risenga maluleke statistician-general statisticssouth africa

A Poverty Mapping Overview of the Poorest

Provinces, Metros, Districts and Localities

in South Africa

Statistics South Africa

Risenga MalulekeStatistician-General

Surveys & Censuses

Sector statistics

Registers/Administrative records

Line Departments

Stats SA

Official Statistics

Policy analysis

Research

Input Process Output OutcomeOutput ImpactOutcome

Statistical Production System

Income, Inequality and Poverty Dynamics

Who are the poor and where do they reside?

Stats SA applies and measures various definitions of poverty

Subjective poverty (self-perceived)

Money-metric (lack of income/expenditure)

Multidimensional poverty (lack of basic services, education, etc.)

Inequality (Gini coefficient, share of expenditure, etc.)

Threshold of absolute deprivation. The amount of money required to purchase the minimum required daily energy intake

Food Poverty Line

R585

Austere threshold below which one has to choose between food and important non-food items

Lower-Bound Poverty Line

R840

Upper-Bound Poverty Line

R1268Threshold of relative deprivation below which people cannot afford the minimum desired lifestyle by most South Africans

Source: National Poverty Lines

National Poverty Lines based on April 2020 prices

R219

Food Poverty Line ; R585

R370

Lower-bound Poverty Line (LBPL); R840

R575

Upper-bound Poverty Line (UBPL); R1 268

R0

R200

R400

R600

R800

R1 000

R1 200

R1 400

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020Source: National Poverty Lines

National Poverty Line Series from 2006 to 2020

Upper-Bound Poverty Line Lower-Bound Poverty Line

Non Poor45,5%

Poor55,5%

Non Poor60,0%

Poor40,0%

Non Poor74,8%

Poor25,2%

Food Poverty Line

In 2015, more than a quarter of the population were living below the food poverty line

Source: Living Conditions Survey

Is the Rand value below which individuals are unable to purchase or consume enough food to supply them with minimum per-capita-per-day energy requirement for good health

Provides an austere threshold below which one has to choose between food and important non-food items

Provides an unambiguous threshold of relative deprivation below which people cannot afford the minimum lifestyle desired by most South Africans

Money-metric Poverty headcounts in 2015

28,4%

33,5%

21,4%

25,2%

51,0%

47,6%

36,4%

40,0%

66,6%

62,1%

53,2%

55,5%

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

2006 2009 2011 2015

Perc

enta

ge

Approximately 13,8 million South Africans were living below the FPL in 2015, down from a peak of 16,7 million in 2009.

Poverty headcounts based on the FPL, LBPL and UBPL

8,0

18,0

28,0

38,0

48,0

58,0

2006

2009

2011

2015

2006

2009

2011

2015

2006

2009

2011

2015

2006

2009

2011

2015

2006

2009

2011

2015

2006

2009

2011

2015

2006

2009

2011

2015

2006

2009

2011

2015

2006

2009

2011

2015

LPEC

KZN

MPNC

NW

FS

WC

GP

The poorest three provinces in the country have consistently been Limpopo, Eastern Cape & KwaZulu-Natal.

Gauteng & Western Cape remain the two provinces with the lowest poverty headcounts at 13,6 % & 12,8% respectively.

For Periods 2006 / 2009 / 2011 / 2015Source: Poverty Trends Report

KZN

Poverty Measures of Households (LBPL)

48,3% 45,6%

34,7%38,2%

53,6%49,6%

38,1%

41,7%

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

2006 2009 2011 2015

Perc

enta

ge

Females remain more disadvantaged than males consistently recording a higher headcount, gap and severity measures at each point in time; however, the difference between the sexes is narrowing.


