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