Population Dynamics in South Africa
Report No. 03-01-67
ISBN 978-0-621-43660-0
The South Africa I know, the home I understand
Census 2011:
Population Dynamics in South Africa
Statistics South Africa
Report No. 03-01-67
Pali Lehohla
Statistician-General
Statistics South Africa
ii
Census 2011: Population Dynamics / Statistics South Africa
Published by Statistics South Africa, Private Bag X44, Pretoria 0001
© Statistics South Africa, 2015
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sold or offered for sale in any form whatsoever without prior permission from Stats SA.
Stats SA Library Cataloguing-in-Publication (CIP) Data
Census 2011: Population Dynamics / Statistics South Africa. Pretoria: Statistics South Africa, 2012
Report No. 03-01-67
124pp
ISBN 978-0-621-43660-0
A complete set of Stats SA publications is available at the Stats SA Library and the following libraries:
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Preface
Evidence-based decision-making has become an indispensable practice universally because of
its role in ensuring efficient management of populations, economic and social affairs. It is in this
regard that Statistics South Africa (Stats SA) is mandated to provide the state and other
stakeholders with official statistics on the demographic, economic and social situations of the
country to support planning, monitoring and evaluation of the implementation of programmes
and other initiatives. In fulfilling its mandate prescribed in the Statistics Act, (Act No. 6 of 1999),
Stats SA has conducted three Censuses (1996, 2001 and 2011) and various household-based
surveys. Censuses remain one of the key data sources that provide government planners,
policy-makers and administrators with information on which to base their social and economic
development plans and programmes at all levels of geography. Census information is also used
in the monitoring of national priorities and their achievements, and the universally adopted
Millennium Development Goals. This demand for evidence-based policy-making continues to
create new pressures for the organisation to go beyond statistical releases that profile basic
information and embark on the production of in-depth analytical reports that reveal unique
challenges and opportunities that the citizenry have at all levels of geography. This analytical
work also enhances intellectual debates which are critical for policy reviews and interventions.
The above process is aimed at enabling the organisation to respond to, and support evidencebased policy-making adequately, build analytical capacity and identify emerging population,
socio-economic and social issues that require attention in terms of policy formulation and
research. The monograph series represents the first phase of detailed analytical reports that are
theme-based aimed at addressing topics on education, disability, ageing, nuptiality, age
structure, migration, fertility, and mortality among others.
The age and sex structure of a population is affected by the changes in the population. These
changes could be brought by migration, fertility or mortality. Amongst these three demographic
phenomena, this report attempts to establish the main process that changed population age-sex
structure. It also seeks to explore the possibility of the country to capitalise from the
demographic dividend that stemmed from demographic transition.
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Acknowledgements
Statistics South Africa wishes to express sincere thanks to Lesego Olga Masebe, Lesego
Lefakane, Mercy Shoko, Diego Iturralde, David Baloyi and Princelle Dasappa-Venketsamy for
the contributions they made in the compilation of this monograph.
The organisation’s gratitude also goes out to Dr John Kekovole and Dr Nicole de Wet for their
constructive criticisms, guidance, encouragement and technical expertise provided in the course
of the review of this publication.
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Executive summary
Besides the major findings of the national population and housing results from Census 2011
which were published in 2012, a detailed analysis of the population age-sex structure was
undertaken to provide an insight of the demographic transition over time.
While the population aged 15–64 increased steadily from 1996–2011, the child population (0–
14) shows a decreasing trend across the years. South Africa has an intermediary population,
with the median age ranking between 22 and 25. The white population group had the median
ages of over 30 in all the three years under consideration; and this population group appears to
be older than all other population groups. The overall sex ratio is still in favour of females, it
increased from 92,7 in 1996 to 94,8 in 2011. The highest increase of sex ratio is more
pronounced in the Indian/Asian population group. The burden of children and elderly on those
who are economically productive declined over time, however, the white population group
dependency ratio indicates a stable pattern from 1996 to 2011.
Despite the attempts made to explain the population age-structure of the 2011 Census,
empirical investigation still needs to be done to establish the declining child population aged
within the 5–14 age group that was observed in 2011. Nonetheless, analysis of past mortality
levels and trends, indicate that the marked increase in infant and child mortality, as well as the
decrease in the life expectancy that were observed between 1998 and 2006 might be indicative
of a decrease of this cohort. The intensive government programmes which appeared to have
reduced child mortality rates and thus increased life expectancy, with the improved undercount
rate of children aged 0–4 from 2001 to 2011 could be attributed to a broader base of the 2011
population structure.
Amongst the three processes of population change, fertility seems to have been the main
contributing factor to the change in population age-sex structure over time. The observed fertility
decline that occurred more than four decades ago, resulted in the shift from child population to
youth population aged 20–29. This youth bulge increased markedly from 1996 to 2011, thus
creating a demographic dividend. The question that remained unanswered is whether the
country could benefit from this window of opportunity. Empirical findings with regard to youth
unemployment rates, uncertainties about quality of education and the scourge of HIV/AIDS
among the young population might prohibit the country from benefiting from this demographic
dividend.
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TABLE OF CONTENTS
Chapter 1: Overview .................................................................................................................................................. 1
1.1
Introduction ....................................................................................................................................................... 1
Chapter 2: Assessment of data ................................................................................................................................ 4
2.1
Introduction ....................................................................................................................................................... 4
2.2
Imputation rates ................................................................................................................................................ 4
2.3
Comparison between the National Population Register, 2011 and census, 2011 ......................................... 5
2.4
Distribution of population by age in single years .............................................................................................. 6
2.5
Age ratios .......................................................................................................................................................... 7
2.6
UN Accuracy Index ........................................................................................................................................... 8
2.7
Summary......................................................................................................................................................... 10
Chapter 3:
What could have contributed to the shape of age and sex structure of the population,
1996-2011?................................................................................................................................................................. 11
3.1
Introduction ..................................................................................................................................................... 11
3.2
The population structure of 1996, 2001 and 2011 .......................................................................................... 11
3.3
Mortality, fertility and population age sex structure ........................................................................................ 14
3.4
Change in the proportion of children (0–4) relative to women (15–49) .......................................................... 15
3.5
Migration and population age-sex structure ................................................................................................... 16
3.6
Summary......................................................................................................................................................... 17
Chapter 4: Empirical findings from Censuses 1996, 2001 and 2011 .................................................................. 18
4.1
Introduction ..................................................................................................................................................... 18
4.2
Observed indicators from Censuses 1996, 2001 and 2011 ........................................................................... 18
4.2.1
Broad age groups ........................................................................................................................................... 18
4.2.2
Median age ..................................................................................................................................................... 19
4.2.2.1 Median age by population group .................................................................................................................... 19
4.2.2.2 Median age by province .................................................................................................................................. 19
4.3
Overall sex ratios ............................................................................................................................................ 20
4.4
Dependency ratio ............................................................................................................................................ 21
4.4.1
Total dependency ratios by population group ................................................................................................. 21
4.4.2
Total dependency ratios by province .............................................................................................................. 21
4.4.3
Child dependency ratios by population group ................................................................................................ 22
4.4.4
Child dependency ratios by province .............................................................................................................. 22
4.4.5
Old age dependency ratios by population group ............................................................................................ 23
4.4.6
Old age dependency ratios by province ......................................................................................................... 23
4.5 Summary.............................................................................................................................................................. 24
Chapter 5: Population age-sex structure scenarios ............................................................................................ 25
5.1
Introduction ..................................................................................................................................................... 25
5.2
Projected scenarios ........................................................................................................................................ 25
5.3
Summary.......................................................................................................................................................... 27
Chapter 6: Demographic dividend ......................................................................................................................... 28
6.1
Introduction ..................................................................................................................................................... 28
6.2
The demographic dividend is delivered through several mechanisms ........................................................... 29
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6.2.1
Change in age structure ................................................................................................................................. 29
6.2.2
Change in fertility ............................................................................................................................................ 31
6.2.3
Infant and child mortality ................................................................................................................................. 31
6.2.4
Life expectancy ............................................................................................................................................... 32
6.3
Investing in health ........................................................................................................................................... 33
6.4
Education ........................................................................................................................................................ 35
6.5
Employment .................................................................................................................................................... 38
6.6
Youth and training skills .................................................................................................................................. 41
6.7
The demographic dividend and good governance ......................................................................................... 43
6.8
Labour market policies in South Africa ........................................................................................................... 43
6.8.1
Good governance and savings ....................................................................................................................... 44
6.8.2
Good governance and investor confidence .................................................................................................... 44
6.8.3
Good governance and gender equality .......................................................................................................... 44
6.8.4
Good governance and business ..................................................................................................................... 45
8
References .................................................................................................................................................... 48
9
Appendix......................................................................................................................................................... 54
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List of tables
Table 1: Imputation rates by age and sex, unadjusted Census 2011 ......................................................................... 5
Table 2: UN accuracy index, Censuses 1996, 2001 and 2011 ................................................................................... 9
Table 3: Proportion of children (aged 0–4) relative to women (aged 15–49), 2001 and 2011.................................. 16
Table 4: Projected and reported population size, 1996, 2001 and 2011 .................................................................. 26
Table 5: Projected and reported median ages and dependency ratios, 1996, 2001 and 2011 ................................ 27
Table 6: Composition of employment by skill levels .................................................................................................. 42
Table 7: Composition of skill levels of youth and adults, 1994 and 2014 ................................................................. 42
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List of figures
Figure 1:
Trends in total fertility rate among women aged 15–49, (1970–2010) ......................................................... 2
Figure 2:
Life expectancy in South Africa, 1996–2010 ................................................................................................ 2
Figure 3:
Trends in international migration, Census 2011 ........................................................................................... 3
Figure 4:
Population distribution by age group, Census 2011 and National Population Register ............................... 6
Figure 5:
Population distribution by age in single years and sex, Census 2011 ......................................................... 7
Figure 6:
Age ratios, Censuses 1996, 2001 and 2011 ................................................................................................ 8
Figure 7:
UN Accuracy Index (UNI) by population group, Censuses 1996, 2001 and 2011 ....................................... 9
Figure 8:
UN Accuracy Index by province, Censuses 1996, 2001 and 2011 ............................................................ 10
Figure 9, Figure 10 and Figure 11: Population age sex structure, 1996, 2001 and 2011 .......................................... 13
Figure 12: Under-five and infant mortality rates, 1998–2010....................................................................................... 15
Figure 13: Immigrants into South Africa, (2002–2011) ................................................................................................ 17
Figure 14: Total population and population without immigrants (2002–2011) ............................................................. 17
Figure 15: Distribution of population by functional age groups and sex ...................................................................... 18
Figure 16: Median age by population group ................................................................................................................ 19
Figure 17: Median age by province .............................................................................................................................. 20
Figure 18: Sex ratios by population group ................................................................................................................... 20
Figure 19: Total dependency ratios by population group ............................................................................................. 21
Figure 20: Total dependency ratios by province .......................................................................................................... 22
Figure 21: Child dependency ratios by population group ............................................................................................ 22
Figure 22: Child dependency ratios by province .......................................................................................................... 23
Figure 23: Old age dependency ratios by population group ........................................................................................ 23
Figure 24: Old age dependency ratios by province ..................................................................................................... 24
Figure 25: Policy wheels for creating and earning the demographic dividend ............................................................ 29
Figure 26: Population of South Africa, 1985 ................................................................................................................ 30
Figure 27: Population of South Africa, 2011 ................................................................................................................ 30
Figure 28: Trends in total fertility, all women aged 15–49, 1985–2011 ....................................................................... 31
Figure 29: Estimates of South Africa’s under-5 mortality rate compared with South Africa’s MDG target .................. 32
Figure 30: Life expectancy at birth by sex, 2002–2012 ............................................................................................... 32
Figure 31: HIV prevalence by sex and age, South Africa, 2012 .................................................................................. 34
Figure 32: Percentage distribution of deaths by age and year of death, 2008–2012 .................................................. 34
Figure 33: Secondary gross enrolment rate and enrolment ratios for children aged 7-13 years ................................ 35
Figure 34: Proportion of 15–17 year-olds who have completed Grade 7 and higher, ................................................. 36
Figure 35: Percentage distribution of headcount enrolment in public higher education institutions by major field of
study, 2011 ................................................................................................................................................. 37
Figure 36: Trends in working age population (’000) .................................................................................................... 39
Figure 37: Percentage change in working age population among the youth and adults, 2008–2014 ......................... 39
Figure 38: Share of youth in working age population and in employment, 2008 and 2014 ......................................... 40
Figure 39: Unemployment rates 15-64, 2008-2014 ..................................................................................................... 40
Figure 40: Absorption rates of youth and adults in South Africa.................................................................................. 41
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List of abbreviations
AIDS:
Acquired Immune Deficiency Syndrome
AfDB:
African Development Bank
ARSF:
Age Ratio Score for Female
ARSM:
Age Ratio Score for Male
ASSA:
Acturial Society of South Africa
CHET:
Centre for Higher Education and Training
CPI:
Consumer Price Index
DBE:
Department of Basic Education
DoH:
Department of Health
FAO:
Food and Agriculture Organisation
FET:
Further Education and Training
FETI:
Further Education and Training Institute
GER:
Gross Enrolment Rate
GDP:
Gross Domestic Product
HIV:
Human Immunodeficiency Virus
ILO:
International Labour Organization
IMR:
Infant Mortality Rate
MDGs:
Millennium Development Goals
NEET:
Young people not in Education, Employment and Training
NDP:
National Development Plan
NPR:
National Population Register
OECD:
Organization for Economic Cooperation and Development
PMTCT:
Prevention of Mother to Child Transmission
PIRLS:
Progress in International Reading and Literacy Studies
PPD:
Partners in Population and Development
SRS:
Sex Ratio Score
Stats SA:
Statistics South Africa
TIMMS:
Trends in International Mathematics and Science Study
TFR:
Total Fertility Rate
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UN:
United Nations
UNAIDS:
United Nations Program on AIDS
UNDP:
United Nations Development Programme
UNECA:
United Nations Economic Commission for Africa
UNICEF:
United Nations Children's Fund
UNI:
United Nations Accuracy index
US:
United States
USA:
United States of America
USAID:
United States Agency for International Development
WHO:
World Health Organization
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Chapter 1: Overview
1.1
Introduction
The analysis of age-sex structure is one of the most basic ways of understanding population
change over time. The distribution of a population by age and sex is very important for socioeconomic and demographic purposes. Simelane, (2002) in the study of demographic
description of the 1996 population noted that age and sex analysis may be used to, amongst
other variables, evaluate, adjust and reconcile the completeness and accuracy of census
counts. In addition to these, the structure can be used to project the total population and its
components. The age-sex distribution of a population is a cross-cutting variable used in
planning as it is intrinsically linked to all aspects of the lifecycle, including childhood, education,
marriage, childbearing, entry into the labour market, retirement, ageing, morbidity and mortality
(Udjo, 2005).
Information on the size, distribution and characteristics of a country’s population is essential for
describing and assessing its economic, social and demographic circumstances and for
developing sound policies and programmes. The statistics are used as a critical reference to
ensure equity in the distribution of wealth, government services and funds among various
regions and districts of a country to fund for education and health services (UN, 1998).
The age and the sex structure of a population are the most important demographic
characteristics captured by a population census. These variables however, can be altered by
change in mortality, fertility and migration. For instance, births occur at age zero, hence the rise
in fertility increases the proportion of children at younger ages and the growth rate. Conversely,
when fertility declines, high proportions of children that stemmed from high fertility rates
progress to youthful ages, which consequently lead to youth bulge. South Africa, relative to
other African countries, achieved a decline in fertility for the past four decades (Caldwell and
Caldwell, 1993 and 2003). Figure 1 provides trends in fertility rates from 1970–2010.
Corresponding to the decline in fertility, the structure of the population indicates an increase in
the proportion of the population aged 20–29 (see Figure 11).
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Figure 1: Trends in total fertility rate among women aged 15–49, (1970–2010)
Interpolation
6
Udjo (2005)
5
Moultrie and Dorrington
(2004)
4
Statistics South Africa
3
2
1
0
1970
1980
1990
2000
2010
On the other hand, mortality occurs at all ages therefore its effect depends on the age incidence
of the change in the risk of dying. While South Africa, along with other sub-Saharan African
countries had experienced a mortality decline, this was later reversed by the HIV/AIDS
epidemic. Reniers, et al. 2011 argue, “HIV/AIDS presents the most drastic reversals in adult
mortality that have been documented to date in Southern Africa in particular, the mortality gains
made during the previous four decades have been wiped out in less than ten years”. South
Africa has recorded an improvement in life expectancy since 2006 as shown in Figure 2. This
mortality pattern relates to the decreasing pattern of child and infant mortality discussed in
subsequent sections.
Life expectancy
Figure 2: Life expectancy in South Africa, 1996–2010
62
61
60
59
58
57
56
55
54
53
1996
1998
2000
2002
2004
2006
2008
2010
Year
Source: Midyear population estimates, Statistics South Africa, 2011
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Migration, like death can occur at any age, thus its effects on the structure depends on a
particular age and sex of migrants. Figure 3 shows an increasing trend of in-migration into
South Africa across time. Its impact is indicated in the Indian/Asian population group sex ratios
of the population aged 20–39 that have increased from less than 100 in 2001 to over 113 in
2011 (Stats SA, 2012a).
160000
140000
120000
100000
80000
60000
40000
20000
0
Male
2011
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
Female
1985
Migrant population
Figure 3: Trends in international migration, Census 2011
Year
Age-sex data compiled from population censuses and surveys are often subject to a number of
irregularities, especially in developing countries. Misreporting of sex is generally rare but age
misreporting affects the quality of age data. Mason and Cope (1987) concluded that there are
sources that could be attributed to age misreporting in censuses. Amongst them are ignorance
of actual age, miscommunication between interviewers and informants and errors in recording
or processing. Though the overall age-sex reporting in the country appears to have improved in
2011, there are variations when data is disaggregated by population group.
Stats SA, (2012a) have published basic statistics pertaining to size, composition and structure
of the population. This study builds on this theme by further attempting to identify specific
demographic processes that could have impacted on the 2011 age and sex structure. This
monograph is divided into 7 chapters. This chapter has provided an overview of the entire
monograph. The 2nd chapter focuses on the assessment of the quality of age and sex data.
Chapter 3 provides further insight into the relationship between demographic processes and
population age-sex structure. The 4th chapter presents empirical findings of the change in
population composition. Chapter 5 investigates further which demographic factor amongst
fertility, mortality and migration could have significantly contributed to the current age and sex
structure by employing various assumptions. Chapter 6 focuses on the issues pertaining to the
demographic dividend. The last chapter presents the conclusions and discussions.
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Chapter 2: Assessment of data
2.1
Introduction
As mentioned earlier, errors in age and sex are inherent. However, Pullum (2005) noted that the
challenge is to separate irregularities from structural anomalies. Methods that detect age errors
assume ‘normal’ age-sex distribution, hence the indices inform us how data diverge or conform
to the assumed ‘normal’ distribution. If the pattern of age and sex distribution of the population
is due to structural reasons such as migration or mortality, such inaccuracies should not be
regarded as data errors. This section seeks to appraise the overall quality of age and sex data
using various demographic techniques.
2.2
Imputation rates
The first method used to assess the quality of the census age and sex data is to explore the edit
rules that were applied to the raw data. Stats SA developed edit specifications that were
implemented for each variable. During the editing process, the following five imputation flags
were established: (1) No imputation (variables were left as in the raw data), (2) logical
imputation from blank, (3) logical imputation from non-blank, (4) hot deck imputation from blank,
and (5) hot deck imputation from non-blank. Logical imputations basically utilise other
information in the household pertaining to a specific case to resolve the problem while hot deck
imputation generates values using a pre-determined set which matches the profile of the
affected case (UN, 2010). Table 1 below shows the extent of editing by type of imputation rules
applied to age and sex data.
Table 1 indicates that 79% and 98% of age and sex data, respectively, were not subjected to
any imputations. Only 2% of cases on age were corrected using logical imputations. There is a
marked 18% of data from non-blank that is imputed. This arises from the specification that
derived age from year of birth. Imputation rates using hot-decking for both age and sex were
very insignificant, for instance data on age indicate that hot-decking from non-blank is only
0,2%.
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Table 1: Imputation rates by age and sex, unadjusted Census 2011
Total population
Frequency
%
No imputation
34 916 612
79,0
Logical imputation (from blank)
785 515
1,8
Logical imputation (non-blank)
7 948 793
18,0
Hot deck imputation (from blank)
425 737
1,0
Hot deck imputation (non-blank)
143 740
0,3
Total
44 220 397
100,0
No imputation
43 223 242
97,7
Logical imputation (from blank)
116 819
0,3
Logical imputation (non-blank)
191 281
0,4
Hot deck imputation (from blank)
662 080
1,5
Hot deck imputation (non-blank)
26 975
0,1
Total
44 220 397
100,0
Age
Sex
2.3
Comparison between the National Population Register, 2011 and census, 2011
Numerous techniques of demographic analysis can be employed in the evaluation of age data.
Amongst them is the direct comparison of census results with data from other demographic
record systems, such as vital registration of births and deaths. In this case, the NPR that
corresponds to the same population as enumerated during Census 2011 was considered as a
viable source for comparison purposes. The register has records of South African citizens
whose births were registered and individuals with permanent residence permits. It includes all
births and excludes deaths as of 10 October 2011. Amongst its limitations are that it is affected
by incompleteness of both birth and death registrations and it excludes non-citizens.
Figure 4 shows the distribution of age of the population as recorded in Census 2011 and NPR.
A look at Figure 4 indicates virtually the same pattern except for variations at some age groups.
The figure shows that Census 2011 has relatively more children aged 0–4 than the NPR. This
could be ascribed to the late registrations of births in the register that occurred 12 months prior
to the Census 2011. There is a high number of people in age group 15–44 in the NPR than in
Census 2011. Census counted all the people who were in the country on the Census night
irrespective of whether a person is a registered or unregistered migrant while the NPR has
information on citizens only. The variation between the two sources at elderly population age
groups may be due to deaths that had occurred but had not been removed from the NPR.
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Notwithstanding all this, it is imperative to note that the distribution evidenced in Census 2011
was noted in previous data collection events dating back to the 2007 Community Survey.
Figure 4: Population distribution by age group, Census 2011 and National Population
Register 2011
6000000
Population
5000000
4000000
3000000
NPR
2000000
census 2011
1000000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
0
Age in 5yrs
Source: Own computation from Census 2011 and NPR data, 2011
2.4
Distribution of population by age in single years
One most common error of age misreporting is age heaping and this is apparent when age is
distributed in single years. The benefit of graphing the population by single years of age and sex
is that occurrences of data heaping by age are made visible from the start. Findings in Figure 5
indicate fluctuations on the pattern at some ages for males and females. In addition to what was
mentioned above, Shryrock and Siegel (1976) suggest that one of the key processes that
underlie most age misreporting is the distortion of age to meet preconceptions or social norms
about the relationship of age to other social characteristics or events. The results show troughs
and peaks at some ages particularly those ending with 0 or 5. This may imply data problems in
terms of age heaping and the overall quality of data. It is therefore essential to assess the
extent of anomalies depicted above. The following analysis provides insight into age heaping
and explores robustness of data on age and sex.
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Figure 5: Population distribution by age in single years and sex, Census 2011
700000
600000
Population
500000
400000
Male
300000
Female
200000
100000
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
115
120
0
2.5
Age ratios
Age heaping on particular ages are more easily identified graphically than through calculated
measures, however, age ratios can provide an indication of possible undercounts or
displacements between age groups. The age ratio for a given age group is the ratio of twice the
population in that age group to the sum of the population in each of the adjacent age groups
(Moultrie, et al., 2013). If no irregularities are to be identified in the census data, the age ratios
should equal 100. If the index is over a 100, it represents over-enumeration of the age groups.
In the case of under-enumeration, the ratio is less than 100. A glance at Figure 6 indicates
irregularities at ages 5–9, 10–14 in 2011 relative to previous censuses for both males and
females. Stats SA (2012a) indicates that age groups 5–9 and 10–14 had the lowest undercount
of 11,4% and 11,1% respectively. It might be that the cohorts have been affected by the
mortality pattern that prevailed between 1996 and 2011, hence the deviation from 100 might not
be considered to be data error. The deviation from standard index for ages 20–29 might also
not be a problem of data. It is indicated that the country had positive migration rates compared
to previous censuses (Stats SA, 2012a).
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Figure 6: Age ratios, Censuses 1996, 2001 and 2011
2.6
UN Accuracy Index
Another way of evaluating the quality of distribution of a population by age and sex data is by
employing the Age-sex Accuracy Index developed by the United Nations. This index establishes
the accuracy of the population structure by sex and 5-year age groups (UN, 1955).
Table 2 shows a UN joint score that is positioned at 18,2 in 2011 relative to 18,7 in 2001, which
indicates consistency in the quality of data over time. Since the index is below 20, it can be
concluded that data for 2011 are reliable. It is essential to note that the ARSF in 2001 (4,2) and
2011 (3,8) are less than those pertaining to males, signifying a smoother age pattern for
females. The joint score is a combination of age ratio and sex ratio, therefore its value is
determined by the characteristics of age ratio and sex ratio scores. There was no change in the
SRS from 2001 to 2011. The pattern could be ascertained by a slight decline of UN joint score.
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Table 2: UN Accuracy Index, Censuses 1996, 2001 and 2011
Census year
SRS
ARSF
ARSM
UN
Index
1996
3,3
4,1
3,7
17,7
2001
3,4
4,2
4,4
18,7
% Change
3,0
2,4
18,9
5,6
2011
3,4
3,8
4,1
18,2
% Change
0,0
-9,5
-6,8
-2,6
Figure 7 shows the UN Accuracy Index from 1996 to 2011 for population groups. What is
strikingly notable is the increase in the index for the Indian/Asian population group. This
signifies the worsening of the reporting of age-sex data amongst the Indian/Asian population
group. The indices of coloured and white populations are below 20 across the years from 1996
to 2011, and this is indicative that age-sex data for these population groups seemed to be more
accurately reported.