Poverty headcounts by sex (LBPL)

62,8%

53,7%

39,7%

39,5%

39,1%

40,1%

49,5%

0 10 20 30 40 50 60 70 80

0-17

18-24

25-34

35-44

45-54

55-64

65+

Poverty in 2006

Children

Elderly

Percentage

Money metric poverty in 2006 (LBPL)


Money metric poverty by age group

51,0%

43,6%

34,7%

30,1%

27,8%

29,9%

30,1%

0 10 20 30 40 50 60 70 80

0-17

18-24

25-34

35-44

45-54

55-64

65+ - 19,3% Points

-11,8%Points

-10,0% Points

-11,3% Points

- 10,0 %Points

-9,4 %Points

- 5,0%Points

Poverty in 2015

Children

Elderly

Percentage

Change in money metric poverty between 2006 and 2015 (LBPL)

Elderly saw the greatest reduction in money metric poverty


Household size: 2,4

Household income: R199 267

Ownership of dwelling: 60,1%

Piped water inside/on site: 86,7%

2,4

Electricity: 91,4%

Piped water inside/on site:

Household size: 4,6

Household income: R46 624

Ownership of dwelling: 78,2%

Piped water inside/on site: 59%

78,2%

Electricity: 80,5%Electricity: 80,5%

Poor Households Non-poor Households

Household Expenditure R31 669

Flush toilet: 39,3% Flush toilet: 80,8%

Household Expenditure R151 097

39,3%

Non-poor households had better access to services compared to poor households

Profile of Poor and Non-poor households (Money Metric)

The South African Multidimensional Poverty Index (SAMPI) provides a more holistic view of poverty

Health

Education

Child mortality

Years of schooling

School attendance

The four dimensions of the SAMPI

Living standards

Lighting

Heating

Cooking

Water

Sanitation

Economic activity

Unemployment

Dwelling

Assets

(death of child under 5)

(completed 5 years of schooling)

(school-aged child out of school)

(no electricity)

(no electricity)

(no electricity)

(no piped water)

(informal/traditional/caravan/tent)

(no flush toilet)

(no radio/TV/phone/car)

(adults unemployed)

Deprivation cut-offs

17,9%

8,0%7,0%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

RSA

Multidimensional Poverty headcount by Geographic Various levels 2001-2016

SAMPI

SourceThe South African MPI2002 2003 2004 2005 2006 2007 2008 2009 2010 2012 2013 2014 2015

Headcount poverty decreased from 17,9% in 2001 to 7,0% in 2016

• In 2001 wide dispersion of Poverty with Msinga having a poverty Headcount of around 60%

• Between 2001 and 2011 poverty generally declines for all municipalities

• However between 2011 and 2016 poverty trends diverge between municipalities

Multidimensional Poverty by Municipalities 2001-2016

Msinga Headcount 59,8%


Intsika YethuHeadcount 27,7%


KZN

82

7

3 6

4

9 5

1

22%

23%

23%

23%

23%

23%

23%

24%

25%

28%

Ngquza Hill

Emalahleni

Umhlabuyalingana

Mbizana

Ntabankulu

Engcobo

Port St Johns

Umzimvubu

Msinga

Intsika Yethu12

34

Engcobo5Ntabankulu6

Mbizana7Umhlabuyalingana8

Ngquza Hill10Emalahleni9

10

ECHigh levels of

poverty in rural areas of SA

Location of the ten poorest municipalities (by headcount) in 2016.

SourceThe South African MPI

District and Municipal View of Poverty 2016


Data from Census 2011 allows us to zoom into poverty hotspots

Mapping the poverty headcount by ward - 2011


Mapping the poverty headcount by MunicipalityEastern Cape – 2011


Mapping the poverty headcount by ward Port St Johns – 2011


Census 2001, 2011 and CS 2016

40 5233

10 Years

5 Years

Multidimensional Poverty Drivers

14,0%

12,9%

19,5%

20,7%

23,3%

20,8%

24,1%

27,8%

32,9%

26,5%

21,7%

28,8%

39,2%

41,8%

41,9%

42,3%

44,0%

46,2%

48,9%

52,5%

WC

GP

RSA

KZN

FS

MP

NC

NW

EC

LP

Female Male

52,5% of Female headed households in LP do not have an employed household memberHouseholds without and employed household member by sex of household head, 2018

