Upload
questa
View
41
Download
0
Embed Size (px)
DESCRIPTION
Cities and Poverty Research. City Economic Development Think-Tank 19 November 2002. Project Structure. Recognising Urban Poverty. Urban Growth SA reflects global and regional trends in urban population growth The big picture is of consistent growth - PowerPoint PPT Presentation
Citation preview
P D GIsandla Institute
Cities and Poverty Research
City Economic Development Think-Tank
19 November 2002
P D GIsandla Institute
Project Structure
Poverty Alleviation
Recognizingandunderstanding
Recordingandmonitoring
Respondingandintervening
P D GIsandla Institute
Recognising Urban Poverty
Urban Growth SA reflects global and regional trends in urban
population growth The big picture is of consistent growth Within this there are different patterns in the
rate, location and population that are growing
P D GIsandla Institute
Urban growth - race
Urban Population by Race, 1911-1996
-
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
1910 1920 1930 1940 1950 1960 1970 1980 1990
Years
Po
pu
lati
on Africans
Whites
Coloureds
Indians
P D GIsandla Institute
Urban growth - gender
African Urban Population by Gender, 1911-1996
-
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
1910 1920 1930 1940 1950 1960 1970 1980 1990
Years
Po
pu
lati
on
Men
Women
P D GIsandla Institute
Urban growth - location
Population of Metropolitan Centres, 1875-1996
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
1875 1895 1915 1935 1955 1975 1995
Years
Po
pu
lati
on
Greater Johannesburg
Greater Cape Town
Greater Durban
Port Elizabeth
P D GIsandla Institute
Urbanisation of poverty
Three main reasons for the urbanisation of poverty The natural growth of the poor population
within cities Growing urban inequality Poor people moving to cities
P D GIsandla Institute
Who are the urban poor in SA
If there is a typical ‘face of poverty’ in South Africa then this picture is no longer only a
rural women engaged in subsistence agricultural production. It is an HIV child living
in an environmentally degraded informal settlement in a rapidly growing city - without services who is subjected to organised and
household violence and is vulnerable to global economic and political trends.
FS Mufamadi, Minister For Provincial and Local Government, SACN Launch 7 October 2002
P D GIsandla Institute
Who are the urban poor in SA?
Total Urban Population, by Race1996
-1300000 -800000 -300000 200000 700000 1200000
0-4 Years
5-9 Years
10-14 Years
15-19 Years
20-24 Years
25-29 Years
30-34 Years
35-39 Years
40-44 Years
45-49 Years
50-54 Years
55-59 Years
60-64 Years
65-69 Years
70-74 Years
75-79 Years
80-84 Years
85-89 Years
90-94 Years
95-99 Years
Population
Indian Men
Coloured Men
White Men
African Men
Indian Women
Coloured Women
White Women
African Women
P D GIsandla Institute
Who are the urban poor in SA?
1996 City Population by Race
0
250,000
500,000
750,000
1,000,000
1,250,000
1,500,000
1,750,000
2,000,000
2,250,000
2,500,000
2,750,000
3,000,000
Joburg East Rand Pretoria Durban Pieter- maritzburg Cape Town Port Elizabeth Buffalo City Mangaung
Popu
lation
Indian/Asian
Coloured
White
African/Black
P D GIsandla Institute
Poverty definition
Poverty is more than a lack of income. Poverty exists when an individual or a household’s
access to income, jobs and/or infrastructure is inadequate or sufficiently unequal to
prohibit full access to opportunities in society. The condition of poverty is caused by a combination of social, economic, spatial,
environmental and political factors.