Figure 7: UN Accuracy Index (UNI) by population group, Censuses 1996, 2001 and 2011
25
Index
20
15
10
5
0
Black African
Coloured
Indian/Asian
White
1996
22,8
14,9
14,5
13,5
2001
21,2
14,8
16,6
17,4
2011
21,3
16,2
22,5
13,6
The results in Figure 8 indicate that Northern Cape amongst all provinces had lowest UNI for
both 1996 and 2011, while Western Cape had the lowest only for 2001. On the other hand, the
results indicate that although Eastern Cape, Limpopo and Mpumalanga had indices higher than
20, the quality of data improved over time. For instance, Limpopo had an index of 30,6 in 1996
that decreased to 23,4 in 2011. In contrast, Western Cape and Gauteng showed a worsening
age and sex reporting with the Gauteng index increasing from 23,3 in 1996 to 28,3 in 2011.
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Figure 8: UN Accuracy Index by province, Censuses 1996, 2001 and 2011
35
30
Index
25
20
15
10
5
0
2.7
WC
EC
NC
FS
KZN
NW
GP
MP
LP
SA
1996
13,4
27,8
13,8
23,7
23,2
21,5
23,3
28,7
30,6
17,7
2001
13,9
26,5
14,6
17,7
24,0
22,2
25
26,1
29,1
18,7
2011
19,5
21,0
12,7
19,6
21
21
28,3
22,4
23,4
18,2
Summary
Overall, the analysis on assessment of data indicates that the quality of data pertaining to age
and sex is reasonable, hence can be used for further analysis. However, findings observed in
the UN accuracy indices of Indian/Asian and black Africans indicate some irregularities in the
reported data. Age ratios index shows fluctuations in ratios across the years, particularly in
younger and adult age groups.
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Chapter 3: What could have contributed to the shape of age and sex
structure of the population, 1996-2011?
3.1
Introduction
The age-sex structure refers to the distribution of age and sex within a population and it is
determined by the history of births and deaths at each age as well as by the number of migrants
by age that have entered and left the population. The processes of fertility, mortality and
migration together determine not only the current size of the population but also the distribution
of age and sex. A comparative analysis of sizes of specific age groups relative to the others or
to the population as a whole is recognised as having an important role in the development
process (Naire, 2011). The analysis of age and sex structure is one of the most basic ways of
understanding population change over time (US Census Bureau, 2010). Obaid, (2007) noted
the existence of demographic change between developed countries, where populations are
ageing and many developing countries where populations continue to grow at rapid rates at
young generations.
High fertility and declining mortality were the leading components of demographic change of the
20th century but currently low fertility and ageing are the dominant demographic issues of the
21st century. A review of the demographic history of South Africa reflects two episodes that
could change the structure of a population. South Africa recorded a fertility decline from 7,1
children per woman in the 1950s to 2,8 children per woman in 2007 which is matchless in any
African country (Moultrie and Timaeus, 2003). In the midst of fertility transition, the sudden
scourge of HIV/AIDS befell sub-Saharan Africa and that had a devastating effect on mortality.
Findings of the antenatal clinic HIV survey conducted in 1998 (DoH, 1999) revealed that in
1990; 4,2 million people in South Africa were infected with HIV and less than 1% of women
attending antenatal clinic were infected, however, the proportion increased drastically to 22,1%
in 1998. Evidence from sub-Saharan Africa suggests that fertility is between 25% and 40%
lower in HIV positive women than among uninfected women (Zaba and Gregson 1998, Ryder,
et al., 1991).
3.2
The population structure of 1996, 2001 and 2011
Figures 9 to 11 indicate the population structure of South Africa for 1996, 2001 and 2011. In
1996, the structure reflects the shrinking base, suggesting a population that is experiencing a
decline in fertility. This was evidenced by the study carried out by Udjo, (1997) where his
findings indicated a decline of TFR from 4,2 in 1980 to 3,3 in 1996. As the population
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progressed to 2001, a similar structure holds at the base, signifying the population that could be
experiencing a decrease in the number of births. Moultrie and Dorrington (2004) explored
fertility from Census 2001 and established a further decline in TFR from 3,23 in 1996 to 2,84 in
2001.
Surprisingly, the 2011 age-sex structure exhibits an unusual pattern relative to the previous
censuses. A glance at the structure indicates two fluctuations in age groups 0–4 and 5–14, the
widening pattern of the structure at 0–4 that signifies the increase in the number of children
aged 0–4 followed by the narrow pattern of children aged 5–14.
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Figure 9, Figure 10 and Figure 11: Population age sex structure, 1996, 2001 and 2011
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3.3
14
Mortality, fertility and population age sex structure
Most empirical findings of mortality fertility links have evolved around birth intervals, parental
replacement and hoarding strategies and the continued pursuit is still an important debate left
behind (Preston, 1978). Heuveline, (2003) suggested that one other possible way to understand
the structure of the population is to establish trends of fertility in response to past mortality
crises particularly where fertility has not been controlled. He found that in Cambodia, although
there was a post-war baby boom, the post-war crisis rebound does not necessarily indicate an
influence on fertility behaviour.
Several studies indicate that when living conditions improve after a period of extreme hardship,
for example, plague, epidemic war or famine, fertility often, but not always recovers temporarily
(Lindstrom and Berhanu, 1999). It is because that if fewer births occur during a crisis, an
unusual number of women are prone to the risk of conception that results in record number of
births one to two years later. Lee, (1997) suggested that post-crisis fertility are driven by marital
fertility surge that result from an increase in the number of susceptible women following the
absence of conception during a crisis. Bongaarts and Watkins, (1996) argued that fertility
transitions occurred in different demographic and economic contexts; however an impressive
mortality decline constitutes an aspect of changing environment in which transitions have
occurred. Can this typical response explain the increase in the number of children five years
prior to Census 2011 in South Africa?
Extremely high adult mortality levels in some of the south eastern African countries are not the
sole result of the HIV/AIDS epidemic, but due to the triple burden of infectious and chronic
diseases, as well as external injuries (Reniers, et al., 2011). In Southern Africa in particular, the
mortality gains made during the previous four decades have been wiped out in less than ten
years (ibid). The trend in IMR as per MDG report 2013 as shown in Figure 12, suggests an
increase from 26 infant deaths per 1 000 live births to 48 infant deaths per 1 000 live births from
1998 to 2007 and a decrease since 2007 to 2010. In 2010, IMR was at 38 infant deaths per
1 000 live births. A similar pattern holds for under-five mortality rate.
HIV/AIDS prevalence in pregnant mothers increased significantly from 1999. Mother-to-child
transmission decreased from 8% to almost 3% in 2011 (Sherman, et al., 2012). The country
implemented a PMTCT programme in 2002 and a comprehensive national antiretroviral
programme in 2004 and has seen an improvement in the quality and practice (Barron, et al.,
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2013). The programme formed part of the 2003 National Strategic Plan and included increased
provision of nevirapine, the extension of treatment to all HIV-infected pregnant mothers and
their children and the expansion of related health-care services, such as “voluntary counselling
and testing”, (ibid). The guidance and recommendations have changed over time.
There are many plausible factors that could have shaped the structure of the population from
1996–2011. Could the increase in mortality from around 1998 to 2006/7 possibly be due to the
high infant mortality and partly due to the high mother–to-child HIV transmission contributed to
the current age and sex structure? The decline in fertility due to the association between fertility
and HIV/AIDS could have also to some degree contributed to the shape of the structure. Other
authorities, however have not found evidence that HIV/AIDS affected fertility (Fortson, 2009).
Linking mortality pattern to the structure, particularly at younger ages could suggest that
fluctuation in the mortality pattern could have, to a certain extent, contributed to the decreasing
number of cohort 5–14. Lee, (2003) indicates that reduction in infectious and contagious
diseases as well as improvement of health increase the life expectancy. Figure 12 shows the
infant mortality and under-five mortality from 1998–2010. The trend reflects an increasing
pattern from 1998 to 2007 that then declines towards 2010.
80
70
60
50
40
30
20
10
0
IMR
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
U5MR
1998
Mortality rate
Figure 12: Under-five and infant mortality rates, 1998–2010
Year
Source: Stats SA, MDG country report, 2013
3.4
Change in the proportion of children (0–4) relative to women (15–49)
Change in the age structure and particularly fluctuations in the number of women in the
reproductive age group, affects fertility levels. Table 3 indicates that the number of children in
the age group 0–4 (1,05) has slightly increased from 2001 to 2011 relative to women aged 15–
49 (-0,34). This pattern suggests a number of underlying phenomena, amongst them are that
the actual number of children born per woman may have increased despite the declining fertility
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(Retherford and Thapa 1998). Further, the declining mortality rates might have improved the
survival rates, that led to the increased population aged 0–4 and lastly, the improvement of data
quality could decrease under-reporting of children, and therefore increase the proportions at this
age group (Subedi, 1998). Stats SA (2004 and 2012b) indicated that net undercount rate of
children under-five improved slightly from 16,8% in 2001 to 15,1% in 2011.
Table 3: Proportion of children (aged 0–4) relative to women (aged 15–49), 2001 and 2011
2001
2011
2001
2011
Percentage difference
Number
Number
%
%
%
0-4
4 449 816
5 685 452
9,9
11,0
1,1
15-49
12 641 970
14 423 494
28,2
27,9
-0,3
Age group
3.5
Migration and population age-sex structure
Migrants tend to be a selected group of individuals when compared to both the population of
origin as well as the population of destination (Newell, 1988). Migration selectivity is as a result
of the propensity of certain age groups and sex to migrate more than other ages. Researchers
reveal that age specific schedules of migration peak in young children (resulting from children
migrating with their parents), then migration rates decrease in teenage years and sharply
increase in early adult ages and decrease thereafter (Rogers,et al., 2010). Figure 13 presents
the structure of immigrants into South Africa between 2002 and 2011. As expected, the majority
of immigrants were males within economically active population. In Figure 14, the difference
between the shaded bars and the line bars is the contribution of immigrants to the total
population of the country. The findings reveal that nationally, the contribution of the number of
immigrants to the population structure was insignificant; however the effect is noticeable in the
Indian/Asian population group (Stats SA, 2012a).
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Figure 13: Immigrants into South Africa, (2002–2011)
80+
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5- 9
0- 4
Female
Male
250000
200000
150000
100000
50000
0
50000
100000
150000
Figure 14: Total population and population without immigrants (2002–2011)
80+
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5- 9
0- 4
Male
4000000 3000000 2000000 1000000
3.6
Female
0
1000000 2000000 3000000 4000000
Summary
From the findings it could be suggested that the shrinking of the population aged 5–9 and 10–14
coincided with the increased infant and child mortality from 1998 to 2007. Congruent to child
mortality decline from 2007, the population structure in 2011 indicates an increasing population
aged 0–4 relative to 2001, the linkage between the two demographic processes could suggest
that the improvement of the healthcare system might have resulted in the survival of children 0–
4. One likely factor could be the improvement of data collection in this age group. Migration did
not show any significant effect on the unusual pattern of Census 2011 population structure.
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Chapter 4: Empirical findings from Censuses 1996, 2001 and 2011
4.1
Introduction
South Africa has led the fertility transition in Africa, with all the populations evidencing fertility
decline from 1965, followed by other parts of Southern Africa. In the midst of declining fertility,
the country was confronted with a scourge of HIV/AIDS. Subsequent to these changes, it is vital
to investigate the impact of the changes in the population composition. Although some
indicators on population structure and composition were published in 2012, analysis of the
population age-sex structure was undertaken to provide an insight of the demographic transition
over time.
The following section attempts to investigate the extent to which these demographic changes
affected the population from 1996 to 2011. Different summary measures and measure of central
age are used to describe the population composition and measure the shifts in the population
age structure.
4.2
Observed indicators from Censuses 1996, 2001 and 2011
4.2.1
Broad age groups
Figure 15 indicates the decreasing trend in the proportion of male and female population aged
0–14 over time. The proportion for males decreased from 35,6% in 1996 to 30,3% in 2011 while
that for females decreased from 33,2% in 1996 to 28,1% in 2011. The proportion for the
economically active population (age group 15–64) remained relatively consistent, with the
proportion for males and females increasing from an average of 61% in 1996 to 65% in 2011.
% population
Figure 15: Distribution of population by functional age groups and sex
70
60
50
40
30
20
10
0
0-14
15-64
65+
Male
Female
1996
35,6
33,2
60,5
61,1
3,9
5,7
Male
Female
2001
33,4
30,8
62,8
63,2
3,8
6,0
Male
Female
2007
32,1
30,0
63,8
63,4
4,1
6,6
Male
Female
2011
30,3
28,1
65,6
65,4
4,1
6,5
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Median age
The median age of the population is defined as that age that divides the total population in half.
A population may be described as young if the median age is below 20, intermediate if the
median age lies anywhere from 20 to 29 and old if the median is over or equal to 30 years
(Hobbs, 2004).
4.2.2.1
Median age by population group
According to Figure 16 the median age suggests that the country has an intermediate median
age, this means that the country is neither young nor old. Nonetheless, there are disparities by
population groups. The white population group has consistently shown a higher median age that
is between 33 and 39 over time compared to other population groups. This is indicative of a
population that is ageing. The other population groups have median ages that reflect
populations in the intermediary stage.
Median age
Figure 16: Median age by population group
45
40
35
30
25
20
15
10
5
0
White
Indian/Asian
Coloured
Black African
RSA
1996
33
26
23
21
22
2001
35
29
24
22
23
2011
39
32
27
24
25
4.2.2.2
Median age by province
Figure 17 shows disparities between provincial median ages. Gauteng and Western Cape are
the two provinces with the highest median ages of 28 compared to all other provinces. Stats SA
(2012a) revealed that Western Cape and Gauteng had the highest in-flow of migrants between
2001 and 2011 and it is likely that the median ages of these provinces increased due to age and
sex selectivity of migration. In 2011, the median ages for all provinces are above 20, with
Limpopo having the lowest median age at 22 years. This is followed by the Eastern Cape and
KwaZulu-Natal with median ages of 23 years.
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Figure 17: Median age by province
30
Median age
25
20
15
10
5
0
EC
FS
GP
KZN
LP
MP
NW
NC
WC
RSA
1996
19
24
27
21
17
21
22
24
26
22
2001
20
24
27
21
19
21
24
25
26
23
2011
23
26
28
23
22
24
26
26
28
25
4.3
Overall sex ratios
Sex ratios provide the number of males for every 100 females. It shows the predominance of
males over females and if it is less than 100, then the reverse is true. Traditionally, sex ratios at
birth are high and decrease steadily as age increases. The sex ratios have always been
favourable for females. From the results in Figure 18, South Africa has consistently had a lower
sex ratio ranging from 93 males per 100 females in 1996 to 95 male per 100 females in 2011.
There is a stable sex ratio for black African, coloured and white population groups across the
three censuses ranging between (92 males per 100 females) and (95 males per 100 females).
On the other hand, the Indian/Asian population group shows a noticeable increasing sex ratio
from 96 males per 100 females in 1996 to 101 males per 100 females in 2011.
Figure 18: Sex ratios by population group
102,0
100,0
98,0
Sex Ratio
96,0
94,0
92,0
90,0
88,0
86,0
Black African
Coloured
Indian/Asian
White
RSA
1996
92,0
94,0
96,0
95,2
92,7
2001
91,1
92,6
95,6
94,0
91,7
2011
94,4
93,3
100,6
94,8
94,8
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21
Dependency ratio
The dependency ratio is an indicator of potential dependency burden of children and the elderly
on those who are of economically productive ages in a population.
4.4.1
Total dependency ratios by population group
The results in Figure 19 indicate a decrease of total dependency ratio from 64,4 in 1996 to 52,7
in 2011. This means that in 2011, every 100 persons of economically active population (15–64),
were expected to cater for 52,7 of which 44,5 were children and 8,2 were adults (See Figures
21 and 23). Total dependency ratios have been declining for all population groups except for the
white population. The analysis of this indicator reflects the relationship between dependency
ratios and total fertility rates in various sub-population groups.
Figure 19: Total dependency ratios by population group
4.4.2
Total dependency ratios by province
Figure 20 suggests that across the years, Limpopo and the Eastern Cape are the provinces with
the highest dependency ratios compared to other provinces. This is expected due to the outflow
of migrants to other provinces within the country. All the other provinces, except Gauteng in
2001 and 2011, suggest a decline in total dependency ratios over time. Gauteng also has the
lowest total age ratios compared to all other provinces, this also can be explained by the high
influx of people from other provinces and countries who increased the number of the working
age population in the province.
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Dependency ratio
Figure 20: Total dependency ratios by province
100,0
90,0
80,0
70,0
60,0
50,0
40,0
30,0
20,0
10,0
0,0
FS
GP
KZN
LP
MP
NW
NC
WC
RSA
1996 82,7
56,4
42,9
68,9
92,4
70,7
63,2
65,9
52,4
64,4
2001 75,0
55,4
38,7
65,4
81,0
67,0
57,0
60,1
48,2
58,7
2011 66,0
52,9
39,0
58,5
67,3
56,0
54,5
55,7
45,0
52,7
4.4.3
EC
Child dependency ratios by population group
Findings depicted in Figure 21 indicate that nationally, the child dependency ratio has been
declining. A similar pattern is noted in population groups with the white population having the
lowest ratio. It is interesting to note that both the Indian/Asian and the white populations exhibit
child dependency ratios that are consistent with those observed in more developed regions,
while black African population ratios are comparable to those seen in less developed regions
(Rowland, 2003).
Dependency ratio
Figure 21: Child dependency ratios by population group
4.4.4
70,0
60,0
50,0
40,0
30,0
20,0
10,0
0,0
Black African
Coloured
Indian/Asian
White
Total
1996
61,6
52,9
40,4
31,4
56,4
2001
55,2
47,3
32,7
27,2
50,9
2011
47,8
42,4
27,5
24,3
44,5
Child dependency ratios by province
Provincial distribution of child dependency ratio in Figure 22 indicates that while Limpopo and
Eastern Cape have the highest child dependency ratios, Gauteng and Western Cape had the
lowest ratios. The pattern revealed by child dependency ratios corroborates with fertility
schedules across the country.
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Figure 22: Child dependency ratios by province
4.4.5
Old age dependency ratios by population group
Old age dependency also provides an indication of ageing in a population. The results in Figure
23 suggest that old age dependency for the country seems to be consistently around 8. The old
age dependency ratio for the white population is the one that is the most striking (increasing
from 15 in 1996 to 21 in 2011). Amongst all population groups, black African population group
old age dependency ratios have shown a steady decrease over time.
Figure 23: Old age dependency ratios by population group
4.4.6
Old age dependency ratios by province
In Figure 24, Limpopo and the Eastern Cape provinces have the highest old age dependency
ratios. Gauteng amongst all the provinces indicated the lowest old age dependency ratio across
the years. In terms of migration, Limpopo and Eastern Cape are the most sending provinces.
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The age selectivity theory of migration could have contributed to the age composition in each
province.
Figure 24: Old age dependency ratios by province
4.5
Summary
The findings indicate that there have been changes in the population age-sex structure since
1996 to 2011. The distribution of population by broad age groups shows a decreasing child
population (0–14) from 1996 to 2011. Notwithstanding the gradual increase of the median age
from 1996 to 2011, the population of South Africa is still in an intermediary stage with the
median age that is positioned between 20 and 29. Across the years, the median age of the
white population signifies a population that is ageing, whilst that of the black African population
is characterised by a young population. Sex ratios have been increasing over time, however
nationally; female population still exceeds male population. The Indian/Asian population sex
ratio stands at almost 101, indicating a population with a slightly high proportion of males than
females. While total dependency ratios showed a declining trend over time, the black African
population had the highest total dependency ratios across the years. Provincially, Limpopo and
Eastern Cape had the highest child and old age dependency ratios relative to other provinces.
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Chapter 5: Population age-sex structure scenarios
5.1
Introduction
Given the composition of the population and its development over time, this section attempts to
investigate the main demographic process that could have shaped the structure of the
population from 1996 to 2011. To be able to do that, three scenarios of projecting the population
from 1985 to 2011 were performed. The method is employed to find out what the size and the
structure of the population would be if each of the demographic process is left to be constant.
The hypothesis is that, if the output of the demographic process differ from the reported profiles
then that process contributed to change in the structure of the population. Otherwise, if it mimics
the reported profile, then that process did not affect the population structure.
The scenarios are based on the following assumptions:
 The first scenario is what the structure of the population would be with the assumption
that TFR did not change from 1985.
 In the second scenario, we determine what the shape of the structure would be with an
assumption of constant mortality.
 In the last scenario, we project the population using mortality and fertility estimates over
time and set migration to be constant.
5.2
Projected scenarios
Input data for mortality indicators used in the analysis are from the mid-year population
estimates of 2011 while TFR and migration rates employed are from censuses.
Tables 4 and 5 show the results of all the scenarios and reported figures from censuses. It is
evident that if fertility remains constant over time from 1985, the population size diverges
markedly from the reported one. The effect is more pronounced in 2011 where the projected
population is positioned at 57 million if fertility was assumed not have changed. The pattern is
suggestive that the decline in fertility made a major effect in the evolution of the population
structure.
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Mortality seemed to be second in shaping the structure of the population in 2011. As indicated
in Table 4, when life expectancy did not vary from 1985, the projected population indicated
lower population size than the reported one. Regardless of the sudden increase in mortality
rates between the late 1990s and the mid-2000s, the country encountered an improvement in
life expectancy at birth over time, hence the population size with variation of rates in mortality
exceeds the one with constant mortality except for 1996 where the reported and the projected
populations were almost the same. UNAIDS, (2014) noted a gradual increase in life expectancy
at birth in South Africa from 60,6 years in 1985 to 62,0 years in 19971 which dramatically
decreased to 52,1 years in 2004. By 2011, the life expectancy was at 58,1 (Stats SA, 2013).
The analysis in Figures 13 and 14 above indicate that migration appeared to have not altered
the population structure. The reported population size and the projected one in the three
censuses points are virtually the same despite constant migration. The implication is that apart
from the increasing inflow of migrants into the country over time (see Figure 3), its effect on the
population structure is insignificant.
The argument on the effect of fertility and mortality is likewise demonstrated in Table 5 below.
With constant fertility, the projected proportion of children in the age group 0–14 is higher than
the reported population in all three points. The effects still hold when mortality is kept constant,
with considerable variations between the reported and projected populations aged 0–14 in 1996
and 2001.
The median ages and child dependency ratios in Table 5 confirm that fertility is the main
demographic process that shaped the structure from 1996 to 2011. When fertility remained
unchanged the population appeared to be very young with the median ages of 21 across the
three years and the projected child dependency ratios indicate an increasing trend over time.
Table 4: Projected and reported population size, 1996, 2001 and 2011
Demographics
Census 1996
Census 2001
Census 2011
40 583 572
44 819 777
51 770 560
Fertility constant
42 099 355
46 865 846
56 969 797
Mortality constant
41 024 712
44 146 836
49 175 367
Migration constant
41 298 329
44 675 621
49 709 343
Reported population size from
censuses
1
Spectrum UNAIDS, 2014 Life expectancy figures
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Table 5: Projected and reported median ages and dependency ratios, 1996, 2001 and
2011
Indicators
Fertility constant
Median age
Projected
Mortality constant
Migration constant
Reported
Projected
Reported
Projected
Reported
1996
21
22
21
22
21
22
2001
21
23
22
23
22
23
2011
21
25
23
25
23
25
Reported
Projected
Reported
Projected
Reported
Child dependency ratio
Projected
1996
62,6
56,4
59,6
56,4
59,8
56,4
2001
64,0
50,9
56,7
50,9
57,3
50,9
2011
66,6
44,5
51,7
44,5
53,1
44,5
5.3 Summary
Fertility, then mortality seem to be the contributing demographic processes that changed the
population age and sex structure over time. If fertility is constant, the proportion of children aged
0–14 increased markedly, thus increasing the size of the population, far above the reported
figures. The projected decreasing median ages affirm that fertility transition altered the structure
of the population. Mortality to a certain extent did alter the population structure particularly in the
children population. If assuming no mortality change, the projected total population size
decreases considerably compared to the reported one. Migration as discussed earlier did not
have any impact on age-sex distribution over time.
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Chapter 6: Demographic dividend
6.1
Introduction
One of the developmental challenges that are facing the world is to create an environment of
economic growth. The South Asian “Asian Tigers“ such as Hong Kong, Korea, Singapore and
Taiwan came up with a comprehensive way to make this growth a reality and this was triggered
by what was known as the demographic dividend. It is the accelerated economic growth that
begins with changes in the age structure as it transition from high to low fertility and mortality
rates and the subsequent change in the age structure (Gribble and Bremner, 2012). According
to Ross (2004), the demographic dividend occurs when falling birth and mortality rates change
the age distribution so that fewer investments are needed to meet the needs of the youngest
age groups and resources are released for investments in economic development and family
welfare. The generations of children born during periods of high fertility finally leave the
dependent years and enter the labour market, however, good policies during demographic
transition are required to educate and train them so that they become skilled, educated and
contribute to a productive labour force which can boost the economy.
A country can make use of the window of opportunity for rapid economic growth if the relevant
social and economic policies are directed towards health, education, governance and economy
(UNECA, 2013a). The Asian Tigers invested heavily in health, family planning, education and
economic reforms of their young population. According to estimates by Williamson (1997), since
the 1970s, between a quarter and a third of economic growth in the South Asian countries could
be attributed to the demographic dividend. However, the demographic dividend is not
permanent and the window of opportunity is limited. The large young adults will progress to the
elderly population followed by a cohort of fertility decline. This change will be followed by an
increase in the elderly dependency ratio and the pressure will be on caring for the needs of the
elderly. On the other hand, the dividend is not automatic, it requires a set of investments and
policy commitments. Some countries will take advantage of the released resources and some
will not until the window of opportunity closes (Ross, 2004).