Source: Marginal Groups Indicator Report 2018

43%43%

59%

73%

16%

12%

9%

3%

30%35%

20%

10%

9%6%

10%

10%

1,2

3,32

2,18

4,31

0 10 20 30 40 50 60 70 80 90 100

LPECNCMPNW

FSKZNRSAGP

WC

Salaries Remittances Other SourcesGrantsPensions

Percentage distribution of sources of household income by province, 2018

Source: GHS 2018

Grants remain a significant source of income for SA households, particularly in rural areas

Vulnerability to hunger at an individual and household level has been declining whilst access to grants has been increasing.

22,8%

9,7%

27,7%

11,3%12,8%

31,0%30,8%

44,3%

2002 2004 2006 2008 2010 2012 2014 2016 2018

Grants and Vulnerability to hunger 2002 - 2018

Grant: persons

Grant: households

Vulnerability to hunger: persons

Vulnerability to hunger: HH

Source: GHS 2018

4,6% 5,

2% 6,2%

10,1

%

8,3%

11,6

%

14,3

%

14,6

%

10,8

%

9,5%

3,8%

8,1%

9,6% 11

,3%

11,6

%

14,4

%

14,8

%

15,3

%

15,6

% 17,4

%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

LP GP EC FS RSA MP NC NW KZN WC

Male Female

Limpopo as a whole (4,2%) had the lowest percentage of households male or female that reported suffering from hunger

Source: Marginal Groups Indicator Report 2018

Households that reported hunger

29

Indigent households identified by

municipalities

Beneficiaries

Water Electricity Sewerage and sanitation

Solid waste management

Western Cape 370 639 360 571 365 839 357 619 357 016

Eastern Cape 797 103 516 972 337 832 274 686 221 946

Northern Cape 73 831 67 015 69 548 63 200 63 841

Free State 178 740 147 304 164 215 144 414 146 737

KwaZulu-Natal 769 258 643 560 296 034 347 792 465 588

North West 162 170 99 492 146 996 74 643 79 573

Gauteng 863 221 744 844 407 788 384 352 814 705

Mpumalanga 109 318 103 125 104 447 90 741 93 664

Limpopo 307 163 127 550 131 448 44 603 42 493

South Africa 3 631 443 2 810 433 2 024 147 1 782 050 2 285 563

Poor households as determined by municipalities ; The basis on which a municipality determines if a household is indigent varies across municipalities, even within same province

3,6 Million Indigent households identified by municipalities

Indigent households registered with municipalities: 2018

A gender disaggregated perspective on poverty and inequality as experienced by Women, Youth and People Disabilities?

SA Disability Prevalence Stood at 7,7%

6,3%

6,4%

6,7%

7,6%

7,7%

8,6%

8,6%

8,8%

10,7%

11,0%

Western Cape

Limpopo

Gauteng

Mpumalanga

South Africa

Eastern Cape

KwaZulu-Natal

North West

Northern Cape

Free State

The disability Prevalence for the FS was 3,3% Points more than the SA

average

Distribution of population aged 5 years and older by district, disability status UN Disability Index

Disability prevalence was measured using a computed index based on the general health and functioning question asked in CS 2016

Male Female Both SexesPersons with Disability R35 153 R21 245 R27 143Persons without disability R60 421 R39 033 R49 977

R0

R10 000

R20 000

R30 000

R40 000

R50 000

R60 000

R70 000

Aver

age

annu

al p

erso

nal I

ncom

e

Source: Profile of persons with disabilities in South Africa Census 2011

Average annual personal income of persons with disabilities is lower compared to those with no disabilities.