P D GIsandla Institute
Poverty definition
PovertyPoverty
IncomeIncome
UnemploymentUnemployment
LiteracyLiteracy
EnergyEnergy
WaterWater
DisabilityDisability
GenderGender
Environmental HealthEnvironmental Health
HealthHealth
TransportTransport
HousingHousing
CrimeCrime
WasteWaste CDICDIGiniGini
P D GIsandla Institute
Recording and monitoring poverty
Choose the appropriate indicators of urban poverty
Select the correct scale Monitor vulnerable groups Identify sectoral weaknesses Use up-to-date, reliable data
P D GIsandla Institute
Choose the right indicator
P D GIsandla Institute
Select the right scale
P D GIsandla Institute
Identify vulnerable groups
0%
10%
20%
30%
40%
50%
60%
70%
80%
Formal Dwelling
Formal Backyard Dwelling
Informal Dwelling
Informal Backyard Dwelling
Dwelling type by race in Cape Town
Total population
African population
P D GIsandla Institute
Making complex data useful
Must be understood by all stakeholders Must be flexible - accommodate new data
and refinement Must interface with other data e.g. budget,
provincial data, community priorities etc. Must be authoritative - locally and
internationally and internally and externally
P D GIsandla Institute
The City Development Index
PMB/Msunduzi Standard of Living Index CDI
39.6
11.0
54.0
34.7
49.0
49.3
0.0
20.0
40.0
60.0
80.0
100.0CDI =
Infrastructure =
Waste =
Health =
Education =
City Product =
All Cities Standard of Living Index CDI
35.13
56.00
75.89
56.30
61.92
56.30
0.00
20.00
40.00
60.00
80.00
100.00CDI =
Infrastructure =
Waste =
Health =
Education =
Product =
P D GIsandla Institute
Customising the CDI for SA
CDI AVAILABLEDATA
REASONABLEDATA ‘WISH
LIST’
COMMENT ON SOUTHAFRICAN RELEVANCE
PRODUCT(Gross GeographicalProduct (GGP))
We used MeanHouseholdIncome(Census 1996)normalised to100 as asubstitute forGGP.
GGP figures areavailable, but havenot been calculatedfor all the cities.
The product figure fails to capturethe other dimensions of growth – forexample investment,competitiveness, exports, tourism,employment, building plans passed,car sales, house prices, localinflation, skills etc.
P D GIsandla Institute
Customising the CDI for SA
CDI AVAILABLEDATA
REASONABLEDATA ‘WISH
LIST’
COMMENT ON SOUTHAFRICAN RELEVANCE
EDUCATION(Literacy x 25 +combined enrolment)
In the absenceof cityenrolmentfigures weused onlyliteracy (9years)(literacy x50).
Enrolment figures areavailable in theProvinces, but havenot been calculatedfor at the city or subcity scale.
Given the nature of formalemployment in cities it may also beappropriate to measure levels oftertiary education.
P D GIsandla Institute
Customising the CDI for SA
CDI AVAILABLEDATA
REASONABLEDATA ‘WISH
LIST’
COMMENT ON SOUTHAFRICAN RELEVANCE
HEALTHLife expectancy – 25x50/60 +32Child mortality x50/31.92
We usedprovincialestimates ofinfantmortalityinstead ofchild mortality
Child mortalityfigures are availablefrom the ante natalsurveys, but are notcalibrated at the cityscale.Life expectancy hasnot been calibrated atthe city scale
Recognising that there is a danger ofdouble counting, it is important thatHIV/Aids and TB figures arereflected in the health index.Similarly, the impact on mortalityand health services of the hightransport accident figures means thatthis data could also be used.
P D GIsandla Institute
Customising the CDI for SA
CDI AVAILABLEDATA
REASONABLEDATA ‘WISHLIST’
COMMENT ON SOUTHAFRICAN RELEVANCE
INFRASTUCTUREWater connectionx25Sewerage connectionx25Electricity x25Telephone x25
Census 1996data was used…definelevels
City data may bemore up to date thanthe census.
Service levels may need to beadjusted.Given the housing backlog and theongoing demands associated withurban growth we felt housing shouldbe included – but that only informalbackyard shacks and informalsettlements should be defined asinadequate to recognise rentalhousing as an important urbanshelter choice.