The proportion of the world large population between the ages of 12–24 years living in Africa is
expected to rise from 18% in 2012 to 28% in 2040. The UN’s latest population projections put
Africa at 2 billion people in 2040. For many African countries, the accelerated economic growth
of the demographic dividend remains a possibility. Amongst the challenges that threaten the
process are high fertility rates, poor governance and political instability (AFIDEP, 2013).
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Figure 25: Policy wheels for creating and earning the demographic dividend
Source: Gribble & Bremner, 2012
The above model in Figure 25 shows that for a demographic dividend to occur one needs much
more than just an appropriate age structure. Investment in health, education and economic
development are required along with adherence to corporate governance principles in order for
a nation to derive benefits from a demographic dividend. This is what the chapter attempts to
show.
6.2
The demographic dividend is delivered through several mechanisms
6.2.1
Change in age structure
The accelerated economic growth of the demographic dividend should start with prioritising
strategic investments to lower fertility and child mortality. As the total fertility decreases, the
proportion of children aged 0–14 begins to decrease relative to the population aged 15–64. The
decline in fertility will reduce age dependency, economic dependency and lead to a greater
resource mobilisation and finally improved socio-economic status (Eloundou-Enygue, 2013).
This demographic transition does not automatically accelerate economic growth. In subSaharan countries and elsewhere, where fertility is declining, it is vital to establish an enabling
policy environment for children and youth to benefit from education, health, employment
opportunities and livelihood creation to become productive adult workers in the near future
(USAID, 2012). Until countries address their extremely young age structure, they will not
achieve their full potential for economic growth that comes through the demographic dividend.
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For countries to realise a demographic dividend, they need to make investments that lead to
having a smaller school-age population and a larger working-age population (UNECA, 2013b).
Figures 26 and 27 present the population structure of 1985 and 2011. The structure transitioned
from the ‘young’ population structure characterised by high mortality and fertility rates to a
mature one with declining mortality rates. As the cohort matures, the population represents an
age structure with a high proportion of young people relative to children. According to Bloom, et
al., (2003) the concept of demographic dividend explains the possible economic consequences
of the excess population particularly in the working age group. As alluded earlier, given the right
policy environment, countries can benefit from the demographic dividend. The policy reviews
should be aimed at the following areas: public health, family planning, education and economic
policies that promote labour-market flexibility and openness to trade, and savings (Urdal, 2011).
Figure 26: Population of South Africa, 1985
Source: Acturial Society of South Africa (ASSA), 2008
Figure 27: Population of South Africa, 2011
Source: Statistics South Africa, 2012
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6.2.2
31
Change in fertility
South Africa witnessed fertility transition from the 1960’s. Figure 28 suggests that fertility is still
declining to date. The rate of fertility in South Africa is among the lowest in sub-Saharan African
countries (Moultrie and Timaeus, 2003). In addition, life expectancy increased from 57,1 years
in 2009 to 61,3 years in 2012 (Dorrington et. al, 2014). Adult mortality shows a decreasing
pattern from 46% in 2009 to 38% in 2012 (ibid).The transition brought a change in the
population age structure with fewer young children relative to working age population.
Figure 28: Trends in total fertility, all women aged 15–49, 1985–2011
Source: Udjo (1997, 1998); Dorrington et al. (1999); Moultrie and Dorrington (2004); Udjo, (2014); Sibanda and Zuberi, (1999) and
Calculated estimates from Census 2011
6.2.3
Infant and child mortality
Secondly, governments need to invest in reducing child mortality in order to produce a healthy
potential future workforce. Although South Africa has made progress in reducing child mortality,
the rate is much higher than the global average that stood at 51 deaths per 1 000 live births
(UNICEF, 2013). If the country continues to invest in immunisation to ensure children survival,
this will give a greater impetus for reduction of fertility rates. When families are confident that
their children will survive, then they will give birth to fewer children.
Figure 29 indicates under-5-mortality projections from different models. South Africa did not
reach the MDG target of 20 deaths per 1 000 live births by 2015, however all the projections
indicate a decreasing under-5-mortality except for the trend projections of Grenne and Gakusi.
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Figure 29: Estimates of South Africa’s under-5 mortality rate compared with South
Africa’s MDG target
Source: Nannan et.al, 2012
6.2.4
Life expectancy
The level of life expectancy is influenced by fluctuations of child and infant mortality. People
tend to lose years of life if they die in their early stages of life. It is apparent that the gradual
decrease of infant and child mortality has, amongst other factors, contributed to an increase in
life expectancy. As presented in Figure 30, the improvement in life expectancy began to be
evident from 2007 onwards.
life expectancy
Figure 30: Life expectancy at birth by sex, 2002–2012
64,0
62,0
60,0
58,0
56,0
54,0
52,0
50,0
48,0
46,0
44,0
Male
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
50,0 49,5 49,3 49,4 50,2 51,7 53,3 54,6 55,5 56,1 56,8
Female 55,2 54,4 53,9 53,6 54,6 56,1 57,6 58,8 59,5 60,0 60,5
Total
52,7 52,1 51,7 51,6 52,5 54,0 55,5 56,8 57,6 58,1 58,7
Source: Stats SA, Mid-year population estimates 2013
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6.3
33
Investing in health
Evidence from Arora, (2001) and Fogel, (1990)] suggests that better health facilities improve
economic production. For a country to have a very productive labour force, it does not only need
to be educated and skilled, but also to have a healthy population. More critically, countries need
to invest in child nutrition as it affects how well children can learn and also impacts the quality of
the future labour force, some examples are: ensuring that infants receive good medical care,
protecting women’s reproductive health and stressing the health of children and teenagers
(UNECA, 2013a).
As these children are growing to adolescent age group 10–19 (WHO), they should have access
to reproductive health services to avoid, amongst others, unwanted pregnancies, HIV and
sexually transmitted diseases. Investment and promoting healthy life styles during young
adulthood ensure transition into healthy adults who can participate productively in the economy
(Megquier and Belohlav, 2014). The question that remains is whether the country has invested
on the health of the generation of high fertility era to prepare them to participate actively in the
economy as they reach adulthood stage of life.
The leading causes of youth mortality worldwide are among others, injury, respiratory infections,
HIV/AIDS and meningitis (USAID, 2012). According to the 2014 Human Sciences Research
Council National HIV Prevalence, Incidence and Behaviour Survey, South Africa has the
highest HIV/AIDS infections in the world estimated to be 6 300 000 (per total population). The
HIV/AIDS prevalence has also increased from 10,6% in 2008 to 12,2% in 2012 (ibid). This
number is projected to rise to 7,3 million by 2030. Figure 31 indicates that the highest
prevalence is more pronounced in population between the ages of 20 and 44. The impact of
HIV/AIDS is depleting the working-age population. The total number of persons living with
HIV/AIDS increased from an estimated 4,21 million in 2008 to an estimated 5,38 million in 2011.
The estimated prevalence of persons aged 15–49 has increased steadily, but has been stable
for the past ten years with an estimate of 16,0% in 2001 to 16,6% in 2011 (Stats SA, 2011).
The population of South Africa has a proportionately high number of people of working age
group and a low number of children and elderly people, the challenge is that high
unemployment and HIV/AIDS have produced many more dependents than would have been the
case (NDP, 2011). Statistically the country is in a position to benefit from a demographic
dividend, however, the challenges of joblessness and HIV/AIDS are major burdens on working
age population, hence preventing South Africa to maximise from the benefits of the dividend.
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34
40
35
30
25
20
15
10
5
0
36
28,4
28,8
31,6
28
25,6
19,7
17,4 17,3
15,8
15,5 14,8
13,4
9,7
0-14
5,5
4,6
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
male
female
male
female
male
female
male
female
male
female
male
female
male
female
male
female
male
female
0,7
2,4
female
5,6 5,1
male
female
2,3 2,4
male
HIV Prevalence
Figure 31: HIV prevalence by sex and age, South Africa, 2012
60 +
Source: HSRC, 2012
The age-specific deaths in South Africa have been decreasing over time as seen in Figure 32;
but the peak of deaths is still among the economically active population. It is well documented
that a population’s health status affects the country’s economic performance in terms of
economic growth and social development (Arora and Mayer, 2001).
Figure 32: Percentage distribution of deaths by age and year of death, 2008–2012
10
9
8
7
6
5
4
3
2
1
0
0
1-4
5-9
2008
7,7
2,6
0,8
10-14 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75 - 79 80 - 84
0,7
1,5
4,0
7,1
9,1
9,0
7,8
7,1
6,4
6,1
5,3
5,7
5,0
5,0
3,9
2009
6,8
2,2
0,8
0,8
1,5
3,8
6,8
8,5
8,6
7,6
7,2
6,6
6,4
5,8
5,8
5,4
5,3
4,3
2010
6,3
2,4
0,8
0,8
1,5
3,7
6,6
8,0
8,2
7,5
7,2
6,8
6,4
6,4
5,8
5,9
5,1
4,8
2011
5,6
2,0
0,9
0,8
1,5
3,4
6,1
7,3
7,8
7,1
7,0
6,9
6,8
6,9
6,1
6,5
5,5
5,2
2012
5,6
2,2
1,0
0,8
1,5
3,3
5,9
7,0
7,5
6,9
6,7
6,9
6,8
7,1
6,3
6,6
5,8
5,5
Source: Stats SA (2014d), Mortality and causes of deaths in South Africa, 2012: Findings from death notification.
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6.4
35
Education
For a country to take advantage of a window of opportunity brought about by a change in the
population age structure, the quantity and the quality of education needs to be improved. Links
between education and economic development are well established. At this stage the goal is to
provide quality education that produces internationally competitive and life journey graduates
(Eloundou-Enygue, 2013). Secondary education, tertiary education and vocational training need
to be expanded and made relevant in order for youth to develop the skills required for
productive employment (PPD, 2011).
According to the South African Schools Act, (1996), school is compulsory for children aged 7–
15 years. The Education law Amendment Bill of 2002 set the age admission into Grade 1 as the
year in which the child turns seven (DBE, 2010). The compulsory school attendance band is
compulsory from grade 1–9 for all learners. From Grade 10–12, learners may decide to follow a
different path included in FET (DoE, and School Realities, 2009). One of the goals of DoE is to
ensure that all children have access to complete free and compulsory primary education of
good quality.
Figure 33: Secondary gross enrolment rate and enrolment ratios for children aged 7-13
years
Source: DoE, Education Statistics in South Africa: 2002-2007, DoE, School Realities: 2008-2009 and General Household Survey 2002–
2009, Statistics South Africa
Results in Figure 33 show that the enrollment ratios for the 7–13 year-olds are already high with
an increase of about 2% points from 2002–2009. The data further suggest that, South Africa is
characterised by high enrollment rates in secondary schools with GER (Gross enrollment rate)
that is at 85% in 2009.
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Figure 34:
36
Proportion of 15–17 year-olds who have completed Grade 7 and higher,
% Completed Grade 7 and higher
2002–2009
100,0
90,0
80,0
70,0
60,0
50,0
40,0
30,0
20,0
10,0
0,0
2002
2003
2004
2005
2006
2007
2008
2009
15 year olds
68,7
68,6
71,8
74,6
78,0
79,0
78,5
79,9
16 year olds
78,6
81,6
81,9
84,8
84,6
86,7
86,2
88,3
17 year olds
85,7
86,7
88,7
90,0
90,9
91,5
91,0
91,8
18 year olds
89,6
90,2
90,0
90,2
91,7
93,2
92,1
93,8
Source: Statistics South Africa, General Household Survey, 2002–2009
The picture depicted in Figure 34 indicates that South Africa has made some strides in
attempting to achieve MDG 2 – that of achieving universal primary education. The proportions
of learners aged 18 who have completed primary education is on average about 92% and has
been steadily increasing over time.
South Africa fares fairly well by international comparisons in terms of educational attainment up
to grade 11. The rate of completion of Grade 12 is low by international standards, however, if it
can be increased from about 40% to 50% it could match the rates of countries like Taiwan (van
der Berg, et al., 2011). Although the matric pass rate dropped from 2013 (78,2%) to 75,8% in
2014, the pass rate shows an increasing trend from 67,8% in 2010 to 75,8% in 2014. Despite
these indicators, South Africa still has a high unemployment rate amongst the youth that is at
36,1% in 2014 relative to 15,6% of adults in the same year (Stats SA, 2014b).
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Figure 35: Percentage distribution of headcount enrolment in public higher education
institutions by major field of study, 2011
35
30
28,1
30,8
23,5
25
17,6
20
15
10
5
0
Science, Engineering
and Technology
Business and
Management
Education
Humanity and Social
Science
Source: Department of Higher Education and Training, 2013
On completion of secondary education, a learner generally progresses to tertiary institution.
Figure 35 indicates that the majority (30,8%) of students enrolled in public higher institutions
were in the field of Business and Management, followed by Science Engineering and
Technology (28,1%). The South African education system can now be recognised to have
attained near universal access, however the system remains largely in a poor state of affairs 2. In
the past five years the country has seen the doubling of the education budget but still it has
failed to reverse its low exam results and the standard of teaching3. If the country is to
contribute to the economy in a meaningful way, serious interventions are needed to improve the
quality of teaching and learning. In recent times, the quality of education has become
uppermost in people’s minds, especially in the face of increasing unemployment and
inequalities. South Africa participated in international tests of educational achievements.
Amongst them are TIMMS and PIRLS. Findings from TIMMS indicate that between 2002 and
2011 the performance of Grade 9 pupils in both Mathematics and Science increased by 67
points and 64 points respectively. However, it must be noted that South Africa’s overall
performance is still the worst of all middle-income countries that took part in the tests (Spaull,
2013).
2
It isn’t about money, 2011. The Education Fix, 7 April. Available at: http://www. Visualeconomics.com/ how- the – countries- spend- their –
money/
3
South Africa teacher strike shuts schools, compounds educational crisis, 2011. Bloomberg News, 31 August. Available at
http://www.bloomberg.com/news/2010
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In 2003, DoE found out that 61% of grade 3 learners could not read and write at the appropriate
level for their age4. South Africa was also the lowest performing participant in Mathematics and
Science in the 2003 trends and international maths and science study.
6.5
Employment
South African real domestic product increased by only 0,6% quarter-on-quarter in the second
quarter of 2014 (Stats SA, 2014b). South Africa’s rate of economic growth, as measured by real
GDP increased from an average of 2,7% per annum during 1997–2003 to 5,2% per annum
during 2004–2007, but decreased to 2,2% per annum during 2008–2013 (ibid). On the other
hand, results presented in Figure 39 over the period 2008 to 2014, suggest that the
unemployment rate among youth has been consistently higher than that of adults by a large
margin. There are however several issues aggravating the situation. One such issue is the skills
shortage in major industries.
Is South Africa producing the necessary skills relevent to labour market in order to accelerate
the economy? AfDB, OECD and UNDP (2012) observed that high vacancy rates in the
presence of large scale unemployment confirm the existence of skills mismatches and are
especially substantial in middle income countries. Although there are large numbers of
unemployed young people and a constantly growing labour supply, many enterprises in Africa
struggle to fill open positions. In Egypt, for instance about 1,5 million young people are
unemployed while at the same time private sector firms cannot fill 600 000 vacancies. In South
Africa the situation is even more extreme, with 3 million young people in NEET (young people
not in Education, Employment or Training) and 600 000 unemployed university graduates
versus 800 000 vacancies (ILO, 2011).
4
Centre for evaluation and assessment, 2006
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Figure 36: Trends in working age population (’000)
25000
working age population
20000
15000
10000
5000
0
2008
2009
2010
2011
2012
2013
2014
15-34
18209
18405
18608
18824
19053
19283
19504
35-64
13336
13732
14124
14511
14892
15274
15672
Source: Stats SA, 2014a
Figure 37: Percentage change in working age population among the youth and adults,
2008–2014
3,50
3,00
% change
2,50
2,00
1,50
1,00
0,50
0,00
2008-2009
2009-2010
2010-2011
2011-2012
2012-2013
2013-2014
15-34
1,08
1,10
1,16
1,22
1,21
1,15
35-64
2,97
2,13
2,74
2,63
2,57
2,61
Source: Stats SA, 2014a
As indicated in Figure 36, through the period 2008–2014, a larger share of the working age
population is accounted for by youth compared to adults (35–64), yet the annual percentage
changes in the employed population among adults across all the years, though steadily
declining, is higher than among the youth population (Figure 37). The figure suggests that the
adult working age population increased at a faster pace than the young employed population
and their share in employment increased from 42,3% in 2008 to 44,6% in 2014 (Stats SA,
2014a).
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share of youth
Figure 38: Share of youth in working age population and in employment, 2008 and 2014
70
60
50
40
30
20
10
0
Share of youth in working age
population
share of youth in employment
2008
57,4
44,7
2014
55,4
39,9
Source: Stats SA, 2014a
Youth in South Africa are vulnerable in the labour market. Figure 38 indicates that although the
share of youth in working age population was 57,4% and 55,4% in 2008 and 2014 respectively,
their share in employment decreased from 44,7% in 2008 to almost 40% in 2014.
unemployment rate
Figure 39: Unemployment rates 15-64, 2008-2014
40
30
20
10
0
2008
2009
2010
2011
2012
2013
2014
15-34
32,7
33,7
35,7
36,1
35,8
36,2
36,1
35-64
13,4
12,4
14,9
14,4
15,1
15,0
15,6
15-64
23,2
23,0
25,1
24,8
25,0
25,0
25,2
Source: Stats SA, 2014a
Although unemployment rates for all age groups show a steady increasing trend, youth
unemployment rates are noticeably higher than that of adults across the period 2008–2014
(Figure 39). This corroborates with the scarcity of job opportunities as seen in the absorption
rates in Figure 40 below. Over time, the absorption rates for youth are lower compared to other
age groups and it is indicative that government and private sector policies should be
readdressed to create more employment opportunities for youth.
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Absorbtion rate
Figure 40: Absorption rates of youth and adults in South Africa
70
60
50
40
30
20
10
0
2008
2009
2010
2011
2012
2013
2014
15-34
35,5
34,2
31,1
30,3
30,8
30,3
30,8
35-64
59,8
60,6
56,7
56,5
56,5
57
57,8
15-64
45,8
45,5
42,2
41,7
42,1
42,1
42,8
Source: Stats SA, 2014a
In July 2010, the OECD released a survey of South Africa, which revealed that South Africa had
the worst rate of unemployment for youth between the ages 15 and 24 among the 36 countries
surveyed. The results suggest that 50% employment rate for working age youth is lagging
behind other middle income and emerging market economies which employ about 80% youth.
Youth unemployment is a supply side problem since the number of jobs created in the economy
is too small (Altam and Marock, 2008). On the other hand, youth unemployment is a supply side
problem because many young South Africans lack the appropriate skills, work-related
capabilities and higher education qualifications required for a high skill economy. The 2009
CHET publication describes the post-school education as being characterised by the following:
a large annual outflow of students from schooling without meaningful further educational
opportunities, post-school institutions architecture that limits further educational oppurtunities for
youth and a recapitilised FET colleges sectors that requires further training (CHET & FETI,
2012).
6.6
Youth and training skills
Although education indicators seem to have reached the universal standard, research indicates
that only a small proportion of South Africans further their education post schooling (Breier and
Mabizela, 2008) even if given the high returns of tertiary education in the labour market (Keswell
and Poswel, 2004). Training presents an alternative channel through which individuals can
acquire skills to increase their productivity and improve their prospects on the job market.
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Table 6: Composition of employment by skill levels
1994
(% of total)
20,6
47,0
32,4
100,0
Skill level
Skilled
Semi-skilled
Low skilled
total
2014
(% of total)
25,2
46,2
28,5
100,0
Change
(% point)
+4,6
-0,8
-3,9
-
Source: Stats SA, 2014c
Table 7: Composition of skill levels of youth and adults, 1994 and 2014
15–34
Skill level
Skilled
Semi-skilled
Low-skilled
Total
1994
% of
total
19,4
50,7
29,9
100,0
2014
% of
total
20,8
52,3
27,0
100,0
35–64
Change
(% point)
1,3
1,6
-2,9
-
1994
% of
total
22,3
46,7
30,9
100,0
2014
% of
total
28,1
42,4
29,5
100,0
Change
(% point)
5,8
-4,4
-1,4
-
Source: Stats SA, Computed from OHS 1994 and QLFS Quarter 2, 2014
The composition of the workforce by level of skills of 1994 and 2014 is shown in Table 6. There
was a shift in the compostion of skilled labour force of +4,6% from 1994 to 2014 whilst the
proportion of the composition of low-skilled labour force shows a decreasing trend with a
percentage change point of -3,9.
The analysis of composition of skills level of youths and adults as well as the percentage
change points are shown in Table 7. Although the composition of skilled labour force of both
adults and youth indicate a positive shift, an increase in the compostion of skilled adults (5,8%)
is higher than that of youth (1,3%). With regards to composition of semi-skilled labour force,
there was a significant decreasing shift of -4.4% of adults while that of youth increased by 1,6%.
Data suggest that the level of composition of skilled adults surpasses that of youth. In South
Africa the role of the level of aggregate demand in employment outcomes is well appreciated
since there has been a speculation about the rate of GDP growth that would lead to a significant
reduction of unemployment. However, the nature of unemployment is considered to be
structural, with a feature of mismatch between the skills endowments of the labour force and the
nature of skills demanded by employers (Bhorat and Hodge 1999).
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43
The demographic dividend and good governance
Governance is defined by the United Nations as, “the process of decisions-making and the
process by which decisions are implemented or not implemented” (United Nations Economic
and Social Commission for Asia and the Pacific, 2009). Good governance ensures that
corruption is reduced and that the views of minority groups and vulnerable groups in society are
included in decision making. Decisions should also respond to the current and future needs of
the country. In addition to factors covered, for the demographic dividend to be realised,
adequate governance policies need to be in place. Part of the policy considerations is the
quality of government institutions, labour market regulation, macroeconomic management and
openness to trade and capital flows (Drummond, Thakoor, & Yu, 2014). These can advance
economic growth and boost the benefits of the dividend. Open economies assist in market and
investor confidence which may assist the country to realise the dividend.
6.8
Labour market policies in South Africa
South Africa’s labour market has undergone transformation since 1994 to redress inequalities
created by apartheid policies. This has led to the following legislations which support the labour
market transformation:
- Labour Relations Act
- Basic Conditions of Employment
- Employment Equality Act
- Skills Development Act
According to the NDP (2011), achieving full employment is integral to improve the living
standards of South Africans. The aim of the NDP is to reduce the unemployment rate to 6% by
2030. This will entail creating 11 million more jobs. To achieve this, the economy should grow
on average by 5,4% every year.
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44
Good governance and savings
Governance influences a country’s savings and investments, which is integral to reach the
potential of the demographic dividend. A country’s citizens will save if it is safe and profitable.
Government must promote savings by promoting stability of prices, low inflation and
transparency and efficiency in financial institutions (Bloom, et al., 2003). The headline
Consumer Price Index (CPI) annual inflation rate for South Africa in January 2015 was 4,4%. In
January 2014 it was 3,7%. This indicates an increase in inflation in South Africa which makes it
more difficult for South Africans to save. The South African government needs to look at
controlling inflation rates in order to promote savings. The NDP (2011) emphasises the
importance of reducing inequality in South Africa which is core to good governance.
6.8.2
Good governance and investor confidence
Good governance is imperative in securing investment in the country to achieve the
demographic dividend. An enabling environment for a demographic dividend requires good
governance. A country must implement comprehensive governance policies to optimise the
opportunity of the dividend. This will assist in drawing domestic and foreign investments in the
South African economy. Investments will lead to job creation and increased economic growth. If
this is not conducted, a country may not be able to take advantage of the dividend (Amjad,
2013). There may be high unemployment rates, especially for young people who are entering
the working age population. Established legal systems and rules of law (contract law and
financial standards) should be instituted to increase investor confidence (Eloundou-Enyegue,
2013). Reducing corruption and an efficiently operating government will also serve to increase
investor confidence. Corruption is an effect of weak governance (Gribble & Bremner, 2012).
6.8.3
Good governance and gender equality
An attribute of good governance is the promotion of gender equality. The demographic
transition is promoted by gender equality (MEPD, 2014). When women are able to access
family planning, they can make choices about fertility. This also influences the health of women.
If there is gender equality in society, women can enter the work force and contribute to a
family’s wellbeing (Royan & Sathar, 2013). Women with higher-paying jobs are able to improve
the lives of their children. Women can also be encouraged to save and invest if there are
policies that provide for access to credit and rights to inherit property and assets. Women who
own land are more likely to produce food for their families (FAO, 2011). Women are more likely
than men to use their income to improve the health and wellbeing of their families. If women’s
access to assets is improved, this will assist the country in achieving the demographic dividend.
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Gender equality is also demonstrated by the number of women in legislatures and management
positions. Violence against women contributes to deaths and disability and lost opportunities for
women (Gribble & Bremner, 2012). South Africa has implemented the following policies that
serve to protect women and empower them: Domestic Violence Act (1998), the Criminal Law
Act (Sexual Offences and Related Matters, 2007 and the Promotion of Equality and Prevention
of Unfair Discrimination Act, 2000.
6.8.4
Good governance and business
The World Bank/ IFC’s ‘Ease of Doing Business Rank’ gives the view of the ability of a country
to attract business. According to the 2015 data, South Africa rates 43 out of 189 economies.
This is a drop from 37 in the 2014 data (World Bank Group, 2014).
The World Economic Forum’s ‘Global Competitiveness Report’ considers factors that contribute
to the demographic dividend. These are infrastructure, health, primary and higher education and
training. Competitiveness is defined as the “set of institutions, policies and factors that
determine the level of productivity of a country”. According to the 2014 data, South Africa rates
56 out of 144 countries in competitiveness. This is a drop from 53 in the 2013 data (World
Economic Forum, 2014).
