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

6. 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. Factor1 Factor2 Factor3 Factor4 1. Semi-skilled labour 0.95802 - - - 2. Unskilled labour 0-94657 - - - 3. Public Phone 0.94395 - - - 4. Substandard accommodation0-93107 - - - 5. No electricity 0.92731 - - - 6. High room crowdedness0.92299 - - - 7. No refuse removal 0.91490 - - - 8. Lo household income 0.90527 - - - 9. Average accommodation0.89287 - - - 10. Low personal income 0.88870 - - - 11. Black population 0.88564 - - - 12. Black potentially econ. active0.87780 - - - 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. active0.79697 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 0.78082 0.48354 - - 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. active0.46321 0.40536 - 0.77827 46. Indian actually econ. active0.48237 - - 0.76559 Factor analysis of social, economic and development indicators

15. 1. Economically the SA community is still fragmented. 2. Each of the three factors represents a different population group. 3. The Black population remains strongly associated with the poverty indicators no. 1-17. 4. However, the most positive variables 11-31 and 36-39 load high on both the black and white factors which means that the Black population’s 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. 5. The Coloured and Asian profiles are unlikely to match the White and Black population’s profiles because they are spatially concentrated in certain areas and are numerically small. Conclusions drawn from factor analysis