P D GIsandla Institute
Customising the CDI for SA
CDI AVAILABLEDATA
REASONABLEDATA ‘WISHLIST’
COMMENT ON SOUTHAFRICAN RELEVANCE
WASTEWastewater treatedx50 + formal solidwaste disposal
Formal solidwaste disposal(Census 1996)
Data on waste watertreated is availablefrom the cities.
The focus of this ‘brown agenda’indicator could be expanded torecognise air pollutants, possiblyusing a proxy health indicator suchas upper respiratory tract infections.
P D GIsandla Institute
Gaps in the CDI
Does not capture all dimensions of poverty Infrastructure heavy
Not all locally specific poverty dynamics are addressed - e.g. segregation
Key aspects of city development are not included
P D GIsandla Institute
Introducing SAPIC
South African Poverty Indicator (SAPIC)
0
20
40
60
80
100SAPIC
Safety and Security
Good governance
Spatial integration
Social and economic exclusion
A poverty adjusted CDI
P D GIsandla Institute
SAPIC and budget
SAPIC and Budget Indicator
0
20
40
60
80
100SAPIC
Safety and Security
Good governance
Spatial integration
Social and economic exclusion
A poverty adjusted CDI
Budget indicator SAPIC
P D GIsandla Institute
Introducing SAPIC
SAPIC(Possible indicators)
DATA ‘WISH LIST’ AND DATA ISSUES RELEVANCE TO POVERTY INSOUTH AFRICAN CITIES
SAFETY ANDSECURITYBlack male victimsbetween 16 and 30 whoare homicide victims.Police per 10000populationJuvenile offenders per10000 populationProportion ofalcohol/drug relatedcrimes.
City and sub city scale collation of crime,prison, and medical data.The weighting and formation of the indexneeds to balance issues of access to justice,negative impacts of crime and violence andthe dependence on criminal livelihoodswithin poor communities.Figures on crimes against women andchildren are not included in this part of theSAPIC as they are used as proxy indicatorsof social exclusion.
Although all South Africans are negativelyaffected by crime, the poor bare the bruntof the violence and social dislocation ofcrime.Crime in South African cities, especiallyamong poor communities, is closelyassociated with drug and alcohol trade andabuse.Unchecked criminality as a livelihoodstrategy among poor households maythreaten overall city governance and publicsafety.
P D GIsandla Institute
Introducing SAPIC
SAPIC(Possible indicators)
DATA ‘WISH LIST’ ANDDATA ISSUES
RELEVANCE TO POVERTY IN SOUTHAFRICAN CITIES
GOOD GOVERNACEProject viability -financial indicators,Institutionaltransformation,Participatory IDP, etc
These indicators draw from theDepartment of Provincial andLocal Government’s (DPLG’s)Key Performance Indicators(KPI’s). They are collected at amunicipal scale intended forreporting to national government.The proposed indicators would notbe appropriate for sub cityapplication, for instance in an IDP,where alternatives should beproposed.
Although all citizens benefit from sound financialpractice, transparent government and effectiveparticipatory processes, the poor are most likely togain from democratic and good governance. They arealso most likely to suffer from municipal fiscal crisisand corruption. Without democracy and participatoryforums their voices cannot be heard on how the cityshould be run.Despite its prominence in the pro-poor literaturegood city governance is not an area where there hasbeen much work on urban indicators and we havetherefore adopted some of DPLG’s general KPIs forlocal government.
P D GIsandla Institute
Introducing SAPIC
SAPIC(Possible indicators)
DATA ‘WISH LIST’ AND DATA ISSUES RELEVANCE TO POVERTY INSOUTH AFRICAN CITIES
SPATIALINTEGRATION
Affordability ofcommuter fares x25Accessibility to publictransport x25Door to door journeytimes x 25Proportion of thepopulation strandedwithout access totransport x25
Transport is used as a proxy indicator for spatialisolation and exclusion.Collection of the data at the city (and sub city)scale is required for the inclusion of theindicator as proposed.Elements of the index overlap with the CDI andthere is an ambiguity over the definition ofsecure tenure with a possible over emphasis onownership over rental.