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Chapter 7: Conclusions and discussions
The assessment of age-sex data suggests that overall, data is reasonable. Further, analysis of
assessing age heaping reveals that data is reliable. However, there are variations when
assessing data on sub-populations. Gauteng and Western Cape provinces experienced a
worsening reporting of data from 1996 to 2011. The most probable explanation could be that
Gauteng and Western Cape are the major migrant receiving provinces of the country, also from
neighbouring countries. This might have affected the reporting of information from these
populations since some of the migrants are in the country illegally. The importance of age and
sex data cannot be underestimated as mentioned earlier. Training, particularly on the
questionnaire should be emphasised and be given sufficient time to lessen the errors observed
in the data.
In trying to analyse the population age structure, it was found that there was an indentation in
the population aged 5–14 and the increased proportion of children 0–4 compared to 2001 agesex population structure. The most likely reason for the declining proportion of children aged 5–
14 might be that it is the cohort that survived the increasing infant and child mortality between
1997 and 2006. As alluded in the monograph, many factors could have contributed to the
increase of the population aged 0–4. Amongst them are; survival rates brought about by the
decreasing infant and child mortality or the improvement in the reporting of children.
Furthermore, the analysis of the age-sex structure suggests that fertility is the main
phenomenon that affected the population structure. The change in the 2011 population structure
is brought about by the persistent decrease of fertility from the 1960’s. With regards to mortality,
the projected figure differs marginally to the reported one. Notwithstanding the sudden increase
in the mortality between the late 1990’s and the mid-2000’s, the country had seen an
improvement in life expectancy at birth over time, hence the population size with the variation of
mortality rates exceeds the one with constant mortality.
Migration appeared to have not altered the population structure. The fact we know is that there
are illegal migrants in the country. However census only captures migrants that are declaring
themselves. What we know is that illegal migration to South Africa is inevitable. Though
measures were put in place to attempt to collect information on migration from all non-citizens
irrespective of the legal status, we do not know if all the illegal migrants were reported.
The oustanding finding is that the country has fewer elderly and children relative to those of
working age. This is a similar profile of Asian countries which capitalise on the change of
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population structure to stimulate the economy. Reaping the benefits, from the working age
population will only be possible if sound policies on education, skills and health are put in place.
The results from labour and HIV/AIDS statistics figures indicate that the youth of the country are
still vulnerable in terms of unemployment and also HIV/AIDS is prevalent amongst them. If high
unemployment rates and HIV/AIDS prevalence are not managed, this window of opportunity
that South Africa has will become a perfect storm. The population between the ages of 15 and
29 will make up more than a quarter of the total population until 2030. The challenge is to
provide skills, educate, manage HIV/AIDS and put all the working age population to work. By
doing that, there is a real opportunity to build and improve a stronger economy resulting from
the demographic transition.
It is projected that in South Africa, the window of opportunity will close by 2030 as the elderly
population over 64 will be rising from 5% in 2014 to 7% in 2023 and to over 8% by 2027 ( NDP,
2011). UN suggests that this is when the country’s population in demographic terms is regarded
as old. The number of South Africans over 64 will rise from 2,2 million now to 4,4 million. The
demographic dividend has a finite window of opportunity and is not automatic, the timing of
policies to capitalise on the window of opportunity is critical.
South Africa presents an illustration of how demographic transition that is not accompanied by
sustained job orientated economic reforms can get countries to miss out on harnessing the full
demographic dividend. The country has high unemployment rates among the youth that has led
to increasing unrest. Failure to transform the economy and ensure creation of jobs for the
population can result in social and political instability when the youthful surplus labour force is
not economiccally engaged (Ministry of Finance, Planning and Economic Development, 2014).
Unless South Africa implements policies that address health challenges, appropriate skills for
labour market and sound corporate governance principles that are adhered to, then it will lose
out on the dividend even though its demographic transition is ideal and the population structure
is in a position to maximise such a benefit.
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48
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9 Appendix
Appendix 1. Distribution of local municipalities by age and sex
EC101:
Camdeboo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
2 626
2 493
2 473
2 621
2 316
1 992
1 613
1 483
1 550
1 348
1 160
953
794
557
393
244
131
87
24 835
2 611
2 518
2 452
2 482
2 266
1 884
1 665
1 650
1 590
1 521
1 342
1 112
938
738
559
394
236
199
26 158
5 238
5 011
4 925
5 103
4 582
3 876
3 279
3 133
3 141
2 869
2 502
2 065
1 732
1 296
953
638
366
286
50 993
EC103: Ikwezi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
593
535
503
423
406
375
343
336
284
315
303
199
150
134
71
43
27
15
5 055
Female
566
603
475
463
418
342
367
354
331
358
328
253
183
151
119
84
54
36
5 482
Total
1 159
1 138
978
886
824
716
709
690
614
673
631
452
333
285
190
127
81
51
10 537
EC102: Blue
Crane Route
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC104: Makana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
2 172
1 741
1 645
1 558
1 428
1 401
1 150
1 189
1 054
1 011
962
769
570
414
320
152
80
66
17 680
Male
3 676
3 211
3 046
3 710
4 730
3 764
2 901
2 485
2 424
2 126
1 855
1 453
944
680
542
305
186
136
38 175
Total
1 784
1 712
1 463
1 615
1 432
1 359
1 163
1 247
1 195
1 192
1 053
901
714
517
457
239
162
116
18 322
3 956
3 453
3 108
3 173
2 859
2 760
2 314
2 436
2 248
2 203
2 015
1 670
1 284
931
778
391
242
182
36 002
Female
3 504
3 239
2 934
3 981
5 229
3 733
2 653
2 838
2 835
2 631
2 304
1 783
1 398
979
913
568
312
382
42 215
Total
7 180
6 450
5 981
7 692
9 959
7 497
5 554
5 323
5 258
4 757
4 159
3 236
2 342
1 659
1 455
873
498
518
80 390
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
55
EC105: Ndlambe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
3 006
2 593
2 261
2 469
2 549
2 509
2 032
2 014
1 767
1 571
1 477
1 223
1 052
814
734
424
282
258
29 035
EC107: Baviaans
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
1 000
942
820
779
711
621
577
572
559
526
456
382
261
209
135
83
45
31
8 709
Female
2 796
2 538
2 243
2 464
2 408
2 571
2 266
2 218
2 269
1 998
1 906
1 526
1 362
1 039
1 003
588
453
492
32 141
Female
1 006
906
849
708
705
670
604
571
567
579
475
454
307
249
187
106
60
48
9 052
Total
5 803
5 131
4 505
4 933
4 958
5 080
4 298
4 232
4 035
3 569
3 383
2 749
2 413
1 853
1 737
1 012
735
750
61 176
Total
2 006
1 847
1 669
1 487
1 416
1 291
1 181
1 143
1 126
1 105
931
836
569
458
322
189
105
79
17 761
EC106: Sundays River
Valley
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC108: Kouga
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
2 885
2 366
2 084
2 394
2 958
3 001
2 346
2 234
1 892
1 505
1 272
940
680
457
357
166
117
107
27 761
2 797
2 473
1 973
2 166
2 386
2 471
1 995
2 021
1 895
1 640
1 406
1 115
771
536
471
275
166
183
26 743
5 682
4 839
4 057
4 560
5 344
5 473
4 341
4 254
3 787
3 145
2 679
2 055
1 451
993
829
441
283
290
54 504
Male
5 350
4 294
3 723
3 806
4 328
4 672
3 951
3 594
3 245
2 725
2 130
1 848
1 511
1 201
1 035
652
317
208
48 591
Female
5 016
4 284
3 735
3 839
4 337
4 612
3 731
3 493
3 202
2 966
2 412
2 030
1 899
1 482
1 160
802
497
470
49 967
Total
10 366
8 579
7 458
7 645
8 665
9 284
7 682
7 087
6 447
5 691
4 542
3 878
3 410
2 684
2 195
1 454
814
678
98 558
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
EC109: Kou-Kamma
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC121: Mbhashe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
56
Male
2 306
1 917
1 815
1 561
1 989
1 934
1 581
1 578
1 370
1 212
987
792
550
354
236
127
57
40
20 405
Male
15 894
15 844
17 913
18 129
10 318
6 634
4 724
3 907
3 066
3 259
3 495
3 200
3 194
2 527
2 438
1 277
864
544
117 230
Female
2 274
1 997
1 778
1 536
1 762
1 875
1 470
1 494
1 385
1 243
1 016
797
599
382
305
169
94
82
20 258
Female
15 489
15 494
16 194
17 247
11 145
7 667
6 325
6 396
6 015
6 252
6 363
5 105
4 970
3 597
3 768
2 523
1 878
1 250
137 679
Total
4 580
3 914
3 593
3 097
3 751
3 809
3 052
3 071
2 755
2 454
2 004
1 589
1 149
735
541
297
151
122
40 663
Total
31 383
31 338
34 107
35 376
21 463
14 301
11 049
10 304
9 081
9 512
9 858
8 306
8 164
6 125
6 206
3 800
2 742
1 794
254 909
EC122:
Mnquma
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC123: Great Kei
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
14 938
14 230
15 226
16 609
10 596
7 372
5 568
4 756
4 035
3 994
4 421
4 108
3 805
2 870
2 463
1 347
943
593
117 873
Male
2 202
1 898
1 675
2 002
1 803
1 423
1 186
1 008
1 026
885
912
757
563
449
493
212
124
86
18 703
14 627
13 948
13 641
15 327
10 822
7 434
5 900
6 174
6 604
6 804
7 224
6 400
5 195
4 123
4 030
2 837
1 868
1 560
134 517
Female
2 142
1 771
1 491
1 868
1 532
1 383
1 267
1 254
1 323
1 209
1 213
949
614
656
758
409
259
190
20 287
Total
29 566
28 178
28 867
31 936
21 417
14 806
11 467
10 930
10 639
10 798
11 644
10 508
9 000
6 993
6 493
4 184
2 811
2 153
252 390
Total
4 344
3 669
3 166
3 870
3 335
2 806
2 453
2 262
2 349
2 093
2 125
1 706
1 177
1 105
1 251
622
383
276
38 991
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
EC124: Amahlathi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC126: Ngqushwa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
57
Male
7 244
6 383
5 925
6 846
5 384
4 080
3 175
2 954
2 853
2 817
2 742
2 413
1 999
1 364
1 221
583
359
305
58 647
Male
4 074
3 727
3 289
3 809
2 775
2 122
1 751
1 726
1 514
1 581
1 637
1 565
1 310
1 041
924
510
314
316
33 984
Female
7 008
6 377
5 494
6 384
4 708
3 803
3 289
3 334
3 679
3 810
3 874
3 200
2 392
1 907
2 019
1 158
890
806
64 131
Female
4 106
3 563
2 898
3 334
2 478
2 074
1 819
1 992
2 118
2 274
2 263
2 028
1 782
1 499
1 525
1 136
709
609
38 206
Total
14 252
12 760
11 419
13 229
10 092
7 883
6 464
6 288
6 532
6 627
6 615
5 613
4 390
3 270
3 240
1 741
1 249
1 112
122 778
Total
8 180
7 290
6 187
7 142
5 253
4 195
3 570
3 719
3 631
3 855
3 900
3 593
3 092
2 540
2 449
1 646
1 024
924
72 190
EC127: Nkonkobe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC128: Nxuba
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
6 891
6 329
5 590
6 661
6 519
4 603
3 346
3 232
3 119
3 082
2 931
2 424
1 999
1 568
1 277
663
460
440
61 133
Male
1 333
1 224
1 112
1 215
958
899
728
732
621
635
545
527
350
307
240
121
72
59
11 677
Female
6 720
6 143
4 923
5 872
6 290
4 356
3 357
3 718
3 882
3 894
3 702
3 213
2 622
2 173
1 897
1 253
981
987
65 982
Total
13 611
12 472
10 513
12 534
12 809
8 959
6 703
6 950
7 001
6 976
6 633
5 637
4 621
3 741
3 174
1 915
1 441
1 427
127 115
Female
1 363
1 187
1 063
1 169
914
868
750
738
750
794
670
627
446
376
379
232
129
130
12 587
Total
2 695
2 411
2 175
2 385
1 872
1 767
1 478
1 470
1 371
1 429
1 216
1 154
796
683
619
353
201
189
24 264
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
EC131: Inxuba
Yethemba
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC133: Inkwanca
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
58
Male
Female
Total
3 502
3 040
2 932
3 049
2 973
2 693
2 091
2 134
1 952
1 758
1 619
1 330
1 012
630
436
253
147
118
31 671
3 484
3 217
2 929
2 916
2 690
2 776
2 235
2 200
2 214
1 978
1 890
1 582
1 278
894
675
378
265
287
33 889
Male
1 305
1 165
1 043
1 055
1 082
877
758
615
531
492
447
428
341
204
156
82
44
51
10 676
Female
1 304
1 027
963
1 029
1 049
896
689
687
614
654
591
520
388
229
312
136
109
99
11 295
Male
6 987
6 258
5 861
5 965
5 663
5 468
4 325
4 335
4 167
3 736
3 509
2 912
2 291
1 524
1 111
631
412
405
65 560
EC132:
Tsolwana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Total
2 609
2 192
2 006
2 084
2 131
1 773
1 448
1 302
1 144
1 145
1 038
948
729
433
468
218
152
150
21 971
EC134: Lukanji
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
10 208
10 081
9 190
10 041
9 129
7 644
5 704
5 212
4 536
4 229
3 917
3 286
2 586
1 714
1 479
732
495
436
90 619
1 959
1 838
1 827
1 820
1 442
1 129
837
805
683
681
584
550
474
352
318
166
98
93
15 656
Female
Total
1 924
1 898
1 581
1 738
1 435
1 145
936
949
930
862
907
896
713
506
489
357
187
172
17 625
3 884
3 735
3 408
3 558
2 877
2 273
1 773
1 755
1 613
1 544
1 491
1 446
1 187
857
807
523
284
265
33 281
Female
10 381
9 930
8 465
9 718
9 035
7 634
6 023
5 934
5 737
5 772
5 426
4 406
3 351
2 376
2 459
1 500
986
970
100 103
Total
20 589
20 011
17 656
19 759
18 164
15 278
11 726
11 147
10 272
10 000
9 343
7 693
5 938
4 089
3 938
2 233
1 481
1 406
190 723
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
EC135: Intsika
Yethu
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC137: Engcobo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
59
Male
8 971
9 129
9 017
9 438
5 579
4 049
2 825
2 584
2 126
2 310
2 555
2 468
2 386
1 848
1 632
886
611
384
68 797
Male
10 577
10 348
10 568
10 419
5 360
3 886
2 802
2 594
2 304
2 177
2 242
2 087
1 997
1 388
1 516
819
559
312
71 953
Female
Total
8 565
8 827
7 975
8 146
4 984
3 587
2 995
3 244
3 580
3 967
4 174
3 857
3 488
2 446
2 665
1 675
1 383
1 017
76 575
17 536
17 955
16 992
17 584
10 563
7 635
5 820
5 828
5 706
6 277
6 729
6 326
5 873
4 294
4 297
2 562
1 994
1 401
145 372
Female
10 190
10 011
9 754
9 499
6 338
4 815
3 879
4 152
3 936
3 867
3 935
3 278
2 646
2 091
2 150
1 516
866
636
83 560
Total
20 768
20 359
20 322
19 917
11 698
8 701
6 681
6 746
6 240
6 044
6 177
5 365
4 642
3 480
3 666
2 335
1 425
948
155 513
EC136:
Emalahleni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC138: Sakhisizwe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
7 242
7 580
6 806
7 722
4 923
3 314
2 523
2 362
1 890
1 867
2 213
1 993
1 860
1 370
1 418
707
426
403
56 620
Male
3 984
3 618
3 661
4 025
2 902
2 109
1 661
1 334
1 197
1 117
1 188
1 115
910
641
526
291
201
165
30 646
Female
Total
7 086
7 083
6 102
6 920
4 506
3 159
2 533
2 664
2 919
3 163
3 514
3 027
2 653
1 953
2 385
1 409
907
858
62 839
14 328
14 663
12 908
14 642
9 429
6 473
5 056
5 026
4 809
5 030
5 727
5 020
4 514
3 323
3 803
2 116
1 333
1 261
119 460
Female
3 980
3 544
3 444
3 592
2 629
1 998
1 638
1 627
1 595
1 685
1 704
1 331
1 215
772
833
601
402
344
32 936
Total
7 964
7 162
7 105
7 617
5 532
4 107
3 299
2 962
2 792
2 801
2 892
2 447
2 125
1 413
1 359
892
603
509
63 582
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
EC141: Elundini
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC143: Maletswai
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
60
Male
8 188
8 164
8 860
9 013
6 441
4 397
3 189
2 743
2 210
2 074
2 120
2 095
1 938
1 382
1 156
765
453
294
65 482
Male
2 531
2 406
2 158
2 043
1 990
1 823
1 577
1 346
1 124
867
769
644
544
353
250
152
71
87
20 735
Female
7 949
7 829
7 866
8 166
5 746
4 302
3 453
3 512
3 387
3 508
3 587
3 189
2 804
2 032
1 911
1 666
1 044
706
72 658
Female
2 644
2 388
2 064
2 100
2 187
2 137
1 632
1 513
1 236
1 118
1 055
810
673
442
386
262
192
228
23 065
Total
16 137
15 993
16 726
17 180
12 187
8 699
6 641
6 256
5 597
5 582
5 707
5 285
4 742
3 414
3 067
2 431
1 497
1 000
138 141
EC142: Senqu
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Total
5 176
4 793
4 222
4 142
4 177
3 959
3 209
2 858
2 360
1 985
1 825
1 454
1 217
794
636
413
263
315
43 800
EC144: Gariep
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
8 030
7 533
7 554
8 269
6 223
4 264
3 570
3 074
2 520
2 235
2 116
1 967
1 863
1 271
1 051
707
324
236
62 804
Male
1 904
1 823
1 571
1 577
1 581
1 370
1 159
1 058
835
831
736
673
439
345
244
132
69
73
16 420
Female
7 783
7 589
7 088
7 732
6 484
5 024
4 061
3 660
3 163
3 241
3 205
2 971
2 604
1 962
1 719
1 548
869
642
71 346
Female
1 939
1 835
1 596
1 508
1 373
1 321
1 130
1 101
946
994
805
766
610
361
397
223
192
160
17 256
Total
15 812
15 123
14 642
16 001
12 707
9 288
7 631
6 734
5 683
5 476
5 321
4 937
4 467
3 233
2 770
2 255
1 192
878
134 150
Total
3 843
3 658
3 168
3 084
2 954
2 691
2 289
2 159
1 781
1 826
1 541
1 439
1 049
706
641
355
261
233
33 677
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
EC153: Ngquza
Hill
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC155:
Nyandeni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
61
Male
20 642
19 373
19 720
19 286
11 954
8 277
6 115
4 705
3 272
3 153
3 140
2 498
2 122
1 429
1 461
813
646
368
128 974
Male
19 945
19 426
20 370
20 713
13 174
8 397
5 884
4 785
3 820
3 491
3 593
2 937
2 523
1 640
1 624
885
639
394
134 241
Female
Total
20 192
19 511
18 590
19 058
12 299
9 698
8 012
6 881
6 348
5 701
5 312
3 956
3 576
2 801
2 772
2 140
1 731
929
149 507
40 833
38 884
38 311
38 344
24 253
17 975
14 127
11 586
9 620
8 855
8 451
6 454
5 698
4 230
4 233
2 953
2 377
1 297
278 481
Female
19 655
19 387
19 127
20 188
13 680
10 314
8 293
7 665
6 619
6 418
5 792
4 651
3 845
2 582
3 118
2 284
1 580
950
156 149
Total
39 600
38 813
39 496
40 901
26 854
18 711
14 177
12 450
10 440
9 909
9 386
7 589
6 368
4 222
4 742
3 169
2 219
1 344
290 390
EC154: Port St
Johns
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC156:
Mhlontlo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
11 407
11 124
11 184
11 358
6 433
4 026
2 815
2 301
1 760
1 649
1 794
1 493
1 365
838
876
505
344
210
71 482
Male
Female
11 265
11 025
10 384
11 240
7 060
5 046
4 172
3 811
3 431
3 353
3 141
2 400
2 309
1 501
1 807
1 200
967
546
84 654
Female
12 605
12 055
12 303
13 209
8 518
5 109
3 636
3 155
2 627
2 626
2 556
2 392
2 094
1 597
1 278
799
570
312
87 440
12 265
11 559
11 327
12 169
8 357
5 836
4 749
4 615
4 466
4 622
4 495
3 962
3 359
2 432
2 323
1 986
1 290
975
100 786
Total
22 672
22 149
21 568
22 597
13 493
9 071
6 986
6 112
5 190
5 003
4 935
3 893
3 674
2 338
2 682
1 706
1 311
755
156 136
Total
24 870
23 614
23 630
25 378
16 875
10 945
8 385
7 771
7 093
7 248
7 051
6 353
5 453
4 028
3 601
2 785
1 860
1 288
188 226
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
EC157:
King
0
- 4 Sabata
Dalindyebo
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EC442:
Umzimvubu
0
-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
62
Male
27 168
25 770
27 109
30 240
22 445
16 315
11 320
9 248
7 621
6 789
6 085
5 226
4 328
2 703
2 485
1 410
1 018
671
207 951
Male
12 854
12 135
12 212
12 507
8 523
5 659
4 329
3 579
2 823
2 516
2 625
2 201
2 040
1 500
1 065
679
441
259
87 946
Female
26 792
25 443
25 745
31 038
25 476
19 486
14 662
13 698
12 011
10 603
9 817
7 941
6 391
3 837
4 446
2 778
1 981
1 614
243 760
Female
12 311
12 149
11 724
12 078
8 852
6 714
5 383
5 042
4 450
4 439
4 209
3 966
3 425
2 646
2 192
1 939
1 298
859
103 674
Total
53 961
51 213
52 854
61 279
47 921
35 801
25 982
22 946
19 632
17 392
15 902
13 167
10 720
6 540
6 931
4 188
2 999
2 284
451 710
EC441:
Matatiele
0
-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Total
25 164
24 284
23 935
24 585
17 375
12 373
9 712
8 621
7 273
6 955
6 834
6 167
5 465
4 145
3 256
2 618
1 739
1 118
191 620
EC443:
Mbizana
0
-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
13 018
13 104
13 631
13 190
8 639
5 697
4 431
3 730
2 945
2 992
2 932
2 616
2 346
1 678
1 164
795
450
317
93 675
Female
12 881
12 720
12 970
12 915
8 968
6 898
5 699
5 387
4 656
4 705
4 546
4 327
3 756
2 941
2 320
2 117
1 335
1 027
110 168
Male
21 565
20 702
21 124
19 605
11 174
7 448
4 827
3 921
3 093
2 882
2 990
2 412
2 210
1 323
1 309
815
617
315
128 332
Female
21 224
19 786
20 110
19 685
12 909
9 796
7 400
7 068
6 483
5 883
5 510
3 786
3 650
2 746
2 728
2 055
1 797
958
153 573
Total
25 898
25 823
26 601
26 105
17 608
12 595
10 130
9 117
7 601
7 697
7 478
6 943
6 102
4 619
3 484
2 912
1 786
1 344
203 843
Total
42 789
40 489
41 234
39 290
24 082
17 244
12 226
10 989
9 576
8 765
8 500
6 198
5 860
4 069
4 038
2 870
2 413
1 273
281 905
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
EC444:
Ntabankulu
0
-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NMA: Nelson
Mandela
Bay
0
-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
63
Male
8 984
8 424
8 545
8 213
5 514
3 557
2 433
1 887
1 400
1 400
1 404
1 198
1 135
789
674
425
359
194
56 534
Male
55 884
49 558
43 692
49 795
56 968
52 720
42 877
39 716
35 817
31 697
28 190
22 771
16 717
