P D G Isandla Institute Cities and Poverty Research Indicators Working Group 7 November 2002

P D GIsandla Institute

Cities and Poverty Research

Indicators Working Group7 November 2002


Project Structure

Poverty Alleviation

Recognizingandunderstanding

Recordingandmonitoring

Respondingandintervening


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


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


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


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


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


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


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


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


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.


Poverty definition

PovertyPoverty

IncomeIncome

UnemploymentUnemployment

LiteracyLiteracy

EnergyEnergy

WaterWater

DisabilityDisability

GenderGender

Environmental HealthEnvironmental Health

HealthHealth

TransportTransport

HousingHousing

CrimeCrime

WasteWaste CDICDIGiniGini


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


Choose the right indicator


Select the right scale


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


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


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 =


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.



CDI AVAILABLEDATA


LIST’


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.



CDI AVAILABLEDATA


LIST’


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.



CDI AVAILABLEDATA

REASONABLEDATA ‘WISHLIST’


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.



CDI AVAILABLEDATA

REASONABLEDATA ‘WISHLIST’


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.


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


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


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


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.


Introducing SAPIC


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.


Introducing SAPIC


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.


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.


Introducing SAPIC


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.


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


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


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


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.


Project Structure

Poverty Alleviation

Recognizingandunderstanding

Recordingandmonitoring

Respondingandintervening


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


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

Documents

P D G Isandla Institute Cities and Poverty Research Indicators Working Group 7 November 2002