Slums Index:% households without tenure% households without water% households without sanitation and otherservices% households without permanent structures
The legacy of apartheid planning andthe high cost of well located land fornew subsidy based housingdevelopment means that the urban poorin South African are located on theperiphery, far from jobs and subject toexpensive travel. Extensive subsidiescurrently maintain this pattern of raceand class segregation and mitigateagainst the integration of cities in linewith urban reconstruction policyframeworks that are designed toenhance the opportunities of the poor.There are some questions around theappropriateness of the UN Slums Index.
P D GIsandla Institute
Introducing SAPICSAPIC
(Possible indicators)DATA ‘WISH LIST’ AND DATA ISSUES RELEVANCE TO
POVERTY INSOUTH AFRICAN
CITIESSOCIAL ANDECONOMICEXCLUSIONRDI (RacialDevelopment Index) =HDI of Africans as aproportion of that of thepopulation as a whole.GDI (GenderDevelopment Index)RapeGini coefficient forAfricansReported child abuseper 10000 of populationUnemployment(extended definition)
The HDI is a globally accepted index of well being. HDI(Human Development Index) indicators include longevity,education and income – these can all be extracted from theSouth African census at the city and sub city scale andcalculated using the apartheid race classification of African as aproxy for racist exclusion.The UN’s GDI (Gender Development Index) uses the samevariables as the HDI but measures the performance of womenrelative to that of men. It is used here as a proxy indicator ofgender discrimination.
Although rape and child abuse figures are notoriouslyunderreported, they are collected and can be used to reflect fearand vulnerability.Gini coefficients measure inequality – traditionally in income.The use of the African Gini is designed to show that race is nolonger a reliable predictor of poverty, as there is increasinglyextreme inequality within ‘race’ groups. Similar measures couldbe made of any ‘race’ group.
Key lines of exclusionand marginality inSouth Africa includeracism, sexism,language discriminationand xenophobia.
P D GIsandla Institute
Introducing SAPIC
SAPIC(Possible indicators)
DATA ‘WISH LIST’ AND DATAISSUES
RELEVANCE TO POVERTY IN SOUTHAFRICAN CITIES
POVERTYADJUSTED CDICDI for AfricansCDI for residents ofinformal backyards andinformal settlementsCDI for the lowestincome quintile
Not all variables of the CDI can beadjusted for race or for housing typeand income quintile. But theinfrastructure, waste, health andeducation variables can bedisaggregated in this way and ifincome rather than GGP is used forthe product Census 1996 can beused to calculate the povertyadjusted CDI.
The CDI is a solid general measure of poverty, butit measures average performance and, especially inhighly unequal contexts such as South Africancities, fails to reflect the position of the poorest ofthe poor. By running the CDI for Africans (thepopulation most negatively impacted by apartheid);the lowest income quintile and those in informalsettlements (the housing and infrastructure poorest)we establish a general idea of development from theperspective of the poor of the city.
