Towards A Development Index Framework to Measure and Manage Development

1
Towards A Development Index Framework to Measure
and Manage Development
2
Development Index Framework

• What a DIF is
• Provides an economic and social DNA-structure
• Makes dissimilar characteristics comparable
• Gives an overview of development conditions
• User friendly policy instrument

3
Composition of the DIF

• The DIF
• Consists of a wide range of development indices
• Make provision for multi-dimensional comparisons
• Each index group makes provision for a direct
  3x4-way comparison between development
  characteristics
• Compares characteristics within municipalities /
  urban / rural areas
• Compares characteristics between municipalities /
  urban / rural areas
• Ranks characteristics within municipalities /
  urban / rural areas longitudinally
• Ranks characteristics between municipalities /
  urban / rural areas longitudinally
• Makes provision for comparisons between index
  groups
• Is GIS-friendly

4
Types of indices

• Size indicators
• Economic indicators
• Social indicators
• Developmental indicators

5
Examples of indices in the next three slides

• Example of intra-municipal profile versus profile
  of municipalities within a province
• Example of urban / rural profile within
  municipalities and between municipalities within
  a province
• Example of intra-regional (DC) profile versus
  profile of DCs within a province

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Economic catchment areas 2003
7
Overlap between districts and catchment areas -
Best fit
8
Overlap between districts and catchment areas
Medium fit
9
Overlap between districts and catchment areas
Worst fit
10
Profiles within and between municipalities in a
province
11
Urban/rural profiles within and between
municipalities
12
Regional profiles within District Council areas
13
Example of thematic maps showing indices
14

Factor analysis of social, economic and
development indicators
Factor1 Factor2
Factor3 Factor4   1. Semi-skilled
labour 0.95802 - -
- 2. Unskilled labour 0-94657
- - - 3.
Public Phone 0.94395 -
- - 4.
Substandard accommodation 0-93107 -
- - 5. No electricity
0.92731 - -
- 6. High room crowdedness 0.92299
- - - 7.
No refuse removal 0.91490 -
- - 8. Lo household income
0.90527 - -
- 9. Average accommodation 0.89287 -
- - 10. Low
personal income 0.88870 -
- - 11. Black population
0.88564 - -
- 12. Black potentially econ.
active 0.87780 - -
- 13. Rooms crowded 0.85773
- - -
14. Skilled labour 0.84427
0.41137 - - 15.
Communal water source 0.84087 -
- - 16. No
phone 0.83635 -
- - 17. Rooms not
crowded 0.83429 - -
- 18. Highly Skilled
labour 0.80793 0.40536 -
- 19. Black actual econ.
active 0.79697 0.47751 -
- 20. Div of labour. quaternary
0.79373 0.45226 - -
21. Private water source 0.79248
0.52651 - -
22. Rural settlement 0.78550 -
- - 23.
Private phone [phone]
- - 24. Electrified
0-77484 0.50878 -
- 25. Medium personal income
0.70720 0.54200 - -
26. High household income 0.67554
0.51731 - -
27. Div. of labour tertiary 0.66953
0.55259 - - 28.
High personal income 0.61886 0.59383
- - 29. Medium
household income 0.61703 0.59175 -
- 30. Div of labour
secondary 0.61423 0.56835 -
- 31. Housing quality
temporary 0.60165 0.46660 -
- _____________________________________
__________________________________ 32. White
population - 0.87256
- - 33. White
potentially econ. active -
0.87081 - -
34. White actually econ. active -
0.86222 - -
35. Div. of labour primary -
0.74880 - -
36. Full refuse removal 0.42456
0.73582 - - 37.
Urban population 0.43393 0.70564
- - 38. Semi-perm.
housing 0.63619 0.67863 -
- 39. Rural population
0.46029 0.59109 - -
40. White total econ. active -
0.55814 0.42751
- ____________________________________________
___________________________ 41.
Coloured population - -
0.94186 - 42. Colrd
potentially econ. active - -
0.93782 - 43. Colrd
actually econ. active - -
0.92398 - ______________________
_________________________________________________
44. Indian population 0.44739
0.41390 - 0.78061
45. Indian potentially econ. active 0.46321
0.40536 - 0.77827 46.
Indian actually econ. active 0.48237 -
- 0.76559  
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Conclusions drawn from factor analysis

• Economically the SA community is still
  fragmented.
• Each of the three factors represents a
  different population group.
• The Black population remains strongly
  associated with the poverty indicators no. 1-17.
• However, the most positive variables 11-31 and
  36-39 load high on both the black and white
  factors which means that the Black populations
  profile also corresponds well with the positive
  factors. In fact the higher loadings of these
  positive variables on Factor 1 (the Black factor)
  than on Factor 2 (the White factor) indicates
  that the Black population is beginning to
  dominate that part of the economy more than the
  White population does numerically.
• The Coloured and Asian profiles are unlikely
  to match the White and Black populations
  profiles because they are spatially concentrated
  in certain areas and are numerically small.
•  

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