10 714
7 448
4 499
2 328
1 603
552 994
Female
8 905
8 508
8 120
8 401
5 695
4 156
3 148
2 927
2 692
2 806
2 528
2 215
1 951
1 396
1 336
1 172
882
604
67 442
Female
54 522
48 163
42 451
51 016
58 483
53 883
44 527
42 369
40 692
37 716
34 542
27 610
21 106
14 642
11 345
7 661
4 668
3 724
599 121
Total
17 889
16 931
16 664
16 614
11 208
7 713
5 582
4 814
4 092
4 206
3 933
3 413
3 086
2 184
2 010
1 597
1 241
798
123 976
Total
110 406
97 721
86 143
100 811
115 451
106 603
87 405
82 085
76 509
69 414
62 732
50 381
37 822
25 357
18 793
12 160
6 997
5 326
1 152 115
BUF: Buffalo
City
0
-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
39 292
33 365
28 646
33 356
37 658
34 235
28 329
24 844
21 990
19 442
17 100
14 011
9 850
6 241
5 026
2 539
1 502
1 131
358 557
Female
38 211
32 323
27 478
34 674
38 913
36 190
30 052
27 670
26 141
24 521
22 369
17 519
11 836
9 585
8 160
5 089
3 262
2 650
396 644
Total
77 503
65 688
56 124
68 031
76 571
70 425
58 381
52 514
48 131
43 962
39 469
31 530
21 685
15 825
13 186
7 628
4 764
3 781
755 200
FS161:
Letsemeng
0
-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
2 088
1 876
1 850
1 828
1 963
1 911
1 692
1 409
1 145
1 027
915
674
568
354
237
155
80
78
19 852
Female
2 114
1 808
1 741
1 791
1 648
1 388
1 330
1 258
1 186
1 059
927
756
573
378
335
233
126
125
18 777
Total
4 202
3 684
3 592
3 618
3 612
3 298
3 022
2 668
2 332
2 086
1 843
1 430
1 141
732
572
388
206
203
38 628
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
FS162:
Kopanong
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
FS164: Naledi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
64
Male
2 654
2 513
2 138
2 208
2 323
2 255
1 779
1 509
1 451
1 209
1 039
934
746
509
372
228
124
93
24 083
Male
1 469
1 347
1 265
1 266
1 138
864
794
597
602
444
368
380
300
210
170
108
39
48
11 409
Female
Total
2 643
2 529
2 130
2 181
2 211
2 018
1 641
1 609
1 528
1 386
1 167
1 072
967
656
549
354
221
224
25 087
5 298
5 043
4 268
4 389
4 534
4 272
3 420
3 118
2 979
2 595
2 206
2 006
1 712
1 165
921
582
345
318
49 171
Female
1 390
1 300
1 138
1 271
1 170
1 014
887
835
675
587
564
545
459
328
299
225
110
109
12 905
Total
2 858
2 647
2 403
2 537
2 308
1 878
1 681
1 431
1 277
1 031
932
925
759
538
470
332
150
157
24 314
FS163:
Mohokare
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
FS181:
Masilonyana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
2 000
1 857
1 677
1 613
1 722
1 429
1 138
942
832
720
596
543
435
296
277
117
78
42
16 314
3 445
3 158
2 928
2 846
3 121
2 782
2 483
2 091
2 165
1 995
1 687
1 135
753
516
379
239
139
99
31 961
Female
1 926
1 954
1 576
1 635
1 645
1 538
1 146
1 133
971
885
755
703
581
374
402
307
167
135
17 831
Female
3 461
3 160
2 715
2 981
2 883
2 528
2 137
2 062
1 803
1 650
1 407
1 284
985
729
634
447
253
256
31 374
Total
3 926
3 811
3 253
3 248
3 367
2 967
2 284
2 075
1 803
1 605
1 351
1 245
1 016
670
679
424
245
177
34 146
Total
6 906
6 318
5 643
5 828
6 004
5 310
4 619
4 153
3 968
3 644
3 094
2 419
1 738
1 244
1 012
686
392
355
63 334
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
FS182:
Tokologo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
FS184:
Matjhabeng
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
65
Male
1 659
1 573
1 423
1 311
1 354
1 181
1 134
1 023
842
716
630
515
360
306
153
128
63
40
14 410
Male
21 337
17 533
16 908
18 493
22 087
20 131
15 917
12 611
11 792
13 119
11 565
7 790
4 883
3 052
2 055
1 234
606
395
201 509
Female
1 555
1 410
1 423
1 485
1 275
1 101
1 019
1 015
785
746
658
613
473
329
218
216
124
130
14 576
Female
21 002
17 552
16 565
18 629
20 564
18 456
14 876
13 775
13 648
13 162
10 945
8 310
5 871
4 086
3 194
2 282
1 111
924
204 952
Total
3 214
2 983
2 846
2 796
2 629
2 283
2 153
2 037
1 628
1 463
1 287
1 128
833
635
371
344
186
170
28 986
Total
42 339
35 085
33 473
37 122
42 651
38 586
30 793
26 386
25 440
26 281
22 511
16 100
10 755
7 138
5 249
3 516
1 717
1 319
406 461
FS183:
Tswelopele
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
FS185:
Nala
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
2 894
2 630
2 516
2 124
2 459
2 052
1 531
1 254
1 157
1 046
873
794
539
413
233
174
107
68
22 864
Male
4 958
4 490
3 805
3 721
3 847
3 384
2 709
2 260
1 899
1 747
1 661
1 347
1 226
800
508
280
128
99
38 867
2 958
2 621
2 456
2 246
2 327
2 154
1 626
1 429
1 379
1 228
1 050
961
731
513
415
346
175
147
24 761
Female
5 127
4 279
3 876
3 787
3 944
3 577
2 885
2 533
2 277
2 089
1 898
1 834
1 372
1 053
724
543
309
245
42 353
Total
5 851
5 251
4 973
4 370
4 786
4 206
3 157
2 682
2 536
2 274
1 923
1 755
1 270
926
648
520
283
215
47 625
Total
10 085
8 768
7 681
7 509
7 791
6 961
5 594
4 793
4 176
3 836
3 560
3 181
2 597
1 853
1 232
823
437
344
81 220
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
FS191:
Setsoto
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
FS193:
Nketoana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
66
Male
Female
Total
6 591
6 159
5 431
5 675
5 259
4 700
3 783
3 203
2 573
2 227
1 949
1 692
1 276
858
549
355
193
159
52 633
6 559
6 145
5 256
5 651
5 455
5 057
4 288
3 775
3 344
3 088
2 569
2 401
1 922
1 437
1 176
885
506
451
59 964
13 150
12 305
10 688
11 325
10 714
9 758
8 071
6 978
5 917
5 316
4 517
4 093
3 199
2 294
1 726
1 240
699
609
112 597
Male
Female
Total
3 527
3 230
2 875
2 973
2 950
2 519
2 028
1 696
1 299
1 356
1 108
985
819
513
321
185
131
96
28 611
3 591
3 328
2 931
2 910
2 921
2 654
2 173
1 851
1 647
1 643
1 441
1 341
1 015
749
563
411
302
242
31 713
7 118
6 558
5 806
5 883
5 871
5 173
4 201
3 547
2 946
2 999
2 549
2 326
1 834
1 262
884
596
433
338
60 324
FS192:
Dihlabeng
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
FS194:
Maluti a
Phofung
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
6 752
6 236
6 019
5 549
6 031
6 027
5 054
4 212
3 610
2 984
2 608
2 023
1 546
1 032
646
397
255
171
61 153
7 034
6 160
5 660
5 685
6 049
6 135
5 364
4 692
4 171
3 897
3 181
2 741
2 293
1 461
1 154
883
535
455
67 551
Female
19 604
18 480
17 256
19 676
16 468
12 389
9 446
7 958
6 848
6 302
5 198
4 314
3 457
2 313
1 552
978
517
453
153 209
19 386
18 365
16 605
19 274
17 915
15 420
12 157
10 694
10 336
9 613
8 243
7 190
5 400
3 850
2 995
2 357
1 524
1 252
182 575
Total
13 786
12 396
11 679
11 235
12 080
12 162
10 418
8 904
7 781
6 881
5 789
4 764
3 839
2 493
1 800
1 280
790
627
128 704
Total
38 991
36 845
33 861
38 950
34 383
27 809
21 603
18 652
17 184
15 914
13 441
11 504
8 856
6 163
4 547
3 335
2 041
1 705
335 784
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
FS195:
Phumelela
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
FS201:
Moqhaka
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
67
Male
2 616
2 545
2 389
2 263
2 340
1 971
1 741
1 408
1 240
1 063
969
855
643
414
323
201
97
86
23 162
Male
8 135
7 076
6 627
7 360
8 209
7 231
6 220
5 412
5 118
4 778
4 077
3 077
2 341
1 548
1 025
644
340
258
79 477
Female
2 596
2 627
2 363
2 284
2 306
1 991
1 584
1 460
1 323
1 314
1 219
1 005
786
563
476
332
216
164
24 611
Female
8 188
6 945
6 390
7 086
7 165
6 615
5 796
5 333
4 767
4 777
4 325
3 785
3 197
2 161
1 697
1 320
814
693
81 055
Total
5 212
5 172
4 752
4 548
4 645
3 962
3 324
2 868
2 563
2 377
2 188
1 861
1 429
977
799
533
313
250
47 772
Total
16 324
14 021
13 018
14 445
15 374
13 846
12 016
10 744
9 885
9 555
8 403
6 861
5 538
3 709
2 722
1 964
1 155
951
160 532
FS196:
Mantsopa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
FS203:
Ngwathe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
3 049
2 732
2 425
2 576
2 407
2 289
1 799
1 450
1 324
1 095
895
824
580
400
282
130
84
62
24 402
Male
Female
2 946
2 638
2 427
2 540
2 434
2 250
1 851
1 673
1 470
1 372
1 242
1 130
862
557
494
346
218
203
26 654
Female
6 804
5 991
5 364
5 357
5 632
5 150
4 192
3 439
3 100
2 656
2 359
2 244
1 767
1 324
935
564
314
231
57 424
6 700
6 018
5 405
5 391
5 506
5 091
4 280
3 948
3 560
3 498
2 942
2 857
2 272
1 938
1 419
1 057
631
583
63 096
Total
5 994
5 370
4 852
5 116
4 841
4 540
3 650
3 123
2 794
2 467
2 137
1 954
1 443
957
776
476
302
265
51 056
Total
13 504
12 009
10 769
10 749
11 138
10 242
8 472
7 386
6 660
6 153
5 301
5 101
4 039
3 263
2 354
1 621
944
814
120 520
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
FS204:
Metsimaholo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
MAN:
Mangaung
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
68
Male
7 828
6 255
5 843
6 375
8 534
9 038
7 707
6 243
5 028
4 056
3 319
2 666
1 968
1 234
752
392
222
177
77 636
Male
37 636
32 930
30 565
35 116
43 249
37 755
30 767
25 590
22 012
17 637
14 806
11 280
8 506
5 745
3 917
2 325
1 253
1 098
362 186
Female
7 573
6 186
5 552
6 156
7 106
7 075
6 105
5 171
4 658
4 126
3 360
2 670
2 001
1 418
1 024
653
355
281
71 472
Female
37 712
32 716
29 262
35 840
41 397
35 550
28 594
26 952
24 835
21 874
18 265
14 969
11 929
8 445
6 611
4 798
2 828
2 668
385 245
Total
15 401
12 441
11 395
12 532
15 639
16 113
13 812
11 414
9 686
8 182
6 679
5 337
3 969
2 652
1 776
1 045
578
458
149 108
Total
75 348
65 646
59 827
70 956
84 646
73 305
59 360
52 542
46 847
39 511
33 071
26 249
20 435
14 190
10 528
7 123
4 080
3 766
747 431
FS205:
Mafube
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
GT421:
Emfuleni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
3 174
3 080
2 919
2 749
2 822
2 504
2 102
1 642
1 383
1 265
1 129
957
690
548
353
227
159
103
27 805
Male
Female
3 214
3 088
2 803
2 993
2 643
2 494
1 987
1 837
1 594
1 556
1 419
1 227
942
692
665
403
300
213
30 071
Female
35 524
29 017
27 881
31 327
40 016
36 578
31 135
26 682
22 322
19 091
17 105
13 825
9 935
6 237
4 024
2 241
1 099
825
354 862
35 700
29 434
26 870
32 556
39 200
34 253
29 446
25 938
23 169
22 167
19 433
15 904
11 703
7 647
5 739
3 755
2 165
1 722
366 800
Total
6 388
6 168
5 722
5 742
5 465
4 998
4 089
3 478
2 977
2 820
2 548
2 184
1 632
1 240
1 018
630
459
316
57 876
Total
71 223
58 451
54 751
63 883
79 217
70 831
60 580
52 620
45 491
41 258
36 538
29 729
21 638
13 883
9 763
5 997
3 264
2 546
721 663
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
GT422:
Midvaal
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
GT481:
Mogale City
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
69
Male
4 176
3 555
3 454
3 633
4 693
5 235
4 406
4 155
3 529
2 960
2 676
2 176
1 744
1 219
794
441
208
124
49 178
Male
17 052
13 539
12 626
13 682
21 915
21 665
18 374
15 574
12 437
10 491
8 777
6 878
4 830
2 961
1 995
1 111
648
425
184 981
Female
4 203
3 466
3 250
3 983
4 198
4 254
3 643
3 549
3 267
2 953
2 418
2 067
1 643
1 277
871
510
288
283
46 123
Female
16 858
13 656
12 282
13 787
17 702
19 109
15 931
13 778
12 334
11 121
9 262
7 271
5 000
3 339
2 511
1 574
1 086
841
177 441
Total
8 379
7 021
6 704
7 616
8 892
9 490
8 048
7 704
6 795
5 913
5 094
4 243
3 387
2 496
1 666
950
495
407
95 301
Total
33 910
27 195
24 908
27 469
39 617
40 774
34 305
29 352
24 771
21 612
18 038
14 150
9 830
6 300
4 507
2 685
1 734
1 266
362 422
GT423: Lesedi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
GT482:
Randfontein
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
5 019
4 245
3 904
4 173
5 247
5 817
4 740
4 010
3 325
2 854
2 278
1 929
1 449
951
688
348
200
142
51 317
Male
Female
4 943
4 048
3 723
4 105
4 601
4 617
3 824
3 447
3 142
2 775
2 433
1 990
1 501
1 101
864
566
304
221
48 203
Female
7 010
5 877
5 662
5 778
7 591
7 838
6 946
5 854
5 006
4 708
4 238
3 218
2 131
1 303
840
481
247
158
74 885
6 961
5 914
5 680
5 823
7 179
7 121
6 013
5 612
5 322
4 881
4 260
3 189
2 140
1 593
1 122
780
468
342
74 400
Total
9 962
8 293
7 626
8 278
9 848
10 434
8 563
7 457
6 467
5 628
4 711
3 919
2 949
2 052
1 552
914
504
363
99 520
Total
13 971
11 791
11 342
11 600
14 770
14 959
12 959
11 466
10 328
9 589
8 498
6 408
4 271
2 896
1 962
1 261
715
499
149 286
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
GT483:
Westonaria
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
EKU:
Ekurhuleni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
70
Male
5 619
4 125
3 840
4 122
6 569
7 778
6 365
5 270
4 752
4 906
3 838
2 080
843
473
296
155
63
58
61 152
Male
159 573
120 401
109 665
117 978
178 087
206 472
177 151
143 250
111 303
88 126
70 704
54 877
37 476
22 858
14 721
7 912
4 236
2 933
1 627 724
Female
5 669
4 215
3 933
4 232
5 768
6 123
4 856
4 007
3 341
2 826
2 090
1 290
873
550
369
227
145
103
50 615
Female
157 404
118 902
106 519
120 727
168 641
180 575
144 082
121 052
100 649
88 112
72 718
56 938
40 537
27 222
19 702
12 802
8 115
6 052
1 550 747
Total
11 288
8 339
7 773
8 354
12 337
13 901
11 220
9 276
8 093
7 732
5 927
3 370
1 716
1 022
665
382
208
161
111 767
Total
316 977
239 303
216 183
238 705
346 727
387 047
321 232
264 302
211 951
176 239
143 422
111 814
78 014
50 080
34 423
20 715
12 352
8 985
3 178 470
GT484:
Merafong
City
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
JHB: City of
Johannesburg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
9 613
7 448
6 999
7 254
10 328
11 872
10 350
9 323
9 068
8 845
7 220
4 053
1 950
1 156
827
414
269
168
107 157
9 507
7 303
6 749
7 737
9 632
9 920
8 111
6 867
6 017
5 606
4 130
2 947
2 048
1 336
1 061
672
419
301
90 363
Male
Female
218 590
160 226
139 589
154 707
252 752
296 584
251 469
200 038
150 576
114 646
91 062
70 634
49 644
30 348
20 271
11 638
6 959
5 405
2 225 137
214 133
158 239
138 035
160 409
249 660
276 223
220 565
177 828
143 170
121 831
101 871
80 898
58 040
37 559
28 527
19 411
12 630
10 662
2 209 690
Total
19 119
14 751
13 748
14 991
19 960
21 792
18 461
16 190
15 086
14 451
11 349
6 999
3 999
2 491
1 888
1 087
688
469
197 520
Total
432 722
318 465
277 624
315 116
502 412
572 807
472 034
377 866
293 746
236 477
192 933
151 531
107 684
67 906
48 797
31 049
19 589
16 068
4 434 827
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
TSH:
City of
Tshwane
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
71
Male
Female
137 848
106 232
97 308
112 418
170 994
174 314
145 298
121 415
98 807
78 813
65 524
51 149
35 611
23 244
15 799
9 331
5 513
3 866
1 453 483
KZN214:
UMuziwabantu
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
7 159
6 456
6 367
6 326
4 126
3 067
2 117
1 844
1 446
1 415
1 134
970
931
496
412
207
162
120
44 754
136 018
105 659
94 045
116 160
169 851
164 499
132 071
114 373
98 319
85 378
70 816
55 090
40 576
29 258
21 887
14 984
10 398
8 624
1 468 005
Female
6 979
6 107
5 995
6 267
4 506
3 816
2 769
2 524
2 332
2 369
1 838
1 444
1 394
1 004
998
709
489
262
51 802
Total
273 866
211 891
191 352
228 577
340 844
338 813
277 369
235 789
197 126
164 191
136 340
106 239
76 187
52 501
37 686
24 315
15 912
12 490
2 921 488
Total
14 139
12 563
12 362
12 593
8 632
6 882
4 885
4 368
3 779
3 784
2 971
2 413
2 325
1 499
1 410
915
651
382
96 556
KZN213:
Umzumbe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN215:
Ezingoleni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
10 836
9 801
9 729
10 238
7 344
5 241
3 702
3 107
2 535
2 544
2 256
2 153
2 103
1 181
985
452
335
277
74 819
Male
10 600
9 578
9 191
10 007
7 427
5 979
4 274
3 861
3 872
4 270
3 660
2 988
3 038
2 194
1 855
1 390
1 253
719
86 156
Female
3 613
3 261
3 121
3 372
2 326
1 753
1 308
1 076
774
784
688
624
564
305
269
133
77
55
24 101
3 718
3 106
2 960
3 255
2 469
2 089
1 671
1 463
1 195
1 339
1 187
966
885
670
629
357
287
192
28 439
Total
21 435
19 379
18 919
20 245
14 771
11 219
7 976
6 968
6 407
6 814
5 916
5 141
5 141
3 375
2 841
1 842
1 588
996
160 975
Total
7 331
6 367
6 081
6 626
4 795
3 842
2 979
2 538
1 969
2 123
1 875
1 590
1 448
975
898
490
364
247
52 540
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN216:
Hibiscus
Coast
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN212:
Umdoni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
72
Male
13 641
11 359
11 565
12 949
12 746
12 713
8 805
7 641
5 809
5 075
4 329
3 942
3 463
2 449
2 140
1 294
753
458
121 131
Male
4 002
3 311
3 336
3 749
4 128
3 994
2 906
2 538
1 941
1 938
1 492
1 328
1 176
921
724
393
271
146
38 294
Female
Total
13 373
11 138
11 183
13 403
13 177
13 262
9 910
8 406
7 061
6 947
6 025
5 017
4 665
3 669
3 144
2 127
1 451
1 047
135 004
27 014
22 497
22 748
26 352
25 923
25 975
18 715
16 047
12 870
12 022
10 353
8 959
8 127
6 118
5 285
3 421
2 204
1 505
256 135
Female
Total
3 824
3 348
3 317
3 818
4 089
3 737
2 943
2 593
2 217
2 213
1 833
1 568
1 457
1 314
970
638
431
271
40 581
7 825
6 659
6 653
7 566
8 217
7 732
5 849
5 131
4 157
4 151
3 325
2 896
2 633
2 235
1 694
1 031
702
417
78 875
KZN211:
Vulamehlo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN221:
uMshwathi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
5 224
4 578
4 751
4 753
3 535
2 496
1 744
1 645
1 237
1 344
1 265
1 004
1 032
582
411
203
154
104
36 062
Male
6 424
5 582
5 769
6 101
5 360
4 508
3 444
2 971
2 160
2 031
1 714
1 487
1 243
689
475
223
163
139
50 484
5 109
4 508
4 361
4 751
3 728
2 915
2 197
1 948
1 832
1 982
1 868
1 432
1 349
1 037
847
608
523
346
41 341
Female
6 160
5 464
5 482
6 150
5 478
4 641
3 582
3 218
2 657
2 758
2 478
2 033
1 955
1 192
983
703
518
438
55 890
Total
10 333
9 087
9 112
9 504
7 263
5 411
3 941
3 593
3 070
3 326
3 133
2 436
2 380
1 619
1 258
811
677
450
77 403
Total
12 584
11 047
11 251
12 251
10 838
9 149
7 026
6 189
4 817
4 789
4 192
3 520
3 198
1 880
1 458
926
681
577
106 374
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
73
KZN222:
uMngeni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
KZN224:
Impendle
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
4 145
3 670
3 697
4 579
4 697
4 726
3 756
3 494
2 687
1 989
1 635
1 464
1 247
951
816
599
411
286
44 849
2 243
2 132
2 075
2 060
1 501
1 051
708
628
514
502
453
525
446
241
182
95
75
59
15 493
Female
3 893
3 586
3 576
4 205
4 511
4 619
3 821
3 566
2 978
2 616
2 113
1 944
1 818
1 279
1 203
885
677
570
47 861
Female
2 163
2 000
1 865
1 990
1 524
1 191
872
724
727
774
807
803
682
453
365
301
227
145
17 612
Total
8 038
7 256
7 273
8 785
9 208
9 345
7 578
7 061
5 665
4 606
3 747
3 409
3 065
2 230
2 018
1 484
1 088
856
92 710
Total
4 406
4 132
3 940
4 050
3 025
2 241
1 581
1 352
1 242
1 276
1 259
1 328
1 128
695
547
396
302
204
33 105
KZN223:
Mpofana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
KZN225:
The
Msunduzi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
2 099
1 853
1 878
1 946
2 061
2 007
1 530
1 224
911
769
599
544
480
236
150
95
63
41
18 487
29 827
26 229
27 002
30 645
35 701
31 887
24 548
21 998
16 464
12 816
10 563
8 967
7 142
4 317
2 907
1 693
1 031
718
294 454
Female
2 073
1 821
1 768
1 771
2 008
2 013
1 545
1 311
1 183
969
819
706
627
326
243
154
144
135
19 617
Female
29 351
25 923
26 281
31 392
35 977
32 610
25 106
23 048
19 012
17 401
14 234
12 871
10 556
6 948
5 302
3 616
2 563
1 892
324 082
Total
4 172
3 675
3 646
3 717
4 069
4 020
3 075
2 535
2 094
1 738
1 418
1 249
1 107
562
393
249
206
176
38 103
Total
59 178
52 152
53 283
62 036
71 678
64 496
49 655
45 046
35 476
30 218
24 796
21 838
17 698
11 264
8 209
5 308
3 594
2 611
618 536
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN226:
Mkhambathini
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN232:
Emnambithi/Lady
smith
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
74
Male
Female
3 677
3 208
3 174
3 441
3 423
3 011
2 186
1 805
1 366
1 237
955
961
773
459
255
152
119
67
30 270
3 720
3 143
3 085
3 306
3 395
3 108
2 306
1 969
1 656
1 609
1 331
1 194
1 042
625
528
333
305
216
32 872
Male
Female
14 406
12 602
12 720
12 305
11 267
10 907
8 240
6 924
5 251
4 397
3 720
3 182
2 285
1 272
1 022
509
349
259
111 617
14 504
12 740
12 071
12 452
12 291
11 695
9 124
7 474
6 339
6 105
5 489
4 619
3 724
2 416
1 942
1 257
860
719
125 820
Total
7 397
6 351
6 259
6 747
6 818
6 119
4 492
3 774
3 022
2 846
2 286
2 155
1 815
1 084
783
486
424
284
63 142
Total
28 910
25 342
24 791
24 757
23 557
22 602
17 364
14 398
11 590
10 502
9 210
7 801
6 008
3 688
2 964
1 766
1 210
978
237 437
KZN227:
Richmond
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN233:
Indaka
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
4 138
3 718
3 515
3 369
3 421
3 112
2 424
2 033
1 541
1 170
938
832
694
366
285
134
91
101
31 883
Male
8 366
7 359
6 972
6 748
3 904
2 591
1 918
1 662
1 191
1 299
1 081
980
972
518
452
220
124
154
46 509
3 888
3 570
3 233
3 387
3 340
2 936
2 306
2 124
1 759
1 699
1 268
1 293
964
622
519
383
373
248
33 910
Female
7 943
7 106
6 652
6 883
5 146
4 010
2 794
2 247
2 025
2 254
1 963
1 766
1 777
1 189
1 053
776
575
448
56 607
Total
8 026
7 288
6 748
6 756
6 760
6 047
4 730
4 157
3 300
2 869
2 206
2 125
1 658
989
804
517
464
349
65 793
Total
16 308
14 464
13 623
13 631
9 049
6 602
4 712
3 909
3 216
3 553
3 044
2 747
2 750
1 707
1 505
996
700
602
103 116
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN234:
Umtshezi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN236:
Imbabazane
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
75
Male
5 109
4 626
4 519
4 218
4 053
3 563
2 600
2 222
1 829
1 595
1 210
1 057
876
466
337
150
102
87
38 615
Male
7 900
6 861
6 724
6 867
5 296
4 286
2 906
2 511
1 880
1 823
1 592
1 449
1 198
621
443
202
139
120
52 817
Female
5 014
4 623
4 678
4 467
4 142
4 072
3 061