P D GIsandla Institute
Calculating quality of life indices
Contents 1 Human poverty index
2 Human development Index
3 Gender - related development index
4 Gini coefficient
5 Poverty line
6 Cities development index
7 Poverty gap index
P D GIsandla Institute
Inequality indicators - Gini coefficients (Jhb - Africans)
0 Income groups (R)
5,776 None
27,992 R1-2400
79,617 R2401-6000
161,417 R6001-12000
142,271 R12001-18000
89,623 R18001-30000
37,685 R30001-42000
17,841 R42001-54000
10,305 R54001-72000
4,669 R72001-96000
2,910 R96001-132000
1,614 R132001-192000
890 R192001-360000
373 R360001 or more
582983
Gini coefficient 0.46
Lorenz Curve
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 20% 40% 60% 80% 100%
No. of Individuals
P D GIsandla Institute
Gender-related Development IndexGender - related Development Index
Indicators
Population share Percent share of total population
Longevity
Knowledge Adult literacy
Decent standard of livingAdjusted real GDP per capita
% share of the econ active population
Ratio of female non-agri wage to male non agri wage
Data (male) Data (female)
Population share 0.49 0.51
Longevity 76.70 82.80
Adult literacy 99.00 99.00
Combined first, second and third level gross enrolment ration79.00 77.00
% share of economically active population 0.59 0.41
Ratio of female non agri wage to male non agri wage 0.75 0.75
Adjusted real GDP per capita 6231.00 6231.00
Max (male) Min (male) Max (female) Min (female)
Longevity 76.7 22.5 82.8 27.5
Adult literacy 100 0 100 0
Combined first, second and third level gross enrolment ration100 0 100 0
Index
Longevity 0.913Adult literacy 0.920
Decent standard of living 0.885
GDI 0.906
Measures
Combined first, second and third level gross enrolment ration
life expectancy at birth
P D GIsandla Institute
Poverty lines (eThekweni)
Poverty Lines
Income groups (R) Poverty Lines Number Percent Poverty lines
None None 73,909 13%
R1-2400 Below 2400 per annum 21,283 4% One of the easiest ways to establish who
R2401-6000 Below 6000 per annum 60,144 11% the poor are is to establish the percentage
R6001-12000 Below 12000 per annum 65,149 11% of people who are living below the
R12001-18000 Below 18000 per annum 66,788 12% poverty line.
R18001-30000 Below 30000 per annum 73,494 13%
R30001-42000 Below 42000 per annum 45,081 8% povety lines are a rough measure of
R42001-54000 Below 54000 per annum 35,654 6% identifying the poor. However
R54001-72000 Below 72000 per annum 39,284 7% poverty lines are the easiest measure to
R72001-96000 Below 96000 per annum 27,611 5% determine poverty.
R96001-132000 Below 132000 per annum 28,756 5%
R132001-192000 Below 192000 per annum 18,179 3%
R192001-360000 Below 360000 per annum 11,591 2%
R360001 or more 3,030 1%
569953 100%
Select poverty line
% and No. of people below the poverty line 50.40% 287,273 50.4 percent of people are living below the poverty line
Distribution below the Poverty Line
0%
5%
10%
15%
20%
25%
None Below2400p.a.
Below6000p.a.
Below12000p.a.
Below18000p.a.
Below 18000 p.a.
P D GIsandla Institute
Project Structure
Poverty Alleviation
Recognizingandunderstanding
Recordingandmonitoring
Respondingandintervening
P D GIsandla Institute
Responding and intervening
Focus of intervention SACN GroupService delivery andpoverty
Finance andrestructuring
Pro-poor sectorsupport
LED
Land-use planning –transport and poverty
Transport
HIV/Aids and poverty HIV group
City
De
velo
pm
ent
Str
ate
gie
s
Environment andpoverty
Ind
icator G
rou
p
P D GIsandla Institute
Conclusion
Further information from the project is available on www.sacities.net
Recognising and understandingpoverty
Recording and monitoringpoverty
Responding to poverty andintervening
South Africancommitments tosustainable urbandevelopment,Different approaches toaddressing urban poverty,The dynamics of urbangrowth in global, regionaland national patterns ofpoverty,The urbanisation ofpoverty,Key definitions,Web Sources on urbanpoverty
Census-based Profiles ofSACN members – data bycity and sector,Composite indicatorsincluding:- City Development
Index,- Gini coefficients,- Gender Development
Index- Human Development
Index- Poverty Lines- Poverty GapsSouth African PovertyIndex for Cities Proposal
Urban development andHIV/AidsPro-poor local economicdevelopment – a sectoralapproachEnvironment and povertyreliefTransportation, spatialplanning and povertyalleviationPro-poor service delivery –affordability and willingness-to-pay