HIV/AIDS AND THE ECONOMIC
DEMOGRAPHY OF TSHWANE
Prof Carel van AardtBureau of Market Research
The situation
One of the highest per capita HIV/AIDS prevalence and infection rates in the world
About 2000+ new infections per day An estimated 4.7 to 6.5 million South
Africans already HIV positive More than 300000 people died because
of AIDS related diseases during 2001. Expected to rise to about 800000+ by 2008
HIV prevalence trends
05101520
25303540
1995 1997 1997 1998 1999 2000
South Africa
Eastern Cape
Free State
Gauteng
Kw aZulu-Natal
Mpumalanga
Northern Cape
NorthernProvinceNorth West
Western Cape
Why is prevalence so high in S.A.?
Social and family disruption High mobility and good transport High poverty and low education
levels High level of STDs, low status of
women Low contraceptive prevalence Many sexual partners Culture and risk behavior Fear of admitting status (denial)
HIV/AIDS lifecycle: 2001-2010
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
1 2 3 4 5 6 7 8 9 10
Terminal
AIDS
AR Complex
Progressive
Silent
Acute
Key uncertainties
HIV/AIDS parameters, progress and epidemiological pattern
Institutional capacity to deal with it Efficacy of drugs and vaccines Efficacy of macro-, meso- and
micro- responses to HIV/AIDS Multipliers and mediating variables Economy, education, business
Black population, 2001
0
20
40
60
80
100
120
(100,000) (80,000) (60,000) (40,000) (20,000) 0 20,000 40,000 60,000 80,000 100,000
Male Population Female
Age
Black population, 2006
0
20
40
60
80
100
120
(100,000) (80,000) (60,000) (40,000) (20,000) 0 20,000 40,000 60,000 80,000 100,000
Male Population Female
Age
Black population, 2011
0
20
40
60
80
100
120
(100,000) (80,000) (60,000) (40,000) (20,000) 0 20,000 40,000 60,000 80,000 100,000
Male Population Female
Age
Black population, 1996 to 2011
0
200,000
400,000
600,000
800,000
1,000,000
!996 2001 2006 2011
Male
Female
Stochastic distribution: Black population
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
1996 2001 2006 2011
95%high
66%high
mu
66%low
95%low
White population, 2001
0
20
40
60
80
100
120
(40,000) (30,000) (20,000) (10,000) 0 10,000 20,000 30,000 40,000
Male Population Female
Age
White population, 2006
0
20
40
60
80
100
120
(40,000) (30,000) (20,000) (10,000) 0 10,000 20,000 30,000 40,000
Male Population Female
Age
White population, 2011
0
20
40
60
80
100
120
(40,000) (30,000) (20,000) (10,000) 0 10,000 20,000 30,000 40,000
Male Population Female
Age
Stochastic distribution: White population
0100,000200,000300,000400,000500,000600,000700,000800,000
1996 2001 2006 2011
95%high
66%high
mu
66%low
95%low
Asian population, 2001
0
20
40
60
80
100
120
(1,500) (1,000) (500) 0 500 1,000 1,500
Male Population Female
Age
Asian population, 2006
0
20
40
60
80
100
120
(1,500) (1,000) (500) 0 500 1,000 1,500
Male Population Female
Age
Asian population, 2011
0
20
40
60
80
100
120
(1,500) (1,000) (500) 0 500 1,000 1,500
Male Population Female
Age
Stochastic distribution: Asian population
05,000
10,00015,00020,00025,00030,00035,00040,000
1996 2001 2006 2011
95%high
66%high
mu
66%low
95%low
Coloured population, 2001
0
20
40
60
80
100
120
(2,500) (2,000) (1,500) (1,000) (500) 0 500 1,000 1,500 2,000 2,500
Male Population Female
Age
Coloured population, 2006
0
20
40
60
80
100
120
(2,500) (2,000) (1,500) (1,000) (500) 0 500 1,000 1,500 2,000 2,500
Male Population Female
Age
Coloured population, 2011
0
20
40
60
80
100
120
(2,500) (2,000) (1,500) (1,000) (500) 0 500 1,000 1,500 2,000 2,500
Male Population Female
Age
Stochastic distribution: Coloured population
0
10,000
20,000
30,000
40,000
50,000
1996 2001 2006 2011
95%high
66%high
mu
66%low
95%low
Impacts (1)
Demographic – size and structure Labour supply and demand -
outsourcing Skills availability and skills formation Income impacts Expenditure and savings patterns Health sector – cost and effort Entrepreneurship Economic structure and capital
intensification
Impacts (2)
Households – negative spiral Local government income and
expenditure Economy of scale effects Factor flight and lower GDP per capita Development and poverty Priorities of people (medicines vs.
education) Formal sector behavior
Prospects
Substantially less people by 2010 Loss of a large pool of highly skilled
people and entrepreneurs Strong growth in unemployment and
poverty Decline in business confidence and
growth of tax base Economic growth and development Social and political instability Productivity and production
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