2 829
2 411
2 331
1 662
1 504
1 407
801
614
359
313
251
44 538
Female
7 600
6 740
6 551
6 958
5 665
5 018
3 629
2 897
2 537
2 572
2 291
2 063
2 045
1 139
978
616
533
425
60 256
Total
10 122
9 249
9 197
8 684
8 194
7 635
5 661
5 050
4 240
3 925
2 872
2 561
2 282
1 267
950
510
415
338
83 153
Total
15 500
13 600
13 276
13 825
10 961
9 305
6 535
5 408
4 417
4 395
3 883
3 512
3 243
1 759
1 420
818
672
545
113 073
KZN235:
Okhahlamba
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN271:
Umhlabuyalin
gana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
9 399
8 447
8 488
8 110
5 404
4 381
3 342
2 878
2 283
2 026
1 698
1 635
1 449
735
642
342
203
164
61 624
9 370
8 287
7 836
7 794
6 325
5 760
4 254
3 351
3 191
2 915
2 497
2 436
2 053
1 403
1 149
811
595
417
70 443
18 769
16 734
16 323
15 904
11 729
10 141
7 596
6 229
5 474
4 941
4 195
4 071
3 502
2 138
1 791
1 153
799
581
132 068
Male
Female
Total
11 291
10 181
10 697
10 014
6 855
4 699
3 346
3 060
2 586
2 347
1 739
1 374
1 130
735
735
326
373
281
71 769
10 989
9 794
9 982
10 030
8 057
6 614
5 261
4 465
4 170
3 682
2 777
1 876
1 790
1 215
1 433
1 082
1 087
663
84 967
22 281
19 974
20 679
20 044
14 912
11 313
8 608
7 525
6 756
6 030
4 515
3 250
2 920
1 949
2 168
1 408
1 460
945
156 736
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
76
KZN272: Jozini
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
13 855
12 414
12 725
12 399
8 708
5 772
4 084
3 291
2 724
2 454
2 264
1 767
1 254
802
695
342
324
243
86 116
Female
13 490
12 166
12 301
12 470
10 321
8 442
5 993
4 842
4 385
3 899
3 131
2 240
1 783
1 229
1 272
969
873
580
100 386
Total
27 345
24 579
25 025
24 869
19 029
14 215
10 077
8 133
7 109
6 353
5 396
4 007
3 037
2 032
1 966
1 311
1 197
823
186 502
KZN273: The
Big 5 False
Bay
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN274:
Hlabisa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
KZN275:
Mtubatuba
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
5 373
4 727
4 830
5 030
3 154
2 140
1 381
1 124
927
888
830
775
638
415
292
161
145
113
32 942
5 417
4 627
4 778
4 826
3 908
2 953
1 973
1 684
1 502
1 557
1 385
1 134
900
630
633
442
343
289
38 983
10 790
9 353
9 608
9 856
7 062
5 093
3 354
2 808
2 430
2 445
2 215
1 909
1 539
1 045
926
603
487
402
71 925
Male
2 343
2 092
2 283
2 220
1 628
1 406
978
777
602
526
463
360
286
159
153
88
82
58
16 505
Male
Female
2 323
2 076
2 109
2 104
1 939
1 724
1 269
1 021
863
809
594
498
430
294
281
186
144
89
18 753
Female
12 765
11 175
11 112
10 931
8 307
6 259
4 301
3 568
2 653
2 461
2 137
1 636
1 333
788
849
429
354
256
81 314
12 339
11 022
10 655
11 103
9 585
8 303
5 921
4 616
3 859
3 595
3 523
2 540
1 932
1 374
1 399
994
812
539
94 111
Total
4 666
4 168
4 392
4 324
3 567
3 129
2 247
1 798
1 465
1 335
1 056
858
717
453
434
274
226
147
35 258
Total
25 104
22 197
21 767
22 034
17 892
14 563
10 222
8 184
6 511
6 057
5 661
4 176
3 265
2 162
2 247
1 423
1 166
795
175 425
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN282:
uMhlathuze
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN281:
Mfolozi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
77
Male
18 252
15 354
15 735
16 767
19 534
19 217
14 076
11 847
8 479
6 703
5 696
4 430
2 942
1 641
1 094
494
354
326
162 942
Male
8 039
7 239
7 098
7 292
6 338
5 554
4 103
3 136
2 128
1 942
1 817
1 437
1 059
654
523
249
204
208
59 020
Female
18 070
15 402
15 296
18 542
21 450
19 681
14 177
11 052
8 757
7 725
6 413
4 626
3 554
2 161
1 785
1 178
985
661
171 517
Female
8 176
6 994
6 802
7 049
6 901
6 072
4 189
3 162
2 513
2 683
2 565
1 755
1 380
947
928
651
652
447
63 869
Total
36 322
30 756
31 031
35 309
40 984
38 899
28 253
22 900
17 236
14 428
12 109
9 056
6 496
3 803
2 879
1 672
1 339
987
334 459
Total
16 215
14 234
13 900
14 342
13 239
11 626
8 292
6 298
4 641
4 626
4 382
3 192
2 438
1 601
1 451
900
856
654
122 889
KZN286:
Nkandla
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN283:
Ntambanana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
7 924
7 422
7 847
7 733
4 535
2 943
2 008
1 718
1 461
1 402
1 202
1 373
1 176
741
519
303
190
151
50 647
8 215
7 313
7 380
7 921
5 667
4 497
3 225
2 610
2 470
2 703
2 401
2 329
1 952
1 330
1 339
938
792
686
63 770
Male
Female
5 176
4 684
4 823
5 061
3 460
2 467
1 635
1 345
1 019
1 041
928
831
654
397
328
156
174
100
34 280
5 184
4 633
4 674
4 864
3 999
3 182
2 196
1 762
1 481
1 719
1 727
1 087
1 008
658
695
502
397
288
40 057
Total
16 138
14 735
15 228
15 654
10 203
7 440
5 233
4 328
3 931
4 105
3 603
3 702
3 128
2 071
1 858
1 240
982
837
114 416
Total
10 361
9 317
9 497
9 925
7 460
5 648
3 831
3 108
2 500
2 761
2 655
1 918
1 662
1 055
1 023
658
571
388
74 336
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN284:
uMlalazi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN431:
Ingwe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
78
Male
14 297
12 799
13 145
13 351
9 742
7 068
5 057
4 434
3 555
3 336
2 791
2 552
2 379
1 469
1 121
538
419
355
98 407
Male
7 403
6 516
6 491
6 293
4 397
3 210
2 214
1 955
1 591
1 491
1 371
1 212
1 129
608
395
208
169
99
46 752
Female
Total
14 217
12 532
12 543
13 235
11 168
9 006
6 480
5 578
5 000
5 250
4 351
3 689
4 083
2 127
2 281
1 581
1 211
863
115 194
28 515
25 331
25 688
26 586
20 910
16 074
11 537
10 012
8 554
8 586
7 142
6 241
6 461
3 596
3 402
2 119
1 631
1 218
213 601
Female
Total
7 151
6 559
5 858
6 001
4 840
3 932
2 991
2 569
2 419
2 385
1 972
1 877
1 649
1 173
895
658
500
367
53 795
14 554
13 075
12 349
12 295
9 238
7 142
5 205
4 525
4 010
3 876
3 342
3 089
2 778
1 780
1 289
867
669
466
100 548
KZN285:
Mthonjaneni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN432:
Kwa Sani
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
3 339
2 944
2 992
2 771
2 180
1 732
1 259
971
833
773
660
536
430
236
172
106
73
95
22 102
Male
659
528
443
524
768
917
612
566
385
310
252
230
170
160
87
37
24
15
6 688
3 344
2 864
2 851
2 767
2 530
2 204
1 580
1 273
1 156
1 204
951
781
639
374
451
300
259
190
25 716
Female
579
476
437
456
628
702
522
512
435
343
287
239
207
164
82
67
43
30
6 210
Total
6 683
5 808
5 843
5 538
4 710
3 936
2 838
2 244
1 990
1 977
1 611
1 316
1 068
611
623
406
331
285
47 818
Total
1 238
1 004
880
980
1 396
1 619
1 135
1 078
820
654
540
469
377
324
169
104
66
45
12 898
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN433:
Greater
Kokstad
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN435:
Umzimkhulu
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
79
Male
3 688
3 223
3 141
3 210
3 639
3 703
2 951
2 449
1 749
1 323
1 153
759
455
259
174
82
38
34
32 032
Male
13 420
12 027
11 862
11 997
7 343
4 803
3 598
3 075
2 452
2 390
2 267
1 828
1 738
1 185
760
497
326
181
81 749
Female
3 783
3 116
3 224
3 485
3 828
3 559
2 801
2 416
2 057
1 675
1 288
923
606
417
329
210
137
94
33 950
Female
13 076
11 853
11 377
12 224
9 102
6 814
5 266
4 323
4 096
3 924
3 607
3 229
2 756
2 144
1 690
1 477
1 000
593
98 553
Total
7 471
6 339
6 365
6 695
7 467
7 262
5 752
4 865
3 806
2 998
2 442
1 682
1 061
676
503
292
176
128
65 981
Total
26 496
23 880
23 239
24 222
16 445
11 618
8 865
7 398
6 548
6 314
5 873
5 057
4 494
3 329
2 450
1 973
1 326
775
180 302
KZN434:
Ubuhlebezw
e
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN241:
Endumeni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
6 970
6 185
6 157
6 225
4 654
3 563
2 480
2 147
1 727
1 696
1 392
1 313
1 168
599
435
225
171
138
47 246
6 761
6 080
5 836
6 177
4 820
4 070
2 916
2 645
2 595
2 667
2 252
2 115
1 728
1 195
888
607
595
498
54 445
13 731
12 264
11 993
12 403
9 474
7 633
5 396
4 792
4 322
4 363
3 644
3 428
2 896
1 795
1 323
832
766
636
101 691
Male
Female
Total
3 427
3 298
3 439
3 582
3 417
3 134
2 325
2 012
1 609
1 374
1 121
953
730
537
327
179
92
82
31 637
3 440
3 449
3 308
3 467
3 164
3 081
2 224
2 049
1 791
1 831
1 371
1 186
939
703
517
335
201
170
33 225
6 867
6 746
6 747
7 049
6 582
6 215
4 548
4 061
3 399
3 206
2 492
2 139
1 668
1 240
843
514
294
252
64 862
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN242:
Nqutu
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN245:
Umvoti
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
80
Male
12 403
11 638
11 686
11 085
6 561
4 497
3 146
2 640
2 146
2 016
1 653
1 816
1 491
961
677
342
221
216
75 194
Male
6 541
5 737
6 134
5 987
4 375
3 448
2 547
2 190
1 823
1 634
1 370
1 242
1 038
640
425
199
142
132
45 601
Female
12 389
11 256
10 781
10 801
8 394
6 503
4 771
3 819
3 407
3 619
3 117
2 900
2 361
1 747
1 558
1 091
923
676
90 113
Female
6 683
5 857
5 975
6 285
5 421
4 644
3 684
3 151
2 846
2 968
2 326
2 057
1 700
1 312
940
540
510
593
57 491
Total
24 791
22 894
22 467
21 886
14 954
11 000
7 917
6 459
5 553
5 635
4 770
4 716
3 852
2 709
2 235
1 432
1 144
892
165 307
Total
13 225
11 593
12 109
12 271
9 796
8 091
6 231
5 340
4 669
4 602
3 695
3 299
2 738
1 952
1 366
738
652
724
103 093
KZN244:
Msinga
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN252:
Newcastle
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
13 946
12 792
12 577
11 636
5 600
3 748
2 875
2 590
2 115
2 037
1 657
1 630
1 531
946
655
299
285
227
77 143
Male
20 292
19 641
19 157
20 432
19 748
16 498
12 039
10 178
7 774
6 380
5 929
5 244
3 860
2 375
1 631
820
472
374
172 846
Female
13 972
12 431
11 951
12 094
8 334
6 937
5 240
4 565
3 962
4 112
3 180
3 266
3 125
2 034
1 859
1 121
1 233
1 018
100 433
Female
20 446
19 176
18 249
20 095
19 411
17 052
13 126
11 251
9 827
9 291
8 537
7 383
5 844
3 725
2 956
1 917
1 220
884
190 390
Total
27 918
25 223
24 528
23 730
13 934
10 684
8 114
7 154
6 077
6 149
4 837
4 896
4 656
2 980
2 514
1 420
1 519
1 245
177 577
Total
40 738
38 816
37 406
40 527
39 160
33 550
25 165
21 430
17 601
15 671
14 466
12 627
9 705
6 100
4 587
2 737
1 692
1 259
363 236
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN253:
Emadlangeni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN263:
Abaqulusi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
81
Male
2 095
2 166
2 112
1 950
1 703
1 457
1 230
1 044
717
657
587
607
399
310
207
88
86
70
17 486
Male
13 426
12 820
12 862
12 783
10 220
8 541
6 203
5 047
3 893
3 375
3 044
2 682
2 077
1 227
1 067
549
325
333
100 474
Female
2 053
2 006
1 916
1 767
1 495
1 386
1 087
990
834
740
673
587
436
297
276
179
142
93
16 956
Female
13 423
12 546
12 325
12 495
10 563
9 374
6 753
5 613
4 979
5 036
4 342
3 886
2 867
1 858
1 758
1 192
923
653
110 586
Total
4 148
4 173
4 027
3 717
3 197
2 843
2 317
2 034
1 551
1 398
1 260
1 194
835
608
482
266
228
163
34 442
Total
26 848
25 366
25 187
25 278
20 783
17 915
12 956
10 659
8 872
8 411
7 386
6 568
4 945
3 085
2 825
1 741
1 248
986
211 060
KZN254:
Dannhauser
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN261:
eDumbe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
6 778
6 331
6 497
6 579
4 977
3 557
2 576
2 089
1 598
1 615
1 480
1 414
1 170
719
482
251
149
117
48 380
Male
5 701
5 431
5 412
4 998
3 809
2 769
1 986
1 647
1 377
1 191
1 071
961
668
492
381
217
210
125
38 447
6 958
6 448
6 054
6 289
5 060
4 106
3 086
2 404
2 109
2 213
2 058
1 936
1 631
1 133
962
629
376
329
53 781
Female
5 799
5 399
5 065
4 755
4 257
3 508
2 486
1 906
1 844
1 805
1 718
1 400
995
720
771
438
411
327
43 605
Total
13 735
12 779
12 552
12 868
10 037
7 663
5 663
4 493
3 707
3 827
3 538
3 350
2 801
1 851
1 444
881
525
446
102 161
Total
11 500
10 830
10 478
9 753
8 066
6 276
4 472
3 553
3 221
2 996
2 790
2 362
1 663
1 212
1 152
655
621
452
82 053
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
KZN262:
UPhongolo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN266:
Ulundi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
82
Male
Female
Total
8 838
8 042
8 155
7 961
6 380
4 997
3 354
2 615
2 021
1 635
1 665
1 315
970
613
538
263
184
182
59 728
8 962
8 120
7 910
7 897
7 087
5 766
3 894
3 192
2 793
2 554
2 318
1 873
1 467
992
1 022
685
573
406
67 510
17 800
16 162
16 065
15 858
13 467
10 762
7 247
5 806
4 814
4 188
3 983
3 188
2 438
1 605
1 560
948
757
588
127 238
Male
Female
Total
13 034
12 193
12 466
12 123
8 223
6 028
3 938
3 332
2 577
2 570
2 389
1 982
1 596
909
715
401
274
312
85 061
13 634
12 311
11 973
12 170
10 098
8 398
5 756
4 715
4 158
4 386
3 804
3 276
2 460
1 425
1 600
1 194
1 021
878
103 255
26 668
24 504
24 439
24 293
18 321
14 426
9 694
8 047
6 735
6 956
6 192
5 258
4 057
2 334
2 315
1 595
1 295
1 189
188 317
KZN265:
Nongoma
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
KZN294:
Maphumulo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
14 885
12 893
13 613
13 708
8 467
5 393
3 442
2 922
2 360
2 182
2 167
1 979
1 664
1 008
851
416
301
239
88 490
14 623
12 860
12 987
13 361
10 714
8 103
5 458
4 316
3 947
4 350
3 318
3 138
2 684
1 765
1 718
1 273
1 067
737
106 418
29 508
25 753
26 600
27 069
19 181
13 496
8 900
7 238
6 307
6 532
5 485
5 117
4 348
2 772
2 569
1 690
1 368
975
194 908
Male
Female
Total
7 099
6 234
6 785
6 538
3 557
2 303
1 627
1 516
1 230
1 204
1 084
1 170
1 164
636
497
236
189
153
43 221
6 834
6 078
6 273
6 766
4 689
3 459
2 476
2 141
2 059
2 273
2 030
1 860
1 984
1 254
1 171
850
754
553
53 503
13 933
12 312
13 057
13 304
8 246
5 762
4 103
3 657
3 289
3 477
3 114
3 030
3 147
1 890
1 668
1 086
942
707
96 724
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
83
KZN291:
Mandeni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
KZN293:
Ndwedwe
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
8 864
7 283
7 203
7 754
7 436
6 314
4 712
3 942
2 904
2 428
1 907
1 588
1 226
753
480
251
165
122
65 332
9 593
8 332
8 727
8 967
6 391
4 777
3 535
3 074
2 260
2 312
2 047
1 925
1 742
1 011
774
373
248
208
66 296
Female
8 786
7 209
6 897
7 675
7 763
7 420
5 457
4 355
3 544
3 493
2 602
2 013
1 941
1 099
969
706
482
334
72 746
Female
9 310
8 243
8 043
8 832
6 905
5 470
3 866
3 294
3 030
3 340
3 106
2 717
2 577
1 811
1 429
987
918
649
74 524
Total
17 650
14 492
14 100
15 429
15 199
13 734
10 169
8 297
6 448
5 921
4 510
3 601
3 167
1 852
1 449
957
647
456
138 078
Total
18 903
16 575
16 770
17 799
13 296
10 247
7 401
6 368
5 290
5 652
5 153
4 642
4 319
2 822
2 203
1 359
1 166
857
140 820
KZN292:
KwaDukuza
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
ETH:
eThekwini
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
13 320
10 539
10 023
10 241
13 096
13 995
10 372
8 687
6 298
4 673
3 662
2 847
2 483
1 614
1 146
582
338
245
114 160
Male
165 537
135 124
136 579
156 287
207 947
203 289
154 042
125 590
96 104
78 473
65 340
53 412
42 673
26 164
16 971
9 516
5 371
3 988
1 682 406
13 211
10 369
9 570
10 818
12 873
12 716
9 598
8 201
6 669
5 630
4 475
3 666
3 157
2 189
1 691
1 006
672
516
117 028
Female
162 435
133 421
133 184
162 252
204 778
192 292
145 125
121 792
103 063
96 459
80 383
66 559
54 828
37 328
27 862
18 050
11 658
8 484
1 759 955
Total
26 531
20 908
19 592
21 059
25 969
26 710
19 970
16 888
12 967
10 302
8 138
6 513
5 640
3 803
2 837
1 588
1 010
761
231 187
Total
327 972
268 545
269 763
318 539
412 726
395 581
299 167
247 382
199 167
174 931
145 722
119 972
97 501
63 492
44 833
27 566
17 029
12 473
3 442 361
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
LIM331:
Greater
Giyani
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
LIM333:
Greater
Tzaneen
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
84
Male
16 074
14 594
14 623
16 205
12 012
7 297
5 122
4 208
3 539
3 273
2 708
2 300
1 866
1 425
1 341
736
459
343
108 124
Male
24 007
18 877
19 714
21 780
20 565
15 951
11 910
10 160
8 753
7 454
5 909
5 161
4 183
2 720
2 134
1 003
685
592
181 558
Female
16 068
14 591
13 818
15 848
13 407
10 782
8 594
7 982
6 598
6 289
5 454
3 908
2 836
2 644
2 871
1 854
1 448
1 102
136 094
Female
24 002
18 989
18 606
21 502
19 949
17 852
14 523
13 470
11 985
11 561
8 748
6 877
5 304
4 115
3 902
2 775
2 451
1 928
208 536
Total
32 143
29 186
28 441
32 053
25 418
18 079
13 715
12 191
10 137
9 562
8 162
6 208
4 701
4 069
4 212
2 590
1 907
1 445
244 217
Total
48 009
37 866
38 320
43 282
40 514
33 802
26 433
23 630
20 738
19 015
14 656
12 038
9 487
6 835
6 036
3 777
3 135
2 519
390 095
LIM332:
Greater Letaba
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
LIM334: BaPhalaborwa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
13 539
11 381
11 983
14 501
11 160
6 611
4 727
4 246
3 328
2 908
2 525
2 211
1 979
1 496
1 325
600
416
369
95 305
9 722
7 536
7 531
7 125
8 142
6 845
5 406
4 658
3 892
3 105
2 697
2 420
1 664
961
654
339
203
118
73 017
Female
13 597
11 577
11 311
13 775
10 972
8 924
7 193
6 522
6 001
5 679
4 482
3 723
3 133
2 964
2 936
1 827
1 508
1 273
117 396
Female
9 715
7 850
7 221
7 558
7 998
7 374
6 022
5 399
4 402
3 790
2 853
2 253
1 622
1 206
959
632
444
321
77 620
Total
27 135
22 958
23 294
28 276
22 132
15 535
11 919
10 767
9 329
8 587
7 007
5 934
5 112
4 460
4 261
2 428
1 924
1 642
212 701
Total
19 437
15 386
14 752
14 683
16 140
14 219
11 428
10 057
8 293
6 895
5 550
4 673
3 286
2 167
1 613
972
647
439
150 637
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
LIM335:
Maruleng
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
LIM343:
Thulamela
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
85
Male
6 039
5 201
5 244
5 704
4 796
3 467
2 556
2 081
1 861
1 602
1 321
1 071
975
653
475
231
181
118
43 577
Male
39 250
34 960
35 680
40 554
32 275
20 525
14 513
11 874
10 299
9 422
7 373
5 946
5 109
3 592
2 967
1 723
1 451
1 138
278 650
Female
5 997
5 153
4 954
5 589
4 915
4 497
3 558
3 067
2 865
2 512
2 026
1 531
1 227
874
809
702
575
430
51 280
Female
39 096
35 095
33 795
39 455
32 433
25 761
22 002
20 223
17 082
16 599
13 672
10 246
8 171
5 885
5 069
5 687
5 267
4 275
339 812
Total
12 036
10 353
10 198
11 293
9 711
7 964
6 114
5 148
4 726
4 114
3 347
2 601
2 202
1 528
1 284
933
756
548
94 857
Total
78 346
70 055
69 475
80 009
64 708
46 286
36 516
32 096
27 381
26 020
21 045
16 192
13 280
9 477
8 037
7 409
6 717
5 413
618 462
LIM342: Mutale
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
6 029
5 794
6 025
6 099
4 241
2 605
1 955
1 611
1 505
1 365
1 056
828
704
485
447
282
235
281
41 546
Female
5 987
5 680
5 571
5 837
4 446
3 763
3 089
2 973
2 434
2 249
1 888
1 314
1 115
799
758
772
740
907
50 324
Total
12 017
11 474
11 596
11 936
8 687
6 367
5 045
4 584
3 939
3 614
2 944
2 143
1 820
1 284
1 205
1 054
975
1 188
91 870
LIM341: Musina
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
4 486
2 717
2 603
2 945
4 307
4 714
3 748
2 807
1 973
1 279
981
701
485
327
175
89
88
81
34 506
Female
4 253
2 738
2 461
2 902
4 235
4 593
3 352
2 818
1 892
1 332
1 014
738
502
321
269
185
124
126
33 853
Total
8 739
5 455
5 064
5 847
8 542
9 307
7 100
5 625
3 864
2 611
1 995
1 439
987
648
443
274
212
207
68 359
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
LIM344:
Makhado
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
LIM352:
Aganang
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
32 690
28 718
29 904
31 664
24 445
17 210
13 346
11 344
9 980
8 907
7 129
6 102
4 621
3 317
3 232
1 735
1 338
1 115
236 795
8 400
7 980
8 328
8 995
5 154
2 913
2 107
1 918
1 884
1 859
1 829
1 739
1 891
1 345
1 265
724
435
406
59 171
86
Female
32 109
28 179
27 772
30 033
24 377
20 658
17 925
16 749
14 532
13 342
11 639
9 226
6 814
6 355
5 300
5 650
4 556
4 020
279 236
Female
8 558
7 865
7 875
8 318
5 527
4 185
3 079
3 060
2 901
3 313
2 843
2 782
2 763
2 315
2 282
1 592
1 361
1 372
71 992
Total
64 799
56 896
57 676
61 697
48 822
37 868
31 270
28 093
24 511
22 249
18 768
15 327
11 435
9 672
8 531
7 385
5 895
5 135
516 031
Total
16 958
15 845
16 203
17 314
10 681
7 098
5 186
4 977
4 785
5 172
4 673
4 522
4 654
3 660
3 547
2 315
1 796
1 778
131 164
LIM351:
Blouberg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
LIM353:
Molemole
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
11 251
10 331
10 922
10 960
7 360
4 019
3 025
2 618
2 427
2 035
1 861
1 727
1 713
1 233
1 134
699
425
413
74 152
Male
7 003
6 102
6 389
7 089
5 044
3 400
2 467
2 182
1 901
1 731
1 391
1 378
1 213
964
691
423
287
226
49 881
Total
10 578
10 328
10 024
10 765
7 758
5 597
4 532
4 347
3 889
4 250
3 102
2 639
2 735
2 136
2 106
1 457
1 184
1 049
88 476
21 829
20 659
20 946
21 724
15 118
9 616
7 557
6 965
6 317
6 285
4 963
4 366
4 447
3 370
3 240
2 156
1 610
1 462
162 629
Female
Total
7 130
5 979
5 932
6 627
4 907
4 173
3 170
3 140
2 825
2 895
2 250
1 996
1 819
1 393
1 457
1 000
934
811
58 440
14 133
12 081
12 321
13 716
9 951
7 573
5 637
5 322
4 726
4 625
3 641
3 374
3 032
2 357
2 148
1 423
1 222
1 038
108 321
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
LIM354:
Polokwane
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
LIM361:
Thabazimbi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
87
Male
Female
35 722
29 723
29 927
33 633
36 309
30 166
22 964
18 280
15 558
13 294
10 462
8 965
6 349
4 208
3 210
1 666
975
823
302 233
Male
4 018
2 695
2 327
2 543
5 424
7 338
6 318
5 003
3 641
3 674
3 045
1 945
830
448
297
142
101
87
49 877
35 596
29 667
28 775
33 736
34 886
30 776
23 776
21 652
18 723
16 839
12 794
10 323
8 232
6 313
5 332
3 783
2 817
2 746
326 766
Female
4 071
2 610
2 293
2 481
3 807
4 467
3 578
2 977
2 578
2 203
1 627
1 043
631
398
239
160
114
80
35 357