Average annual personal income by sex and disability status

Persons without disabilities: only 7,4% had

no formal education and about 11% had

tertiary education

Disability and level of education

24,6%Persons with disabilities showed the highest proportion with

no formal education (24,6%) and had the lowest proportion

that had attained higher education (5,1%)

7,4%

Source: Profile of persons with disabilities in South Africa

In 2018 women’s median monthly earnings were 76% of men's median monthly earnings

Women with tertiary education experienced a narrower pay gap in 2018, earning 92,3% of men’s earnings2018, earning 92,3% of men’s earnings

Limpopo has the highest gender pay gap - Females earned 66,2% of men’s median monthly earnings in 2018men’s median monthly earnings in 2018

8,1%

13,0%

24,0%

33,8%

30,1%

6,1%

14,4%

19,0%

26,3%

23,3%

White

Indian/Asian

Coloured

BlackAfrican

Both sexes

Unemployment Rateby Population Group

Q2:2020 Q1:2020

8,0%

11,7%

24,7%

31,6%

28,3%

6,5%

11,3%

20,5%

24,8%

22,1%

Male Unemployment RateBy Population Group

Q2:2020 Q1:2020

Black women are the most vulnerable with unemployment rate of over 30%.

8,2%

15,3%

23,2%

36,5%

32,4%

5,6%

20,3%

17,4%

28,2%

24,8%

Female Unemployment Rate by Population Group

Q2:2020 Q1:2020

37

OFFICIAL unemployment rate by population group and sex

Black African women are the most vulnerable with an unemployment rate above 28,0%.

10,1%

18,7%

31,0%

44,1%

39,7%

14,3%

26,2%

33,1%

46,3%

42,0%

White

Indian/Asian

Coloured

BlackAfrican

Both sexes

Expanded unemployment rate by population group

Q2:2020 Q1:2020

9,8%

15,3%

31,8%

40,4%

36,5%

14,0%

19,7%

33,3%

42,9%

38,9%

Male expanded unemployment rate

Q2:2020 Q1:2020

10,5%

24,2%

30,0%

48,2%

43,4%

14,6%

36,6%

33,0%

50,2%

45,7%

Female expanded unemployment rate

Q2:2020 Q1:2020

Irrespective of gender, the black African and coloured population groups remain vulnerable in the labour market.

38

EXPANDED unemployment rate by population group and sex

EducationWorkNEET

Those young people

(15-34 years) who are

categorised as NEET

are considered to be

disengaged from both

work and education.

Youth NEET rate is calculated as the total number of youth who are NEET as a proportion of the total

youth-specific working-age population

?

Not in employment, education or training (NEET)

39

36,8%

41,7%43,9%

47,9%

20%

25%

30%

35%

40%

45%

50%

Q2: 2019 Q2: 2020

FEMALE NEET

MALE NEET

NEET (15-34 years) by gender

Over 9,2 million (44,7%) out of 20,5 million young people aged 15-34 years were not in employment, education or training (NEET). The overall NEET rate increased by 4,4 percentage points y/y.

20%

25%

30%

Female NEETUp by 4,0 percentage points

Male NEETUp by 4,9 percentage points

15-34 YEARS

40Source: QLFS Q2 2020

There is an undeniable relationship between poverty and education.

Percentage of those aged 5 – 24 years who attend educational institution, 2018

There is noticeable representation of learners who are older than the ideal graduation age in primary and secondary schools.

0%

20%

40%

60%

80%

100%

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

TVET

University

Primary School

Secondary School

Pre

Scho

ol

Not in Education or Training

Source: GHS 2018

Main reasons given by persons aged 7 to 18 years for not attending an educational institution, 2018

Over a fifth of learners cited a lack of money as the main reason for not attending an educational institution. Some reasons for not attending an educational institution are particularly affected by gender.

3,5%

7,5%

7,9%

9,8%

10,8%

13,3%

22,9%

24,2%

Working at home

Education is useless

Family commitments

Completed education

Illness and disability

Other

Poor academic performance

No money for fees

M F

Vast gender disparities in “family commitment” and “education is useless”

14.4%0,2%

11,8%3,9%

Source: GHS 2018

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risenga maluleke statistician-general statisticssouth africa

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