst teachers. However, the age groups at which these prevalence rates apply, and the gender to which they apply, are actually fairly coincident with the demographics of the teaching force. But let us be conservative, let us use these numbers to help us reason in terms of schools, and let us assume that the variability (not necessarily the mean) of HIV-prevalence rates at the hospitals bears some reasonable relation to the variability in eventual HIV/AIDS-incidence rates in regions but a little lower. It seems safe to assume that in some schools 1 teacher out of 10 will be affected by HIV/AIDS, and will likely die, whereas in other schools, 4 out of 10 teachers will be affected and will likely die.12

12 Note that this does not mean that the death rates at those schools in any given year will be 10% and 40% respectively. All other things being equal, if the disease lasts, say, seven years from infection to death, a prevalence rate of 50% would be approximately equivalent to a death rate of 7% (50/7). To see why, note that the prevalence of death amongst humans is 100% (we will all die sooner or later), but it takes us about 65 years to die, on average, so the death rate at any moment is about 100/65 or 1.5%.

It is unreasonable to suppose that any system of human resources allocation that is centralised (in actual resource allocation using a provincially driven post allocation model) and yet participatory (in consulting many stakeholders for every transaction) will be able to cope with this problem. The problems of participatory coordination, but from a centralised resource allocation will simply overwhelm the administrative capacity of the system. It would seem that the human resources provisioning system will have to be either far more directive and non-participatory (the locus of allocation matching the locus of deployment decision-making, and transactions costs being lowered) than it is now, or far more decentralised (again, the locus of allocation now being made coincident with the locus of deployment decisions). The present mix is quite attractive in some ways, but it can work only in a highly stable and predictable environment where its high transaction costs can be ignored.

One may be tempted to take comfort in the notion that these wide micro-regional or regional differences are to be expected only in a country in the early stages of the epidemic. Data from countries with mature epidemics, however, suggest (only suggest) that the problem gets, if anything, worse as the epidemic matures. The following data (Table 4) from Uganda illustrate the point.

Table 4: Regionality of HIV prevalence in a mature epidemic, Uganda 1998


Prevalence rate

Two lowest regions


Matany

1.3


Pallisa

2.6

Two middle regions


Mbale

6.3


Soroti

7.7

Two highest regions


Kagadi

11.5


Mbara

10.9

Source: UNAIDS/WHO Epidemiological Fact Sheet, 2000 Update, http://www.unaids.org/hivaidsinfo/statistics/june00/fact_sheets/pdfs/uganda.pdf

As can be noted, the ratio of highest to lowest prevalence rates is about 5 to 1, thus, in fact, higher than that in South Africa. Countries other than Uganda show exactly the same pattern, but we are not showing the data so as not to clutter the presentation. However, note that the absolute magnitude of the prevalence rates is lower in Uganda than in KwaZulu Natal (admittedly perhaps the worst-affected overall region in South Africa). The administrative nightmare of dealing with a high variance really only shows up if the averages are also high, which is the case in KwaZulu Natal and in other badly affected regions of South Africa.