Total
71 317
59 390
58 703
67 369
71 195
60 942
46 741
39 932
34 280
30 133
23 257
19 287
14 581
10 522
8 542
5 449
3 793
3 569
628 999
Total
8 089
5 305
4 620
5 024
9 230
11 805
9 897
7 979
6 220
5 877
4 672
2 989
1 461
846
536
302
216
167
85 234
LIM355:
LepeleNkumpi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
LIM362:
Lephalale
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
15 323
13 646
12 804
14 272
10 124
7 031
5 090
4 366
4 083
3 738
3 199
2 930
2 442
1 798
1 761
923
684
589
104 805
15 316
13 402
12 425
13 552
10 286
8 534
6 883
6 797
6 279
6 077
5 439
4 532
3 832
3 402
2 818
2 167
1 971
1 833
125 545
30 639
27 049
25 229
27 823
20 409
15 565
11 974
11 163
10 363
9 815
8 638
7 462
6 274
5 200
4 579
3 089
2 655
2 423
230 350
Male
Female
Total
6 043
4 740
4 574
5 157
8 604
8 956
6 399
4 556
3 350
2 991
2 532
1 836
1 240
604
548
303
196
192
62 819
5 849
4 724
4 411
4 903
6 385
5 721
4 135
3 526
2 806
2 688
2 032
1 636
1 238
869
745
585
380
315
52 948
11 891
9 464
8 985
10 059
14 990
14 677
10 533
8 082
6 157
5 678
4 564
3 472
2 479
1 472
1 293
888
576
507
115 767
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
88
LIM364:
Mookgopong
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
LIM366: BelaBela
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
1 989
1 257
1 286
1 371
2 255
2 289
1 658
1 324
1 052
878
767
578
541
404
303
194
127
55
18 329
3 501
3 000
2 843
2 957
3 696
3 610
2 927
2 323
2 025
1 655
1 424
1 101
968
703
434
302
177
108
33 754
Female
1 975
1 358
1 288
1 244
1 743
1 804
1 354
1 299
1 138
996
661
601
541
421
360
254
149
125
17 310
Female
3 455
3 058
2 808
2 777
3 126
3 097
2 379
2 290
1 955
1 838
1 507
1 255
970
735
612
423
264
199
32 746
Total
3 964
2 615
2 574
2 615
3 998
4 093
3 013
2 623
2 190
1 874
1 427
1 179
1 083
824
663
449
276
180
35 640
Total
6 956
6 059
5 651
5 734
6 821
6 707
5 305
4 613
3 980
3 493
2 931
2 356
1 938
1 439
1 045
725
440
307
66 500
LIM365:
Modimolle
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
LIM367:
Mogalakwena
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
4 076
3 466
3 164
3 263
3 671
3 651
2 899
2 443
1 867
1 591
1 245
1 012
707
585
495
277
161
116
34 686
Male
4 055
3 310
3 054
2 938
3 358
3 134
2 559
2 379
1 949
1 753
1 331
1 112
820
700
585
364
245
182
33 827
Female
19 520
17 004
16 762
18 674
14 354
10 892
8 456
7 106
5 824
5 501
4 645
3 960
3 425
2 486
2 417
1 196
813
668
143 702
19 669
16 747
15 942
17 566
14 724
12 344
9 534
9 353
7 774
7 887
6 577
5 836
4 932
4 159
4 169
2 769
2 187
1 811
163 980
Total
8 130
6 776
6 218
6 202
7 029
6 784
5 458
4 821
3 816
3 344
2 576
2 124
1 527
1 284
1 080
640
406
297
68 513
Total
39 189
33 751
32 704
36 240
29 078
23 236
17 990
16 459
13 598
13 388
11 222
9 796
8 356
6 645
6 587
3 964
2 999
2 480
307 682
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
LIM471:
Ephraim
Mogale
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
LIM473:
Makhuduthamaga
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
89
Male
8 415
7 308
6 797
7 323
6 038
4 435
3 262
2 704
2 326
2 100
1 767
1 548
1 328
918
886
393
333
326
58 207
Female
8 147
6 938
6 310
6 804
5 983
5 178
3 942
3 684
3 160
3 028
2 538
2 147
1 877
1 767
1 324
954
905
756
65 442
Total
16 562
14 246
13 107
14 127
12 021
9 612
7 204
6 388
5 486
5 128
4 305
3 694
3 206
2 685
2 209
1 347
1 238
1 082
123 648
Male
Female
Total
19 346
17 035
16 586
16 848
11 169
7 110
5 213
4 522
4 048
3 541
3 191
3 126
2 811
2 056
2 134
1 082
802
663
121 282
18 897
17 001
15 602
16 459
12 435
10 610
8 819
8 002
7 116
6 866
5 781
5 326
4 971
4 895
3 541
2 533
2 165
2 057
153 075
38 243
34 036
32 187
33 306
23 604
17 720
14 032
12 523
11 164
10 407
8 972
8 452
7 782
6 950
5 675
3 615
2 966
2 721
274 358
LIM472:
Elias
Motsoaledi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
LIM474:
Fetakgomo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
16 696
14 483
14 211
14 840
12 198
8 288
6 127
5 258
4 350
4 064
3 501
3 064
2 605
2 108
1 742
820
554
595
115 503
Male
6 077
5 422
5 183
5 441
4 013
3 099
2 318
1 894
1 574
1 493
1 271
1 228
976
644
751
381
274
217
42 258
16 507
14 618
13 579
14 663
12 335
9 986
7 727
6 987
5 985
6 147
4 920
4 403
4 246
3 777
2 743
1 868
1 775
1 594
133 860
Female
6 150
5 447
4 931
5 442
4 342
3 907
3 033
2 763
2 450
2 523
1 972
1 737
1 496
1 650
1 261
925
734
774
51 536
Total
33 204
29 101
27 790
29 503
24 533
18 275
13 854
12 244
10 335
10 210
8 422
7 467
6 851
5 885
4 485
2 687
2 329
2 188
249 363
Total
12 227
10 869
10 114
10 882
8 355
7 006
5 351
4 657
4 024
4 016
3 243
2 965
2 472
2 294
2 013
1 306
1 009
991
93 795
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
LIM475:
Greater
Tubatse
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
MP302:
Msukaligwa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
90
Male
22 141
18 759
17 676
18 448
17 627
15 294
11 868
9 075
6 939
6 113
4 472
3 882
2 846
1 786
1 708
766
536
461
160 398
Male
8 301
7 590
7 030
7 532
8 089
7 969
5 829
4 794
4 125
3 427
3 001
2 417
1 656
969
649
365
223
148
74 113
Female
Total
22 027
18 329
17 042
18 172
18 251
16 460
12 700
10 224
8 425
8 087
5 541
4 652
3 648
3 662
2 686
2 048
1 661
1 663
175 278
44 168
37 089
34 718
36 620
35 877
31 754
24 568
19 300
15 363
14 201
10 013
8 534
6 493
5 448
4 394
2 814
2 197
2 124
335 676
Female
Total
8 273
7 271
6 944
7 542
7 908
7 520
5 359
4 741
4 191
3 921
3 238
2 673
1 970
1 192
1 082
638
438
363
75 264
16 575
14 861
13 974
15 073
15 997
15 489
11 188
9 535
8 316
7 348
6 239
5 090
3 625
2 160
1 731
1 004
661
511
149 377
MP301: Albert
Luthuli
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
MP303:
Mkhondo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
11 877
11 183
11 179
11 818
9 015
6 874
4 688
3 908
3 385
2 980
2 627
2 244
1 788
1 309
1 001
535
405
372
87 188
Male
Female
11 537
11 153
10 873
11 745
9 704
7 828
5 455
4 908
4 813
4 471
3 934
3 499
2 657
1 787
1 697
1 132
836
795
98 822
Female
10 949
10 423
10 113
9 980
8 452
7 192
5 145
4 562
3 822
3 093
2 449
2 060
1 512
905
741
362
273
232
82 263
10 737
10 657
10 043
9 946
9 006
7 371
5 406
5 079
4 350
4 180
3 343
2 768
2 064
1 552
1 368
743
666
439
89 719
Total
23 414
22 336
22 052
23 563
18 719
14 702
10 143
8 816
8 198
7 452
6 561
5 743
4 444
3 096
2 698
1 667
1 240
1 166
186 010
Total
21 686
21 081
20 156
19 926
17 458
14 564
10 551
9 641
8 172
7 273
5 792
4 828
3 576
2 457
2 109
1 104
939
671
171 982
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
91
MP304: Pixley Ka
Seme
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
MP306:
Dipaleseng
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
5 126
4 679
4 756
4 548
3 889
3 158
2 433
2 130
1 718
1 615
1 469
1 198
991
658
528
306
178
140
39 520
2 319
1 902
1 796
1 991
2 204
2 302
1 871
1 538
1 255
1 048
904
738
608
390
303
133
95
65
21 462
Female
5 077
4 804
4 608
4 627
4 066
3 332
2 617
2 331
2 298
2 196
1 842
1 700
1 294
980
779
497
359
310
43 715
Female
2 272
1 827
1 836
1 972
2 013
1 912
1 550
1 379
1 138
1 130
918
834
655
535
414
266
154
121
20 928
Total
10 203
9 483
9 364
9 175
7 955
6 490
5 049
4 461
4 016
3 811
3 311
2 898
2 285
1 638
1 307
803
537
450
83 235
Total
4 592
3 729
3 632
3 963
4 217
4 214
3 420
2 917
2 393
2 179
1 822
1 571
1 264
925
717
399
249
186
42 390
MP305:
Lekwa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
MP307:
Govan Mbeki
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
6 030
5 279
5 332
5 556
6 285
5 606
4 477
3 917
3 360
2 968
2 674
2 322
1 504
990
627
335
229
157
57 647
5 990
5 277
5 176
5 528
5 387
5 311
4 196
3 907
3 443
3 443
2 917
2 234
1 749
1 247
978
604
373
253
58 014
Female
15 129
12 843
11 778
12 898
17 261
18 512
14 306
11 027
9 202
8 330
7 741
5 608
3 272
1 962
1 020
703
328
291
152 211
15 176
12 761
11 517
12 842
14 449
14 829
11 136
9 795
8 910
8 689
7 338
5 021
3 308
2 321
1 834
1 123
717
560
142 326
Total
12 021
10 556
10 508
11 084
11 671
10 917
8 673
7 825
6 804
6 411
5 591
4 556
3 253
2 237
1 606
939
602
409
115 662
Total
30 305
25 604
23 295
25 740
31 710
33 341
25 441
20 822
18 112
17 019
15 079
10 629
6 580
4 283
2 854
1 826
1 045
851
294 538
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
MP311: Victor
Khanye
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
MP313:
Steve
Tshwete
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
92
Male
Female
3 990
3 349
3 415
3 433
4 114
4 188
3 335
2 868
2 443
2 069
1 683
1 381
1 002
656
440
238
126
87
38 816
Male
11 127
9 048
8 663
9 484
13 025
14 273
11 152
9 578
8 197
6 863
6 131
4 654
3 046
1 791
1 131
647
355
244
119 411
4 063
3 322
3 146
3 347
3 516
3 538
2 772
2 558
2 040
2 144
1 686
1 343
1 144
704
536
361
221
193
36 636
Female
11 117
9 059
8 554
9 425
11 116
11 336
9 009
8 379
7 499
6 741
5 408
4 202
2 895
2 085
1 494
945
649
509
110 421
Total
8 053
6 672
6 561
6 780
7 629
7 726
6 107
5 426
4 483
4 213
3 369
2 724
2 146
1 361
976
598
347
279
75 452
Total
22 245
18 107
17 218
18 909
24 141
25 609
20 161
17 957
15 696
13 604
11 539
8 855
5 940
3 876
2 625
1 592
1 003
753
229 831
MP312:
Emalahleni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
MP314:
Emakhazeni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
19 803
15 598
14 683
15 997
24 156
26 728
20 716
16 553
13 298
11 941
10 585
7 885
4 840
2 555
1 699
864
464
387
208 751
Male
19 821
15 257
14 390
16 218
20 249
20 191
15 483
13 678
11 943
11 084
8 810
6 784
4 629
2 973
2 276
1 302
886
741
186 715
Female
2 482
2 146
2 185
2 223
2 507
2 649
2 029
1 693
1 446
1 172
931
780
679
491
330
156
125
75
24 099
2 376
2 066
1 946
2 164
2 293
2 167
1 734
1 522
1 422
1 242
1 044
899
676
550
401
248
180
188
23 117
Total
39 625
30 855
29 073
32 214
44 405
46 920
36 199
30 231
25 240
23 026
19 395
14 668
9 469
5 527
3 975
2 166
1 349
1 128
395 466
Total
4 859
4 212
4 130
4 387
4 799
4 817
3 762
3 215
2 867
2 413
1 975
1 679
1 355
1 042
731
403
305
264
47 216
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
MP315:
Thembisile
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
MP321:
Thaba
Chweu
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
93
Male
Female
Total
18 094
16 536
15 535
16 753
15 794
13 157
10 195
8 743
7 475
6 609
5 362
4 687
3 570
2 133
1 348
669
520
497
147 676
18 134
16 323
15 111
17 030
15 547
13 924
11 151
10 175
8 665
8 378
7 227
6 158
4 808
3 204
2 652
1 698
1 316
1 279
162 783
36 227
32 859
30 646
33 783
31 341
27 081
21 346
18 919
16 140
14 986
12 590
10 845
8 379
5 337
4 000
2 368
1 836
1 776
310 458
Male
Female
Total
4 804
3 805
3 863
4 131
5 362
5 889
4 727
4 226
3 503
2 958
2 217
1 768
1 181
793
557
311
190
129
50 415
4 795
3 781
3 714
4 136
4 718
4 939
3 993
3 680
3 181
2 867
2 158
1 844
1 301
942
778
486
366
293
47 972
9 600
7 586
7 577
8 267
10 080
10 829
8 720
7 907
6 684
5 825
4 375
3 612
2 483
1 736
1 336
796
555
422
98 387
MP316:
Dr JS
Moroka
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
MP322:
Mbombela
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
14 765
13 255
13 124
14 195
12 019
8 991
6 820
5 857
5 169
4 899
4 270
3 999
3 426
2 483
1 882
965
749
629
117 494
14 732
13 191
12 382
13 829
11 780
10 261
7 779
7 160
6 578
6 256
5 382
5 057
4 731
4 042
3 315
2 188
1 800
1 747
132 211
29 497
26 446
25 505
28 024
23 799
19 252
14 599
13 016
11 747
11 154
9 652
9 056
8 157
6 525
5 197
3 153
2 549
2 376
249 705
Male
Female
Total
33 577
26 920
28 174
29 563
32 704
31 279
23 103
19 459
15 057
13 028
9 779
7 784
5 595
3 582
2 736
1 419
1 055
937
285 750
32 699
27 000
27 282
30 838
33 338
31 638
23 479
21 297
17 622
15 475
11 037
9 650
6 594
4 399
4 139
2 646
2 102
1 808
303 044
66 275
53 919
55 456
60 401
66 043
62 917
46 582
40 757
32 679
28 503
20 816
17 435
12 189
7 982
6 875
4 065
3 157
2 745
588 794
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
94
MP323: Umjindi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
3 512
2 892
2 896
3 074
3 880
4 295
3 310
2 716
2 190
1 864
1 434
1 109
721
470
397
199
98
82
35 141
Female
3 443
2 890
2 787
3 127
3 260
3 365
2 490
2 360
1 969
1 602
1 328
1 035
800
545
415
295
186
118
32 016
Total
6 955
5 782
5 683
6 201
7 140
7 661
5 800
5 076
4 159
3 466
2 761
2 144
1 521
1 015
812
495
285
200
67 156
MP324:
Nkomazi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
MP325:
Bushbuckridge
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
35 470
32 634
33 228
33 911
27 017
18 457
13 363
10 710
8 998
7 528
5 675
5 186
4 513
3 068
2 801
1 371
1 143
949
246 023
34 892
32 283
31 845
33 904
29 316
24 801
19 278
16 788
14 553
12 976
9 700
8 956
6 594
5 424
4 823
3 628
3 273
2 192
295 224
70 362
64 917
65 074
67 815
56 333
43 258
32 641
27 498
23 552
20 504
15 375
14 142
11 107
8 492
7 623
4 999
4 417
3 140
541 248
NW371:
Moretele
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
24 460
21 690
23 267
24 406
21 460
17 594
12 510
9 784
7 364
6 452
4 606
3 864
2 856
1 846
1 716
886
718
594
186 074
Male
24 607
22 077
23 178
24 564
22 642
19 718
14 667
12 105
10 216
8 200
5 832
5 022
3 813
2 681
2 874
1 953
1 756
1 049
206 956
Female
11 047
9 375
8 664
9 636
9 333
8 150
6 541
5 181
4 159
3 758
3 464
3 337
2 870
2 125
1 528
936
596
491
91 193
11 192
9 383
8 175
9 039
8 673
8 083
6 195
5 394
4 625
4 057
4 058
3 926
3 357
2 945
2 377
1 784
1 285
1 208
95 755
Total
49 067
43 767
46 445
48 971
44 102
37 312
27 177
21 889
17 580
14 652
10 438
8 887
6 669
4 528
4 591
2 839
2 474
1 643
393 030
Total
22 239
18 757
16 839
18 675
18 006
16 232
12 736
10 575
8 784
7 815
7 522
7 263
6 227
5 070
3 905
2 720
1 881
1 699
186 947
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NW372:
Madibeng
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NW374:
Kgetlengrivier
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
95
Male
Female
26 819
18 792
16 698
19 132
28 599
30 403
25 529
20 136
15 780
14 224
12 029
9 462
6 221
4 312
2 717
1 518
916
705
253 991
26 121
18 682
15 577
17 985
23 656
23 421
18 324
16 072
13 057
11 829
9 895
8 479
6 271
4 888
3 569
2 567
1 601
1 394
223 390
Male
Female
2 933
2 417
2 196
2 277
2 553
2 590
2 149
1 982
1 713
1 494
1 341
1 043
916
575
345
225
170
114
27 034
2 599
2 262
2 164
2 246
2 105
1 989
1 762
1 681
1 490
1 298
1 084
970
788
519
415
288
206
148
24 015
Total
52 940
37 474
32 275
37 117
52 255
53 825
43 852
36 209
28 837
26 053
21 923
17 941
12 492
9 200
6 286
4 085
2 517
2 100
477 381
Total
5 533
4 678
4 360
4 523
4 658
4 579
3 911
3 663
3 203
2 793
2 425
2 013
1 704
1 095
760
514
377
262
51 049
NW373:
Rustenburg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NW375:
Moses Kotane
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
28 861
20 335
17 891
20 234
31 696
37 664
34 163
27 039
22 340
21 401
16 465
10 672
5 517
3 141
1 941
1 252
680
503
301 796
Male
28 098
19 911
17 364
19 543
27 107
29 081
23 293
19 246
16 582
13 598
10 488
7 274
5 202
3 746
2 814
1 944
1 285
1 204
247 779
Female
14278
11230
10187
11374
12027
11617
9341
7490
6166
5844
5614
4601
3415
2826
1942
1241
756
565
120515
14127
11273
9700
10682
11133
9996
7941
7263
6779
6482
5958
5010
4239
3577
2926
2126
1420
1406
122038
Total
56 959
40 247
35 256
39 777
58 803
66 745
57 455
46 284
38 923
34 999
26 953
17 946
10 718
6 887
4 755
3 196
1 965
1 707
549 575
Total
28405
22503
19887
22056
23160
21613
17282
14753
12945
12326
11572
9611
7655
6403
4868
3367
2176
1972
242554
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NW381: Ratlou
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NW383:
Mafikeng
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
96
Male
7549
6766
6769
6156
4172
3157
2562
2260
2018
1869
1909
1574
1415
1178
835
532
307
281
51310
Male
16 690
14 329
14 442
15 764
15 061
12 657
10 063
8 638
8 110
6 977
5 884
4 330
3 137
2 205
1 512
905
495
443
141 642
Female
7389
6662
6439
5689
4490
3670
2930
2826
2546
2525
2289
2026
1743
1554
1146
861
623
621
56029
Female
16 502
13 857
13 883
15 717
14 821
12 638
10 356
10 287
9 390
8 172
6 716
5 280
3 745
2 942
2 214
1 453
958
954
149 885
Total
14938
13428
13208
11845
8663
6827
5492
5086
4565
4395
4198
3600
3157
2732
1981
1393
929
903
107339
Total
33 192
28 186
28 325
31 482
29 882
25 295
20 419
18 925
17 500
15 149
12 600
9 610
6 882
5 147
3 726
2 358
1 453
1 397
291 527
NW382:
Tswaing
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NW384:
Ditsobotla
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
8 116
7 405
7 644
6 687
5 434
4 448
3 613
3 254
2 933
2 810
2 765
2 130
1 633
1 187
827
478
302
262
61 931
7 634
7 065
6 892
6 158
5 470
4 715
3 619
3 516
3 144
3 062
2 713
2 301
1 658
1 427
1 122
822
502
465
62 287
Male
Female
10 717
9 087
8 523
7 739
7 864
7 407
6 308
5 542
4 945
4 248
3 745
3 120
2 100
1 576
1 156
576
364
280
85 297
10 038
8 774
7 874
7 631
7 332
6 934
5 664
5 427
4 666
4 406
3 842
3 236
2 444
1 847
1 417
964
616
493
83 605
Total
15 751
14 470
14 536
12 845
10 904
9 163
7 233
6 770
6 078
5 872
5 479
4 430
3 292
2 614
1 950
1 300
804
727
124 218
Total
20 754
17 861
16 397
15 371
15 196
14 342
11 972
10 969
9 611
8 654
7 587
6 356
4 544
3 423
2 573
1 540
981
772
168 902
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NW385:
Ramotshere
Moiloa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NW393:
Mamusa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
97
Male
9 482
8 100
7 859
7 333
6 888
5 319
4 793
4 258
3 609
3 171
3 067
2 715
2 179
1 691
1 193
726
450
386
73 220
Male
4 132
3 847
3 309
3 046
2 686
2 417
1 888
1 717
1 366
1 310
1 166
1 032
755
500
372
172
122
104
29 941
Female
8 972
7 824
7 291
7 033
6 537
5 698
4 833
4 800
4 271
4 005
3 529
3 233
2 658
2 221
1 629
1 142
892
925
77 494
Female
3 927
3 755
3 072
2 748
2 825
2 654
2 002
1 732
1 438
1 446
1 220
1 107
732
564
429
308
240
215
30 414
Total
18 454
15 924
15 150
14 366
13 425
11 017
9 626
9 058
7 881
7 176
6 596
5 948
4 837
3 912
2 822
1 868
1 342
1 311
150 713
Total
8 059
7 602
6 381
5 794
5 511
5 071
3 890
3 449
2 804
2 756
2 386
2 140
1 487
1 064
801
480
362
319
60 355
NW392: Naledi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
4 060
3 474
3 094
3 053
2 889
3 010
2 520
2 494
2 002
1 799
1 690
1 241
829
520
357
258
126
86
33 502
Female
3 844
3 253
2 958
2 999
2 911
2 945
2 378
2 258
1 973
1 859
1 681
1 314
895
733
465
375
228
210
33 279
Total
7 904
6 727
6 052
6 052
5 800
5 955
4 899
4 751
3 975
3 658
3 372
2 555
1 724
1 253
822
632
354
296
66 781
NW394:
Greater Taung
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
11476
10755
10105
10254
7288
5181
4293
3720
3414
3301
3232
2765
2518
2077
1452
961
534
429
83756
11291
10662
9365
9644
7676
6490
5477
5176
4521
4338
3915
3599
3196
2808
2165
1608
978
978
93886
22767
21416
19470
19898
14964
11671
9770
8896
7935
7638
7147
6364
5714
4886
3617
2569
1512
1407
177642
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NW396: LekwaTeemane
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NW401:
Ventersdorp
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
98
Male
3 064
2 889
2 496
2 498
2 607
2 272
2 060
1 688
1 464
1 348
1 120
992
731
558
357
235
123
100
26 600
Male
3 872
3 298
2 997
2 763
2 564
2 355
1 883
1 911
1 523
1 372
1 094
1 084
809
653
476
288
174
130
29 246
Female
3 152
2 848
2 506
2 445
2 335
2 109
1 881
1 652
1 446
1 423
1 102
1 051
830
650
506
326
185
203
26 648
Female
3 310
3 035
2 557
2 318
2 468
2 332
1 988
1 832
1 458
1 395
1 093
1 070
837
578
472
314
196
201
27 456
Total
6 215
5 736
5 002
4 942
4 942
4 381
3 941
3 340
2 909
2 771
2 222
2 043
1 561
1 208
862
561
308
303
53 248
Total
7 183
6 333
5 554
5 081
5 032
4 687
3 871
3 743
2 981
2 768
2 187
2 154
1 645
1 231
948
602
370
331
56 702
NW397:
Kagisano/Molopo
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NW402:
Tlokwe City
Council
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
7 772
6 658
5 807
5 779
3 803
3 410
2 775
2 598
2 338
2 215
2 049
1 706
1 392
1 081
699
383
244
207
50 918
Male
7 691
6 650
6 408
6 930
9 788
7 836
6 348
5 816
5 280
4 352
3 877
2 933
2 222
1 567
1 071
625
349
225
79 967
Female
7 477
6 505
5 579
5 390
4 229
3 795
3 361
3 266
2 874
2 869
2 405
2 043
1 445
1 224
806
636
462
505
54 870
Female
7 656
6 438
6 150
7 217
9 844
7 347
6 240
5 869
5 281
4 979
4 177
3 491
2 670
1 994
1 484
965
524
465
82 794
Total
15 249
13 163
11 387
11 169
8 033
7 205
6 136
5 864
5 212
5 083
4 454
3 749
2 837
2 305
1 505
1 019
706
712
105 789
Total
15 347
13 089
12 558
14 148
19 633
15 184
12 588
11 686
10 560
9 331
8 054
6 424
4 891
3 561
2 555
1 590
873
690
162 762
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NW403: City
of Matlosana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC061:
Richtersveld
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
99
Male
Female
Total
21 625
18 343
17 497
17 617
18 532
18 570
15 793
14 349
13 049
11 990
10 332
7 537
5 217
3 591
2 513
1 476
819
515
199 364
21 092
17 662
16 393
16 843
18 640
18 310
15 381
13 535
12 934
11 952
10 150
8 007
5 890
4 540
3 380
2 297
1 330
976
199 311
42 717
36 004
33 890
34 460
37 172
36 880
31 174
27 883
25 983
23 941
20 482
15 544
11 107
8 131
5 893
3 773
2 150
1 491
398 676
Male
Female
Total
481
482
525
487
475
674
524
506
526
447
365
286
191
140
84
54
30
23
6 300
456
440
