Non Financial Census of Municipalities

Transcription

1 Non Financial Census of Municipalities Pali Lehohla Statistician-General Statistics South Africa Cape Town 22 October

2 Outline of Presentation Oversight Role of the Portfolio Committee Using Stats SA data for Monitoring and Evaluation Situation Analysis. What is the current position? Integrated Data Analysis Maputo Corridor Analysis Infrastructure development Causes of Death My Ward My Councilor Stats SA Services 2

3 The four dimensions of poverty Health Education Deprivation cut-offs Living standards Economic activity Child mortality (death of child under 5) Years of schooling (completed 5 years of schooling) School attendance (school-aged child out of school) Lighting (no electricity) Heating (no electricity) Cooking (no electricity) Water (no piped water) Sanitation Dwelling Assets (no flush toilet) (informal/traditional/caravan/tent) (no radio/tv/phone/car) Unemployment (adults unemployed) 3

4 INDIGENTS 4

5 Free Basic Service Policy (Rebates): From 20 6kl free per household per month 50kwh free per household per month R50 average for Sewerage and sanitation Target group = indigent households R50 average for Solid waste management 5

6 Number of indigent households registered with municipalities per province: ,4 million indigent households GP

7 Water services 7

8 Consumer units with access to water South Africa: consumer units receiving water service 2009: 9,7 million 2013: 11,8 million 88% of consumer units that had access to water in 2009 received water within RDP standard Consumer units with access to water inside the yard 2009: 6,7 million 2013: 7,9 million Access increased to 90% in

9 6kl free per household per month Number of consumer units receiving basic water and free basic water NC GP NW FS LP MP KZN Basic water Free basic water 2013 financial year WC EC ,8 million consumer units 9

10 Poverty headcount by municipality (SAMPI) Mapping the poverty headcount by Municipality Eastern Cape (SAMPI) 10

11 Poverty headcount by municipality (SAMPI) 11

12 Poverty headcount by municipality (SAMPI) 12

13 Percentage of households that have no toilet facility by improved sanitation per province 13

14 Mapping the poverty headcount by municipality 2011 (SAMPI) 14

15 Measuring poverty 100% R321 R620 Upper-bound poverty line R443 Lower-bound poverty line Food poverty line 80% 60% 40% 20% 8% 0% 0% 20% 40% 60% 80% 100% 33% 15

16 Headcount KZN EC 8 5 Where are the poorest municipalities located? Census

17 Mapping the poverty headcount by ward

18 Mapping the poverty headcount by ward

19 Economic activity Living standard Education Health Poverty drivers in South Africa Unemployment Assets Dwelling Sanitation Water Cooking Heating Lighting School attendance Years of schooling Child mortality Men Women Total Unemployment rate Unemployment is now the major driver of poverty in the country THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 19 19

20 20

21 Major service delivery protests, by year ( ) 21

22 Major service delivery protests, 2012, by province 22

23 Hotspots for service delivery protests in SA Police crowd control data Map of protests in South Africa 01/01/ /11/

24 The Maputo Development Corridor Problem Statement According to Spatial Development Initiatives (1999), some of the objectives of the MDC include: 1. rehabilitating the primary infrastructure network along the corridor 2. maximising investment in both the inherent corridor area and in the added opportunities 3. maximising social development, employment opportunities and increase the participation of historically disadvantaged people 24

25 The scope of the research 25

26 Reported positive impacts of the MDC on local communities 26

27 Reported negative impacts of the MDC on local communities 27

28 Selected indicators Description Variable(s) Demographic characteristics 1. Total population 2. Population aged as a proxy for the labour force group: 1996, 2001, 2007 and 2011 Educational characteristics Educational level for the population aged 15-64: 1996, 2001 and 2007 and 2011 Categories 1. Less than primary education (used as a proxy for unskilled labour) 2. Achieved high school education and above (used as a proxy for skilled labour) Employment Percentage of the labour force employed (all sector employment): 1996, 2001, 2007 and 2011 Grouped employment sector Agriculture and mining Infrastructure related Wholesale, Migration in vs out Number of persons aged who reported that they moved from other places to others between 2001 and 2011: census 2011 Provision of basic services to households Percentage of households with piped water in dwelling or yard: 1996, 2001, 2007 and