472
394
434
533
444
435
439
454
328
251
215
166
87
73
34
27
5 682
937
922
997
881
910
1 207
968
941
965
902
692
537
406
306
172
128
64
50
11 982
NW404:
Maquassi Hills
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC062:
Nama Khoi
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
4 949
4 407
3 611
3 503
3 724
3 422
2 734
2 446
2 088
1 917
1 707
1 448
955
761
460
272
157
119
38 680
Male
Female
4 794
4 297
3 576
3 429
3 629
3 567
2 702
2 389
2 151
2 023
1 700
1 438
1 106
809
622
378
266
237
39 114
Female
1 968
1 966
2 137
2 319
1 839
1 715
1 641
1 613
1 568
1 456
1 291
1 137
890
671
500
257
137
110
23 215
1 789
1 830
2 009
2 128
1 773
1 735
1 677
1 622
1 778
1 592
1 425
1 276
1 002
799
576
393
241
183
23 826
Total
9 743
8 704
7 187
6 932
7 353
6 990
5 437
4 834
4 239
3 940
3 407
2 886
2 061
1 570
1 083
650
423
356
77 794
Total
3 757
3 795
4 146
4 447
3 613
3 450
3 318
3 234
3 346
3 047
2 716
2 413
1 892
1 471
1 076
650
378
293
47 041
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NC064: Kamiesberg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC066: Karoo
Hoogland
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
100
Male
458
455
502
528
348
306
292
309
337
362
298
254
232
166
125
86
46
31
5 136
Male
558
644
609
515
384
390
371
441
456
449
360
302
237
226
143
62
45
62
6 253
Female
407
398
481
405
328
331
293
321
327
376
309
261
233
220
144
98
63
56
5 051
Female
584
537
551
502
415
370
351
478
449
430
331
339
272
255
175
113
76
106
6 335
Total
865
853
982
933
677
637
585
630
664
738
607
515
465
386
269
184
109
87
10 187
Total
1 141
1 181
1 160
1 017
799
760
722
918
905
879
691
642
509
481
318
175
121
169
12 588
NC065: Hantam
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
1 035
1 023
1 040
997
837
793
632
696
729
721
620
525
411
292
215
120
71
53
10 809
Female
907
938
981
901
790
755
597
702
753
753
664
549
441
356
258
173
140
111
10 769
Total
1 942
1 961
2 021
1 898
1 627
1 548
1 228
1 398
1 481
1 474
1 284
1 075
852
648
472
293
211
163
21 578
NC067: Khâi-Ma
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
567
583
544
521
691
657
602
515
470
378
323
263
163
110
77
56
18
21
6 560
Female
537
494
496
492
595
557
474
402
413
352
307
224
156
155
98
63
45
44
5 905
Total
1 104
1 078
1 041
1 013
1 285
1 214
1 075
917
883
729
630
487
319
265
176
120
63
65
12 465
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NC071: Ubuntu
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC073:
Emthanjeni
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
101
Male
1 066
1 057
974
831
820
733
609
619
559
494
427
333
265
197
110
78
24
29
9 225
Female
1 087
1 101
907
822
774
660
591
616
571
527
424
406
295
213
152
105
60
66
9 376
Male
Female
2 356
2 298
2 144
2 046
1 763
1 688
1 492
1 324
1 169
1 070
984
787
613
446
273
150
66
51
20 722
2 247
2 274
2 103
1 988
1 760
1 688
1 412
1 242
1 326
1 248
1 086
979
798
566
404
258
132
122
21 634
Total
2 153
2 158
1 881
1 652
1 594
1 394
1 200
1 235
1 130
1 021
851
739
560
410
263
183
84
95
18 601
Total
4 603
4 573
4 248
4 034
3 523
3 376
2 904
2 565
2 495
2 317
2 070
1 766
1 411
1 012
678
409
198
174
42 356
NC072:
Umsobomvu
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC074:
Kareeberg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
1 451
1 474
1 482
1 395
1 179
1 280
1 065
839
766
699
540
483
397
279
155
113
50
43
13 689
Male
599
538
580
458
478
435
295
381
391
357
313
289
241
166
106
67
37
23
5 755
1 573
1 542
1 392
1 294
1 225
1 228
1 060
807
833
838
735
654
499
338
293
142
105
127
14 687
Female
625
574
514
522
454
386
304
380
372
370
330
290
250
182
118
103
62
80
5 918
Total
3 025
3 016
2 874
2 689
2 404
2 508
2 124
1 646
1 599
1 537
1 275
1 137
896
618
447
255
155
170
28 376
Total
1 224
1 112
1 094
980
931
822
599
761
763
727
643
580
491
348
224
170
100
103
11 673
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NC075:
Renosterberg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC077:
Siyathemba
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
102
Male
575
656
555
483
469
370
372
309
317
285
270
223
185
122
97
44
22
18
5 371
Male
1 121
1 129
1 124
991
858
915
792
677
697
632
535
453
335
206
136
88
37
32
10 759
Female
589
640
587
406
443
420
390
366
320
324
252
269
222
146
119
54
31
29
5 607
Female
1 095
1 074
1 100
940
869
781
692
631
698
625
640
503
386
289
216
149
69
73
10 832
Total
1 164
1 296
1 142
888
912
790
762
675
638
609
522
492
406
267
215
98
53
47
10 978
Total
2 216
2 203
2 224
1 931
1 727
1 696
1 484
1 308
1 395
1 257
1 175
956
722
495
352
237
105
106
21 591
NC076:
Thembelihle
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC078:
Siyancuma
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
874
848
825
723
711
629
556
504
475
444
397
304
250
149
126
79
38
43
7 976
Male
Female
783
721
793
717
671
624
495
471
512
418
425
292
236
178
147
115
54
72
7 724
Female
2 165
1 946
1 933
1 793
1 541
1 623
1 277
1 209
1 143
949
779
721
513
404
242
156
92
84
18 570
1 990
1 931
1 836
1 750
1 501
1 522
1 272
1 168
1 101
1 062
882
722
532
392
306
250
146
141
18 505
Total
1 657
1 568
1 618
1 440
1 382
1 253
1 052
975
987
863
822
596
486
327
273
194
92
115
15 701
Total
4 156
3 877
3 770
3 543
3 041
3 144
2 549
2 377
2 244
2 010
1 661
1 444
1 045
797
548
406
238
225
37 076
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NC081: Mier
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC083: //Khara
Hais
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
103
Male
411
391
343
341
291
273
223
250
231
195
169
119
129
77
68
36
22
30
3 599
Male
4 771
4 606
4 785
4 667
4 220
4 077
3 586
3 151
3 010
2 450
2 051
1 470
1 106
752
606
380
191
168
46 047
Female
346
392
331
337
283
202
214
208
220
169
141
178
115
83
62
52
30
39
3 404
Female
4 601
4 542
4 583
4 574
4 053
3 787
3 423
3 150
3 157
2 853
2 305
1 845
1 486
1 060
845
603
295
284
47 447
Total
758
783
674
679
574
475
437
458
451
364
310
296
245
160
129
89
52
69
7 003
Total
9 371
9 148
9 368
9 242
8 274
7 864
7 009
6 301
6 168
5 303
4 356
3 315
2 592
1 811
1 451
983
486
452
93 494
NC082: Kai
!Garib
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
NC084: !Kheis
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
2 845
2 699
2 716
3 274
4 636
3 973
3 073
2 470
2 067
1 690
1 491
1 139
765
558
393
255
117
118
34 278
2 695
2 596
2 523
2 991
3 803
3 217
2 502
2 157
1 870
1 771
1 484
1 105
945
637
521
352
204
219
31 591
5 540
5 294
5 238
6 265
8 439
7 191
5 575
4 627
3 937
3 460
2 975
2 244
1 710
1 195
914
607
321
337
65 869
Male
1 010
968
971
746
627
597
694
577
479
494
335
321
243
142
101
64
12
28
8 408
Female
960
991
923
781
553
593
579
547
484
409
377
342
253
163
113
87
35
38
8 229
Total
1 970
1 959
1 894
1 527
1 179
1 190
1 274
1 124
962
903
712
663
496
305
214
151
47
66
16 637
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
104
NC085:
Tsantsabane
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
NC091: Sol
Plaatjie
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
1 844
1 500
1 616
1 559
2 064
2 135
1 813
1 346
1 083
925
786
602
473
254
187
98
49
31
18 363
13 170
11 618
11 039
11 557
11 696
11 671
9 942
8 280
7 228
6 191
5 427
4 083
3 143
2 014
1 418
905
486
344
120 212
Female
1 845
1 542
1 460
1 459
1 696
1 600
1 310
1 206
992
851
790
563
488
344
241
167
80
98
16 730
Female
12 607
11 064
10 700
11 537
11 878
11 859
10 091
8 839
8 114
7 184
6 407
5 197
3 964
2 831
2 239
1 562
961
794
127 829
Total
3 690
3 042
3 075
3 018
3 759
3 735
3 123
2 552
2 074
1 776
1 576
1 164
961
598
427
265
129
129
35 093
Total
25 777
22 682
21 739
23 095
23 573
23 530
20 033
17 119
15 341
13 375
11 834
9 280
7 107
4 845
3 657
2 467
1 447
1 138
248 041
NC086:
Kgatelopele
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
NC092:
Dikgatlong
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
931
941
875
917
843
927
814
643
587
568
523
358
223
136
89
59
14
26
9 472
1 046
901
815
790
865
882
705
589
602
581
425
330
242
171
122
60
44
46
9 215
Female
2 651
2 460
2 373
2 137
2 173
1 993
1 800
1 479
1 367
1 097
1 079
788
657
437
252
164
82
73
23 062
2 583
2 500
2 258
2 162
2 161
2 071
1 742
1 546
1 337
1 328
1 069
896
672
543
356
278
138
137
23 778
Total
1 977
1 842
1 690
1 707
1 708
1 809
1 519
1 232
1 189
1 149
948
688
465
308
211
119
57
72
18 687
Total
5 234
4 959
4 632
4 299
4 334
4 064
3 543
3 025
2 704
2 425
2 148
1 684
1 328
980
608
442
221
210
46 841
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
105
NC093:
Magareng
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
NC451: Joe
Morolong
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
1 338
1 312
1 249
1 155
1 211
932
793
668
586
551
545
480
300
252
181
90
49
38
11 732
6 500
6 110
5 446
4 794
3 112
2 292
1 939
1 701
1 579
1 474
1 565
1 489
1 102
815
555
369
245
174
41 262
Female
1 352
1 183
1 167
1 153
1 112
982
913
769
688
711
573
523
412
322
263
170
94
84
12 473
Female
6 093
6 032
5 112
4 629
3 917
3 311
2 890
2 519
2 249
2 341
2 189
1 935
1 480
1 135
890
690
429
426
48 268
Total
2 690
2 495
2 417
2 307
2 323
1 914
1 706
1 438
1 274
1 262
1 118
1 003
713
574
444
260
143
122
24 204
Total
12 593
12 143
10 558
9 423
7 029
5 603
4 830
4 220
3 828
3 815
3 754
3 424
2 582
1 950
1 445
1 059
674
600
89 530
NC094:
Phokwane
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
NC452: GaSegonyana
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
3 707
3 419
3 179
3 101
2 747
2 482
2 173
1 846
1 608
1 512
1 289
1 088
898
557
419
244
130
93
30 491
3 571
3 340
3 161
3 033
2 870
2 712
2 267
2 094
1 795
1 683
1 569
1 267
987
766
544
383
250
216
32 509
Female
5 765
4 941
4 745
4 721
4 326
3 997
3 551
2 933
2 436
2 046
1 748
1 383
969
624
363
236
144
67
44994
5 400
4 953
4 637
4 761
4 368
4 391
3 908
3 350
2 720
2 504
2 134
1 735
1 248
852
658
484
316
242
48658
Total
7 278
6 760
6 340
6 134
5 617
5 194
4 440
3 940
3 403
3 194
2 858
2 356
1 885
1 322
963
627
380
310
63 000
Total
11 164
9 894
9 382
9 482
8 693
8 388
7 458
6 282
5 156
4 551
3 882
3 117
2 216
1 476
1 021
720
460
308
93651
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
NC453:
Gamagara
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC012:
Cederberg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
106
Male
2 032
1 736
1 700
1 541
2 642
3 333
2 623
1 892
1 405
1 106
996
773
467
209
113
65
40
37
22 710
Male
2 390
2 079
2 063
2 057
2 319
2 362
1 956
1 760
1 753
1 601
1 425
1 011
733
623
412
239
127
82
24 994
Female
1 902
1 677
1 545
1 610
2 060
2 283
1 858
1 432
1 037
1 023
871
591
395
225
160
118
60
60
18 907
Female
2 159
2 026
2 010
1 985
2 266
2 191
1 839
1 820
1 942
1 617
1 406
1 044
771
565
466
307
195
167
24 774
Total
3 934
3 412
3 245
3 151
4 703
5 616
4 481
3 324
2 442
2 129
1 866
1 364
862
434
274
183
100
97
41 617
Total
4 549
4 105
4 073
4 042
4 585
4 553
3 795
3 580
3 695
3 217
2 831
2 055
1 504
1 188
878
546
322
248
49 768
WC011:
Matzikama
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC013:
Bergrivier
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
3 296
2 917
2 860
2 974
3 180
2 816
2 485
2 309
2 351
2 260
1 888
1 461
1 026
732
489
296
174
110
33 624
3 238
2 942
2 748
3 045
3 028
2 751
2 348
2 244
2 311
2 128
1 852
1 438
1 052
843
563
469
281
242
33 523
6 535
5 859
5 608
6 019
6 208
5 567
4 833
4 553
4 662
4 388
3 740
2 899
2 078
1 575
1 052
765
455
352
67 147
Male
Female
Total
2 709
2 521
2 498
2 535
2 584
2 622
2 225
2 360
2 258
1 861
1 591
1 422
1 030
708
583
316
154
83
30 060
2 736
2 477
2 489
2 705
2 941
2 709
2 268
2 436
2 262
2 140
1 690
1 391
1 108
824
718
428
289
224
31 837
5 445
4 999
4 987
5 240
5 525
5 331
4 493
4 796
4 520
4 000
3 282
2 814
2 137
1 532
1 301
744
443
307
61 897
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
WC014:
Saldanha Bay
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC022:
Witzenberg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
107
Male
4 978
3 876
3 784
3 874
5 177
5 172
4 113
3 767
3 614
2 887
2 471
1 889
1 457
1 030
663
366
179
92
49 389
Male
5 238
4 729
5 043
5 024
6 586
6 597
5 403
4 928
4 213
3 449
2 741
2 020
1 448
898
558
363
172
145
59 554
Female
4 800
3 880
3 772
3 847
5 118
5 085
3 878
3 720
3 460
3 110
2 681
2 057
1 528
1 126
770
483
287
202
49 804
Female
5 070
4 612
4 767
4 964
5 436
5 637
4 518
4 400
4 234
3 590
2 828
2 160
1 456
1 002
696
467
301
253
56 392
Total
9 778
7 755
7 556
7 721
10 294
10 257
7 991
7 487
7 074
5 997
5 152
3 946
2 985
2 157
1 433
850
466
294
99 193
Total
10 308
9 341
9 810
9 988
12 022
12 234
9 921
9 328
8 448
7 039
5 569
4 180
2 904
1 900
1 254
830
472
398
115 946
WC015:
Swartland
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC023:
Drakenstein
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
5 257
4 562
4 504
4 523
5 608
5 665
4 311
4 303
4 357
3 598
2 879
2 216
1 836
1 158
770
542
246
138
56 472
5 105
4 581
4 468
4 886
5 509
5 458
4 133
4 135
4 273
3 604
2 994
2 369
1 973
1 334
979
710
403
376
57 290
10 361
9 142
8 972
9 409
11 116
11 124
8 444
8 438
8 631
7 202
5 873
4 585
3 809
2 492
1 749
1 252
649
514
113 762
Male
Female
Total
12 038
10 092
10 275
11 683
12 984
11 820
8 912
9 041
8 942
7 855
6 438
4 740
3 407
2 144
1 478
888
465
321
123 525
11 745
10 049
10 093
11 607
12 397
11 252
8 802
9 058
9 595
8 652
7 223
5 403
4 027
2 722
2 090
1 386
895
742
127 737
23 783
20 141
20 368
23 290
25 381
23 072
17 714
18 099
18 536
16 508
13 660
10 143
7 434
4 867
3 568
2 275
1 360
1 063
251 262
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
108
WC024:
Stellenbosch
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
WC026:
Langeberg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
6 765
5 592
5 503
7 370
11 359
7 967
6 023
5 123
4 933
4 185
3 417
2 655
2 052
1 339
874
515
316
188
76 176
4 894
4 489
4 627
4 309
4 241
4 138
3 445
3 271
3 463
2 941
2 271
1 808
1 376
1 045
689
434
249
199
47 891
Female
6 663
5 533
5 489
8 046
11 606
7 764
5 775
5 340
5 127
4 703
3 795
3 079
2 213
1 569
1 151
774
498
431
79 557
Female
4 893
4 412
4 444
4 335
4 262
4 066
3 357
3 534
3 758
3 201
2 631
2 024
1 595
1 159
881
641
345
295
49 834
Total
13 428
11 125
10 992
15 417
22 965
15 731
11 799
10 463
10 060
8 888
7 212
5 733
4 266
2 908
2 025
1 289
814
618
155 733
Total
9 787
8 901
9 071
8 644
8 503
8 205
6 802
6 804
7 222
6 142
4 902
3 832
2 971
2 203
1 570
1 076
594
494
97 724
WC025: Breede
Valley
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC034:
Swellendam
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
8 540
7 328
7 475
7 691
7 561
7 453
6 177
5 769
5 684
4 631
3 808
3 024
2 286
1 435
1 034
567
357
248
81 067
8 165
7 416
7 495
7 718
7 660
7 517
6 159
6 256
5 952
5 343
4 672
3 491
2 719
1 784
1 411
965
563
474
85 758
16 705
14 744
14 970
15 409
15 221
14 970
12 335
12 025
11 635
9 975
8 480
6 515
5 005
3 219
2 445
1 532
920
722
166 825
Male
Female
Total
1 660
1 497
1 567
1 569
1 582
1 465
1 302
1 243
1 215
1 219
980
817
630
437
316
206
120
66
17 891
1 671
1 442
1 555
1 477
1 477
1 486
1 172
1 330
1 239
1 257
1 017
763
664
513
396
259
177
129
18 025
3 331
2 939
3 122
3 046
3 059
2 951
2 474
2 573
2 454
2 476
1 997
1 580
1 295
950
711
465
297
195
35 916
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
WC031:
Theewaterskloof
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC033: Cape
Agulhas
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
109
Male
Female
Total
5 068
4 559
4 399
4 474
5 370
6 230
4 455
4 424
4 097
3 465
2 828
2 004
1 607
1 060
691
389
193
150
55 463
4 945
4 383
4 371
4 554
4 684
5 142
4 014
4 062
3 916
3 435
2 850
2 106
1 747
1 152
811
555
300
301
53 327
10 013
8 942
8 770
9 028
10 054
11 372
8 470
8 486
8 013
6 899
5 677
4 110
3 354
2 212
1 502
944
493
451
108 790
Male
Female
Total
1 356
1 195
1 342
1 247
1 354
1 411
1 158
1 131
1 226
1 087
999
762
642
518
378
237
129
56
16 229
1 335
1 211
1 301
1 406
1 351
1 396
1 009
1 111
1 271
1 178
1 025
799
780
579
472
272
188
124
16 808
2 691
2 406
2 644
2 654
2 705
2 808
2 167
2 242
2 496
2 264
2 024
1 561
1 422
1 097
850
509
317
180
33 038
WC032:
Overstrand
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC041:
Kannaland
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
3 366
2 721
2 557
2 455
3 321
4 201
3 672
3 017
2 647
2 051
1 709
1 541
1 844
1 729
1 365
895
430
267
39 786
Male
1 218
1 119
1 207
1 078
1 053
847
691
757
786
754
638
520
458
316
256
167
68
62
11 995
Female
3 402
2 688
2 541
2 681
3 209
3 992
3 258
2 734
2 550
2 065
1 907
1 882
2 067
1 947
1 585
945
644
548
40 646
Female
1 234
1 118
1 221
1 123
971
946
777
828
908
821
718
578
475
373
292
168
117
103
12 772
Total
6 768
5 409
5 097
5 136
6 530
8 193
6 930
5 751
5 197
4 116
3 615
3 423
3 911
3 676
2 950
1 839
1 074
815
80 432
Total
2 452
2 238
2 428
2 201
2 024
1 794
1 468
1 585
1 694
1 575
1 356
1 098
933
688
548
335
185
165
24 767
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
WC042:
Hessequa
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC044:
George
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
110
Male
2 195
2 081
2 224
1 950
1 794
1 974
1 568
1 642
1 851
1 725
1 445
1 295
1 189
991
743
480
237
142
25 525
Male
9 287
8 389
8 130
8 265
8 472
8 815
7 346
7 002
6 641
5 859
4 823
3 692
3 125
2 078
1 548
937
539
361
95 310
Female
2 155
2 065
2 105
2 020
1 896
2 112
1 539
1 722
2 060
1 849
1 630
1 430
1 296
1 164
836
583
368
287
27 117
Female
9 136
8 094
7 917
8 454
8 528
8 604
7 077
7 267
7 086
6 327
5 187
4 224
3 554
2 415
1 864
1 252
751
625
98 362
Total
4 351
4 146
4 329
3 970
3 689
4 086
3 107
3 364
3 911
3 574
3 075
2 725
2 485
2 155
1 579
1 062
604
428
52 642
Total
18 423
16 483
16 047
16 718
17 000
17 419
14 423
14 269
13 728
12 186
10 010
7 915
6 679
4 493
3 412
2 190
1 290
986
193 672
WC043:
Mossel
Bay
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC045:
Oudtshoorn
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
3 924
3 437
3 185
3 225
3 605
4 064
3 427
3 103
3 074
2 582
2 320
1 938
1 796
1 532
1 206
747
360
227
43 751
Male
3 653
3 307
3 179
3 355
3 727
4 061
3 325
3 226
3 163
2 789
2 506
2 334
2 107
1 819
1 336
835
542
416
45 679
Female
4 763
4 507
4 629
4 751
4 068
3 425
2 801
3 066
2 960
2 547
2 300
1 817
1 437
1 095
767
490
304
187
45 913
4 590
4 419
4 576
4 525
4 037
3 786
3 156
3 464
3 359
3 197
2 715
2 297
1 877
1 415
1 082
717
461
346
50 021
Total
7 577
6 743
6 363
6 580
7 332
8 125
6 752
6 330
6 237
5 371
4 826
4 272
3 903
3 350
2 542
1 582
902
643
89 430
Total
9 353
8 927
9 205
9 276
8 105
7 211
5 957
6 530
6 320
5 744
5 014
4 114
3 314
2 510
1 848
1 207
765
534
95 933
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
WC047: Bitou
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
WC051:
Laingsburg
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
111
Male
2 394
1 937
1 806
1 876
2 088
2 489
2 394
2 188
1 748
1 458
1 135
850
678
516
458
244
110
98
24 468
Female
2 247
2 105
1 897
1 806
2 136
2 485
2 207
2 053
1 762
1 447
1 188
914
745
678
424
282
175
141
24 694
Male
Female
407
358
352
326
295
335
310
297
304
257
249
193
163
120
74
45
30
18
4 134
398
349
332
341
276
294
293
333
320
276
243
222
166
117
78
55
31
32
4 155
Total
4 641
4 042
3 703
3 682
4 224
4 974
4 601
4 241
3 511
2 905
2 323
1 763
1 423
1 195
882
527
285
240
49 162
WC048: Knysna
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
3 363
2 717
2 498
2 494
2 897
3 307
2 946
2 653
2 453
1 853
1 585
1 320
1 162
998
859
441
235
174
33 957
Female
3 185
2 601
2 649
2 631
2 946
3 223
2 775
2 646
2 334
2 021
1 732
1 519
1 424
1 066
856
500
292
302
34 702
Total
6 548
5 319
5 147
5 125
5 844
6 530
5 721
5 299
4 787
3 874
3 318
2 838
2 585
2 064
1 714
941
527
477
68 659
Total
WC052: Prince
Albert
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
Female
Total
806
707
684
667
571
629
603
630
624
533
492
414
329
238
152
99
61
50
8 289
652
601
653
597
573
481
469
430
428
458
331
251
192
161
112
63
24
20
6 496
656
699
625
544
558
524
421
425
460
417
347
275
228
149
140
84
53
35
6 640
1 308
1 299
1 278
1 141
1 131
1 005
890
855
888
875
678
527
420
310
252
147
77
55
13 136
Census 2011: Population Dynamics
Report 03-01-67
Statistics South Africa
WC053: Beaufort
West
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
112
Male
2 864
2 515
2 488
2 214
1 951
1 818
1 556
1 581
1 564
1 377
1 236
993
730
499
363
200
101
87
24 137
Female
2 701
2 509
2 524
2 137
2 020
1 890
1 691
1 706
1 710
1 493
1 405
1 114
865
591
475
304
165
148
25 449
Total
5 564
5 025
5 012
4 351
3 971
3 708
3 247
3 287
3 274
2 870
2 641
2 106
1 595
1 090
839
504
267
236
49 586
CPT: City
of Cape
Town
0-4
5-9
10 - 14
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 44
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85+
Total
Male
189 429
146 011
136 113
146 861
191 986
201 998
166 643
144 030
120 720
102 579
86 118
65 020
49 273
32 945
23 674
14 066
7 932
5 299
1 830 699
Female
180 867
143 416
132 493
154 508
193 503
198 700
160 021
141 591
126 701
116 666
98 817
77 929
60 544
42 198
32 464
22 364
14 862
11 683
1 909 327
Total
370 296
289 427
268 606
301 370
385 489
400 698
326 664
285 622
247 421
219 245
184 935
142 949
109 817
75 143
56 137
36 430
22 794
16 983
3 740 026
Census 2011: Population Dynamics
Report 03-01-67