29 Percentage Percentage of population 70 aged employed Year MDC region Mpumalanga Province South Africa 29

30 Year Percentage of persons aged employed by employment sector Wholesale, retail and services Infrastructure related Agriculture and mining Wholesale, retail and services Infrastructure related Agriculture and mining SOUTH AFRICA MPUMALANGA MDC REGION Wholesale, retail and services Infrastructure related Agriculture and mining Percentage employed 30

31 Number of persons aged Number of persons aged Numbers of in and out migrants: In migrants into MDC municipalities Out migrants from MDC municipalities Year moved Year moved Victor Khanye Emalahleni Steve Tshwete Victor Khanye Emalahleni Steve Tshwete Emakhazeni Mbombela Nkomazi Emakhazeni Mbombela Nkomazi 31

32 Building plans passed 32

33 Value of building plans passed by province, 2013 GP R35,3bn 41% / LP R2,3bn 3% South Africa R86,1bn NW R3,8bn 4% MP R4,1bn 5% NC R1,0bn 1% FS R2,5bn 3% KZN R13,8bn 16% WC R18,6bn 22% EC R4,8bn 6% Municipalities in Gauteng recorded building plans to the value of R35,3 billion, 41% of the national total 33

34 Buildings completed 34

35 Value of buildings completed by province, 2013 GP R21,3bn 41% / LP R0,7bn 1% South Africa R52,2bn NW R1,7bn 3% MP R2,0bn 4% NC R0,4bn 1% FS R1,1bn 2% KZN R7,0bn 13% WC R15,7bn 30% EC R2,3bn 4% Municipalities in Gauteng recorded completed buildings to the value of R21,3 billion, 41% of the national total 35

36 2006 Number of buildings completed 2013 In both 2006 and 2013, Cape Town and Tswhane completed the most number of buildings 36

37 Links to Capital Spending Public capital spending impetus for private sector investments. Cities generally have larger capital budgets than other municipalities. Highest spending in services infrastructure (transport, water, electricity, sanitation) followed by Housing for 2007/8 to 2011/12. Capital expenditure (% of municipal budget) CoJ CoCT RLM Average Economic (%) Social (%) Housing (%) Infrastructure

38 SDF priorities: corridor development EW, NS, public transport management areas; nodal development; marginalised areas; nonprioritized areas. Wards containing SDF features generally attracted higher levels of capital spending. Average investment in the corridors was the highest, followed 38 by the mixed-use & industrial nodes, and the marginalised areas.

39 Census 2001 Population Census 2011 Population 39

40 Statistics are about: People Places 40

41 For USE in Outcome: use of evidence Planning (baseline information for NDP) Monitoring & Evaluation (measuring development and impact) Policy development (increasing rationale for making decisions for better policies) Decision-making (decision making in government, subnational, business & the public) Increased knowledge, understanding and use by the leadership, citizens and state 41

42 The importance of a spatial information frame? Postal services Address provision for household mail delivery Retail and business services Deliver goods, financial services, business location Emergency services Rapid response to save lives Disaster management Service delivery Provide basic services to consumer units Data collection Plan for the future Inform urban and regional planning Valuation roll Property descriptions and rates valuation An accurate and complete statistical frame 42

43 Geo-information integration Link customer unit to fundamental data Using the customer unit as the spatial integrator Benefits 43

44 and the need to know 44

45 Industrial revolution of data 45

46 by-product unstructured and unfiltered product structured and planned clear concept & method high cost centralised point-in-time regulated HUGE macro-level manageable size 46

47 Proportion with flush toilet 47

48 Red indicates wards that have less than 5% households with flush toilets 48

49 Proportion with refuse removal 49

50 50

51 Proportion unemployed 51

52 Red colour means unemployment rate greater than 71% 52

53 %distribution of informal dwellings at ward level 53

54 Informal dwelling Red is less than 10% 54

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56 Green it greater than 48% 56

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58 RED is household with less than 5% access to piped water 58

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60 Red shows 90% or greater 60

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62 Red is less than 10% 62

63 Wards 63

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65 Piped water inside dwellin The red indicates household with piped water inside dwelling between 65 0 and 3.98%

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67 Flush toilet The red Indicates household with f 67 lush toilets

68 Main place 68

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70 No access to piped wate Red indicates at least 50% Households don t have 70 access to

71 Youth Unemployment Hot spot analysis 71

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73 Red indicates areas with high under-5 mortality 73

74 Training and Analysis All Stats SA data is free Free Supercross training Spatial analysis Integrated Statistical analysis 74

75 The South Africa I know, the home I understand Thank you! 75